system

Abstract

We provide the system. [Solution] A means of obtaining performance data of actual athletes, A means of using artificial intelligence to calculate a competitor's predicted score based on the aforementioned performance data, A means of providing the user with the aforementioned prediction points and enabling the formation of a fictional team, A method for users to compete against each other and display the results as a ranking, A means of providing an interface for users to share information or communicate, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Current fantasy sports lack transparency when predicting the performance of competitors, and there is a problem that reliable information is insufficient for users. Furthermore, during the process of users' fictional team formation and competitions, effective means for real-time performance and information exchange with other users are not sufficiently provided. As a result, users' engagement has decreased, and the needs for a fresher experience have not been met.

Means for Solving the Problems

[0005] This invention provides transparent information to users by acquiring performance data of real athletes and calculating predicted points for those athletes using artificial intelligence based on that data. Furthermore, it provides a function that allows users to form fictional teams using the predicted points of athletes, and includes a means for users to compete against each other and display the results as rankings. In addition, by providing an interface for users to share information and communicate with each other, it enhances real-time data updates and community functions, thereby improving user engagement.

[0006] "Competitor" refers to a player or athlete who participates in a sporting event.

[0007] "Performance data" refers to information that shows records such as scores, points, and assists achieved by athletes in matches and competitions.

[0008] "Prediction points" refer to an indicator that quantifies an athlete's future performance, calculated by artificial intelligence based on the athlete's past performance data and real-time data.

[0009] "Artificial intelligence" refers to systems and programs that have the ability to process and predict / analyze athlete performance data using machine learning and data analysis techniques.

[0010] A "fictional team" refers to a virtual team composed of multiple players selected by the user, and competes in a context different from actual matches.

[0011] A "user" refers to an individual or group that uses this system to obtain information about competitors or to form fictional teams.

[0012] A "ranking" refers to a list where fictional teams created by users are compared to other teams, and the results of the competition are displayed as rankings.

[0013] An "interface" refers to a mechanism that provides functions and interactive screens to make it easier for users to operate a system, enabling them to acquire information and input data.

[0014] "Community features" refer to providing functions such as discussion forums and chat boards that allow users to share information and exchange opinions with each other. [Brief explanation of the drawing]

[0015] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for a sports fantasy simulation game, characterized by the ability for users to assemble fictional teams and compete for points using performance data of real athletes. Detailed embodiments are shown below.

[0037] First, the server connects to an external data provider to retrieve player performance data in real time. This data includes goals, assists, defensive records, and more. The retrieved data is stored in a database and prepared for analysis.

[0038] Next, the server uses artificial intelligence with the stored data to calculate prediction points for each athlete. This allows for predictions of future performance, taking into account the athlete's past results and current performance. The AI ​​model's algorithm and the sources of the data used for predictions are recorded and disclosed to the user as needed.

[0039] The terminal provides users with a visual interface of player information and predicted points. Users can use this interface to search for and draft players they want to include in their team. Once the fictional team is formed, the team information is saved on the server.

[0040] Next, as the match progresses, the server continuously retrieves new performance data and updates the points for the user's fictional team in real time. This information is displayed to the user through their device, allowing them to track their team's performance in real time.

[0041] Furthermore, the server hosts tournaments among users and operates a ranking system based on these tournaments. Rankings are calculated based on the points users earn, and top users receive rewards.

[0042] Finally, the device provides community features, allowing users to discuss game strategies and player information. This enables users to exchange information with other players and improve their team composition and strategies.

[0043] As a concrete example, if user A uses this system, user A can select competitor B to be on their team. Competitor B's real-time performance is acquired, and the points predicted by the AI ​​model are reflected in user A's points. At this point, user A can communicate with other users through the terminal and receive advice on the best team composition. In this way, the present invention enhances the user experience and enables the provision of interactive competitions.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server retrieves real-time performance data from an external sports data provider. Using an API, it collects detailed data such as player goals, assists, and playing time, and stores it in the server's database.

[0047] Step 2:

[0048] The server uses an artificial intelligence algorithm to calculate predicted points for each athlete based on stored performance data. The algorithm considers past performance data and current performance to quantify the athlete's future performance.

[0049] Step 3:

[0050] The device provides the user with player information via an interface. Based on this information, the user drafts players to form a fictional team. The information of the selected players is sent to the server, and the user's team composition is recorded.

[0051] Step 4:

[0052] The server periodically updates performance data during the match and calculates the user's team points based on the players' real-time performance. The updated point information is sent from the server to the terminal and displayed to the user in real time.

[0053] Step 5:

[0054] The server will host tournaments among users and operate a ranking system. It will tally points earned by users and update the rankings at regular intervals. A system will be established to reward top-ranking users.

[0055] Step 6:

[0056] The device provides community features, creating a space where users can discuss strategies and player information. This feature allows users to exchange information with other participants and improve their own team building strategies.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Traditional sports fantasy simulation games suffer from several challenges: the process of acquiring real-time performance data of athletes and calculating predicted evaluation values ​​is opaque, and the limited information available to users about the athletes they can choose from hinders the improvement of the game experience. Furthermore, insufficient communication and information sharing among users prevents the game from providing a truly interactive competitive environment.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate predicted evaluation values ​​of athletes based on the performance information, and means for providing terminals for users to share information or communicate. This enables users to select athletes based on more accurate and transparent evaluations, and to enhance the appeal of the game through real-time information acquisition and improved interactive communication.

[0062] "Statistical information of actual athletes" refers to specific and measurable data recorded by athletes in actual sports competitions, such as the number of goals scored, assists, and defensive records.

[0063] A "predictive evaluation value" is a numerical value that estimates an athlete's future performance, calculated using a machine learning algorithm based on past performance data and current performance.

[0064] "Machine learning" is a type of artificial intelligence technology in which algorithms learn patterns based on data and perform predictions and classifications.

[0065] "Users" refers to individuals or teams who use this system to form fictional competitive groups and enjoy competing.

[0066] A "terminal" is a computer, smartphone, or other electronic device that a user uses to access the system.

[0067] "Rankings" refer to information that shows the rankings of users based on the results of competitions and the evaluation scores obtained.

[0068] A "server" is a computer system that performs core processing within a system and is responsible for storing, analyzing, and providing data.

[0069] This invention is a system for realizing a fantasy simulation game themed around sports competitions. This system is characterized by the use of performance data from real athletes, allowing users to assemble fictional teams and compete against each other. The following describes specific embodiments of this invention.

[0070] This system primarily consists of a server, terminals, and users. The server connects to external data providers via the internet and retrieves data using a RESTful API. Player performance information, including goals scored, assists, and defensive records, is stored in a database. The server also has a mechanism to calculate predictive evaluation values ​​for players based on the retrieved data, using machine learning software such as TENSORFLOW®. This allows for predictions that consider not only past performance but also current performance.

[0071] The device visually displays player information and predicted evaluation values ​​through its user interface. Users can use this interface to draft players for their own fictional teams. In particular, the device is built using React and other modern web technologies, providing a dynamic and responsive interface.

[0072] Users can strategically assemble fictional competitive teams based on real-time, ever-changing information. The server updates its database whenever new match data arrives, promptly pushing information about the user's team to their device. This process utilizes WebSockets to ensure immediacy.

[0073] For example, if a user views a player's information on their device and decides to add that player to their team, the server can record the player's performance data and predicted evaluation value in a database and provide points based on the user's requested immediate information. This allows users to adjust their strategies during the game and enjoy a more fulfilling experience.

[0074] An example of a prompt would be, "Please describe a system that acquires real-time athlete performance data and predicts team points in a fantasy sports game." This would allow for a detailed explanation using a generative AI model.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server connects to an external data provider and retrieves player performance information via an API. Using the API endpoint and authentication information as input, it obtains performance data such as goals scored, assists, and defensive records as output. The data is received in JSON format and stored in a temporary database. This storage process includes checking for duplicate data and validating the data format.

[0078] Step 2:

[0079] The server inputs stored performance data into a machine learning model to calculate a predicted evaluation score for each player. The input includes the player's past performance and current performance data. The output is a predicted evaluation score indicating their potential performance in the next season, calculated by a neural network using TensorFlow. This evaluation score takes into account the player's current state and future potential.

[0080] Step 3:

[0081] The terminal provides users with athlete information and predicted evaluation values ​​in a visual format. Users can search for athletes and view their data through the interface. Athlete information is retrieved from the server as input, and the information is displayed visually as output in a web interface using React. This display is dynamically updated and responds immediately to user interaction.

[0082] Step 4:

[0083] Users draft players into a fictional team via their device. The selected player information is used as input, and the newly formed team information is output. The device sends this information to a server, which stores it in a database. Once the team formation is complete, the information is immediately shared with other users.

[0084] Step 5:

[0085] The server continuously retrieves new performance data as the match progresses and updates the database. It receives new match results as input and provides updated player statistics and user team points as output. This update process is handled automatically and notified to the user's terminal in real time via WebSocket.

[0086] Step 6:

[0087] The terminal displays updated information to the user in real time. It uses updated data from the server as input and provides the user with the latest team performance as output. Users can see points changing in real time and instantly review their team's strategy.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] There is a need to enhance the user experience related to sports content and provide a more real-time, interactive, and participatory simulation experience by utilizing actual performance data of athletes. Furthermore, there is a lack of environments where users can interact with other users and visually enjoy real-time data fluctuations.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring performance data of real athletes, means for using information processing technology to calculate predicted measurement values ​​for athletes based on the performance data, and means for providing the predicted measurement values ​​to users and enabling the formation of a fictional group. This makes it possible for users to track the performance of athletes in real time while watching a sports event and to visually check the performance of their own group.

[0093] "Competitors" refer to real athletes or players who participate in sporting events.

[0094] "Performance data" refers to information that represents the results and records achieved by athletes in sporting events.

[0095] "Predicted performance" refers to the result of calculating future performance based on an athlete's past performance data.

[0096] "Information processing technology" includes computational methods and techniques for collecting, analyzing, and processing data.

[0097] "Users" refer to individuals who use the system or application of this patent to form fictitious teams or receive data.

[0098] A "fictional group" is a collection of sports teams or athletes formed by users that do not actually exist in reality.

[0099] A "visual display device" refers to a device that directly presents information to the user's eyes, and includes smart glasses and head-mounted displays.

[0100] The system implementing this invention is comprised of multiple computer devices and network technologies. The server connects to an external data provider and periodically collects athlete performance data. This data collection utilizes real-time data acquisition via an API. The performance data is stored in a database on the server, allowing for rapid access and processing.

[0101] The server utilizes generative AI models as an information processing technology to calculate predicted performance metrics for athletes based on collected performance data. This AI model is built using Python and data analysis libraries such as scikit-learn, and predicts future performance from large amounts of performance data. The AI ​​model records the basis for its predictions and can present sources and rationale to users to maintain transparency.

[0102] The device uses smart glasses or a head-mounted display as a visual display device to provide users with predictive measurements and real-time performance information. This allows users to track athlete data in real time during sporting events and check the performance of a hypothetical group.

[0103] For example, if a user is watching a soccer match, wearing smart glasses will visually display player data in sync with the progress of the game. This allows the user to check the score of their virtual team in real time.

[0104] An example of a prompt for the generative AI model is, "Who are the players to watch in the next soccer match?" This allows the AI ​​to suggest players based on past performance and the latest data.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server connects to an external data provider's API to retrieve athlete performance data. This process collects the data received from the API in JSON format and stores it in the server's internal database. The input is the response data from the API, which, once stored in the database, is output as performance information used in subsequent processing.

[0108] Step 2:

[0109] The server runs a generative AI model using stored performance data to calculate predicted performance metrics for each athlete. The input is historical performance data, and data analysis is performed using Python and scikit-learn to predict future performance. As a result of this process, predicted performance metrics for each athlete are output, and the basis for these predictions is also maintained to ensure transparency.

