system

Abstract

The system according to the embodiment aims to properly evaluate the market value of athletes and use this information to help build teams. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects player performance data, past results, and contract information. The analysis unit analyzes the data collected by the collection unit and calculates the market value of the player. The proposal unit proposes player combinations based on the market value calculated by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to properly evaluate the abilities and market value of athletes, making it difficult to make decisions when building a team.

[0005] The system according to the embodiment aims to properly evaluate the market value of athletes and use this information to help build teams. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects performance data, past results, and contract information of players. The analysis unit analyzes the data collected by the collection unit and calculates the market value of the players. The proposal unit proposes player combinations based on the market value calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can properly evaluate the market value of athletes and can be useful in team building. [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) The platform (PF) according to an embodiment of the present invention is a system that instantly calculates the market value of players and supports team-building decisions. This system collects player performance data, past performance, contract information, and other data, and then uses AI to analyze the data to calculate market value and propose optimal player combinations. For example, detailed data such as a player's movements during a game, playing style, number of goals scored, and number of assists is collected. In the case of soccer players, data such as the distance traveled during a game, pass success rate, and number of shots taken can be collected. This allows for a detailed understanding of a player's performance. Next, AI analyzes the collected data and calculates the player's market value. The AI ​​evaluates the player's abilities and performance based on the collected data and calculates market value. For example, the AI ​​comprehensively evaluates the player's number of goals scored, number of assists, and movements during a game to calculate market value. This allows for an accurate evaluation of a player's abilities and market value. Furthermore, the AI ​​proposes optimal player combinations for team building based on the calculated market value. Based on the calculated market value, the AI ​​proposes player combinations that are optimal for the team's tactics and strategy. For example, a team's performance can be maximized by balancing players with strong offensive and defensive abilities. This can be used to help with team-building decisions. By instantly calculating a player's market value, it is possible to make quick and appropriate decisions during contract negotiations and trades. It can also maximize team performance by proposing the best player combination for a team's tactics and strategy. This can help with team-building decisions in response to rising player contracts and broadcasting fees. The platform can instantly calculate a player's market value and support decisions regarding team building.

[0029] The platform according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects player performance data, past performance, and contract information. The player performance data includes, for example, movements during a game, number of goals scored, number of assists, etc. The collection unit can collect, for example, movements during a game using GPS data or motion analysis technology. The collection unit can also collect number of goals scored and number of assists using official records or game video analysis. The collection unit can also collect player contract information as data such as contract period, contract amount, and contract conditions. The analysis unit analyzes the data collected by the collection unit and calculates the player's market value. For example, the analysis unit evaluates the player's abilities and performance based on the collected data and calculates the market value. The analysis unit can comprehensively evaluate the player's number of goals scored, number of assists, movements during a game, etc. to calculate the market value. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can calculate the player's market value using an AI model that inputs the collected data and outputs market value. The suggestion unit proposes player combinations based on the market values ​​calculated by the analysis unit. For example, the suggestion unit proposes player combinations that are optimal for the team's tactics and strategy based on the calculated market values. The suggestion unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose player combinations using an AI model that inputs the calculated market values ​​and outputs optimal player combinations. This allows the platform according to the embodiment to instantly calculate the market values ​​of players and assist in making decisions regarding team building.

[0030] The collection unit can collect detailed data on a player's movements during a game, playing style, number of goals scored, and number of assists. The collection unit, for example, collects movements during a game using GPS data or motion analysis technology. For example, the collection unit can obtain a player's running distance and number of sprints from GPS data. The collection unit can also analyze a player's movements using video analysis technology to understand a player's playing style. For example, the collection unit can collect a player's pass success rate and dribble success rate using video analysis technology. The collection unit can also collect the number of goals scored and the number of assists scored using official records or game video analysis. For example, the collection unit can obtain the number of goals scored and the number of assists scored from official records after the game. The collection unit can also analyze game video to identify scoring scenes and assist scenes. This allows the collection unit to understand a player's performance in detail. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on a player's movements and playing style during a game into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can evaluate the player's abilities and performance based on the collected data and calculate the player's market value. The analysis unit, for example, evaluates the player's abilities and performance based on the collected data. For example, the analysis unit can comprehensively evaluate the player's number of goals scored, number of assists, movements during a game, etc., and calculate the player's market value. The analysis unit can evaluate the player's abilities such as speed, stamina, and technical ability. For example, the analysis unit can evaluate the player's speed based on the number of times the player sprints and the distance he runs. The analysis unit can also evaluate the player's stamina based on the player's movements during a game. Furthermore, the analysis unit can evaluate the player's technical ability based on the pass success rate and dribble success rate. This allows the analysis unit to appropriately evaluate the player's abilities and market value. 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 calculate the player's market value using an AI model that inputs the collected data and outputs market value.

