system

Abstract

The system according to this embodiment aims to calculate points based on performance data of real and virtual players and to manage tournaments. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a calculation unit, a management unit, and a provision unit. The reception unit accepts the selection of players. The analysis unit analyzes the performance data of the players accepted by the reception unit. The calculation unit calculates the players' points based on the data analyzed by the analysis unit. The management unit manages the tournament based on the points calculated by the calculation unit. The provision unit provides the tournament results managed by the management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in fantasy sports, there is a problem that points are calculated only based on the performance data of real players, and future performance cannot be predicted and reflected.

[0005] The system according to the embodiment aims to calculate points based on the performance data of real players and virtual players and manage tournaments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a calculation unit, a management unit, and a provision unit. The reception unit accepts the selection of players. The analysis unit analyzes the performance data of the players accepted by the reception unit. The calculation unit calculates the players' points based on the data analyzed by the analysis unit. The management unit manages the tournament based on the points calculated by the calculation unit. The provision unit provides the tournament results managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can calculate points and manage tournaments based on performance data of real and virtual players. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fantasy sports provision system according to an embodiment of the present invention is a system that provides a new form of fantasy sports in which real professional athletes and virtual athletes generated by a generative AI compete in the same league. This system calculates athletes' points not only based on their current performance but also on their future performance predicted by the generative AI. For example, a user selects real professional athletes and virtual athletes generated by the generative AI to form a team. Next, the generative AI calculates the athletes' points based on their current performance data and predicted future performance data. For example, the generative AI analyzes the athletes' past match data and training data to predict their future performance. This predicted data is reflected in the calculation of the athletes' points. Furthermore, to ensure transparency of the generative AI, a function is provided to explain to the user how the generative AI makes its predictions. For example, details of the generative AI's prediction model and the source of the data are made public, providing users with reliable information. In addition, by utilizing real-time data, the actual match results and the generative AI's predictions are integrated in real time, and users can check the points as the match progresses. A tournament format is introduced, allowing users to compete against each other. For example, the system motivates participants by offering tournaments within the fantasy AI league and rewards for top performers. It also attracts diverse sports fans by covering multiple sports such as soccer, basketball, and baseball. Finally, it introduces community features, providing a platform for users to share information and engage in discussions. This promotes interaction among fans and expands the enjoyment of fantasy sports. For instance, users can exchange opinions on their team's strategy or player performance. In this way, the fantasy sports system offers a new form of fantasy sports where real professional athletes and AI-generated virtual athletes compete in the same league. By combining transparency in the AI ​​generation, real-time data utilization, tournament formats, diverse sports offerings, and community features, it expands the possibilities of fantasy sports.This allows fantasy sports delivery systems to offer users a new form of fantasy sports experience.

[0029] The fantasy sports provision system according to this embodiment comprises a reception unit, an analysis unit, a calculation unit, a management unit, and a provision unit. The reception unit accepts the selection of athlete. The selection of athlete includes, for example, real professional athletes or virtual athletes generated by a generative AI, but is not limited to such examples. The reception unit provides, for example, an interface for the user to select an athlete. The reception unit can also collect athlete performance data. For example, it collects the athlete's past match data and training data. The analysis unit analyzes the athlete performance data accepted by the reception unit. For example, the analysis unit analyzes the athlete's past match data and training data to predict future performance. The analysis unit can use a generative AI to analyze the athlete's performance data. For example, the generative AI predicts future performance based on the athlete's past match data. The analysis unit can also analyze the athlete's training data to predict future performance. The calculation unit calculates the athlete's points based on the data analyzed by the analysis unit. For example, the calculation unit calculates points based on the athlete's current performance data and future performance prediction data. The calculation unit can use a generative AI to calculate the athlete's points. For example, the generating AI calculates points based on player performance data. The calculation unit can also calculate player points in real time. The management unit manages tournaments based on the points calculated by the calculation unit. The management unit manages tournaments within a fantasy AI league, for example. The management unit can manage the progress of tournaments and provide rewards to top performers. The delivery unit provides the results of tournaments managed by the management unit. The delivery unit notifies users of the tournament results, for example. The delivery unit can provide tournament results using the generating AI. For example, the generating AI provides feedback to users based on the tournament results. The delivery unit can also provide tournament results in real time. As a result, the fantasy sports delivery system according to this embodiment can consistently handle everything from player selection to providing tournament results.

[0030] The reception desk accepts player selections. Player selections include, but are not limited to, real-world professional athletes or virtual athletes generated by AI. The reception desk provides, for example, an interface for users to select players. Through this interface, users can view player profiles, past performance, training data, etc., and make their selections. The interface is designed to be intuitive and easy to use, making it easy for users to select players. The reception desk can also collect player performance data. For example, it can collect players' past match data and training data. This data is important for evaluating player performance and is provided as reference information when users select players. Furthermore, the reception desk can update player data in real time, providing users with the latest information. For example, if a player participates in a new match, the data from that match is immediately reflected in the system. This allows users to always select players based on the latest information. The reception desk plays a crucial role in efficiently and accurately receiving user player selections.

[0031] The analysis unit analyzes the player's performance data received by the reception unit. For example, the analysis unit analyzes the player's past match data and training data to predict future performance. The analysis unit can use generative AI to analyze the player's performance data. Specifically, the generative AI predicts future performance based on the player's past match data. The generative AI uses deep learning technology to consider various factors that affect the player's performance and make highly accurate predictions. For example, it can take into account the player's physical condition, the strength of the opponent, and the match environment. The analysis unit can also analyze the player's training data to predict future performance. Training data includes the player's training content, training frequency, and training results. By analyzing this data, it is possible to predict the player's growth and performance improvement. Furthermore, the analysis unit can analyze the player's performance data in real time and provide the latest prediction results. This allows users to always evaluate the player's performance based on the latest information. The analysis unit plays a crucial role in accurately predicting player performance and providing useful information to users.

[0032] The calculation unit calculates player points based on data analyzed by the analysis unit. For example, the calculation unit calculates points based on a player's current performance data and predicted future performance data. The calculation unit can also calculate player points using generative AI. Specifically, the generative AI calculates points based on player performance data. The generative AI considers various factors that affect a player's performance to perform highly accurate point calculations. For example, it calculates points based on in-game performance data such as points scored, assists, and rebounds. The calculation unit can also calculate player points in real time. This allows users to always check the latest point information. Furthermore, the calculation unit uses algorithms to ensure fairness and transparency in calculating player points. For example, when calculating points based on player performance data, it makes adjustments to prevent bias towards specific players or teams. This allows users to enjoy fantasy sports in a fair competitive environment. The calculation unit plays a crucial role in accurately and fairly calculating player points and providing users with reliable information.

[0033] The Management Department manages tournaments based on points calculated by the Calculation Department. For example, the Management Department manages tournaments within the Fantasy AI League. The Management Department can manage the progress of tournaments and provide rewards to top performers. Specifically, the Management Department sets the tournament schedule and records the results of each match. The tournament progress is updated in real time, so users can always check the latest information. The Management Department also manages the system for providing rewards to top performers. For example, it can provide users with rewards such as points, virtual items, or cash depending on their tournament performance. Furthermore, the Management Department sets rules and guidelines to ensure the fairness of tournaments and monitors their compliance. This allows users to participate in tournaments in a fair competitive environment. The Management Department plays a crucial role in smoothly managing the progress of tournaments and providing users with a fair and transparent competitive environment.

[0034] The service provider provides tournament results managed by the administration department. For example, the service provider notifies users of tournament results. The service provider can use generative AI to provide tournament results. Specifically, the generative AI provides feedback to users based on the tournament results. Based on the user's player selection and strategy, the generative AI can suggest advice and areas for improvement for the next tournament. The service provider can also provide tournament results in real time, allowing users to always check the latest results. Furthermore, the service provider can provide tournament results in various formats. For example, results can be displayed via websites and mobile apps, as well as notified to users via email or push notifications. This allows users to check results in a way that suits their preferences. The service provider plays a crucial role in providing tournament results quickly and accurately, and in offering valuable feedback to users.

[0035] The analysis unit can analyze a player's past match data and training data to predict their future performance. For example, the analysis unit can predict future performance based on a player's past match data. The analysis unit can also use generative AI to analyze a player's past match data and predict future performance. For example, the generative AI takes a player's past match data as input and outputs future performance. The analysis unit can also analyze a player's training data to predict future performance. For example, the analysis unit predicts future performance based on a player's training data. By predicting a player's future performance, the accuracy of point calculation is improved. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input a player's past match data into the generative AI and have the generative AI perform the prediction of future performance.

