System

Abstract

An object of a system according to an embodiment is to analyze game data and provide optimal tactical advice.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a training unit. The collection unit collects game data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit provides tactical advice based on the analysis result obtained by the analysis unit. The training unit performs training based on the advice provided by the suggestion 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 provide tactical advice based on match data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze match data and provide optimal tactical advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a training unit. The collection unit collects match data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit provides tactical advice based on the analysis results obtained by the analysis unit. The training unit performs training based on the advice provided by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze match data and provide optimal tactical advice. [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 tactical advice system according to an embodiment of the present invention collects game data, analyzes it using a generation AI, provides tactical advice, and conducts training. The tactical advice system collects gameplay data from players and teams, analyzes it using a generation AI, and provides optimal tactics for individual players and teams. This mechanism allows players and teams to receive effective training and build strategic consistency. For example, the tactical advice system collects detailed data on movements and performance during a game, as well as the status of tactical execution. For example, in a soccer game, data such as each player's running distance, pass success rate, and number of shots is collected. The tactical advice system then analyzes the collected data using a generation AI. The generation AI then identifies the strengths and weaknesses of players and teams based on the collected data and proposes optimal tactics. For example, the generation AI can analyze soccer game data to identify the preferred playing style of a specific player and the tactical tendencies of the entire team. The tactical advice system then allows players and teams to receive effective training based on the tactical advice provided by the generation AI. For example, a specific player can improve their performance by practicing shooting and passing based on the tactics proposed by the generation AI. It is also possible to conduct training to build tactical coherence across the entire team. This allows the tactical advice system to improve player and team performance and secure a strategic advantage. This allows the tactical advice system to improve player and team performance and build strategic coherence. For example, players can improve their teamwork, which can improve the performance of the entire team.

[0029] A tactical advice system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a training unit. The collection unit collects match data. The match data includes, but is not limited to, player movements, performance, and tactical execution status. The collection unit collects, for example, data on player movements, performance, and tactical execution status during a match. For example, the collection unit can collect player position data and speed data. The collection unit can also collect player success rates and the number of mistakes. The collection unit can also collect tactical success rates and execution times. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit identifies strengths and weaknesses of players or teams based on the collected data. The analysis unit can also identify the preferred playing style of a specific player and the tactical tendencies of the entire team. The analysis unit can also use the generation AI to perform a detailed analysis of player or team performance. The proposal unit uses the generation AI to provide tactical advice based on the analysis results obtained by the analysis unit. Examples of tactical advice include, but are not limited to, suggested changes to tactics and changes to player positioning. For example, the suggestion unit may suggest tactics based on a particular player's preferred playing style. The suggestion unit may also suggest tactics based on the tactical tendencies of the entire team. The suggestion unit may also use the generation AI to provide specific tactical advice to players or teams. The training unit conducts training based on the tactics proposed by the generation AI. Examples of training include, but are not limited to, shooting practice, passing practice, and physical training. For example, the training unit conducts shooting practice and passing practice for a particular player. The training unit may also conduct training to build tactical consistency across the entire team. The training unit may also conduct training to improve player or team performance based on the tactics proposed by the generation AI.As a result, the tactical advice system according to the embodiment can improve the performance of a player or a team and build strategic consistency. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit may perform training to improve the performance of a player or a team based on the tactics proposed by the generation AI.

[0030] The collection unit can collect data on movements, performance, and tactical execution status during a match. The collection unit collects, for example, data on movements, performance, and tactical execution status during a match. For example, the collection unit can collect player position data and speed data. The collection unit can also collect player success rates and the number of mistakes. The collection unit can also collect tactical success rates and execution times. In this way, by collecting detailed data during a match, it is possible to understand the performance of players and teams in detail. 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 data on movements, performance, and tactical execution status during a match to a generation AI and cause the generation AI to collect the data.

[0031] The analysis unit can identify the strengths and weaknesses of a player or a team based on the collected data. The analysis unit, for example, identifies the strengths and weaknesses of a player or a team based on the collected data. For example, the analysis unit can analyze a player's performance data and identify strengths and weaknesses. The analysis unit can also analyze the execution status of tactics and identify the strengths and weaknesses of a team. The analysis unit can also use a generation AI to identify the strengths and weaknesses of a player or a team in detail. This makes it possible to provide more effective tactical advice by identifying the strengths and weaknesses of a player or a team. Some or all of the above-described processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify strengths and weaknesses.

[0032] The suggestion unit can grasp the playing style favored by a specific player or the tactical tendencies of the entire team. The suggestion unit, for example, grasps the playing style favored by a specific player. For example, the suggestion unit grasps the player's movement patterns and favorite plays. The suggestion unit can also grasp the tactical tendencies of the entire team. For example, the suggestion unit grasps the team's tactical patterns and the success rate of tactics. The suggestion unit can also use a generation AI to grasp the tactical tendencies of players and teams in detail. By grasping the tactical tendencies of players and teams in this way, more specific tactical advice can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the player's movement patterns and favorite plays into the generation AI and cause the generation AI to grasp the tactical tendencies.

[0033] The training unit allows a specific player to practice shooting or passing based on the tactics proposed by the generation AI. The training unit, for example, allows a specific player to practice shooting or passing based on the tactics proposed by the generation AI. For example, the training unit allows a player to practice shooting. The training unit can also allow a player to practice passing. The training unit can also perform training to improve the player's performance based on the tactics proposed by the generation AI. In this way, the player's performance can be improved by training based on the tactics proposed by the generation AI. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can cause the generation AI to perform shooting or passing practice for the player based on the tactics proposed by the generation AI.

[0034] The training unit can conduct training to build tactical unity across the entire team. The training unit, for example, conducts training to build tactical unity across the entire team. For example, the training unit conducts team coordination practice across the entire team. The training unit can also conduct repetitive practice of tactics. The training unit can also conduct training to improve the performance of the entire team based on the tactics proposed by the generation AI. In this way, training to build tactical unity across the entire team can improve team performance. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can cause the generation AI to conduct team coordination practice across the entire team and repetitive practice of tactics based on the tactics proposed by the generation AI.