[0032] The proposal unit can propose a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. The proposal unit, for example, proposes a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. For example, the proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. The proposal unit can propose a combination of players based on the players' market value, taking into account positional balance and tactical suitability. For example, when adopting offensive tactics, the proposal unit can arrange many players with high offensive ability. Also, when adopting defensive tactics, the proposal unit can arrange many players with high defensive ability. This can be useful for determining team building. Some or all of the above-mentioned processing by the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that inputs the calculated market value and outputs an optimal combination of players.

[0033] The proposal unit can arrange players with offensive ability and players with defensive ability in a balanced manner. For example, the proposal unit arranges players with offensive ability and players with defensive ability in a balanced manner. For example, the proposal unit can arrange forward players with high offensive ability and defender players with high defensive ability in a balanced manner. The proposal unit can also arrange attacking midfielders and defensive midfielders in a balanced manner. This allows the proposal unit to maximize team performance. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose player arrangements taking into account the balance between offensive ability and defensive ability based on the market value of the players.

[0034] The proposal unit can propose a combination of players to maximize team performance. The proposal unit, for example, proposes a combination of players to maximize team performance. For example, the proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. The proposal unit can propose a combination of players based on the market value of the players, taking into consideration positional balance and tactical suitability. For example, when an offensive tactic is adopted, the proposal unit can arrange many players with high offensive ability. Also, when a defensive tactic is adopted, the proposal unit can arrange many players with high defensive ability. In this way, the proposal unit can maximize team performance. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that outputs an optimal combination of players based on the market value of the players.

[0035] The collection unit can analyze a player's past performance data and select an optimal collection method. The collection unit, for example, analyzes a player's past performance data and selects an optimal collection method. For example, the collection unit can analyze a player's past game data and select a data collection method that suits a specific playing style. The collection unit can also focus on collecting data on scoring scenes and assist scenes based on the player's past performance. Furthermore, the collection unit can select a data collection method that matches the contract renewal period by referring to the player's past contract information. This allows the collection unit to select an optimal collection method based on the player's past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into a generation AI and cause the generation AI to select an optimal collection method.

[0036] The collection unit can filter the performance data based on the player's current physical condition and condition when collecting the performance data. For example, the collection unit can filter the performance data based on the player's current physical condition and condition when collecting the performance data. For example, if the player is injured, the collection unit can prioritize collecting data that is not affected by the injury. Also, if the player is fatigued, the collection unit can collect data that is not affected by fatigue. Furthermore, if the player is in top condition, the collection unit can collect data that reflects that state. In this way, the collection unit can collect data that corresponds to the player's physical condition and condition. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's physical condition data to the generation AI and cause the generation AI to perform filtering.

[0037] When collecting performance data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the player. For example, when collecting performance data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the player. For example, when collecting performance data, the collection unit can prioritize collecting performance data at the home stadium when the player plays a home game. Furthermore, when the player plays an away game, the collection unit can prioritize collecting performance data at the away stadium. Furthermore, when the player plays a game in a specific region, the collection unit can prioritize collecting data related to the climate and environment of that region. In this way, the collection unit can collect highly relevant data based on the geographical location information of the player. Some or all of the above-mentioned 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 geographical location information of the player to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the social media activities of the players and collect related data when collecting performance data. For example, the collection unit can analyze the social media activities of the players and collect related data when collecting performance data. For example, the collection unit can collect performance data during a game based on content posted by the players on social media before the game. The collection unit can also collect post-game performance data based on content posted by the players on social media after the game. Furthermore, if the player participates in a specific event or campaign, the collection unit can collect data related to the activities. This allows the collection unit to collect related data based on the player's social media activities. Some or all of the above-mentioned 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 social media data into the generation AI and cause the generation AI to collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of a player during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of a player during analysis. For example, the analysis unit can perform a detailed analysis of an important player and provide detailed data. For an average player, the analysis unit can also perform a basic analysis and provide key data. Furthermore, for a rookie player, the analysis unit can perform an analysis that anticipates future growth and evaluates potential. This allows the analysis unit to provide analysis results with an appropriate level of detail depending on the importance of the player. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input player importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the player's category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the player's category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes scoring ability and shooting accuracy to a forward player. The analysis unit can also apply an analysis algorithm that emphasizes pass success rate and number of assists to a midfielder player. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes defensive ability and tackle success rate to a defender player. This allows the analysis unit to apply an appropriate analysis algorithm depending on the player's category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input player category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the timing of submission of the player's performance data during analysis. For example, the analysis unit can determine the analysis priority based on the timing of submission of the player's performance data during analysis. For example, the analysis unit can prioritize analysis of the most recent game data to evaluate the most recent performance. The analysis unit can also prioritize analysis of data from important games to evaluate the impact of the games. Furthermore, the analysis unit can analyze long-term data to evaluate the growth and changes of the player. This allows the analysis unit to perform analysis with appropriate priorities based on the timing of submission of the player's performance data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the timing of submission of performance data to the generation AI and cause the generation AI to determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of players during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of players during analysis. For example, the analysis unit can prioritize analyzing data of key players on a team to evaluate the performance of the entire team. The analysis unit can also prioritize analyzing data of newly joined players to evaluate their fitness for the team. Furthermore, the analysis unit can prioritize analyzing data of players who are scheduled to leave the team to evaluate their impact on the team. This allows the analysis unit to perform analysis in an appropriate order based on the relevance of players. 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 player relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The proposal unit can adjust the level of detail of the proposal based on the market value of the player when making a proposal. For example, the proposal unit can adjust the level of detail of the proposal based on the market value of the player when making a proposal. For example, the proposal unit can make a detailed proposal and provide detailed data for a player with a high market value. The proposal unit can also make a basic proposal and provide key data for a player with an average market value. Furthermore, the proposal unit can make a proposal that anticipates future growth for a rookie player and evaluate the player's potential. This allows the proposal unit to provide a proposal with an appropriate level of detail depending on the player's market value. Some or all of the above-described processing in the proposal unit can be performed using AI, for example, or without AI. For example, the proposal unit can input player market value data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0044] The suggestion unit can apply different suggestion algorithms depending on the player's category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the player's category when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm that emphasizes scoring ability and shooting accuracy to a forward player. The suggestion unit can also apply a suggestion algorithm that emphasizes pass success rate and number of assists to a midfielder player. Furthermore, the suggestion unit can apply a suggestion algorithm that emphasizes defensive ability and tackle success rate to a defender player. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the player's category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input player category data to a generation AI and cause the generation AI to apply the suggestion algorithm.