[0110] Step 3:

[0111] The terminal distributes the generated predicted measurements to a visual display device, providing information to the user. The input is the predicted measurements received from the server, which are displayed through the user interface of smart glasses or a head-mounted display. This output allows the user to check the predicted performance of the athlete in real time.

[0112] Step 4:

[0113] Users can evaluate the performance of a fictional group in a sporting event they are watching through a visual display device. Users can receive real-time feedback and use prompts to instruct the generative AI model to make new predictions. The input here is the user's interaction, and the output is the AI ​​model's recommendations and predictions based on the requested information.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention aims to improve user engagement by combining an emotion engine with a sports fantasy simulation system. Specific embodiments are described below.

[0116] First, the server retrieves player performance data in real time from an external data provider and stores it in a database. This data retrieval is performed periodically and includes information such as players' goals, assists, and defensive records.

[0117] Next, the server uses an artificial intelligence algorithm based on the stored performance data to calculate predicted points for each athlete. The user checks these predicted points through their device and assembles a fictional team. The user's selected team information is managed on the server.

[0118] A key feature of this invention is that the terminal is equipped with an emotion engine that analyzes the user's input and operation history, and recognizes the user's emotions in real time. For example, the emotion engine recognizes whether the user is happy or stressed based on the user's writing and clicking speed, facial expression data (if a camera is being used), etc.

[0119] This allows the server to dynamically adjust the interface it provides to the user based on the results of its emotion recognition. For example, if a user expresses dissatisfaction with the game's outcome, the interface can display encouraging messages or hints from the players.

[0120] Furthermore, the device provides a messaging function for users to communicate with each other. In this process, the emotion engine adjusts the tone of the messages to be appropriate according to the user's emotional state. For example, congratulatory messages are prioritized for users who are overjoyed, while encouraging messages are prioritized for users who are stressed.

[0121] For example, if user X uses this system and their team loses, the emotion engine senses user X's frustration and dissatisfaction, and provides feedback such as "advice for continuing next time" displayed on the device. In this way, an approach that responds to the user's emotions can increase user satisfaction and the frequency of system use.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server retrieves real-time performance data from an external sports data provider using an API. The data includes detailed information such as player scores, assists, and defensive actions. The retrieved data is stored in a database to prepare for subsequent processing.

[0125] Step 2:

[0126] The server uses an artificial intelligence algorithm to calculate athlete prediction scores based on stored performance data. The AI ​​model analyzes past performance history and predicts future performance considering current performance. The results are stored in a database in a format that users can use.

[0127] Step 3:

[0128] The device provides the user with an interface containing performance data and predicted points. The user uses this information to create a fictional team through screen touches and clicks. The created team information is saved to the server based on the user's actions.

[0129] Step 4:

[0130] The device activates an emotion engine that analyzes emotions based on user input and interaction. By analyzing and evaluating data such as input speed, operation patterns, and even facial recognition (optional), it recognizes the user's emotional state in real time.

[0131] Step 5:

[0132] The server dynamically adjusts the interface content based on the user's emotional state. For example, for a user showing signs of stress, the UI might display relaxation tips or encouraging messages.

[0133] Step 6:

[0134] The device provides community features to facilitate communication between users. Here, the emotion engine adjusts the tone of messages according to the user's emotions, assisting in smoother interaction with other users.

[0135] This process allows the entire system to respond flexibly to the user's emotions, enriching the gaming experience and improving user satisfaction.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] When users form virtual sports teams, it is essential that performance data and interaction with other users are handled smoothly. However, with conventional systems, this process cannot accommodate the emotional state of the users, making it difficult to improve user satisfaction.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for acquiring performance data of real athletes, means for using intelligent processing to calculate the athlete's predicted points based on the performance data, and means for analyzing the user's operation history and identifying their emotional state. This makes it possible to provide information and adjust the screen according to the user's emotional state.

[0141] "Performance data" refers to numerical information that quantifies the performance of athletes in competitions, such as goals scored, assists, and defensive records.

[0142] "Intelligent processing" refers to processing methods using computer programs to make predictions and analyses based on data, and is particularly achieved through the use of artificial intelligence.

[0143] "Prediction points" are an index calculated by numerically evaluating a player's future performance based on past performance data.

[0144] A "user" refers to an individual who operates this system and forms a virtual sports organization.

[0145] "Operation history" refers to a record of a series of actions, such as clicks and inputs, that a user performs when using the system.

[0146] "Emotional state" refers to the result of identifying the manifestation of emotions based on the user's psychological or physiological responses.

[0147] "Screen display" refers to the visual information interface provided to the user through the system, meaning a screen from which operations and information can be viewed.

[0148] A "virtual group" refers to a fictional team composed of athletes selected by the user, whose performance is based on actual competition results.

[0149] This system primarily consists of a server, terminals, and users. The server retrieves performance data of real athletes from external data sources via a REST API and stores it in a database. MySQL® is often used for this database. The data includes specific performance indicators such as goals scored, assists, and defensive records.

[0150] The server uses the stored performance data to perform intelligent processing to calculate predicted points for each athlete. This intelligent processing uses libraries such as Python's Scikit-learn, and a machine learning model makes predictions based on past performance data and other relevant information.

[0151] Users connect to the internet via their devices and operate through a web browser. They refer to the provided prediction points and use drag-and-drop operations to form virtual groups. The formed team information is sent to a server and managed individually.

[0152] Furthermore, the device is equipped with a function to identify emotional states. This uses an emotion AI library such as Affectiva to analyze the user's emotional state from their operation history. Specifically, it analyzes the user's keystrokes, mouse movements, and facial expression data to recognize emotions.

[0153] Based on the results of this emotion recognition, the server can dynamically adjust the screen display provided to the user. For example, if the user expresses dissatisfaction with the system's results, it can display encouraging messages or operational hints on the terminal.

[0154] As a concrete example, suppose a user's team loses a match while using the system. At this point, the emotion engine detects the user's frustration and dissatisfaction and displays a message on the device saying, "You're getting closer to success by trying again." This kind of feedback improves user satisfaction.

[0155] An example of a prompt message is, "How can I recognize user emotions in real time and dynamically adjust the interface?" Using this prompt can improve the user experience in the system.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server retrieves player performance data from an external data source. The input data is performance information in JSON format sent to the server via an API request. Specifically, the server sends a request to the external API and receives scores, assists, and defensive records for each player. The received information is stored in the database in preparation for subsequent processing. The output is the state in which the latest player performance data has been saved in the database.

[0159] Step 2:

[0160] The server uses intelligent processing to calculate predicted scores based on performance data stored in the database. Individual athlete data is used as input, which is passed to the Python Scikit-learn library for calculations. Specific data calculations include regression analysis based on past performance metrics. The output is the predicted score for each athlete, which is also stored in the database.

[0161] Step 3:

[0162] Users check their predicted points using a web browser on their device. The input data is the predicted points of the competitors provided by the server. Specifically, users assemble virtual teams using drag-and-drop operations through an interface provided in the browser. Once the team is assembled, the team information is sent to the server and managed on a per-user basis. The output is the data for the virtual teams assembled by the users.

[0163] Step 4:

[0164] The terminal records the user's operation history and analyzes their emotional state using an emotion AI library. Inputs include keystrokes, mouse movements, and, if necessary, facial expression data. Specifically, this data is passed to the Affectiva library, and the program identifies the user's emotional state (e.g., stress or joy). The output is the identified emotional state data.

[0165] Step 5:

[0166] The server receives emotional state data and dynamically adjusts the screen display according to the user's emotions. The input is emotional state data sent from the terminal. Specifically, the server performs conditional branching and, for example, if dissatisfaction is detected, creates an interface that provides words of encouragement or suggestions for the next steps. The output is the adjusted screen instructions, and the terminal updates the screen in real time according to these instructions.

[0167] Step 6:

[0168] The device operates a content delivery function for communication between users. Inputs include messages from other users and adjustment instructions based on emotions. Specifically, it adjusts the message tone to encourage positive communication. Output is a user interface displaying the adjusted message.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] In sports fantasy simulations, there is a need to increase user engagement and provide a more intuitive and personalized experience. However, traditional systems lack sufficient feedback and visual content adjustments that respond to user emotions, which hinders improved satisfaction.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate a predictive evaluation of athletes based on the performance information, and means for using an emotion recognition engine to analyze the user's emotions and adjust the visual content. This makes it possible to provide appropriately customized content based on the user's emotional state.

[0174] "Performance data" refers to performance data of athletes in sports competitions, including scores, assists, defensive records, etc.

[0175] "Predictive evaluation" refers to a future performance indicator of an athlete, calculated using a machine learning algorithm based on acquired performance data.

[0176] Machine learning is a technique that uses algorithms and statistical models to identify patterns in data and perform predictions and classifications.

[0177] "User" refers to an individual or group that uses a sports fantasy simulation system to form a fictional team and participate in a competition.

[0178] "Display means" refers to an interface or device for visually presenting information to a user.

[0179] An "emotion recognition engine" is a technology that analyzes and determines a user's emotional state from their input and facial expression data, and adjusts the system's response and content accordingly.

[0180] "Real-time" means that information is processed or displayed immediately the moment it is generated, without any delay.

[0181] To implement this invention, it is necessary to construct a system in which the roles of server, terminal, and user are clearly defined. The server is primarily responsible for the following processes:

[0182] First, the server retrieves player performance information in real time from external data providers. This information includes detailed records of a player's performance in each match, such as goals scored, assists, and defensive records.

[0183] The server then uses this performance information to calculate predictive evaluations using machine learning algorithms. These machine learning algorithms utilize libraries such as TensorFlow and PyTorch, and are designed to provide highly accurate predictions.

[0184] The server then provides users with predictive evaluations via their terminals, allowing them to create their own fictional groups. Based on these evaluations, users can select players, form specific groups, and enjoy their performance as a competition.

[0185] Furthermore, the device is equipped with an emotion recognition engine that analyzes input data and facial expression data when the user operates the system to determine the user's emotional state. This includes facial expression recognition technology using OpenCV and text analysis using NLTK.

[0186] Depending on the user's emotional state, the device can appropriately adjust the visual content. For example, if a user is dissatisfied with the outcome of a match, the device can support the user by displaying hints and advice obtained from the server.

[0187] This entire process allows users to engage more deeply through a more personalized experience. An example of using the generative AI model is a prompt such as, "Generate a highlight video that matches the user's state of happiness."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server retrieves performance data in real time from external data providers. Inputs are player match data, including goals, assists, and defensive records. This data is processed and stored in a database. Output is a structured set of performance information.

[0191] Step 2:

[0192] The server uses a machine learning algorithm to convert performance data into predictive evaluations. The input is the performance data saved in Step 1. As part of the data processing, highly relevant features are selected, and a model is applied using TensorFlow to calculate the predictive evaluation. The output is the predictive evaluation for each player.

[0193] Step 3:

[0194] The server provides predictive evaluations to the user via the terminal. The input is the predictive evaluation generated in step 2. The evaluations are displayed on the terminal screen, and an interface is provided that allows the user to select players and form a hypothetical group. The output is information about the group selected by the user.

[0195] Step 4:

[0196] The device acquires user input and facial expression data, which are then analyzed by an emotion recognition engine. Input consists of user operation data and (if possible) facial expression data from the camera. OpenCV and NLTK are used to analyze the data and determine the user's emotional state. The output is an evaluation of the user's emotional state.

[0197] Step 5:

[0198] The server dynamically adjusts the interface and content based on the user's emotional state. The input is the emotional state obtained in step 4. Depending on the emotion, the display shows encouraging messages or hints for the next step. The output is personalized feedback for the user.