[0035] The collection unit can analyze the player's past performance data and select the optimal data collection method. The collection unit, for example, analyzes the player's past performance data and selects the optimal data collection method. For example, the collection unit analyzes the player's past match data and collects data at times when a specific performance indicator is high. The collection unit can also collect data from the player's past performance data, focusing on specific actions or playing styles. The collection unit can also collect data at specific scenes during a match based on the player's past performance data. In this way, the optimal data collection method can be selected by analyzing the player's past performance 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 the player's past performance data to a generation AI and cause the generation AI to select the optimal data collection method.

[0036] The collection unit can filter data based on the player's current physical condition or fatigue level when collecting data. For example, the collection unit can filter data based on the player's current physical condition or fatigue level when collecting data. For example, the collection unit can monitor the player's heart rate and fatigue level in real time and collect data when the player is in good physical condition. The collection unit can also temporarily suspend data collection when the player is in poor physical condition and resume it after the player has recovered. The collection unit can also reduce the frequency of data collection when the player's fatigue level is high and collect only important data. By filtering data collection based on the player's physical condition and fatigue level, more accurate data can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the player's heart rate and fatigue level data into the generation AI and have the generation AI perform data collection filtering.

[0037] The collection unit can select the optimal collection means depending on the player's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the player's input method (audio, text, video, etc.) when collecting data. For example, if the player prefers audio input, the collection unit can preferentially collect audio data. Also, if the player prefers text input, the collection unit can preferentially collect text data. Also, if the player prefers video input, the collection unit can preferentially collect video data. In this way, data can be collected efficiently by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the player when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the geographical location information of the player into consideration when collecting data. For example, if the player is playing a game in a specific area, the collection unit can prioritize collecting environmental data about that area. Furthermore, if the player is playing a game in a specific stadium, the collection unit can prioritize collecting data about the characteristics of that stadium. Furthermore, if the player is playing a game under specific climatic conditions, the collection unit can prioritize collecting data about the climatic conditions. In this way, highly relevant data can be collected preferentially by taking the geographical location information of the player into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the player to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the player's social media activities and collect relevant data during data collection. For example, the collection unit may analyze the player's social media activities and collect relevant data during data collection. For example, the collection unit may collect game impressions and feedback shared by the player on social media. The collection unit may also collect performance data of other players the player follows on social media. The collection unit may also collect opinions and trends in communities in which the player participates on social media. This allows relevant data to be collected by analyzing the player's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit may input the player's social media data into the generation AI and cause the generation AI to collect relevant data.

[0040] The collection unit can adjust the collection method when collecting data by reflecting the player's past feedback. For example, the collection unit adjusts the collection method when collecting data by reflecting the player's past feedback. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the player in the past. The collection unit can also adjust the type of data to collect based on feedback provided by the player in the past. The collection unit can also adjust the timing of data collection based on feedback provided by the player in the past. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's feedback data to the generation AI and cause the generation AI to adjust the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis of important data and provides specific advice. The analysis unit can also perform a concise analysis of less important data and provide only the main points. The analysis unit can also determine the priority of the analysis according to the importance of the data and start analyzing important data first. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an analysis algorithm based on a specific performance indicator to performance data. The analysis unit can also apply an analysis algorithm for grasping tactical trends to tactical data. The analysis unit can also apply an analysis algorithm for sentiment analysis to feedback data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis can be performed. 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 data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis based on the player's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results during analysis. For example, the analysis unit can compare the current analysis results with the player's past analysis results and improve the accuracy. The analysis unit can also extract specific patterns from the player's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm based on the player's past analysis results and improve the accuracy. In this way, the analysis accuracy is improved by referring to the player's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the player's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the order of analysis based on the time of data collection during analysis. The analysis unit, for example, determines the order of analysis based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the latest data and provides real-time advice. The analysis unit can also prioritize analyzing data from important matches and provide tactical advice. The analysis unit can also prioritize analyzing current data while referring to past data. In this way, by determining the order of analysis based on the time of data collection, real-time advice can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the order of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data and provides specific advice. The analysis unit can also postpone analysis of less relevant data and start analysis from important data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can start from important data. 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the player's level of knowledge during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the player's level of knowledge during analysis. For example, if the player is a beginner, the analysis unit can avoid technical terms and provide analysis results in simple language. If the player is an intermediate player, the analysis unit can use appropriate technical terms and provide detailed analysis results. If the player is an advanced player, the analysis unit can use more technical terms and provide more advanced analysis results. By adjusting the use of technical terms in the analysis according to the player's level of knowledge, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the player's level of knowledge into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the tactic when making a proposal. The suggestion unit, for example, adjusts the level of detail of the proposal based on the importance of the tactic when making a proposal. For example, the suggestion unit makes detailed suggestions and provides specific advice for important tactics. The suggestion unit can also make concise suggestions and provide only the main points for less important tactics. The suggestion unit can also determine the priority of the suggestions according to the importance of the tactics and make suggestions starting with the important tactics. In this way, by adjusting the level of detail of the proposal based on the importance of the tactics, detailed suggestions can be made for important tactics. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the tactics to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different proposal algorithms depending on the category of the tactic when making a proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the tactic when making a proposal. For example, the suggestion unit applies a proposal algorithm based on a specific attacking pattern to an offensive tactic. The suggestion unit can also apply a proposal algorithm for grasping defensive tendencies to a defensive tactic. The suggestion unit can also apply a proposal algorithm for performing sentiment analysis to a feedback tactic. In this way, by applying different proposal algorithms depending on the category of the tactic, more accurate proposals can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of the tactic to a generation AI and cause the generation AI to apply the proposal algorithm.