[0045] The proposal unit can determine the priority of proposals based on when the player's market value was calculated when making a proposal. The proposal unit, for example, determines the priority of proposals based on when the player's market value was calculated when making a proposal. For example, the proposal unit can make the latest proposal based on the most recent market value. The proposal unit can also prioritize proposals in line with important contract negotiation periods. Furthermore, the proposal unit can make proposals that anticipate future growth based on long-term market value. This allows the proposal unit to make proposals with appropriate priority based on when the player's market value was calculated. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input market value calculation time data into the generation AI and have the generation AI determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of players when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of players when making suggestions. For example, the suggestion unit can prioritize suggestions for key players of the team to maximize the performance of the entire team. The suggestion unit can also prioritize suggestions for new players and evaluate their fitness for the team. Furthermore, the suggestion unit can prioritize suggestions for players who are scheduled to leave the team and evaluate their impact on the team. This allows the suggestion unit to make suggestions in an appropriate order based on the relevance of players. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input player relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] In addition to athlete performance data, the collection unit can also collect athlete health data. For example, by collecting biometric data such as athlete heart rate, blood pressure, and oxygen saturation, the physical condition of the athlete can be grasped in real time. The collection unit can also collect athlete sleep and diet data to comprehensively evaluate the athlete's condition. Furthermore, based on the athlete's health data, the collection unit can identify factors that affect the athlete's performance and suggest appropriate training and rest. This allows the collection unit to gain a detailed understanding of the athlete's health condition and provide data to maximize the athlete's performance.

[0049] In addition to a player's market value, the proposal department can also evaluate the player's future growth potential. For example, the proposal department can evaluate the player's future growth potential based on the player's age, experience, and training history. The proposal department can also evaluate the player's future growth potential based on the player's technical skills and physical abilities. Furthermore, the proposal department can propose a player development plan and training program based on the player's future growth potential. This allows the proposal department to gain a detailed understanding of the player's future growth potential and use it to develop and train the player.

[0050] The collection unit can also collect environmental data about players in addition to their performance data. For example, it can collect environmental data such as the temperature, humidity, and wind speed at the players' training grounds and game venues to understand the factors that affect players' performance. It can also collect the players' travel distances and travel times to evaluate their fatigue levels. Furthermore, the collection unit can suggest environmental conditions that are optimal for the players' performance based on the players' environmental data. This allows the collection unit to obtain a detailed understanding of the players' environmental data and provide data to maximize the players' performance.

[0051] In addition to player performance data, the analysis unit can also analyze a player's role within a team. For example, it can evaluate a player's role within a team based on the player's position and playing style. It can also analyze a player's movements and passing during a game to evaluate the degree of teamwork between players. Furthermore, the analysis unit can propose tactics and strategies to maximize a player's performance based on the player's role within a team. In this way, the analysis unit can gain a detailed understanding of a player's role within a team and provide data to maximize a player's performance.

