System

Abstract

A system is provided.SOLUTION: A system includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for selecting game detailed information by the user, means for acquiring data for providing the game detailed information from a database, means for analyzing game data in real time and displaying an analysis result, and means for receiving a request for information on a player from the user and providing player information.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] Beginners in sports viewing face the challenge of not knowing what to watch and enjoy during a game, while advanced viewers face the challenge of having to work hard to gather a variety of information. Furthermore, media outlets want to improve the value of the information they acquire at great expense. There is a need for a system that can resolve these challenges while providing a richer sports viewing experience. [Means for solving the problem]

[0005] It provides a means for receiving authentication information entered by the user from a terminal and authenticating the user based on that information. It also includes a means for the user to select detailed match information and a means for retrieving that information from a database, thereby providing the user with the match data they need. It also provides a means for analyzing match data in real time and displaying the analysis results, making it easier to understand the match situation. The system includes a means for receiving requests for player information from users and providing player information, allowing for quick access to individual player information. It also includes a means for a chatbot to receive user input, analyze questions using natural language processing, and generate and provide appropriate answers, helping to resolve users' questions. By continuously acquiring match data in real time and analyzing it using an AI engine, it is possible to create a system that can provide more accurate match information.

[0006] A "terminal" is an electronic device that a user accesses for input and display.

[0007] "Credentials" are the identifying information a User uses to access a System, including email address and password.

[0008] A "user" is an individual who uses the system to watch sports, gather information, and place bets.

[0009] A "server" is a central device that processes and stores data and provides information in cooperation with user terminals.

[0010] A "database" is a system that collects and manages data such as match information and player information.

[0011] "Match Data" refers to information regarding real-time match conditions and player performance.

[0012] An "AI engine" is a system that analyzes data using artificial intelligence technologies such as machine learning and image analysis.

[0013] "Real-time" means that ongoing events and data are processed and provided immediately.

[0014] A "chatbot" is a program that responds to user questions in a dialogue format, using natural language processing (NLP) technology.

[0015] "Natural Language Processing (NLP)" is an artificial intelligence technique for analyzing human language, used to understand user input and generate appropriate responses.

[0016] "Detailed match information" is detailed data about a specific match, including opposing teams, scores, player information, and the like.

[0017] "Analysis results" refers to information obtained after analyzing match data, and includes player performance and team tactical data. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from their terminals and acquire, analyze, and display game information in real time. An embodiment of the system will be described in detail below.

[0040] User Authentication

[0041] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[0042] Match selection and information provision

[0043] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[0044] Real-time data analysis and provision

[0045] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[0046] Providing individual player information

[0047] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[0048] Chatbot operation

[0049] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[0050] Sports betting features

[0051] (Note: The following applies once sports betting is legalized)

[0052] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[0053] Specific examples

[0054] As a specific example, consider a scene in which a user is watching a basketball game.

[0055] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[0056] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[0057] As described above, the system of the present invention allows users to analyze a game in real time and intuitively obtain detailed player information, greatly improving the enjoyment of watching sports.

[0058] The processing flow will be explained below.

[0059] User Authentication

[0060] Step 1:

[0061] The user starts the device and opens the application's login screen.

[0062] Step 2:

[0063] The user enters their email address and password and clicks the "Login" button.

[0064] Step 3:

[0065] The terminal transmits the entered authentication information to the server.

[0066] Step 4:

[0067] The server receives the authentication information and checks it against the user information in its database.

[0068] Step 5:

[0069] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[0070] Step 6:

[0071] The device displays the dashboard screen.

[0072] Match selection and information provision

[0073] Step 1:

[0074] A user views a list of matches from the dashboard.

[0075] Step 2:

[0076] The server retrieves the day's match information from the database and sends it to the terminal.

[0077] Step 3:

[0078] The game information acquired by the device is displayed as a list.

[0079] Step 4:

[0080] The user selects the match they wish to view.

[0081] Step 5:

[0082] The device sends the selected match ID to the server.

[0083] Step 6:

[0084] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[0085] Step 7:

[0086] The server sends the detailed information to the terminal.

[0087] Step 8:

[0088] Displays detailed match data acquired by the device.

[0089] Real-time data analysis and provision

[0090] Step 1:

[0091] The server collects match data in real time.

[0092] Step 2:

[0093] The server obtains data using the real-time API of the match information provider.

[0094] Step 3:

[0095] The server sends the acquired data to the AI ​​engine.

[0096] Step 4:

[0097] The server analyzes the data using an AI engine and generates analysis results.

[0098] Step 5:

[0099] The server sends the analysis results to the device.

[0100] Step 6:

[0101] The device updates the data in real time and displays it on the screen.

[0102] Providing individual player information

[0103] Step 1:

[0104] The user clicks on a particular player icon.

[0105] Step 2:

[0106] The device sends an information request including the player ID to the server.

[0107] Step 3:

[0108] The server retrieves the player's latest profile, performance, and past match data from the database.

[0109] Step 4:

[0110] The server transmits the acquired information to the terminal.

[0111] Step 5:

[0112] The terminal displays player information.

[0113] Chatbot operation

[0114] Step 1:

[0115] The user types a question into the chatbot.

[0116] Step 2:

[0117] The terminal sends a question to the server.

[0118] Step 3:

[0119] The server sends the question to a natural language processing (NLP) engine.

[0120] Step 4:

[0121] The server analyzes the question and understands its meaning.

[0122] Step 5:

[0123] The server generates the appropriate database query.

[0124] Step 6:

[0125] The server creates an answer based on information obtained from the database.

[0126] Step 7:

[0127] The server sends the response to the terminal.

[0128] Step 8:

[0129] The device will display the answer.

[0130] Sports betting feature (after lifting the ban)

[0131] Step 1:

[0132] The user selects a betting option (next goal prediction, match result prediction, etc.).

[0133] Step 2:

[0134] The device sends the selection to the server.

[0135] Step 3:

[0136] The server processes the bet and updates the user's betting account.

[0137] Step 4:

[0138] The server performs the calculation based on the bet amount and odds.

[0139] Step 5:

[0140] The server sends a bet confirmation message to the terminal.

[0141] Step 6:

[0142] The device will display a confirmation message.

[0143] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[0144] Example 1

[0145] 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."

[0146] Conventional sports viewing systems have the problem of making it difficult to easily obtain and analyze detailed match data and player information in real time. They also lack the functionality to quickly and accurately respond when users request detailed information about specific players or matches. Furthermore, they lack the ability to respond to questions via chatbots or incorporate AI engines for real-time analysis, creating a need for an improved user experience.

[0147] 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.

[0148] In this invention, the server includes means for receiving authentication information input by a user from a communication device, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving information for providing detailed match information from an information repository, means for analyzing the match information in real time and displaying the analysis results, means for receiving requests for player information from users and providing the player information, and means for instantly collecting data during the match and analyzing it with an AI engine. This allows users to easily obtain and analyze detailed match data and player information in real time, enabling quick and accurate responses using chatbots and instant analysis of data during the match.

[0149] A "user" is someone who uses the system to obtain match information and player data to enhance the viewing experience.

[0150] A "communication device" is a device, such as a smartphone, tablet, or personal computer, that allows a user to access the system over the Internet.

[0151] "Authentication information" refers to the identification information a user enters to log in to a system, typically a combination of an email address and a password.

[0152] An "information repository" is a database or data storage system where the system stores and manages match information, player data, etc.

[0153] "Analysis results" are the output of match data and player performance data processed by the AI ​​engine.

[0154] "Real-time" refers to the immediacy of obtaining data instantly in accordance with the progress of the game and providing analytical results.

[0155] A "request" is an action in which a user requests specific information (for example, data about a specific player) from the system.

[0156] The "AI engine" is a component that uses artificial intelligence technology to analyze match data and generate performance and statistical information.

[0157] A "chatbot" is an interactive program that receives natural language input from a user, analyzes the question, and generates an appropriate response.

[0158] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from a communication device and acquire, analyze, and display game information in real time. The following describes an embodiment of the present invention in detail.

[0159] User Authentication

[0160] The user starts up the communication device and enters their email address and password on the login screen. The communication device sends this authentication information to the server. The server receives the authentication information and compares it with the registration information stored in a database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the communication device. The communication device displays the dashboard screen, and the user can proceed with game selection.

[0161] Match selection and information provision

[0162] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the communication device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the communication device. The communication device then displays the retrieved detailed data about the match.

[0163] Real-time data analysis and provision

[0164] While the game is in progress, the server collects game data in real time. The real-time data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (e.g., Player A's shooting success rate and number of rebounds) are immediately sent to the user's communication device, and the screen is updated in real time.

[0165] Providing individual player information

[0166] When a user clicks on a specific player's icon, a request for player information is sent from the communication device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the communication device. The user can then view detailed player information.

[0167] Chatbot operation

[0168] When a user enters a question into the chatbot, the communication device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the communication device and displayed on the screen.

[0169] Sports betting features

[0170] (Note: The following applies once sports betting is legalized)

[0171] Once the user selects a betting option (e.g., next goal prediction, match outcome prediction, etc.), the communication device sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the communication device. The communication device displays the confirmation message and notifies the user.

[0172] Specific examples

[0173] As a specific example, consider a scene in which a user is watching a basketball game.

[0174] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves match data from the database and sends it to the user's communication device. During the match, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the communication device. When the user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[0175] Example prompts for generative AI models

[0176] 1. "Log in with your account to view today's matches."

[0177] 2. "Analyze and display real-time performance data for Player A in Match X."

[0178] 3. "What is Player B's profile and what is his scoring percentage this season?"

[0179] 4. "What are the betting options for predicting the next goal?"

[0180] By inputting the above prompt sentences into a generative AI model, ideal response examples that will help the user are generated.

[0181] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0182] Step 1:

[0183] A user starts up a communication device and runs an application.

[0184] What happens: A user taps an app icon to open it.

[0185] Input: User action.

[0186] Output: Launches the app and displays the login screen.

[0187] Step 2:

[0188] The user enters their email address and password on the login screen.

[0189] Specific behavior: The user enters their email address and password into the login form and clicks the login button.

[0190] Enter your email address and password.

[0191] Output: Sends authentication information to the server.

[0192] Step 3:

[0193] The device sends the login information to the server.

[0194] Specific operation: The device sends the entered authentication information to the server via an HTTP POST request.

[0195] Input: Credentials (email address, password).

[0196] Output: Sending authentication information to the server.

[0197] Step 4:

[0198] The server receives the authentication information and checks it against the information stored in a database.

[0199] What happens: The server runs a database query to verify that the email address and password entered match.

[0200] Input: Your credentials.

[0201] Output: Authentication result.

[0202] Step 5:

[0203] If authentication is successful, the server returns an authentication token and the user's dashboard data to the device.

[0204] What happens: The server generates an authentication token and sends back an HTTP response containing the dashboard data.

[0205] Input: Authentication results, dashboard data.

[0206] Output: Authentication token, sending dashboard data.

[0207] Step 6:

[0208] The device displays the dashboard screen.

[0209] Specific operation: Build and display a user interface based on the dashboard data acquired by the device.

[0210] Inputs: Auth token, dashboard data.

[0211] Output: Display of the dashboard screen.

[0212] Step 7:

[0213] The user clicks the "Match List" button in the dashboard to display the match list.

[0214] Specific behavior: The user clicks the "List of Matches" button.

[0215] Input: User action.

[0216] Output: Display a list of matches.

[0217] Step 8:

[0218] The device obtains the match list data from the server.

[0219] Specific operation: The terminal sends an HTTP GET request containing the list data, and the server retrieves the match list from the database and returns it.

[0220] Input: The request to the server.

[0221] Output: Receive match list data.

[0222] Step 9:

[0223] The user selects the match they want to watch from the list of matches.

[0224] What happens: The user clicks on a match.

[0225] Input: User action.

[0226] Output: Get the selected match ID.

[0227] Step 10:

[0228] The device sends the selected match ID to the server.

[0229] Specific behavior: The device sends an HTTP POST request to the server containing the ID of the selected match.

[0230] Input: Selected match ID.

[0231] Output: Sends the match ID to the server.

[0232] Step 11:

[0233] The server retrieves the details of the selected match from the database.

[0234] What happens: The server executes a database query to retrieve detailed data for the match.

[0235] Input: Match ID.

[0236] Output: Get detailed match data.

[0237] Step 12:

[0238] The server sends the match detail data to the terminal.

[0239] Specific operation: The server returns an HTTP response containing the acquired match data to the device.

[0240] Input: Match details data.

[0241] Output: Sending data to the terminal.

[0242] Step 13:

[0243] Displays detailed match data acquired by the device.

[0244] Specific operation: The device updates the user interface based on the match data and displays detailed match information.

[0245] Input: Match details data.

[0246] Output: Display detailed match information.

[0247] Step 14:

[0248] As the match progresses, the server collects match data in real time.

[0249] Specific operation: The server periodically retrieves data from the match information provider using an API.

[0250] Input: Match progress data.

[0251] Output: Real-time data collection.

[0252] Step 15:

[0253] The server analyzes the match data using an AI engine.

[0254] Specific operation: The server inputs game data into the AI ​​engine and analyzes the players' shooting success rate, number of rebounds, etc.

[0255] Input: Real-time match data.

[0256] Output: Generates analysis results.

[0257] Step 16:

[0258] The server transmits the analysis results to the user terminal.

[0259] Specific operation: The server returns an HTTP response containing the analysis results to the user device.

