System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring the action of a player and game information in real time, a means for analyzing the acquired data and generating the information of the player and play prediction, a means for automatically generating a quiz on the basis of the generated prediction information, a means for displaying the generated quiz and the analysis result on a user terminal, and a means for receiving the quiz answer of the user and displaying the result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] Today's sports spectators often find it difficult to understand what to watch and enjoy during a game. Collecting player information from various sources is also a significant effort, placing a burden on spectators of all levels, from beginners to advanced players. Furthermore, even if sports betting is legalized in the future, there will be a lack of a foundation for providing safe and fair information. For this reason, there is a need for a system that allows sports spectators to obtain player information in real time and enjoy the game more deeply. [Means for solving the problem]

[0006] The present invention relates to a system that acquires player movements and game information in real time, analyzes the acquired information, and generates player information and play predictions. Specifically, the system includes a means for acquiring player movements and game information in real time, a means for analyzing the acquired data and generating player information and play predictions, a means for automatically generating quizzes based on the generated prediction information, a means for displaying the generated quizzes and analysis results on a user terminal, and a means for receiving users' quiz answers and displaying the results. This system allows sports spectators to enjoy detailed player information and play predictions in real time, further enhancing their enjoyment of the game. Furthermore, the system will function as a platform for providing safe and fair information even if the sports betting market is legalized in the future.

[0007] Below are definitions of important words:

[0008] "Real time" means processing or displaying information or data continuously in real time.

[0009] "Athlete actions" refers to the series of physical movements and actions that athletes perform during a game.

[0010] "Match information" refers to data related to a sports match, such as detailed information about scores, player positions, ball movement, etc.

[0011] "Means of acquisition" refers to the methods or devices used to collect or acquire the required information or data.

[0012] "Analysis" refers to the processing of acquired data to understand its contents and convert it into meaningful information.

[0013] "Athlete information" refers to detailed data about athletes, such as physical data and past performance.

[0014] "Play prediction" means predicting the future actions of players or teams and the development of the game.

[0015] "Generating means" refers to a method or device for creating or generating new data or information.

[0016] "Quiz" refers to a question-type question designed to test a user's specific knowledge or understanding.

[0017] "Means for automatic generation" refers to a method or device in which a program or device creates content such as a quiz without human intervention.

[0018] "User terminal" refers to a device used by a user to receive or input information, such as a smartphone or computer.

[0019] "Displaying means" refers to a method or device for visually presenting information or data to a user.

[0020] "Receiving" means receiving data or information sent from a sender.

[0021] "Results" refers to the final output or conclusion of a task or process. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] This invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a platform for providing safe and fair information even if the sports betting market is legalized in the future.

[0044] An embodiment of this system is described below.

[0045] System configuration

[0046] 1. Data Collection Methods

[0047] The server works in conjunction with sports media to receive real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled.

[0048] 2. Data analysis methods

[0049] The server captures real-time data and runs it through an AI image analysis engine, which tags the player's movements and situations to generate detailed player information.

[0050] The server uses machine learning models to generate play predictions based on past match data and the current match situation.

[0051] 3. Quiz Generation Method

[0052] The server automatically generates quizzes based on the analyzed player information and play prediction data, such as "Will player B score a shot on the next play?"

[0053] 4. Quiz display method

[0054] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[0055] 5. Means of receiving responses

[0056] The terminal receives the user's answers to the quiz and transmits them to the server.

[0057] 6. Results display means

[0058] The server receives the user's quiz answers, checks the results against the actual progress of the game, and sends the results back to the user's terminal.

[0059] The device displays the results received from the server to the user, displaying a message such as "Your prediction was correct!"

[0060] Specific examples

[0061] Specific examples of data collection methods

[0062] The server receives real-time video feeds from live J.League matches and also obtains player status data (heart rate, distance traveled, etc.) from the data stream.

[0063] Specific examples of data analysis methods

[0064] The server analyzes the real-time video feed with an image analysis engine to identify player movements, for example, Player A's shooting attempts, and calculates the shooting success rate.

[0065] Examples of quiz generation methods

[0066] The server automatically generates a quiz question: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0067] Examples of quiz display methods

[0068] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[0069] Specific examples of response receiving methods

[0070] The device receives the user's answer and sends it to the server. For example, if the user selects "Yes," the information is sent to the server.

[0071] Examples of results display methods

[0072] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[0073] The device displays the results to the user, along with the accuracy rate and other statistics.

[0074] As described above, the present invention is a system that acquires player movements and game information in real time, analyzes it, and generates a variety of information to provide to spectators. This allows spectators to enjoy the game more, and when the sports betting market is opened up, they will be able to place bets based on highly reliable information.

[0075] The processing flow will be explained below.

[0076] Understood. Below I will explain the specific flow for each processing step.

[0077] Step 1:

[0078] The server receives real-time game video feeds and player status data from sports-related media, specifically collecting data such as player positioning, heart rate, and distance traveled.

[0079] Step 2:

[0080] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[0081] Step 3:

[0082] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[0083] Step 4:

[0084] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[0085] Step 5:

[0086] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[0087] Step 6:

[0088] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[0089] Step 7:

[0090] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[0091] Step 8:

[0092] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[0093] Step 9:

[0094] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0095] Step 10:

[0096] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0097] Step 11:

[0098] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] Conventional sports viewing systems have a problem in that they lack the means for spectators to perform detailed analysis or make predictions in real time. Furthermore, there has been no effective system for instantly analyzing players' movements and game information during a game, and then generating quizzes based on that information to provide to users. This has led to a need for a way to keep spectators engaged and gain a deeper understanding of the game. Furthermore, even when the sports betting market is legalized in the future, a platform is needed that allows spectators to obtain information safely and fairly.

[0102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0103] In this invention, the server includes means for acquiring player movements and competition information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, means for the server to perform analysis and quiz generation, and means for the user terminal to display quizzes and send answers. This allows spectators to understand the details of the game movements in real time and enjoy predictions, and can provide reliable information even when the sports betting market is lifted.

[0104] "Means for acquiring player movements and competition information in real time" refers to a system for collecting player position, movement, heart rate, distance traveled, and other movement data during a match, as well as the progress of the match, in real time.

[0105] "Means for analyzing acquired data and generating player information and play predictions" refers to a system that generates detailed player information and predicts future plays based on collected player movement data and match information.

[0106] "Means for automatically generating quizzes based on generated prediction information" refers to a system that automatically generates quiz-style content based on analyzed player information and play prediction data.

[0107] "Means for displaying the generated quiz and analysis results on the user's device" refers to a system for displaying the automatically generated quiz and its related information on the device used by the user.

[0108] "Means for receiving users' quiz answers and displaying the results" refers to a system for collecting the answers users give to quizzes, analyzing the results, and providing feedback to users.

[0109] "Server" refers to the central computer system that analyzes data, automatically generates quizzes, and evaluates the results.

[0110] "User device" refers to a device, such as a smartphone or tablet, that a user uses to answer quizzes and view analysis results.

[0111] The present invention relates to a system that acquires player movements and competition information in real time, analyzes the information, and generates detailed player information and play predictions. This system provides a tool for spectators to enjoy the game more, and also serves as a platform for providing safe and fair information when the sports betting market is legalized in the future. An embodiment of this system is described below in detail.

[0112] Data collection methods

[0113] The server works with sports broadcasting agencies to obtain real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled. Specifically, the server obtains game broadcast data through an API and receives player GPS location information, heart rate sensor data, and distance traveled tracking data.

[0114] Examples:

[0115] The server receives real-time video feeds from live J.League matches, and also obtains player status data such as heart rate and distance traveled from the data stream.

[0116] Data Analysis Methods

[0117] The server inputs the collected data into an AI image analysis engine, which tags the player's movements and situations. It then uses a machine learning model to generate play predictions based on the analysis data and past match data. Specifically, the video feed is passed through the image analysis engine, and when, for example, Player A goes into shooting motion, that movement is detected and the shooting success rate is calculated. It also compares the shooting success rate data from the past 10 matches with the current situation to predict the probability that Player A will make a shot on the next play.

[0118] Examples:

[0119] The server analyzes the real-time video feed with an image analysis engine to identify the player's movements. It analyzes the movement of Player A when he attempts a shot and calculates the shooting success rate.

[0120] Quiz generation method

[0121] The server automatically generates a quiz based on the analyzed player information and play prediction data. Specifically, it generates a quiz such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0122] Examples:

[0123] The server generates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0124] Quiz display method

[0125] The device displays the generated quiz and related analysis results to the user. The user answers the quiz and can check the results on the device. Specifically, a quiz screen is displayed on the user's smartphone or tablet, asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[0126] Examples:

[0127] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[0128] Response receiving method

[0129] The device receives the user's answers to the quiz and sends them to the server. Specifically, when the user presses the "Yes" or "No" button, the device collects the user's selection and sends it to the server.

[0130] Examples:

[0131] The device receives the user's answer and sends it to the server. If the user selects "yes," the information is sent to the server.

[0132] Results display means

[0133] The server receives the user's quiz answers, compares them with the actual game results, and returns the results to the user's device. Specifically, the server monitors the progress of the game, checks whether Player A actually scored a goal, and generates a result to compare with the user's answer. The result is then sent to the device and fed back to the user.

[0134] Examples:

[0135] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[0136] The device displays the results to the user, along with the accuracy rate and other statistics.

[0137] Example prompt sentences (input to generative AI models)

[0138] Generate a program to create a system that captures player movements and match information in real time, analyzes it, and provides it to spectators. Please use the following detailed specifications:

[0139] 1. The server receives real-time game video feeds and player status data via API.

[0140] 2. The server analyzes the data using an AI image analysis engine to identify player movements and then uses machine learning models to predict plays.

[0141] 3. The server generates a quiz in the form of questions based on the analysis data and sends the quiz to the user.

[0142] 4. The device displays the quiz to the user and sends the user's answers to the server.

[0143] 5. Finally, the server evaluates the match results and sends the results based on the user's answers back to the device, which then displays the results to the user.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1: Data collection

[0146] The server receives real-time game video feeds and player status data from sports broadcasters via APIs, including player heart rate, distance traveled, and GPS location.

[0147] Input: Match video feed, player status data

[0148] Data processing: Synchronizing video data and status data

[0149] Output: Synchronized real-time dataset

[0150] Specific operation: The server obtains J.League match broadcast data via an API, and obtains data from each player's heart rate sensor, data tracking running distance, GPS location information, etc.

[0151] Step 2: Data analysis

[0152] The server inputs the collected data into an AI image analysis engine, which tags players' movements and determines their situations. It also uses machine learning models to analyze past match data and current match information to generate play predictions.

[0153] Input: Synchronized real-time dataset

[0154] Data processing: Image analysis and machine learning model analysis

[0155] Output: Analyzed player information and play prediction data

[0156] Specific operation: The server passes the acquired video feed through an AI image analysis engine to analyze Player A's shooting motion and positional relationship. It also inputs data from the past 10 games into a machine learning model to calculate Player A's shooting success rate.

[0157] Step 3: Quiz Generation

[0158] The server automatically generates quizzes based on the analyzed player information and play prediction data. The generated quizzes are based on real-time information.

[0159] Input: Analyzed player information, play prediction data

[0160] Data processing: Automatic generation of quizzes

[0161] Output: Auto-generated quiz

[0162] Specific behavior: The server creates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0163] Step 4: Quiz display

[0164] The terminal displays the generated quiz and related information to the user, who can then answer the quiz.

[0165] Input: Auto-generated quiz, related information

[0166] Data processing: Display of quiz screen

[0167] Output: Quiz and related information displayed on the user's device

[0168] Specific operation: The device displays a quiz on a smartphone or tablet asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[0169] Step 5: Receiving a response

[0170] The terminal receives the user's answers to the quiz and transmits them to the server.

[0171] Input: User's quiz answer

[0172] Data processing: Sending user response data

[0173] Output: Data sent to the server

[0174] Specific operation: The user presses the "Yes" or "No" button, and the result is sent to the server via the terminal.

[0175] Step 6: View the results

[0176] The server receives the user's quiz answers, compares them with the actual game results, and returns the evaluation results to the user's device and displays them to the user.

[0177] Input: User quiz answers, actual match results

[0178] Data processing: Comparing and evaluating answers with real-world results

[0179] Output: Evaluation results

[0180] Specific operation: The server monitors the game, and when Player A scores a shot, it generates a message saying "Your prediction was correct!" and sends it to the device. The device displays this result to the user, along with the accuracy rate and other statistical information.

[0181] (Application example 1)

[0182] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0183] When watching sports, spectators want to be able to understand the progress of the game and the actions of the players in real time, allowing them to enjoy the game more deeply. However, current systems do not fully meet this requirement, and few of them include interactive elements. Furthermore, if sports betting is legalized in the future, a system that provides reliable prediction information will be necessary. There is a need for a system that can solve these issues and improve the value of the spectator experience.

[0184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0185] In this invention, the server includes means for acquiring player movement and match information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, and means for the user terminal being a head-mounted display and interactively displaying the analysis results and quizzes, thereby enabling users to interactively enjoy player movement predictions and related quizzes in real time through the head-mounted display.

[0186] "Real-time" refers to the immediate acquisition and processing of ongoing data and information.

[0187] "Athlete actions" means all physical actions and movements made by an athlete in a sporting event.

[0188] "Match Information" includes all data related to a match, such as the progress of a sporting event, scores, and player status.

[0189] "Analyzing data" means processing acquired data using technologies such as machine learning models and AI image analysis engines to derive useful information and predictions.

[0190] "Play prediction" is the process of estimating the next actions and outcomes that a player or team is likely to take based on past data and real-time movement information.

[0191] "Automatically generating quizzes" means that a computer automatically creates quiz-style questions based on analyzed information.

[0192] "Analysis results" refers to detailed information and prediction results regarding the player's movements and plays obtained by the data analysis means.

[0193] "User terminal" means a digital device, such as a head-mounted display, through which a user receives information or provides input.

[0194] "Interactively displayed" refers to a state in which the user can react and operate in real time to the information displayed through the device.

[0195] A "head-mounted display" refers to a device that displays information when worn on the user's head.

[0196] "Receiving quiz answers" means that the system acquires the responses given by the user to the quiz.

[0197] "Displaying the results" means displaying feedback based on the user's quiz answers and the actual match results.

[0198] This invention is a system that acquires player movements and game information in real time, analyzes it, and generates player information and play predictions. It provides an effective tool for spectators to enjoy the game more, and can also accommodate future sports betting markets. Specific embodiments of the system are described below.

[0199] System configuration

[0200] Hardware Configuration

[0201] 1. Server

[0202] Use a high-performance computer with a stable network connection to receive player status data and game video feeds in real time, and the server should preferably be equipped with a GPU or TPU for data analysis.

[0203] 2. Head-Mounted Display (HMD)

[0204] It is used by spectators to interactively view information and quizzes in real time, and requires a high-resolution display and a comfortable HMD.

[0205] Software Configuration

[0206] 1. Data Collection Methods

[0207] The server receives real-time game data through a sports data API, including player positioning, movement, heart rate, distance traveled, and game progress.

[0208] 2. Data analysis methods

[0209] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow), thereby generating predictions of player movements and plays.

[0210] 3. Quiz Generation Method

[0211] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0212] 4. Quiz display method

[0213] The user's HMD visually displays the generated quiz and related analysis results, and the user can answer the quiz through the HMD.

[0214] 5. Means of receiving responses

[0215] The HMD receives the user's answers to the quiz and sends them to a server, which processes the user's responses immediately.

[0216] 6. Results display means

[0217] The server receives the user's answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[0218] Specific examples

[0219] For example, if the motion of Player A attempting to shoot in a J.League match is analyzed in real time, the server predicts that "Player A has a 75% chance of scoring on the next play." The user's HMD then displays a quiz question: "Player A's current shooting success rate is 75%. Will he score on the next play?" If the user answers "Yes," the answer is sent to the server, and the result "Your prediction was correct!" is displayed based on the actual match results.

[0220] Prompt Sentence Examples

[0221] "Design an interactive quiz platform for watching sports that predicts Player A's actions and success rate in real time and asks him if he will be successful on his next play."

[0222] This system will enable spectators to enjoy the game more deeply and, in the future, will enable the provision of reliable information that can also be used for the sports betting market.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] The server retrieves real-time match data from a sports data API, including player positioning, movement, heart rate, distance traveled, and match progress.

[0226] Input: Real-time data from the Sports Data API

[0227] Output: Match and player status data obtained

[0228] Step 2:

[0229] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and machine learning models (e.g., TensorFlow), which generates play predictions based on players' movements and past performance data.

[0230] Input: Retrieved match and player status data

[0231] Output: Analyzed player movement information and play prediction results

[0232] Step 3:

[0233] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates a question such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0234] Input: Analyzed player movement information and play prediction results

[0235] Output: Auto-generated quiz

[0236] Step 4:

[0237] The device (user's HMD) visually displays the generated quiz and the analysis results, and the user answers the displayed quiz.

[0238] Input: Auto-generated quiz and analysis results

[0239] Output: User response interface

[0240] Step 5:

[0241] The device receives the user's answers to the quiz and sends them to the server, which processes the user's responses immediately.

[0242] Input: User's quiz answer

[0243] Output: User response data

[0244] Step 6:

[0245] The server receives the user's quiz answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[0246] Input: User response data and match progress data

[0247] Output: Result message displayed on the HMD

[0248] In this way, the invention provides a system that provides spectators with an interactive, real-time experience, enhancing the enjoyment of watching sports.

[0249] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0250] The present invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a foundation for providing safe and fair information even if the sports betting market is legalized in the future. The present invention also incorporates an "emotion engine" that recognizes user emotions and optimizes services based on them.

[0251] An embodiment of this system is described below.

[0252] System configuration

[0253] 1. Data Collection Methods

[0254] The server connects with sports media and receives real-time player status data and game video feeds, collecting data such as player positioning, heart rate, and distance traveled.

[0255] 2. Data analysis methods

[0256] The server then analyzes the received data through an AI image analysis engine and machine learning models, which identify players' movements and playing tendencies and update the database in real time.

[0257] 3. Quiz Generation Method

[0258] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Will player A score on the next play?"

[0259] 4. Quiz display method

[0260] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[0261] 5. Means of receiving responses

[0262] The terminal receives the user's answers to the quiz and transmits them to the server.

[0263] 6. Results display means

[0264] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0265] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0266] The device displays the results to the user, along with the accuracy rate and other statistics.

[0267] 7. Emotion recognition means

[0268] The server uses an emotion engine to analyze the user's feedback and emotional state when answering the quiz. It acquires and analyzes the user's facial expression data and emotion labels, and stores the results.

[0269] 8. Emotional reflection means

[0270] The server adjusts the quiz and display content based on the user's emotional state: for example, if the user is excited, it presents a more challenging quiz, and if the user is relaxed, it presents more information-oriented content.

[0271] The device displays the most appropriate quiz and analysis results for the user based on the emotional reflection information sent from the server.

[0272] 9. Statistical Analysis of Emotional Data

[0273] The server collects the emotional data provided by the user and performs statistical analysis, which allows the server to analyze the user's behavioral trends and emotional change patterns and provide more personalized services.

[0274] The server will use these results to readjust the machine learning model and reflect this in future service provision.