[0052] The collection unit can also collect training data of players in addition to performance data of players. For example, the collection unit can collect the content, intensity, and frequency of the player's training sessions to evaluate the effectiveness of the player's training. The collection unit can also collect the player's heart rate and calories burned during training to understand the player's physical condition. Furthermore, the collection unit can propose a training program to maximize the player's performance based on the player's training data. In this way, the collection unit can obtain a detailed understanding of the player's training data and provide data to maximize the player's performance.

[0053] The proposal department can evaluate the contract terms of a player in addition to the player's market value. For example, the proposal department can evaluate the contract terms of a player based on the player's contract period, contract amount, and contract terms. The proposal department can also evaluate the contract terms of a player based on the player's market value and performance data. Furthermore, the proposal department can propose contract renewals or trades for a player based on the contract terms of the player. This allows the proposal department to gain a detailed understanding of the player's contract terms, which can be useful in contract negotiations and trades.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects player performance data, past performance, and contract information. Player performance data includes, for example, movements during a game, number of goals scored, number of assists, etc. The collection unit can collect movements during a game, for example, using GPS data or motion analysis technology. The collection unit can also collect number of goals scored and number of assists using official records or game video analysis. Furthermore, the collection unit can collect player contract information such as contract period, contract amount, contract conditions, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the market value of the player. For example, the analysis unit evaluates the player's abilities and performance based on the collected data and calculates the market value. The analysis unit can calculate the market value by comprehensively evaluating the player's number of goals scored, number of assists, movements during the game, etc. 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 calculate the market value of the player using an AI model that inputs the collected data and outputs market value. Step 3: The proposal unit proposes a combination of players based on the market value calculated by the analysis unit. For example, the proposal unit proposes a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. The proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that inputs the calculated market value and outputs the optimal combination of players.

[0056] (Example 2) The platform (PF) according to an embodiment of the present invention is a system that instantly calculates the market value of players and supports team-building decisions. This system collects player performance data, past performance, contract information, and other data, and then uses AI to analyze the data to calculate market value and propose optimal player combinations. For example, detailed data such as a player's movements during a game, playing style, number of goals scored, and number of assists is collected. In the case of soccer players, data such as the distance traveled during a game, pass success rate, and number of shots taken can be collected. This allows for a detailed understanding of a player's performance. Next, AI analyzes the collected data and calculates the player's market value. The AI ​​evaluates the player's abilities and performance based on the collected data and calculates market value. For example, the AI ​​comprehensively evaluates the player's number of goals scored, number of assists, and movements during a game to calculate market value. This allows for an accurate evaluation of a player's abilities and market value. Furthermore, the AI ​​proposes optimal player combinations for team building based on the calculated market value. Based on the calculated market value, the AI ​​proposes player combinations that are optimal for the team's tactics and strategy. For example, a team's performance can be maximized by balancing players with strong offensive and defensive abilities. This can be used to help with team-building decisions. By instantly calculating a player's market value, it is possible to make quick and appropriate decisions during contract negotiations and trades. It can also maximize team performance by proposing the best player combination for a team's tactics and strategy. This can help with team-building decisions in response to rising player contracts and broadcasting fees. The platform can instantly calculate a player's market value and support decisions regarding team building.

[0057] The platform according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects player performance data, past performance, and contract information. The player performance data includes, for example, movements during a game, number of goals scored, number of assists, etc. The collection unit can collect, for example, movements during a game using GPS data or motion analysis technology. The collection unit can also collect number of goals scored and number of assists using official records or game video analysis. The collection unit can also collect player contract information as data such as contract period, contract amount, and contract conditions. The analysis unit analyzes the data collected by the collection unit and calculates the player's market value. For example, the analysis unit evaluates the player's abilities and performance based on the collected data and calculates the market value. The analysis unit can comprehensively evaluate the player's number of goals scored, number of assists, movements during a game, etc. to calculate the market value. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can calculate the player's market value using an AI model that inputs the collected data and outputs market value. The suggestion unit proposes player combinations based on the market values ​​calculated by the analysis unit. For example, the suggestion unit proposes player combinations that are optimal for the team's tactics and strategy based on the calculated market values. The suggestion unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose player combinations using an AI model that inputs the calculated market values ​​and outputs optimal player combinations. This allows the platform according to the embodiment to instantly calculate the market values ​​of players and assist in making decisions regarding team building.

[0058] The collection unit can collect detailed data on a player's movements during a game, playing style, number of goals scored, and number of assists. The collection unit, for example, collects movements during a game using GPS data or motion analysis technology. For example, the collection unit can obtain a player's running distance and number of sprints from GPS data. The collection unit can also analyze a player's movements using video analysis technology to understand a player's playing style. For example, the collection unit can collect a player's pass success rate and dribble success rate using video analysis technology. The collection unit can also collect the number of goals scored and the number of assists scored using official records or game video analysis. For example, the collection unit can obtain the number of goals scored and the number of assists scored from official records after the game. The collection unit can also analyze game video to identify scoring scenes and assist scenes. This allows the collection unit to understand a player's performance in detail. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on a player's movements and playing style during a game into a generation AI and have the generation AI analyze the data.