[0036] The provider can provide users with reliable information by disclosing details of the generative AI's predictive model and data sources. For example, the provider can disclose details of the generative AI's predictive model. The provider can disclose the algorithm used by the generative AI's predictive model and how the model is trained. For example, the provider can disclose the algorithm used by the generative AI's predictive model. The provider can also disclose how the generative AI's predictive model is trained. The provider can disclose data sources. The provider can disclose the sources and collection methods of the data used by the generative AI. For example, the provider can disclose the sources of the data used by the generative AI. The provider can also disclose how the data used by the generative AI is collected. This ensures transparency of the generative AI by providing users with reliable information. Some or all of the above processing in the provider may be performed using the generative AI or not. For example, the provider can input details of the generative AI's predictive model into the generative AI and have the generative AI generate the information to be disclosed to users.

[0037] The service provider can collect real-time data and integrate actual match results with predictions from the generating AI in real time. For example, the service provider can collect real-time data on the progress of the match and player performance. The service provider can collect real-time data using the generating AI. For example, the service provider can input the progress of the match into the generating AI and collect real-time data. The service provider can also input player performance data into the generating AI and collect real-time data. The service provider integrates actual match results with predictions from the generating AI in real time. The service provider can integrate actual match results with predictions from the generating AI in real time using the generating AI. For example, the service provider can input actual match results into the generating AI and integrate them with the predictions from the generating AI. The service provider can also integrate the predictions from the generating AI with the actual match results. This allows users to check the points as the match progresses by integrating match results and predictions from the generating AI in real time. Some or all of the above processing in the service provider may be performed using the generating AI or not using the generating AI. For example, the service provider can input the actual match results and the generation AI's predictions into the generation AI and have the generation AI perform a process to integrate them in real time.

[0038] The management department can manage tournaments within the Fantasy AI League and provide rewards to top performers. For example, the management department can manage the progress of tournaments. The management department can manage the progress of tournaments using a generative AI. For example, the management department can input the progress of tournaments into a generative AI and manage it. The management department can also provide rewards to top performers. The management department can provide rewards to top performers using a generative AI. For example, the management department can input the rewards for top performers into a generative AI and provide them. This increases participant motivation by managing tournaments and providing rewards to top performers. Some or all of the above processes in the management department may be performed using a generative AI or not. For example, the management department can input the progress of tournaments into a generative AI and have the generative AI execute the process of managing it.

[0039] The service provider can target multiple sports, such as soccer, basketball, and baseball. For example, the service provider can target sports such as soccer, basketball, and baseball. The service provider can target multiple sports using a generative AI. For example, the service provider can input sports such as soccer, basketball, and baseball into the generative AI and target them. This allows the service provider to attract a diverse range of sports fans by targeting multiple sports. Some or all of the above-described processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input information targeting multiple sports into the generative AI and have the generative AI execute the target processing.

[0040] The service provider can provide community features that allow users to share information and engage in discussions. For example, the service provider can provide forums and chat functions. The service provider can provide community features using generative AI. For example, the service provider can input forums and chat functions into the generative AI and provide them. The service provider can also provide information sharing functions. The service provider can provide information sharing functions using generative AI. For example, the service provider can input information sharing functions into the generative AI and provide them. This promotes interaction among users and expands the enjoyment of fantasy sports. Some or all of the above-described processes in the service provider may be performed using generative AI or not. For example, the service provider can input community functions into the generative AI and have the generative AI execute the processes to provide them.

[0041] The reception desk can analyze the user's past player selection history and propose the optimal player selection method. For example, the reception desk can recommend similar players based on the performance data of players the user has selected in the past. The reception desk can analyze the user's past player selection history using generative AI. For example, the reception desk can input the user's past player selection history into the generative AI and analyze it. The reception desk can also analyze the trends of players the user has selected in the past and propose players that match their preferences. The reception desk can propose the optimal player selection method using generative AI. For example, the reception desk can input the user's past player selection history into the generative AI and propose the optimal player selection method. Furthermore, the reception desk can prioritize displaying players from specific teams or leagues based on the user's past player selection history. In this way, by analyzing the past player selection history, the reception desk can propose the optimal player selection method to the user. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's past player selection history into the generative AI and have the generative AI perform the analysis.

[0042] The reception desk can filter players based on the user's current sport of interest and the players' performance tendencies when selecting players. For example, the reception desk can prioritize displaying players from the sport the user is currently interested in. The reception desk can use generative AI to identify the user's current sport of interest. For example, the reception desk can input the user's past selection data into the generative AI to identify the sport of interest. The reception desk can also recommend similar players based on the performance tendencies of players the user has previously given high ratings to. The reception desk can use generative AI to analyze the performance tendencies of players. For example, the reception desk can input the player's past performance data into the generative AI to analyze the performance tendencies. Furthermore, the reception desk can display players reflecting the latest match data based on the user's current sport of interest. This allows for more appropriate player selection by filtering based on the user's sport of interest and the players' performance tendencies. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's sport of interest and the players' performance tendencies into the generative AI and have the generative AI perform the filtering process.

[0043] The reception desk can prioritize displaying players with high relevance by considering the user's geographical location when selecting players. For example, the reception desk can prioritize displaying players from local teams based on the user's geographical location. The reception desk can collect the user's geographical location information using a generative AI. For example, the reception desk can input the user's GPS data into the generative AI to collect geographical location information. The reception desk can also prioritize displaying players participating in nearby matches based on the user's geographical location. The reception desk can identify highly relevant players using a generative AI. For example, the reception desk can input player performance data into the generative AI to identify highly relevant players. Furthermore, the reception desk can prioritize displaying popular local players based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant players can be prioritized. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's geographical location information into the generative AI and have the generative AI perform the process of identifying highly relevant players.

[0044] The reception desk can analyze a user's social media activity when selecting a player and recommend relevant players. For example, the reception desk can recommend relevant players based on the user's "likes" and comments on social media. The reception desk can analyze a user's social media activity using generative AI. For example, the reception desk can input the user's social media data into the generative AI and analyze it. The reception desk can also recommend players based on the interests of the user's followers and friends. The reception desk can identify relevant players using generative AI. For example, the reception desk can input player performance data into the generative AI to identify relevant players. Furthermore, the reception desk can analyze the user's social media activity history and recommend players that they might be interested in. In this way, relevant players can be recommended by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's social media data into the generative AI and have the generative AI perform the process of recommending relevant players.

[0045] The analysis unit can analyze a player's past match data and training data to predict future performance, taking into account the player's health condition and training environment. For example, the analysis unit can predict future performance by considering the player's past injuries and rehabilitation status. The analysis unit can use generative AI to consider the player's health condition and training environment. For example, the analysis unit can input the player's medical data into the generative AI to consider their health condition. The analysis unit can also predict future performance by considering the player's training environment (quality of facilities, quality of trainers, etc.). The analysis unit can use generative AI to consider the training environment. For example, the analysis unit can input the player's training data into the generative AI to consider the training environment. Furthermore, the analysis unit predicts future performance by considering the player's health condition (physical condition, fatigue level, etc.). This improves the accuracy of future performance predictions by considering the player's health condition and training environment. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input the player's health condition and training environment into the generative AI and have the generative AI perform the prediction of future performance.

[0046] The analysis unit can improve the accuracy of its analysis by considering external factors that affect player performance (such as weather and the strength of the opponent) during the analysis. For example, the analysis unit analyzes player performance while considering weather (rain, wind, temperature, etc.). The analysis unit can consider external factors using generative AI. For example, the analysis unit inputs weather data into the generative AI and analyzes player performance. The analysis unit also analyzes player performance while considering the strength and tactics of the opponent. The analysis unit can consider the strength of the opponent using generative AI. For example, the analysis unit inputs opponent data into the generative AI and analyzes player performance. Furthermore, the analysis unit analyzes player performance while considering the location of the match (home, away). This improves the accuracy of the analysis by considering external factors. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input external factors into the generative AI and have the generative AI execute processing to improve the accuracy of the analysis.

[0047] The analysis unit can perform analysis while considering the geographical distribution of players. For example, the analysis unit can perform analysis while considering the players' birthplace and current place of residence. The analysis unit can use generative AI to consider the geographical distribution of players. For example, the analysis unit can input the players' place of residence data into the generative AI and perform the analysis. The analysis unit can also perform analysis while considering the players' match locations (home, away). The analysis unit can use generative AI to consider match locations. For example, the analysis unit can input match location data into the generative AI and perform the analysis. Furthermore, the analysis unit can perform analysis while considering the players' training locations. This improves the accuracy of the analysis by considering the geographical distribution of players. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the players' geographical distribution into the generative AI and have the generative AI perform the analysis.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and news articles about the player during the analysis process. For example, the analysis unit performs analysis by referring to the latest news articles about the player. The analysis unit can refer to relevant literature and news articles using a generative AI. For example, the analysis unit inputs news article data into the generative AI and performs analysis. The analysis unit also performs analysis by referring to academic papers and research data about the player. The analysis unit can refer to academic papers using a generative AI. For example, the analysis unit inputs academic paper data into the generative AI and performs analysis. Furthermore, the analysis unit performs analysis by referring to interview articles and comments about the player. This improves the accuracy of the analysis by referring to relevant literature and news articles. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input relevant literature and news articles into the generative AI and have the generative AI perform the analysis.