[0275] Specific examples

[0276] Specific examples of data collection methods

[0277] The server receives real-time video feeds from major league teams and also obtains real-time player status data (heart rate, distance traveled, etc.) from a number of sensors.

[0278] Specific examples of data analysis methods

[0279] The server processes the video feed with an AI image analysis engine to analyze player movements, for example identifying when Player B receives a pass and attempts a shot.

[0280] Examples of quiz generation methods

[0281] The server automatically generates a quiz question: "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0282] Examples of quiz display methods

[0283] The device will display the quiz along with related information such as "Player B's shooting percentage in the last 10 games: 70%." The user can select "Yes" or "No."

[0284] Specific examples of response receiving methods

[0285] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0286] Examples of results display methods

[0287] The server receives the user's answers and checks them against the actual game results, for example, to see if Player B actually scored a shot.

[0288] The device will then display the result to the user: "Your prediction was correct!"

[0289] Specific examples of emotion recognition

[0290] The server uses an emotion engine to analyze the user's facial expression data sent from the device and determine whether the user is excited or relaxed.

[0291] Examples of emotional reflection

[0292] The server senses that the user's emotional state is agitated and provides the next quiz with a slightly higher difficulty.

[0293] Based on this, the device will display new quizzes and related information to the user.

[0294] Examples of statistical analysis of emotion data

[0295] The server analyzes the collected emotional data of many users and learns emotional patterns.

[0296] Based on this, the server will optimize future service provision and improve the user experience.

[0297] As described above, this invention is a system that acquires player movements and match information in real time, analyzes it, and generates a variety of information to provide to spectators. Furthermore, by recognizing and analyzing user emotions and optimizing services based on that, it is possible to provide spectators with a richer sports viewing experience.

[0298] The processing flow will be explained below.

[0299] Step 1:

[0300] The server receives real-time game video feeds and player status data from sports-related media, including player positioning, heart rate, and distance traveled.

[0301] Step 2:

[0302] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[0303] Step 3:

[0304] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[0305] Step 4:

[0306] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[0307] Step 5:

[0308] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[0309] Step 6:

[0310] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[0311] Step 7:

[0312] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[0313] Step 8:

[0314] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[0315] Step 9:

[0316] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0317] Step 10:

[0318] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0319] Step 11:

[0320] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[0321] Step 12:

[0322] The device captures the user's facial expression data and transmits it to the server, where the facial expression data is captured in real time.

[0323] Step 13:

[0324] The server analyzes the received facial expression data using an emotion engine, which identifies the user's emotional state (excited, relaxed, etc.).

[0325] Step 14:

[0326] The server adjusts the quiz and presentation content based on the user's emotional state, for example generating more challenging quizzes if the user is excited, or providing more information-heavy content if the user is relaxed.

[0327] Step 15:

[0328] The server statistically analyzes users' emotional data and uses it to optimize services, including learning emotional patterns and analyzing trends in emotional changes.

[0329] Step 16:

[0330] The device displays tailored content sent from the server to the user, allowing the user to receive information and quizzes optimized for their emotional state.

[0331] These are the specific processing steps of the sports viewing support system that combines the emotion engine. This system allows users to enjoy the game more fully and provides a personalized experience based on their emotions.

[0332] Example 2

[0333] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] Conventional sports viewing systems have difficulty obtaining real-time information about players' movements and matches, limiting the information provided to spectators. As a result, there are a lack of tools that allow spectators to enjoy the match more deeply. In addition, it is difficult to provide personalized services that respond to user emotions, making it impossible to maximize spectator interest and satisfaction.

[0335] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0336] In this invention, the server includes a means for acquiring player movements and game information in real time, a means for analyzing the acquired data to generate player information and play predictions, and a means for automatically generating quizzes based on the generated prediction information. This allows spectators to enjoy the game more. The server also includes a means for displaying the generated quizzes and analysis results on a user terminal, and a means for receiving the user's quiz answers and displaying the results. Additionally, the server includes a means for recognizing and analyzing user emotion data, a means for adjusting the next quiz and display content based on the analyzed emotion data, and a means for statistically analyzing the user emotion data to optimize the system. This makes it possible to provide spectators with detailed player information and play predictions in real time, and to provide personalized quizzes and information based on the user's emotions.

[0337] "Means for acquiring player movements and match information in real time" refers to devices or programs for acquiring data such as player movements, positions, heart rates, and distance traveled during a match, as well as video feeds of the match, in real time.

[0338] "Means of analyzing acquired data and generating player information and play predictions" refers to the process of using an AI image analysis engine and machine learning models to analyze player movements and playing tendencies based on received match data, and generate play predictions.

[0339] The "means for automatically generating quizzes based on the generated prediction information" refers to a program or tool for automatically generating quizzes using analyzed player information and play prediction data.

[0340] The "means for displaying the generated quiz and analysis results on the user's terminal" refers to a user interface or communication means for displaying the quiz and analysis results automatically generated by the server on the user's terminal.

[0341] "Means for receiving the user's quiz answers and displaying the results" refers to a device or program that receives the content of the quiz answered by the user, processes and evaluates it, and displays it to the user.

[0342] "Means for recognizing and analyzing user emotional data" refers to an emotion engine or AI model that recognizes and analyzes emotions based on the user's facial expressions and behavioral data.

[0343] The "means for adjusting the next quiz or display content based on the analyzed emotional data" is a program for adjusting the difficulty level of the next quiz or display content based on the user's emotional state analyzed by the emotion engine.

[0344] "Means for statistically analyzing user emotional data and optimizing the system" refers to the process of statistically analyzing collected user emotional data and using the results to improve the overall system performance and user experience.

[0345] The present invention relates to a system that acquires player movements and game information in real time, analyzes the acquired information, and generates player information and play predictions, allowing spectators to enjoy the game more. Specific embodiments for carrying out the present invention will be described in detail below.

[0346] System configuration

[0347] 1. Data Collection Methods

[0348] The server works with sports media to obtain real-time player status data (heart rate, distance traveled, etc.) and game video feeds using APIs. As a specific example, it uses the SportRadar API to collect player data from major leagues.

[0349] 2. Data analysis methods

[0350] The server analyzes the received match data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements and playing tendencies, and updates the database in real time. For example, it identifies the movement of Player B receiving a pass and attempting a shot.

[0351] 3. Quiz Generation Method

[0352] The server uses a natural language generation tool (e.g., OpenAI GPT-3) to automatically generate questions based on the analyzed data. For example, it generates a question such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0353] 4. Quiz display method

[0354] The terminal displays the quiz and the associated analysis results sent from the server to the user, and the user views the quiz on the user interface of the terminal and selects "yes" or "no."

[0355] 5. Means of receiving responses

[0356] The terminal receives the answer given by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0357] 6. Results display means

[0358] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player B actually scored a shot.

[0359] The terminal displays the evaluation result to the user, saying "Your prediction was correct!"

[0360] 7. Emotion recognition means

[0361] The server analyzes the user's facial expression data sent from the device using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed.

[0362] 8. Emotional reflection means

[0363] The server adjusts the difficulty of the next quiz based on the results of the emotion engine, for example, if the user is excited, it will provide a slightly more difficult quiz.

[0364] The terminal displays the new quiz and related information to the user.

[0365] 9. Statistical Analysis of Emotional Data

[0366] The server statistically analyzes the collected emotion data to identify user emotion patterns, thereby improving overall system performance and user experience.

[0367] The server will optimize the system based on these results and reflect them in future service provision.

[0368] Specific examples

[0369] Specific examples of data collection methods

[0370] The server calls the SportRadar API to retrieve real-time game data (such as player heart rate, distance traveled, positioning, etc.) and live feeds from major league games.

[0371] Specific examples of data analysis methods

[0372] The server uses an AI image analysis engine (TensorFlow, OpenCV) to analyze the players' movements from the video data. It analyzes the movements of Player B when he receives the pass and attempts to shoot, and stores the results in a database.

[0373] Examples of quiz generation methods

[0374] Based on the analyzed data, the server uses OpenAI GPT-3 to generate a quiz such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0375] Examples of quiz display methods

[0376] The device displays the quiz sent from the server on the user interface and provides information such as "Player B's shooting success rate in the last 10 games: 70%." The user selects "Yes" or "No."

[0377] Specific examples of response receiving methods

[0378] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0379] Examples of results display methods

[0380] The server receives the user's answer and compares it with the actual game result. It checks whether Player B actually made the shot and sends the result "Your prediction was correct!" to the terminal and displays it to the user.

[0381] Specific examples of emotion recognition methods

[0382] The device captures the user's facial expressions with a camera and sends the facial data to a server, which then uses the Microsoft Azure Emotion API to analyze the data and determine whether the user is excited or relaxed.

[0383] Examples of emotional reflection

[0384] The server adjusts the difficulty of the next quiz based on the user's emotional state. For example, if the user is excited, the next quiz will be more difficult. The terminal displays the new quiz and related information to the user.

[0385] Examples of statistical analysis of emotion data

[0386] The server statistically analyzes the emotion data collected from a large number of users to identify emotion patterns, which will optimize future service provision and improve user experience.

[0387] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0388] Step 1:

[0389] Real-time data collection

[0390] The server works with sports media and uses APIs to obtain player status data (heart rate, distance traveled, etc.) and game video feeds in real time. The input is API data provided by the sports media, and the output is the obtained player status data and game video feeds. Specifically, the server calls the SportRadar API to obtain player data from major leagues.

[0391] Step 2:

[0392] Data analysis

[0393] The server analyzes the acquired data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements, positioning, and playing tendencies. The input is a real-time game video feed and status data, and the output is analyzed player movement information and play prediction data. As a concrete example, the server identifies from the video feed the movement of Player B receiving a pass and attempting a shot, and stores that data in a database.

[0394] Step 3:

[0395] Generate a quiz

[0396] The server automatically generates quizzes using a natural language generation tool (e.g., OpenAI GPT-3) based on the analyzed player information and play prediction data. The input is the analyzed player information and play prediction data, and the output is a quiz generated in natural language. For example, a quiz may be generated that asks, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?" The server sends this generation prompt and receives the generated quiz.

[0397] Step 4:

[0398] View Quiz

[0399] The terminal displays the quiz sent from the server and the related analysis results to the user. The input is the quiz data sent from the server, and the output is the quiz and analysis results displayed on the user interface. Specifically, the terminal displays the quiz along with the information "Player B's shooting success rate in the last 10 games: 70%" and allows the user to select "Yes" or "No."

[0400] Step 5:

[0401] Receiving user responses

[0402] The terminal receives the answer selected by the user and sends it to the server. The input is the answer information selected by the user, and the output is the answer data sent to the server. For example, if the user selects "Yes," the terminal sends that data to the server. This specific operation is performed by the terminal receiving the input from the user and transferring it to the server.

[0403] Step 6:

[0404] Evaluation of answer results

[0405] The server receives the user's answers and evaluates the results by comparing them with the actual progress of the game. The input is the user's answer data and actual game data, and the output is the evaluation result. For example, the server checks whether player B actually scored a shot and evaluates the result. This evaluation result is communicated to the user in the form of a message such as "Your prediction was accurate."

[0406] Step 7:

[0407] Emotional Data Recognition

[0408] The device captures the user's facial expression with a camera and sends the data to a server. The server analyzes the received facial expression data using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed. The input is the user's facial expression data, and the output is analyzed emotional state data. As a concrete example, the device uses a camera to take a picture of the user's face and sends the data to a server.

[0409] Step 8:

[0410] Adjustments based on user emotions

[0411] The server adjusts the difficulty and content of the next quiz based on the analysis results of the emotion engine. The input is the analyzed emotion data, and the output is the adjusted quiz data. For example, if the user is excited, the server adjusts the difficulty of the next quiz to be higher. The terminal then displays the new quiz content to the user.

[0412] Step 9:

[0413] Statistical analysis of emotion data

[0414] The server statistically analyzes the collected emotional data of many users and identifies emotional patterns. The input is the collected emotional data, and the output is the statistically analyzed emotional patterns. The server uses these analysis results to optimize the system and reflect them in future service provision.

[0415] Through these steps, the system can provide detailed player information and play predictions in real time, as well as provide personalized quizzes and information based on the user's emotions.

[0416] (Application example 2)

[0417] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0418] Conventional sports viewing systems tend to make spectators watch the game monotonously, limiting the enjoyment of the experience. Furthermore, it is difficult to provide personalized content that reflects the user's emotional state. This can lead to a poor user experience and a lack of sustained interest in watching the game. Furthermore, when the sports betting market is legalized, fair and secure information provision will be required, but current systems have difficulty meeting this requirement.

[0419] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring player movements and game information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on the user terminal, means for receiving the user's quiz answers and displaying the results, means for recognizing the user's emotional state and adjusting the quiz and display content based on that state, and means for statistically analyzing the collected emotional data and reflecting it in the next service provision. This allows spectators to enjoy the game more interestingly, improves the user experience, and enables fair and safe information provision in the sports betting market.

[0420] "Means for obtaining player movements and match information in real time" refers to a method for instantly obtaining player data and match progress during a sports event.

[0421] "Means for analyzing acquired data and generating player information and play predictions" refers to a method for using acquired data to analyze player movements and game developments and predict future plays.

[0422] The "means for automatically generating quizzes based on generated prediction information" is a method for automatically creating quiz-style questions using prediction data generated by analysis.

[0423] "Means for displaying the generated quiz and analysis results on the user's terminal" refers to a method for displaying the generated quiz and analysis results on the terminal used by the user.

[0424] The "means for receiving the user's quiz answers and displaying the results" is a method for receiving the answers to the quiz answered by the user and displaying the results again on the user's terminal.

[0425] "Means for recognizing the emotional state of a user and adjusting quizzes and display content based on that state" refers to a method for recognizing the emotions of a user and providing quizzes and display content in an appropriate form according to those emotions.

[0426] "Means of statistically analyzing collected emotional data and reflecting it in the next service provision" refers to a method of statistically analyzing emotional data collected from users and improving and optimizing the content of the next service provision based on the results.

[0427] This invention is a system that acquires and analyzes the movements of athletes and game information in real time, and automatically generates and displays quizzes based on the analysis. Specific embodiments for carrying out the invention are described below.

[0428] System configuration

[0429] The system mainly consists of a server and a user terminal. Each function is as follows:

[0430] Data collection

[0431] The server obtains real-time information about player movements and match information during sporting events by receiving real-time video feeds and player status data (e.g., heart rate, distance traveled) through a remote API.

[0432] Data analysis

[0433] The server analyzes the received data using a machine learning model and an AI image analysis engine. This allows it to identify the players' movements and playing tendencies and predict future plays. For example, OpenCV is used as the AI ​​image analysis engine and TensorFlow is used as the machine learning model for this process.

[0434] Quiz Generation

[0435] The server automatically generates a quiz based on the analysis results and play predictions. For example, it generates a quiz such as "Will Player A's next shot be successful?" and sends it to the user's device.

[0436] Quiz display

[0437] The user device displays the generated quiz and related information to the user. The user answers the quiz through the device, and the answers are sent to the server. The device can be a smartphone or a head-mounted display.

[0438] Receiving responses and displaying results

[0439] The server receives the user's answer and compares it with the actual game result. For example, it checks whether Player A actually made a shot, and sends the result to the user's device as "The user's prediction was correct."

[0440] emotion recognition

[0441] The server analyzes the user's facial expression data sent from the user's device and recognizes the user's emotional state using an emotion engine (e.g., Hugging Face emotion recognition model), thereby determining whether the user is excited or relaxed.

[0442] emotional reflection

[0443] The server then tailors the quiz and content displayed based on the user's perceived emotional state, for example providing a more challenging quiz if the user is excited, or displaying more information-focused content if the user is relaxed.

[0444] Statistical analysis of emotion data

[0445] The server statistically analyzes the collected emotional data and reflects it in the next service provision. This analysis clarifies the user's behavioral trends and emotional change patterns, making it possible to provide more personalized services.

[0446] Specific examples

[0447] Below is a concrete example of how this system can be used.

[0448] Service flow

[0449] Consider a series of steps: generate a quiz about the next play of player A in a major league game ID 12345, analyze the user's emotional state, and display a quiz of appropriate difficulty.

[0450] 1. The server collects data for match ID 12345.

[0451] 2. The server analyzes the data, predicts the next play, and generates a quiz.

[0452] 3. A quiz will be displayed on the user's device: "Will Player A's next shot be successful? (Past success rate: 70%)"

[0453] 4. The user selects an answer and the data is sent back to the server.

[0454] 5. The server compares the match results and displays them to the user.

[0455] 6. At the same time, the user's facial expression data is analyzed to recognize emotions.

[0456] 7. The server adjusts the next quiz or display content depending on the user's emotional state.

[0457] In this way, spectators can enjoy the sporting event more fully, and fair and safe information provision is achieved.

[0458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0459] Step 1:

[0460] The server identifies the match ID and retrieves real-time video feeds and player status data through an external API. This process involves sending an API request to receive data such as the video feed, player heart rate, and distance traveled. The input is the match ID, and the output is the real-time video feed and player status data.

[0461] Step 2:

[0462] The server analyzes the received data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow). It extracts each frame from the video feed, analyzes the player's movements and positions, and stores the results in a database. At the same time, it analyzes player status data to predict playing tendencies and performance. The inputs are the real-time video feed and player status data, and the outputs are the analysis results and play prediction data.

[0463] Step 3:

[0464] The server automatically generates a quiz based on the analysis results and play prediction data. For example, it generates a quiz such as "Will Player A's next shot be successful?" The input is the analysis results and play prediction data, and the output is the generated quiz.

[0465] Step 4:

[0466] The server sends the generated quiz and related information to the user's device. The user's device (smartphone or head-mounted display) displays the quiz and supplementary information (e.g., past success rates and player performance data). The input is the generated quiz, and the output is the quiz displayed on the user's device.

[0467] Step 5:

[0468] The user answers the presented quiz. The user terminal receives the user's answers and sends them to the server. The input is the user's answers, and the output is the answer data sent to the server.

[0469] Step 6:

[0470] The server compares the user's answer with the actual result of the game. For example, it checks whether Player A actually scored a shot and determines whether the user's answer was correct or incorrect. The input is the user's answer and the game result data, and the output is the evaluation result.

[0471] Step 7:

[0472] The server sends the evaluation results to the user's device and displays the results and explanations. It provides feedback such as "Your prediction was correct!" The input is the evaluation results, and the output is the result display on the user's device.

[0473] Step 8:

[0474] The user device captures the user's facial expression data with a camera and sends it to the server. The input is the user's facial expression data, and the output is the data sent to the server.

[0475] Step 9:

[0476] The server analyzes the facial expression data using an emotion engine (e.g., Hugging Face emotion recognition model) to determine the user's emotional state. The input is the user's facial expression data, and the output is the recognized emotional state.

[0477] Step 10:

[0478] The server adjusts the next quiz or display content based on the recognized emotional state. For example, if the user is excited, it provides a more difficult quiz. The input is the recognized emotional state, and the output is the adjusted quiz or display content.

[0479] Step 11:

[0480] The server statistically analyzes the collected emotion data and reflects it in the next service provision. This analysis allows the server to learn user behavioral trends and emotion change patterns and improve the service. The input is the collected emotion data, and the output is the results of the statistical analysis.