[0049] The suggestion unit can improve the accuracy of the suggestion based on the player's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results when making a suggestion. For example, the suggestion unit can compare the current suggestion results with the player's past suggestion results to improve accuracy. The suggestion unit can also extract specific patterns from the player's past suggestion results and reflect them in the current suggestion. The suggestion unit can also adjust the suggestion algorithm based on the player's past suggestion results to improve accuracy. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the player's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] The suggestion unit can determine the order of proposals based on the time of submission of tactics when making proposals. The suggestion unit, for example, determines the order of proposals based on the time of submission of tactics when making proposals. For example, the suggestion unit prioritizes proposing the most effective tactics before an important match. The suggestion unit can also propose tactics for the next match based on feedback after the match. The suggestion unit can also dynamically adjust the priority of tactics at specific times during the season. In this way, by determining the order of proposals based on the time of submission of tactics, important tactics can be prioritized for proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the time of submission of tactics to a generation AI and cause the generation AI to determine the order of proposals.

[0051] The suggestion unit can adjust the order of proposals based on the relevance of the tactics when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the tactics when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant tactics and provides specific advice. The suggestion unit can also postpone less relevant tactics and make proposals starting with important tactics. The suggestion unit can also dynamically adjust the order of proposals according to the relevance of the tactics. In this way, by adjusting the order of proposals based on the relevance of the tactics, it is possible to make proposals starting with important tactics. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of tactics to a generation AI and cause the generation AI to adjust the order of proposals.

[0052] The suggestion unit may adjust the use of technical terms in the suggestion according to the player's knowledge level when making the suggestion. For example, the suggestion unit may adjust the use of technical terms in the suggestion according to the player's knowledge level when making the suggestion. For example, if the player is a beginner, the suggestion unit may avoid technical terms and provide a suggestion in simple language. If the player is an intermediate player, the suggestion unit may use appropriate technical terms and provide a detailed suggestion. If the player is an advanced player, the suggestion unit may use a lot of technical terms and provide a more advanced suggestion. By adjusting the use of technical terms in the suggestion according to the player's knowledge level, it is possible to provide a more understandable suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the player's knowledge level into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The training unit can analyze the player's past performance during training and select an appropriate training method. For example, the training unit can analyze the player's past performance during training and select an appropriate training method. For example, the training unit can select a training method to improve a specific skill based on the player's past performance data. The training unit can also select a training method to compensate for a weakness based on the player's past performance data. The training unit can also select a training method to improve overall performance based on the player's past performance data. In this way, the optimal training method can be selected by analyzing the player's past performance. Some or all of the above-described processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the player's past performance data into a generation AI and have the generation AI select a training method.

[0054] The training unit can customize training methods based on the player's current physical condition and fatigue level during training. For example, the training unit can customize training methods based on the player's current physical condition and fatigue level during training. For example, the training unit can monitor the player's heart rate and fatigue level in real time and train when the player is in good physical condition. The training unit can also temporarily suspend training if the player is in poor physical condition and resume it after recovery. The training unit can also adjust the intensity of training and only perform important training if the player's fatigue level is high. This allows for more effective training by customizing training methods based on the player's physical condition and fatigue level. Some or all of the above-described processing in the training unit can be performed using, or without, AI. For example, the training unit can input data on the player's physical condition and fatigue level into a generation AI and have the generation AI customize the training methods.

[0055] The training unit can adjust the training method by reflecting the player's feedback during training. For example, the training unit can adjust the training method by reflecting the player's feedback during training. For example, the training unit can adjust the frequency of training based on the feedback provided by the player. The training unit can also adjust the content of training based on the feedback provided by the player. The training unit can also adjust the timing of training based on the feedback provided by the player. In this way, the training method can be improved by reflecting the player's feedback. Some or all of the above-mentioned processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the player's feedback data into the generation AI and cause the generation AI to adjust the training method.

[0056] The training unit can select an appropriate training method based on the player's geographical location information during training. For example, the training unit selects the optimal training method by taking the player's geographical location information into consideration during training. For example, if the player is training in a specific area, the training unit can select a training method suitable for the local environment. Furthermore, if the player is training in a specific stadium, the training unit can select a training method suitable for the characteristics of the stadium. Furthermore, if the player is training under specific climatic conditions, the training unit can select a training method suitable for the climatic conditions. In this way, the optimal training method can be selected by taking the player's geographical location information into consideration. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the player's geographical location data into the generation AI and cause the generation AI to select a training method.

[0057] The training unit can analyze the player's social media activity during training to suggest training methods. For example, the training unit can analyze the player's social media activity during training to suggest training methods. For example, the training unit can suggest training methods based on the player's training impressions and feedback shared on social media. The training unit can also refer to the training methods of other players the player follows on social media. The training unit can also suggest training methods based on the opinions and trends of the community in which the player participates on social media. In this way, more effective training methods can be suggested by analyzing the player's social media activity. Some or all of the above-mentioned processing in the training unit may be performed using, or without, AI. For example, the training unit can input the player's social media data into a generation AI and have the generation AI suggest training methods.

[0058] The training unit can adjust the training method during training by reflecting the player's past feedback. For example, the training unit can adjust the training method during training by reflecting the player's past feedback. For example, the training unit can adjust the frequency of training based on feedback provided by the player in the past. The training unit can also adjust the content of training based on feedback provided by the player in the past. The training unit can also adjust the timing of training based on feedback provided by the player in the past. In this way, the training method can be customized by reflecting the player's past feedback. Some or all of the above-described processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the player's feedback data into a generation AI and cause the generation AI to adjust the training method.

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

[0060] The collection unit can analyze a player's past performance data and select the optimal data collection method. For example, it can analyze a player's past match data and collect data when specific performance indicators are high. It can also collect data focusing on specific actions or playing styles. It can also collect data at specific scenes during a match. In this way, the optimal data collection method can be selected by analyzing a player's past performance data.