[0260] Input: Analysis results.

[0261] Output: Sending data to the user's terminal.

[0262] Step 17:

[0263] The device displays the analysis results in real time.

[0264] Specific operation: The device receives the analysis data and displays it by updating the screen in real time.

[0265] Input: Analysis result data.

[0266] Output: Real-time display.

[0267] Step 18:

[0268] The user clicks on a specific player icon to request player information.

[0269] Specific behavior: The user clicks on a player icon.

[0270] Input: User action.

[0271] Output: Send player information request.

[0272] Step 19:

[0273] The terminal sends a player information request to the server.

[0274] Specific operation: The device sends an HTTP GET request including the player ID to the server.

[0275] Input: Player ID.

[0276] Output: Sends a request to the server.

[0277] Step 20:

[0278] The server retrieves the latest information about the player from the database.

[0279] What happens: The server executes a database query to retrieve player stats, profiles, etc.

[0280] Input: Player ID.

[0281] Output: Get player information.

[0282] Step 21:

[0283] The server sends player information to the terminal.

[0284] Specific operation: The server returns an HTTP response containing the acquired player information to the terminal.

[0285] Input: Player information data.

[0286] Output: Sending data to the terminal.

[0287] Step 22:

[0288] The device displays detailed information about the player.

[0289] Specific operation: The device updates and displays the user interface based on the player information.

[0290] Input: Player information data.

[0291] Output: Display player details.

[0292] Step 23:

[0293] The user types a question into the chatbot.

[0294] Specific behavior: The user enters a question into the chatbot's input field and clicks the submit button.

[0295] Input: The user's question.

[0296] Output: Sending question data.

[0297] Step 24:

[0298] The terminal sends a question to the server.

[0299] Specific operation: The device sends an HTTP POST request containing a question to the server.

[0300] Input: Question data.

[0301] Output: Sending data to the server.

[0302] Step 25:

[0303] The server analyzes the question using an NLP engine.

[0304] Specific operation: The server inputs a question into the NLP engine and analyzes the question.

[0305] Input: Question data.

[0306] Output: Analysis results.

[0307] Step 26:

[0308] The server generates a database query to retrieve the required information.

[0309] Specific operation: The server generates a database query based on the analysis results and retrieves the required information from the database.

[0310] Input: Analysis results.

[0311] Output: Obtain the required information.

[0312] Step 27:

[0313] The server sends the generated response to the terminal.

[0314] Specific operation: The server returns an HTTP response containing the answer to the device.

[0315] Input: Response data.

[0316] Output: Sending data to the terminal.

[0317] Step 28:

[0318] The device will display the answer on the screen.

[0319] Specific operation: The device updates the screen based on the response data and displays it to the user.

[0320] Input: Response data.

[0321] Output: Display the answer.

[0322] Step 29:

[0323] The user selects a betting option.

[0324] What happens: A user selects an option on a betting screen, such as predicting the next goal.

[0325] Input: User action.

[0326] Output: Selection of bet options.

[0327] Step 30:

[0328] The terminal transmits the bet details to the server.

[0329] What happens: The device sends an HTTP POST request containing the bet details to the server.

[0330] Input: Bet details.

[0331] Output: Sending data to the server.

[0332] Step 31:

[0333] The server processes the bet and updates the user's betting account.

[0334] What happens: The server saves the bet details to a database and updates the betting account information.

[0335] Input: Bet details.

[0336] Output: Updated account information.

[0337] Step 32:

[0338] The server sends a bet confirmation message to the terminal.

[0339] Specific operation: The server sends an HTTP response containing a bet confirmation message back to the terminal.

[0340] Input: A confirmation message.

[0341] Output: Sending data to the terminal.

[0342] Step 33:

[0343] The terminal displays a confirmation message to notify the user.

[0344] Specific behavior: The device displays a confirmation message and issues an alert to notify the user.

[0345] Input: A confirmation message.

[0346] Output: Message display and notification.

[0347] (Application example 1)

[0348] 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."

[0349] In conventional sports viewing systems, users had to obtain game information individually, making it difficult to follow the progress of the game in real time. Furthermore, analysis of player information and game data was often delayed, resulting in a lack of a satisfying viewing experience. Furthermore, there was no system that combined sports betting and natural language question-answering, preventing users from getting a consistent experience.

[0350] 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.

[0351] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, means for users to obtain information in real time while watching live streaming, means for users to select betting options and process the contents of the selections, and means for analyzing natural language questions using a generative AI model and providing appropriate answers. This allows users to analyze matches in real time and intuitively obtain detailed player information, significantly improving the enjoyment of sports watching.

[0352] "User authentication information" refers to personal identification information, such as an email address or password, that a user enters to access a system.

[0353] A "database" is a collection of information that stores detailed match information, player information, analysis results, and other data in an organized manner and can be quickly accessed as needed.

[0354] "Real-time analysis" is the process of instantly analyzing data collected during a match and providing the results immediately to the user's device.

[0355] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, that a user uses to access the system.

[0356] "Detailed match information" refers to detailed data about the match, such as player information, team information, and match progress.

[0357] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and performs advanced tasks such as natural language processing and data analysis.

[0358] A "chatbot" is a system that automatically responds to questions and requests from users, and operates using natural language processing technology.

[0359] "Sports betting options" are options that allow users to predict outcomes or place bets on specific events during or before a game.

[0360] "Live streaming" is a technology that allows users to watch game footage in real time via the Internet.

[0361] "Natural language processing" is a technology that enables computers to understand, analyze, and respond to human language.

[0362] The present invention provides a system that allows users to obtain real-time game information and intuitively access detailed player data when watching a sporting event. The system includes a server, a database, a user terminal, and a generative AI model.

[0363] User Authentication

[0364] A user launches a sports viewing app and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registration information in the SQLite database and returns the user's dashboard data along with an authentication token. The user can then proceed to select a match from the dashboard screen.

[0365] Match selection and information provision

[0366] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the user's device. The user can then display the retrieved detailed match data on their device.

[0367] Real-time data analysis and provision

[0368] While the game is in progress, the server collects game data in real time. This data is acquired from game information providers and analyzed by an AI engine. The analysis results (for example, a player's shooting success rate or number of rebounds) are immediately sent to the user's device, and the screen is updated. This allows the user to check the progress of the game in real time.

[0369] Providing individual player information

[0370] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the user's device. The user can then view detailed player information.

[0371] Chatbot operation

[0372] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a generative AI model (using natural language processing technology such as Spacy, for example) and analyzes the question. Based on the analysis results, it generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is the player's scoring percentage this season?", the server retrieves the player's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[0373] Sports betting features

[0374] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[0375] Specific examples

[0376] As a concrete example, consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data (for example, shooting percentage and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on a player's profile, the server retrieves detailed information about the player and displays it on the device. Also, when the user asks the chatbot, "What is the player's scoring percentage this season?", the server analyzes the question using a generative AI model, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[0377] An example prompt would be "How can I bet 1000 yen on the next goal?"

[0378] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0379] Step 1:

[0380] The user operates the device and launches the application. The device displays a login screen, where the user enters their email address and password. After completing the entry, the authentication information is sent to the server.

[0381] Input: Email address, password

[0382] Output: Send authentication information

[0383] Step 2:

[0384] The server receives the authentication information and compares it with the registration information in the database. If the comparison is successful, the server generates an authentication token and the user's dashboard data and returns them to the device.

[0385] Input: Credentials

[0386] Output: Authentication token, dashboard data

[0387] Behavior: Database lookup

[0388] Step 3:

[0389] Based on the received authentication token and dashboard data, the user terminal displays the dashboard screen, allowing the user to proceed with game selection.

[0390] Input: Auth token, dashboard data

[0391] Output: Dashboard screen display

[0392] Operation: Screen update

[0393] Step 4:

[0394] The user displays a list of matches from the dashboard on their device and selects the match they want to watch. The selected match ID is sent from the device to the server.

[0395] Input: Match selection

[0396] Output: Match ID

[0397] Action: Match selection operation

[0398] Step 5:

[0399] The server retrieves detailed information about the selected game (player information, team information, game progress, etc.) from the database and sends it to the user's terminal.

[0400] Input: Match ID

[0401] Output: Match details

[0402] Action:Database access

[0403] Step 6:

[0404] The user terminal displays the acquired detailed data of the match, allowing the user to check the detailed information while watching the match.

[0405] Input: Match details

[0406] Output: Match details screen

[0407] Operation: Screen display

[0408] Step 7:

[0409] During the match, the server collects match data in real time and analyzes it using an AI engine, with the analysis results immediately sent to the user's device.

[0410] Input: Real-time match data

[0411] Output: Analysis results

[0412] Operation: Data collection and analysis

[0413] Step 8:

[0414] The user device updates the screen based on the received analysis results and displays the player's performance data in real time.

[0415] Input: Analysis results

[0416] Output: Updated match screen

[0417] Operation: Screen update

[0418] Step 9:

[0419] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server, which retrieves the player's latest profile, performance, and past match data from the database and sends it to the user's device.

[0420] Input: Player Request

[0421] Output: Player details

[0422] Action:Database access

[0423] Step 10:

[0424] The user terminal displays the received detailed player information so that the user can check it.

[0425] Input: Player details

[0426] Output: Player information screen

[0427] Operation: Screen display

[0428] Step 11:

[0429] When a user types a question into the chatbot, the device sends the question to the server, which analyzes the question using a generative AI model and generates the appropriate database query to retrieve the required information.

[0430] Input: Question

[0431] Output: Answer generation

[0432] Behavior: Analysis by generative AI models

[0433] Step 12:

[0434] The server generates an answer and sends it to the user's device, which then displays it on the screen. For example, if you ask, "What is the player's scoring percentage this season?", the server retrieves the relevant data and generates an answer.

[0435] Input: Generated Answer

[0436] Output: Answer displayed on the screen

[0437] Operation: Screen display

[0438] Step 13:

[0439] When a user selects a betting option, the terminal transmits the bet to the server, which processes the bet and updates the user's betting account.

[0440] Input: Bet details

[0441] Output: Bet result

[0442] Action: Bet processing

[0443] Step 14:

[0444] The server performs calculations based on the bet amount and odds, and sends a bet confirmation message to the terminal, which then displays the confirmation message and notifies the user.

[0445] Input: Calculation result

[0446] Output:Confirmation message

[0447] Operation: calculation, notification

[0448] Example prompt: "How do I bet 1000 yen on the next goal?"

[0449] 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.

[0450] The present invention is a system for enhancing sports viewing, allowing users to access the system from their devices and acquire, analyze, and display game information in real time. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides content optimized for the user. An embodiment of this system is described in detail below.

[0451] User Authentication

[0452] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[0453] Match selection and information provision

[0454] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[0455] Real-time data analysis and provision

[0456] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[0457] Providing individual player information

[0458] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[0459] Chatbot operation

[0460] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[0461] Sports betting features

[0462] (Note: The following applies once sports betting is legalized)

[0463] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[0464] Operating the Emotion Engine

[0465] When a user uses the system, the emotion engine analyzes their emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotion data. For example, if the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is calm, it will provide detailed tactical analysis information and past player data.

[0466] In addition, customizing the interface (for example, changing the color scheme or layout) according to the user's emotional state allows the user to use the system more comfortably.

[0467] Specific examples

[0468] As a specific example, consider a scene in which a user is watching a basketball game.

[0469] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[0470] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[0471] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[0472] As described above, the system of the present invention allows users to analyze the game in real time and receive detailed player information and information based on the user's emotions, greatly improving the enjoyment of watching sports.

[0473] The processing flow will be explained below.

[0474] User Authentication

[0475] User authentication process flow

[0476] Step 1:

[0477] The user starts the device and opens the application's login screen.

[0478] Step 2:

[0479] The user enters their email address and password and clicks the "Login" button.

[0480] Step 3:

[0481] The terminal transmits the entered authentication information to the server.

[0482] Step 4:

[0483] The server receives the authentication information and checks it against the user information in its database.

[0484] Step 5:

[0485] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[0486] Step 6:

[0487] The device displays the dashboard screen.

[0488] Match selection and information provision

[0489] Match selection and information provision processing flow

[0490] Step 1:

[0491] A user views a list of matches from the dashboard.

[0492] Step 2:

[0493] The server retrieves the day's match information from the database and sends it to the terminal.

[0494] Step 3:

[0495] The game information acquired by the device is displayed as a list.

[0496] Step 4:

[0497] The user selects the match they wish to view.

[0498] Step 5:

[0499] The device sends the selected match ID to the server.

[0500] Step 6:

[0501] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[0502] Step 7:

[0503] The server sends the detailed information to the terminal.

[0504] Step 8:

[0505] Displays detailed match data acquired by the device.

[0506] Real-time data analysis and provision

[0507] Real-time data analysis and provision process flow

[0508] Step 1:

[0509] The server collects match data in real time.

[0510] Step 2:

[0511] The server obtains data using the real-time API of the match information provider.

[0512] Step 3:

[0513] The server sends the acquired data to the AI ​​engine.

[0514] Step 4:

[0515] The server analyzes the data using an AI engine and generates analysis results.

[0516] Step 5:

[0517] The server sends the analysis results to the device.

[0518] Step 6:

[0519] The device updates the data in real time and displays it on the screen.

[0520] Providing individual player information

[0521] Processing flow for providing individual player information

[0522] Step 1:

[0523] The user clicks on a particular player icon.

[0524] Step 2:

[0525] The device sends an information request including the player ID to the server.