[0059] The analysis unit can evaluate the player's abilities and performance based on the collected data and calculate the player's market value. The analysis unit, for example, evaluates the player's abilities and performance based on the collected data. For example, the analysis unit can comprehensively evaluate the player's number of goals scored, number of assists, movements during a game, etc., and calculate the player's market value. The analysis unit can evaluate the player's abilities such as speed, stamina, and technical ability. For example, the analysis unit can evaluate the player's speed based on the number of times the player sprints and the distance he runs. The analysis unit can also evaluate the player's stamina based on the player's movements during a game. Furthermore, the analysis unit can evaluate the player's technical ability based on the pass success rate and dribble success rate. This allows the analysis unit to appropriately evaluate the player's abilities and market value. 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 calculate the player's market value using an AI model that inputs the collected data and outputs market value.

[0060] The proposal unit can propose a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. The proposal unit, for example, proposes a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. For example, the proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. The proposal unit can propose a combination of players based on the players' market value, taking into account positional balance and tactical suitability. For example, when adopting offensive tactics, the proposal unit can arrange many players with high offensive ability. Also, when adopting defensive tactics, the proposal unit can arrange many players with high defensive ability. This can be useful for determining team building. Some or all of the above-mentioned processing by the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that inputs the calculated market value and outputs an optimal combination of players.

[0061] The proposal unit can arrange players with offensive ability and players with defensive ability in a balanced manner. For example, the proposal unit arranges players with offensive ability and players with defensive ability in a balanced manner. For example, the proposal unit can arrange forward players with high offensive ability and defender players with high defensive ability in a balanced manner. The proposal unit can also arrange attacking midfielders and defensive midfielders in a balanced manner. This allows the proposal unit to maximize team performance. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose player arrangements taking into account the balance between offensive ability and defensive ability based on the market value of the players.

[0062] The proposal unit can propose a combination of players to maximize team performance. The proposal unit, for example, proposes a combination of players to maximize team performance. For example, the proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. The proposal unit can propose a combination of players based on the market value of the players, taking into consideration positional balance and tactical suitability. For example, when an offensive tactic is adopted, the proposal unit can arrange many players with high offensive ability. Also, when a defensive tactic is adopted, the proposal unit can arrange many players with high defensive ability. In this way, the proposal unit can maximize team performance. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that outputs an optimal combination of players based on the market value of the players.

[0063] The collection unit can estimate a player's emotions and adjust the timing of performance data collection based on the estimated player's emotions. For example, the collection unit estimates a player's emotions and adjusts the timing of performance data collection based on the estimated player's emotions. For example, if a player is nervous before a game, the collection unit avoids collecting data immediately before the start of the game and prioritizes collecting data during warm-ups. Furthermore, if a player is relaxed during a game, the collection unit can collect data at important play moments during the game. Furthermore, if a player is tired after a game, the collection unit can avoid collecting data immediately after the end of the game and collect data after a break. This allows the collection unit to collect data at appropriate times depending on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input emotional data of a player into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0064] The collection unit can analyze a player's past performance data and select an optimal collection method. The collection unit, for example, analyzes a player's past performance data and selects an optimal collection method. For example, the collection unit can analyze a player's past game data and select a data collection method that suits a specific playing style. The collection unit can also focus on collecting data on scoring scenes and assist scenes based on the player's past performance. Furthermore, the collection unit can select a data collection method that matches the contract renewal period by referring to the player's past contract information. This allows the collection unit to select an optimal collection method based on the player's past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into a generation AI and cause the generation AI to select an optimal collection method.

[0065] The collection unit can filter the performance data based on the player's current physical condition and condition when collecting the performance data. For example, the collection unit can filter the performance data based on the player's current physical condition and condition when collecting the performance data. For example, if the player is injured, the collection unit can prioritize collecting data that is not affected by the injury. Also, if the player is fatigued, the collection unit can collect data that is not affected by fatigue. Furthermore, if the player is in top condition, the collection unit can collect data that reflects that state. In this way, the collection unit can collect data that corresponds to the player's physical condition and condition. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's physical condition data to the generation AI and cause the generation AI to perform filtering.

[0066] 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 nervous, the collection unit can prioritize collecting data to help the player relax. Furthermore, if the player is relaxed, the collection unit can prioritize collecting data that helps the player improve their performance. Furthermore, if the player is excited, the collection unit can prioritize collecting data to help the player stay calm. In this way, the collection unit can determine the priority of data to be collected 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 collection unit may be performed using an AI, for example, or without 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.