[0061] When collecting data, the collection unit can filter the data based on the player's current physical condition and fatigue level. For example, the collection unit can monitor the player's heart rate and fatigue level in real time and collect data when the player is in good physical condition. In addition, if the player is in poor physical condition, the collection unit can temporarily suspend data collection and resume it after the player has recovered. Furthermore, if the player is highly fatigued, the frequency of data collection can be reduced and only important data can be collected. In this way, by filtering data collection based on the player's physical condition and fatigue level, more accurate data can be collected.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis of important data and provide specific advice. It can also perform a concise analysis of less important data and provide only the main points. Furthermore, it can determine the priority of the analysis based on the importance of the data and start analyzing the important data first. In this way, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis of important data.

[0063] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tactic. For example, detailed proposals can be made for important tactics, providing specific advice. Also, for less important tactics, concise proposals can be made, providing only the main points. Furthermore, the priority of the proposals can be determined according to the importance of the tactics, and suggestions can be made starting with the most important tactics. In this way, by adjusting the level of detail of the proposal based on the importance of the tactic, detailed proposals can be made for important tactics.

[0064] During training, the training department can analyze a player's past performance and select an appropriate training method. For example, a training method for strengthening a specific skill can be selected based on the player's past performance data. A training method for compensating for a weakness can also be selected based on the player's past performance data. Furthermore, a training method for improving overall performance can also be selected based on the player's past performance data. In this way, the optimal training method can be selected by analyzing a player's past performance.

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

[0066] Step 1: The collection unit collects match data. The match data includes player movements, performance, tactical execution status, etc. For example, the collection unit collects player position data, speed data, success rate, number of mistakes, tactical success rate, and execution time. Step 2: The analysis unit uses the generative AI to analyze the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, the analysis unit identifies the strengths and weaknesses of players and teams based on the collected data, and understands the preferred playing styles of specific players and the tactical tendencies of the team as a whole. Step 3: The suggestion unit uses the generation AI to provide tactical advice based on the analysis results obtained by the analysis unit. The tactical advice may include suggested tactical changes and player positioning changes. For example, the suggestion unit may suggest tactics based on the preferred playing style of a specific player and tactical tendencies of the entire team. Step 4: The training team conducts training based on the tactics proposed by the generative AI. Training includes shooting practice, passing practice, physical training, etc. For example, the training team may conduct shooting practice and passing practice for specific players to build tactical consistency across the team.

[0067] (Example 2) A tactical advice system according to an embodiment of the present invention collects game data, analyzes it using a generation AI, provides tactical advice, and conducts training. The tactical advice system collects gameplay data from players and teams, analyzes it using a generation AI, and provides optimal tactics for individual players and teams. This mechanism allows players and teams to receive effective training and build strategic consistency. For example, the tactical advice system collects detailed data on movements and performance during a game, as well as the status of tactical execution. For example, in a soccer game, data such as each player's running distance, pass success rate, and number of shots is collected. The tactical advice system then analyzes the collected data using a generation AI. The generation AI then identifies the strengths and weaknesses of players and teams based on the collected data and proposes optimal tactics. For example, the generation AI can analyze soccer game data to identify the preferred playing style of a specific player and the tactical tendencies of the entire team. The tactical advice system then allows players and teams to receive effective training based on the tactical advice provided by the generation AI. For example, a specific player can improve their performance by practicing shooting and passing based on the tactics proposed by the generation AI. It is also possible to conduct training to build tactical coherence across the entire team. This allows the tactical advice system to improve player and team performance and secure a strategic advantage. This allows the tactical advice system to improve player and team performance and build strategic coherence. For example, players can improve their teamwork, which can improve the performance of the entire team.

[0068] A tactical advice system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a training unit. The collection unit collects match data. The match data includes, but is not limited to, player movements, performance, and tactical execution status. The collection unit collects, for example, data on player movements, performance, and tactical execution status during a match. For example, the collection unit can collect player position data and speed data. The collection unit can also collect player success rates and the number of mistakes. The collection unit can also collect tactical success rates and execution times. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit identifies strengths and weaknesses of players or teams based on the collected data. The analysis unit can also identify the preferred playing style of a specific player and the tactical tendencies of the entire team. The analysis unit can also use the generation AI to perform a detailed analysis of player or team performance. The proposal unit uses the generation AI to provide tactical advice based on the analysis results obtained by the analysis unit. Examples of tactical advice include, but are not limited to, suggested changes to tactics and changes to player positioning. For example, the suggestion unit may suggest tactics based on a particular player's preferred playing style. The suggestion unit may also suggest tactics based on the tactical tendencies of the entire team. The suggestion unit may also use the generation AI to provide specific tactical advice to players or teams. The training unit conducts training based on the tactics proposed by the generation AI. Examples of training include, but are not limited to, shooting practice, passing practice, and physical training. For example, the training unit conducts shooting practice and passing practice for a particular player. The training unit may also conduct training to build tactical consistency across the entire team. The training unit may also conduct training to improve player or team performance based on the tactics proposed by the generation AI.As a result, the tactical advice system according to the embodiment can improve the performance of a player or a team and build strategic consistency. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit may perform training to improve the performance of a player or a team based on the tactics proposed by the generation AI.

[0069] The collection unit can collect data on movements, performance, and tactical execution status during a match. The collection unit collects, for example, data on movements, performance, and tactical execution status during a match. For example, the collection unit can collect player position data and speed data. The collection unit can also collect player success rates and the number of mistakes. The collection unit can also collect tactical success rates and execution times. In this way, by collecting detailed data during a match, it is possible to understand the performance of players and teams in detail. 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 data on movements, performance, and tactical execution status during a match to a generation AI and cause the generation AI to collect the data.

[0070] The analysis unit can identify the strengths and weaknesses of a player or a team based on the collected data. The analysis unit, for example, identifies the strengths and weaknesses of a player or a team based on the collected data. For example, the analysis unit can analyze a player's performance data and identify strengths and weaknesses. The analysis unit can also analyze the execution status of tactics and identify the strengths and weaknesses of a team. The analysis unit can also use a generation AI to identify the strengths and weaknesses of a player or a team in detail. This makes it possible to provide more effective tactical advice by identifying the strengths and weaknesses of a player or a team. Some or all of the above-described processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify strengths and weaknesses.