[0199] Step 6:

[0200] Based on the customized feedback, users modify their behavior or develop new strategies. The input is the feedback from step 5. The output is increased user satisfaction and willingness to reuse the system.

[0201] 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.

[0202] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0208] 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.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0210] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] 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.

[0212] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The 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.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention is a system for a sports fantasy simulation game, characterized by the ability for users to assemble fictional teams and compete for points using performance data of real athletes. Detailed embodiments are shown below.

[0218] First, the server connects to an external data provider to retrieve player performance data in real time. This data includes goals, assists, defensive records, and more. The retrieved data is stored in a database and prepared for analysis.

[0219] Next, the server uses artificial intelligence with the stored data to calculate prediction points for each athlete. This allows for predictions of future performance, taking into account the athlete's past results and current performance. The AI ​​model's algorithm and the sources of the data used for predictions are recorded and disclosed to the user as needed.

[0220] The terminal provides users with a visual interface of player information and predicted points. Users can use this interface to search for and draft players they want to include in their team. Once the fictional team is formed, the team information is saved on the server.

[0221] Next, as the match progresses, the server continuously retrieves new performance data and updates the points for the user's fictional team in real time. This information is displayed to the user through their device, allowing them to track their team's performance in real time.

[0222] Furthermore, the server hosts tournaments among users and operates a ranking system based on these tournaments. Rankings are calculated based on the points users earn, and top users receive rewards.

[0223] Finally, the device provides community features, allowing users to discuss game strategies and player information. This enables users to exchange information with other players and improve their team composition and strategies.

[0224] As a concrete example, if user A uses this system, user A can select competitor B to be on their team. Competitor B's real-time performance is acquired, and the points predicted by the AI ​​model are reflected in user A's points. At this point, user A can communicate with other users through the terminal and receive advice on the best team composition. In this way, the present invention enhances the user experience and enables the provision of interactive competitions.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server retrieves real-time performance data from an external sports data provider. Using an API, it collects detailed data such as player goals, assists, and playing time, and stores it in the server's database.

[0228] Step 2:

[0229] The server uses an artificial intelligence algorithm to calculate predicted points for each athlete based on stored performance data. The algorithm considers past performance data and current performance to quantify the athlete's future performance.

[0230] Step 3:

[0231] The device provides the user with player information via an interface. Based on this information, the user drafts players to form a fictional team. The information of the selected players is sent to the server, and the user's team composition is recorded.

[0232] Step 4:

[0233] The server periodically updates performance data during the match and calculates the user's team points based on the players' real-time performance. The updated point information is sent from the server to the terminal and displayed to the user in real time.

[0234] Step 5:

[0235] The server will host tournaments among users and operate a ranking system. It will tally points earned by users and update the rankings at regular intervals. A system will be established to reward top-ranking users.

[0236] Step 6:

[0237] The device provides community features, creating a space where users can discuss strategies and player information. This feature allows users to exchange information with other participants and improve their own team building strategies.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] Traditional sports fantasy simulation games suffer from several challenges: the process of acquiring real-time performance data of athletes and calculating predicted evaluation values ​​is opaque, and the limited information available to users about the athletes they can choose from hinders the improvement of the game experience. Furthermore, insufficient communication and information sharing among users prevents the game from providing a truly interactive competitive environment.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate predicted evaluation values ​​of athletes based on the performance information, and means for providing terminals for users to share information or communicate. This enables users to select athletes based on more accurate and transparent evaluations, and to enhance the appeal of the game through real-time information acquisition and improved interactive communication.

[0243] "Statistical information of actual athletes" refers to specific and measurable data recorded by athletes in actual sports competitions, such as the number of goals scored, assists, and defensive records.

[0244] A "predictive evaluation value" is a numerical value that estimates an athlete's future performance, calculated using a machine learning algorithm based on past performance data and current performance.

[0245] "Machine learning" is a type of artificial intelligence technology in which algorithms learn patterns based on data and perform predictions and classifications.

[0246] "Users" refers to individuals or teams who use this system to form fictional competitive groups and enjoy competing.

[0247] A "terminal" is a computer, smartphone, or other electronic device that a user uses to access the system.

[0248] "Rankings" refer to information that shows the rankings of users based on the results of competitions and the evaluation scores obtained.

[0249] A "server" is a computer system that performs core processing within a system and is responsible for storing, analyzing, and providing data.

[0250] This invention is a system for realizing a fantasy simulation game themed around sports competitions. This system is characterized by the use of performance data from real athletes, allowing users to assemble fictional teams and compete against each other. The following describes specific embodiments of this invention.

[0251] This system primarily consists of a server, terminals, and users. The server connects to external data providers via the internet and retrieves data using a RESTful API. Player performance information, including goals scored, assists, and defensive records, is stored in a database. The server also has a mechanism to calculate predictive evaluation values ​​for players based on the retrieved data using machine learning software such as TensorFlow. This allows for predictions that consider not only past performance but also current performance.

[0252] The device visually displays player information and predicted evaluation values ​​through its user interface. Users can use this interface to draft players for their own fictional teams. In particular, the device is built using React and other modern web technologies, providing a dynamic and responsive interface.

[0253] Users can strategically assemble fictional competitive teams based on real-time, ever-changing information. The server updates its database whenever new match data arrives, promptly pushing information about the user's team to their device. This process utilizes WebSockets to ensure immediacy.

[0254] For example, if a user views a player's information on their device and decides to add that player to their team, the server can record the player's performance data and predicted evaluation value in a database and provide points based on the user's requested immediate information. This allows users to adjust their strategies during the game and enjoy a more fulfilling experience.

[0255] An example of a prompt would be, "Please describe a system that acquires real-time athlete performance data and predicts team points in a fantasy sports game." This would allow for a detailed explanation using a generative AI model.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The server connects to an external data provider and retrieves player performance information via an API. Using the API endpoint and authentication information as input, it obtains performance data such as goals scored, assists, and defensive records as output. The data is received in JSON format and stored in a temporary database. This storage process includes checking for duplicate data and validating the data format.

[0259] Step 2:

[0260] The server inputs stored performance data into a machine learning model to calculate a predicted evaluation score for each player. The input includes the player's past performance and current performance data. The output is a predicted evaluation score indicating their potential performance in the next season, calculated by a neural network using TensorFlow. This evaluation score takes into account the player's current state and future potential.

[0261] Step 3:

[0262] The terminal provides users with athlete information and predicted evaluation values ​​in a visual format. Users can search for athletes and view their data through the interface. Athlete information is retrieved from the server as input, and the information is displayed visually as output in a web interface using React. This display is dynamically updated and responds immediately to user interaction.

[0263] Step 4:

[0264] Users draft players into a fictional team via their device. The selected player information is used as input, and the newly formed team information is output. The device sends this information to a server, which stores it in a database. Once the team formation is complete, the information is immediately shared with other users.

[0265] Step 5:

[0266] The server continuously retrieves new performance data as the match progresses and updates the database. It receives new match results as input and provides updated player statistics and user team points as output. This update process is handled automatically and notified to the user's terminal in real time via WebSocket.

[0267] Step 6:

[0268] The terminal displays updated information to the user in real time. It uses updated data from the server as input and provides the user with the latest team performance as output. Users can see points changing in real time and instantly review their team's strategy.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] There is a need to enhance the user experience related to sports content and provide a more real-time, interactive, and participatory simulation experience by utilizing actual performance data of athletes. Furthermore, there is a lack of environments where users can interact with other users and visually enjoy real-time data fluctuations.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for acquiring performance data of real athletes, means for using information processing technology to calculate predicted measurement values ​​for athletes based on the performance data, and means for providing the predicted measurement values ​​to users and enabling the formation of a fictional group. This makes it possible for users to track the performance of athletes in real time while watching a sports event and to visually check the performance of their own group.

[0274] "Competitors" refer to real athletes or players who participate in sporting events.

[0275] "Performance data" refers to information that represents the results and records achieved by athletes in sporting events.

[0276] "Predicted performance" refers to the result of calculating future performance based on an athlete's past performance data.

[0277] "Information processing technology" includes computational methods and techniques for collecting, analyzing, and processing data.

[0278] "Users" refer to individuals who use the system or application of this patent to form fictitious teams or receive data.

[0279] A "fictional group" is a collection of sports teams or athletes formed by users that do not actually exist in reality.

[0280] A "visual display device" refers to a device that directly presents information to the user's eyes, and includes smart glasses and head-mounted displays.

[0281] The system for implementing this invention is composed of multiple computer devices and network technologies. The server connects to external data providers and periodically collects the performance data of competitors. For this data collection, the acquisition of real-time data through APIs is utilized. The performance data is stored in a database within the server to enable rapid access and processing.

[0282] The server makes full use of the generative AI model as an information processing technology and calculates the predicted measurement values of competitors based on the collected performance data. This AI model is constructed using data analysis libraries such as Python and scikit-learn, and predicts future performance from a large amount of performance data. The AI model records the basis for the prediction and can present the source and basis to users to maintain transparency.

[0283] The terminal uses smart glasses or head-mounted displays as visual display devices to provide users with predicted measurement values and real-time performance information. Thereby, users can track the data of competitors in real time during sports events and check the performance of an imaginary group.

[0284] As a specific example, when a user uses it while watching a soccer game, if wearing smart glasses, the data of the players will be visually displayed according to the progress of the game. Thereby, the user can check the score of their imaginary group in real time.

[0285] As an example of the prompt text for the generative AI model, it can be input in the form of "Who are the players to watch in the next soccer game?" Thereby, the AI can present recommended players based on past performance and the latest data.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server connects to an external data provider's API to retrieve athlete performance data. This process collects the data received from the API in JSON format and stores it in the server's internal database. The input is the response data from the API, which, once stored in the database, is output as performance information used in subsequent processing.

[0289] Step 2:

[0290] The server runs a generative AI model using stored performance data to calculate predicted performance metrics for each athlete. The input is historical performance data, and data analysis is performed using Python and scikit-learn to predict future performance. As a result of this process, predicted performance metrics for each athlete are output, and the basis for these predictions is also maintained to ensure transparency.

[0291] Step 3:

[0292] The terminal distributes the generated predicted measurements to a visual display device, providing information to the user. The input is the predicted measurements received from the server, which are displayed through the user interface of smart glasses or a head-mounted display. This output allows the user to check the predicted performance of the athlete in real time.

[0293] Step 4:

[0294] Users can evaluate the performance of a fictional group in a sporting event they are watching through a visual display device. Users can receive real-time feedback and use prompts to instruct the generative AI model to make new predictions. The input here is the user's interaction, and the output is the AI ​​model's recommendations and predictions based on the requested information.

[0295] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0296] This invention aims to improve user engagement by combining an emotion engine with a sports fantasy simulation system. Specific embodiments are described below.

[0297] First, the server retrieves player performance data in real time from an external data provider and stores it in a database. This data retrieval is performed periodically and includes information such as players' goals, assists, and defensive records.

[0298] Next, the server uses an artificial intelligence algorithm based on the stored performance data to calculate predicted points for each athlete. The user checks these predicted points through their device and assembles a fictional team. The user's selected team information is managed on the server.

[0299] A key feature of this invention is that the terminal is equipped with an emotion engine that analyzes the user's input and operation history, and recognizes the user's emotions in real time. For example, the emotion engine recognizes whether the user is happy or stressed based on the user's writing and clicking speed, facial expression data (if a camera is being used), etc.

[0300] This allows the server to dynamically adjust the interface it provides to the user based on the results of its emotion recognition. For example, if a user expresses dissatisfaction with the game's outcome, the interface can display encouraging messages or hints from the players.