[0481] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0482] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0483] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0484] [Second embodiment]

[0485] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0486] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0487] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0488] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0489] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0490] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0491] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0492] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0493] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0494] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0495] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0496] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0497] This invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a platform for providing safe and fair information even if the sports betting market is legalized in the future.

[0498] An embodiment of this system is described below.

[0499] System configuration

[0500] 1. Data Collection Methods

[0501] The server works in conjunction with sports media to receive real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled.

[0502] 2. Data analysis methods

[0503] The server captures real-time data and runs it through an AI image analysis engine, which tags the player's movements and situations to generate detailed player information.

[0504] The server uses machine learning models to generate play predictions based on past match data and the current match situation.

[0505] 3. Quiz Generation Method

[0506] The server automatically generates quizzes based on the analyzed player information and play prediction data, such as "Will player B score a shot on the next play?"

[0507] 4. Quiz display method

[0508] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[0509] 5. Means of receiving responses

[0510] The terminal receives the user's answers to the quiz and transmits them to the server.

[0511] 6. Results display means

[0512] The server receives the user's quiz answers, checks the results against the actual progress of the game, and sends the results back to the user's terminal.

[0513] The device displays the results received from the server to the user, displaying a message such as "Your prediction was correct!"

[0514] Specific examples

[0515] Specific examples of data collection methods

[0516] The server receives real-time video feeds from live J.League matches and also obtains player status data (heart rate, distance traveled, etc.) from the data stream.

[0517] Specific examples of data analysis methods

[0518] The server analyzes the real-time video feed with an image analysis engine to identify player movements, for example, Player A's shooting attempts, and calculates the shooting success rate.

[0519] Examples of quiz generation methods

[0520] The server automatically generates a quiz question: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0521] Examples of quiz display methods

[0522] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[0523] Specific examples of response receiving methods

[0524] The device receives the user's answer and sends it to the server. For example, if the user selects "Yes," the information is sent to the server.

[0525] Examples of results display methods

[0526] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[0527] The device displays the results to the user, along with the accuracy rate and other statistics.

[0528] As described above, the present invention is a system that acquires player movements and game information in real time, analyzes it, and generates a variety of information to provide to spectators. This allows spectators to enjoy the game more, and when the sports betting market is opened up, they will be able to place bets based on highly reliable information.

[0529] The processing flow will be explained below.

[0530] Understood. Below I will explain the specific flow for each processing step.

[0531] Step 1:

[0532] The server receives real-time game video feeds and player status data from sports-related media, specifically collecting data such as player positioning, heart rate, and distance traveled.

[0533] Step 2:

[0534] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[0535] Step 3:

[0536] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[0537] Step 4:

[0538] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[0539] Step 5:

[0540] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[0541] Step 6:

[0542] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[0543] Step 7:

[0544] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[0545] Step 8:

[0546] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[0547] Step 9:

[0548] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0549] Step 10:

[0550] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0551] Step 11:

[0552] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[0553] Example 1

[0554] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] Conventional sports viewing systems have a problem in that they lack the means for spectators to perform detailed analysis or make predictions in real time. Furthermore, there has been no effective system for instantly analyzing players' movements and game information during a game, and then generating quizzes based on that information to provide to users. This has led to a need for a way to keep spectators engaged and gain a deeper understanding of the game. Furthermore, even when the sports betting market is legalized in the future, a platform is needed that allows spectators to obtain information safely and fairly.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0557] In this invention, the server includes means for acquiring player movements and competition information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, means for the server to perform analysis and quiz generation, and means for the user terminal to display quizzes and send answers. This allows spectators to understand the details of the game movements in real time and enjoy predictions, and can provide reliable information even when the sports betting market is lifted.

[0558] "Means for acquiring player movements and competition information in real time" refers to a system for collecting player position, movement, heart rate, distance traveled, and other movement data during a match, as well as the progress of the match, in real time.

[0559] "Means for analyzing acquired data and generating player information and play predictions" refers to a system that generates detailed player information and predicts future plays based on collected player movement data and match information.

[0560] "Means for automatically generating quizzes based on generated prediction information" refers to a system that automatically generates quiz-style content based on analyzed player information and play prediction data.

[0561] "Means for displaying the generated quiz and analysis results on the user's device" refers to a system for displaying the automatically generated quiz and its related information on the device used by the user.

[0562] "Means for receiving users' quiz answers and displaying the results" refers to a system for collecting the answers users give to quizzes, analyzing the results, and providing feedback to users.

[0563] "Server" refers to the central computer system that analyzes data, automatically generates quizzes, and evaluates the results.

[0564] "User device" refers to a device, such as a smartphone or tablet, that a user uses to answer quizzes and view analysis results.

[0565] The present invention relates to a system that acquires player movements and competition information in real time, analyzes the information, and generates detailed player information and play predictions. This system provides a tool for spectators to enjoy the game more, and also serves as a platform for providing safe and fair information when the sports betting market is legalized in the future. An embodiment of this system is described below in detail.

[0566] Data collection methods

[0567] The server works with sports broadcasting agencies to obtain real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled. Specifically, the server obtains game broadcast data through an API and receives player GPS location information, heart rate sensor data, and distance traveled tracking data.

[0568] Examples:

[0569] The server receives real-time video feeds from live J.League matches, and also obtains player status data such as heart rate and distance traveled from the data stream.

[0570] Data Analysis Methods

[0571] The server inputs the collected data into an AI image analysis engine, which tags the player's movements and situations. It then uses a machine learning model to generate play predictions based on the analysis data and past match data. Specifically, the video feed is passed through the image analysis engine, and when, for example, Player A goes into shooting motion, that movement is detected and the shooting success rate is calculated. It also compares the shooting success rate data from the past 10 matches with the current situation to predict the probability that Player A will make a shot on the next play.

[0572] Examples:

[0573] The server analyzes the real-time video feed with an image analysis engine to identify the player's movements. It analyzes the movement of Player A when he attempts a shot and calculates the shooting success rate.

[0574] Quiz generation method

[0575] The server automatically generates a quiz based on the analyzed player information and play prediction data. Specifically, it generates a quiz such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0576] Examples:

[0577] The server generates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0578] Quiz display method

[0579] The device displays the generated quiz and related analysis results to the user. The user answers the quiz and can check the results on the device. Specifically, a quiz screen is displayed on the user's smartphone or tablet, asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[0580] Examples:

[0581] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[0582] Response receiving method

[0583] The device receives the user's answers to the quiz and sends them to the server. Specifically, when the user presses the "Yes" or "No" button, the device collects the user's selection and sends it to the server.

[0584] Examples:

[0585] The device receives the user's answer and sends it to the server. If the user selects "yes," the information is sent to the server.

[0586] Results display means

[0587] The server receives the user's quiz answers, compares them with the actual game results, and returns the results to the user's device. Specifically, the server monitors the progress of the game, checks whether Player A actually scored a goal, and generates a result to compare with the user's answer. The result is then sent to the device and fed back to the user.

[0588] Examples:

[0589] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[0590] The device displays the results to the user, along with the accuracy rate and other statistics.

[0591] Example prompt sentences (input to generative AI models)

[0592] Generate a program to create a system that captures player movements and match information in real time, analyzes it, and provides it to spectators. Please use the following detailed specifications:

[0593] 1. The server receives real-time game video feeds and player status data via API.

[0594] 2. The server analyzes the data using an AI image analysis engine to identify player movements and then uses machine learning models to predict plays.

[0595] 3. The server generates a quiz in the form of questions based on the analysis data and sends the quiz to the user.

[0596] 4. The device displays the quiz to the user and sends the user's answers to the server.

[0597] 5. Finally, the server evaluates the match results and sends the results based on the user's answers back to the device, which then displays the results to the user.

[0598] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0599] Step 1: Data collection

[0600] The server receives real-time game video feeds and player status data from sports broadcasters via APIs, including player heart rate, distance traveled, and GPS location.

[0601] Input: Match video feed, player status data

[0602] Data processing: Synchronizing video data and status data

[0603] Output: Synchronized real-time dataset

[0604] Specific operation: The server obtains J.League match broadcast data via an API, and obtains data from each player's heart rate sensor, data tracking running distance, GPS location information, etc.

[0605] Step 2: Data analysis

[0606] The server inputs the collected data into an AI image analysis engine, which tags players' movements and determines their situations. It also uses machine learning models to analyze past match data and current match information to generate play predictions.

[0607] Input: Synchronized real-time dataset

[0608] Data processing: Image analysis and machine learning model analysis

[0609] Output: Analyzed player information and play prediction data

[0610] Specific operation: The server passes the acquired video feed through an AI image analysis engine to analyze Player A's shooting motion and positional relationship. It also inputs data from the past 10 games into a machine learning model to calculate Player A's shooting success rate.

[0611] Step 3: Quiz Generation

[0612] The server automatically generates quizzes based on the analyzed player information and play prediction data. The generated quizzes are based on real-time information.

[0613] Input: Analyzed player information, play prediction data

[0614] Data processing: Automatic generation of quizzes

[0615] Output: Auto-generated quiz

[0616] Specific behavior: The server creates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0617] Step 4: Quiz display

[0618] The terminal displays the generated quiz and related information to the user, who can then answer the quiz.

[0619] Input: Auto-generated quiz, related information

[0620] Data processing: Display of quiz screen

[0621] Output: Quiz and related information displayed on the user's device

[0622] Specific operation: The device displays a quiz on a smartphone or tablet asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[0623] Step 5: Receiving a response

[0624] The terminal receives the user's answers to the quiz and transmits them to the server.

[0625] Input: User's quiz answer

[0626] Data processing: Sending user response data

[0627] Output: Data sent to the server

[0628] Specific operation: The user presses the "Yes" or "No" button, and the result is sent to the server via the terminal.

[0629] Step 6: View the results

[0630] The server receives the user's quiz answers, compares them with the actual game results, and returns the evaluation results to the user's device and displays them to the user.

[0631] Input: User quiz answers, actual match results

[0632] Data processing: Comparing and evaluating answers with real-world results

[0633] Output: Evaluation results

[0634] Specific operation: The server monitors the game, and when Player A scores a shot, it generates a message saying "Your prediction was correct!" and sends it to the device. The device displays this result to the user, along with the accuracy rate and other statistical information.

[0635] (Application example 1)

[0636] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] When watching sports, spectators want to be able to understand the progress of the game and the actions of the players in real time, allowing them to enjoy the game more deeply. However, current systems do not fully meet this requirement, and few of them include interactive elements. Furthermore, if sports betting is legalized in the future, a system that provides reliable prediction information will be necessary. There is a need for a system that can solve these issues and improve the value of the spectator experience.

[0638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0639] In this invention, the server includes means for acquiring player movement and match information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, and means for the user terminal being a head-mounted display and interactively displaying the analysis results and quizzes, thereby enabling users to interactively enjoy player movement predictions and related quizzes in real time through the head-mounted display.

[0640] "Real-time" refers to the immediate acquisition and processing of ongoing data and information.

[0641] "Athlete actions" means all physical actions and movements made by an athlete in a sporting event.

[0642] "Match Information" includes all data related to a match, such as the progress of a sporting event, scores, and player status.

[0643] "Analyzing data" means processing acquired data using technologies such as machine learning models and AI image analysis engines to derive useful information and predictions.

[0644] "Play prediction" is the process of estimating the next actions and outcomes that a player or team is likely to take based on past data and real-time movement information.

[0645] "Automatically generating quizzes" means that a computer automatically creates quiz-style questions based on analyzed information.

[0646] "Analysis results" refers to detailed information and prediction results regarding the player's movements and plays obtained by the data analysis means.

[0647] "User terminal" means a digital device, such as a head-mounted display, through which a user receives information or provides input.

[0648] "Interactively displayed" refers to a state in which the user can react and operate in real time to the information displayed through the device.

[0649] A "head-mounted display" refers to a device that displays information when worn on the user's head.

[0650] "Receiving quiz answers" means that the system acquires the responses given by the user to the quiz.

[0651] "Displaying the results" means displaying feedback based on the user's quiz answers and the actual match results.

[0652] This invention is a system that acquires player movements and game information in real time, analyzes it, and generates player information and play predictions. It provides an effective tool for spectators to enjoy the game more, and can also accommodate future sports betting markets. Specific embodiments of the system are described below.

[0653] System configuration

[0654] Hardware Configuration

[0655] 1. Server

[0656] Use a high-performance computer with a stable network connection to receive player status data and game video feeds in real time, and the server should preferably be equipped with a GPU or TPU for data analysis.

[0657] 2. Head-Mounted Display (HMD)

[0658] It is used by spectators to interactively view information and quizzes in real time, and requires a high-resolution display and a comfortable HMD.

[0659] Software Configuration

[0660] 1. Data Collection Methods

[0661] The server receives real-time game data through a sports data API, including player positioning, movement, heart rate, distance traveled, and game progress.

[0662] 2. Data analysis methods

[0663] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow), thereby generating predictions of player movements and plays.

[0664] 3. Quiz Generation Method

[0665] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0666] 4. Quiz display method

[0667] The user's HMD visually displays the generated quiz and related analysis results, and the user can answer the quiz through the HMD.

[0668] 5. Means of receiving responses

[0669] The HMD receives the user's answers to the quiz and sends them to a server, which processes the user's responses immediately.

[0670] 6. Results display means

[0671] The server receives the user's answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[0672] Specific examples

[0673] For example, if the motion of Player A attempting to shoot in a J.League match is analyzed in real time, the server predicts that "Player A has a 75% chance of scoring on the next play." The user's HMD then displays a quiz question: "Player A's current shooting success rate is 75%. Will he score on the next play?" If the user answers "Yes," the answer is sent to the server, and the result "Your prediction was correct!" is displayed based on the actual match results.

[0674] Prompt Sentence Examples

[0675] "Design an interactive quiz platform for watching sports that predicts Player A's actions and success rate in real time and asks him if he will be successful on his next play."

[0676] This system will enable spectators to enjoy the game more deeply and, in the future, will enable the provision of reliable information that can also be used for the sports betting market.

[0677] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0678] Step 1:

[0679] The server retrieves real-time match data from a sports data API, including player positioning, movement, heart rate, distance traveled, and match progress.

[0680] Input: Real-time data from the Sports Data API

[0681] Output: Match and player status data obtained

[0682] Step 2:

[0683] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and machine learning models (e.g., TensorFlow), which generates play predictions based on players' movements and past performance data.

[0684] Input: Retrieved match and player status data

[0685] Output: Analyzed player movement information and play prediction results

[0686] Step 3:

[0687] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates a question such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[0688] Input: Analyzed player movement information and play prediction results

[0689] Output: Auto-generated quiz

[0690] Step 4:

[0691] The device (user's HMD) visually displays the generated quiz and the analysis results, and the user answers the displayed quiz.

[0692] Input: Auto-generated quiz and analysis results

[0693] Output: User response interface

[0694] Step 5:

[0695] The device receives the user's answers to the quiz and sends them to the server, which processes the user's responses immediately.

[0696] Input: User's quiz answer

[0697] Output: User response data

[0698] Step 6:

[0699] The server receives the user's quiz answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[0700] Input: User response data and match progress data

[0701] Output: Result message displayed on the HMD

[0702] In this way, the invention provides a system that provides spectators with an interactive, real-time experience, enhancing the enjoyment of watching sports.

[0703] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0704] The present invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a foundation for providing safe and fair information even if the sports betting market is legalized in the future. The present invention also incorporates an "emotion engine" that recognizes user emotions and optimizes services based on them.

[0705] An embodiment of this system is described below.

[0706] System configuration

[0707] 1. Data Collection Methods

[0708] The server connects with sports media and receives real-time player status data and game video feeds, collecting data such as player positioning, heart rate, and distance traveled.

[0709] 2. Data analysis methods

[0710] The server then analyzes the received data through an AI image analysis engine and machine learning models, which identify players' movements and playing tendencies and update the database in real time.

[0711] 3. Quiz Generation Method

[0712] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Will player A score on the next play?"

[0713] 4. Quiz display method

[0714] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[0715] 5. Means of receiving responses

[0716] The terminal receives the user's answers to the quiz and transmits them to the server.

[0717] 6. Results display means

[0718] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0719] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0720] The device displays the results to the user, along with the accuracy rate and other statistics.

[0721] 7. Emotion recognition means

[0722] The server uses an emotion engine to analyze the user's feedback and emotional state when answering the quiz. It acquires and analyzes the user's facial expression data and emotion labels, and stores the results.

[0723] 8. Emotional reflection means

[0724] The server adjusts the quiz and display content based on the user's emotional state: for example, if the user is excited, it presents a more challenging quiz, and if the user is relaxed, it presents more information-oriented content.

[0725] The device displays the most appropriate quiz and analysis results for the user based on the emotional reflection information sent from the server.

[0726] 9. Statistical Analysis of Emotional Data

[0727] The server collects the emotional data provided by the user and performs statistical analysis, which allows the server to analyze the user's behavioral trends and emotional change patterns and provide more personalized services.

[0728] The server will use these results to readjust the machine learning model and reflect this in future service provision.

[0729] Specific examples

[0730] Specific examples of data collection methods

[0731] The server receives real-time video feeds from major league teams and also obtains real-time player status data (heart rate, distance traveled, etc.) from a number of sensors.

[0732] Specific examples of data analysis methods

[0733] The server processes the video feed with an AI image analysis engine to analyze player movements, for example identifying when Player B receives a pass and attempts a shot.

[0734] Examples of quiz generation methods

[0735] The server automatically generates a quiz question: "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0736] Examples of quiz display methods

[0737] The device will display the quiz along with related information such as "Player B's shooting percentage in the last 10 games: 70%." The user can select "Yes" or "No."

[0738] Specific examples of response receiving methods

[0739] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0740] Examples of results display methods

[0741] The server receives the user's answers and checks them against the actual game results, for example, to see if Player B actually scored a shot.

[0742] The device will then display the result to the user: "Your prediction was correct!"

[0743] Specific examples of emotion recognition

[0744] The server uses an emotion engine to analyze the user's facial expression data sent from the device and determine whether the user is excited or relaxed.

[0745] Examples of emotional reflection

[0746] The server senses that the user's emotional state is agitated and provides the next quiz with a slightly higher difficulty.

[0747] Based on this, the device will display new quizzes and related information to the user.

[0748] Examples of statistical analysis of emotion data

[0749] The server analyzes the collected emotional data of many users and learns emotional patterns.

[0750] Based on this, the server will optimize future service provision and improve the user experience.

[0751] As described above, this invention is a system that acquires player movements and match information in real time, analyzes it, and generates a variety of information to provide to spectators. Furthermore, by recognizing and analyzing user emotions and optimizing services based on that, it is possible to provide spectators with a richer sports viewing experience.

[0752] The processing flow will be explained below.

[0753] Step 1:

[0754] The server receives real-time game video feeds and player status data from sports-related media, including player positioning, heart rate, and distance traveled.

[0755] Step 2:

[0756] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[0757] Step 3:

[0758] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[0759] Step 4:

[0760] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[0761] Step 5:

[0762] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[0763] Step 6:

[0764] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[0765] Step 7:

[0766] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[0767] Step 8:

[0768] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[0769] Step 9:

[0770] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[0771] Step 10:

[0772] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[0773] Step 11:

[0774] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[0775] Step 12:

[0776] The device captures the user's facial expression data and transmits it to the server, where the facial expression data is captured in real time.