[0049] The calculation unit can consider the player's motivation and mental state in addition to the player's performance data when calculating points. For example, the calculation unit calculates points considering the player's motivation (pre-match comments and interviews). The calculation unit can consider the player's motivation using a generation AI. For example, the calculation unit inputs the player's interview data into the generation AI and considers the motivation. The calculation unit also calculates points considering the player's mental state (facial expressions and attitude during the match). The calculation unit can consider the mental state using a generation AI. For example, the calculation unit inputs the player's facial expression data into the generation AI and considers the mental state. Furthermore, the calculation unit analyzes the player's mental state in past matches and reflects this in the point calculation. This improves the accuracy of point calculation by considering the player's motivation and mental state. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the player's motivation and mental state into a generation AI and have the generation AI perform the point calculation process.

[0050] The calculation unit can consider past injuries and rehabilitation status that affect a player's performance when calculating points. For example, the calculation unit calculates points considering a player's past injury history. The calculation unit can consider past injuries using a generation AI. For example, the calculation unit inputs the player's medical records into the generation AI and considers the injury history. The calculation unit also calculates points considering the player's rehabilitation status (rehabilitation progress and training content). The calculation unit can consider rehabilitation status using a generation AI. For example, the calculation unit inputs rehabilitation data into the generation AI and considers the rehabilitation status. Furthermore, the calculation unit analyzes the player's recovery status from injury and reflects this in the point calculation. This improves the accuracy of point calculation by considering past injuries and rehabilitation status. Some or all of the above processing in the calculation unit may be performed using a generation AI or not. For example, the calculation unit can input the player's injury and rehabilitation status into the generation AI and have the generation AI perform the point calculation process.

[0051] The calculation unit can perform point calculations while considering the geographical distribution of players. For example, the calculation unit can calculate points while considering the players' birthplace and current place of residence. The calculation unit can consider the geographical distribution of players using a generation AI. For example, the calculation unit can input the players' place of residence data into the generation AI and perform the calculation. The calculation unit can also calculate points while considering the players' match locations (home, away). The calculation unit can consider the match locations using a generation AI. For example, the calculation unit can input the match location data into the generation AI and perform the calculation. Furthermore, the calculation unit can calculate points while considering the players' training locations. This improves the accuracy of point calculation by considering the geographical distribution of players. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the calculation unit can input the players' geographical distribution into a generation AI and have the generation AI perform the calculation.

[0052] The calculation unit can improve the accuracy of point calculations by referring to relevant literature and news articles about the players. For example, the calculation unit calculates points by referring to the latest news articles about the players. The calculation unit can refer to relevant literature and news articles using a generation AI. For example, the calculation unit inputs news article data into the generation AI and performs calculations. The calculation unit also calculates points by referring to academic papers and research data about the players. The calculation unit can refer to academic papers using a generation AI. For example, the calculation unit inputs academic paper data into the generation AI and performs calculations. Furthermore, the calculation unit calculates points by referring to interview articles and comments about the players. This improves the accuracy of point calculations by referring to relevant literature and news articles. Some or all of the above processes in the calculation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the calculation unit can input relevant literature and news articles into a generation AI and have the generation AI perform the calculations.

[0053] The management department can select the optimal management method when managing tournaments by referring to the user's past tournament participation history. For example, the management department can propose the optimal management method based on the format of tournaments the user has participated in in the past. The management department can refer to the user's past tournament participation history using a generative AI. For example, the management department can input the user's tournament participation history data into the generative AI and refer to it. The management department can also analyze the user's past tournament results and select an appropriate tournament format. The management department can select a tournament format using a generative AI. For example, the management department can input the user's performance data into the generative AI and select a tournament format. Furthermore, the management department can propose a tournament format that suits the user's preferences based on their past tournament participation history. In this way, the optimal tournament management method can be selected by referring to the past tournament participation history. Some or all of the above processes in the management department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management department can input the user's tournament participation history into the generative AI and have the generative AI execute the process of selecting a management method.

[0054] The management unit can customize tournament management based on the user's current interests in sports and tournament formats. For example, the management unit can prioritize displaying tournaments for sports the user is currently interested in. The management unit can use generative AI to identify the user's interests in sports. For example, the management unit can input the user's past selection data into the generative AI to identify the interests. The management unit can also customize the tournament format to suit the user's preferences. The management unit can use generative AI to customize the tournament format. For example, the management unit can input tournament format data into the generative AI and customize it. Furthermore, the management unit provides the latest tournament information based on the user's current interests in sports. This allows for more appropriate tournament management by customizing based on the user's interests in sports and tournament formats. Some or all of the above processes in the management unit may be performed using generative AI or not. For example, the management unit can input the user's interests in sports and tournament formats into the generative AI and have the generative AI perform the customization process.

[0055] The management unit can select the optimal management method when managing tournaments, taking into account the user's geographical location information. For example, the management unit can prioritize displaying local tournaments based on the user's geographical location information. The management unit can collect the user's geographical location information using a generative AI. For example, the management unit can input the user's GPS data into the generative AI to collect geographical location information. The management unit can also provide information on nearby tournaments based on the user's geographical location information. The management unit can select the optimal management method using a generative AI. For example, the management unit can input the user's geographical location information into the generative AI to select a management method. Furthermore, the management unit can prioritize displaying popular local tournaments based on the user's geographical location information. In this way, the optimal tournament management method can be selected by taking into account the user's geographical location information. Some or all of the above processes in the management unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management unit can input the user's geographical location information into the generative AI and have the generative AI execute the process of selecting a management method.

[0056] The management department can analyze users' social media activity and suggest tournament options when managing tournaments. For example, the management department can recommend relevant tournaments based on users' "likes" and comments on social media. The management department can analyze users' social media activity using generative AI. For example, the management department can input users' social media data into the generative AI and analyze it. The management department can also recommend tournaments based on the interests of users' followers and friends. The management department can identify relevant tournaments using generative AI. For example, the management department can input tournament data into the generative AI and identify relevant tournaments. Furthermore, the management department can analyze users' social media activity history and recommend tournaments that they might be interested in. In this way, relevant tournaments can be recommended by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input users' social media data into the generative AI and have the generative AI perform the process of suggesting tournament options.

[0057] The service provider can select the optimal service delivery method by referring to the user's past information delivery history at the time of delivery. For example, the service provider can provide relevant information based on information the user has previously viewed. The service provider can refer to the user's past information delivery history using a generation AI. For example, the service provider can input the user's information delivery history data into the generation AI and refer to it. The service provider can also analyze the user's past information delivery history and provide information that suits their preferences. The service provider can select the optimal service delivery method using a generation AI. For example, the service provider can input the user's information delivery history data into the generation AI and select a delivery method. Furthermore, the service provider can predict and propose information to be delivered at a specific time based on the user's past information delivery history. This allows the service provider to select the optimal service delivery method by referring to past information delivery history. Some or all of the above processes in the service provider may be performed using a generation AI, or they may not be performed using a generation AI. For example, the service provider can input the user's information delivery history into the generation AI and have the generation AI execute the process of selecting a delivery method.

[0058] The information provider can customize the information provided based on the user's current sports of interest and the performance trends of athletes. For example, the provider can prioritize providing information on sports that the user is currently interested in. The provider can identify the user's sports of interest using generative AI. For example, the provider can input the user's past selection data into the generative AI to identify the sports of interest. The provider can also customize the information based on the performance trends of athletes to match the user's preferences. The provider can customize the information using generative AI. For example, the provider can input athlete performance data into the generative AI to customize the information. Furthermore, the provider can provide information that reflects the latest match data based on the user's current sports of interest. This makes it possible to provide more appropriate information by customizing it based on the user's sports of interest and the performance trends of athletes. Some or all of the above processing in the provider can be performed using generative AI or not. For example, the provider can input the user's sports of interest and the performance trends of athletes into the generative AI and have the generative AI perform the process of customizing the information.

[0059] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing local sports information based on the user's geographical location information. The service provider can collect the user's geographical location information using a generative AI. For example, the service provider can input the user's GPS data into the generative AI to collect geographical location information. The service provider can also provide information on nearby matches based on the user's geographical location information. The service provider can select the optimal service delivery method using a generative AI. For example, the service provider can input the user's geographical location information into the generative AI to select a service delivery method. Furthermore, the service provider can prioritize providing information on popular local sports based on the user's geographical location information. In this way, the service provider can select the optimal information delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's geographical location information into the generative AI and have the generative AI execute the process of selecting a service delivery method.