[0301] Furthermore, the terminal provides a messaging function for users to communicate with each other. At this time, the emotion engine adjusts the tone of the message to be appropriate according to the user's emotional state. For example, congratulatory words are preferentially displayed to users in a state of overflowing joy, and encouraging messages are preferentially displayed to users in a state of receiving stress.

[0302] As a specific example, when user X uses this system and their team loses, the emotion engine senses user X's anxiety and dissatisfaction, and feedback such as "advice for continuing next" is displayed on the terminal. By taking an approach according to the user's emotions in this way, the user's satisfaction and the frequency of using the system can be increased.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The server obtains real-time score data from an external sports data provider using an API. The data includes detailed information such as the scores of competitors, assists, and defensive actions. By saving the obtained data in a database, it is prepared for subsequent processing.

[0306] Step 2:

[0307] The server calculates the predicted points of the competitor using an artificial intelligence algorithm based on the saved score data. The AI model analyzes the past performance history and predicts the future performance considering the current performance. The result is stored in the database in a form that can be used by the user.

[0308] Step 3:

[0309] The device provides the user with an interface containing performance data and predicted points. The user uses this information to create a fictional team through screen touches and clicks. The created team information is saved to the server based on the user's actions.

[0310] Step 4:

[0311] The device activates an emotion engine that analyzes emotions based on user input and interaction. By analyzing and evaluating data such as input speed, operation patterns, and even facial recognition (optional), it recognizes the user's emotional state in real time.

[0312] Step 5:

[0313] The server dynamically adjusts the interface content based on the user's emotional state. For example, for a user showing signs of stress, the UI might display relaxation tips or encouraging messages.

[0314] Step 6:

[0315] The device provides community features to facilitate communication between users. Here, the emotion engine adjusts the tone of messages according to the user's emotions, assisting in smoother interaction with other users.

[0316] This process allows the entire system to respond flexibly to the user's emotions, enriching the gaming experience and improving user satisfaction.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] When users form virtual sports teams, it is essential that performance data and interaction with other users are handled smoothly. However, with conventional systems, this process cannot accommodate the emotional state of the users, making it difficult to improve user satisfaction.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes means for acquiring performance data of real athletes, means for using intelligent processing to calculate the athlete's predicted points based on the performance data, and means for analyzing the user's operation history and identifying their emotional state. This makes it possible to provide information and adjust the screen according to the user's emotional state.

[0322] "Performance data" refers to numerical information that quantifies the performance of athletes in competitions, such as goals scored, assists, and defensive records.

[0323] "Intelligent processing" refers to processing methods using computer programs to make predictions and analyses based on data, and is particularly achieved through the use of artificial intelligence.

[0324] "Prediction points" are an index calculated by numerically evaluating a player's future performance based on past performance data.

[0325] A "user" refers to an individual who operates this system and forms a virtual sports organization.

[0326] "Operation history" refers to a record of a series of actions, such as clicks and inputs, that a user performs when using the system.

[0327] "Emotional state" refers to the result of identifying the manifestation of emotions based on the user's psychological or physiological responses.

[0328] "Screen display" refers to the visual information interface provided to the user through the system, meaning a screen from which operations and information can be viewed.

[0329] A "virtual group" refers to a fictional team composed of athletes selected by the user, whose performance is based on actual competition results.

[0330] This system primarily consists of a server, terminals, and users. The server retrieves performance data of real athletes from external data sources via a REST API and stores it in a database. MySQL is often used for this database. The data includes specific performance indicators such as goals scored, assists, and defensive records.

[0331] The server uses the stored performance data to perform intelligent processing to calculate predicted points for each athlete. This intelligent processing uses libraries such as Python's Scikit-learn, and a machine learning model makes predictions based on past performance data and other relevant information.

[0332] Users connect to the internet via their devices and operate through a web browser. They refer to the provided prediction points and use drag-and-drop operations to form virtual groups. The formed team information is sent to a server and managed individually.

[0333] Furthermore, the device is equipped with a function to identify emotional states. This uses an emotion AI library such as Affectiva to analyze the user's emotional state from their operation history. Specifically, it analyzes the user's keystrokes, mouse movements, and facial expression data to recognize emotions.

[0334] Based on the results of this emotion recognition, the server can dynamically adjust the screen display provided to the user. For example, if the user expresses dissatisfaction with the system's results, it can display encouraging messages or operational hints on the terminal.

[0335] As a concrete example, suppose a user's team loses a match while using the system. At this point, the emotion engine detects the user's frustration and dissatisfaction and displays a message on the device saying, "You're getting closer to success by trying again." This kind of feedback improves user satisfaction.

[0336] An example of a prompt message is, "How can I recognize user emotions in real time and dynamically adjust the interface?" Using this prompt can improve the user experience in the system.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The server retrieves player performance data from an external data source. The input data is performance information in JSON format sent to the server via an API request. Specifically, the server sends a request to the external API and receives scores, assists, and defensive records for each player. The received information is stored in the database in preparation for subsequent processing. The output is the state in which the latest player performance data has been saved in the database.

[0340] Step 2:

[0341] The server uses intelligent processing to calculate predicted scores based on performance data stored in the database. Individual athlete data is used as input, which is passed to the Python Scikit-learn library for calculations. Specific data calculations include regression analysis based on past performance metrics. The output is the predicted score for each athlete, which is also stored in the database.

[0342] Step 3:

[0343] Users check their predicted points using a web browser on their device. The input data is the predicted points of the competitors provided by the server. Specifically, users assemble virtual teams using drag-and-drop operations through an interface provided in the browser. Once the team is assembled, the team information is sent to the server and managed on a per-user basis. The output is the data for the virtual teams assembled by the users.

[0344] Step 4:

[0345] The terminal records the user's operation history and analyzes their emotional state using an emotion AI library. Inputs include keystrokes, mouse movements, and, if necessary, facial expression data. Specifically, this data is passed to the Affectiva library, and the program identifies the user's emotional state (e.g., stress or joy). The output is the identified emotional state data.

[0346] Step 5:

[0347] The server receives emotional state data and dynamically adjusts the screen display according to the user's emotions. The input is emotional state data sent from the terminal. Specifically, the server performs conditional branching and, for example, if dissatisfaction is detected, creates an interface that provides words of encouragement or suggestions for the next steps. The output is the adjusted screen instructions, and the terminal updates the screen in real time according to these instructions.

[0348] Step 6:

[0349] The device operates a content delivery function for communication between users. Inputs include messages from other users and adjustment instructions based on emotions. Specifically, it adjusts the message tone to encourage positive communication. Output is a user interface displaying the adjusted message.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0352] In sports fantasy simulations, there is a need to increase user engagement and provide a more intuitive and personalized experience. However, traditional systems lack sufficient feedback and visual content adjustments that respond to user emotions, which hinders improved satisfaction.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate a predictive evaluation of athletes based on the performance information, and means for using an emotion recognition engine to analyze the user's emotions and adjust the visual content. This makes it possible to provide appropriately customized content based on the user's emotional state.

[0355] "Performance data" refers to performance data of athletes in sports competitions, including scores, assists, defensive records, etc.

[0356] "Predictive evaluation" refers to a future performance indicator of an athlete, calculated using a machine learning algorithm based on acquired performance data.

[0357] Machine learning is a technique that uses algorithms and statistical models to identify patterns in data and perform predictions and classifications.

[0358] "User" refers to an individual or group that uses a sports fantasy simulation system to form a fictional team and participate in a competition.

[0359] "Display means" refers to an interface or device for visually presenting information to a user.

[0360] An "emotion recognition engine" is a technology that analyzes and determines a user's emotional state from their input and facial expression data, and adjusts the system's response and content accordingly.

[0361] "Real-time" means that information is processed or displayed immediately the moment it is generated, without any delay.

[0362] To implement this invention, it is necessary to construct a system in which the roles of server, terminal, and user are clearly defined. The server is primarily responsible for the following processes:

[0363] First, the server retrieves player performance information in real time from external data providers. This information includes detailed records of a player's performance in each match, such as goals scored, assists, and defensive records.

[0364] The server then uses this performance information to calculate predictive evaluations using machine learning algorithms. These machine learning algorithms utilize libraries such as TensorFlow and PyTorch, and are designed to provide highly accurate predictions.

[0365] The server then provides users with predictive evaluations via their terminals, allowing them to create their own fictional groups. Based on these evaluations, users can select players, form specific groups, and enjoy their performance as a competition.

[0366] Furthermore, the device is equipped with an emotion recognition engine that analyzes input data and facial expression data when the user operates the system to determine the user's emotional state. This includes facial expression recognition technology using OpenCV and text analysis using NLTK.

[0367] Depending on the user's emotional state, the device can appropriately adjust the visual content. For example, if a user is dissatisfied with the outcome of a match, the device can support the user by displaying hints and advice obtained from the server.

[0368] This entire process allows users to engage more deeply through a more personalized experience. An example of using the generative AI model is a prompt such as, "Generate a highlight video that matches the user's state of happiness."

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server retrieves performance data in real time from external data providers. Inputs are player match data, including goals, assists, and defensive records. This data is processed and stored in a database. Output is a structured set of performance information.

[0372] Step 2:

[0373] The server uses a machine learning algorithm to convert performance data into predictive evaluations. The input is the performance data saved in Step 1. As part of the data processing, highly relevant features are selected, and a model is applied using TensorFlow to calculate the predictive evaluation. The output is the predictive evaluation for each player.

[0374] Step 3:

[0375] The server provides predictive evaluations to the user via the terminal. The input is the predictive evaluation generated in step 2. The evaluations are displayed on the terminal screen, and an interface is provided that allows the user to select players and form a hypothetical group. The output is information about the group selected by the user.

[0376] Step 4:

[0377] The device acquires user input and facial expression data, which are then analyzed by an emotion recognition engine. Input consists of user operation data and (if possible) facial expression data from the camera. OpenCV and NLTK are used to analyze the data and determine the user's emotional state. The output is an evaluation of the user's emotional state.

[0378] Step 5:

[0379] The server dynamically adjusts the interface and content based on the user's emotional state. The input is the emotional state obtained in step 4. Depending on the emotion, the display shows encouraging messages or hints for the next step. The output is personalized feedback for the user.

[0380] Step 6:

[0381] Based on the customized feedback, users modify their behavior or develop new strategies. The input is the feedback from step 5. The output is increased user satisfaction and willingness to reuse the system.

[0382] 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.

[0383] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0389] 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.

[0390] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0391] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0392] 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.

[0393] 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.

[0394] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The 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.

[0396] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0397] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0398] This invention is a system for a sports fantasy simulation game, characterized by the ability for users to assemble fictional teams and compete for points using performance data of real athletes. Detailed embodiments are shown below.

[0399] First, the server connects to an external data provider to retrieve player performance data in real time. This data includes goals, assists, defensive records, and more. The retrieved data is stored in a database and prepared for analysis.

[0400] Next, the server uses artificial intelligence with the stored data to calculate prediction points for each athlete. This allows for predictions of future performance, taking into account the athlete's past results and current performance. The AI ​​model's algorithm and the sources of the data used for predictions are recorded and disclosed to the user as needed.

[0401] The terminal provides users with a visual interface of player information and predicted points. Users can use this interface to search for and draft players they want to include in their team. Once the fictional team is formed, the team information is saved on the server.

[0402] Next, as the match progresses, the server continuously retrieves new performance data and updates the points for the user's fictional team in real time. This information is displayed to the user through their device, allowing them to track their team's performance in real time.

[0403] Furthermore, the server hosts tournaments among users and operates a ranking system based on these tournaments. Rankings are calculated based on the points users earn, and top users receive rewards.

[0404] Finally, the device provides community features, allowing users to discuss game strategies and player information. This enables users to exchange information with other players and improve their team composition and strategies.