[0777] Step 13:

[0778] The server analyzes the received facial expression data using an emotion engine, which identifies the user's emotional state (excited, relaxed, etc.).

[0779] Step 14:

[0780] The server adjusts the quiz and presentation content based on the user's emotional state, for example generating more challenging quizzes if the user is excited, or providing more information-heavy content if the user is relaxed.

[0781] Step 15:

[0782] The server statistically analyzes users' emotional data and uses it to optimize services, including learning emotional patterns and analyzing trends in emotional changes.

[0783] Step 16:

[0784] The device displays tailored content sent from the server to the user, allowing the user to receive information and quizzes optimized for their emotional state.

[0785] These are the specific processing steps of the sports viewing support system that combines the emotion engine. This system allows users to enjoy the game more fully and provides a personalized experience based on their emotions.

[0786] Example 2

[0787] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] Conventional sports viewing systems have difficulty obtaining real-time information about players' movements and matches, limiting the information provided to spectators. As a result, there are a lack of tools that allow spectators to enjoy the match more deeply. In addition, it is difficult to provide personalized services that respond to user emotions, making it impossible to maximize spectator interest and satisfaction.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0790] In this invention, the server includes a means for acquiring player movements and game information in real time, a means for analyzing the acquired data to generate player information and play predictions, and a means for automatically generating quizzes based on the generated prediction information. This allows spectators to enjoy the game more. The server also includes a means for displaying the generated quizzes and analysis results on a user terminal, and a means for receiving the user's quiz answers and displaying the results. Additionally, the server includes a means for recognizing and analyzing user emotion data, a means for adjusting the next quiz and display content based on the analyzed emotion data, and a means for statistically analyzing the user emotion data to optimize the system. This makes it possible to provide spectators with detailed player information and play predictions in real time, and to provide personalized quizzes and information based on the user's emotions.

[0791] "Means for acquiring player movements and match information in real time" refers to devices or programs for acquiring data such as player movements, positions, heart rates, and distance traveled during a match, as well as video feeds of the match, in real time.

[0792] "Means of analyzing acquired data and generating player information and play predictions" refers to the process of using an AI image analysis engine and machine learning models to analyze player movements and playing tendencies based on received match data, and generate play predictions.

[0793] The "means for automatically generating quizzes based on the generated prediction information" refers to a program or tool for automatically generating quizzes using analyzed player information and play prediction data.

[0794] The "means for displaying the generated quiz and analysis results on the user's terminal" refers to a user interface or communication means for displaying the quiz and analysis results automatically generated by the server on the user's terminal.

[0795] "Means for receiving the user's quiz answers and displaying the results" refers to a device or program that receives the content of the quiz answered by the user, processes and evaluates it, and displays it to the user.

[0796] "Means for recognizing and analyzing user emotional data" refers to an emotion engine or AI model that recognizes and analyzes emotions based on the user's facial expressions and behavioral data.

[0797] The "means for adjusting the next quiz or display content based on the analyzed emotional data" is a program for adjusting the difficulty level of the next quiz or display content based on the user's emotional state analyzed by the emotion engine.

[0798] "Means for statistically analyzing user emotional data and optimizing the system" refers to the process of statistically analyzing collected user emotional data and using the results to improve the overall system performance and user experience.

[0799] The present invention relates to a system that acquires player movements and game information in real time, analyzes the acquired information, and generates player information and play predictions, allowing spectators to enjoy the game more. Specific embodiments for carrying out the present invention will be described in detail below.

[0800] System configuration

[0801] 1. Data Collection Methods

[0802] The server works with sports media to obtain real-time player status data (heart rate, distance traveled, etc.) and game video feeds using APIs. As a specific example, it uses the SportRadar API to collect player data from major leagues.

[0803] 2. Data analysis methods

[0804] The server analyzes the received match data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements and playing tendencies, and updates the database in real time. For example, it identifies the movement of Player B receiving a pass and attempting a shot.

[0805] 3. Quiz Generation Method

[0806] The server uses a natural language generation tool (e.g., OpenAI GPT-3) to automatically generate questions based on the analyzed data. For example, it generates a question such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0807] 4. Quiz display method

[0808] The terminal displays the quiz and the associated analysis results sent from the server to the user, and the user views the quiz on the user interface of the terminal and selects "yes" or "no."

[0809] 5. Means of receiving responses

[0810] The terminal receives the answer given by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0811] 6. Results display means

[0812] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player B actually scored a shot.

[0813] The terminal displays the evaluation result to the user, saying "Your prediction was correct!"

[0814] 7. Emotion recognition means

[0815] The server analyzes the user's facial expression data sent from the device using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed.

[0816] 8. Emotional reflection means

[0817] The server adjusts the difficulty of the next quiz based on the results of the emotion engine, for example, if the user is excited, it will provide a slightly more difficult quiz.

[0818] The terminal displays the new quiz and related information to the user.

[0819] 9. Statistical Analysis of Emotional Data

[0820] The server statistically analyzes the collected emotion data to identify user emotion patterns, thereby improving overall system performance and user experience.

[0821] The server will optimize the system based on these results and reflect them in future service provision.

[0822] Specific examples

[0823] Specific examples of data collection methods

[0824] The server calls the SportRadar API to retrieve real-time game data (such as player heart rate, distance traveled, positioning, etc.) and live feeds from major league games.

[0825] Specific examples of data analysis methods

[0826] The server uses an AI image analysis engine (TensorFlow, OpenCV) to analyze the players' movements from the video data. It analyzes the movements of Player B when he receives the pass and attempts to shoot, and stores the results in a database.

[0827] Examples of quiz generation methods

[0828] Based on the analyzed data, the server uses OpenAI GPT-3 to generate a quiz such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[0829] Examples of quiz display methods

[0830] The device displays the quiz sent from the server on the user interface and provides information such as "Player B's shooting success rate in the last 10 games: 70%." The user selects "Yes" or "No."

[0831] Specific examples of response receiving methods

[0832] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[0833] Examples of results display methods

[0834] The server receives the user's answer and compares it with the actual game result. It checks whether Player B actually made the shot and sends the result "Your prediction was correct!" to the terminal and displays it to the user.

[0835] Specific examples of emotion recognition methods

[0836] The device captures the user's facial expressions with a camera and sends the facial data to a server, which then uses the Microsoft Azure Emotion API to analyze the data and determine whether the user is excited or relaxed.

[0837] Examples of emotional reflection

[0838] The server adjusts the difficulty of the next quiz based on the user's emotional state. For example, if the user is excited, the next quiz will be more difficult. The terminal displays the new quiz and related information to the user.

[0839] Examples of statistical analysis of emotion data

[0840] The server statistically analyzes the emotion data collected from a large number of users to identify emotion patterns, which will optimize future service provision and improve user experience.

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1:

[0843] Real-time data collection

[0844] The server works with sports media and uses APIs to obtain player status data (heart rate, distance traveled, etc.) and game video feeds in real time. The input is API data provided by the sports media, and the output is the obtained player status data and game video feeds. Specifically, the server calls the SportRadar API to obtain player data from major leagues.

[0845] Step 2:

[0846] Data analysis

[0847] The server analyzes the acquired data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements, positioning, and playing tendencies. The input is a real-time game video feed and status data, and the output is analyzed player movement information and play prediction data. As a concrete example, the server identifies from the video feed the movement of Player B receiving a pass and attempting a shot, and stores that data in a database.

[0848] Step 3:

[0849] Generate a quiz

[0850] The server automatically generates quizzes using a natural language generation tool (e.g., OpenAI GPT-3) based on the analyzed player information and play prediction data. The input is the analyzed player information and play prediction data, and the output is a quiz generated in natural language. For example, a quiz may be generated that asks, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?" The server sends this generation prompt and receives the generated quiz.

[0851] Step 4:

[0852] View Quiz

[0853] The terminal displays the quiz sent from the server and the related analysis results to the user. The input is the quiz data sent from the server, and the output is the quiz and analysis results displayed on the user interface. Specifically, the terminal displays the quiz along with the information "Player B's shooting success rate in the last 10 games: 70%" and allows the user to select "Yes" or "No."

[0854] Step 5:

[0855] Receiving user responses

[0856] The terminal receives the answer selected by the user and sends it to the server. The input is the answer information selected by the user, and the output is the answer data sent to the server. For example, if the user selects "Yes," the terminal sends that data to the server. This specific operation is performed by the terminal receiving the input from the user and transferring it to the server.

[0857] Step 6:

[0858] Evaluation of answer results

[0859] The server receives the user's answers and evaluates the results by comparing them with the actual progress of the game. The input is the user's answer data and actual game data, and the output is the evaluation result. For example, the server checks whether player B actually scored a shot and evaluates the result. This evaluation result is communicated to the user in the form of a message such as "Your prediction was accurate."

[0860] Step 7:

[0861] Emotional Data Recognition

[0862] The device captures the user's facial expression with a camera and sends the data to a server. The server analyzes the received facial expression data using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed. The input is the user's facial expression data, and the output is analyzed emotional state data. As a concrete example, the device uses a camera to take a picture of the user's face and sends the data to a server.

[0863] Step 8:

[0864] Adjustments based on user emotions

[0865] The server adjusts the difficulty and content of the next quiz based on the analysis results of the emotion engine. The input is the analyzed emotion data, and the output is the adjusted quiz data. For example, if the user is excited, the server adjusts the difficulty of the next quiz to be higher. The terminal then displays the new quiz content to the user.

[0866] Step 9:

[0867] Statistical analysis of emotion data

[0868] The server statistically analyzes the collected emotional data of many users and identifies emotional patterns. The input is the collected emotional data, and the output is the statistically analyzed emotional patterns. The server uses these analysis results to optimize the system and reflect them in future service provision.

[0869] Through these steps, the system can provide detailed player information and play predictions in real time, as well as provide personalized quizzes and information based on the user's emotions.

[0870] (Application example 2)

[0871] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0872] Conventional sports viewing systems tend to make spectators watch the game monotonously, limiting the enjoyment of the experience. Furthermore, it is difficult to provide personalized content that reflects the user's emotional state. This can lead to a poor user experience and a lack of sustained interest in watching the game. Furthermore, when the sports betting market is legalized, fair and secure information provision will be required, but current systems have difficulty meeting this requirement.

[0873] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring player movements and game information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on the user terminal, means for receiving the user's quiz answers and displaying the results, means for recognizing the user's emotional state and adjusting the quiz and display content based on that state, and means for statistically analyzing the collected emotional data and reflecting it in the next service provision. This allows spectators to enjoy the game more interestingly, improves the user experience, and enables fair and safe information provision in the sports betting market.

[0874] "Means for obtaining player movements and match information in real time" refers to a method for instantly obtaining player data and match progress during a sports event.

[0875] "Means for analyzing acquired data and generating player information and play predictions" refers to a method for using acquired data to analyze player movements and game developments and predict future plays.

[0876] The "means for automatically generating quizzes based on generated prediction information" is a method for automatically creating quiz-style questions using prediction data generated by analysis.

[0877] "Means for displaying the generated quiz and analysis results on the user's terminal" refers to a method for displaying the generated quiz and analysis results on the terminal used by the user.

[0878] The "means for receiving the user's quiz answers and displaying the results" is a method for receiving the answers to the quiz answered by the user and displaying the results again on the user's terminal.

[0879] "Means for recognizing the emotional state of a user and adjusting quizzes and display content based on that state" refers to a method for recognizing the emotions of a user and providing quizzes and display content in an appropriate form according to those emotions.

[0880] "Means of statistically analyzing collected emotional data and reflecting it in the next service provision" refers to a method of statistically analyzing emotional data collected from users and improving and optimizing the content of the next service provision based on the results.

[0881] This invention is a system that acquires and analyzes the movements of athletes and game information in real time, and automatically generates and displays quizzes based on the analysis. Specific embodiments for carrying out the invention are described below.

[0882] System configuration

[0883] The system mainly consists of a server and a user terminal. Each function is as follows:

[0884] Data collection

[0885] The server obtains real-time information about player movements and match information during sporting events by receiving real-time video feeds and player status data (e.g., heart rate, distance traveled) through a remote API.

[0886] Data analysis

[0887] The server analyzes the received data using a machine learning model and an AI image analysis engine. This allows it to identify the players' movements and playing tendencies and predict future plays. For example, OpenCV is used as the AI ​​image analysis engine and TensorFlow is used as the machine learning model for this process.

[0888] Quiz Generation

[0889] The server automatically generates a quiz based on the analysis results and play predictions. For example, it generates a quiz such as "Will Player A's next shot be successful?" and sends it to the user's device.

[0890] Quiz display

[0891] The user device displays the generated quiz and related information to the user. The user answers the quiz through the device, and the answers are sent to the server. The device can be a smartphone or a head-mounted display.

[0892] Receiving responses and displaying results

[0893] The server receives the user's answer and compares it with the actual game result. For example, it checks whether Player A actually made a shot, and sends the result to the user's device as "The user's prediction was correct."

[0894] emotion recognition

[0895] The server analyzes the user's facial expression data sent from the user's device and recognizes the user's emotional state using an emotion engine (e.g., Hugging Face emotion recognition model), thereby determining whether the user is excited or relaxed.

[0896] emotional reflection

[0897] The server then tailors the quiz and content displayed based on the user's perceived emotional state, for example providing a more challenging quiz if the user is excited, or displaying more information-focused content if the user is relaxed.

[0898] Statistical analysis of emotion data

[0899] The server statistically analyzes the collected emotional data and reflects it in the next service provision. This analysis clarifies the user's behavioral trends and emotional change patterns, making it possible to provide more personalized services.

[0900] Specific examples

[0901] Below is a concrete example of how this system can be used.

[0902] Service flow

[0903] Consider a series of steps: generate a quiz about the next play of player A in a major league game ID 12345, analyze the user's emotional state, and display a quiz of appropriate difficulty.

[0904] 1. The server collects data for match ID 12345.

[0905] 2. The server analyzes the data, predicts the next play, and generates a quiz.

[0906] 3. A quiz will be displayed on the user's device: "Will Player A's next shot be successful? (Past success rate: 70%)"

[0907] 4. The user selects an answer and the data is sent back to the server.

[0908] 5. The server compares the match results and displays them to the user.

[0909] 6. At the same time, the user's facial expression data is analyzed to recognize emotions.

[0910] 7. The server adjusts the next quiz or display content depending on the user's emotional state.

[0911] In this way, spectators can enjoy the sporting event more fully, and fair and safe information provision is achieved.

[0912] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0913] Step 1:

[0914] The server identifies the match ID and retrieves real-time video feeds and player status data through an external API. This process involves sending an API request to receive data such as the video feed, player heart rate, and distance traveled. The input is the match ID, and the output is the real-time video feed and player status data.

[0915] Step 2:

[0916] The server analyzes the received data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow). It extracts each frame from the video feed, analyzes the player's movements and positions, and stores the results in a database. At the same time, it analyzes player status data to predict playing tendencies and performance. The inputs are the real-time video feed and player status data, and the outputs are the analysis results and play prediction data.

[0917] Step 3:

[0918] The server automatically generates a quiz based on the analysis results and play prediction data. For example, it generates a quiz such as "Will Player A's next shot be successful?" The input is the analysis results and play prediction data, and the output is the generated quiz.

[0919] Step 4:

[0920] The server sends the generated quiz and related information to the user's device. The user's device (smartphone or head-mounted display) displays the quiz and supplementary information (e.g., past success rates and player performance data). The input is the generated quiz, and the output is the quiz displayed on the user's device.

[0921] Step 5:

[0922] The user answers the presented quiz. The user terminal receives the user's answers and sends them to the server. The input is the user's answers, and the output is the answer data sent to the server.

[0923] Step 6:

[0924] The server compares the user's answer with the actual result of the game. For example, it checks whether Player A actually scored a shot and determines whether the user's answer was correct or incorrect. The input is the user's answer and the game result data, and the output is the evaluation result.

[0925] Step 7:

[0926] The server sends the evaluation results to the user's device and displays the results and explanations. It provides feedback such as "Your prediction was correct!" The input is the evaluation results, and the output is the result display on the user's device.

[0927] Step 8:

[0928] The user device captures the user's facial expression data with a camera and sends it to the server. The input is the user's facial expression data, and the output is the data sent to the server.

[0929] Step 9:

[0930] The server analyzes the facial expression data using an emotion engine (e.g., Hugging Face emotion recognition model) to determine the user's emotional state. The input is the user's facial expression data, and the output is the recognized emotional state.

[0931] Step 10:

[0932] The server adjusts the next quiz or display content based on the recognized emotional state. For example, if the user is excited, it provides a more difficult quiz. The input is the recognized emotional state, and the output is the adjusted quiz or display content.

[0933] Step 11:

[0934] The server statistically analyzes the collected emotion data and reflects it in the next service provision. This analysis allows the server to learn user behavioral trends and emotion change patterns and improve the service. The input is the collected emotion data, and the output is the results of the statistical analysis.

[0935] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0936] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0937] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0941] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0942] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0943] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0944] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0945] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0946] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0947] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0948] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0949] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0950] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0951] This invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a platform for providing safe and fair information even if the sports betting market is legalized in the future.

[0952] An embodiment of this system is described below.

[0953] System configuration

[0954] 1. Data Collection Methods

[0955] The server works in conjunction with sports media to receive real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled.

[0956] 2. Data analysis methods

[0957] The server captures real-time data and runs it through an AI image analysis engine, which tags the player's movements and situations to generate detailed player information.

[0958] The server uses machine learning models to generate play predictions based on past match data and the current match situation.

[0959] 3. Quiz Generation Method

[0960] The server automatically generates quizzes based on the analyzed player information and play prediction data, such as "Will player B score a shot on the next play?"

[0961] 4. Quiz display method

[0962] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[0963] 5. Means of receiving responses

[0964] The terminal receives the user's answers to the quiz and transmits them to the server.

[0965] 6. Results display means

[0966] The server receives the user's quiz answers, checks the results against the actual progress of the game, and sends the results back to the user's terminal.

[0967] The device displays the results received from the server to the user, displaying a message such as "Your prediction was correct!"

[0968] Specific examples

[0969] Specific examples of data collection methods

[0970] The server receives real-time video feeds from live J.League matches and also obtains player status data (heart rate, distance traveled, etc.) from the data stream.

[0971] Specific examples of data analysis methods

[0972] The server analyzes the real-time video feed with an image analysis engine to identify player movements, for example, Player A's shooting attempts, and calculates the shooting success rate.

[0973] Examples of quiz generation methods

[0974] The server automatically generates a quiz question: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[0975] Examples of quiz display methods

[0976] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[0977] Specific examples of response receiving methods

[0978] The device receives the user's answer and sends it to the server. For example, if the user selects "Yes," the information is sent to the server.

[0979] Examples of results display methods

[0980] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[0981] The device displays the results to the user, along with the accuracy rate and other statistics.

[0982] As described above, the present invention is a system that acquires player movements and game information in real time, analyzes it, and generates a variety of information to provide to spectators. This allows spectators to enjoy the game more, and when the sports betting market is opened up, they will be able to place bets based on highly reliable information.

[0983] The processing flow will be explained below.

[0984] Understood. Below I will explain the specific flow for each processing step.