[0067] When collecting performance data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the player. For example, when collecting performance data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the player. For example, when collecting performance data, the collection unit can prioritize collecting performance data at the home stadium when the player plays a home game. Furthermore, when the player plays an away game, the collection unit can prioritize collecting performance data at the away stadium. Furthermore, when the player plays a game in a specific region, the collection unit can prioritize collecting data related to the climate and environment of that region. In this way, the collection unit can collect highly relevant data based on the geographical location information of the player. Some or all of the above-mentioned 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 geographical location information of the player to the generation AI and cause the generation AI to collect highly relevant data.

[0068] The collection unit can analyze the social media activities of the players and collect related data when collecting performance data. For example, the collection unit can analyze the social media activities of the players and collect related data when collecting performance data. For example, the collection unit can collect performance data during a game based on content posted by the players on social media before the game. The collection unit can also collect post-game performance data based on content posted by the players on social media after the game. Furthermore, if the player participates in a specific event or campaign, the collection unit can collect data related to the activities. This allows the collection unit to collect related data based on the player's social media activities. Some or all of the above-mentioned 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 social media data into the generation AI and cause the generation AI to collect related data.

[0069] The analysis unit can estimate the player's emotions and adjust the method of expressing the analysis based on the estimated player's emotions. For example, the analysis unit can estimate the player's emotions and adjust the method of expressing the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the player is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the player is excited, the analysis unit can provide visually stimulating analysis results. This allows the analysis unit to provide analysis results in an appropriate expression method depending 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 may be performed using, for example, an AI, or may be performed without using 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 method of expressing the analysis.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of a player during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of a player during analysis. For example, the analysis unit can perform a detailed analysis of an important player and provide detailed data. For an average player, the analysis unit can also perform a basic analysis and provide key data. Furthermore, for a rookie player, the analysis unit can perform an analysis that anticipates future growth and evaluates potential. This allows the analysis unit to provide analysis results with an appropriate level of detail depending on the importance of the player. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input player importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the player's category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the player's category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes scoring ability and shooting accuracy to a forward player. The analysis unit can also apply an analysis algorithm that emphasizes pass success rate and number of assists to a midfielder player. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes defensive ability and tackle success rate to a defender player. This allows the analysis unit to apply an appropriate analysis algorithm depending on the player's category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input player category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0072] 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. Furthermore, if the player is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis unit to provide an analysis result of an appropriate length depending 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 length of the analysis.

[0073] The analysis unit can determine the analysis priority based on the timing of submission of the player's performance data during analysis. For example, the analysis unit can determine the analysis priority based on the timing of submission of the player's performance data during analysis. For example, the analysis unit can prioritize analysis of the most recent game data to evaluate the most recent performance. The analysis unit can also prioritize analysis of data from important games to evaluate the impact of the games. Furthermore, the analysis unit can analyze long-term data to evaluate the growth and changes of the player. This allows the analysis unit to perform analysis with appropriate priorities based on the timing of submission of the player's performance data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the timing of submission of performance data to the generation AI and cause the generation AI to determine the analysis priority.

[0074] The analysis unit can adjust the order of analysis based on the relevance of players during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of players during analysis. For example, the analysis unit can prioritize analyzing data of key players on a team to evaluate the performance of the entire team. The analysis unit can also prioritize analyzing data of newly joined players to evaluate their fitness for the team. Furthermore, the analysis unit can prioritize analyzing data of players who are scheduled to leave the team to evaluate their impact on the team. This allows the analysis unit to perform analysis in an appropriate order based on the relevance of players. 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 player relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0075] The suggestion unit can estimate the player's emotions and adjust the way the suggestion is expressed based on the estimated player's emotions. For example, the suggestion unit can estimate the player's emotions and adjust the way the suggestion is expressed based on the estimated player's emotions. For example, if the player is nervous, the suggestion unit can provide a simple, highly visible suggestion. If the player is relaxed, the suggestion unit can provide a detailed suggestion. If the player is excited, the suggestion unit can provide a visually stimulating suggestion. This allows the suggestion unit to provide a suggestion in an appropriate way according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 suggestion unit can be performed using an AI, for example, 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 the suggestion is expressed.

[0076] The proposal unit can adjust the level of detail of the proposal based on the market value of the player when making a proposal. For example, the proposal unit can adjust the level of detail of the proposal based on the market value of the player when making a proposal. For example, the proposal unit can make a detailed proposal and provide detailed data for a player with a high market value. The proposal unit can also make a basic proposal and provide key data for a player with an average market value. Furthermore, the proposal unit can make a proposal that anticipates future growth for a rookie player and evaluate the player's potential. This allows the proposal unit to provide a proposal with an appropriate level of detail depending on the player's market value. Some or all of the above-described processing in the proposal unit can be performed using AI, for example, or without AI. For example, the proposal unit can input player market value data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0077] The suggestion unit can apply different suggestion algorithms depending on the player's category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the player's category when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm that emphasizes scoring ability and shooting accuracy to a forward player. The suggestion unit can also apply a suggestion algorithm that emphasizes pass success rate and number of assists to a midfielder player. Furthermore, the suggestion unit can apply a suggestion algorithm that emphasizes defensive ability and tackle success rate to a defender player. This allows the suggestion unit to apply an appropriate suggestion algorithm depending on the player's category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input player category data to a generation AI and cause the generation AI to apply the suggestion algorithm.