[0071] The suggestion unit can grasp the playing style favored by a specific player or the tactical tendencies of the entire team. The suggestion unit, for example, grasps the playing style favored by a specific player. For example, the suggestion unit grasps the player's movement patterns and favorite plays. The suggestion unit can also grasp the tactical tendencies of the entire team. For example, the suggestion unit grasps the team's tactical patterns and the success rate of tactics. The suggestion unit can also use a generation AI to grasp the tactical tendencies of players and teams in detail. By grasping the tactical tendencies of players and teams in this way, more specific tactical advice can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the player's movement patterns and favorite plays into the generation AI and cause the generation AI to grasp the tactical tendencies.

[0072] The training unit allows a specific player to practice shooting or passing based on the tactics proposed by the generation AI. The training unit, for example, allows a specific player to practice shooting or passing based on the tactics proposed by the generation AI. For example, the training unit allows a player to practice shooting. The training unit can also allow a player to practice passing. The training unit can also perform training to improve the player's performance based on the tactics proposed by the generation AI. In this way, the player's performance can be improved by training based on the tactics proposed by the generation AI. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can cause the generation AI to perform shooting or passing practice for the player based on the tactics proposed by the generation AI.

[0073] The training unit can conduct training to build tactical unity across the entire team. The training unit, for example, conducts training to build tactical unity across the entire team. For example, the training unit conducts team coordination practice across the entire team. The training unit can also conduct repetitive practice of tactics. The training unit can also conduct training to improve the performance of the entire team based on the tactics proposed by the generation AI. In this way, training to build tactical unity across the entire team can improve team performance. Some or all of the above-described processing in the training unit may be performed using AI, for example, or may be performed without using AI. For example, the training unit can cause the generation AI to conduct team coordination practice across the entire team and repetitive practice of tactics based on the tactics proposed by the generation AI.

[0074] The collection unit can estimate the player's emotions and adjust the timing of data collection based on the estimated player's emotions. For example, the collection unit estimates the player's emotions and adjusts the timing of data collection based on the estimated player's emotions. For example, if the player is nervous, the collection unit avoids collecting data immediately after the start of the game and collects data when the player is relaxed. Furthermore, if the player is concentrating, the collection unit can collect data in the middle of the game to help the player maintain their concentration. Furthermore, if the player is tired, the collection unit can avoid collecting data after the game ends and collect data during breaks. This allows for more appropriate data collection by adjusting the timing of data collection according to the player'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 can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the player's emotional data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can analyze the player's past performance data and select the optimal data collection method. The collection unit, for example, analyzes the player's past performance data and selects the optimal data collection method. For example, the collection unit analyzes the player's past match data and collects data at times when a specific performance indicator is high. The collection unit can also collect data from the player's past performance data, focusing on specific actions or playing styles. The collection unit can also collect data at specific scenes during a match based on the player's past performance data. In this way, the optimal data collection method can be selected by analyzing the player's past performance 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 the player's past performance data to a generation AI and cause the generation AI to select the optimal data collection method.

[0076] The collection unit can filter data based on the player's current physical condition or fatigue level when collecting data. For example, the collection unit can filter data based on the player's current physical condition or fatigue level when collecting data. For example, the collection unit can monitor the player's heart rate and fatigue level in real time and collect data when the player is in good physical condition. The collection unit can also temporarily suspend data collection when the player is in poor physical condition and resume it after the player has recovered. The collection unit can also reduce the frequency of data collection when the player's fatigue level is high and collect only important data. By filtering data collection based on the player's physical condition and fatigue level, more accurate data can be collected. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the player's heart rate and fatigue level data into the generation AI and have the generation AI perform data collection filtering.

[0077] The collection unit can select the optimal collection means depending on the player's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the player's input method (audio, text, video, etc.) when collecting data. For example, if the player prefers audio input, the collection unit can preferentially collect audio data. Also, if the player prefers text input, the collection unit can preferentially collect text data. Also, if the player prefers video input, the collection unit can preferentially collect video data. In this way, data can be collected efficiently by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0078] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the estimated player's emotions. The collection unit, for example, estimates the player's emotions and determines the priority of data to be collected based on the estimated player's emotions. For example, if the player is tense, the collection unit can prioritize collecting data to help the player relax. Furthermore, if the player is concentrating, the collection unit can prioritize collecting data to help the player maintain their concentration. Furthermore, if the player is tired, the collection unit can prioritize collecting data that helps the player recover from fatigue. Thus, by prioritizing data based on the player's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the player's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] The collection unit can prioritize collecting highly relevant data based on the geographical location information of the player when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the geographical location information of the player into consideration when collecting data. For example, if the player is playing a game in a specific area, the collection unit can prioritize collecting environmental data about that area. Furthermore, if the player is playing a game in a specific stadium, the collection unit can prioritize collecting data about the characteristics of that stadium. Furthermore, if the player is playing a game under specific climatic conditions, the collection unit can prioritize collecting data about the climatic conditions. In this way, highly relevant data can be collected preferentially by taking the geographical location information of the player into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the player to the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit may analyze the player's social media activities and collect relevant data during data collection. For example, the collection unit may analyze the player's social media activities and collect relevant data during data collection. For example, the collection unit may collect game impressions and feedback shared by the player on social media. The collection unit may also collect performance data of other players the player follows on social media. The collection unit may also collect opinions and trends in communities in which the player participates on social media. This allows relevant data to be collected by analyzing the player's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit may input the player's social media data into the generation AI and cause the generation AI to collect relevant data.

[0081] The collection unit can adjust the collection method when collecting data by reflecting the player's past feedback. For example, the collection unit adjusts the collection method when collecting data by reflecting the player's past feedback. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the player in the past. The collection unit can also adjust the type of data to collect based on feedback provided by the player in the past. The collection unit can also adjust the timing of data collection based on feedback provided by the player in the past. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's feedback data to the generation AI and cause the generation AI to adjust the collection method.