[0405] As a concrete example, if user A uses this system, user A can select competitor B to be on their team. Competitor B's real-time performance is acquired, and the points predicted by the AI ​​model are reflected in user A's points. At this point, user A can communicate with other users through the terminal and receive advice on the best team composition. In this way, the present invention enhances the user experience and enables the provision of interactive competitions.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The server retrieves real-time performance data from an external sports data provider. Using an API, it collects detailed data such as player goals, assists, and playing time, and stores it in the server's database.

[0409] Step 2:

[0410] The server uses an artificial intelligence algorithm to calculate predicted points for each athlete based on stored performance data. The algorithm considers past performance data and current performance to quantify the athlete's future performance.

[0411] Step 3:

[0412] The device provides the user with player information via an interface. Based on this information, the user drafts players to form a fictional team. The information of the selected players is sent to the server, and the user's team composition is recorded.

[0413] Step 4:

[0414] The server periodically updates performance data during the match and calculates the user's team points based on the players' real-time performance. The updated point information is sent from the server to the terminal and displayed to the user in real time.

[0415] Step 5:

[0416] The server will host tournaments among users and operate a ranking system. It will tally points earned by users and update the rankings at regular intervals. A system will be established to reward top-ranking users.

[0417] Step 6:

[0418] The device provides community features, creating a space where users can discuss strategies and player information. This feature allows users to exchange information with other participants and improve their own team building strategies.

[0419] (Example 1)

[0420] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0421] Traditional sports fantasy simulation games suffer from several challenges: the process of acquiring real-time performance data of athletes and calculating predicted evaluation values ​​is opaque, and the limited information available to users about the athletes they can choose from hinders the improvement of the game experience. Furthermore, insufficient communication and information sharing among users prevents the game from providing a truly interactive competitive environment.

[0422] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0423] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate predicted evaluation values ​​of athletes based on the performance information, and means for providing terminals for users to share information or communicate. This enables users to select athletes based on more accurate and transparent evaluations, and to enhance the appeal of the game through real-time information acquisition and improved interactive communication.

[0424] "Statistical information of actual athletes" refers to specific and measurable data recorded by athletes in actual sports competitions, such as the number of goals scored, assists, and defensive records.

[0425] A "predictive evaluation value" is a numerical value that estimates an athlete's future performance, calculated using a machine learning algorithm based on past performance data and current performance.

[0426] "Machine learning" is a type of artificial intelligence technology in which algorithms learn patterns based on data and perform predictions and classifications.

[0427] "Users" refers to individuals or teams who use this system to form fictional competitive groups and enjoy competing.

[0428] A "terminal" is a computer, smartphone, or other electronic device that a user uses to access the system.

[0429] "Rankings" refer to information that shows the rankings of users based on the results of competitions and the evaluation scores obtained.

[0430] A "server" is a computer system that performs core processing within a system and is responsible for storing, analyzing, and providing data.

[0431] This invention is a system for realizing a fantasy simulation game themed around sports competitions. This system is characterized by the use of performance data from real athletes, allowing users to assemble fictional teams and compete against each other. The following describes specific embodiments of this invention.

[0432] This system primarily consists of a server, terminals, and users. The server connects to external data providers via the internet and retrieves data using a RESTful API. Player performance information, including goals scored, assists, and defensive records, is stored in a database. The server also has a mechanism to calculate predictive evaluation values ​​for players based on the retrieved data using machine learning software such as TensorFlow. This allows for predictions that consider not only past performance but also current performance.

[0433] The device visually displays player information and predicted evaluation values ​​through its user interface. Users can use this interface to draft players for their own fictional teams. In particular, the device is built using React and other modern web technologies, providing a dynamic and responsive interface.

[0434] Users can strategically assemble fictional competitive teams based on real-time, ever-changing information. The server updates its database whenever new match data arrives, promptly pushing information about the user's team to their device. This process utilizes WebSockets to ensure immediacy.

[0435] For example, if a user views a player's information on their device and decides to add that player to their team, the server can record the player's performance data and predicted evaluation value in a database and provide points based on the user's requested immediate information. This allows users to adjust their strategies during the game and enjoy a more fulfilling experience.

[0436] An example of a prompt would be, "Please describe a system that acquires real-time athlete performance data and predicts team points in a fantasy sports game." This would allow for a detailed explanation using a generative AI model.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The server connects to an external data provider and retrieves player performance information via an API. Using the API endpoint and authentication information as input, it obtains performance data such as goals scored, assists, and defensive records as output. The data is received in JSON format and stored in a temporary database. This storage process includes checking for duplicate data and validating the data format.

[0440] Step 2:

[0441] The server inputs stored performance data into a machine learning model to calculate a predicted evaluation score for each player. The input includes the player's past performance and current performance data. The output is a predicted evaluation score indicating their potential performance in the next season, calculated by a neural network using TensorFlow. This evaluation score takes into account the player's current state and future potential.

[0442] Step 3:

[0443] The terminal provides users with athlete information and predicted evaluation values ​​in a visual format. Users can search for athletes and view their data through the interface. Athlete information is retrieved from the server as input, and the information is displayed visually as output in a web interface using React. This display is dynamically updated and responds immediately to user interaction.

[0444] Step 4:

[0445] Users draft players into a fictional team via their device. The selected player information is used as input, and the newly formed team information is output. The device sends this information to a server, which stores it in a database. Once the team formation is complete, the information is immediately shared with other users.

[0446] Step 5:

[0447] The server continuously retrieves new performance data as the match progresses and updates the database. It receives new match results as input and provides updated player statistics and user team points as output. This update process is handled automatically and notified to the user's terminal in real time via WebSocket.

[0448] Step 6:

[0449] The terminal displays updated information to the user in real time. It uses updated data from the server as input and provides the user with the latest team performance as output. Users can see points changing in real time and instantly review their team's strategy.

[0450] (Application Example 1)

[0451] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0452] There is a need to enhance the user experience related to sports content and provide a more real-time, interactive, and participatory simulation experience by utilizing actual performance data of athletes. Furthermore, there is a lack of environments where users can interact with other users and visually enjoy real-time data fluctuations.

[0453] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0454] In this invention, the server includes means for acquiring performance data of real athletes, means for using information processing technology to calculate predicted measurement values ​​for athletes based on the performance data, and means for providing the predicted measurement values ​​to users and enabling the formation of a fictional group. This makes it possible for users to track the performance of athletes in real time while watching a sports event and to visually check the performance of their own group.

[0455] "Competitors" refer to real athletes or players who participate in sporting events.

[0456] "Performance data" refers to information that represents the results and records achieved by athletes in sporting events.

[0457] "Predicted performance" refers to the result of calculating future performance based on an athlete's past performance data.

[0458] "Information processing technology" includes computational methods and techniques for collecting, analyzing, and processing data.

[0459] "Users" refer to individuals who use the system or application of this patent to form fictitious teams or receive data.

[0460] A "fictional group" is a collection of sports teams or athletes formed by users that do not actually exist in reality.

[0461] A "visual display device" refers to a device that directly presents information to the user's eyes, and includes smart glasses and head-mounted displays.

[0462] The system implementing this invention is comprised of multiple computer devices and network technologies. The server connects to an external data provider and periodically collects athlete performance data. This data collection utilizes real-time data acquisition via an API. The performance data is stored in a database on the server, allowing for rapid access and processing.

[0463] The server utilizes generative AI models as an information processing technology to calculate predicted performance metrics for athletes based on collected performance data. This AI model is built using Python and data analysis libraries such as scikit-learn, and predicts future performance from large amounts of performance data. The AI ​​model records the basis for its predictions and can present sources and rationale to users to maintain transparency.

[0464] The device uses smart glasses or a head-mounted display as a visual display device to provide users with predictive measurements and real-time performance information. This allows users to track athlete data in real time during sporting events and check the performance of a hypothetical group.

[0465] For example, if a user is watching a soccer match, wearing smart glasses will visually display player data in sync with the progress of the game. This allows the user to check the score of their virtual team in real time.

[0466] An example of a prompt for the generative AI model is, "Who are the players to watch in the next soccer match?" This allows the AI ​​to suggest players based on past performance and the latest data.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The server connects to an external data provider's API to retrieve athlete performance data. This process collects the data received from the API in JSON format and stores it in the server's internal database. The input is the response data from the API, which, once stored in the database, is output as performance information used in subsequent processing.

[0470] Step 2:

[0471] The server runs a generative AI model using stored performance data to calculate predicted performance metrics for each athlete. The input is historical performance data, and data analysis is performed using Python and scikit-learn to predict future performance. As a result of this process, predicted performance metrics for each athlete are output, and the basis for these predictions is also maintained to ensure transparency.

[0472] Step 3:

[0473] The terminal distributes the generated predicted measurements to a visual display device, providing information to the user. The input is the predicted measurements received from the server, which are displayed through the user interface of smart glasses or a head-mounted display. This output allows the user to check the predicted performance of the athlete in real time.

[0474] Step 4:

[0475] Users can evaluate the performance of a fictional group in a sporting event they are watching through a visual display device. Users can receive real-time feedback and use prompts to instruct the generative AI model to make new predictions. The input here is the user's interaction, and the output is the AI ​​model's recommendations and predictions based on the requested information.

[0476] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0477] This invention aims to improve user engagement by combining an emotion engine with a sports fantasy simulation system. Specific embodiments are described below.

[0478] First, the server retrieves player performance data in real time from an external data provider and stores it in a database. This data retrieval is performed periodically and includes information such as players' goals, assists, and defensive records.

[0479] Next, the server uses an artificial intelligence algorithm based on the stored performance data to calculate predicted points for each athlete. The user checks these predicted points through their device and assembles a fictional team. The user's selected team information is managed on the server.

[0480] A key feature of this invention is that the terminal is equipped with an emotion engine that analyzes the user's input and operation history, and recognizes the user's emotions in real time. For example, the emotion engine recognizes whether the user is happy or stressed based on the user's writing and clicking speed, facial expression data (if a camera is being used), etc.

[0481] This allows the server to dynamically adjust the interface it provides to the user based on the results of its emotion recognition. For example, if a user expresses dissatisfaction with the game's outcome, the interface can display encouraging messages or hints from the players.

[0482] Furthermore, the device provides a messaging function for users to communicate with each other. In this process, the emotion engine adjusts the tone of the messages to be appropriate according to the user's emotional state. For example, congratulatory messages are prioritized for users who are overjoyed, while encouraging messages are prioritized for users who are stressed.

[0483] For example, if user X uses this system and their team loses, the emotion engine senses user X's frustration and dissatisfaction, and provides feedback such as "advice for continuing next time" displayed on the device. In this way, an approach that responds to the user's emotions can increase user satisfaction and the frequency of system use.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The server retrieves real-time performance data from an external sports data provider using an API. The data includes detailed information such as player scores, assists, and defensive actions. The retrieved data is stored in a database to prepare for subsequent processing.

[0487] Step 2:

[0488] The server uses an artificial intelligence algorithm to calculate athlete prediction scores based on stored performance data. The AI ​​model analyzes past performance history and predicts future performance considering current performance. The results are stored in a database in a format that users can use.

[0489] Step 3:

[0490] The device provides the user with an interface containing performance data and predicted points. The user uses this information to create a fictional team through screen touches and clicks. The created team information is saved to the server based on the user's actions.

[0491] Step 4:

[0492] The device activates an emotion engine that analyzes emotions based on user input and interaction. By analyzing and evaluating data such as input speed, operation patterns, and even facial recognition (optional), it recognizes the user's emotional state in real time.

[0493] Step 5:

[0494] The server dynamically adjusts the interface content based on the user's emotional state. For example, for a user showing signs of stress, the UI might display relaxation tips or encouraging messages.