[0060] The service provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the service provider can provide relevant information based on the user's "likes" and comments on social media. The service provider can analyze the user's social media activity using generative AI. For example, the service provider can input the user's social media data into the generative AI and analyze it. The service provider can also make recommendations based on information that the user's followers and friends are interested in. The service provider can identify relevant information using generative AI. For example, the service provider can input information data into the generative AI and identify relevant information. Furthermore, the service provider can analyze the user's social media activity history and provide information that is likely to be of interest. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media data into the generative AI and have the generative AI perform the process of proposing means of providing information.

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

[0062] The reception desk can recommend players to users when they select a player, taking into account not only the player's past performance data but also their health status and training environment. For example, it can evaluate a player's health status based on their past injuries and rehabilitation history to assist in player selection. It can also predict a player's performance by considering their training environment (quality of facilities, quality of trainers, etc.) to assist in player selection. Furthermore, it can evaluate a player's current health status (physical condition, fatigue level, etc.) in real time to assist in player selection. This allows users to select a more appropriate player after considering their health status and training environment.

[0063] The analysis unit can consider psychological factors (motivation, stress levels, etc.) of players when analyzing their performance. For example, it can analyze players' pre-game comments and interviews to assess their motivation. It can also analyze players' facial expressions and attitudes during games to assess their stress levels. Furthermore, it can analyze psychological factors in players' past games to evaluate their impact on performance. By considering players' psychological factors, it becomes possible to predict performance with greater accuracy.

[0064] The management department can select the optimal management method when managing tournaments by referring to the user's past tournament participation history. For example, it can suggest the optimal management method based on the format of tournaments the user has participated in in the past. It can also analyze the user's past tournament results and select an appropriate tournament format. Furthermore, it can suggest a tournament format that suits the user's preferences based on their past tournament participation history. In this way, the optimal tournament management method can be selected by referring to past tournament participation history.

[0065] The analysis unit can perform analyses of player performance while considering the players' geographical distribution. For example, it can consider the players' hometowns and current places of residence. It can also consider the players' match locations (home and away). Furthermore, it can consider the players' training locations. By considering the players' geographical distribution, the accuracy of the analysis is improved.

[0066] The calculation unit can consider not only the player's performance data but also their motivation and mental state when calculating points. For example, it can calculate points considering the player's motivation (pre-match comments and interviews). It can also calculate points considering the player's mental state (facial expressions and attitude during the match). Furthermore, it can analyze the player's mental state in past matches and reflect that in the point calculation. By considering the player's motivation and mental state, the accuracy of the point calculation is improved.

[0067] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, it can prioritize providing local sports information based on the user's geographical location information. It can also provide information on nearby matches based on the user's geographical location information. Furthermore, it can prioritize providing information on popular local sports based on the user's geographical location information. In this way, the optimal information delivery method can be selected by considering the user's geographical location information.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk accepts player selections. Player selections include real professional athletes and virtual athletes generated by a generator AI. The reception desk provides an interface for users to select players and can also collect player performance data. For example, it collects players' past match data and training data. Step 2: The analysis unit analyzes the player's performance data received by the reception unit. The analysis unit analyzes the player's past match data and training data to predict future performance. Generative AI can also be used to analyze the player's performance data. Step 3: The calculation unit calculates the player's points based on the data analyzed by the analysis unit. The calculation unit calculates the points based on the player's current performance data and future performance prediction data. It is also possible to calculate the player's points using generative AI. Step 4: The management unit manages the tournament based on the points calculated by the calculation unit. The management unit can manage the tournament within the Fantasy AI League, monitor the progress of the tournament, and provide rewards to top performers. Step 5: The provisioning unit provides the tournament results managed by the management unit. The provisioning unit can notify users of the tournament results and provide the tournament results using a generating AI.

[0070] (Example of form 2) The fantasy sports provision system according to an embodiment of the present invention is a system that provides a new form of fantasy sports in which real professional athletes and virtual athletes generated by a generative AI compete in the same league. This system calculates athletes' points not only based on their current performance but also on their future performance predicted by the generative AI. For example, a user selects real professional athletes and virtual athletes generated by the generative AI to form a team. Next, the generative AI calculates the athletes' points based on their current performance data and predicted future performance data. For example, the generative AI analyzes the athletes' past match data and training data to predict their future performance. This predicted data is reflected in the calculation of the athletes' points. Furthermore, to ensure transparency of the generative AI, a function is provided to explain to the user how the generative AI makes its predictions. For example, details of the generative AI's prediction model and the source of the data are made public, providing users with reliable information. In addition, by utilizing real-time data, the actual match results and the generative AI's predictions are integrated in real time, and users can check the points as the match progresses. A tournament format is introduced, allowing users to compete against each other. For example, the system motivates participants by offering tournaments within the fantasy AI league and rewards for top performers. It also attracts diverse sports fans by covering multiple sports such as soccer, basketball, and baseball. Finally, it introduces community features, providing a platform for users to share information and engage in discussions. This promotes interaction among fans and expands the enjoyment of fantasy sports. For instance, users can exchange opinions on their team's strategy or player performance. In this way, the fantasy sports system offers a new form of fantasy sports where real professional athletes and AI-generated virtual athletes compete in the same league. By combining transparency in the AI ​​generation, real-time data utilization, tournament formats, diverse sports offerings, and community features, it expands the possibilities of fantasy sports.This allows fantasy sports delivery systems to offer users a new form of fantasy sports experience.

[0071] The fantasy sports provision system according to this embodiment comprises a reception unit, an analysis unit, a calculation unit, a management unit, and a provision unit. The reception unit accepts the selection of athlete. The selection of athlete includes, for example, real professional athletes or virtual athletes generated by a generative AI, but is not limited to such examples. The reception unit provides, for example, an interface for the user to select an athlete. The reception unit can also collect athlete performance data. For example, it collects the athlete's past match data and training data. The analysis unit analyzes the athlete performance data accepted by the reception unit. For example, the analysis unit analyzes the athlete's past match data and training data to predict future performance. The analysis unit can use a generative AI to analyze the athlete's performance data. For example, the generative AI predicts future performance based on the athlete's past match data. The analysis unit can also analyze the athlete's training data to predict future performance. The calculation unit calculates the athlete's points based on the data analyzed by the analysis unit. For example, the calculation unit calculates points based on the athlete's current performance data and future performance prediction data. The calculation unit can use a generative AI to calculate the athlete's points. For example, the generating AI calculates points based on player performance data. The calculation unit can also calculate player points in real time. The management unit manages tournaments based on the points calculated by the calculation unit. The management unit manages tournaments within a fantasy AI league, for example. The management unit can manage the progress of tournaments and provide rewards to top performers. The delivery unit provides the results of tournaments managed by the management unit. The delivery unit notifies users of the tournament results, for example. The delivery unit can provide tournament results using the generating AI. For example, the generating AI provides feedback to users based on the tournament results. The delivery unit can also provide tournament results in real time. As a result, the fantasy sports delivery system according to this embodiment can consistently handle everything from player selection to providing tournament results.

[0072] The reception desk accepts player selections. Player selections include, but are not limited to, real-world professional athletes or virtual athletes generated by AI. The reception desk provides, for example, an interface for users to select players. Through this interface, users can view player profiles, past performance, training data, etc., and make their selections. The interface is designed to be intuitive and easy to use, making it easy for users to select players. The reception desk can also collect player performance data. For example, it can collect players' past match data and training data. This data is important for evaluating player performance and is provided as reference information when users select players. Furthermore, the reception desk can update player data in real time, providing users with the latest information. For example, if a player participates in a new match, the data from that match is immediately reflected in the system. This allows users to always select players based on the latest information. The reception desk plays a crucial role in efficiently and accurately receiving user player selections.

[0073] The analysis unit analyzes the player's performance data received by the reception unit. For example, the analysis unit analyzes the player's past match data and training data to predict future performance. The analysis unit can use generative AI to analyze the player's performance data. Specifically, the generative AI predicts future performance based on the player's past match data. The generative AI uses deep learning technology to consider various factors that affect the player's performance and make highly accurate predictions. For example, it can take into account the player's physical condition, the strength of the opponent, and the match environment. The analysis unit can also analyze the player's training data to predict future performance. Training data includes the player's training content, training frequency, and training results. By analyzing this data, it is possible to predict the player's growth and performance improvement. Furthermore, the analysis unit can analyze the player's performance data in real time and provide the latest prediction results. This allows users to always evaluate the player's performance based on the latest information. The analysis unit plays a crucial role in accurately predicting player performance and providing useful information to users.