[0985] Step 1:

[0986] The server receives real-time game video feeds and player status data from sports-related media, specifically collecting data such as player positioning, heart rate, and distance traveled.

[0987] Step 2:

[0988] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[0989] Step 3:

[0990] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[0991] Step 4:

[0992] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[0993] Step 5:

[0994] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[0995] Step 6:

[0996] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[0997] Step 7:

[0998] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[0999] Step 8:

[1000] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[1001] Step 9:

[1002] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1003] Step 10:

[1004] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1005] Step 11:

[1006] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[1007] Example 1

[1008] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1009] Conventional sports viewing systems have a problem in that they lack the means for spectators to perform detailed analysis or make predictions in real time. Furthermore, there has been no effective system for instantly analyzing players' movements and game information during a game, and then generating quizzes based on that information to provide to users. This has led to a need for a way to keep spectators engaged and gain a deeper understanding of the game. Furthermore, even when the sports betting market is legalized in the future, a platform is needed that allows spectators to obtain information safely and fairly.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1011] In this invention, the server includes means for acquiring player movements and competition information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, means for the server to perform analysis and quiz generation, and means for the user terminal to display quizzes and send answers. This allows spectators to understand the details of the game movements in real time and enjoy predictions, and can provide reliable information even when the sports betting market is lifted.

[1012] "Means for acquiring player movements and competition information in real time" refers to a system for collecting player position, movement, heart rate, distance traveled, and other movement data during a match, as well as the progress of the match, in real time.

[1013] "Means for analyzing acquired data and generating player information and play predictions" refers to a system that generates detailed player information and predicts future plays based on collected player movement data and match information.

[1014] "Means for automatically generating quizzes based on generated prediction information" refers to a system that automatically generates quiz-style content based on analyzed player information and play prediction data.

[1015] "Means for displaying the generated quiz and analysis results on the user's device" refers to a system for displaying the automatically generated quiz and its related information on the device used by the user.

[1016] "Means for receiving users' quiz answers and displaying the results" refers to a system for collecting the answers users give to quizzes, analyzing the results, and providing feedback to users.

[1017] "Server" refers to the central computer system that analyzes data, automatically generates quizzes, and evaluates the results.

[1018] "User device" refers to a device, such as a smartphone or tablet, that a user uses to answer quizzes and view analysis results.

[1019] The present invention relates to a system that acquires player movements and competition information in real time, analyzes the information, and generates detailed player information and play predictions. This system provides a tool for spectators to enjoy the game more, and also serves as a platform for providing safe and fair information when the sports betting market is legalized in the future. An embodiment of this system is described below in detail.

[1020] Data collection methods

[1021] The server works with sports broadcasting agencies to obtain real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled. Specifically, the server obtains game broadcast data through an API and receives player GPS location information, heart rate sensor data, and distance traveled tracking data.

[1022] Examples:

[1023] The server receives real-time video feeds from live J.League matches, and also obtains player status data such as heart rate and distance traveled from the data stream.

[1024] Data Analysis Methods

[1025] The server inputs the collected data into an AI image analysis engine, which tags the player's movements and situations. It then uses a machine learning model to generate play predictions based on the analysis data and past match data. Specifically, the video feed is passed through the image analysis engine, and when, for example, Player A goes into shooting motion, that movement is detected and the shooting success rate is calculated. It also compares the shooting success rate data from the past 10 matches with the current situation to predict the probability that Player A will make a shot on the next play.

[1026] Examples:

[1027] The server analyzes the real-time video feed with an image analysis engine to identify the player's movements. It analyzes the movement of Player A when he attempts a shot and calculates the shooting success rate.

[1028] Quiz generation method

[1029] The server automatically generates a quiz based on the analyzed player information and play prediction data. Specifically, it generates a quiz such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1030] Examples:

[1031] The server generates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[1032] Quiz display method

[1033] The device displays the generated quiz and related analysis results to the user. The user answers the quiz and can check the results on the device. Specifically, a quiz screen is displayed on the user's smartphone or tablet, asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[1034] Examples:

[1035] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[1036] Response receiving method

[1037] The device receives the user's answers to the quiz and sends them to the server. Specifically, when the user presses the "Yes" or "No" button, the device collects the user's selection and sends it to the server.

[1038] Examples:

[1039] The device receives the user's answer and sends it to the server. If the user selects "yes," the information is sent to the server.

[1040] Results display means

[1041] The server receives the user's quiz answers, compares them with the actual game results, and returns the results to the user's device. Specifically, the server monitors the progress of the game, checks whether Player A actually scored a goal, and generates a result to compare with the user's answer. The result is then sent to the device and fed back to the user.

[1042] Examples:

[1043] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[1044] The device displays the results to the user, along with the accuracy rate and other statistics.

[1045] Example prompt sentences (input to generative AI models)

[1046] Generate a program to create a system that captures player movements and match information in real time, analyzes it, and provides it to spectators. Please use the following detailed specifications:

[1047] 1. The server receives real-time game video feeds and player status data via API.

[1048] 2. The server analyzes the data using an AI image analysis engine to identify player movements and then uses machine learning models to predict plays.

[1049] 3. The server generates a quiz in the form of questions based on the analysis data and sends the quiz to the user.

[1050] 4. The device displays the quiz to the user and sends the user's answers to the server.

[1051] 5. Finally, the server evaluates the match results and sends the results based on the user's answers back to the device, which then displays the results to the user.

[1052] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1053] Step 1: Data collection

[1054] The server receives real-time game video feeds and player status data from sports broadcasters via APIs, including player heart rate, distance traveled, and GPS location.

[1055] Input: Match video feed, player status data

[1056] Data processing: Synchronizing video data and status data

[1057] Output: Synchronized real-time dataset

[1058] Specific operation: The server obtains J.League match broadcast data via an API, and obtains data from each player's heart rate sensor, data tracking running distance, GPS location information, etc.

[1059] Step 2: Data analysis

[1060] The server inputs the collected data into an AI image analysis engine, which tags players' movements and determines their situations. It also uses machine learning models to analyze past match data and current match information to generate play predictions.

[1061] Input: Synchronized real-time dataset

[1062] Data processing: Image analysis and machine learning model analysis

[1063] Output: Analyzed player information and play prediction data

[1064] Specific operation: The server passes the acquired video feed through an AI image analysis engine to analyze Player A's shooting motion and positional relationship. It also inputs data from the past 10 games into a machine learning model to calculate Player A's shooting success rate.

[1065] Step 3: Quiz Generation

[1066] The server automatically generates quizzes based on the analyzed player information and play prediction data. The generated quizzes are based on real-time information.

[1067] Input: Analyzed player information, play prediction data

[1068] Data processing: Automatic generation of quizzes

[1069] Output: Auto-generated quiz

[1070] Specific behavior: The server creates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[1071] Step 4: Quiz display

[1072] The terminal displays the generated quiz and related information to the user, who can then answer the quiz.

[1073] Input: Auto-generated quiz, related information

[1074] Data processing: Display of quiz screen

[1075] Output: Quiz and related information displayed on the user's device

[1076] Specific operation: The device displays a quiz on a smartphone or tablet asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[1077] Step 5: Receiving a response

[1078] The terminal receives the user's answers to the quiz and transmits them to the server.

[1079] Input: User's quiz answer

[1080] Data processing: Sending user response data

[1081] Output: Data sent to the server

[1082] Specific operation: The user presses the "Yes" or "No" button, and the result is sent to the server via the terminal.

[1083] Step 6: View the results

[1084] The server receives the user's quiz answers, compares them with the actual game results, and returns the evaluation results to the user's device and displays them to the user.

[1085] Input: User quiz answers, actual match results

[1086] Data processing: Comparing and evaluating answers with real-world results

[1087] Output: Evaluation results

[1088] Specific operation: The server monitors the game, and when Player A scores a shot, it generates a message saying "Your prediction was correct!" and sends it to the device. The device displays this result to the user, along with the accuracy rate and other statistical information.

[1089] (Application example 1)

[1090] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1091] When watching sports, spectators want to be able to understand the progress of the game and the actions of the players in real time, allowing them to enjoy the game more deeply. However, current systems do not fully meet this requirement, and few of them include interactive elements. Furthermore, if sports betting is legalized in the future, a system that provides reliable prediction information will be necessary. There is a need for a system that can solve these issues and improve the value of the spectator experience.

[1092] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1093] In this invention, the server includes means for acquiring player movement and match information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, and means for the user terminal being a head-mounted display and interactively displaying the analysis results and quizzes, thereby enabling users to interactively enjoy player movement predictions and related quizzes in real time through the head-mounted display.

[1094] "Real-time" refers to the immediate acquisition and processing of ongoing data and information.

[1095] "Athlete actions" means all physical actions and movements made by an athlete in a sporting event.

[1096] "Match Information" includes all data related to a match, such as the progress of a sporting event, scores, and player status.

[1097] "Analyzing data" means processing acquired data using technologies such as machine learning models and AI image analysis engines to derive useful information and predictions.

[1098] "Play prediction" is the process of estimating the next actions and outcomes that a player or team is likely to take based on past data and real-time movement information.

[1099] "Automatically generating quizzes" means that a computer automatically creates quiz-style questions based on analyzed information.

[1100] "Analysis results" refers to detailed information and prediction results regarding the player's movements and plays obtained by the data analysis means.

[1101] "User terminal" means a digital device, such as a head-mounted display, through which a user receives information or provides input.

[1102] "Interactively displayed" refers to a state in which the user can react and operate in real time to the information displayed through the device.

[1103] A "head-mounted display" refers to a device that displays information when worn on the user's head.

[1104] "Receiving quiz answers" means that the system acquires the responses given by the user to the quiz.

[1105] "Displaying the results" means displaying feedback based on the user's quiz answers and the actual match results.

[1106] This invention is a system that acquires player movements and game information in real time, analyzes it, and generates player information and play predictions. It provides an effective tool for spectators to enjoy the game more, and can also accommodate future sports betting markets. Specific embodiments of the system are described below.

[1107] System configuration

[1108] Hardware Configuration

[1109] 1. Server

[1110] Use a high-performance computer with a stable network connection to receive player status data and game video feeds in real time, and the server should preferably be equipped with a GPU or TPU for data analysis.

[1111] 2. Head-Mounted Display (HMD)

[1112] It is used by spectators to interactively view information and quizzes in real time, and requires a high-resolution display and a comfortable HMD.

[1113] Software Configuration

[1114] 1. Data Collection Methods

[1115] The server receives real-time game data through a sports data API, including player positioning, movement, heart rate, distance traveled, and game progress.

[1116] 2. Data analysis methods

[1117] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow), thereby generating predictions of player movements and plays.

[1118] 3. Quiz Generation Method

[1119] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1120] 4. Quiz display method

[1121] The user's HMD visually displays the generated quiz and related analysis results, and the user can answer the quiz through the HMD.

[1122] 5. Means of receiving responses

[1123] The HMD receives the user's answers to the quiz and sends them to a server, which processes the user's responses immediately.

[1124] 6. Results display means

[1125] The server receives the user's answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[1126] Specific examples

[1127] For example, if the motion of Player A attempting to shoot in a J.League match is analyzed in real time, the server predicts that "Player A has a 75% chance of scoring on the next play." The user's HMD then displays a quiz question: "Player A's current shooting success rate is 75%. Will he score on the next play?" If the user answers "Yes," the answer is sent to the server, and the result "Your prediction was correct!" is displayed based on the actual match results.

[1128] Prompt Sentence Examples

[1129] "Design an interactive quiz platform for watching sports that predicts Player A's actions and success rate in real time and asks him if he will be successful on his next play."

[1130] This system will enable spectators to enjoy the game more deeply and, in the future, will enable the provision of reliable information that can also be used for the sports betting market.

[1131] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1132] Step 1:

[1133] The server retrieves real-time match data from a sports data API, including player positioning, movement, heart rate, distance traveled, and match progress.

[1134] Input: Real-time data from the Sports Data API

[1135] Output: Match and player status data obtained

[1136] Step 2:

[1137] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and machine learning models (e.g., TensorFlow), which generates play predictions based on players' movements and past performance data.

[1138] Input: Retrieved match and player status data

[1139] Output: Analyzed player movement information and play prediction results

[1140] Step 3:

[1141] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates a question such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1142] Input: Analyzed player movement information and play prediction results

[1143] Output: Auto-generated quiz

[1144] Step 4:

[1145] The device (user's HMD) visually displays the generated quiz and the analysis results, and the user answers the displayed quiz.

[1146] Input: Auto-generated quiz and analysis results

[1147] Output: User response interface

[1148] Step 5:

[1149] The device receives the user's answers to the quiz and sends them to the server, which processes the user's responses immediately.

[1150] Input: User's quiz answer

[1151] Output: User response data

[1152] Step 6:

[1153] The server receives the user's quiz answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[1154] Input: User response data and match progress data

[1155] Output: Result message displayed on the HMD

[1156] In this way, the invention provides a system that provides spectators with an interactive, real-time experience, enhancing the enjoyment of watching sports.

[1157] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1158] The present invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a foundation for providing safe and fair information even if the sports betting market is legalized in the future. The present invention also incorporates an "emotion engine" that recognizes user emotions and optimizes services based on them.

[1159] An embodiment of this system is described below.

[1160] System configuration

[1161] 1. Data Collection Methods

[1162] The server connects with sports media and receives real-time player status data and game video feeds, collecting data such as player positioning, heart rate, and distance traveled.

[1163] 2. Data analysis methods

[1164] The server then analyzes the received data through an AI image analysis engine and machine learning models, which identify players' movements and playing tendencies and update the database in real time.

[1165] 3. Quiz Generation Method

[1166] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Will player A score on the next play?"

[1167] 4. Quiz display method

[1168] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[1169] 5. Means of receiving responses

[1170] The terminal receives the user's answers to the quiz and transmits them to the server.

[1171] 6. Results display means

[1172] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1173] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1174] The device displays the results to the user, along with the accuracy rate and other statistics.

[1175] 7. Emotion recognition means

[1176] The server uses an emotion engine to analyze the user's feedback and emotional state when answering the quiz. It acquires and analyzes the user's facial expression data and emotion labels, and stores the results.

[1177] 8. Emotional reflection means

[1178] The server adjusts the quiz and display content based on the user's emotional state: for example, if the user is excited, it presents a more challenging quiz, and if the user is relaxed, it presents more information-oriented content.

[1179] The device displays the most appropriate quiz and analysis results for the user based on the emotional reflection information sent from the server.

[1180] 9. Statistical Analysis of Emotional Data

[1181] The server collects the emotional data provided by the user and performs statistical analysis, which allows the server to analyze the user's behavioral trends and emotional change patterns and provide more personalized services.

[1182] The server will use these results to readjust the machine learning model and reflect this in future service provision.

[1183] Specific examples

[1184] Specific examples of data collection methods

[1185] The server receives real-time video feeds from major league teams and also obtains real-time player status data (heart rate, distance traveled, etc.) from a number of sensors.

[1186] Specific examples of data analysis methods

[1187] The server processes the video feed with an AI image analysis engine to analyze player movements, for example identifying when Player B receives a pass and attempts a shot.

[1188] Examples of quiz generation methods

[1189] The server automatically generates a quiz question: "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1190] Examples of quiz display methods

[1191] The device will display the quiz along with related information such as "Player B's shooting percentage in the last 10 games: 70%." The user can select "Yes" or "No."

[1192] Specific examples of response receiving methods

[1193] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1194] Examples of results display methods

[1195] The server receives the user's answers and checks them against the actual game results, for example, to see if Player B actually scored a shot.

[1196] The device will then display the result to the user: "Your prediction was correct!"

[1197] Specific examples of emotion recognition

[1198] The server uses an emotion engine to analyze the user's facial expression data sent from the device and determine whether the user is excited or relaxed.

[1199] Examples of emotional reflection

[1200] The server senses that the user's emotional state is agitated and provides the next quiz with a slightly higher difficulty.

[1201] Based on this, the device will display new quizzes and related information to the user.

[1202] Examples of statistical analysis of emotion data

[1203] The server analyzes the collected emotional data of many users and learns emotional patterns.

[1204] Based on this, the server will optimize future service provision and improve the user experience.

[1205] As described above, this invention is a system that acquires player movements and match information in real time, analyzes it, and generates a variety of information to provide to spectators. Furthermore, by recognizing and analyzing user emotions and optimizing services based on that, it is possible to provide spectators with a richer sports viewing experience.

[1206] The processing flow will be explained below.

[1207] Step 1:

[1208] The server receives real-time game video feeds and player status data from sports-related media, including player positioning, heart rate, and distance traveled.

[1209] Step 2:

[1210] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[1211] Step 3:

[1212] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[1213] Step 4:

[1214] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[1215] Step 5:

[1216] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[1217] Step 6:

[1218] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[1219] Step 7:

[1220] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[1221] Step 8:

[1222] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[1223] Step 9:

[1224] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1225] Step 10:

[1226] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1227] Step 11:

[1228] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[1229] Step 12:

[1230] The device captures the user's facial expression data and transmits it to the server, where the facial expression data is captured in real time.

[1231] Step 13:

[1232] The server analyzes the received facial expression data using an emotion engine, which identifies the user's emotional state (excited, relaxed, etc.).

[1233] Step 14:

[1234] The server adjusts the quiz and presentation content based on the user's emotional state, for example generating more challenging quizzes if the user is excited, or providing more information-heavy content if the user is relaxed.

[1235] Step 15:

[1236] The server statistically analyzes users' emotional data and uses it to optimize services, including learning emotional patterns and analyzing trends in emotional changes.

[1237] Step 16:

[1238] The device displays tailored content sent from the server to the user, allowing the user to receive information and quizzes optimized for their emotional state.

[1239] These are the specific processing steps of the sports viewing support system that combines the emotion engine. This system allows users to enjoy the game more fully and provides a personalized experience based on their emotions.

[1240] Example 2

[1241] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1242] Conventional sports viewing systems have difficulty obtaining real-time information about players' movements and matches, limiting the information provided to spectators. As a result, there are a lack of tools that allow spectators to enjoy the match more deeply. In addition, it is difficult to provide personalized services that respond to user emotions, making it impossible to maximize spectator interest and satisfaction.

[1243] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1244] In this invention, the server includes a means for acquiring player movements and game information in real time, a means for analyzing the acquired data to generate player information and play predictions, and a means for automatically generating quizzes based on the generated prediction information. This allows spectators to enjoy the game more. The server also includes a means for displaying the generated quizzes and analysis results on a user terminal, and a means for receiving the user's quiz answers and displaying the results. Additionally, the server includes a means for recognizing and analyzing user emotion data, a means for adjusting the next quiz and display content based on the analyzed emotion data, and a means for statistically analyzing the user emotion data to optimize the system. This makes it possible to provide spectators with detailed player information and play predictions in real time, and to provide personalized quizzes and information based on the user's emotions.

[1245] "Means for acquiring player movements and match information in real time" refers to devices or programs for acquiring data such as player movements, positions, heart rates, and distance traveled during a match, as well as video feeds of the match, in real time.

[1246] "Means of analyzing acquired data and generating player information and play predictions" refers to the process of using an AI image analysis engine and machine learning models to analyze player movements and playing tendencies based on received match data, and generate play predictions.

[1247] The "means for automatically generating quizzes based on the generated prediction information" refers to a program or tool for automatically generating quizzes using analyzed player information and play prediction data.