[0495] Step 6:

[0496] The device provides community features to facilitate communication between users. Here, the emotion engine adjusts the tone of messages according to the user's emotions, assisting in smoother interaction with other users.

[0497] This process allows the entire system to respond flexibly to the user's emotions, enriching the gaming experience and improving user satisfaction.

[0498] (Example 2)

[0499] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] When users form virtual sports teams, it is essential that performance data and interaction with other users are handled smoothly. However, with conventional systems, this process cannot accommodate the emotional state of the users, making it difficult to improve user satisfaction.

[0501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0502] In this invention, the server includes means for acquiring performance data of real athletes, means for using intelligent processing to calculate the athlete's predicted points based on the performance data, and means for analyzing the user's operation history and identifying their emotional state. This makes it possible to provide information and adjust the screen according to the user's emotional state.

[0503] "Performance data" refers to numerical information that quantifies the performance of athletes in competitions, such as goals scored, assists, and defensive records.

[0504] "Intelligent processing" refers to processing methods using computer programs to make predictions and analyses based on data, and is particularly achieved through the use of artificial intelligence.

[0505] "Prediction points" are an index calculated by numerically evaluating a player's future performance based on past performance data.

[0506] A "user" refers to an individual who operates this system and forms a virtual sports organization.

[0507] "Operation history" refers to a record of a series of actions, such as clicks and inputs, that a user performs when using the system.

[0508] "Emotional state" refers to the result of identifying the manifestation of emotions based on the user's psychological or physiological responses.

[0509] "Screen display" refers to the visual information interface provided to the user through the system, meaning a screen from which operations and information can be viewed.

[0510] A "virtual group" refers to a fictional team composed of athletes selected by the user, whose performance is based on actual competition results.

[0511] This system primarily consists of a server, terminals, and users. The server retrieves performance data of real athletes from external data sources via a REST API and stores it in a database. MySQL is often used for this database. The data includes specific performance indicators such as goals scored, assists, and defensive records.

[0512] The server uses the stored performance data to perform intelligent processing to calculate predicted points for each athlete. This intelligent processing uses libraries such as Python's Scikit-learn, and a machine learning model makes predictions based on past performance data and other relevant information.

[0513] Users connect to the internet via their devices and operate through a web browser. They refer to the provided prediction points and use drag-and-drop operations to form virtual groups. The formed team information is sent to a server and managed individually.

[0514] Furthermore, the device is equipped with a function to identify emotional states. This uses an emotion AI library such as Affectiva to analyze the user's emotional state from their operation history. Specifically, it analyzes the user's keystrokes, mouse movements, and facial expression data to recognize emotions.

[0515] Based on the results of this emotion recognition, the server can dynamically adjust the screen display provided to the user. For example, if the user expresses dissatisfaction with the system's results, it can display encouraging messages or operational hints on the terminal.

[0516] As a concrete example, suppose a user's team loses a match while using the system. At this point, the emotion engine detects the user's frustration and dissatisfaction and displays a message on the device saying, "You're getting closer to success by trying again." This kind of feedback improves user satisfaction.

[0517] An example of a prompt message is, "How can I recognize user emotions in real time and dynamically adjust the interface?" Using this prompt can improve the user experience in the system.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The server retrieves player performance data from an external data source. The input data is performance information in JSON format sent to the server via an API request. Specifically, the server sends a request to the external API and receives scores, assists, and defensive records for each player. The received information is stored in the database in preparation for subsequent processing. The output is the state in which the latest player performance data has been saved in the database.

[0521] Step 2:

[0522] The server uses intelligent processing to calculate predicted scores based on performance data stored in the database. Individual athlete data is used as input, which is passed to the Python Scikit-learn library for calculations. Specific data calculations include regression analysis based on past performance metrics. The output is the predicted score for each athlete, which is also stored in the database.

[0523] Step 3:

[0524] Users check their predicted points using a web browser on their device. The input data is the predicted points of the competitors provided by the server. Specifically, users assemble virtual teams using drag-and-drop operations through an interface provided in the browser. Once the team is assembled, the team information is sent to the server and managed on a per-user basis. The output is the data for the virtual teams assembled by the users.

[0525] Step 4:

[0526] The terminal records the user's operation history and analyzes their emotional state using an emotion AI library. Inputs include keystrokes, mouse movements, and, if necessary, facial expression data. Specifically, this data is passed to the Affectiva library, and the program identifies the user's emotional state (e.g., stress or joy). The output is the identified emotional state data.

[0527] Step 5:

[0528] The server receives emotional state data and dynamically adjusts the screen display according to the user's emotions. The input is emotional state data sent from the terminal. Specifically, the server performs conditional branching and, for example, if dissatisfaction is detected, creates an interface that provides words of encouragement or suggestions for the next steps. The output is the adjusted screen instructions, and the terminal updates the screen in real time according to these instructions.

[0529] Step 6:

[0530] The device operates a content delivery function for communication between users. Inputs include messages from other users and adjustment instructions based on emotions. Specifically, it adjusts the message tone to encourage positive communication. Output is a user interface displaying the adjusted message.

[0531] (Application Example 2)

[0532] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] In sports fantasy simulations, there is a need to increase user engagement and provide a more intuitive and personalized experience. However, traditional systems lack sufficient feedback and visual content adjustments that respond to user emotions, which hinders improved satisfaction.

[0534] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0535] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate a predictive evaluation of athletes based on the performance information, and means for using an emotion recognition engine to analyze the user's emotions and adjust the visual content. This makes it possible to provide appropriately customized content based on the user's emotional state.

[0536] "Performance data" refers to performance data of athletes in sports competitions, including scores, assists, defensive records, etc.

[0537] "Predictive evaluation" refers to a future performance indicator of an athlete, calculated using a machine learning algorithm based on acquired performance data.

[0538] Machine learning is a technique that uses algorithms and statistical models to identify patterns in data and perform predictions and classifications.

[0539] "User" refers to an individual or group that uses a sports fantasy simulation system to form a fictional team and participate in a competition.

[0540] "Display means" refers to an interface or device for visually presenting information to a user.

[0541] An "emotion recognition engine" is a technology that analyzes and determines a user's emotional state from their input and facial expression data, and adjusts the system's response and content accordingly.

[0542] "Real-time" means that information is processed or displayed immediately the moment it is generated, without any delay.

[0543] To implement this invention, it is necessary to construct a system in which the roles of server, terminal, and user are clearly defined. The server is primarily responsible for the following processes:

[0544] First, the server retrieves player performance information in real time from external data providers. This information includes detailed records of a player's performance in each match, such as goals scored, assists, and defensive records.

[0545] The server then uses this performance information to calculate predictive evaluations using machine learning algorithms. These machine learning algorithms utilize libraries such as TensorFlow and PyTorch, and are designed to provide highly accurate predictions.

[0546] The server then provides users with predictive evaluations via their terminals, allowing them to create their own fictional groups. Based on these evaluations, users can select players, form specific groups, and enjoy their performance as a competition.

[0547] Furthermore, the device is equipped with an emotion recognition engine that analyzes input data and facial expression data when the user operates the system to determine the user's emotional state. This includes facial expression recognition technology using OpenCV and text analysis using NLTK.

[0548] Depending on the user's emotional state, the device can appropriately adjust the visual content. For example, if a user is dissatisfied with the outcome of a match, the device can support the user by displaying hints and advice obtained from the server.

[0549] This entire process allows users to engage more deeply through a more personalized experience. An example of using the generative AI model is a prompt such as, "Generate a highlight video that matches the user's state of happiness."

[0550] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0551] Step 1:

[0552] The server retrieves performance data in real time from external data providers. Inputs are player match data, including goals, assists, and defensive records. This data is processed and stored in a database. Output is a structured set of performance information.

[0553] Step 2:

[0554] The server uses a machine learning algorithm to convert performance data into predictive evaluations. The input is the performance data saved in Step 1. As part of the data processing, highly relevant features are selected, and a model is applied using TensorFlow to calculate the predictive evaluation. The output is the predictive evaluation for each player.

[0555] Step 3:

[0556] The server provides predictive evaluations to the user via the terminal. The input is the predictive evaluation generated in step 2. The evaluations are displayed on the terminal screen, and an interface is provided that allows the user to select players and form a hypothetical group. The output is information about the group selected by the user.

[0557] Step 4:

[0558] The device acquires user input and facial expression data, which are then analyzed by an emotion recognition engine. Input consists of user operation data and (if possible) facial expression data from the camera. OpenCV and NLTK are used to analyze the data and determine the user's emotional state. The output is an evaluation of the user's emotional state.

[0559] Step 5:

[0560] The server dynamically adjusts the interface and content based on the user's emotional state. The input is the emotional state obtained in step 4. Depending on the emotion, the display shows encouraging messages or hints for the next step. The output is personalized feedback for the user.

[0561] Step 6:

[0562] Based on the customized feedback, users modify their behavior or develop new strategies. The input is the feedback from step 5. The output is increased user satisfaction and willingness to reuse the system.

[0563] 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.

[0564] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0565] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] 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.

[0569] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0570] 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.

[0571] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0572] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0573] 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.

[0574] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0575] 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.

[0576] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0577] The 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.

[0578] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0579] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0580] This invention is a system for a sports fantasy simulation game, characterized by the ability for users to assemble fictional teams and compete for points using performance data of real athletes. Detailed embodiments are shown below.

[0581] First, the server connects to an external data provider to retrieve player performance data in real time. This data includes goals, assists, defensive records, and more. The retrieved data is stored in a database and prepared for analysis.

[0582] Next, the server uses artificial intelligence with the stored data to calculate prediction points for each athlete. This allows for predictions of future performance, taking into account the athlete's past results and current performance. The AI ​​model's algorithm and the sources of the data used for predictions are recorded and disclosed to the user as needed.

[0583] The terminal provides users with a visual interface of player information and predicted points. Users can use this interface to search for and draft players they want to include in their team. Once the fictional team is formed, the team information is saved on the server.

[0584] Next, as the match progresses, the server continuously retrieves new performance data and updates the points for the user's fictional team in real time. This information is displayed to the user through their device, allowing them to track their team's performance in real time.

[0585] Furthermore, the server hosts tournaments among users and operates a ranking system based on these tournaments. Rankings are calculated based on the points users earn, and top users receive rewards.

[0586] Finally, the device provides community features, allowing users to discuss game strategies and player information. This enables users to exchange information with other players and improve their team composition and strategies.

[0587] As a concrete example, if user A uses this system, user A can select competitor B to be on their team. Competitor B's real-time performance is acquired, and the points predicted by the AI ​​model are reflected in user A's points. At this point, user A can communicate with other users through the terminal and receive advice on the best team composition. In this way, the present invention enhances the user experience and enables the provision of interactive competitions.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The server retrieves real-time performance data from an external sports data provider. Using an API, it collects detailed data such as player goals, assists, and playing time, and stores it in the server's database.

[0591] Step 2:

[0592] The server uses an artificial intelligence algorithm to calculate predicted points for each athlete based on stored performance data. The algorithm considers past performance data and current performance to quantify the athlete's future performance.

[0593] Step 3:

[0594] The device provides the user with player information via an interface. Based on this information, the user drafts players to form a fictional team. The information of the selected players is sent to the server, and the user's team composition is recorded.

[0595] Step 4:

[0596] The server periodically updates performance data during the match and calculates the user's team points based on the players' real-time performance. The updated point information is sent from the server to the terminal and displayed to the user in real time.

[0597] Step 5:

[0598] The server will host tournaments among users and operate a ranking system. It will tally points earned by users and update the rankings at regular intervals. A system will be established to reward top-ranking users.