[0526] Step 3:

[0527] The server retrieves the player's latest profile, performance, and past match data from the database.

[0528] Step 4:

[0529] The server transmits the acquired information to the terminal.

[0530] Step 5:

[0531] The terminal displays player information.

[0532] Chatbot operation

[0533] Chatbot operation process flow

[0534] Step 1:

[0535] The user types a question into the chatbot.

[0536] Step 2:

[0537] The terminal sends a question to the server.

[0538] Step 3:

[0539] The server sends the question to a natural language processing (NLP) engine.

[0540] Step 4:

[0541] The server analyzes the question and understands its meaning.

[0542] Step 5:

[0543] The server generates the appropriate database query.

[0544] Step 6:

[0545] The server creates an answer based on information obtained from the database.

[0546] Step 7:

[0547] The server sends the response to the terminal.

[0548] Step 8:

[0549] The device will display the answer.

[0550] Sports betting feature (after lifting the ban)

[0551] Sports betting function processing flow

[0552] Step 1:

[0553] The user selects a betting option (next goal prediction, match result prediction, etc.).

[0554] Step 2:

[0555] The device sends the selection to the server.

[0556] Step 3:

[0557] The server processes the bet and updates the user's betting account.

[0558] Step 4:

[0559] The server performs the calculation based on the bet amount and odds.

[0560] Step 5:

[0561] The server sends a bet confirmation message to the terminal.

[0562] Step 6:

[0563] The device will display a confirmation message.

[0564] Operating the Emotion Engine

[0565] Emotion Engine Operation Process Flow

[0566] Step 1:

[0567] The user begins using the system and performs input or actions (e.g., posting a comment, asking a question to the chatbot, etc.).

[0568] Step 2:

[0569] The server sends user input and behavioral data to the emotion engine.

[0570] Step 3:

[0571] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0572] Step 4:

[0573] The server selects appropriate content and information based on the recognized emotion data.

[0574] Step 5:

[0575] The server sends the selected content and information to the terminal.

[0576] Step 6:

[0577] The device displays an interface and content that corresponds to the user's emotional state.

[0578] Specific examples

[0579] As a specific example, consider a scene in which a user is watching a basketball game.

[0580] Processing flow of specific example

[0581] Step 1:

[0582] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches.

[0583] Step 2:

[0584] The server retrieves the match data from the database and sends it to the user's device.

[0585] Step 3:

[0586] Displays detailed match data acquired by the device.

[0587] Step 4:

[0588] The server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and transmits it to the terminal.

[0589] Step 5:

[0590] When a user clicks on the profile of Player A, the server retrieves detailed information about Player A and displays it on the device.

[0591] Step 6:

[0592] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. The answer is then displayed on the device.

[0593] Step 7:

[0594] The emotion engine analyzes the user's behavior, and if the user is excited, the server will recommend recent highlight videos and notable plays. If the user is relaxed, the server will provide past data and detailed tactical information.

[0595] Step 8:

[0596] The interface and content are adjusted and displayed according to the user's emotional state.

[0597] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[0598] Example 2

[0599] 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."

[0600] Current sports viewing systems can obtain game information in real time, but they have problems in that they cannot provide optimal content or customize the interface according to the user's emotions and interests. Furthermore, there are limited means to provide detailed information about players or to provide a natural interactive experience in response to user input. As a result, users are unable to efficiently obtain information while watching sports, which can lead to a decrease in satisfaction.

[0601] 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.

[0602] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from the user and providing the player information, and means for analyzing the user's emotional state and providing optimal content and an interface based on the analysis results. This allows the user to obtain match information in real time and be provided with optimal information and content based on their emotions and interests, enabling a more fulfilling sports viewing experience.

[0603] "Authentication information" refers to information such as an email address and password that a user uses to identify themselves.

[0604] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets.

[0605] "Server" refers to a computer system that processes data and communicates with other devices over a network.

[0606] A "database" refers to a computer system or software that organizes and stores specific information.

[0607] "Match data" refers to information generated during a sports match (such as scores, player movements, and the progress of the match).

[0608] "Analysis" refers to the process of analyzing data in detail, including providing information based on the results.

[0609] "Player Information" refers to information related to a specific player, such as profile, performance, and past match data.

[0610] "Emotional state" refers to the user's subjective feelings (excitement, relaxation, etc.), and includes analyzing this to understand the user's current mental state.

[0611] "Content" refers to information and media provided to users (e.g., game highlights, statistics, news articles, etc.).

[0612] "Interface" refers to the screens and operating elements that allow a user to interact with a system.

[0613] A "chatbot" refers to a software program that automatically converses or interacts with a user.

[0614] "Natural language processing" refers to the technology that allows computers to understand human language and generate appropriate responses.

[0615] This invention is a system that allows users to enhance their sports viewing experience, and has the ability to acquire, analyze, and provide game information in real time. In addition, by combining it with an emotion engine that analyzes user emotions, it is possible to provide content optimized for users.

[0616] User Authentication

[0617] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[0618] Match selection and information provision

[0619] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[0620] Real-time data analysis and provision

[0621] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[0622] Providing individual player information

[0623] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[0624] Chatbot operation

[0625] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[0626] Sports betting features

[0627] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[0628] Operating the Emotion Engine

[0629] When a user uses the system, the emotion engine analyzes the user's emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotional data. For example, if the user is excited, the emotion engine will suggest recent highlights and notable plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and past player data. In addition, the system can customize the interface (for example, change the color scheme or layout) according to the user's emotional state, making it easier for users to use the system.

[0630] Specific examples

[0631] As a specific example, consider a scene in which a user is watching a basketball game.

[0632] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[0633] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[0634] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[0635] Example prompts to input to the generative AI model

[0636] For example, prompts like the one below can be input into a generative AI model to generate content tailored to the user's interests.

[0637] 1. "What are the highlights of the match between Team X and Team Y?"

[0638] 2. "Please give me detailed performance data for Player A this season."

[0639] 3. "What is Player B's scoring percentage this season?"

[0640] 4. "Please explain any notable tactics or plays from recent matches."

[0641] 5. "Use an emotion engine to generate content recommendations for excited users."

[0642] These prompts allow the generative AI model to automatically generate detailed and interesting content tailored to the user's needs.

[0643] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0644] User Authentication

[0645] Step 1:

[0646] The user starts up the device and the login screen is displayed. The user enters their email address and password. Based on the input (email address and password), the device generates the data to be sent.

[0647] Step 2:

[0648] The terminal sends the entered authentication information to the server. The server receives this authentication information, compares it with a database based on the input (authentication information), and performs a calculation to determine whether or not authentication is possible.

[0649] Step 3:

[0650] The server checks the registration information in the database to see if authentication is successful. If authentication is successful, the server generates an authentication token and dashboard data for the user. Generates output (authentication token and dashboard data).

[0651] Step 4:

[0652] The server returns the authentication token and dashboard data to the device. The device prepares to display the dashboard screen based on the received data (authentication token and dashboard data).

[0653] Step 5:

[0654] The terminal displays the user's dashboard screen and the user can proceed to select a match. The output (dashboard screen) is displayed.

[0655] Match selection and information provision

[0656] Step 6:

[0657] The user displays the match list from the dashboard. The device sends a request to the server to get the match list. Input (a match list request) is generated.

[0658] Step 7:

[0659] The server retrieves a list of matches from the match database, retrieves data from the database, and outputs it as a list of matches.

[0660] Step 8:

[0661] The server sends a list of matches to the terminal, which displays the list of matches based on the received data (list of matches).

[0662] Step 9:

[0663] The user selects the match they want to view. The device sends the selected match ID to the server. An input (request for match ID) is generated.

[0664] Step 10:

[0665] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database. The server retrieves the data from the database and outputs it as detailed information about the match.

[0666] Step 11:

[0667] The server sends detailed match data to the terminal, which then displays the match details screen based on the received data (detailed match data).

[0668] Real-time data analysis and provision

[0669] Step 12:

[0670] While the match is in progress, the server collects match data in real time from match information providers. Data collection is performed based on input (real-time match data).

[0671] Step 13:

[0672] The server analyzes the collected match data using an AI engine. Data analysis is performed based on the input (collected data) and analysis results are generated.

[0673] Step 14:

[0674] The server sends the analysis results to the user's terminal. The terminal generates output (updated screen data) that updates the screen in real time based on the received data (analysis results).

[0675] Step 15:

[0676] The user terminal updates the screen in real time based on the analysis data and displays the analysis results. Output (display of analysis results) is performed.

[0677] Providing individual player information

[0678] Step 16:

[0679] The user clicks on a specific player icon. The device sends a request for player information to the server. An input (request for player information) is generated.

[0680] Step 17:

[0681] The server retrieves the player's latest profile, performance, and past match data from the database, and outputs the data as player information.

[0682] Step 18:

[0683] The server sends player information to the terminal. The terminal prepares to display the player details screen based on the received data (player information).

[0684] Step 19:

[0685] The terminal displays detailed player information to the user. Output (player details screen) is performed.

[0686] Chatbot operation

[0687] Step 20:

[0688] The user inputs a question to the chatbot. The device sends the question to the server. A request is generated based on the input (question data).

[0689] Step 21:

[0690] The server sends the question to a natural language processing (NLP) engine, which analyzes the question. Data analysis is performed based on the input (question data), and analysis results are generated.

[0691] Step 22:

[0692] The server generates an appropriate database query based on the analysis results. A query is generated based on the input (analysis results).

[0693] Step 23:

[0694] The server retrieves the necessary information from the database, retrieves data from the database, and generates response data.

[0695] Step 24:

[0696] The server generates a response based on the information it has acquired and sends it to the terminal. It generates output (response data).

[0697] Step 25:

[0698] The device displays the generated answer to the user. Output (answer screen) is generated.

[0699] Sports betting features

[0700] Step 26:

[0701] User selects betting options (e.g. next goal prediction, match outcome prediction, etc.) Device sends bet to server Generates input (bet request)

[0702] Step 27:

[0703] The server processes the bet details and updates the user's betting account. The account is updated based on the input (bet details).

[0704] Step 28:

[0705] The server performs calculations based on the bet amount and odds. It performs calculations based on the input (bet amount and odds) and generates a result.

[0706] Step 29:

[0707] The server sends a bet confirmation message to the terminal. Generates an output (confirmation message).

[0708] Step 30:

[0709] The terminal displays a confirmation message and notifies the user. Output (display of confirmation message) is performed.

[0710] Operating the Emotion Engine

[0711] Step 31:

[0712] When a user uses the system, the emotion engine analyzes emotions from the user's input and behavior. Emotion analysis is performed based on the input (user behavior data).

[0713] Step 32:

[0714] The server recommends appropriate content and information based on the analyzed emotional data. Data recommendations are made based on the input (emotional data) and recommended content is generated.

[0715] Step 33:

[0716] If the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and historical player data. An output (recommended content) is generated.

[0717] Step 34:

[0718] The interface is customized (e.g., color scheme or layout changes) according to the user's emotional state, and an output (customized interface) is generated.

[0719] Specific examples

[0720] Step 35:

[0721] As a concrete example, consider a scenario where a user is watching a basketball game. The user opens the app, logs in, and selects the "Team X vs Team Y" game from the list of games. The server retrieves game data from the database and sends it to the user's device. Data is retrieved based on input (a request for game data). During the game, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[0722] Step 36:

[0723] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. An output (answer data) is generated, and the answer is displayed to the user.

[0724] Step 37:

[0725] The emotion engine analyzes the user's emotional state. For example, if the user is determined to be excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information. The output (recommended content) is generated.

[0726] (Application example 2)

[0727] 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."

[0728] Conventional sports viewing systems only provide game information and display analysis results, without customizing the content based on the user's emotions. As a result, the same information is provided regardless of whether the user is excited or relaxed. It is necessary to resolve this shortcoming and enhance the sports viewing experience by recommending optimal content based on the user's emotional state.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0730] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, and means for analyzing user emotions and recommending content based on the analysis results, thereby making it possible to provide optimal content according to the user's emotional state.

[0731] "User authentication" is the process of verifying a user's identity based on authentication information entered by the user from a terminal.

[0732] "Detailed match information" refers to data related to the match, such as player information, team information, and match progress.

[0733] "Real-time analysis" is the process of collecting data in a timely manner while the game is in progress and analyzing that data immediately.

[0734] "Player information" refers to detailed data about a specific player, such as the player's profile, performance, and past match data.

[0735] "Emotion analysis" is the process of identifying an emotional state from a user's input and behavior and generating emotional data.

[0736] "Content recommendation" is a function that suggests the best content to watch based on data such as the user's emotional state.

[0737] A "chatbot" is a dialogue system that automatically responds to text entered by a user.

[0738] "Natural language processing (NLP)" is a technology that uses computers to understand, analyze, and generate human language.

[0739] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[0740] The present invention is a system for enhancing the real-time viewing of sports. Specific embodiments of the present invention will be described below.

[0741] Overall system overview

[0742] The system includes a user device (smartphone, computer, etc.), a server, a database, an artificial intelligence (AI) engine, and a natural language processing (NLP) engine. Users log in from their devices and the system acquires, analyzes, and displays match information in real time. It also uses an emotion analysis engine to analyze the user's emotional state and provide optimal content.

[0743] User Authentication

[0744] When a user starts up a device and enters their authentication information (email address and password), the device sends this information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server sends an authentication token and the user's dashboard data back to the device, which displays it and allows the user to proceed to the next step.