[0082] The analysis unit can estimate the player's emotions and adjust the presentation method of the analysis based on the estimated player's emotions. For example, the analysis unit can estimate the player's emotions and adjust the presentation method of the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis unit can provide a simple, highly visible analysis result. If the player is relaxed, the analysis unit can provide a detailed analysis result. If the player is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis of important data and provides specific advice. The analysis unit can also perform a concise analysis of less important data and provide only the main points. The analysis unit can also determine the priority of the analysis according to the importance of the data and start analyzing important data first. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an analysis algorithm based on a specific performance indicator to performance data. The analysis unit can also apply an analysis algorithm for grasping tactical trends to tactical data. The analysis unit can also apply an analysis algorithm for sentiment analysis to feedback data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis can be performed. 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 data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis based on the player's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results during analysis. For example, the analysis unit can compare the current analysis results with the player's past analysis results and improve the accuracy. The analysis unit can also extract specific patterns from the player's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm based on the player's past analysis results and improve the accuracy. In this way, the analysis accuracy is improved by referring to the player's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the player's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the player's emotions and adjust the length of the analysis based on the estimated player's emotions. For example, the analysis unit can estimate the player's emotions and adjust the length of the analysis based on the estimated player's emotions. For example, if the player is in a hurry, the analysis unit can provide a short, concise analysis result. If the player is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. If the player is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis based on the player's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] The analysis unit can determine the order of analysis based on the time of data collection during analysis. The analysis unit, for example, determines the order of analysis based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the latest data and provides real-time advice. The analysis unit can also prioritize analyzing data from important matches and provide tactical advice. The analysis unit can also prioritize analyzing current data while referring to past data. In this way, by determining the order of analysis based on the time of data collection, real-time advice can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection to the generation AI and have the generation AI determine the order of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data and provides specific advice. The analysis unit can also postpone analysis of less relevant data and start analysis from important data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can start from important data. 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the player's level of knowledge during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the player's level of knowledge during analysis. For example, if the player is a beginner, the analysis unit can avoid technical terms and provide analysis results in simple language. If the player is an intermediate player, the analysis unit can use appropriate technical terms and provide detailed analysis results. If the player is an advanced player, the analysis unit can use more technical terms and provide more advanced analysis results. By adjusting the use of technical terms in the analysis according to the player's level of knowledge, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the player's level of knowledge into the generation AI and cause the generation AI to adjust the use of technical terms.

[0090] The suggestion unit can estimate the player's emotions and adjust the way suggestions are expressed based on the estimated player's emotions. For example, the suggestion unit can estimate the player's emotions and adjust the way suggestions are expressed based on the estimated player's emotions. For example, if the player is nervous, the suggestion unit can provide simple, highly visible suggestions. If the player is relaxed, the suggestion unit can provide detailed suggestions. If the player is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions to be provided by adjusting the way suggestions are expressed based on the player's emotions. The 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0091] The suggestion unit can adjust the level of detail of the proposal based on the importance of the tactic when making a proposal. The suggestion unit, for example, adjusts the level of detail of the proposal based on the importance of the tactic when making a proposal. For example, the suggestion unit makes detailed suggestions and provides specific advice for important tactics. The suggestion unit can also make concise suggestions and provide only the main points for less important tactics. The suggestion unit can also determine the priority of the suggestions according to the importance of the tactics and make suggestions starting with the important tactics. In this way, by adjusting the level of detail of the proposal based on the importance of the tactics, detailed suggestions can be made for important tactics. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the tactics to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0092] The suggestion unit can apply different proposal algorithms depending on the category of the tactic when making a proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the tactic when making a proposal. For example, the suggestion unit applies a proposal algorithm based on a specific attacking pattern to an offensive tactic. The suggestion unit can also apply a proposal algorithm for grasping defensive tendencies to a defensive tactic. The suggestion unit can also apply a proposal algorithm for performing sentiment analysis to a feedback tactic. In this way, by applying different proposal algorithms depending on the category of the tactic, more accurate proposals can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of the tactic to a generation AI and cause the generation AI to apply the proposal algorithm.

[0093] The suggestion unit can improve the accuracy of the suggestion based on the player's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results when making a suggestion. For example, the suggestion unit can compare the current suggestion results with the player's past suggestion results to improve accuracy. The suggestion unit can also extract specific patterns from the player's past suggestion results and reflect them in the current suggestion. The suggestion unit can also adjust the suggestion algorithm based on the player's past suggestion results to improve accuracy. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the player's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0094] The suggestion unit can estimate the player's emotions and adjust the length of the suggestions based on the estimated player's emotions. For example, the suggestion unit can estimate the player's emotions and adjust the length of the suggestions based on the estimated player's emotions. For example, if the player is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the player is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the player is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for adjusting the length of the suggestions based on the player's emotions to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using an AI, or without an AI. For example, the suggestion unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0095] The suggestion unit can determine the order of proposals based on the time of submission of tactics when making proposals. The suggestion unit, for example, determines the order of proposals based on the time of submission of tactics when making proposals. For example, the suggestion unit prioritizes proposing the most effective tactics before an important match. The suggestion unit can also propose tactics for the next match based on feedback after the match. The suggestion unit can also dynamically adjust the priority of tactics at specific times during the season. In this way, by determining the order of proposals based on the time of submission of tactics, important tactics can be prioritized for proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the time of submission of tactics to a generation AI and cause the generation AI to determine the order of proposals.

[0096] The suggestion unit can adjust the order of proposals based on the relevance of the tactics when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the tactics when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant tactics and provides specific advice. The suggestion unit can also postpone less relevant tactics and make proposals starting with important tactics. The suggestion unit can also dynamically adjust the order of proposals according to the relevance of the tactics. In this way, by adjusting the order of proposals based on the relevance of the tactics, it is possible to make proposals starting with important tactics. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of tactics to a generation AI and cause the generation AI to adjust the order of proposals.