[0599] Step 6:

[0600] The device provides community features, creating a space where users can discuss strategies and player information. This feature allows users to exchange information with other participants and improve their own team building strategies.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] Traditional sports fantasy simulation games suffer from several challenges: the process of acquiring real-time performance data of athletes and calculating predicted evaluation values ​​is opaque, and the limited information available to users about the athletes they can choose from hinders the improvement of the game experience. Furthermore, insufficient communication and information sharing among users prevents the game from providing a truly interactive competitive environment.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0605] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate predicted evaluation values ​​of athletes based on the performance information, and means for providing terminals for users to share information or communicate. This enables users to select athletes based on more accurate and transparent evaluations, and to enhance the appeal of the game through real-time information acquisition and improved interactive communication.

[0606] "Statistical information of actual athletes" refers to specific and measurable data recorded by athletes in actual sports competitions, such as the number of goals scored, assists, and defensive records.

[0607] A "predictive evaluation value" is a numerical value that estimates an athlete's future performance, calculated using a machine learning algorithm based on past performance data and current performance.

[0608] "Machine learning" is a type of artificial intelligence technology in which algorithms learn patterns based on data and perform predictions and classifications.

[0609] "Users" refers to individuals or teams who use this system to form fictional competitive groups and enjoy competing.

[0610] A "terminal" is a computer, smartphone, or other electronic device that a user uses to access the system.

[0611] "Rankings" refer to information that shows the rankings of users based on the results of competitions and the evaluation scores obtained.

[0612] A "server" is a computer system that performs core processing within a system and is responsible for storing, analyzing, and providing data.

[0613] This invention is a system for realizing a fantasy simulation game themed around sports competitions. This system is characterized by the use of performance data from real athletes, allowing users to assemble fictional teams and compete against each other. The following describes specific embodiments of this invention.

[0614] This system primarily consists of a server, terminals, and users. The server connects to external data providers via the internet and retrieves data using a RESTful API. Player performance information, including goals scored, assists, and defensive records, is stored in a database. The server also has a mechanism to calculate predictive evaluation values ​​for players based on the retrieved data using machine learning software such as TensorFlow. This allows for predictions that consider not only past performance but also current performance.

[0615] The device visually displays player information and predicted evaluation values ​​through its user interface. Users can use this interface to draft players for their own fictional teams. In particular, the device is built using React and other modern web technologies, providing a dynamic and responsive interface.

[0616] Users can strategically assemble fictional competitive teams based on real-time, ever-changing information. The server updates its database whenever new match data arrives, promptly pushing information about the user's team to their device. This process utilizes WebSockets to ensure immediacy.

[0617] For example, if a user views a player's information on their device and decides to add that player to their team, the server can record the player's performance data and predicted evaluation value in a database and provide points based on the user's requested immediate information. This allows users to adjust their strategies during the game and enjoy a more fulfilling experience.

[0618] An example of a prompt would be, "Please describe a system that acquires real-time athlete performance data and predicts team points in a fantasy sports game." This would allow for a detailed explanation using a generative AI model.

[0619] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0620] Step 1:

[0621] The server connects to an external data provider and retrieves player performance information via an API. Using the API endpoint and authentication information as input, it obtains performance data such as goals scored, assists, and defensive records as output. The data is received in JSON format and stored in a temporary database. This storage process includes checking for duplicate data and validating the data format.

[0622] Step 2:

[0623] The server inputs stored performance data into a machine learning model to calculate a predicted evaluation score for each player. The input includes the player's past performance and current performance data. The output is a predicted evaluation score indicating their potential performance in the next season, calculated by a neural network using TensorFlow. This evaluation score takes into account the player's current state and future potential.

[0624] Step 3:

[0625] The terminal provides users with athlete information and predicted evaluation values ​​in a visual format. Users can search for athletes and view their data through the interface. Athlete information is retrieved from the server as input, and the information is displayed visually as output in a web interface using React. This display is dynamically updated and responds immediately to user interaction.

[0626] Step 4:

[0627] Users draft players into a fictional team via their device. The selected player information is used as input, and the newly formed team information is output. The device sends this information to a server, which stores it in a database. Once the team formation is complete, the information is immediately shared with other users.

[0628] Step 5:

[0629] The server continuously retrieves new performance data as the match progresses and updates the database. It receives new match results as input and provides updated player statistics and user team points as output. This update process is handled automatically and notified to the user's terminal in real time via WebSocket.

[0630] Step 6:

[0631] The terminal displays updated information to the user in real time. It uses updated data from the server as input and provides the user with the latest team performance as output. Users can see points changing in real time and instantly review their team's strategy.

[0632] (Application Example 1)

[0633] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] There is a need to enhance the user experience related to sports content and provide a more real-time, interactive, and participatory simulation experience by utilizing actual performance data of athletes. Furthermore, there is a lack of environments where users can interact with other users and visually enjoy real-time data fluctuations.

[0635] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0636] In this invention, the server includes means for acquiring performance data of real athletes, means for using information processing technology to calculate predicted measurement values ​​for athletes based on the performance data, and means for providing the predicted measurement values ​​to users and enabling the formation of a fictional group. This makes it possible for users to track the performance of athletes in real time while watching a sports event and to visually check the performance of their own group.

[0637] "Competitors" refer to real athletes or players who participate in sporting events.

[0638] "Performance data" refers to information that represents the results and records achieved by athletes in sporting events.

[0639] "Predicted performance" refers to the result of calculating future performance based on an athlete's past performance data.

[0640] "Information processing technology" includes computational methods and techniques for collecting, analyzing, and processing data.

[0641] "Users" refer to individuals who use the system or application of this patent to form fictitious teams or receive data.

[0642] A "fictional group" is a collection of sports teams or athletes formed by users that do not actually exist in reality.

[0643] A "visual display device" refers to a device that directly presents information to the user's eyes, and includes smart glasses and head-mounted displays.

[0644] The system implementing this invention is comprised of multiple computer devices and network technologies. The server connects to an external data provider and periodically collects athlete performance data. This data collection utilizes real-time data acquisition via an API. The performance data is stored in a database on the server, allowing for rapid access and processing.

[0645] The server utilizes generative AI models as an information processing technology to calculate predicted performance metrics for athletes based on collected performance data. This AI model is built using Python and data analysis libraries such as scikit-learn, and predicts future performance from large amounts of performance data. The AI ​​model records the basis for its predictions and can present sources and rationale to users to maintain transparency.

[0646] The device uses smart glasses or a head-mounted display as a visual display device to provide users with predictive measurements and real-time performance information. This allows users to track athlete data in real time during sporting events and check the performance of a hypothetical group.

[0647] For example, if a user is watching a soccer match, wearing smart glasses will visually display player data in sync with the progress of the game. This allows the user to check the score of their virtual team in real time.

[0648] An example of a prompt for the generative AI model is, "Who are the players to watch in the next soccer match?" This allows the AI ​​to suggest players based on past performance and the latest data.

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The server connects to an external data provider's API to retrieve athlete performance data. This process collects the data received from the API in JSON format and stores it in the server's internal database. The input is the response data from the API, which, once stored in the database, is output as performance information used in subsequent processing.

[0652] Step 2:

[0653] The server runs a generative AI model using stored performance data to calculate predicted performance metrics for each athlete. The input is historical performance data, and data analysis is performed using Python and scikit-learn to predict future performance. As a result of this process, predicted performance metrics for each athlete are output, and the basis for these predictions is also maintained to ensure transparency.

[0654] Step 3:

[0655] The terminal distributes the generated predicted measurements to a visual display device, providing information to the user. The input is the predicted measurements received from the server, which are displayed through the user interface of smart glasses or a head-mounted display. This output allows the user to check the predicted performance of the athlete in real time.

[0656] Step 4:

[0657] Users can evaluate the performance of a fictional group in a sporting event they are watching through a visual display device. Users can receive real-time feedback and use prompts to instruct the generative AI model to make new predictions. The input here is the user's interaction, and the output is the AI ​​model's recommendations and predictions based on the requested information.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] This invention aims to improve user engagement by combining an emotion engine with a sports fantasy simulation system. Specific embodiments are described below.

[0660] First, the server retrieves player performance data in real time from an external data provider and stores it in a database. This data retrieval is performed periodically and includes information such as players' goals, assists, and defensive records.

[0661] Next, the server uses an artificial intelligence algorithm based on the stored performance data to calculate predicted points for each athlete. The user checks these predicted points through their device and assembles a fictional team. The user's selected team information is managed on the server.

[0662] A key feature of this invention is that the terminal is equipped with an emotion engine that analyzes the user's input and operation history, and recognizes the user's emotions in real time. For example, the emotion engine recognizes whether the user is happy or stressed based on the user's writing and clicking speed, facial expression data (if a camera is being used), etc.

[0663] This allows the server to dynamically adjust the interface it provides to the user based on the results of its emotion recognition. For example, if a user expresses dissatisfaction with the game's outcome, the interface can display encouraging messages or hints from the players.

[0664] Furthermore, the device provides a messaging function for users to communicate with each other. In this process, the emotion engine adjusts the tone of the messages to be appropriate according to the user's emotional state. For example, congratulatory messages are prioritized for users who are overjoyed, while encouraging messages are prioritized for users who are stressed.

[0665] For example, if user X uses this system and their team loses, the emotion engine senses user X's frustration and dissatisfaction, and provides feedback such as "advice for continuing next time" displayed on the device. In this way, an approach that responds to the user's emotions can increase user satisfaction and the frequency of system use.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The server retrieves real-time performance data from an external sports data provider using an API. The data includes detailed information such as player scores, assists, and defensive actions. The retrieved data is stored in a database to prepare for subsequent processing.

[0669] Step 2:

[0670] The server uses an artificial intelligence algorithm to calculate athlete prediction scores based on stored performance data. The AI ​​model analyzes past performance history and predicts future performance considering current performance. The results are stored in a database in a format that users can use.

[0671] Step 3:

[0672] The device provides the user with an interface containing performance data and predicted points. The user uses this information to create a fictional team through screen touches and clicks. The created team information is saved to the server based on the user's actions.

[0673] Step 4:

[0674] The device activates an emotion engine that analyzes emotions based on user input and interaction. By analyzing and evaluating data such as input speed, operation patterns, and even facial recognition (optional), it recognizes the user's emotional state in real time.

[0675] Step 5:

[0676] The server dynamically adjusts the interface content based on the user's emotional state. For example, for a user showing signs of stress, the UI might display relaxation tips or encouraging messages.

[0677] Step 6:

[0678] The device provides community features to facilitate communication between users. Here, the emotion engine adjusts the tone of messages according to the user's emotions, assisting in smoother interaction with other users.

[0679] This process allows the entire system to respond flexibly to the user's emotions, enriching the gaming experience and improving user satisfaction.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] When users form virtual sports teams, it is essential that performance data and interaction with other users are handled smoothly. However, with conventional systems, this process cannot accommodate the emotional state of the users, making it difficult to improve user satisfaction.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes means for acquiring performance data of real athletes, means for using intelligent processing to calculate the athlete's predicted points based on the performance data, and means for analyzing the user's operation history and identifying their emotional state. This makes it possible to provide information and adjust the screen according to the user's emotional state.

[0685] "Performance data" refers to numerical information that quantifies the performance of athletes in competitions, such as goals scored, assists, and defensive records.

[0686] "Intelligent processing" refers to processing methods using computer programs to make predictions and analyses based on data, and is particularly achieved through the use of artificial intelligence.

[0687] "Prediction points" are an index calculated by numerically evaluating a player's future performance based on past performance data.

[0688] A "user" refers to an individual who operates this system and forms a virtual sports organization.

[0689] "Operation history" refers to a record of a series of actions, such as clicks and inputs, that a user performs when using the system.

[0690] "Emotional state" refers to the result of identifying the manifestation of emotions based on the user's psychological or physiological responses.