[0745] Match selection and information provision

[0746] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server, which retrieves detailed information about the match (player information, team information, match progress, etc.) from the database. The retrieved information is sent to the device and displayed to the user.

[0747] Real-time data analysis and provision

[0748] The server collects game data in real time during the game and analyzes it using an AI engine. The analysis results (e.g., players' shooting success rates, number of rebounds, etc.) are immediately sent to the user's device and updated in real time.

[0749] Providing individual player information

[0750] When a user clicks on a specific player's icon, the device sends a request for player information to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device, allowing the user to view detailed player information.

[0751] Chatbot operation

[0752] When a user types a question into the chatbot, the device sends it to the server, which uses an NLP engine to analyze the question, generate the appropriate database query, and retrieve the necessary information. The generated answer is then sent to the device and displayed to the user.

[0753] Content recommendation using emotion engine

[0754] While the user is using the system, the emotion engine analyzes the user's input and behavior to generate emotion data. Based on the analysis results, the server recommends appropriate content (such as highlight videos or detailed tactical information).

[0755] Specific examples

[0756] Consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data in real time and provides it to the user's device. If the user inputs a question into the chatbot, such as "What is Player X's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. If the collected emotional data indicates that the user is excited, the server will recommend recent highlight videos or notable play scenes.

[0757] Prompt Sentence Examples

[0758] "If a user types, 'I'm really excited about this match!', generate a sentiment analysis result like this. The analysis result should follow this format:

[0759] {

[0760] "happiness": 0.7,

[0761] "anger": 0.1,

[0762] "surprise": 0.2

[0763] }

[0764] "

[0765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0766] Step 1:

[0767] The user turns on the device and provides authentication information by entering an email address and password.

[0768] Input: User credentials (email address and password)

[0769] Output: Sending authentication information to the server

[0770] Action: The device sends the entered authentication information to the server.

[0771] Step 2:

[0772] The server receives the authentication information and compares it with the registration information in the database. If authentication is successful, it generates an authentication token and the user's dashboard data and returns them to the device.

[0773] Input: The authentication information sent to the server

[0774] Output: Authentication token and user dashboard data

[0775] How it works: The server checks against its database and generates an authentication result.

[0776] Step 3:

[0777] The device receives the authentication token and displays a list of matches on the user's dashboard screen.

[0778] Input: Authentication token and dashboard data sent from the server

[0779] Output: Display of dashboard screen

[0780] What it does: Your device will refresh the dashboard screen and display a list of matches.

[0781] Step 4:

[0782] The user selects the match they want to watch from the dashboard and sends the match ID to the server.

[0783] Input: Match ID selected by user

[0784] Output: Match ID sent to server

[0785] Action: The device sends the selected match ID to the server.

[0786] Step 5:

[0787] The server retrieves detailed information about the match from the database and sends that information to the terminal.

[0788] Input: Match ID sent to the server

[0789] Output: Detailed information about the match

[0790] Operation: The server retrieves data from the database and sends it to the terminal.

[0791] Step 6:

[0792] The terminal receives the detailed information about the match and displays it to the user.

[0793] Input: Match details sent from the server

[0794] Output: Display match information

[0795] What it does: The device displays the match information on the screen.

[0796] Step 7:

[0797] The server collects match data in real time while the match is in progress, analyzes it using an AI engine, and sends the analysis results to the device.

[0798] Input: Match data collected in real time

[0799] Output: Sending analysis results to the terminal

[0800] How it works: The server uses an AI engine to analyze the data and sends the results to the device.

[0801] Step 8:

[0802] The terminal receives the analysis results in real time and displays them to the user.

[0803] Input: Analysis results sent from the server

[0804] Output: Display of analysis results

[0805] Operation: The device reflects and displays the analysis results on the screen.

[0806] Step 9:

[0807] When a user clicks on a particular player icon, the terminal sends a request for player information to the server.

[0808] Input: The player icon clicked by the user

[0809] Output: Request to server for player information

[0810] Operation: The device sends a request for player information to the server.

[0811] Step 10:

[0812] The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the terminal.

[0813] Input: Player information request sent from the device

[0814] Output: Detailed information about the player

[0815] Operation: The server retrieves player information from the database and returns it to the terminal.

[0816] Step 11:

[0817] The terminal receives the detailed information of the player and displays it to the user.

[0818] Input: Player details sent from the server

[0819] Output: Display player information

[0820] Action: The device displays player information on the screen.

[0821] Step 12:

[0822] When a user inputs a question to the chatbot, the terminal sends the question to the server.

[0823] Input: The question entered by the user

[0824] Output: Sending the question to the server

[0825] Operation: The device sends a query to the server.

[0826] Step 13:

[0827] The server analyzes the question using an NLP engine, generates the appropriate database query to retrieve the required information, and sends the generated answer to the device.

[0828] Input: Question sent from terminal

[0829] Output: The generated answer

[0830] How it works: The server uses an NLP engine to analyze the question, generate an answer, and send it to the device.

[0831] Step 14:

[0832] The terminal receives the response and displays it to the user.

[0833] Input: The answer sent by the server

[0834] Output: Show answer

[0835] Action: The device displays the answer on the screen.

[0836] Step 15:

[0837] The emotion engine analyzes user input and behavior, generates emotion data, and provides content appropriate for the device from the server.

[0838] Input: User input and behavioral data

[0839] Output: Parsed emotion data and corresponding content

[0840] Operation: The server uses the emotion engine to analyze the emotion data and sends the corresponding content to the device.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] [Second embodiment]

[0845] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0846] 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.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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).

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from their terminals and acquire, analyze, and display game information in real time. An embodiment of the system will be described in detail below.

[0858] User Authentication

[0859] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[0860] Match selection and information provision

[0861] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[0862] Real-time data analysis and provision

[0863] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[0864] Providing individual player information

[0865] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[0866] Chatbot operation

[0867] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[0868] Sports betting features

[0869] (Note: The following applies once sports betting is legalized)

[0870] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[0871] Specific examples

[0872] As a specific example, consider a scene in which a user is watching a basketball game.

[0873] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[0874] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[0875] As described above, the system of the present invention allows users to analyze a game in real time and intuitively obtain detailed player information, greatly improving the enjoyment of watching sports.

[0876] The processing flow will be explained below.

[0877] User Authentication

[0878] Step 1:

[0879] The user starts the device and opens the application's login screen.

[0880] Step 2:

[0881] The user enters their email address and password and clicks the "Login" button.

[0882] Step 3:

[0883] The terminal transmits the entered authentication information to the server.

[0884] Step 4:

[0885] The server receives the authentication information and checks it against the user information in its database.

[0886] Step 5:

[0887] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[0888] Step 6:

[0889] The device displays the dashboard screen.

[0890] Match selection and information provision

[0891] Step 1:

[0892] A user views a list of matches from the dashboard.

[0893] Step 2:

[0894] The server retrieves the day's match information from the database and sends it to the terminal.

[0895] Step 3:

[0896] The game information acquired by the device is displayed as a list.

[0897] Step 4:

[0898] The user selects the match they wish to view.

[0899] Step 5:

[0900] The device sends the selected match ID to the server.

[0901] Step 6:

[0902] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[0903] Step 7:

[0904] The server sends the detailed information to the terminal.

[0905] Step 8:

[0906] Displays detailed match data acquired by the device.

[0907] Real-time data analysis and provision

[0908] Step 1:

[0909] The server collects match data in real time.

[0910] Step 2:

[0911] The server obtains data using the real-time API of the match information provider.

[0912] Step 3:

[0913] The server sends the acquired data to the AI ​​engine.

[0914] Step 4:

[0915] The server analyzes the data using an AI engine and generates analysis results.

[0916] Step 5:

[0917] The server sends the analysis results to the device.

[0918] Step 6:

[0919] The device updates the data in real time and displays it on the screen.

[0920] Providing individual player information

[0921] Step 1:

[0922] The user clicks on a particular player icon.

[0923] Step 2:

[0924] The device sends an information request including the player ID to the server.

[0925] Step 3:

[0926] The server retrieves the player's latest profile, performance, and past match data from the database.

[0927] Step 4:

[0928] The server transmits the acquired information to the terminal.

[0929] Step 5:

[0930] The terminal displays player information.

[0931] Chatbot operation

[0932] Step 1:

[0933] The user types a question into the chatbot.

[0934] Step 2:

[0935] The terminal sends a question to the server.

[0936] Step 3:

[0937] The server sends the question to a natural language processing (NLP) engine.

[0938] Step 4:

[0939] The server analyzes the question and understands its meaning.

[0940] Step 5:

[0941] The server generates the appropriate database query.

[0942] Step 6:

[0943] The server creates an answer based on information obtained from the database.

[0944] Step 7:

[0945] The server sends the response to the terminal.

[0946] Step 8:

[0947] The device will display the answer.

[0948] Sports betting feature (after lifting the ban)

[0949] Step 1:

[0950] The user selects a betting option (next goal prediction, match result prediction, etc.).

[0951] Step 2:

[0952] The device sends the selection to the server.

[0953] Step 3:

[0954] The server processes the bet and updates the user's betting account.

[0955] Step 4:

[0956] The server performs the calculation based on the bet amount and odds.

[0957] Step 5:

[0958] The server sends a bet confirmation message to the terminal.

[0959] Step 6:

[0960] The device will display a confirmation message.

[0961] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[0962] Example 1

[0963] 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."

[0964] Conventional sports viewing systems have the problem of making it difficult to easily obtain and analyze detailed match data and player information in real time. They also lack the functionality to quickly and accurately respond when users request detailed information about specific players or matches. Furthermore, they lack the ability to respond to questions via chatbots or incorporate AI engines for real-time analysis, creating a need for an improved user experience.

[0965] 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.

[0966] In this invention, the server includes means for receiving authentication information input by a user from a communication device, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving information for providing detailed match information from an information repository, means for analyzing the match information in real time and displaying the analysis results, means for receiving requests for player information from users and providing the player information, and means for instantly collecting data during the match and analyzing it with an AI engine. This allows users to easily obtain and analyze detailed match data and player information in real time, enabling quick and accurate responses using chatbots and instant analysis of data during the match.

[0967] A "user" is someone who uses the system to obtain match information and player data to enhance the viewing experience.

[0968] A "communication device" is a device, such as a smartphone, tablet, or personal computer, that allows a user to access the system over the Internet.

[0969] "Authentication information" refers to the identification information a user enters to log in to a system, typically a combination of an email address and a password.

[0970] An "information repository" is a database or data storage system where the system stores and manages match information, player data, etc.

[0971] "Analysis results" are the output of match data and player performance data processed by the AI ​​engine.

[0972] "Real-time" refers to the immediacy of obtaining data instantly in accordance with the progress of the game and providing analytical results.

[0973] A "request" is an action in which a user requests specific information (for example, data about a specific player) from the system.

[0974] The "AI engine" is a component that uses artificial intelligence technology to analyze match data and generate performance and statistical information.

[0975] A "chatbot" is an interactive program that receives natural language input from a user, analyzes the question, and generates an appropriate response.

[0976] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from a communication device and acquire, analyze, and display game information in real time. The following describes an embodiment of the present invention in detail.

[0977] User Authentication

[0978] The user starts up the communication device and enters their email address and password on the login screen. The communication device sends this authentication information to the server. The server receives the authentication information and compares it with the registration information stored in a database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the communication device. The communication device displays the dashboard screen, and the user can proceed with game selection.

[0979] Match selection and information provision

[0980] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the communication device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the communication device. The communication device then displays the retrieved detailed data about the match.

[0981] Real-time data analysis and provision

[0982] While the game is in progress, the server collects game data in real time. The real-time data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (e.g., Player A's shooting success rate and number of rebounds) are immediately sent to the user's communication device, and the screen is updated in real time.

[0983] Providing individual player information

[0984] When a user clicks on a specific player's icon, a request for player information is sent from the communication device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the communication device. The user can then view detailed player information.

[0985] Chatbot operation

[0986] When a user enters a question into the chatbot, the communication device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the communication device and displayed on the screen.

[0987] Sports betting features

[0988] (Note: The following applies once sports betting is legalized)

[0989] Once the user selects a betting option (e.g., next goal prediction, match outcome prediction, etc.), the communication device sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the communication device. The communication device displays the confirmation message and notifies the user.

[0990] Specific examples

[0991] As a specific example, consider a scene in which a user is watching a basketball game.

[0992] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves match data from the database and sends it to the user's communication device. During the match, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the communication device. When the user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[0993] Example prompts for generative AI models

[0994] 1. "Log in with your account to view today's matches."

[0995] 2. "Analyze and display real-time performance data for Player A in Match X."

[0996] 3. "What is Player B's profile and what is his scoring percentage this season?"

[0997] 4. "What are the betting options for predicting the next goal?"

[0998] By inputting the above prompt sentences into a generative AI model, ideal response examples that will help the user are generated.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] A user starts up a communication device and runs an application.

[1002] What happens: A user taps an app icon to open it.

[1003] Input: User action.

[1004] Output: Launches the app and displays the login screen.

[1005] Step 2:

[1006] The user enters their email address and password on the login screen.

[1007] Specific behavior: The user enters their email address and password into the login form and clicks the login button.

[1008] Enter your email address and password.

[1009] Output: Sends authentication information to the server.

[1010] Step 3:

[1011] The device sends the login information to the server.

[1012] Specific operation: The device sends the entered authentication information to the server via an HTTP POST request.