[0074] The calculation unit calculates player points based on data analyzed by the analysis unit. For example, the calculation unit calculates points based on a player's current performance data and predicted future performance data. The calculation unit can also calculate player points using generative AI. Specifically, the generative AI calculates points based on player performance data. The generative AI considers various factors that affect a player's performance to perform highly accurate point calculations. For example, it calculates points based on in-game performance data such as points scored, assists, and rebounds. The calculation unit can also calculate player points in real time. This allows users to always check the latest point information. Furthermore, the calculation unit uses algorithms to ensure fairness and transparency in calculating player points. For example, when calculating points based on player performance data, it makes adjustments to prevent bias towards specific players or teams. This allows users to enjoy fantasy sports in a fair competitive environment. The calculation unit plays a crucial role in accurately and fairly calculating player points and providing users with reliable information.

[0075] The Management Department manages tournaments based on points calculated by the Calculation Department. For example, the Management Department manages tournaments within the Fantasy AI League. The Management Department can manage the progress of tournaments and provide rewards to top performers. Specifically, the Management Department sets the tournament schedule and records the results of each match. The tournament progress is updated in real time, so users can always check the latest information. The Management Department also manages the system for providing rewards to top performers. For example, it can provide users with rewards such as points, virtual items, or cash depending on their tournament performance. Furthermore, the Management Department sets rules and guidelines to ensure the fairness of tournaments and monitors their compliance. This allows users to participate in tournaments in a fair competitive environment. The Management Department plays a crucial role in smoothly managing the progress of tournaments and providing users with a fair and transparent competitive environment.

[0076] The service provider provides tournament results managed by the administration department. For example, the service provider notifies users of tournament results. The service provider can use generative AI to provide tournament results. Specifically, the generative AI provides feedback to users based on the tournament results. Based on the user's player selection and strategy, the generative AI can suggest advice and areas for improvement for the next tournament. The service provider can also provide tournament results in real time, allowing users to always check the latest results. Furthermore, the service provider can provide tournament results in various formats. For example, results can be displayed via websites and mobile apps, as well as notified to users via email or push notifications. This allows users to check results in a way that suits their preferences. The service provider plays a crucial role in providing tournament results quickly and accurately, and in offering valuable feedback to users.

[0077] The analysis unit can analyze a player's past match data and training data to predict their future performance. For example, the analysis unit can predict future performance based on a player's past match data. The analysis unit can also use generative AI to analyze a player's past match data and predict future performance. For example, the generative AI takes a player's past match data as input and outputs future performance. The analysis unit can also analyze a player's training data to predict future performance. For example, the analysis unit predicts future performance based on a player's training data. By predicting a player's future performance, the accuracy of point calculation is improved. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input a player's past match data into the generative AI and have the generative AI perform the prediction of future performance.

[0078] The provider can provide users with reliable information by disclosing details of the generative AI's predictive model and data sources. For example, the provider can disclose details of the generative AI's predictive model. The provider can disclose the algorithm used by the generative AI's predictive model and how the model is trained. For example, the provider can disclose the algorithm used by the generative AI's predictive model. The provider can also disclose how the generative AI's predictive model is trained. The provider can disclose data sources. The provider can disclose the sources and collection methods of the data used by the generative AI. For example, the provider can disclose the sources of the data used by the generative AI. The provider can also disclose how the data used by the generative AI is collected. This ensures transparency of the generative AI by providing users with reliable information. Some or all of the above processing in the provider may be performed using the generative AI or not. For example, the provider can input details of the generative AI's predictive model into the generative AI and have the generative AI generate the information to be disclosed to users.

[0079] The service provider can collect real-time data and integrate actual match results with predictions from the generating AI in real time. For example, the service provider can collect real-time data on the progress of the match and player performance. The service provider can collect real-time data using the generating AI. For example, the service provider can input the progress of the match into the generating AI and collect real-time data. The service provider can also input player performance data into the generating AI and collect real-time data. The service provider integrates actual match results with predictions from the generating AI in real time. The service provider can integrate actual match results with predictions from the generating AI in real time using the generating AI. For example, the service provider can input actual match results into the generating AI and integrate them with the predictions from the generating AI. The service provider can also integrate the predictions from the generating AI with the actual match results. This allows users to check the points as the match progresses by integrating match results and predictions from the generating AI in real time. Some or all of the above processing in the service provider may be performed using the generating AI or not using the generating AI. For example, the service provider can input the actual match results and the generation AI's predictions into the generation AI and have the generation AI perform a process to integrate them in real time.

[0080] The management department can manage tournaments within the Fantasy AI League and provide rewards to top performers. For example, the management department can manage the progress of tournaments. The management department can manage the progress of tournaments using a generative AI. For example, the management department can input the progress of tournaments into a generative AI and manage it. The management department can also provide rewards to top performers. The management department can provide rewards to top performers using a generative AI. For example, the management department can input the rewards for top performers into a generative AI and provide them. This increases participant motivation by managing tournaments and providing rewards to top performers. Some or all of the above processes in the management department may be performed using a generative AI or not. For example, the management department can input the progress of tournaments into a generative AI and have the generative AI execute the process of managing it.

[0081] The service provider can target multiple sports, such as soccer, basketball, and baseball. For example, the service provider can target sports such as soccer, basketball, and baseball. The service provider can target multiple sports using a generative AI. For example, the service provider can input sports such as soccer, basketball, and baseball into the generative AI and target them. This allows the service provider to attract a diverse range of sports fans by targeting multiple sports. Some or all of the above-described processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input information targeting multiple sports into the generative AI and have the generative AI execute the target processing.

[0082] The service provider can provide community features that allow users to share information and engage in discussions. For example, the service provider can provide forums and chat functions. The service provider can provide community features using generative AI. For example, the service provider can input forums and chat functions into the generative AI and provide them. The service provider can also provide information sharing functions. The service provider can provide information sharing functions using generative AI. For example, the service provider can input information sharing functions into the generative AI and provide them. This promotes interaction among users and expands the enjoyment of fantasy sports. Some or all of the above-described processes in the service provider may be performed using generative AI or not. For example, the service provider can input community functions into the generative AI and have the generative AI execute the processes to provide them.

[0083] The reception desk can estimate the user's emotions and assist in player selection based on those emotions. For example, if the user is excited, the reception desk will prioritize displaying popular players to encourage selection. The reception desk can estimate the user's emotions using generative AI. For example, the reception desk inputs the user's facial expression data into the generative AI to estimate emotions. Also, if the user is relaxed, the reception desk will provide detailed player information, allowing the user to carefully select a player. The reception desk can assist in player selection based on the user's emotions using generative AI. For example, the reception desk inputs the user's emotional data into the generative AI to assist in player selection. Furthermore, if the user is stressed, the reception desk will provide a simple interface to reduce the effort required for player selection. This allows for more appropriate player selection by assisting in player selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception desk can analyze the user's past player selection history and propose the optimal player selection method. For example, the reception desk can recommend similar players based on the performance data of players the user has selected in the past. The reception desk can analyze the user's past player selection history using generative AI. For example, the reception desk can input the user's past player selection history into the generative AI and analyze it. The reception desk can also analyze the trends of players the user has selected in the past and propose players that match their preferences. The reception desk can propose the optimal player selection method using generative AI. For example, the reception desk can input the user's past player selection history into the generative AI and propose the optimal player selection method. Furthermore, the reception desk can prioritize displaying players from specific teams or leagues based on the user's past player selection history. In this way, by analyzing the past player selection history, the reception desk can propose the optimal player selection method to the user. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's past player selection history into the generative AI and have the generative AI perform the analysis.

[0085] The reception desk can filter players based on the user's current sport of interest and the players' performance tendencies when selecting players. For example, the reception desk can prioritize displaying players from the sport the user is currently interested in. The reception desk can use generative AI to identify the user's current sport of interest. For example, the reception desk can input the user's past selection data into the generative AI to identify the sport of interest. The reception desk can also recommend similar players based on the performance tendencies of players the user has previously given high ratings to. The reception desk can use generative AI to analyze the performance tendencies of players. For example, the reception desk can input the player's past performance data into the generative AI to analyze the performance tendencies. Furthermore, the reception desk can display players reflecting the latest match data based on the user's current sport of interest. This allows for more appropriate player selection by filtering based on the user's sport of interest and the players' performance tendencies. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's sport of interest and the players' performance tendencies into the generative AI and have the generative AI perform the filtering process.