[0691] "Screen display" refers to the visual information interface provided to the user through the system, meaning a screen from which operations and information can be viewed.

[0692] A "virtual group" refers to a fictional team composed of athletes selected by the user, whose performance is based on actual competition results.

[0693] This system primarily consists of a server, terminals, and users. The server retrieves performance data of real athletes from external data sources via a REST API and stores it in a database. MySQL is often used for this database. The data includes specific performance indicators such as goals scored, assists, and defensive records.

[0694] The server uses the stored performance data to perform intelligent processing to calculate predicted points for each athlete. This intelligent processing uses libraries such as Python's Scikit-learn, and a machine learning model makes predictions based on past performance data and other relevant information.

[0695] Users connect to the internet via their devices and operate through a web browser. They refer to the provided prediction points and use drag-and-drop operations to form virtual groups. The formed team information is sent to a server and managed individually.

[0696] Furthermore, the device is equipped with a function to identify emotional states. This uses an emotion AI library such as Affectiva to analyze the user's emotional state from their operation history. Specifically, it analyzes the user's keystrokes, mouse movements, and facial expression data to recognize emotions.

[0697] Based on the results of this emotion recognition, the server can dynamically adjust the screen display provided to the user. For example, if the user expresses dissatisfaction with the system's results, it can display encouraging messages or operational hints on the terminal.

[0698] As a concrete example, suppose a user's team loses a match while using the system. At this point, the emotion engine detects the user's frustration and dissatisfaction and displays a message on the device saying, "You're getting closer to success by trying again." This kind of feedback improves user satisfaction.

[0699] An example of a prompt message is, "How can I recognize user emotions in real time and dynamically adjust the interface?" Using this prompt can improve the user experience in the system.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server retrieves player performance data from an external data source. The input data is performance information in JSON format sent to the server via an API request. Specifically, the server sends a request to the external API and receives scores, assists, and defensive records for each player. The received information is stored in the database in preparation for subsequent processing. The output is the state in which the latest player performance data has been saved in the database.

[0703] Step 2:

[0704] The server uses intelligent processing to calculate predicted scores based on performance data stored in the database. Individual athlete data is used as input, which is passed to the Python Scikit-learn library for calculations. Specific data calculations include regression analysis based on past performance metrics. The output is the predicted score for each athlete, which is also stored in the database.

[0705] Step 3:

[0706] Users check their predicted points using a web browser on their device. The input data is the predicted points of the competitors provided by the server. Specifically, users assemble virtual teams using drag-and-drop operations through an interface provided in the browser. Once the team is assembled, the team information is sent to the server and managed on a per-user basis. The output is the data for the virtual teams assembled by the users.

[0707] Step 4:

[0708] The terminal records the user's operation history and analyzes their emotional state using an emotion AI library. Inputs include keystrokes, mouse movements, and, if necessary, facial expression data. Specifically, this data is passed to the Affectiva library, and the program identifies the user's emotional state (e.g., stress or joy). The output is the identified emotional state data.

[0709] Step 5:

[0710] The server receives emotional state data and dynamically adjusts the screen display according to the user's emotions. The input is emotional state data sent from the terminal. Specifically, the server performs conditional branching and, for example, if dissatisfaction is detected, creates an interface that provides words of encouragement or suggestions for the next steps. The output is the adjusted screen instructions, and the terminal updates the screen in real time according to these instructions.

[0711] Step 6:

[0712] The device operates a content delivery function for communication between users. Inputs include messages from other users and adjustment instructions based on emotions. Specifically, it adjusts the message tone to encourage positive communication. Output is a user interface displaying the adjusted message.

[0713] (Application Example 2)

[0714] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] In sports fantasy simulations, there is a need to increase user engagement and provide a more intuitive and personalized experience. However, traditional systems lack sufficient feedback and visual content adjustments that respond to user emotions, which hinders improved satisfaction.

[0716] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0717] In this invention, the server includes means for acquiring performance information of real athletes, means for using machine learning to calculate a predictive evaluation of athletes based on the performance information, and means for using an emotion recognition engine to analyze the user's emotions and adjust the visual content. This makes it possible to provide appropriately customized content based on the user's emotional state.

[0718] "Performance data" refers to performance data of athletes in sports competitions, including scores, assists, defensive records, etc.

[0719] "Predictive evaluation" refers to a future performance indicator of an athlete, calculated using a machine learning algorithm based on acquired performance data.

[0720] Machine learning is a technique that uses algorithms and statistical models to identify patterns in data and perform predictions and classifications.

[0721] "User" refers to an individual or group that uses a sports fantasy simulation system to form a fictional team and participate in a competition.

[0722] "Display means" refers to an interface or device for visually presenting information to a user.

[0723] An "emotion recognition engine" is a technology that analyzes and determines a user's emotional state from their input and facial expression data, and adjusts the system's response and content accordingly.

[0724] "Real-time" means that information is processed or displayed immediately the moment it is generated, without any delay.

[0725] To implement this invention, it is necessary to construct a system in which the roles of server, terminal, and user are clearly defined. The server is primarily responsible for the following processes:

[0726] First, the server retrieves player performance information in real time from external data providers. This information includes detailed records of a player's performance in each match, such as goals scored, assists, and defensive records.

[0727] The server then uses this performance information to calculate predictive evaluations using machine learning algorithms. These machine learning algorithms utilize libraries such as TensorFlow and PyTorch, and are designed to provide highly accurate predictions.

[0728] The server then provides users with predictive evaluations via their terminals, allowing them to create their own fictional groups. Based on these evaluations, users can select players, form specific groups, and enjoy their performance as a competition.

[0729] Furthermore, the device is equipped with an emotion recognition engine that analyzes input data and facial expression data when the user operates the system to determine the user's emotional state. This includes facial expression recognition technology using OpenCV and text analysis using NLTK.

[0730] Depending on the user's emotional state, the device can appropriately adjust the visual content. For example, if a user is dissatisfied with the outcome of a match, the device can support the user by displaying hints and advice obtained from the server.

[0731] This entire process allows users to engage more deeply through a more personalized experience. An example of using the generative AI model is a prompt such as, "Generate a highlight video that matches the user's state of happiness."

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] The server retrieves performance data in real time from external data providers. Inputs are player match data, including goals, assists, and defensive records. This data is processed and stored in a database. Output is a structured set of performance information.

[0735] Step 2:

[0736] The server uses a machine learning algorithm to convert performance data into predictive evaluations. The input is the performance data saved in Step 1. As part of the data processing, highly relevant features are selected, and a model is applied using TensorFlow to calculate the predictive evaluation. The output is the predictive evaluation for each player.

[0737] Step 3:

[0738] The server provides predictive evaluations to the user via the terminal. The input is the predictive evaluation generated in step 2. The evaluations are displayed on the terminal screen, and an interface is provided that allows the user to select players and form a hypothetical group. The output is information about the group selected by the user.

[0739] Step 4:

[0740] The device acquires user input and facial expression data, which are then analyzed by an emotion recognition engine. Input consists of user operation data and (if possible) facial expression data from the camera. OpenCV and NLTK are used to analyze the data and determine the user's emotional state. The output is an evaluation of the user's emotional state.

[0741] Step 5:

[0742] The server dynamically adjusts the interface and content based on the user's emotional state. The input is the emotional state obtained in step 4. Depending on the emotion, the display shows encouraging messages or hints for the next step. The output is personalized feedback for the user.

[0743] Step 6:

[0744] Based on the customized feedback, users modify their behavior or develop new strategies. The input is the feedback from step 5. The output is increased user satisfaction and willingness to reuse the system.

[0745] 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.

[0746] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0748] 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.

[0749] Figure 9 shows an 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.

[0750] 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.

[0751] 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.

[0752] 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, motorcycles, etc., 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, for example, based 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.

[0753] 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."

[0754] 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.

[0755] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0756] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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 the like 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.

[0765] 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 as being incorporated by reference.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] A means of obtaining performance data of actual athletes,

[0769] A means of using artificial intelligence to calculate a competitor's predicted score based on the aforementioned performance data,

[0770] A means of providing the user with the aforementioned prediction points and enabling the formation of a fictional team,

[0771] A method for users to compete against each other and display the results as a ranking,

[0772] A means of providing an interface for users to share information or communicate,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the source of the data used, in order to maintain transparency of the prediction algorithm used by the artificial intelligence.

[0776] (Claim 3)

[0777] The system according to claim 1, further comprising means for displaying changes in the athlete's performance data on the user's screen when the data is updated in real time.

[0778] "Example 1"

[0779] (Claim 1)

[0780] A means of obtaining performance information of real athletes,

[0781] A means of using machine learning to calculate a predictive evaluation value of an athlete based on the aforementioned performance information,

[0782] A means to provide the user with the aforementioned predicted evaluation values ​​and enable the formation of a fictional competitive group,

[0783] A means for users to compete against each other and display the results as rankings,

[0784] A means of providing a terminal for users to share information or communicate,

[0785] A means for updating the performance information of the aforementioned athletes in real time and displaying the changes on the user's terminal,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the source of the data used, in order to maintain transparency of the prediction method used by the machine learning.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising means for storing performance information acquired by the server in a database and for deduplication and verification of the data as necessary.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A means of obtaining performance data of actual athletes,

[0794] A means of using information processing technology to calculate predicted measurements of athletes based on the aforementioned performance data,

[0795] A means to provide the aforementioned predicted measurement values ​​to the user and enable the formation of a fictitious group,

[0796] A means for users to compete against each other and display the results as ranks,

[0797] Means for providing a visual display device-based interface for users to share information or interact,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the source of the information used, in order to maintain transparency of the prediction calculation method used by the information processing technology.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for displaying the changes in the athlete's performance data on a user's display device when the data is updated in real time.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] A means of obtaining performance data of actual athletes,

[0806] A means of using intelligent processing to calculate the predicted points of an athlete based on the aforementioned performance data,

[0807] A means for providing the aforementioned prediction points to the user and enabling the formation of a virtual group,

[0808] A means for users to analyze their operation history and identify their emotional state,

[0809] A means for dynamically adjusting the screen display provided to the user according to the aforementioned emotional state,

[0810] A means of providing content that allows users to share information or interact with each other,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the source of the information used, in order to maintain transparency of the prediction algorithm used by the intelligent processing.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for displaying changes in the athlete's performance data on the user's display screen when the data is updated in real time, and means for dynamically displaying a response corresponding to the emotional state.

[0816] "Application example 2 of combining emotional engines"

[0817] (Claim 1)

[0818] A means of obtaining performance information of real athletes,

[0819] A means of using machine learning to calculate a predictive evaluation of an athlete based on the aforementioned performance information,

[0820] A means to provide the aforementioned predictive evaluation to the user and enable the formation of a fictional group,

[0821] A means for users to compete against each other and display the results as rankings,

[0822] A means of display for users to share or interact with information,

[0823] A method using an emotion recognition engine that analyzes the user's emotions and adjusts the visual content,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the origin of the information used, in order to maintain transparency of the prediction method used by the machine learning.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for displaying changes in the athlete's performance information on the user's screen when the information is updated in real time. [Explanation of Symbols]

[0829] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining performance data of actual athletes, A means of using artificial intelligence to calculate a competitor's predicted score based on the aforementioned performance data, A means of providing the user with the aforementioned prediction points and enabling the formation of a fictional team, A method for users to compete against each other and display the results as a ranking, A means of providing an interface for users to share information or communicate, A system that includes this.

2. The system according to claim 1, further comprising means for presenting to the user the basis for the prediction and the source of the data used, in order to maintain transparency of the prediction algorithm used by the artificial intelligence.

3. The system according to claim 1, further comprising means for displaying changes in the athlete's performance data on the user's screen when the data is updated in real time.

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