[1248] The "means for displaying the generated quiz and analysis results on the user's terminal" refers to a user interface or communication means for displaying the quiz and analysis results automatically generated by the server on the user's terminal.

[1249] "Means for receiving the user's quiz answers and displaying the results" refers to a device or program that receives the content of the quiz answered by the user, processes and evaluates it, and displays it to the user.

[1250] "Means for recognizing and analyzing user emotional data" refers to an emotion engine or AI model that recognizes and analyzes emotions based on the user's facial expressions and behavioral data.

[1251] The "means for adjusting the next quiz or display content based on the analyzed emotional data" is a program for adjusting the difficulty level of the next quiz or display content based on the user's emotional state analyzed by the emotion engine.

[1252] "Means for statistically analyzing user emotional data and optimizing the system" refers to the process of statistically analyzing collected user emotional data and using the results to improve the overall system performance and user experience.

[1253] The present invention relates to a system that acquires player movements and game information in real time, analyzes the acquired information, and generates player information and play predictions, allowing spectators to enjoy the game more. Specific embodiments for carrying out the present invention will be described in detail below.

[1254] System configuration

[1255] 1. Data Collection Methods

[1256] The server works with sports media to obtain real-time player status data (heart rate, distance traveled, etc.) and game video feeds using APIs. As a specific example, it uses the SportRadar API to collect player data from major leagues.

[1257] 2. Data analysis methods

[1258] The server analyzes the received match data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements and playing tendencies, and updates the database in real time. For example, it identifies the movement of Player B receiving a pass and attempting a shot.

[1259] 3. Quiz Generation Method

[1260] The server uses a natural language generation tool (e.g., OpenAI GPT-3) to automatically generate questions based on the analyzed data. For example, it generates a question such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1261] 4. Quiz display method

[1262] The terminal displays the quiz and the associated analysis results sent from the server to the user, and the user views the quiz on the user interface of the terminal and selects "yes" or "no."

[1263] 5. Means of receiving responses

[1264] The terminal receives the answer given by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1265] 6. Results display means

[1266] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player B actually scored a shot.

[1267] The terminal displays the evaluation result to the user, saying "Your prediction was correct!"

[1268] 7. Emotion recognition means

[1269] The server analyzes the user's facial expression data sent from the device using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed.

[1270] 8. Emotional reflection means

[1271] The server adjusts the difficulty of the next quiz based on the results of the emotion engine, for example, if the user is excited, it will provide a slightly more difficult quiz.

[1272] The terminal displays the new quiz and related information to the user.

[1273] 9. Statistical Analysis of Emotional Data

[1274] The server statistically analyzes the collected emotion data to identify user emotion patterns, thereby improving overall system performance and user experience.

[1275] The server will optimize the system based on these results and reflect them in future service provision.

[1276] Specific examples

[1277] Specific examples of data collection methods

[1278] The server calls the SportRadar API to retrieve real-time game data (such as player heart rate, distance traveled, positioning, etc.) and live feeds from major league games.

[1279] Specific examples of data analysis methods

[1280] The server uses an AI image analysis engine (TensorFlow, OpenCV) to analyze the players' movements from the video data. It analyzes the movements of Player B when he receives the pass and attempts to shoot, and stores the results in a database.

[1281] Examples of quiz generation methods

[1282] Based on the analyzed data, the server uses OpenAI GPT-3 to generate a quiz such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1283] Examples of quiz display methods

[1284] The device displays the quiz sent from the server on the user interface and provides information such as "Player B's shooting success rate in the last 10 games: 70%." The user selects "Yes" or "No."

[1285] Specific examples of response receiving methods

[1286] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1287] Examples of results display methods

[1288] The server receives the user's answer and compares it with the actual game result. It checks whether Player B actually made the shot and sends the result "Your prediction was correct!" to the terminal and displays it to the user.

[1289] Specific examples of emotion recognition methods

[1290] The device captures the user's facial expressions with a camera and sends the facial data to a server, which then uses the Microsoft Azure Emotion API to analyze the data and determine whether the user is excited or relaxed.

[1291] Examples of emotional reflection

[1292] The server adjusts the difficulty of the next quiz based on the user's emotional state. For example, if the user is excited, the next quiz will be more difficult. The terminal displays the new quiz and related information to the user.

[1293] Examples of statistical analysis of emotion data

[1294] The server statistically analyzes the emotion data collected from a large number of users to identify emotion patterns, which will optimize future service provision and improve user experience.

[1295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1296] Step 1:

[1297] Real-time data collection

[1298] The server works with sports media and uses APIs to obtain player status data (heart rate, distance traveled, etc.) and game video feeds in real time. The input is API data provided by the sports media, and the output is the obtained player status data and game video feeds. Specifically, the server calls the SportRadar API to obtain player data from major leagues.

[1299] Step 2:

[1300] Data analysis

[1301] The server analyzes the acquired data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements, positioning, and playing tendencies. The input is a real-time game video feed and status data, and the output is analyzed player movement information and play prediction data. As a concrete example, the server identifies from the video feed the movement of Player B receiving a pass and attempting a shot, and stores that data in a database.

[1302] Step 3:

[1303] Generate a quiz

[1304] The server automatically generates quizzes using a natural language generation tool (e.g., OpenAI GPT-3) based on the analyzed player information and play prediction data. The input is the analyzed player information and play prediction data, and the output is a quiz generated in natural language. For example, a quiz may be generated that asks, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?" The server sends this generation prompt and receives the generated quiz.

[1305] Step 4:

[1306] View Quiz

[1307] The terminal displays the quiz sent from the server and the related analysis results to the user. The input is the quiz data sent from the server, and the output is the quiz and analysis results displayed on the user interface. Specifically, the terminal displays the quiz along with the information "Player B's shooting success rate in the last 10 games: 70%" and allows the user to select "Yes" or "No."

[1308] Step 5:

[1309] Receiving user responses

[1310] The terminal receives the answer selected by the user and sends it to the server. The input is the answer information selected by the user, and the output is the answer data sent to the server. For example, if the user selects "Yes," the terminal sends that data to the server. This specific operation is performed by the terminal receiving the input from the user and transferring it to the server.

[1311] Step 6:

[1312] Evaluation of answer results

[1313] The server receives the user's answers and evaluates the results by comparing them with the actual progress of the game. The input is the user's answer data and actual game data, and the output is the evaluation result. For example, the server checks whether player B actually scored a shot and evaluates the result. This evaluation result is communicated to the user in the form of a message such as "Your prediction was accurate."

[1314] Step 7:

[1315] Emotional Data Recognition

[1316] The device captures the user's facial expression with a camera and sends the data to a server. The server analyzes the received facial expression data using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed. The input is the user's facial expression data, and the output is analyzed emotional state data. As a concrete example, the device uses a camera to take a picture of the user's face and sends the data to a server.

[1317] Step 8:

[1318] Adjustments based on user emotions

[1319] The server adjusts the difficulty and content of the next quiz based on the analysis results of the emotion engine. The input is the analyzed emotion data, and the output is the adjusted quiz data. For example, if the user is excited, the server adjusts the difficulty of the next quiz to be higher. The terminal then displays the new quiz content to the user.

[1320] Step 9:

[1321] Statistical analysis of emotion data

[1322] The server statistically analyzes the collected emotional data of many users and identifies emotional patterns. The input is the collected emotional data, and the output is the statistically analyzed emotional patterns. The server uses these analysis results to optimize the system and reflect them in future service provision.

[1323] Through these steps, the system can provide detailed player information and play predictions in real time, as well as provide personalized quizzes and information based on the user's emotions.

[1324] (Application example 2)

[1325] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1326] Conventional sports viewing systems tend to make spectators watch the game monotonously, limiting the enjoyment of the experience. Furthermore, it is difficult to provide personalized content that reflects the user's emotional state. This can lead to a poor user experience and a lack of sustained interest in watching the game. Furthermore, when the sports betting market is legalized, fair and secure information provision will be required, but current systems have difficulty meeting this requirement.

[1327] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring player movements and game information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on the user terminal, means for receiving the user's quiz answers and displaying the results, means for recognizing the user's emotional state and adjusting the quiz and display content based on that state, and means for statistically analyzing the collected emotional data and reflecting it in the next service provision. This allows spectators to enjoy the game more interestingly, improves the user experience, and enables fair and safe information provision in the sports betting market.

[1328] "Means for obtaining player movements and match information in real time" refers to a method for instantly obtaining player data and match progress during a sports event.

[1329] "Means for analyzing acquired data and generating player information and play predictions" refers to a method for using acquired data to analyze player movements and game developments and predict future plays.

[1330] The "means for automatically generating quizzes based on generated prediction information" is a method for automatically creating quiz-style questions using prediction data generated by analysis.

[1331] "Means for displaying the generated quiz and analysis results on the user's terminal" refers to a method for displaying the generated quiz and analysis results on the terminal used by the user.

[1332] The "means for receiving the user's quiz answers and displaying the results" is a method for receiving the answers to the quiz answered by the user and displaying the results again on the user's terminal.

[1333] "Means for recognizing the emotional state of a user and adjusting quizzes and display content based on that state" refers to a method for recognizing the emotions of a user and providing quizzes and display content in an appropriate form according to those emotions.

[1334] "Means of statistically analyzing collected emotional data and reflecting it in the next service provision" refers to a method of statistically analyzing emotional data collected from users and improving and optimizing the content of the next service provision based on the results.

[1335] This invention is a system that acquires and analyzes the movements of athletes and game information in real time, and automatically generates and displays quizzes based on the analysis. Specific embodiments for carrying out the invention are described below.

[1336] System configuration

[1337] The system mainly consists of a server and a user terminal. Each function is as follows:

[1338] Data collection

[1339] The server obtains real-time information about player movements and match information during sporting events by receiving real-time video feeds and player status data (e.g., heart rate, distance traveled) through a remote API.

[1340] Data analysis

[1341] The server analyzes the received data using a machine learning model and an AI image analysis engine. This allows it to identify the players' movements and playing tendencies and predict future plays. For example, OpenCV is used as the AI ​​image analysis engine and TensorFlow is used as the machine learning model for this process.

[1342] Quiz Generation

[1343] The server automatically generates a quiz based on the analysis results and play predictions. For example, it generates a quiz such as "Will Player A's next shot be successful?" and sends it to the user's device.

[1344] Quiz display

[1345] The user device displays the generated quiz and related information to the user. The user answers the quiz through the device, and the answers are sent to the server. The device can be a smartphone or a head-mounted display.

[1346] Receiving responses and displaying results

[1347] The server receives the user's answer and compares it with the actual game result. For example, it checks whether Player A actually made a shot, and sends the result to the user's device as "The user's prediction was correct."

[1348] emotion recognition

[1349] The server analyzes the user's facial expression data sent from the user's device and recognizes the user's emotional state using an emotion engine (e.g., Hugging Face emotion recognition model), thereby determining whether the user is excited or relaxed.

[1350] emotional reflection

[1351] The server then tailors the quiz and content displayed based on the user's perceived emotional state, for example providing a more challenging quiz if the user is excited, or displaying more information-focused content if the user is relaxed.

[1352] Statistical analysis of emotion data

[1353] The server statistically analyzes the collected emotional data and reflects it in the next service provision. This analysis clarifies the user's behavioral trends and emotional change patterns, making it possible to provide more personalized services.

[1354] Specific examples

[1355] Below is a concrete example of how this system can be used.

[1356] Service flow

[1357] Consider a series of steps: generate a quiz about the next play of player A in a major league game ID 12345, analyze the user's emotional state, and display a quiz of appropriate difficulty.

[1358] 1. The server collects data for match ID 12345.

[1359] 2. The server analyzes the data, predicts the next play, and generates a quiz.

[1360] 3. A quiz will be displayed on the user's device: "Will Player A's next shot be successful? (Past success rate: 70%)"

[1361] 4. The user selects an answer and the data is sent back to the server.

[1362] 5. The server compares the match results and displays them to the user.

[1363] 6. At the same time, the user's facial expression data is analyzed to recognize emotions.

[1364] 7. The server adjusts the next quiz or display content depending on the user's emotional state.

[1365] In this way, spectators can enjoy the sporting event more fully, and fair and safe information provision is achieved.

[1366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1367] Step 1:

[1368] The server identifies the match ID and retrieves real-time video feeds and player status data through an external API. This process involves sending an API request to receive data such as the video feed, player heart rate, and distance traveled. The input is the match ID, and the output is the real-time video feed and player status data.

[1369] Step 2:

[1370] The server analyzes the received data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow). It extracts each frame from the video feed, analyzes the player's movements and positions, and stores the results in a database. At the same time, it analyzes player status data to predict playing tendencies and performance. The inputs are the real-time video feed and player status data, and the outputs are the analysis results and play prediction data.

[1371] Step 3:

[1372] The server automatically generates a quiz based on the analysis results and play prediction data. For example, it generates a quiz such as "Will Player A's next shot be successful?" The input is the analysis results and play prediction data, and the output is the generated quiz.

[1373] Step 4:

[1374] The server sends the generated quiz and related information to the user's device. The user's device (smartphone or head-mounted display) displays the quiz and supplementary information (e.g., past success rates and player performance data). The input is the generated quiz, and the output is the quiz displayed on the user's device.

[1375] Step 5:

[1376] The user answers the presented quiz. The user terminal receives the user's answers and sends them to the server. The input is the user's answers, and the output is the answer data sent to the server.

[1377] Step 6:

[1378] The server compares the user's answer with the actual result of the game. For example, it checks whether Player A actually scored a shot and determines whether the user's answer was correct or incorrect. The input is the user's answer and the game result data, and the output is the evaluation result.

[1379] Step 7:

[1380] The server sends the evaluation results to the user's device and displays the results and explanations. It provides feedback such as "Your prediction was correct!" The input is the evaluation results, and the output is the result display on the user's device.

[1381] Step 8:

[1382] The user device captures the user's facial expression data with a camera and sends it to the server. The input is the user's facial expression data, and the output is the data sent to the server.

[1383] Step 9:

[1384] The server analyzes the facial expression data using an emotion engine (e.g., Hugging Face emotion recognition model) to determine the user's emotional state. The input is the user's facial expression data, and the output is the recognized emotional state.

[1385] Step 10:

[1386] The server adjusts the next quiz or display content based on the recognized emotional state. For example, if the user is excited, it provides a more difficult quiz. The input is the recognized emotional state, and the output is the adjusted quiz or display content.

[1387] Step 11:

[1388] The server statistically analyzes the collected emotion data and reflects it in the next service provision. This analysis allows the server to learn user behavioral trends and emotion change patterns and improve the service. The input is the collected emotion data, and the output is the results of the statistical analysis.

[1389] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1390] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1391] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1392] [Fourth embodiment]

[1393] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1394] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1395] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1396] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1397] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1399] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1400] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1401] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1402] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1403] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1404] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1405] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1406] This invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a platform for providing safe and fair information even if the sports betting market is legalized in the future.

[1407] An embodiment of this system is described below.

[1408] System configuration

[1409] 1. Data Collection Methods

[1410] The server works in conjunction with sports media to receive real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled.

[1411] 2. Data analysis methods

[1412] The server captures real-time data and runs it through an AI image analysis engine, which tags the player's movements and situations to generate detailed player information.

[1413] The server uses machine learning models to generate play predictions based on past match data and the current match situation.

[1414] 3. Quiz Generation Method

[1415] The server automatically generates quizzes based on the analyzed player information and play prediction data, such as "Will player B score a shot on the next play?"

[1416] 4. Quiz display method

[1417] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[1418] 5. Means of receiving responses

[1419] The terminal receives the user's answers to the quiz and transmits them to the server.

[1420] 6. Results display means

[1421] The server receives the user's quiz answers, checks the results against the actual progress of the game, and sends the results back to the user's terminal.

[1422] The device displays the results received from the server to the user, displaying a message such as "Your prediction was correct!"

[1423] Specific examples

[1424] Specific examples of data collection methods

[1425] The server receives real-time video feeds from live J.League matches and also obtains player status data (heart rate, distance traveled, etc.) from the data stream.

[1426] Specific examples of data analysis methods

[1427] The server analyzes the real-time video feed with an image analysis engine to identify player movements, for example, Player A's shooting attempts, and calculates the shooting success rate.

[1428] Examples of quiz generation methods

[1429] The server automatically generates a quiz question: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[1430] Examples of quiz display methods

[1431] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[1432] Specific examples of response receiving methods

[1433] The device receives the user's answer and sends it to the server. For example, if the user selects "Yes," the information is sent to the server.

[1434] Examples of results display methods

[1435] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[1436] The device displays the results to the user, along with the accuracy rate and other statistics.

[1437] As described above, the present invention is a system that acquires player movements and game information in real time, analyzes it, and generates a variety of information to provide to spectators. This allows spectators to enjoy the game more, and when the sports betting market is opened up, they will be able to place bets based on highly reliable information.

[1438] The processing flow will be explained below.

[1439] Understood. Below I will explain the specific flow for each processing step.

[1440] Step 1:

[1441] The server receives real-time game video feeds and player status data from sports-related media, specifically collecting data such as player positioning, heart rate, and distance traveled.

[1442] Step 2:

[1443] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[1444] Step 3:

[1445] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[1446] Step 4:

[1447] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[1448] Step 5:

[1449] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[1450] Step 6:

[1451] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[1452] Step 7:

[1453] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[1454] Step 8:

[1455] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[1456] Step 9:

[1457] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1458] Step 10:

[1459] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1460] Step 11:

[1461] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[1462] Example 1

[1463] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1464] Conventional sports viewing systems have a problem in that they lack the means for spectators to perform detailed analysis or make predictions in real time. Furthermore, there has been no effective system for instantly analyzing players' movements and game information during a game, and then generating quizzes based on that information to provide to users. This has led to a need for a way to keep spectators engaged and gain a deeper understanding of the game. Furthermore, even when the sports betting market is legalized in the future, a platform is needed that allows spectators to obtain information safely and fairly.

[1465] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1466] In this invention, the server includes means for acquiring player movements and competition information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, means for the server to perform analysis and quiz generation, and means for the user terminal to display quizzes and send answers. This allows spectators to understand the details of the game movements in real time and enjoy predictions, and can provide reliable information even when the sports betting market is lifted.

[1467] "Means for acquiring player movements and competition information in real time" refers to a system for collecting player position, movement, heart rate, distance traveled, and other movement data during a match, as well as the progress of the match, in real time.

[1468] "Means for analyzing acquired data and generating player information and play predictions" refers to a system that generates detailed player information and predicts future plays based on collected player movement data and match information.

[1469] "Means for automatically generating quizzes based on generated prediction information" refers to a system that automatically generates quiz-style content based on analyzed player information and play prediction data.

[1470] "Means for displaying the generated quiz and analysis results on the user's device" refers to a system for displaying the automatically generated quiz and its related information on the device used by the user.

[1471] "Means for receiving users' quiz answers and displaying the results" refers to a system for collecting the answers users give to quizzes, analyzing the results, and providing feedback to users.

[1472] "Server" refers to the central computer system that analyzes data, automatically generates quizzes, and evaluates the results.

[1473] "User device" refers to a device, such as a smartphone or tablet, that a user uses to answer quizzes and view analysis results.