[1013] Input: Credentials (email address, password).

[1014] Output: Sending authentication information to the server.

[1015] Step 4:

[1016] The server receives the authentication information and checks it against the information stored in a database.

[1017] What happens: The server runs a database query to verify that the email address and password entered match.

[1018] Input: Your credentials.

[1019] Output: Authentication result.

[1020] Step 5:

[1021] If authentication is successful, the server returns an authentication token and the user's dashboard data to the device.

[1022] What happens: The server generates an authentication token and sends back an HTTP response containing the dashboard data.

[1023] Input: Authentication results, dashboard data.

[1024] Output: Authentication token, sending dashboard data.

[1025] Step 6:

[1026] The device displays the dashboard screen.

[1027] Specific operation: Build and display a user interface based on the dashboard data acquired by the device.

[1028] Inputs: Auth token, dashboard data.

[1029] Output: Display of the dashboard screen.

[1030] Step 7:

[1031] The user clicks the "Match List" button in the dashboard to display the match list.

[1032] Specific behavior: The user clicks the "List of Matches" button.

[1033] Input: User action.

[1034] Output: Display a list of matches.

[1035] Step 8:

[1036] The device obtains the match list data from the server.

[1037] Specific operation: The terminal sends an HTTP GET request containing the list data, and the server retrieves the match list from the database and returns it.

[1038] Input: The request to the server.

[1039] Output: Receive match list data.

[1040] Step 9:

[1041] The user selects the match they want to watch from the list of matches.

[1042] What happens: The user clicks on a match.

[1043] Input: User action.

[1044] Output: Get the selected match ID.

[1045] Step 10:

[1046] The device sends the selected match ID to the server.

[1047] Specific behavior: The device sends an HTTP POST request to the server containing the ID of the selected match.

[1048] Input: Selected match ID.

[1049] Output: Sends the match ID to the server.

[1050] Step 11:

[1051] The server retrieves the details of the selected match from the database.

[1052] What happens: The server executes a database query to retrieve detailed data for the match.

[1053] Input: Match ID.

[1054] Output: Get detailed match data.

[1055] Step 12:

[1056] The server sends the match detail data to the terminal.

[1057] Specific operation: The server returns an HTTP response containing the acquired match data to the device.

[1058] Input: Match details data.

[1059] Output: Sending data to the terminal.

[1060] Step 13:

[1061] Displays detailed match data acquired by the device.

[1062] Specific operation: The device updates the user interface based on the match data and displays detailed match information.

[1063] Input: Match details data.

[1064] Output: Display detailed match information.

[1065] Step 14:

[1066] As the match progresses, the server collects match data in real time.

[1067] Specific operation: The server periodically retrieves data from the match information provider using an API.

[1068] Input: Match progress data.

[1069] Output: Real-time data collection.

[1070] Step 15:

[1071] The server analyzes the match data using an AI engine.

[1072] Specific operation: The server inputs game data into the AI ​​engine and analyzes the players' shooting success rate, number of rebounds, etc.

[1073] Input: Real-time match data.

[1074] Output: Generates analysis results.

[1075] Step 16:

[1076] The server transmits the analysis results to the user terminal.

[1077] Specific operation: The server returns an HTTP response containing the analysis results to the user device.

[1078] Input: Analysis results.

[1079] Output: Sending data to the user's terminal.

[1080] Step 17:

[1081] The device displays the analysis results in real time.

[1082] Specific operation: The device receives the analysis data and displays it by updating the screen in real time.

[1083] Input: Analysis result data.

[1084] Output: Real-time display.

[1085] Step 18:

[1086] The user clicks on a specific player icon to request player information.

[1087] Specific behavior: The user clicks on a player icon.

[1088] Input: User action.

[1089] Output: Send player information request.

[1090] Step 19:

[1091] The terminal sends a player information request to the server.

[1092] Specific operation: The device sends an HTTP GET request including the player ID to the server.

[1093] Input: Player ID.

[1094] Output: Sends a request to the server.

[1095] Step 20:

[1096] The server retrieves the latest information about the player from the database.

[1097] What happens: The server executes a database query to retrieve player stats, profiles, etc.

[1098] Input: Player ID.

[1099] Output: Get player information.

[1100] Step 21:

[1101] The server sends player information to the terminal.

[1102] Specific operation: The server returns an HTTP response containing the acquired player information to the terminal.

[1103] Input: Player information data.

[1104] Output: Sending data to the terminal.

[1105] Step 22:

[1106] The device displays detailed information about the player.

[1107] Specific operation: The device updates and displays the user interface based on the player information.

[1108] Input: Player information data.

[1109] Output: Display player details.

[1110] Step 23:

[1111] The user types a question into the chatbot.

[1112] Specific behavior: The user enters a question into the chatbot's input field and clicks the submit button.

[1113] Input: The user's question.

[1114] Output: Sending question data.

[1115] Step 24:

[1116] The terminal sends a question to the server.

[1117] Specific operation: The device sends an HTTP POST request containing a question to the server.

[1118] Input: Question data.

[1119] Output: Sending data to the server.

[1120] Step 25:

[1121] The server analyzes the question using an NLP engine.

[1122] Specific operation: The server inputs a question into the NLP engine and analyzes the question.

[1123] Input: Question data.

[1124] Output: Analysis results.

[1125] Step 26:

[1126] The server generates a database query to retrieve the required information.

[1127] Specific operation: The server generates a database query based on the analysis results and retrieves the required information from the database.

[1128] Input: Analysis results.

[1129] Output: Obtain the required information.

[1130] Step 27:

[1131] The server sends the generated response to the terminal.

[1132] Specific operation: The server returns an HTTP response containing the answer to the device.

[1133] Input: Response data.

[1134] Output: Sending data to the terminal.

[1135] Step 28:

[1136] The device will display the answer on the screen.

[1137] Specific operation: The device updates the screen based on the response data and displays it to the user.

[1138] Input: Response data.

[1139] Output: Display the answer.

[1140] Step 29:

[1141] The user selects a betting option.

[1142] What happens: A user selects an option on a betting screen, such as predicting the next goal.

[1143] Input: User action.

[1144] Output: Selection of bet options.

[1145] Step 30:

[1146] The terminal transmits the bet details to the server.

[1147] What happens: The device sends an HTTP POST request containing the bet details to the server.

[1148] Input: Bet details.

[1149] Output: Sending data to the server.

[1150] Step 31:

[1151] The server processes the bet and updates the user's betting account.

[1152] What happens: The server saves the bet details to a database and updates the betting account information.

[1153] Input: Bet details.

[1154] Output: Updated account information.

[1155] Step 32:

[1156] The server sends a bet confirmation message to the terminal.

[1157] Specific operation: The server sends an HTTP response containing a bet confirmation message back to the terminal.

[1158] Input: A confirmation message.

[1159] Output: Sending data to the terminal.

[1160] Step 33:

[1161] The terminal displays a confirmation message to notify the user.

[1162] Specific behavior: The device displays a confirmation message and issues an alert to notify the user.

[1163] Input: A confirmation message.

[1164] Output: Message display and notification.

[1165] (Application example 1)

[1166] 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."

[1167] In conventional sports viewing systems, users had to obtain game information individually, making it difficult to follow the progress of the game in real time. Furthermore, analysis of player information and game data was often delayed, resulting in a lack of a satisfying viewing experience. Furthermore, there was no system that combined sports betting and natural language question-answering, preventing users from getting a consistent experience.

[1168] 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.

[1169] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, means for users to obtain information in real time while watching live streaming, means for users to select betting options and process the contents of the selections, and means for analyzing natural language questions using a generative AI model and providing appropriate answers. This allows users to analyze matches in real time and intuitively obtain detailed player information, significantly improving the enjoyment of sports watching.

[1170] "User authentication information" refers to personal identification information, such as an email address or password, that a user enters to access a system.

[1171] A "database" is a collection of information that stores detailed match information, player information, analysis results, and other data in an organized manner and can be quickly accessed as needed.

[1172] "Real-time analysis" is the process of instantly analyzing data collected during a match and providing the results immediately to the user's device.

[1173] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, that a user uses to access the system.

[1174] "Detailed match information" refers to detailed data about the match, such as player information, team information, and match progress.

[1175] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and performs advanced tasks such as natural language processing and data analysis.

[1176] A "chatbot" is a system that automatically responds to questions and requests from users, and operates using natural language processing technology.

[1177] "Sports betting options" are options that allow users to predict outcomes or place bets on specific events during or before a game.

[1178] "Live streaming" is a technology that allows users to watch game footage in real time via the Internet.

[1179] "Natural language processing" is a technology that enables computers to understand, analyze, and respond to human language.

[1180] The present invention provides a system that allows users to obtain real-time game information and intuitively access detailed player data when watching a sporting event. The system includes a server, a database, a user terminal, and a generative AI model.

[1181] User Authentication

[1182] A user launches a sports viewing app and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registration information in the SQLite database and returns the user's dashboard data along with an authentication token. The user can then proceed to select a match from the dashboard screen.

[1183] Match selection and information provision

[1184] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the user's device. The user can then display the retrieved detailed match data on their device.

[1185] Real-time data analysis and provision

[1186] While the game is in progress, the server collects game data in real time. This data is acquired from game information providers and analyzed by an AI engine. The analysis results (for example, a player's shooting success rate or number of rebounds) are immediately sent to the user's device, and the screen is updated. This allows the user to check the progress of the game in real time.

[1187] Providing individual player information

[1188] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the user's device. The user can then view detailed player information.

[1189] Chatbot operation

[1190] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a generative AI model (using natural language processing technology such as Spacy, for example) and analyzes the question. Based on the analysis results, it generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is the player's scoring percentage this season?", the server retrieves the player's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[1191] Sports betting features

[1192] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[1193] Specific examples

[1194] As a concrete example, consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data (for example, shooting percentage and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on a player's profile, the server retrieves detailed information about the player and displays it on the device. Also, when the user asks the chatbot, "What is the player's scoring percentage this season?", the server analyzes the question using a generative AI model, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[1195] An example prompt would be "How can I bet 1000 yen on the next goal?"

[1196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1197] Step 1:

[1198] The user operates the device and launches the application. The device displays a login screen, where the user enters their email address and password. After completing the entry, the authentication information is sent to the server.

[1199] Input: Email address, password

[1200] Output: Send authentication information

[1201] Step 2:

[1202] The server receives the authentication information and compares it with the registration information in the database. If the comparison is successful, the server generates an authentication token and the user's dashboard data and returns them to the device.

[1203] Input: Credentials

[1204] Output: Authentication token, dashboard data

[1205] Behavior: Database lookup

[1206] Step 3:

[1207] Based on the received authentication token and dashboard data, the user terminal displays the dashboard screen, allowing the user to proceed with game selection.

[1208] Input: Auth token, dashboard data

[1209] Output: Dashboard screen display

[1210] Operation: Screen update

[1211] Step 4:

[1212] The user displays a list of matches from the dashboard on their device and selects the match they want to watch. The selected match ID is sent from the device to the server.

[1213] Input: Match selection

[1214] Output: Match ID

[1215] Action: Match selection operation

[1216] Step 5:

[1217] The server retrieves detailed information about the selected game (player information, team information, game progress, etc.) from the database and sends it to the user's terminal.

[1218] Input: Match ID

[1219] Output: Match details

[1220] Action:Database access

[1221] Step 6:

[1222] The user terminal displays the acquired detailed data of the match, allowing the user to check the detailed information while watching the match.

[1223] Input: Match details

[1224] Output: Match details screen

[1225] Operation: Screen display

[1226] Step 7:

[1227] During the match, the server collects match data in real time and analyzes it using an AI engine, with the analysis results immediately sent to the user's device.

[1228] Input: Real-time match data

[1229] Output: Analysis results

[1230] Operation: Data collection and analysis

[1231] Step 8:

[1232] The user device updates the screen based on the received analysis results and displays the player's performance data in real time.

[1233] Input: Analysis results

[1234] Output: Updated match screen

[1235] Operation: Screen update

[1236] Step 9:

[1237] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server, which retrieves the player's latest profile, performance, and past match data from the database and sends it to the user's device.

[1238] Input: Player Request

[1239] Output: Player details

[1240] Action:Database access

[1241] Step 10:

[1242] The user terminal displays the received detailed player information so that the user can check it.

[1243] Input: Player details

[1244] Output: Player information screen

[1245] Operation: Screen display

[1246] Step 11:

[1247] When a user types a question into the chatbot, the device sends the question to the server, which analyzes the question using a generative AI model and generates the appropriate database query to retrieve the required information.

[1248] Input: Question

[1249] Output: Answer generation

[1250] Behavior: Analysis by generative AI models

[1251] Step 12:

[1252] The server generates an answer and sends it to the user's device, which then displays it on the screen. For example, if you ask, "What is the player's scoring percentage this season?", the server retrieves the relevant data and generates an answer.

[1253] Input: Generated Answer

[1254] Output: Answer displayed on the screen

[1255] Operation: Screen display

[1256] Step 13:

[1257] When a user selects a betting option, the terminal transmits the bet to the server, which processes the bet and updates the user's betting account.

[1258] Input: Bet details

[1259] Output: Bet result

[1260] Action: Bet processing

[1261] Step 14:

[1262] The server performs calculations based on the bet amount and odds, and sends a bet confirmation message to the terminal, which then displays the confirmation message and notifies the user.