[0086] The reception desk can estimate the user's emotions and adjust the player selection order based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize displaying popular players to encourage selection. The reception desk can estimate the user's emotions using generative AI. For example, the reception desk inputs the user's facial expression data into the generative AI to estimate emotions. Also, if the user is relaxed, the reception desk will provide detailed player information, allowing the user to carefully select a player. The reception desk can adjust the player selection order based on the user's emotions using generative AI. For example, the reception desk inputs the user's emotional data into the generative AI to adjust the player selection order. Furthermore, if the user is stressed, the reception desk will provide a simpler interface to reduce the effort required for player selection. This allows for more appropriate player selection by adjusting the player selection order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception desk can prioritize displaying players who are highly relevant to the user's geographical location when the user selects a player. For example, the reception desk can prioritize displaying players from the local team based on the user's geographical location. The reception desk can collect the user's geographical location information using a generative AI. For example, the reception desk can input the user's GPS data into the generative AI to collect geographical location information. The reception desk can also prioritize displaying players participating in nearby matches based on the user's geographical location. The reception desk can identify highly relevant players using a generative AI. For example, the reception desk can input player performance data into the generative AI to identify highly relevant players. Furthermore, the reception desk can prioritize displaying popular local players based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant players can be prioritized. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's geographical location information into the generative AI and have the generative AI perform the process of identifying highly relevant players.

[0088] The reception desk can analyze a user's social media activity when selecting a player and recommend relevant players. For example, the reception desk can recommend relevant players based on the user's "likes" and comments on social media. The reception desk can analyze a user's social media activity using generative AI. For example, the reception desk can input the user's social media data into the generative AI and analyze it. The reception desk can also recommend players based on the interests of the user's followers and friends. The reception desk can identify relevant players using generative AI. For example, the reception desk can input player performance data into the generative AI to identify relevant players. Furthermore, the reception desk can analyze the user's social media activity history and recommend players that they might be interested in. In this way, relevant players can be recommended by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using generative AI or not. For example, the reception desk can input the user's social media data into the generative AI and have the generative AI perform the process of recommending relevant players.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit can estimate the user's emotions using a generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI and estimates the emotions. Also, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can adjust the display method of the analysis results using a generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI and adjusts the display method. Furthermore, if the user is in a hurry, the analysis unit provides a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can analyze a player's past match data and training data to predict future performance, taking into account the player's health condition and training environment. For example, the analysis unit can predict future performance by considering the player's past injuries and rehabilitation status. The analysis unit can use generative AI to consider the player's health condition and training environment. For example, the analysis unit can input the player's medical data into the generative AI to consider their health condition. The analysis unit can also predict future performance by considering the player's training environment (quality of facilities, quality of trainers, etc.). The analysis unit can use generative AI to consider the training environment. For example, the analysis unit can input the player's training data into the generative AI to consider the training environment. Furthermore, the analysis unit predicts future performance by considering the player's health condition (physical condition, fatigue level, etc.). This improves the accuracy of future performance predictions by considering the player's health condition and training environment. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input the player's health condition and training environment into the generative AI and have the generative AI perform the prediction of future performance.

[0091] The analysis unit can improve the accuracy of its analysis by considering external factors that affect player performance (such as weather and the strength of the opponent) during the analysis. For example, the analysis unit analyzes player performance while considering weather (rain, wind, temperature, etc.). The analysis unit can consider external factors using generative AI. For example, the analysis unit inputs weather data into the generative AI and analyzes player performance. The analysis unit also analyzes player performance while considering the strength and tactics of the opponent. The analysis unit can consider the strength of the opponent using generative AI. For example, the analysis unit inputs opponent data into the generative AI and analyzes player performance. Furthermore, the analysis unit analyzes player performance while considering the location of the match (home, away). This improves the accuracy of the analysis by considering external factors. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input external factors into the generative AI and have the generative AI execute processing to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit will prioritize displaying important analysis results. The analysis unit can estimate the user's emotions using generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI to estimate emotions. Also, if the user is relaxed, the analysis unit will display detailed analysis results. The analysis unit can determine the priority of analysis results using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI to determine the priority. Furthermore, if the user is in a hurry, the analysis unit will prioritize displaying analysis results that get straight to the point. This makes it possible to provide more appropriate information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is 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 processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The analysis unit can perform analysis while considering the geographical distribution of players. For example, the analysis unit can perform analysis while considering the players' birthplace and current place of residence. The analysis unit can use generative AI to consider the geographical distribution of players. For example, the analysis unit can input the players' place of residence data into the generative AI and perform the analysis. The analysis unit can also perform analysis while considering the players' match locations (home, away). The analysis unit can use generative AI to consider match locations. For example, the analysis unit can input match location data into the generative AI and perform the analysis. Furthermore, the analysis unit can perform analysis while considering the players' training locations. This improves the accuracy of the analysis by considering the geographical distribution of players. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the players' geographical distribution into the generative AI and have the generative AI perform the analysis.

[0094] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and news articles about the player during the analysis process. For example, the analysis unit performs analysis by referring to the latest news articles about the player. The analysis unit can refer to relevant literature and news articles using a generative AI. For example, the analysis unit inputs news article data into the generative AI and performs analysis. The analysis unit also performs analysis by referring to academic papers and research data about the player. The analysis unit can refer to academic papers using a generative AI. For example, the analysis unit inputs academic paper data into the generative AI and performs analysis. Furthermore, the analysis unit performs analysis by referring to interview articles and comments about the player. This improves the accuracy of the analysis by referring to relevant literature and news articles. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input relevant literature and news articles into the generative AI and have the generative AI perform the analysis.

[0095] The calculation unit can estimate the user's emotions and adjust the point calculation method based on the estimated emotions. For example, if the user is excited, the calculation unit will quickly display the result of the point calculation. The calculation unit can estimate the user's emotions using a generative AI. For example, the calculation unit inputs the user's facial expression data into the generative AI and estimates the emotions. Also, if the user is relaxed, the calculation unit will display the detailed point calculation process. The calculation unit can adjust the point calculation method using a generative AI. For example, the calculation unit inputs the user's emotion data into the generative AI and adjusts the calculation method. Furthermore, if the user is stressed, the calculation unit will display the result of a simple point calculation. This allows for more appropriate point calculation by adjusting the point calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using a generative AI or not. For example, the calculation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The calculation unit can consider the player's motivation and mental state in addition to the player's performance data when calculating points. For example, the calculation unit calculates points considering the player's motivation (pre-match comments and interviews). The calculation unit can consider the player's motivation using a generation AI. For example, the calculation unit inputs the player's interview data into the generation AI and considers the motivation. The calculation unit also calculates points considering the player's mental state (facial expressions and attitude during the match). The calculation unit can consider the mental state using a generation AI. For example, the calculation unit inputs the player's facial expression data into the generation AI and considers the mental state. Furthermore, the calculation unit analyzes the player's mental state in past matches and reflects this in the point calculation. This improves the accuracy of point calculation by considering the player's motivation and mental state. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the calculation unit can input the player's motivation and mental state into a generation AI and have the generation AI perform the point calculation process.

[0097] The calculation unit can consider past injuries and rehabilitation status that affect a player's performance when calculating points. For example, the calculation unit calculates points considering a player's past injury history. The calculation unit can consider past injuries using a generation AI. For example, the calculation unit inputs the player's medical records into the generation AI and considers the injury history. The calculation unit also calculates points considering the player's rehabilitation status (rehabilitation progress and training content). The calculation unit can consider rehabilitation status using a generation AI. For example, the calculation unit inputs rehabilitation data into the generation AI and considers the rehabilitation status. Furthermore, the calculation unit analyzes the player's recovery status from injury and reflects this in the point calculation. This improves the accuracy of point calculation by considering past injuries and rehabilitation status. Some or all of the above processing in the calculation unit may be performed using a generation AI or not. For example, the calculation unit can input the player's injury and rehabilitation status into the generation AI and have the generation AI perform the point calculation process.

[0098] The calculation unit can estimate the user's emotions and determine the priority of point calculations based on the estimated emotions. For example, if the user is excited, the calculation unit will prioritize important point calculations. The calculation unit can estimate the user's emotions using generative AI. For example, the calculation unit inputs the user's facial expression data into the generative AI to estimate emotions. Also, if the user is relaxed, the calculation unit will perform detailed point calculations. The calculation unit can determine the priority of point calculations using generative AI. For example, the calculation unit inputs the user's emotion data into the generative AI to determine the priority. Furthermore, if the user is in a hurry, the calculation unit will prioritize point calculations that get straight to the point. This allows for more appropriate information to be provided by determining the priority of point calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the calculation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The calculation unit can perform point calculations while considering the geographical distribution of players. For example, the calculation unit can calculate points while considering the players' birthplace and current place of residence. The calculation unit can consider the geographical distribution of players using a generation AI. For example, the calculation unit can input the players' place of residence data into the generation AI and perform the calculation. The calculation unit can also calculate points while considering the players' match locations (home, away). The calculation unit can consider the match locations using a generation AI. For example, the calculation unit can input the match location data into the generation AI and perform the calculation. Furthermore, the calculation unit can calculate points while considering the players' training locations. This improves the accuracy of point calculation by considering the geographical distribution of players. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the calculation unit can input the players' geographical distribution into a generation AI and have the generation AI perform the calculation.