[0078] The suggestion unit can estimate the player's emotions and adjust the length of the suggestion based on the estimated player's emotions. For example, the suggestion unit can estimate the player's emotions and adjust the length of the suggestion based on the estimated player's emotions. For example, if the player is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the player is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the player is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to provide a suggestion of an appropriate length depending 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 suggestion unit can be performed using an AI, for example, 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 suggestion.

[0079] The proposal unit can determine the priority of proposals based on when the player's market value was calculated when making a proposal. The proposal unit, for example, determines the priority of proposals based on when the player's market value was calculated when making a proposal. For example, the proposal unit can make the latest proposal based on the most recent market value. The proposal unit can also prioritize proposals in line with important contract negotiation periods. Furthermore, the proposal unit can make proposals that anticipate future growth based on long-term market value. This allows the proposal unit to make proposals with appropriate priority based on when the player's market value was calculated. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input market value calculation time data into the generation AI and have the generation AI determine the priority of proposals.

[0080] The suggestion unit can adjust the order of suggestions based on the relevance of players when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of players when making suggestions. For example, the suggestion unit can prioritize suggestions for key players of the team to maximize the performance of the entire team. The suggestion unit can also prioritize suggestions for new players and evaluate their fitness for the team. Furthermore, the suggestion unit can prioritize suggestions for players who are scheduled to leave the team and evaluate their impact on the team. This allows the suggestion unit to make suggestions in an appropriate order based on the relevance of players. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input player relevance data into the generation AI and cause the generation AI to adjust the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects player performance data using the camera 42 and communication I / F 44 of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the market value of the players based on the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal combination of players based on the calculated market value. Some or all of the collection unit, analysis unit, and proposal unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects player performance data using the camera 42 and communication I / F 44 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the market value of the players based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal combination of players based on the calculated market value. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects player performance data using the camera 42 and communication I / F 44 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the market value of the players based on the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal combination of players based on the calculated market value. Some or all of the collection unit, analysis unit, and proposal unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects player performance data using the camera 42 and communication I / F 44 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the market value of the players based on the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal combination of players based on the calculated market value. Some or all of the collection unit, analysis unit, and proposal unit may be realized, for example, by the control unit 46A of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In addition to athlete performance data, the collection unit can also collect athlete health data. For example, by collecting biometric data such as athlete heart rate, blood pressure, and oxygen saturation, the physical condition of the athlete can be grasped in real time. The collection unit can also collect athlete sleep and diet data to comprehensively evaluate the athlete's condition. Furthermore, based on the athlete's health data, the collection unit can identify factors that affect the athlete's performance and suggest appropriate training and rest. This allows the collection unit to gain a detailed understanding of the athlete's health condition and provide data to maximize the athlete's performance.

[0083] In addition to player performance data, the analysis unit can also analyze the player's psychological state. For example, it can analyze the player's level of tension before a game and their level of concentration during a game to evaluate the player's psychological state. It can also analyze the player's satisfaction and stress level after a game to understand the player's psychological health. Furthermore, the analysis unit can identify factors that affect the player's performance based on the player's psychological state and suggest appropriate mental training or counseling. In this way, the analysis unit can gain a detailed understanding of the player's psychological state and provide data to maximize the player's performance.

[0084] In addition to a player's market value, the proposal department can also evaluate the player's future growth potential. For example, the proposal department can evaluate the player's future growth potential based on the player's age, experience, and training history. The proposal department can also evaluate the player's future growth potential based on the player's technical skills and physical abilities. Furthermore, the proposal department can propose a player development plan and training program based on the player's future growth potential. This allows the proposal department to gain a detailed understanding of the player's future growth potential and use it to develop and train the player.

[0085] The collection unit can also collect environmental data about players in addition to their performance data. For example, it can collect environmental data such as the temperature, humidity, and wind speed at the players' training grounds and game venues to understand the factors that affect players' performance. It can also collect the players' travel distances and travel times to evaluate their fatigue levels. Furthermore, the collection unit can suggest environmental conditions that are optimal for the players' performance based on the players' environmental data. This allows the collection unit to obtain a detailed understanding of the players' environmental data and provide data to maximize the players' performance.