[1263] Input: Calculation result

[1264] Output:Confirmation message

[1265] Operation: calculation, notification

[1266] Example prompt: "How do I bet 1000 yen on the next goal?"

[1267] 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.

[1268] The present invention is a system for enhancing sports viewing, allowing users to access the system from their devices and acquire, analyze, and display game information in real time. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides content optimized for the user. An embodiment of this system is described in detail below.

[1269] User Authentication

[1270] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[1271] Match selection and information provision

[1272] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[1273] Real-time data analysis and provision

[1274] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[1275] Providing individual player information

[1276] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[1277] Chatbot operation

[1278] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[1279] Sports betting features

[1280] (Note: The following applies once sports betting is legalized)

[1281] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[1282] Operating the Emotion Engine

[1283] When a user uses the system, the emotion engine analyzes their emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotion data. For example, if the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is calm, it will provide detailed tactical analysis information and past player data.

[1284] In addition, customizing the interface (for example, changing the color scheme or layout) according to the user's emotional state allows the user to use the system more comfortably.

[1285] Specific examples

[1286] As a specific example, consider a scene in which a user is watching a basketball game.

[1287] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[1288] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[1289] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[1290] As described above, the system of the present invention allows users to analyze the game in real time and receive detailed player information and information based on the user's emotions, greatly improving the enjoyment of watching sports.

[1291] The processing flow will be explained below.

[1292] User Authentication

[1293] User authentication process flow

[1294] Step 1:

[1295] The user starts the device and opens the application's login screen.

[1296] Step 2:

[1297] The user enters their email address and password and clicks the "Login" button.

[1298] Step 3:

[1299] The terminal transmits the entered authentication information to the server.

[1300] Step 4:

[1301] The server receives the authentication information and checks it against the user information in its database.

[1302] Step 5:

[1303] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[1304] Step 6:

[1305] The device displays the dashboard screen.

[1306] Match selection and information provision

[1307] Match selection and information provision processing flow

[1308] Step 1:

[1309] A user views a list of matches from the dashboard.

[1310] Step 2:

[1311] The server retrieves the day's match information from the database and sends it to the terminal.

[1312] Step 3:

[1313] The game information acquired by the device is displayed as a list.

[1314] Step 4:

[1315] The user selects the match they wish to view.

[1316] Step 5:

[1317] The device sends the selected match ID to the server.

[1318] Step 6:

[1319] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[1320] Step 7:

[1321] The server sends the detailed information to the terminal.

[1322] Step 8:

[1323] Displays detailed match data acquired by the device.

[1324] Real-time data analysis and provision

[1325] Real-time data analysis and provision process flow

[1326] Step 1:

[1327] The server collects match data in real time.

[1328] Step 2:

[1329] The server obtains data using the real-time API of the match information provider.

[1330] Step 3:

[1331] The server sends the acquired data to the AI ​​engine.

[1332] Step 4:

[1333] The server analyzes the data using an AI engine and generates analysis results.

[1334] Step 5:

[1335] The server sends the analysis results to the device.

[1336] Step 6:

[1337] The device updates the data in real time and displays it on the screen.

[1338] Providing individual player information

[1339] Processing flow for providing individual player information

[1340] Step 1:

[1341] The user clicks on a particular player icon.

[1342] Step 2:

[1343] The device sends an information request including the player ID to the server.

[1344] Step 3:

[1345] The server retrieves the player's latest profile, performance, and past match data from the database.

[1346] Step 4:

[1347] The server transmits the acquired information to the terminal.

[1348] Step 5:

[1349] The terminal displays player information.

[1350] Chatbot operation

[1351] Chatbot operation process flow

[1352] Step 1:

[1353] The user types a question into the chatbot.

[1354] Step 2:

[1355] The terminal sends a question to the server.

[1356] Step 3:

[1357] The server sends the question to a natural language processing (NLP) engine.

[1358] Step 4:

[1359] The server analyzes the question and understands its meaning.

[1360] Step 5:

[1361] The server generates the appropriate database query.

[1362] Step 6:

[1363] The server creates an answer based on information obtained from the database.

[1364] Step 7:

[1365] The server sends the response to the terminal.

[1366] Step 8:

[1367] The device will display the answer.

[1368] Sports betting feature (after lifting the ban)

[1369] Sports betting function processing flow

[1370] Step 1:

[1371] The user selects a betting option (next goal prediction, match result prediction, etc.).

[1372] Step 2:

[1373] The device sends the selection to the server.

[1374] Step 3:

[1375] The server processes the bet and updates the user's betting account.

[1376] Step 4:

[1377] The server performs the calculation based on the bet amount and odds.

[1378] Step 5:

[1379] The server sends a bet confirmation message to the terminal.

[1380] Step 6:

[1381] The device will display a confirmation message.

[1382] Operating the Emotion Engine

[1383] Emotion Engine Operation Process Flow

[1384] Step 1:

[1385] The user begins using the system and performs input or actions (e.g., posting a comment, asking a question to the chatbot, etc.).

[1386] Step 2:

[1387] The server sends user input and behavioral data to the emotion engine.

[1388] Step 3:

[1389] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1390] Step 4:

[1391] The server selects appropriate content and information based on the recognized emotion data.

[1392] Step 5:

[1393] The server sends the selected content and information to the terminal.

[1394] Step 6:

[1395] The device displays an interface and content that corresponds to the user's emotional state.

[1396] Specific examples

[1397] As a specific example, consider a scene in which a user is watching a basketball game.

[1398] Processing flow of specific example

[1399] Step 1:

[1400] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches.

[1401] Step 2:

[1402] The server retrieves the match data from the database and sends it to the user's device.

[1403] Step 3:

[1404] Displays detailed match data acquired by the device.

[1405] Step 4:

[1406] The server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and transmits it to the terminal.

[1407] Step 5:

[1408] When a user clicks on the profile of Player A, the server retrieves detailed information about Player A and displays it on the device.

[1409] Step 6:

[1410] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. The answer is then displayed on the device.

[1411] Step 7:

[1412] The emotion engine analyzes the user's behavior, and if the user is excited, the server will recommend recent highlight videos and notable plays. If the user is relaxed, the server will provide past data and detailed tactical information.

[1413] Step 8:

[1414] The interface and content are adjusted and displayed according to the user's emotional state.

[1415] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[1416] Example 2

[1417] 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."

[1418] Current sports viewing systems can obtain game information in real time, but they have problems in that they cannot provide optimal content or customize the interface according to the user's emotions and interests. Furthermore, there are limited means to provide detailed information about players or to provide a natural interactive experience in response to user input. As a result, users are unable to efficiently obtain information while watching sports, which can lead to a decrease in satisfaction.

[1419] 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.

[1420] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from the user and providing the player information, and means for analyzing the user's emotional state and providing optimal content and an interface based on the analysis results. This allows the user to obtain match information in real time and be provided with optimal information and content based on their emotions and interests, enabling a more fulfilling sports viewing experience.

[1421] "Authentication information" refers to information such as an email address and password that a user uses to identify themselves.

[1422] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets.

[1423] "Server" refers to a computer system that processes data and communicates with other devices over a network.

[1424] A "database" refers to a computer system or software that organizes and stores specific information.

[1425] "Match data" refers to information generated during a sports match (such as scores, player movements, and the progress of the match).

[1426] "Analysis" refers to the process of analyzing data in detail, including providing information based on the results.

[1427] "Player Information" refers to information related to a specific player, such as profile, performance, and past match data.

[1428] "Emotional state" refers to the user's subjective feelings (excitement, relaxation, etc.), and includes analyzing this to understand the user's current mental state.

[1429] "Content" refers to information and media provided to users (e.g., game highlights, statistics, news articles, etc.).

[1430] "Interface" refers to the screens and operating elements that allow a user to interact with a system.

[1431] A "chatbot" refers to a software program that automatically converses or interacts with a user.

[1432] "Natural language processing" refers to the technology that allows computers to understand human language and generate appropriate responses.

[1433] This invention is a system that allows users to enhance their sports viewing experience, and has the ability to acquire, analyze, and provide game information in real time. In addition, by combining it with an emotion engine that analyzes user emotions, it is possible to provide content optimized for users.

[1434] User Authentication

[1435] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[1436] Match selection and information provision

[1437] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[1438] Real-time data analysis and provision

[1439] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[1440] Providing individual player information

[1441] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[1442] Chatbot operation

[1443] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[1444] Sports betting features

[1445] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[1446] Operating the Emotion Engine

[1447] When a user uses the system, the emotion engine analyzes the user's emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotional data. For example, if the user is excited, the emotion engine will suggest recent highlights and notable plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and past player data. In addition, the system can customize the interface (for example, change the color scheme or layout) according to the user's emotional state, making it easier for users to use the system.

[1448] Specific examples

[1449] As a specific example, consider a scene in which a user is watching a basketball game.

[1450] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[1451] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[1452] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[1453] Example prompts to input to the generative AI model

[1454] For example, prompts like the one below can be input into a generative AI model to generate content tailored to the user's interests.

[1455] 1. "What are the highlights of the match between Team X and Team Y?"

[1456] 2. "Please give me detailed performance data for Player A this season."

[1457] 3. "What is Player B's scoring percentage this season?"

[1458] 4. "Please explain any notable tactics or plays from recent matches."

[1459] 5. "Use an emotion engine to generate content recommendations for excited users."

[1460] These prompts allow the generative AI model to automatically generate detailed and interesting content tailored to the user's needs.

[1461] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1462] User Authentication

[1463] Step 1:

[1464] The user starts up the device and the login screen is displayed. The user enters their email address and password. Based on the input (email address and password), the device generates the data to be sent.

[1465] Step 2:

[1466] The terminal sends the entered authentication information to the server. The server receives this authentication information, compares it with a database based on the input (authentication information), and performs a calculation to determine whether or not authentication is possible.

[1467] Step 3:

[1468] The server checks the registration information in the database to see if authentication is successful. If authentication is successful, the server generates an authentication token and dashboard data for the user. Generates output (authentication token and dashboard data).

[1469] Step 4:

[1470] The server returns the authentication token and dashboard data to the device. The device prepares to display the dashboard screen based on the received data (authentication token and dashboard data).

[1471] Step 5:

[1472] The terminal displays the user's dashboard screen and the user can proceed to select a match. The output (dashboard screen) is displayed.

[1473] Match selection and information provision

[1474] Step 6:

[1475] The user displays the match list from the dashboard. The device sends a request to the server to get the match list. Input (a match list request) is generated.

[1476] Step 7:

[1477] The server retrieves a list of matches from the match database, retrieves data from the database, and outputs it as a list of matches.

[1478] Step 8:

[1479] The server sends a list of matches to the terminal, which displays the list of matches based on the received data (list of matches).

[1480] Step 9:

[1481] The user selects the match they want to view. The device sends the selected match ID to the server. An input (request for match ID) is generated.

[1482] Step 10:

[1483] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database. The server retrieves the data from the database and outputs it as detailed information about the match.

[1484] Step 11:

[1485] The server sends detailed match data to the terminal, which then displays the match details screen based on the received data (detailed match data).

[1486] Real-time data analysis and provision

[1487] Step 12:

[1488] While the match is in progress, the server collects match data in real time from match information providers. Data collection is performed based on input (real-time match data).

[1489] Step 13:

[1490] The server analyzes the collected match data using an AI engine. Data analysis is performed based on the input (collected data) and analysis results are generated.

[1491] Step 14:

[1492] The server sends the analysis results to the user's terminal. The terminal generates output (updated screen data) that updates the screen in real time based on the received data (analysis results).

[1493] Step 15:

[1494] The user terminal updates the screen in real time based on the analysis data and displays the analysis results. Output (display of analysis results) is performed.

[1495] Providing individual player information

[1496] Step 16:

[1497] The user clicks on a specific player icon. The device sends a request for player information to the server. An input (request for player information) is generated.

[1498] Step 17:

[1499] The server retrieves the player's latest profile, performance, and past match data from the database, and outputs the data as player information.

[1500] Step 18:

[1501] The server sends player information to the terminal. The terminal prepares to display the player details screen based on the received data (player information).

[1502] Step 19:

[1503] The terminal displays detailed player information to the user. Output (player details screen) is performed.

[1504] Chatbot operation

[1505] Step 20:

[1506] The user inputs a question to the chatbot. The device sends the question to the server. A request is generated based on the input (question data).

[1507] Step 21:

[1508] The server sends the question to a natural language processing (NLP) engine, which analyzes the question. Data analysis is performed based on the input (question data), and analysis results are generated.

[1509] Step 22:

[1510] The server generates an appropriate database query based on the analysis results. A query is generated based on the input (analysis results).

[1511] Step 23:

[1512] The server retrieves the necessary information from the database, retrieves data from the database, and generates response data.

[1513] Step 24:

[1514] The server generates a response based on the information it has acquired and sends it to the terminal. It generates output (response data).

[1515] Step 25:

[1516] The device displays the generated answer to the user. Output (answer screen) is generated.

[1517] Sports betting features

[1518] Step 26:

[1519] User selects betting options (e.g. next goal prediction, match outcome prediction, etc.) Device sends bet to server Generates input (bet request)

[1520] Step 27:

[1521] The server processes the bet details and updates the user's betting account. The account is updated based on the input (bet details).

[1522] Step 28:

[1523] The server performs calculations based on the bet amount and odds. It performs calculations based on the input (bet amount and odds) and generates a result.

[1524] Step 29:

[1525] The server sends a bet confirmation message to the terminal. Generates an output (confirmation message).