[0100] The calculation unit can improve the accuracy of point calculations by referring to relevant literature and news articles about the players. For example, the calculation unit calculates points by referring to the latest news articles about the players. The calculation unit can refer to relevant literature and news articles using a generation AI. For example, the calculation unit inputs news article data into the generation AI and performs calculations. The calculation unit also calculates points by referring to academic papers and research data about the players. The calculation unit can refer to academic papers using a generation AI. For example, the calculation unit inputs academic paper data into the generation AI and performs calculations. Furthermore, the calculation unit calculates points by referring to interview articles and comments about the players. This improves the accuracy of point calculations by referring to relevant literature and news articles. Some or all of the above processes in the calculation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the calculation unit can input relevant literature and news articles into a generation AI and have the generation AI perform the calculations.

[0101] The management unit can estimate the user's emotions and adjust the tournament management method based on the estimated user emotions. For example, if the user is excited, the management unit will expedite the tournament. The management unit can estimate the user's emotions using generative AI. For example, the management unit inputs the user's facial expression data into the generative AI to estimate the emotions. Also, if the user is relaxed, the management unit will provide detailed tournament information. The management unit can adjust the tournament management method using generative AI. For example, the management unit inputs the user's emotion data into the generative AI to adjust the management method. Furthermore, if the user is stressed, the management unit will provide a simpler tournament management method. This allows for more appropriate tournament management by adjusting the tournament management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using generative AI or not. For example, the management department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0102] The management department can select the optimal management method when managing tournaments by referring to the user's past tournament participation history. For example, the management department can propose the optimal management method based on the format of tournaments the user has participated in in the past. The management department can refer to the user's past tournament participation history using a generative AI. For example, the management department can input the user's tournament participation history data into the generative AI and refer to it. The management department can also analyze the user's past tournament results and select an appropriate tournament format. The management department can select a tournament format using a generative AI. For example, the management department can input the user's performance data into the generative AI and select a tournament format. Furthermore, the management department can propose a tournament format that suits the user's preferences based on their past tournament participation history. In this way, the optimal tournament management method can be selected by referring to the past tournament participation history. Some or all of the above processes in the management department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management department can input the user's tournament participation history into the generative AI and have the generative AI execute the process of selecting a management method.

[0103] The management unit can customize tournament management based on the user's current interests in sports and tournament formats. For example, the management unit can prioritize displaying tournaments for sports the user is currently interested in. The management unit can use generative AI to identify the user's interests in sports. For example, the management unit can input the user's past selection data into the generative AI to identify the interests. The management unit can also customize the tournament format to suit the user's preferences. The management unit can use generative AI to customize the tournament format. For example, the management unit can input tournament format data into the generative AI and customize it. Furthermore, the management unit provides the latest tournament information based on the user's current interests in sports. This allows for more appropriate tournament management by customizing based on the user's interests in sports and tournament formats. Some or all of the above processes in the management unit may be performed using generative AI or not. For example, the management unit can input the user's interests in sports and tournament formats into the generative AI and have the generative AI perform the customization process.

[0104] The management unit can estimate the user's emotions and determine tournament priorities based on those emotions. For example, if the user is excited, the management unit will prioritize displaying important tournaments. The management unit can estimate the user's emotions using generative AI. For example, the management unit inputs the user's facial expression data into the generative AI to estimate the emotions. Also, if the user is relaxed, the management unit will provide detailed tournament information. The management unit can determine tournament priorities using generative AI. For example, the management unit inputs the user's emotion data into the generative AI to determine the priorities. Furthermore, if the user is in a hurry, the management unit will prioritize displaying concise tournament information. This allows for more appropriate information to be provided by determining tournament priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using generative AI or not. For example, the management department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0105] The management unit can select the optimal management method when managing tournaments, taking into account the user's geographical location information. For example, the management unit can prioritize displaying local tournaments based on the user's geographical location information. The management unit can collect the user's geographical location information using a generative AI. For example, the management unit can input the user's GPS data into the generative AI to collect geographical location information. The management unit can also provide information on nearby tournaments based on the user's geographical location information. The management unit can select the optimal management method using a generative AI. For example, the management unit can input the user's geographical location information into the generative AI to select a management method. Furthermore, the management unit can prioritize displaying popular local tournaments based on the user's geographical location information. In this way, the optimal tournament management method can be selected by taking into account the user's geographical location information. Some or all of the above processes in the management unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management unit can input the user's geographical location information into the generative AI and have the generative AI execute the process of selecting a management method.

[0106] The management department can analyze users' social media activity and suggest tournament options when managing tournaments. For example, the management department can recommend relevant tournaments based on users' "likes" and comments on social media. The management department can analyze users' social media activity using generative AI. For example, the management department can input users' social media data into the generative AI and analyze it. The management department can also recommend tournaments based on the interests of users' followers and friends. The management department can identify relevant tournaments using generative AI. For example, the management department can input tournament data into the generative AI and identify relevant tournaments. Furthermore, the management department can analyze users' social media activity history and recommend tournaments that they might be interested in. In this way, relevant tournaments can be recommended by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input users' social media data into the generative AI and have the generative AI perform the process of suggesting tournament options.

[0107] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and highly visible display method. The service provider can estimate the user's emotions using generative AI. For example, the service provider inputs the user's facial expression data into the generative AI and estimates the emotions. Also, if the user is relaxed, the service provider provides a display method that includes detailed information. The service provider can adjust the way the information is displayed using generative AI. For example, the service provider inputs the user's emotion data into the generative AI and adjusts the display method. Furthermore, if the user is in a hurry, the service provider provides a concise display method. By adjusting the way information is displayed according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0108] The service provider can select the optimal service delivery method by referring to the user's past information delivery history at the time of delivery. For example, the service provider can provide relevant information based on information the user has previously viewed. The service provider can refer to the user's past information delivery history using a generation AI. For example, the service provider can input the user's information delivery history data into the generation AI and refer to it. The service provider can also analyze the user's past information delivery history and provide information that suits their preferences. The service provider can select the optimal service delivery method using a generation AI. For example, the service provider can input the user's information delivery history data into the generation AI and select a delivery method. Furthermore, the service provider can predict and propose information to be delivered at a specific time based on the user's past information delivery history. This allows the service provider to select the optimal service delivery method by referring to past information delivery history. Some or all of the above processes in the service provider may be performed using a generation AI, or they may be performed without a generation AI. For example, the service provider can input the user's information delivery history into the generation AI and have the generation AI execute the process of selecting a delivery method.

[0109] The information provider can customize the information provided based on the user's current sports of interest and the performance trends of athletes. For example, the provider can prioritize providing information on sports that the user is currently interested in. The provider can identify the user's sports of interest using generative AI. For example, the provider can input the user's past selection data into the generative AI to identify the sports of interest. The provider can also customize the information based on the performance trends of athletes to match the user's preferences. The provider can customize the information using generative AI. For example, the provider can input athlete performance data into the generative AI to customize the information. Furthermore, the provider can provide information that reflects the latest match data based on the user's current sports of interest. This makes it possible to provide more appropriate information by customizing it based on the user's sports of interest and the performance trends of athletes. Some or all of the above processing in the provider can be performed using generative AI or not. For example, the provider can input the user's sports of interest and the performance trends of athletes into the generative AI and have the generative AI perform the process of customizing the information.

[0110] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is excited, the information provider will prioritize providing important information. The information provider can estimate the user's emotions using generative AI. For example, the information provider can input the user's facial expression data into the generative AI to estimate the emotions. Also, if the user is relaxed, the information provider will provide detailed information. The information provider can determine the priority of information using generative AI. For example, the information provider can input the user's emotion data into the generative AI to determine the priority. Furthermore, if the user is in a hurry, the information provider will prioritize providing concise information. This makes it possible to provide more appropriate information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is 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 processing in the information provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0111] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing local sports information based on the user's geographical location information. The service provider can collect the user's geographical location information using a generative AI. For example, the service provider can input the user's GPS data into the generative AI to collect geographical location information. The service provider can also provide information on nearby matches based on the user's geographical location information. The service provider can select the optimal service delivery method using a generative AI. For example, the service provider can input the user's geographical location information into the generative AI to select a service delivery method. Furthermore, the service provider can prioritize providing information on popular local sports based on the user's geographical location information. In this way, the service provider can select the optimal information delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's geographical location information into the generative AI and have the generative AI execute the process of selecting a service delivery method.