[0086] In addition to player performance data, the analysis unit can also analyze a player's role within a team. For example, it can evaluate a player's role within a team based on the player's position and playing style. It can also analyze a player's movements and passing during a game to evaluate the degree of teamwork between players. Furthermore, the analysis unit can propose tactics and strategies to maximize a player's performance based on the player's role within a team. In this way, the analysis unit can gain a detailed understanding of a player's role within a team and provide data to maximize a player's performance.

[0087] In addition to a player's market value, the proposal department can also evaluate the player's fan popularity. For example, the proposal department can evaluate the player's fan popularity based on the number of followers and engagement rate of the player on social media. The proposal department can also evaluate the player's fan popularity based on the player's performance during games and the content of interviews. Furthermore, the proposal department can propose marketing strategies and fan event plans for the player based on the player's fan popularity. This allows the proposal department to gain a detailed understanding of the player's fan popularity and use it for player marketing and fan engagement.

[0088] The collection unit can also collect training data of players in addition to performance data of players. For example, the collection unit can collect the content, intensity, and frequency of the player's training sessions to evaluate the effectiveness of the player's training. The collection unit can also collect the player's heart rate and calories burned during training to understand the player's physical condition. Furthermore, the collection unit can propose a training program to maximize the player's performance based on the player's training data. In this way, the collection unit can obtain a detailed understanding of the player's training data and provide data to maximize the player's performance.

[0089] In addition to player performance data, the analysis unit can also analyze a player's leadership ability. For example, it can analyze the frequency of a player's instructions and communication during a game to evaluate the player's leadership ability. It can also analyze a player's level of cooperation and trust with their teammates to evaluate the player's leadership ability. Furthermore, the analysis unit can suggest captaincy and leadership training for the player based on the player's leadership ability. This allows the analysis unit to gain a detailed understanding of a player's leadership ability and use it to help with the player's role and leadership within the team.

[0090] The proposal department can evaluate the contract terms of a player in addition to the player's market value. For example, the proposal department can evaluate the contract terms of a player based on the player's contract period, contract amount, and contract terms. The proposal department can also evaluate the contract terms of a player based on the player's market value and performance data. Furthermore, the proposal department can propose contract renewals or trades for a player based on the contract terms of the player. This allows the proposal department to gain a detailed understanding of the player's contract terms, which can be useful in contract negotiations and trades.

[0091] The collection unit can also collect lifestyle data of players in addition to performance data of the players. For example, the collection unit can collect information on the player's sleep time, dietary habits, and stress level to evaluate the player's lifestyle. The collection unit can also collect information on the player's hobbies, interests, and family structure to evaluate the player's lifestyle. Furthermore, the collection unit can identify factors that affect the player's performance based on the player's lifestyle data and make appropriate lifestyle improvement suggestions. In this way, the collection unit can obtain a detailed understanding of the player's lifestyle data and provide data to maximize the player's performance.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects player performance data, past performance, and contract information. Player performance data includes, for example, movements during a game, number of goals scored, number of assists, etc. The collection unit can collect movements during a game, for example, using GPS data or motion analysis technology. The collection unit can also collect number of goals scored and number of assists using official records or game video analysis. Furthermore, the collection unit can collect player contract information such as contract period, contract amount, contract conditions, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the market value of the player. For example, the analysis unit evaluates the player's abilities and performance based on the collected data and calculates the market value. The analysis unit can calculate the market value by comprehensively evaluating the player's number of goals scored, number of assists, movements during the game, etc. 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 calculate the market value of the player using an AI model that inputs the collected data and outputs market value. Step 3: The proposal unit proposes a combination of players based on the market value calculated by the analysis unit. For example, the proposal unit proposes a combination of players that is optimal for the team's tactics and strategy based on the calculated market value. The proposal unit can maximize team performance by arranging players with high offensive ability and players with high defensive ability in a balanced manner. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose a combination of players using an AI model that inputs the calculated market value and outputs the optimal combination of players.

[0094] 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 department that collects player performance data, past performance data, and contract information; an analysis unit that analyzes the data collected by the collection unit and calculates the market value of players; a proposal unit that proposes a combination of players based on the market value calculated by the analysis unit. A system characterized by:

2. The collecting unit Collect detailed data on players' movements or playing style during a match, as well as the number of goals scored and assists 2. The system of claim 1.

3. The analysis unit Based on the collected data, players' abilities and performance are evaluated and their market value is calculated.

2. The system of claim 1.

4. The proposal unit Based on the calculated market value, we propose the best combination of players for the team's tactics and strategy.

2. The system of claim 1.

5. The proposal unit A good balance of offensive and defensive players 2. The system of claim 1.

6. The proposal unit Suggest player combinations to maximize team performance 2. The system of claim 1.

7. The collecting unit Estimate the emotions of the players and adjust the timing of performance data collection based on the estimated emotions of the players.

2. The system of claim 1.

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

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