[1526] Step 30:

[1527] The terminal displays a confirmation message and notifies the user. Output (display of confirmation message) is performed.

[1528] Operating the Emotion Engine

[1529] Step 31:

[1530] When a user uses the system, the emotion engine analyzes emotions from the user's input and behavior. Emotion analysis is performed based on the input (user behavior data).

[1531] Step 32:

[1532] The server recommends appropriate content and information based on the analyzed emotional data. Data recommendations are made based on the input (emotional data) and recommended content is generated.

[1533] Step 33:

[1534] If the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and historical player data. An output (recommended content) is generated.

[1535] Step 34:

[1536] The interface is customized (e.g., color scheme or layout changes) according to the user's emotional state, and an output (customized interface) is generated.

[1537] Specific examples

[1538] Step 35:

[1539] As a concrete example, consider a scenario where a user is watching a basketball game. The user opens the app, logs in, and selects the "Team X vs Team Y" game from the list of games. The server retrieves game data from the database and sends it to the user's device. Data is retrieved based on input (a request for game data). During the game, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[1540] Step 36:

[1541] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. An output (answer data) is generated, and the answer is displayed to the user.

[1542] Step 37:

[1543] The emotion engine analyzes the user's emotional state. For example, if the user is determined to be excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information. The output (recommended content) is generated.

[1544] (Application example 2)

[1545] 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."

[1546] Conventional sports viewing systems only provide game information and display analysis results, without customizing the content based on the user's emotions. As a result, the same information is provided regardless of whether the user is excited or relaxed. It is necessary to resolve this shortcoming and enhance the sports viewing experience by recommending optimal content based on the user's emotional state.

[1547] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1548] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, and means for analyzing user emotions and recommending content based on the analysis results, thereby making it possible to provide optimal content according to the user's emotional state.

[1549] "User authentication" is the process of verifying a user's identity based on authentication information entered by the user from a terminal.

[1550] "Detailed match information" refers to data related to the match, such as player information, team information, and match progress.

[1551] "Real-time analysis" is the process of collecting data in a timely manner while the game is in progress and analyzing that data immediately.

[1552] "Player information" refers to detailed data about a specific player, such as the player's profile, performance, and past match data.

[1553] "Emotion analysis" is the process of identifying an emotional state from a user's input and behavior and generating emotional data.

[1554] "Content recommendation" is a function that suggests the best content to watch based on data such as the user's emotional state.

[1555] A "chatbot" is a dialogue system that automatically responds to text entered by a user.

[1556] "Natural language processing (NLP)" is a technology that uses computers to understand, analyze, and generate human language.

[1557] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[1558] The present invention is a system for enhancing the real-time viewing of sports. Specific embodiments of the present invention will be described below.

[1559] Overall system overview

[1560] The system includes a user device (smartphone, computer, etc.), a server, a database, an artificial intelligence (AI) engine, and a natural language processing (NLP) engine. Users log in from their devices and the system acquires, analyzes, and displays match information in real time. It also uses an emotion analysis engine to analyze the user's emotional state and provide optimal content.

[1561] User Authentication

[1562] When a user starts up a device and enters their authentication information (email address and password), the device sends this information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server sends an authentication token and the user's dashboard data back to the device, which displays it and allows the user to proceed to the next step.

[1563] Match selection and information provision

[1564] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server, which retrieves detailed information about the match (player information, team information, match progress, etc.) from the database. The retrieved information is sent to the device and displayed to the user.

[1565] Real-time data analysis and provision

[1566] The server collects game data in real time during the game and analyzes it using an AI engine. The analysis results (e.g., players' shooting success rates, number of rebounds, etc.) are immediately sent to the user's device and updated in real time.

[1567] Providing individual player information

[1568] When a user clicks on a specific player's icon, the device sends a request for player information to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device, allowing the user to view detailed player information.

[1569] Chatbot operation

[1570] When a user types a question into the chatbot, the device sends it to the server, which uses an NLP engine to analyze the question, generate the appropriate database query, and retrieve the necessary information. The generated answer is then sent to the device and displayed to the user.

[1571] Content recommendation using emotion engine

[1572] While the user is using the system, the emotion engine analyzes the user's input and behavior to generate emotion data. Based on the analysis results, the server recommends appropriate content (such as highlight videos or detailed tactical information).

[1573] Specific examples

[1574] Consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data in real time and provides it to the user's device. If the user inputs a question into the chatbot, such as "What is Player X's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. If the collected emotional data indicates that the user is excited, the server will recommend recent highlight videos or notable play scenes.

[1575] Prompt Sentence Examples

[1576] "If a user types, 'I'm really excited about this match!', generate a sentiment analysis result like this. The analysis result should follow this format:

[1577] {

[1578] "happiness": 0.7,

[1579] "anger": 0.1,

[1580] "surprise": 0.2

[1581] }

[1582] "

[1583] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1584] Step 1:

[1585] The user turns on the device and provides authentication information by entering an email address and password.

[1586] Input: User credentials (email address and password)

[1587] Output: Sending authentication information to the server

[1588] Action: The device sends the entered authentication information to the server.

[1589] Step 2:

[1590] The server receives the authentication information and compares it with the registration information in the database. If authentication is successful, it generates an authentication token and the user's dashboard data and returns them to the device.

[1591] Input: The authentication information sent to the server

[1592] Output: Authentication token and user dashboard data

[1593] How it works: The server checks against its database and generates an authentication result.

[1594] Step 3:

[1595] The device receives the authentication token and displays a list of matches on the user's dashboard screen.

[1596] Input: Authentication token and dashboard data sent from the server

[1597] Output: Display of dashboard screen

[1598] What it does: Your device will refresh the dashboard screen and display a list of matches.

[1599] Step 4:

[1600] The user selects the match they want to watch from the dashboard and sends the match ID to the server.

[1601] Input: Match ID selected by user

[1602] Output: Match ID sent to server

[1603] Action: The device sends the selected match ID to the server.

[1604] Step 5:

[1605] The server retrieves detailed information about the match from the database and sends that information to the terminal.

[1606] Input: Match ID sent to the server

[1607] Output: Detailed information about the match

[1608] Operation: The server retrieves data from the database and sends it to the terminal.

[1609] Step 6:

[1610] The terminal receives the detailed information about the match and displays it to the user.

[1611] Input: Match details sent from the server

[1612] Output: Display match information

[1613] What it does: The device displays the match information on the screen.

[1614] Step 7:

[1615] The server collects match data in real time while the match is in progress, analyzes it using an AI engine, and sends the analysis results to the device.

[1616] Input: Match data collected in real time

[1617] Output: Sending analysis results to the terminal

[1618] How it works: The server uses an AI engine to analyze the data and sends the results to the device.

[1619] Step 8:

[1620] The terminal receives the analysis results in real time and displays them to the user.

[1621] Input: Analysis results sent from the server

[1622] Output: Display of analysis results

[1623] Operation: The device reflects and displays the analysis results on the screen.

[1624] Step 9:

[1625] When a user clicks on a particular player icon, the terminal sends a request for player information to the server.

[1626] Input: The player icon clicked by the user

[1627] Output: Request to server for player information

[1628] Operation: The device sends a request for player information to the server.

[1629] Step 10:

[1630] The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the terminal.

[1631] Input: Player information request sent from the device

[1632] Output: Detailed information about the player

[1633] Operation: The server retrieves player information from the database and returns it to the terminal.

[1634] Step 11:

[1635] The terminal receives the detailed information of the player and displays it to the user.

[1636] Input: Player details sent from the server

[1637] Output: Display player information

[1638] Action: The device displays player information on the screen.

[1639] Step 12:

[1640] When a user inputs a question to the chatbot, the terminal sends the question to the server.

[1641] Input: The question entered by the user

[1642] Output: Sending the question to the server

[1643] Operation: The device sends a query to the server.

[1644] Step 13:

[1645] The server analyzes the question using an NLP engine, generates the appropriate database query to retrieve the required information, and sends the generated answer to the device.

[1646] Input: Question sent from terminal

[1647] Output: The generated answer

[1648] How it works: The server uses an NLP engine to analyze the question, generate an answer, and send it to the device.

[1649] Step 14:

[1650] The terminal receives the response and displays it to the user.

[1651] Input: The answer sent by the server

[1652] Output: Show answer

[1653] Action: The device displays the answer on the screen.

[1654] Step 15:

[1655] The emotion engine analyzes user input and behavior, generates emotion data, and provides content appropriate for the device from the server.

[1656] Input: User input and behavioral data

[1657] Output: Parsed emotion data and corresponding content

[1658] Operation: The server uses the emotion engine to analyze the emotion data and sends the corresponding content to the device.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] [Third embodiment]

[1663] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1664] 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.

[1665] 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).

[1666] 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.

[1667] 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.

[1668] 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).

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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."

[1675] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from their terminals and acquire, analyze, and display game information in real time. An embodiment of the system will be described in detail below.

[1676] User Authentication

[1677] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[1678] Match selection and information provision

[1679] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[1680] Real-time data analysis and provision

[1681] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[1682] Providing individual player information

[1683] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[1684] Chatbot operation

[1685] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[1686] Sports betting features

[1687] (Note: The following applies once sports betting is legalized)

[1688] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[1689] Specific examples

[1690] As a specific example, consider a scene in which a user is watching a basketball game.

[1691] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[1692] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[1693] As described above, the system of the present invention allows users to analyze a game in real time and intuitively obtain detailed player information, greatly improving the enjoyment of watching sports.

[1694] The processing flow will be explained below.

[1695] User Authentication

[1696] Step 1:

[1697] The user starts the device and opens the application's login screen.

[1698] Step 2:

[1699] The user enters their email address and password and clicks the "Login" button.

[1700] Step 3:

[1701] The terminal transmits the entered authentication information to the server.

[1702] Step 4:

[1703] The server receives the authentication information and checks it against the user information in its database.

[1704] Step 5:

[1705] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[1706] Step 6:

[1707] The device displays the dashboard screen.

[1708] Match selection and information provision

[1709] Step 1:

[1710] A user views a list of matches from the dashboard.

[1711] Step 2:

[1712] The server retrieves the day's match information from the database and sends it to the terminal.

[1713] Step 3:

[1714] The game information acquired by the device is displayed as a list.

[1715] Step 4:

[1716] The user selects the match they wish to view.

[1717] Step 5:

[1718] The device sends the selected match ID to the server.

[1719] Step 6:

[1720] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[1721] Step 7:

[1722] The server sends the detailed information to the terminal.

[1723] Step 8:

[1724] Displays detailed match data acquired by the device.

[1725] Real-time data analysis and provision

[1726] Step 1:

[1727] The server collects match data in real time.

[1728] Step 2:

[1729] The server obtains data using the real-time API of the match information provider.

[1730] Step 3:

[1731] The server sends the acquired data to the AI ​​engine.

[1732] Step 4:

[1733] The server analyzes the data using an AI engine and generates analysis results.

[1734] Step 5:

[1735] The server sends the analysis results to the device.

[1736] Step 6:

[1737] The device updates the data in real time and displays it on the screen.

[1738] Providing individual player information

[1739] Step 1:

[1740] The user clicks on a particular player icon.

[1741] Step 2:

[1742] The device sends an information request including the player ID to the server.

[1743] Step 3:

[1744] The server retrieves the player's latest profile, performance, and past match data from the database.

[1745] Step 4:

[1746] The server transmits the acquired information to the terminal.

[1747] Step 5:

[1748] The terminal displays player information.

[1749] Chatbot operation

[1750] Step 1:

[1751] The user types a question into the chatbot.

[1752] Step 2:

[1753] The terminal sends a question to the server.

[1754] Step 3:

[1755] The server sends the question to a natural language processing (NLP) engine.

[1756] Step 4:

[1757] The server analyzes the question and understands its meaning.

[1758] Step 5:

[1759] The server generates the appropriate database query.

[1760] Step 6:

[1761] The server creates an answer based on information obtained from the database.

[1762] Step 7:

[1763] The server sends the response to the terminal.

[1764] Step 8:

[1765] The device will display the answer.

[1766] Sports betting feature (after lifting the ban)

[1767] Step 1:

[1768] The user selects a betting option (next goal prediction, match result prediction, etc.).

[1769] Step 2:

[1770] The device sends the selection to the server.

[1771] Step 3:

[1772] The server processes the bet and updates the user's betting account.

[1773] Step 4:

[1774] The server performs the calculation based on the bet amount and odds.

[1775] Step 5:

[1776] The server sends a bet confirmation message to the terminal.

[1777] Step 6:

[1778] The device will display a confirmation message.

[1779] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[1780] Example 1

[1781] 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."

[1782] Conventional sports viewing systems have the problem of making it difficult to easily obtain and analyze detailed match data and player information in real time. They also lack the functionality to quickly and accurately respond when users request detailed information about specific players or matches. Furthermore, they lack the ability to respond to questions via chatbots or incorporate AI engines for real-time analysis, creating a need for an improved user experience.

[1783] 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.

[1784] In this invention, the server includes means for receiving authentication information input by a user from a communication device, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving information for providing detailed match information from an information repository, means for analyzing the match information in real time and displaying the analysis results, means for receiving requests for player information from users and providing the player information, and means for instantly collecting data during the match and analyzing it with an AI engine. This allows users to easily obtain and analyze detailed match data and player information in real time, enabling quick and accurate responses using chatbots and instant analysis of data during the match.