[0112] The service provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the service provider can provide relevant information based on the user's "likes" and comments on social media. The service provider can analyze the user's social media activity using generative AI. For example, the service provider can input the user's social media data into the generative AI and analyze it. The service provider can also make recommendations based on information that the user's followers and friends are interested in. The service provider can identify relevant information using generative AI. For example, the service provider can input information data into the generative AI and identify relevant information. Furthermore, the service provider can analyze the user's social media activity history and provide information that is likely to be of interest. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's social media data into the generative AI and have the generative AI perform the process of proposing means of providing information.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The reception desk can recommend players to users when they select a player, taking into account not only the player's past performance data but also their health status and training environment. For example, it can evaluate a player's health status based on their past injuries and rehabilitation history to assist in player selection. It can also predict a player's performance by considering their training environment (quality of facilities, quality of trainers, etc.) to assist in player selection. Furthermore, it can evaluate a player's current health status (physical condition, fatigue level, etc.) in real time to assist in player selection. This allows users to select a more appropriate player after considering their health status and training environment.

[0115] The analysis unit can consider psychological factors (motivation, stress levels, etc.) of players when analyzing their performance. For example, it can analyze players' pre-game comments and interviews to assess their motivation. It can also analyze players' facial expressions and attitudes during games to assess their stress levels. Furthermore, it can analyze psychological factors in players' past games to evaluate their impact on performance. By considering players' psychological factors, it becomes possible to predict performance with greater accuracy.

[0116] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials is provided. By adjusting the information display method according to the user's emotions, more appropriate information can be provided.

[0117] The management department can select the optimal management method when managing tournaments by referring to the user's past tournament participation history. For example, it can suggest the optimal management method based on the format of tournaments the user has participated in in the past. It can also analyze the user's past tournament results and select an appropriate tournament format. Furthermore, it can suggest a tournament format that suits the user's preferences based on their past tournament participation history. In this way, the optimal tournament management method can be selected by referring to past tournament participation history.

[0118] The reception desk can estimate the user's emotions and assist in player selection based on those estimates. For example, if the user is excited, it will prioritize displaying popular players to encourage selection. If the user is relaxed, it will provide detailed player information, allowing them to carefully choose a player. Furthermore, if the user is stressed, it will provide a simple interface to reduce the effort required for player selection. In this way, by assisting in player selection according to the user's emotions, more appropriate player selection becomes possible.

[0119] The analysis unit can perform analyses of player performance while considering the players' geographical distribution. For example, it can consider the players' hometowns and current places of residence. It can also consider the players' match locations (home and away). Furthermore, it can consider the players' training locations. By considering the players' geographical distribution, the accuracy of the analysis is improved.

[0120] The information delivery unit can estimate the user's emotions and determine the priority of the information to be delivered based on those emotions. For example, if the user is excited, important information will be prioritized. If the user is relaxed, detailed information will be provided. Furthermore, if the user is in a hurry, concise information will be prioritized. By prioritizing information according to the user's emotions, more appropriate information can be delivered.

[0121] The calculation unit can consider not only the player's performance data but also their motivation and mental state when calculating points. For example, it can calculate points considering the player's motivation (pre-match comments and interviews). It can also calculate points considering the player's mental state (facial expressions and attitude during the match). Furthermore, it can analyze the player's mental state in past matches and reflect that in the point calculation. By considering the player's motivation and mental state, the accuracy of the point calculation is improved.

[0122] The management team can estimate the user's emotions and adjust the tournament management method based on that estimation. For example, if the user is excited, the tournament will proceed quickly. If the user is relaxed, detailed tournament information will be provided. Furthermore, if the user is stressed, a simpler tournament management method will be provided. This allows for more appropriate tournament management by adjusting the tournament management method according to the user's emotions.

[0123] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, it can prioritize providing local sports information based on the user's geographical location information. It can also provide information on nearby matches based on the user's geographical location information. Furthermore, it can prioritize providing information on popular local sports based on the user's geographical location information. In this way, the optimal information delivery method can be selected by considering the user's geographical location information.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception desk accepts player selections. Player selections include real professional athletes and virtual athletes generated by a generator AI. The reception desk provides an interface for users to select players and can also collect player performance data. For example, it collects players' past match data and training data. Step 2: The analysis unit analyzes the player's performance data received by the reception unit. The analysis unit analyzes the player's past match data and training data to predict future performance. Generative AI can also be used to analyze the player's performance data. Step 3: The calculation unit calculates the player's points based on the data analyzed by the analysis unit. The calculation unit calculates the points based on the player's current performance data and future performance prediction data. It is also possible to calculate the player's points using generative AI. Step 4: The management unit manages the tournament based on the points calculated by the calculation unit. The management unit can manage the tournament within the Fantasy AI League, monitor the progress of the tournament, and provide rewards to top performers. Step 5: The provisioning unit provides the tournament results managed by the management unit. The provisioning unit can notify users of the tournament results and provide the tournament results using a generating AI.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, calculation unit, management unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to select a player. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the player's performance data. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the player's points. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the tournament. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and notifies the user of the tournament results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, calculation unit, management unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to select a player. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the player's performance data. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates the player's points. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages the tournament. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the tournament results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, calculation unit, management unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to select a player. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the player's performance data. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the player's points. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the tournament. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and notifies the user of the tournament results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the reception unit, analysis unit, calculation unit, management unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to select a player. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the player's performance data. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the player's points. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the tournament. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user of the tournament results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) The reception desk that accepts player selections, An analysis unit analyzes the athlete's performance data received by the reception unit, A calculation unit that calculates the player's points based on the data analyzed by the aforementioned analysis unit, A management unit manages the tournament based on the points calculated by the calculation unit, The system comprises a provisioning unit that provides tournament results managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzing past match and training data of players to predict future performance. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We will disclose details of our AI predictive models and data sources to provide users with reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It collects real-time data and integrates actual match results with AI predictions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Manage tournaments within the Fantasy AI League and provide rewards to top performers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It covers multiple sports, including soccer, basketball, and baseball. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, It provides a community feature that allows users to share information and engage in discussions with each other. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and assists in player selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is We analyze the user's past player selection history and suggest the optimal player selection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When selecting players, filtering is performed based on the user's current sports of interest and the players' performance trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and adjusts the player selection order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When selecting players, the system prioritizes displaying players with high relevance, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When selecting players, the system analyzes the user's social media activity and recommends relevant players. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing a player's past match and training data to predict future performance, the player's health and training environment are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by taking into account external factors that affect player performance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical distribution of the players will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, we refer to relevant literature and news articles about the players to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit, The system estimates the user's emotions and adjusts the point calculation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, When calculating points, in addition to the player's performance data, their motivation and mental state will also be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit, When calculating points, past injuries and rehabilitation status that may affect a player's performance are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit, The system estimates the user's emotions and determines the priority of point calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit, When calculating points, the geographical distribution of players is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The calculation unit, When calculating points, we refer to relevant literature and news articles about the players to improve the accuracy of the calculations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, The system estimates user sentiment and adjusts tournament management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During tournament management, the system selects the optimal management method by referring to the user's past tournament participation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing tournaments, customize them based on the user's current sports of interest and tournament format. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, The system estimates user sentiment and determines tournament priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing tournaments, the optimal management method is selected by considering the users' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, When managing tournaments, we analyze users' social media activity and suggest tournament strategies. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, the system will select the most suitable method of delivery by referring to the user's past information provision history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, it will be customized based on the user's current interests in sports and the performance trends of the athletes. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and propose methods for providing information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that accepts player selections, An analysis unit analyzes the athlete's performance data received by the reception unit, A calculation unit that calculates the player's points based on the data analyzed by the aforementioned analysis unit, A management unit manages the tournament based on the points calculated by the calculation unit, The system comprises a provisioning unit that provides tournament results managed by the aforementioned management unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzing past match and training data of players to predict future performance. The system according to feature 1.

3. The aforementioned supply unit is, We disclose details of our AI prediction models and data sources to provide users with reliable information. The system according to feature 1.

4. The aforementioned supply unit is, It collects real-time data and integrates actual match results with AI predictions in real time. The system according to feature 1.

5. The aforementioned management department, Manage tournaments within the Fantasy AI League and provide rewards to top performers. The system according to feature 1.

6. The aforementioned supply unit is, It covers multiple sports, including soccer, basketball, and baseball. The system according to feature 1.

7. The aforementioned supply unit is, It provides a community feature that allows users to share information and engage in discussions with each other. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and assists in player selection based on those estimated emotions. The system according to feature 1.

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