[0097] The suggestion unit may adjust the use of technical terms in the suggestion according to the player's knowledge level when making the suggestion. For example, the suggestion unit may adjust the use of technical terms in the suggestion according to the player's knowledge level when making the suggestion. For example, if the player is a beginner, the suggestion unit may avoid technical terms and provide a suggestion in simple language. If the player is an intermediate player, the suggestion unit may use appropriate technical terms and provide a detailed suggestion. If the player is an advanced player, the suggestion unit may use a lot of technical terms and provide a more advanced suggestion. By adjusting the use of technical terms in the suggestion according to the player's knowledge level, it is possible to provide a more understandable suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the player's knowledge level into the generation AI and cause the generation AI to adjust the use of technical terms.

[0098] The training unit can estimate the player's emotions and adjust the training method based on the estimated player's emotions. For example, the training unit can estimate the player's emotions and adjust the training method based on the estimated player's emotions. For example, if the player is nervous, the training unit can suggest a training method to relax the player. If the player is relaxed, the training unit can also suggest a training method to improve concentration. If the player is excited, the training unit can also suggest a training method to release energy. This allows for more effective training by adjusting the training method based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the training unit can be performed using an AI, for example, or without an AI. For example, the training unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the training method.

[0099] The training unit can analyze the player's past performance during training and select an appropriate training method. For example, the training unit can analyze the player's past performance during training and select an appropriate training method. For example, the training unit can select a training method to improve a specific skill based on the player's past performance data. The training unit can also select a training method to compensate for a weakness based on the player's past performance data. The training unit can also select a training method to improve overall performance based on the player's past performance data. In this way, the optimal training method can be selected by analyzing the player's past performance. Some or all of the above-described processing in the training unit can be performed using, for example, AI, or can be performed without using AI. For example, the training unit can input the player's past performance data into a generation AI and have the generation AI select a training method.

[0100] The training unit can customize training methods based on the player's current physical condition and fatigue level during training. For example, the training unit can customize training methods based on the player's current physical condition and fatigue level during training. For example, the training unit can monitor the player's heart rate and fatigue level in real time and train when the player is in good physical condition. The training unit can also temporarily suspend training if the player is in poor physical condition and resume it after recovery. The training unit can also adjust the intensity of training and only perform important training if the player's fatigue level is high. This allows for more effective training by customizing training methods based on the player's physical condition and fatigue level. Some or all of the above-described processing in the training unit can be performed using, or without, AI. For example, the training unit can input data on the player's physical condition and fatigue level into a generation AI and have the generation AI customize the training methods.

[0101] The training unit can adjust the training method by reflecting the player's feedback during training. For example, the training unit can adjust the training method by reflecting the player's feedback during training. For example, the training unit can adjust the frequency of training based on the feedback provided by the player. The training unit can also adjust the content of training based on the feedback provided by the player. The training unit can also adjust the timing of training based on the feedback provided by the player. In this way, the training method can be improved by reflecting the player's feedback. Some or all of the above-mentioned processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the player's feedback data into the generation AI and cause the generation AI to adjust the training method.

[0102] The training unit can estimate the player's emotions and determine training priorities based on the estimated player's emotions. The training unit, for example, estimates the player's emotions and determines training priorities based on the estimated player's emotions. For example, if the player is nervous, the training unit can prioritize training to relax the player. Also, if the player is relaxed, the training unit can prioritize training to improve concentration. Also, if the player is excited, the training unit can prioritize training to release energy. This allows for more effective training by determining training priorities based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the training unit can be performed using an AI, for example, or without an AI. For example, the training unit can input the player's emotion data into the generation AI and have the generation AI determine the training priorities.

[0103] The training unit can select an appropriate training method based on the player's geographical location information during training. For example, the training unit selects the optimal training method by taking the player's geographical location information into consideration during training. For example, if the player is training in a specific area, the training unit can select a training method suitable for the local environment. Furthermore, if the player is training in a specific stadium, the training unit can select a training method suitable for the characteristics of the stadium. Furthermore, if the player is training under specific climatic conditions, the training unit can select a training method suitable for the climatic conditions. In this way, the optimal training method can be selected by taking the player's geographical location information into consideration. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the player's geographical location data into the generation AI and cause the generation AI to select a training method.

[0104] The training unit can analyze the player's social media activity during training to suggest training methods. For example, the training unit can analyze the player's social media activity during training to suggest training methods. For example, the training unit can suggest training methods based on the player's training impressions and feedback shared on social media. The training unit can also refer to the training methods of other players the player follows on social media. The training unit can also suggest training methods based on the opinions and trends of the community in which the player participates on social media. In this way, more effective training methods can be suggested by analyzing the player's social media activity. Some or all of the above-mentioned processing in the training unit may be performed using, or without, AI. For example, the training unit can input the player's social media data into a generation AI and have the generation AI suggest training methods.

[0105] The training unit can adjust the training method during training by reflecting the player's past feedback. For example, the training unit can adjust the training method during training by reflecting the player's past feedback. For example, the training unit can adjust the frequency of training based on feedback provided by the player in the past. The training unit can also adjust the content of training based on feedback provided by the player in the past. The training unit can also adjust the timing of training based on feedback provided by the player in the past. In this way, the training method can be customized by reflecting the player's past feedback. Some or all of the above-described processing in the training unit can be performed using AI, for example, or can be performed without using AI. For example, the training unit can input the player's feedback data into a generation AI and cause the generation AI to adjust the training method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and training unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can provide tactical advice generated by the specific processing unit 290 of the data processing device 12. For example, the training unit can use the control unit 46A of the smart device 14 to perform training based on the tactics suggested by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and training unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 and the microphone 238 of the smart glasses 214. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can provide tactical advice generated by the specific processing unit 290 of the data processing device 12. For example, the training unit can use the control unit 46A of the smart glasses 214 to perform training based on the tactics suggested by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and training unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 or the microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can provide tactical advice generated by the specific processing unit 290 of the data processing device 12. For example, the training unit can use the control unit 46A of the headset type terminal 314 to perform training based on the tactics suggested by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and training unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit can analyze data collected by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can provide tactical advice generated by the specific processing unit 290 of the data processing device 12. For example, the training unit can use the control unit 46A of the robot 414 to perform training based on the tactics suggested by the generation AI.