[1474] The present invention relates to a system that acquires player movements and competition information in real time, analyzes the information, and generates detailed player information and play predictions. This system provides a tool for spectators to enjoy the game more, and also serves as a platform for providing safe and fair information when the sports betting market is legalized in the future. An embodiment of this system is described below in detail.

[1475] Data collection methods

[1476] The server works with sports broadcasting agencies to obtain real-time player status data and game video feeds, including player positioning, movement, heart rate, and distance traveled. Specifically, the server obtains game broadcast data through an API and receives player GPS location information, heart rate sensor data, and distance traveled tracking data.

[1477] Examples:

[1478] The server receives real-time video feeds from live J.League matches, and also obtains player status data such as heart rate and distance traveled from the data stream.

[1479] Data Analysis Methods

[1480] The server inputs the collected data into an AI image analysis engine, which tags the player's movements and situations. It then uses a machine learning model to generate play predictions based on the analysis data and past match data. Specifically, the video feed is passed through the image analysis engine, and when, for example, Player A goes into shooting motion, that movement is detected and the shooting success rate is calculated. It also compares the shooting success rate data from the past 10 matches with the current situation to predict the probability that Player A will make a shot on the next play.

[1481] Examples:

[1482] The server analyzes the real-time video feed with an image analysis engine to identify the player's movements. It analyzes the movement of Player A when he attempts a shot and calculates the shooting success rate.

[1483] Quiz generation method

[1484] The server automatically generates a quiz based on the analyzed player information and play prediction data. Specifically, it generates a quiz such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1485] Examples:

[1486] The server generates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[1487] Quiz display method

[1488] The device displays the generated quiz and related analysis results to the user. The user answers the quiz and can check the results on the device. Specifically, a quiz screen is displayed on the user's smartphone or tablet, asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[1489] Examples:

[1490] The device will display the quiz along with related information to the user, such as "Shooting percentage in last 10 games: 75%." The user answers the quiz with a "yes" or "no" answer.

[1491] Response receiving method

[1492] The device receives the user's answers to the quiz and sends them to the server. Specifically, when the user presses the "Yes" or "No" button, the device collects the user's selection and sends it to the server.

[1493] Examples:

[1494] The device receives the user's answer and sends it to the server. If the user selects "yes," the information is sent to the server.

[1495] Results display means

[1496] The server receives the user's quiz answers, compares them with the actual game results, and returns the results to the user's device. Specifically, the server monitors the progress of the game, checks whether Player A actually scored a goal, and generates a result to compare with the user's answer. The result is then sent to the device and fed back to the user.

[1497] Examples:

[1498] The server receives the user's answer and evaluates it by comparing it with the actual game results. If Player A scores a shot, the server sends a message to the device saying, "Your prediction was correct!"

[1499] The device displays the results to the user, along with the accuracy rate and other statistics.

[1500] Example prompt sentences (input to generative AI models)

[1501] Generate a program to create a system that captures player movements and match information in real time, analyzes it, and provides it to spectators. Please use the following detailed specifications:

[1502] 1. The server receives real-time game video feeds and player status data via API.

[1503] 2. The server analyzes the data using an AI image analysis engine to identify player movements and then uses machine learning models to predict plays.

[1504] 3. The server generates a quiz in the form of questions based on the analysis data and sends the quiz to the user.

[1505] 4. The device displays the quiz to the user and sends the user's answers to the server.

[1506] 5. Finally, the server evaluates the match results and sends the results based on the user's answers back to the device, which then displays the results to the user.

[1507] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1508] Step 1: Data collection

[1509] The server receives real-time game video feeds and player status data from sports broadcasters via APIs, including player heart rate, distance traveled, and GPS location.

[1510] Input: Match video feed, player status data

[1511] Data processing: Synchronizing video data and status data

[1512] Output: Synchronized real-time dataset

[1513] Specific operation: The server obtains J.League match broadcast data via an API, and obtains data from each player's heart rate sensor, data tracking running distance, GPS location information, etc.

[1514] Step 2: Data analysis

[1515] The server inputs the collected data into an AI image analysis engine, which tags players' movements and determines their situations. It also uses machine learning models to analyze past match data and current match information to generate play predictions.

[1516] Input: Synchronized real-time dataset

[1517] Data processing: Image analysis and machine learning model analysis

[1518] Output: Analyzed player information and play prediction data

[1519] Specific operation: The server passes the acquired video feed through an AI image analysis engine to analyze Player A's shooting motion and positional relationship. It also inputs data from the past 10 games into a machine learning model to calculate Player A's shooting success rate.

[1520] Step 3: Quiz Generation

[1521] The server automatically generates quizzes based on the analyzed player information and play prediction data. The generated quizzes are based on real-time information.

[1522] Input: Analyzed player information, play prediction data

[1523] Data processing: Automatic generation of quizzes

[1524] Output: Auto-generated quiz

[1525] Specific behavior: The server creates a quiz: "Player A's current shooting percentage is 75%. Will he make a shot on the next play?"

[1526] Step 4: Quiz display

[1527] The terminal displays the generated quiz and related information to the user, who can then answer the quiz.

[1528] Input: Auto-generated quiz, related information

[1529] Data processing: Display of quiz screen

[1530] Output: Quiz and related information displayed on the user's device

[1531] Specific operation: The device displays a quiz on a smartphone or tablet asking, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?" along with the options "Yes" or "No."

[1532] Step 5: Receiving a response

[1533] The terminal receives the user's answers to the quiz and transmits them to the server.

[1534] Input: User's quiz answer

[1535] Data processing: Sending user response data

[1536] Output: Data sent to the server

[1537] Specific operation: The user presses the "Yes" or "No" button, and the result is sent to the server via the terminal.

[1538] Step 6: View the results

[1539] The server receives the user's quiz answers, compares them with the actual game results, and returns the evaluation results to the user's device and displays them to the user.

[1540] Input: User quiz answers, actual match results

[1541] Data processing: Comparing and evaluating answers with real-world results

[1542] Output: Evaluation results

[1543] Specific operation: The server monitors the game, and when Player A scores a shot, it generates a message saying "Your prediction was correct!" and sends it to the device. The device displays this result to the user, along with the accuracy rate and other statistical information.

[1544] (Application example 1)

[1545] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1546] When watching sports, spectators want to be able to understand the progress of the game and the actions of the players in real time, allowing them to enjoy the game more deeply. However, current systems do not fully meet this requirement, and few of them include interactive elements. Furthermore, if sports betting is legalized in the future, a system that provides reliable prediction information will be necessary. There is a need for a system that can solve these issues and improve the value of the spectator experience.

[1547] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1548] In this invention, the server includes means for acquiring player movement and match information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on a user terminal, means for receiving users' quiz answers and displaying the results, and means for the user terminal being a head-mounted display and interactively displaying the analysis results and quizzes, thereby enabling users to interactively enjoy player movement predictions and related quizzes in real time through the head-mounted display.

[1549] "Real-time" refers to the immediate acquisition and processing of ongoing data and information.

[1550] "Athlete actions" means all physical actions and movements made by an athlete in a sporting event.

[1551] "Match Information" includes all data related to a match, such as the progress of a sporting event, scores, and player status.

[1552] "Analyzing data" means processing acquired data using technologies such as machine learning models and AI image analysis engines to derive useful information and predictions.

[1553] "Play prediction" is the process of estimating the next actions and outcomes that a player or team is likely to take based on past data and real-time movement information.

[1554] "Automatically generating quizzes" means that a computer automatically creates quiz-style questions based on analyzed information.

[1555] "Analysis results" refers to detailed information and prediction results regarding the player's movements and plays obtained by the data analysis means.

[1556] "User terminal" means a digital device, such as a head-mounted display, through which a user receives information or provides input.

[1557] "Interactively displayed" refers to a state in which the user can react and operate in real time to the information displayed through the device.

[1558] A "head-mounted display" refers to a device that displays information when worn on the user's head.

[1559] "Receiving quiz answers" means that the system acquires the responses given by the user to the quiz.

[1560] "Displaying the results" means displaying feedback based on the user's quiz answers and the actual match results.

[1561] This invention is a system that acquires player movements and game information in real time, analyzes it, and generates player information and play predictions. It provides an effective tool for spectators to enjoy the game more, and can also accommodate future sports betting markets. Specific embodiments of the system are described below.

[1562] System configuration

[1563] Hardware Configuration

[1564] 1. Server

[1565] Use a high-performance computer with a stable network connection to receive player status data and game video feeds in real time, and the server should preferably be equipped with a GPU or TPU for data analysis.

[1566] 2. Head-Mounted Display (HMD)

[1567] It is used by spectators to interactively view information and quizzes in real time, and requires a high-resolution display and a comfortable HMD.

[1568] Software Configuration

[1569] 1. Data Collection Methods

[1570] The server receives real-time game data through a sports data API, including player positioning, movement, heart rate, distance traveled, and game progress.

[1571] 2. Data analysis methods

[1572] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow), thereby generating predictions of player movements and plays.

[1573] 3. Quiz Generation Method

[1574] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1575] 4. Quiz display method

[1576] The user's HMD visually displays the generated quiz and related analysis results, and the user can answer the quiz through the HMD.

[1577] 5. Means of receiving responses

[1578] The HMD receives the user's answers to the quiz and sends them to a server, which processes the user's responses immediately.

[1579] 6. Results display means

[1580] The server receives the user's answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[1581] Specific examples

[1582] For example, if the motion of Player A attempting to shoot in a J.League match is analyzed in real time, the server predicts that "Player A has a 75% chance of scoring on the next play." The user's HMD then displays a quiz question: "Player A's current shooting success rate is 75%. Will he score on the next play?" If the user answers "Yes," the answer is sent to the server, and the result "Your prediction was correct!" is displayed based on the actual match results.

[1583] Prompt Sentence Examples

[1584] "Design an interactive quiz platform for watching sports that predicts Player A's actions and success rate in real time and asks him if he will be successful on his next play."

[1585] This system will enable spectators to enjoy the game more deeply and, in the future, will enable the provision of reliable information that can also be used for the sports betting market.

[1586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1587] Step 1:

[1588] The server retrieves real-time match data from a sports data API, including player positioning, movement, heart rate, distance traveled, and match progress.

[1589] Input: Real-time data from the Sports Data API

[1590] Output: Match and player status data obtained

[1591] Step 2:

[1592] The server analyzes the acquired real-time data using an AI image analysis engine (e.g., OpenCV) and machine learning models (e.g., TensorFlow), which generates play predictions based on players' movements and past performance data.

[1593] Input: Retrieved match and player status data

[1594] Output: Analyzed player movement information and play prediction results

[1595] Step 3:

[1596] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates a question such as, "Player A's current shooting success rate is 75%. Will he make a shot on the next play?"

[1597] Input: Analyzed player movement information and play prediction results

[1598] Output: Auto-generated quiz

[1599] Step 4:

[1600] The device (user's HMD) visually displays the generated quiz and the analysis results, and the user answers the displayed quiz.

[1601] Input: Auto-generated quiz and analysis results

[1602] Output: User response interface

[1603] Step 5:

[1604] The device receives the user's answers to the quiz and sends them to the server, which processes the user's responses immediately.

[1605] Input: User's quiz answer

[1606] Output: User response data

[1607] Step 6:

[1608] The server receives the user's quiz answers, checks the results against the actual game progress, and sends the results back to the HMD, displaying a message such as "Your prediction was correct!"

[1609] Input: User response data and match progress data

[1610] Output: Result message displayed on the HMD

[1611] In this way, the invention provides a system that provides spectators with an interactive, real-time experience, enhancing the enjoyment of watching sports.

[1612] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1613] The present invention relates to a system that acquires player movements and game information in real time, analyzes the information, and generates player information and play predictions. This system is particularly targeted at sports spectators from beginners to advanced players, and provides a tool to help spectators enjoy the game more. It will also function as a foundation for providing safe and fair information even if the sports betting market is legalized in the future. The present invention also incorporates an "emotion engine" that recognizes user emotions and optimizes services based on them.

[1614] An embodiment of this system is described below.

[1615] System configuration

[1616] 1. Data Collection Methods

[1617] The server connects with sports media and receives real-time player status data and game video feeds, collecting data such as player positioning, heart rate, and distance traveled.

[1618] 2. Data analysis methods

[1619] The server then analyzes the received data through an AI image analysis engine and machine learning models, which identify players' movements and playing tendencies and update the database in real time.

[1620] 3. Quiz Generation Method

[1621] The server automatically generates quizzes based on the analyzed player information and play prediction data. For example, it creates questions such as, "Will player A score on the next play?"

[1622] 4. Quiz display method

[1623] The device displays the generated quiz and related analysis results to the user, who can then answer the quiz and view the results on the device.

[1624] 5. Means of receiving responses

[1625] The terminal receives the user's answers to the quiz and transmits them to the server.

[1626] 6. Results display means

[1627] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1628] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1629] The device displays the results to the user, along with the accuracy rate and other statistics.

[1630] 7. Emotion recognition means

[1631] The server uses an emotion engine to analyze the user's feedback and emotional state when answering the quiz. It acquires and analyzes the user's facial expression data and emotion labels, and stores the results.

[1632] 8. Emotional reflection means

[1633] The server adjusts the quiz and display content based on the user's emotional state: for example, if the user is excited, it presents a more challenging quiz, and if the user is relaxed, it presents more information-oriented content.

[1634] The device displays the most appropriate quiz and analysis results for the user based on the emotional reflection information sent from the server.

[1635] 9. Statistical Analysis of Emotional Data

[1636] The server collects the emotional data provided by the user and performs statistical analysis, which allows the server to analyze the user's behavioral trends and emotional change patterns and provide more personalized services.

[1637] The server will use these results to readjust the machine learning model and reflect this in future service provision.

[1638] Specific examples

[1639] Specific examples of data collection methods

[1640] The server receives real-time video feeds from major league teams and also obtains real-time player status data (heart rate, distance traveled, etc.) from a number of sensors.

[1641] Specific examples of data analysis methods

[1642] The server processes the video feed with an AI image analysis engine to analyze player movements, for example identifying when Player B receives a pass and attempts a shot.

[1643] Examples of quiz generation methods

[1644] The server automatically generates a quiz question: "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1645] Examples of quiz display methods

[1646] The device will display the quiz along with related information such as "Player B's shooting percentage in the last 10 games: 70%." The user can select "Yes" or "No."

[1647] Specific examples of response receiving methods

[1648] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1649] Examples of results display methods

[1650] The server receives the user's answers and checks them against the actual game results, for example, to see if Player B actually scored a shot.

[1651] The device will then display the result to the user: "Your prediction was correct!"

[1652] Specific examples of emotion recognition

[1653] The server uses an emotion engine to analyze the user's facial expression data sent from the device and determine whether the user is excited or relaxed.

[1654] Examples of emotional reflection

[1655] The server senses that the user's emotional state is agitated and provides the next quiz with a slightly higher difficulty.

[1656] Based on this, the device will display new quizzes and related information to the user.

[1657] Examples of statistical analysis of emotion data

[1658] The server analyzes the collected emotional data of many users and learns emotional patterns.

[1659] Based on this, the server will optimize future service provision and improve the user experience.

[1660] As described above, this invention is a system that acquires player movements and match information in real time, analyzes it, and generates a variety of information to provide to spectators. Furthermore, by recognizing and analyzing user emotions and optimizing services based on that, it is possible to provide spectators with a richer sports viewing experience.

[1661] The processing flow will be explained below.

[1662] Step 1:

[1663] The server receives real-time game video feeds and player status data from sports-related media, including player positioning, heart rate, and distance traveled.

[1664] Step 2:

[1665] The server then passes the received video feed to an AI image analysis engine that analyzes the players' movements, identifying and tagging their positions, movements, and actions such as shots and passes.

[1666] Step 3:

[1667] The server uses the tagged data to update a database with real-time player information, including performance metrics for each player (shooting success rate, passing success rate, etc.).

[1668] Step 4:

[1669] The server then uses the updated data to apply machine learning models to generate predictions about the next play, such as which player will have the ball and attempt a shot next.

[1670] Step 5:

[1671] The server automatically creates quizzes based on the generated play predictions, such as "Will player A score on the next play?"

[1672] Step 6:

[1673] The server then sends the generated quiz to the user's device, which also includes relevant player information and data on playing habits.

[1674] Step 7:

[1675] The device displays the quiz and related information to the user, who can answer the quiz with "yes" or "no."

[1676] Step 8:

[1677] The terminal receives the answers to the quiz entered by the user and transmits the data to the server.

[1678] Step 9:

[1679] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player A actually scored a shot.

[1680] Step 10:

[1681] The server sends the evaluation results to the user's device, including feedback such as "Your prediction was correct!"

[1682] Step 11:

[1683] The device displays the results to the user, allowing the user to see the results of their quiz answers and related statistics.

[1684] Step 12:

[1685] The device captures the user's facial expression data and transmits it to the server, where the facial expression data is captured in real time.

[1686] Step 13:

[1687] The server analyzes the received facial expression data using an emotion engine, which identifies the user's emotional state (excited, relaxed, etc.).

[1688] Step 14:

[1689] The server adjusts the quiz and presentation content based on the user's emotional state, for example generating more challenging quizzes if the user is excited, or providing more information-heavy content if the user is relaxed.

[1690] Step 15:

[1691] The server statistically analyzes users' emotional data and uses it to optimize services, including learning emotional patterns and analyzing trends in emotional changes.

[1692] Step 16:

[1693] The device displays tailored content sent from the server to the user, allowing the user to receive information and quizzes optimized for their emotional state.

[1694] These are the specific processing steps of the sports viewing support system that combines the emotion engine. This system allows users to enjoy the game more fully and provides a personalized experience based on their emotions.

[1695] Example 2

[1696] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1697] Conventional sports viewing systems have difficulty obtaining real-time information about players' movements and matches, limiting the information provided to spectators. As a result, there are a lack of tools that allow spectators to enjoy the match more deeply. In addition, it is difficult to provide personalized services that respond to user emotions, making it impossible to maximize spectator interest and satisfaction.

[1698] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1699] In this invention, the server includes a means for acquiring player movements and game information in real time, a means for analyzing the acquired data to generate player information and play predictions, and a means for automatically generating quizzes based on the generated prediction information. This allows spectators to enjoy the game more. The server also includes a means for displaying the generated quizzes and analysis results on a user terminal, and a means for receiving the user's quiz answers and displaying the results. Additionally, the server includes a means for recognizing and analyzing user emotion data, a means for adjusting the next quiz and display content based on the analyzed emotion data, and a means for statistically analyzing the user emotion data to optimize the system. This makes it possible to provide spectators with detailed player information and play predictions in real time, and to provide personalized quizzes and information based on the user's emotions.

[1700] "Means for acquiring player movements and match information in real time" refers to devices or programs for acquiring data such as player movements, positions, heart rates, and distance traveled during a match, as well as video feeds of the match, in real time.

[1701] "Means of analyzing acquired data and generating player information and play predictions" refers to the process of using an AI image analysis engine and machine learning models to analyze player movements and playing tendencies based on received match data, and generate play predictions.

[1702] The "means for automatically generating quizzes based on the generated prediction information" refers to a program or tool for automatically generating quizzes using analyzed player information and play prediction data.