[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 estimate the player's emotions and determine the priorities of analysis based on the estimated emotions. For example, if the player is nervous, it can prioritize analyzing data to help the player relax. Also, if the player is concentrating, it can prioritize analyzing data to help the player maintain that concentration. Furthermore, if the player is tired, it can prioritize analyzing data that is useful for recovering from fatigue. In this way, by determining the priorities of analysis based on the player's emotions, it is possible to provide more effective analysis results.

[0108] The collection unit can analyze a player's past performance data and select the optimal data collection method. For example, it can analyze a player's past match data and collect data when specific performance indicators are high. It can also collect data focusing on specific actions or playing styles. It can also collect data at specific scenes during a match. In this way, the optimal data collection method can be selected by analyzing a player's past performance data.

[0109] The suggestion unit can estimate the player's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the player is nervous, a simple, highly visible suggestion can be provided. If the player is relaxed, a detailed suggestion can be provided. Furthermore, if the player is excited, a suggestion with a visually stimulating effect can be provided. In this way, by adjusting the way suggestions are expressed based on the player's emotions, more appropriate suggestions can be provided.

[0110] The training unit can estimate the player's emotions and adjust the training method based on the estimated emotions. For example, if the player is nervous, it can suggest a training method to relax the player. Also, if the player is relaxed, it can suggest a training method to improve concentration. Furthermore, if the player is excited, it can suggest a training method to release energy. In this way, by adjusting the training method based on the player's emotions, it is possible to provide more effective training.

[0111] When collecting data, the collection unit can filter the data based on the player's current physical condition and fatigue level. For example, the collection unit can monitor the player's heart rate and fatigue level in real time and collect data when the player is in good physical condition. In addition, if the player is in poor physical condition, the collection unit can temporarily suspend data collection and resume it after the player has recovered. Furthermore, if the player is highly fatigued, the frequency of data collection can be reduced and only important data can be collected. In this way, by filtering data collection based on the player's physical condition and fatigue level, more accurate data can be collected.

[0112] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis of important data and provide specific advice. It can also perform a concise analysis of less important data and provide only the main points. Furthermore, it can determine the priority of the analysis based on the importance of the data and start analyzing the important data first. In this way, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis of important data.

[0113] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tactic. For example, detailed proposals can be made for important tactics, providing specific advice. Also, for less important tactics, concise proposals can be made, providing only the main points. Furthermore, the priority of the proposals can be determined according to the importance of the tactics, and suggestions can be made starting with the most important tactics. In this way, by adjusting the level of detail of the proposal based on the importance of the tactic, detailed proposals can be made for important tactics.

[0114] During training, the training department can analyze a player's past performance and select an appropriate training method. For example, a training method for strengthening a specific skill can be selected based on the player's past performance data. A training method for compensating for a weakness can also be selected based on the player's past performance data. Furthermore, a training method for improving overall performance can also be selected based on the player's past performance data. In this way, the optimal training method can be selected by analyzing a player's past performance.

[0115] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the estimated emotions. For example, if the player is nervous, data to help the player relax can be collected with priority. Also, if the player is concentrating, data to help the player maintain that concentration can be collected with priority. Furthermore, if the player is tired, data to help the player recover from fatigue can be collected with priority. Thus, by determining the priority of data based on the player's emotions, important data can be collected with priority.

[0116] The training department can estimate the player's emotions and determine the priority of training based on the estimated emotions. For example, if the player is nervous, training to relax the player can be prioritized. Also, if the player is relaxed, training to improve concentration can be prioritized. Furthermore, if the player is excited, training to release energy can be prioritized. In this way, by prioritizing training based on the player's emotions, more effective training can be provided.

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

[0118] Step 1: The collection unit collects match data. The match data includes player movements, performance, tactical execution status, etc. For example, the collection unit collects player position data, speed data, success rate, number of mistakes, tactical success rate, and execution time. Step 2: The analysis unit uses the generative AI to analyze the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, the analysis unit identifies the strengths and weaknesses of players and teams based on the collected data, and understands the preferred playing styles of specific players and the tactical tendencies of the team as a whole. Step 3: The suggestion unit uses the generation AI to provide tactical advice based on the analysis results obtained by the analysis unit. The tactical advice may include suggested tactical changes and player positioning changes. For example, the suggestion unit may suggest tactics based on the preferred playing style of a specific player and tactical tendencies of the entire team. Step 4: The training team conducts training based on the tactics proposed by the generative AI. Training includes shooting practice, passing practice, physical training, etc. For example, the training team may conduct shooting practice and passing practice for specific players to build tactical consistency across the team.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0190] [Explanation of symbols]

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

Claims

1. a collection unit that collects match data; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that provides tactical advice based on the analysis results obtained by the analysis unit; a training unit that performs training based on the advice provided by the suggestion unit. A system characterized by:

2. The collecting unit Collect data on movements, performance and tactical execution during a match 2. The system of claim 1.

3. The analysis unit Identify strengths and weaknesses of players and teams based on collected data 2. The system of claim 1.

4. The proposal unit Understand the preferred playing style of a particular player or the tactical tendencies of the entire team 2. The system of claim 1.

5. The training section Specific players practice shooting and passing based on tactics suggested by the generative AI 2. The system of claim 1.

6. The training section Train the whole team to build tactical cohesion 2. The system of claim 1.

7. The collecting unit Estimate player emotions and adjust data collection timing based on the estimated player emotions 2. The system of claim 1.

8. The collecting unit Analyze players' past performance data and select the most appropriate data collection method 2. The system of claim 1.

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