[1703] The "means for displaying the generated quiz and analysis results on the user's terminal" refers to a user interface or communication means for displaying the quiz and analysis results automatically generated by the server on the user's terminal.

[1704] "Means for receiving the user's quiz answers and displaying the results" refers to a device or program that receives the content of the quiz answered by the user, processes and evaluates it, and displays it to the user.

[1705] "Means for recognizing and analyzing user emotional data" refers to an emotion engine or AI model that recognizes and analyzes emotions based on the user's facial expressions and behavioral data.

[1706] The "means for adjusting the next quiz or display content based on the analyzed emotional data" is a program for adjusting the difficulty level of the next quiz or display content based on the user's emotional state analyzed by the emotion engine.

[1707] "Means for statistically analyzing user emotional data and optimizing the system" refers to the process of statistically analyzing collected user emotional data and using the results to improve the overall system performance and user experience.

[1708] The present invention relates to a system that acquires player movements and game information in real time, analyzes the acquired information, and generates player information and play predictions, allowing spectators to enjoy the game more. Specific embodiments for carrying out the present invention will be described in detail below.

[1709] System configuration

[1710] 1. Data Collection Methods

[1711] The server works with sports media to obtain real-time player status data (heart rate, distance traveled, etc.) and game video feeds using APIs. As a specific example, it uses the SportRadar API to collect player data from major leagues.

[1712] 2. Data analysis methods

[1713] The server analyzes the received match data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements and playing tendencies, and updates the database in real time. For example, it identifies the movement of Player B receiving a pass and attempting a shot.

[1714] 3. Quiz Generation Method

[1715] The server uses a natural language generation tool (e.g., OpenAI GPT-3) to automatically generate questions based on the analyzed data. For example, it generates a question such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1716] 4. Quiz display method

[1717] The terminal displays the quiz and the associated analysis results sent from the server to the user, and the user views the quiz on the user interface of the terminal and selects "yes" or "no."

[1718] 5. Means of receiving responses

[1719] The terminal receives the answer given by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1720] 6. Results display means

[1721] The server receives the user's answers and evaluates the results against the actual game progress, for example, checking whether Player B actually scored a shot.

[1722] The terminal displays the evaluation result to the user, saying "Your prediction was correct!"

[1723] 7. Emotion recognition means

[1724] The server analyzes the user's facial expression data sent from the device using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed.

[1725] 8. Emotional reflection means

[1726] The server adjusts the difficulty of the next quiz based on the results of the emotion engine, for example, if the user is excited, it will provide a slightly more difficult quiz.

[1727] The terminal displays the new quiz and related information to the user.

[1728] 9. Statistical Analysis of Emotional Data

[1729] The server statistically analyzes the collected emotion data to identify user emotion patterns, thereby improving overall system performance and user experience.

[1730] The server will optimize the system based on these results and reflect them in future service provision.

[1731] Specific examples

[1732] Specific examples of data collection methods

[1733] The server calls the SportRadar API to retrieve real-time game data (such as player heart rate, distance traveled, positioning, etc.) and live feeds from major league games.

[1734] Specific examples of data analysis methods

[1735] The server uses an AI image analysis engine (TensorFlow, OpenCV) to analyze the players' movements from the video data. It analyzes the movements of Player B when he receives the pass and attempts to shoot, and stores the results in a database.

[1736] Examples of quiz generation methods

[1737] Based on the analyzed data, the server uses OpenAI GPT-3 to generate a quiz such as, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?"

[1738] Examples of quiz display methods

[1739] The device displays the quiz sent from the server on the user interface and provides information such as "Player B's shooting success rate in the last 10 games: 70%." The user selects "Yes" or "No."

[1740] Specific examples of response receiving methods

[1741] The terminal receives the answer selected by the user and transmits it to the server. For example, if the user selects "Yes," the terminal transmits the data to the server.

[1742] Examples of results display methods

[1743] The server receives the user's answer and compares it with the actual game result. It checks whether Player B actually made the shot and sends the result "Your prediction was correct!" to the terminal and displays it to the user.

[1744] Specific examples of emotion recognition methods

[1745] The device captures the user's facial expressions with a camera and sends the facial data to a server, which then uses the Microsoft Azure Emotion API to analyze the data and determine whether the user is excited or relaxed.

[1746] Examples of emotional reflection

[1747] The server adjusts the difficulty of the next quiz based on the user's emotional state. For example, if the user is excited, the next quiz will be more difficult. The terminal displays the new quiz and related information to the user.

[1748] Examples of statistical analysis of emotion data

[1749] The server statistically analyzes the emotion data collected from a large number of users to identify emotion patterns, which will optimize future service provision and improve user experience.

[1750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1751] Step 1:

[1752] Real-time data collection

[1753] The server works with sports media and uses APIs to obtain player status data (heart rate, distance traveled, etc.) and game video feeds in real time. The input is API data provided by the sports media, and the output is the obtained player status data and game video feeds. Specifically, the server calls the SportRadar API to obtain player data from major leagues.

[1754] Step 2:

[1755] Data analysis

[1756] The server analyzes the acquired data using an AI image analysis engine (e.g., TensorFlow, OpenCV) and machine learning models. This analysis identifies players' movements, positioning, and playing tendencies. The input is a real-time game video feed and status data, and the output is analyzed player movement information and play prediction data. As a concrete example, the server identifies from the video feed the movement of Player B receiving a pass and attempting a shot, and stores that data in a database.

[1757] Step 3:

[1758] Generate a quiz

[1759] The server automatically generates quizzes using a natural language generation tool (e.g., OpenAI GPT-3) based on the analyzed player information and play prediction data. The input is the analyzed player information and play prediction data, and the output is a quiz generated in natural language. For example, a quiz may be generated that asks, "Player B's shooting success rate in the past 10 games is 70%. Will his next shot be successful?" The server sends this generation prompt and receives the generated quiz.

[1760] Step 4:

[1761] View Quiz

[1762] The terminal displays the quiz sent from the server and the related analysis results to the user. The input is the quiz data sent from the server, and the output is the quiz and analysis results displayed on the user interface. Specifically, the terminal displays the quiz along with the information "Player B's shooting success rate in the last 10 games: 70%" and allows the user to select "Yes" or "No."

[1763] Step 5:

[1764] Receiving user responses

[1765] The terminal receives the answer selected by the user and sends it to the server. The input is the answer information selected by the user, and the output is the answer data sent to the server. For example, if the user selects "Yes," the terminal sends that data to the server. This specific operation is performed by the terminal receiving the input from the user and transferring it to the server.

[1766] Step 6:

[1767] Evaluation of answer results

[1768] The server receives the user's answers and evaluates the results by comparing them with the actual progress of the game. The input is the user's answer data and actual game data, and the output is the evaluation result. For example, the server checks whether player B actually scored a shot and evaluates the result. This evaluation result is communicated to the user in the form of a message such as "Your prediction was accurate."

[1769] Step 7:

[1770] Emotional Data Recognition

[1771] The device captures the user's facial expression with a camera and sends the data to a server. The server analyzes the received facial expression data using an emotion engine (e.g., Microsoft Azure's Emotion API) to determine whether the user is excited or relaxed. The input is the user's facial expression data, and the output is analyzed emotional state data. As a concrete example, the device uses a camera to take a picture of the user's face and sends the data to a server.

[1772] Step 8:

[1773] Adjustments based on user emotions

[1774] The server adjusts the difficulty and content of the next quiz based on the analysis results of the emotion engine. The input is the analyzed emotion data, and the output is the adjusted quiz data. For example, if the user is excited, the server adjusts the difficulty of the next quiz to be higher. The terminal then displays the new quiz content to the user.

[1775] Step 9:

[1776] Statistical analysis of emotion data

[1777] The server statistically analyzes the collected emotional data of many users and identifies emotional patterns. The input is the collected emotional data, and the output is the statistically analyzed emotional patterns. The server uses these analysis results to optimize the system and reflect them in future service provision.

[1778] Through these steps, the system can provide detailed player information and play predictions in real time, as well as provide personalized quizzes and information based on the user's emotions.

[1779] (Application example 2)

[1780] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1781] Conventional sports viewing systems tend to make spectators watch the game monotonously, limiting the enjoyment of the experience. Furthermore, it is difficult to provide personalized content that reflects the user's emotional state. This can lead to a poor user experience and a lack of sustained interest in watching the game. Furthermore, when the sports betting market is legalized, fair and secure information provision will be required, but current systems have difficulty meeting this requirement.

[1782] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring player movements and game information in real time, means for analyzing the acquired data and generating player information and play predictions, means for automatically generating quizzes based on the generated prediction information, means for displaying the generated quizzes and analysis results on the user terminal, means for receiving the user's quiz answers and displaying the results, means for recognizing the user's emotional state and adjusting the quiz and display content based on that state, and means for statistically analyzing the collected emotional data and reflecting it in the next service provision. This allows spectators to enjoy the game more interestingly, improves the user experience, and enables fair and safe information provision in the sports betting market.

[1783] "Means for obtaining player movements and match information in real time" refers to a method for instantly obtaining player data and match progress during a sports event.

[1784] "Means for analyzing acquired data and generating player information and play predictions" refers to a method for using acquired data to analyze player movements and game developments and predict future plays.

[1785] The "means for automatically generating quizzes based on generated prediction information" is a method for automatically creating quiz-style questions using prediction data generated by analysis.

[1786] "Means for displaying the generated quiz and analysis results on the user's terminal" refers to a method for displaying the generated quiz and analysis results on the terminal used by the user.

[1787] The "means for receiving the user's quiz answers and displaying the results" is a method for receiving the answers to the quiz answered by the user and displaying the results again on the user's terminal.

[1788] "Means for recognizing the emotional state of a user and adjusting quizzes and display content based on that state" refers to a method for recognizing the emotions of a user and providing quizzes and display content in an appropriate form according to those emotions.

[1789] "Means of statistically analyzing collected emotional data and reflecting it in the next service provision" refers to a method of statistically analyzing emotional data collected from users and improving and optimizing the content of the next service provision based on the results.

[1790] This invention is a system that acquires and analyzes the movements of athletes and game information in real time, and automatically generates and displays quizzes based on the analysis. Specific embodiments for carrying out the invention are described below.

[1791] System configuration

[1792] The system mainly consists of a server and a user terminal. Each function is as follows:

[1793] Data collection

[1794] The server obtains real-time information about player movements and match information during sporting events by receiving real-time video feeds and player status data (e.g., heart rate, distance traveled) through a remote API.

[1795] Data analysis

[1796] The server analyzes the received data using a machine learning model and an AI image analysis engine. This allows it to identify the players' movements and playing tendencies and predict future plays. For example, OpenCV is used as the AI ​​image analysis engine and TensorFlow is used as the machine learning model for this process.

[1797] Quiz Generation

[1798] The server automatically generates a quiz based on the analysis results and play predictions. For example, it generates a quiz such as "Will Player A's next shot be successful?" and sends it to the user's device.

[1799] Quiz display

[1800] The user device displays the generated quiz and related information to the user. The user answers the quiz through the device, and the answers are sent to the server. The device can be a smartphone or a head-mounted display.

[1801] Receiving responses and displaying results

[1802] The server receives the user's answer and compares it with the actual game result. For example, it checks whether Player A actually made a shot, and sends the result to the user's device as "The user's prediction was correct."

[1803] emotion recognition

[1804] The server analyzes the user's facial expression data sent from the user's device and recognizes the user's emotional state using an emotion engine (e.g., Hugging Face emotion recognition model), thereby determining whether the user is excited or relaxed.

[1805] emotional reflection

[1806] The server then tailors the quiz and content displayed based on the user's perceived emotional state, for example providing a more challenging quiz if the user is excited, or displaying more information-focused content if the user is relaxed.

[1807] Statistical analysis of emotion data

[1808] The server statistically analyzes the collected emotional data and reflects it in the next service provision. This analysis clarifies the user's behavioral trends and emotional change patterns, making it possible to provide more personalized services.

[1809] Specific examples

[1810] Below is a concrete example of how this system can be used.

[1811] Service flow

[1812] Consider a series of steps: generate a quiz about the next play of player A in a major league game ID 12345, analyze the user's emotional state, and display a quiz of appropriate difficulty.

[1813] 1. The server collects data for match ID 12345.

[1814] 2. The server analyzes the data, predicts the next play, and generates a quiz.

[1815] 3. A quiz will be displayed on the user's device: "Will Player A's next shot be successful? (Past success rate: 70%)"

[1816] 4. The user selects an answer and the data is sent back to the server.

[1817] 5. The server compares the match results and displays them to the user.

[1818] 6. At the same time, the user's facial expression data is analyzed to recognize emotions.

[1819] 7. The server adjusts the next quiz or display content depending on the user's emotional state.

[1820] In this way, spectators can enjoy the sporting event more fully, and fair and safe information provision is achieved.

[1821] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1822] Step 1:

[1823] The server identifies the match ID and retrieves real-time video feeds and player status data through an external API. This process involves sending an API request to receive data such as the video feed, player heart rate, and distance traveled. The input is the match ID, and the output is the real-time video feed and player status data.

[1824] Step 2:

[1825] The server analyzes the received data using an AI image analysis engine (e.g., OpenCV) and a machine learning model (e.g., TensorFlow). It extracts each frame from the video feed, analyzes the player's movements and positions, and stores the results in a database. At the same time, it analyzes player status data to predict playing tendencies and performance. The inputs are the real-time video feed and player status data, and the outputs are the analysis results and play prediction data.

[1826] Step 3:

[1827] The server automatically generates a quiz based on the analysis results and play prediction data. For example, it generates a quiz such as "Will Player A's next shot be successful?" The input is the analysis results and play prediction data, and the output is the generated quiz.

[1828] Step 4:

[1829] The server sends the generated quiz and related information to the user's device. The user's device (smartphone or head-mounted display) displays the quiz and supplementary information (e.g., past success rates and player performance data). The input is the generated quiz, and the output is the quiz displayed on the user's device.

[1830] Step 5:

[1831] The user answers the presented quiz. The user terminal receives the user's answers and sends them to the server. The input is the user's answers, and the output is the answer data sent to the server.

[1832] Step 6:

[1833] The server compares the user's answer with the actual result of the game. For example, it checks whether Player A actually scored a shot and determines whether the user's answer was correct or incorrect. The input is the user's answer and the game result data, and the output is the evaluation result.

[1834] Step 7:

[1835] The server sends the evaluation results to the user's device and displays the results and explanations. It provides feedback such as "Your prediction was correct!" The input is the evaluation results, and the output is the result display on the user's device.

[1836] Step 8:

[1837] The user device captures the user's facial expression data with a camera and sends it to the server. The input is the user's facial expression data, and the output is the data sent to the server.

[1838] Step 9:

[1839] The server analyzes the facial expression data using an emotion engine (e.g., Hugging Face emotion recognition model) to determine the user's emotional state. The input is the user's facial expression data, and the output is the recognized emotional state.

[1840] Step 10:

[1841] The server adjusts the next quiz or display content based on the recognized emotional state. For example, if the user is excited, it provides a more difficult quiz. The input is the recognized emotional state, and the output is the adjusted quiz or display content.

[1842] Step 11:

[1843] The server statistically analyzes the collected emotion data and reflects it in the next service provision. This analysis allows the server to learn user behavioral trends and emotion change patterns and improve the service. The input is the collected emotion data, and the output is the results of the statistical analysis.

[1844] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1845] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1846] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1847] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1848] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1849] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1850] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1851] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1852] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1853] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1854] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1855] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1856] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1857] 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.

[1858] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1859] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1860] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1861] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1862] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1863] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1864] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1865] The following is further disclosed regarding the above embodiment.

[1866] Understood. The draft claims are as follows:

[1867] (Claim 1)

[1868] A means of obtaining player movements and match information in real time,

[1869] A means of analyzing the acquired data and generating player information and play predictions;

[1870] A means for automatically generating a quiz based on the generated prediction information;

[1871] a means for displaying the generated quiz and analysis results on a user device;

[1872] means for receiving the user's quiz responses and displaying the results;

[1873] A system including:

[1874] (Claim 2)

[1875] 10. The system of claim 1, wherein the means for obtaining real-time player action and game information receives real-time video feeds and player status data.

[1876] (Claim 3)

[1877] The system of claim 1, wherein the means for analyzing the acquired data and generating player information and play predictions uses a machine learning model and an AI image analysis engine.

[1878] (Claim 4)

[1879] 2. The system according to claim 1, wherein the means for automatically generating a quiz based on the generated prediction information creates a question about the next play.

[1880] (Claim 5)

[1881] 2. The system according to claim 1, wherein the means for displaying the generated quiz and analysis results on the user terminal constructs and displays a user interface.

[1882] "Example 1"

[1883] (Claim 1)

[1884] A means of obtaining information on athletes' movements and competitions in real time;

[1885] A means of analyzing the acquired data and generating player information and play predictions;

[1886] A means for automatically generating a quiz based on the generated prediction information;

[1887] a means for displaying the generated quiz and analysis results on a user device;

[1888] means for receiving the user's quiz responses and displaying the results;

[1889] a means for the server to perform the analysis and quiz generation;

[1890] A means for the user terminal to display the quiz and send the answers;

[1891] A system including:

[1892] (Claim 2)

[1893] 10. The system of claim 1, wherein the means for obtaining real-time player movement and competition information receives real-time video feeds and player status data.

[1894] (Claim 3)

[1895] The system of claim 1, wherein the means for analyzing the acquired data and generating player information and play predictions uses a machine learning model and an AI image analysis engine.

[1896] "Application Example 1"

[1897] (Claim 1)

[1898] A means of obtaining player movements and match information in real time,

[1899] A means of analyzing the acquired data and generating player information and play predictions;

[1900] A means for automatically generating a quiz based on the generated prediction information;

[1901] a means for displaying the generated quiz and analysis results on a user device;

[1902] means for receiving the user's quiz responses and displaying the results;

[1903] The user device is a head-mounted display, and analysis results and quizzes are displayed interactively.

[1904] A system including:

[1905] (Claim 2)

[1906] 10. The system of claim 1, wherein the means for obtaining real-time player action and game information receives real-time video feeds and player status data.

[1907] (Claim 3)

[1908] The system of claim 1, wherein the means for analyzing the acquired data and generating player information and play predictions uses a machine learning model and an AI image analysis engine.

[1909] "Example 2: Combining Emotion Engines"

[1910] (Claim 1)

[1911] A means of obtaining player movements and match information in real time,

[1912] A means of analyzing the acquired data and generating player information and play predictions;

[1913] A means for automatically generating a quiz based on the generated prediction information;

[1914] ...

Claims

1. A means of obtaining player movements and match information in real time, A means of analyzing the acquired data and generating player information and play predictions; A means for automatically generating a quiz based on the generated prediction information; a means for displaying the generated quiz and analysis results on a user device; means for receiving the user's quiz responses and displaying the results; A system including:

2. 10. The system of claim 1, wherein the means for obtaining real-time player action and game information receives real-time video feeds and player status data.

3. The system of claim 1, wherein the means for analyzing the acquired data and generating player information and play predictions uses a machine learning model and an AI image analysis engine.

4. 2. The system according to claim 1, wherein the means for automatically generating a quiz based on the generated prediction information generates a question about the next play.

5. 2. The system according to claim 1, wherein the means for displaying the generated quiz and analysis results on the user terminal constructs and displays a user interface.

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