[1785] A "user" is someone who uses the system to obtain match information and player data to enhance the viewing experience.

[1786] A "communication device" is a device, such as a smartphone, tablet, or personal computer, that allows a user to access the system over the Internet.

[1787] "Authentication information" refers to the identification information a user enters to log in to a system, typically a combination of an email address and a password.

[1788] An "information repository" is a database or data storage system where the system stores and manages match information, player data, etc.

[1789] "Analysis results" are the output of match data and player performance data processed by the AI ​​engine.

[1790] "Real-time" refers to the immediacy of obtaining data instantly in accordance with the progress of the game and providing analytical results.

[1791] A "request" is an action in which a user requests specific information (for example, data about a specific player) from the system.

[1792] The "AI engine" is a component that uses artificial intelligence technology to analyze match data and generate performance and statistical information.

[1793] A "chatbot" is an interactive program that receives natural language input from a user, analyzes the question, and generates an appropriate response.

[1794] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from a communication device and acquire, analyze, and display game information in real time. The following describes an embodiment of the present invention in detail.

[1795] User Authentication

[1796] The user starts up the communication device and enters their email address and password on the login screen. The communication device sends this authentication information to the server. The server receives the authentication information and compares it with the registration information stored in a database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the communication device. The communication device displays the dashboard screen, and the user can proceed with game selection.

[1797] Match selection and information provision

[1798] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the communication device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the communication device. The communication device then displays the retrieved detailed data about the match.

[1799] Real-time data analysis and provision

[1800] While the game is in progress, the server collects game data in real time. The real-time data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (e.g., Player A's shooting success rate and number of rebounds) are immediately sent to the user's communication device, and the screen is updated in real time.

[1801] Providing individual player information

[1802] When a user clicks on a specific player's icon, a request for player information is sent from the communication device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the communication device. The user can then view detailed player information.

[1803] Chatbot operation

[1804] When a user enters a question into the chatbot, the communication device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the communication device and displayed on the screen.

[1805] Sports betting features

[1806] (Note: The following applies once sports betting is legalized)

[1807] Once the user selects a betting option (e.g., next goal prediction, match outcome prediction, etc.), the communication device sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the communication device. The communication device displays the confirmation message and notifies the user.

[1808] Specific examples

[1809] As a specific example, consider a scene in which a user is watching a basketball game.

[1810] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves match data from the database and sends it to the user's communication device. During the match, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the communication device. When the user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[1811] Example prompts for generative AI models

[1812] 1. "Log in with your account to view today's matches."

[1813] 2. "Analyze and display real-time performance data for Player A in Match X."

[1814] 3. "What is Player B's profile and what is his scoring percentage this season?"

[1815] 4. "What are the betting options for predicting the next goal?"

[1816] By inputting the above prompt sentences into a generative AI model, ideal response examples that will help the user are generated.

[1817] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1818] Step 1:

[1819] A user starts up a communication device and runs an application.

[1820] What happens: A user taps an app icon to open it.

[1821] Input: User action.

[1822] Output: Launches the app and displays the login screen.

[1823] Step 2:

[1824] The user enters their email address and password on the login screen.

[1825] Specific behavior: The user enters their email address and password into the login form and clicks the login button.

[1826] Enter your email address and password.

[1827] Output: Sends authentication information to the server.

[1828] Step 3:

[1829] The device sends the login information to the server.

[1830] Specific operation: The device sends the entered authentication information to the server via an HTTP POST request.

[1831] Input: Credentials (email address, password).

[1832] Output: Sending authentication information to the server.

[1833] Step 4:

[1834] The server receives the authentication information and checks it against the information stored in a database.

[1835] What happens: The server runs a database query to verify that the email address and password entered match.

[1836] Input: Your credentials.

[1837] Output: Authentication result.

[1838] Step 5:

[1839] If authentication is successful, the server returns an authentication token and the user's dashboard data to the device.

[1840] What happens: The server generates an authentication token and sends back an HTTP response containing the dashboard data.

[1841] Input: Authentication results, dashboard data.

[1842] Output: Authentication token, sending dashboard data.

[1843] Step 6:

[1844] The device displays the dashboard screen.

[1845] Specific operation: Build and display a user interface based on the dashboard data acquired by the device.

[1846] Inputs: Auth token, dashboard data.

[1847] Output: Display of the dashboard screen.

[1848] Step 7:

[1849] The user clicks the "Match List" button in the dashboard to display the match list.

[1850] Specific behavior: The user clicks the "List of Matches" button.

[1851] Input: User action.

[1852] Output: Display a list of matches.

[1853] Step 8:

[1854] The device obtains the match list data from the server.

[1855] Specific operation: The terminal sends an HTTP GET request containing the list data, and the server retrieves the match list from the database and returns it.

[1856] Input: The request to the server.

[1857] Output: Receive match list data.

[1858] Step 9:

[1859] The user selects the match they want to watch from the list of matches.

[1860] What happens: The user clicks on a match.

[1861] Input: User action.

[1862] Output: Get the selected match ID.

[1863] Step 10:

[1864] The device sends the selected match ID to the server.

[1865] Specific behavior: The device sends an HTTP POST request to the server containing the ID of the selected match.

[1866] Input: Selected match ID.

[1867] Output: Sends the match ID to the server.

[1868] Step 11:

[1869] The server retrieves the details of the selected match from the database.

[1870] What happens: The server executes a database query to retrieve detailed data for the match.

[1871] Input: Match ID.

[1872] Output: Get detailed match data.

[1873] Step 12:

[1874] The server sends the match detail data to the terminal.

[1875] Specific operation: The server returns an HTTP response containing the acquired match data to the device.

[1876] Input: Match details data.

[1877] Output: Sending data to the terminal.

[1878] Step 13:

[1879] Displays detailed match data acquired by the device.

[1880] Specific operation: The device updates the user interface based on the match data and displays detailed match information.

[1881] Input: Match details data.

[1882] Output: Display detailed match information.

[1883] Step 14:

[1884] As the match progresses, the server collects match data in real time.

[1885] Specific operation: The server periodically retrieves data from the match information provider using an API.

[1886] Input: Match progress data.

[1887] Output: Real-time data collection.

[1888] Step 15:

[1889] The server analyzes the match data using an AI engine.

[1890] Specific operation: The server inputs game data into the AI ​​engine and analyzes the players' shooting success rate, number of rebounds, etc.

[1891] Input: Real-time match data.

[1892] Output: Generates analysis results.

[1893] Step 16:

[1894] The server transmits the analysis results to the user terminal.

[1895] Specific operation: The server returns an HTTP response containing the analysis results to the user device.

[1896] Input: Analysis results.

[1897] Output: Sending data to the user's terminal.

[1898] Step 17:

[1899] The device displays the analysis results in real time.

[1900] Specific operation: The device receives the analysis data and displays it by updating the screen in real time.

[1901] Input: Analysis result data.

[1902] Output: Real-time display.

[1903] Step 18:

[1904] The user clicks on a specific player icon to request player information.

[1905] Specific behavior: The user clicks on a player icon.

[1906] Input: User action.

[1907] Output: Send player information request.

[1908] Step 19:

[1909] The terminal sends a player information request to the server.

[1910] Specific operation: The device sends an HTTP GET request including the player ID to the server.

[1911] Input: Player ID.

[1912] Output: Sends a request to the server.

[1913] Step 20:

[1914] The server retrieves the latest information about the player from the database.

[1915] What happens: The server executes a database query to retrieve player stats, profiles, etc.

[1916] Input: Player ID.

[1917] Output: Get player information.

[1918] Step 21:

[1919] The server sends player information to the terminal.

[1920] Specific operation: The server returns an HTTP response containing the acquired player information to the terminal.

[1921] Input: Player information data.

[1922] Output: Sending data to the terminal.

[1923] Step 22:

[1924] The device displays detailed information about the player.

[1925] Specific operation: The device updates and displays the user interface based on the player information.

[1926] Input: Player information data.

[1927] Output: Display player details.

[1928] Step 23:

[1929] The user types a question into the chatbot.

[1930] Specific behavior: The user enters a question into the chatbot's input field and clicks the submit button.

[1931] Input: The user's question.

[1932] Output: Sending question data.

[1933] Step 24:

[1934] The terminal sends a question to the server.

[1935] Specific operation: The device sends an HTTP POST request containing a question to the server.

[1936] Input: Question data.

[1937] Output: Sending data to the server.

[1938] Step 25:

[1939] The server analyzes the question using an NLP engine.

[1940] Specific operation: The server inputs a question into the NLP engine and analyzes the question.

[1941] Input: Question data.

[1942] Output: Analysis results.

[1943] Step 26:

[1944] The server generates a database query to retrieve the required information.

[1945] Specific operation: The server generates a database query based on the analysis results and retrieves the required information from the database.

[1946] Input: Analysis results.

[1947] Output: Obtain the required information.

[1948] Step 27:

[1949] The server sends the generated response to the terminal.

[1950] Specific operation: The server returns an HTTP response containing the answer to the device.

[1951] Input: Response data.

[1952] Output: Sending data to the terminal.

[1953] Step 28:

[1954] The device will display the answer on the screen.

[1955] Specific operation: The device updates the screen based on the response data and displays it to the user.

[1956] Input: Response data.

[1957] Output: Display the answer.

[1958] Step 29:

[1959] The user selects a betting option.

[1960] What happens: A user selects an option on a betting screen, such as predicting the next goal.

[1961] Input: User action.

[1962] Output: Selection of bet options.

[1963] Step 30:

[1964] The terminal transmits the bet details to the server.

[1965] What happens: The device sends an HTTP POST request containing the bet details to the server.

[1966] Input: Bet details.

[1967] Output: Sending data to the server.

[1968] Step 31:

[1969] The server processes the bet and updates the user's betting account.

[1970] What happens: The server saves the bet details to a database and updates the betting account information.

[1971] Input: Bet details.

[1972] Output: Updated account information.

[1973] Step 32:

[1974] The server sends a bet confirmation message to the terminal.

[1975] Specific operation: The server sends an HTTP response containing a bet confirmation message back to the terminal.

[1976] Input: A confirmation message.

[1977] Output: Sending data to the terminal.

[1978] Step 33:

[1979] The terminal displays a confirmation message to notify the user.

[1980] Specific behavior: The device displays a confirmation message and issues an alert to notify the user.

[1981] Input: A confirmation message.

[1982] Output: Message display and notification.

[1983] (Application example 1)

[1984] 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."

[1985] In conventional sports viewing systems, users had to obtain game information individually, making it difficult to follow the progress of the game in real time. Furthermore, analysis of player information and game data was often delayed, resulting in a lack of a satisfying viewing experience. Furthermore, there was no system that combined sports betting and natural language question-answering, preventing users from getting a consistent experience.

[1986] 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.

[1987] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, means for users to obtain information in real time while watching live streaming, means for users to select betting options and process the contents of the selections, and means for analyzing natural language questions using a generative AI model and providing appropriate answers. This allows users to analyze matches in real time and intuitively obtain detailed player information, significantly improving the enjoyment of sports watching.

[1988] "User authentication information" refers to personal identification information, such as an email address or password, that a user enters to access a system.

[1989] A "database" is a collection of information that stores detailed match information, player information, analysis results, and other data in an organized manner and can be quickly accessed as needed.

[1990] "Real-time analysis" is the process of instantly analyzing data collected during a match and providing the results immediately to the user's device.

[1991] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, that a user uses to access the system.

[1992] "Detailed match information" refers to detailed data about the match, such as player information, team information, and match progress.

[1993] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and performs advanced tasks such as natural language processing and data analysis.

[1994] A "chatbot" is a system that automatically responds to questions and requests from users, and operates using natural language processing technology.

[1995] "Sports betting options" are options that allow users to predict outcomes or place bets on specific events during or before a game.

[1996] "Live streaming" is a technology that allows users to watch game footage in real time via the Internet.

[1997] "Natural language processing" is a technology that enables computers to understand, analyze, and respond to human language.

[1998] The present invention provides a system that allows users to obtain real-time game information and intuitively access detailed player data when watching a sporting event. The system includes a server, a database, a user terminal, and a generative AI model.

[1999] User Authentication

[2000] A user launches a sports viewing app and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registration information in the SQLite database and returns the user's dashboard data along with an authentication token. The user can then proceed to select a match from the dashboard screen.

[2001] Match selection and information provision

[2002] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the user's device. The user can then display the retrieved detailed match data on their device.

[2003] Real-time data analysis and provision

[2004] While the game is in progress, the server collects game data in real time. This data is acquired from game information providers and analyzed by an AI engine. The analysis results (for example, a player's shooting success rate or number of rebounds) are immediately sent to the user's device, and the screen is updated. This allows the user to check the progress of the game in real time.

[2005] Providing individual player information

[2006] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the user's device. The user can then view detailed player information.

[2007] Chatbot operation

[2008] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a generative AI model (using natural language processing technology such as Spacy, for example) and analyzes the question. Based on the analysis results, it generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is the player's scoring percentage this season?", the server retrieves the player's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[2009] Sports betting features

[2010] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[2011] Specific examples

[2012] As a concrete example, consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data (for example, shooting percentage and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on a player's profile, the server retrieves detailed information about the player and displays it on the device. Also, when the user asks the chatbot, "What is the player's scoring percentage this season?", the server analyzes the question using a generative AI model, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[2013] An example prompt would be "How can I bet 1000 yen on the next goal?"

[2014] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2015] Step 1:

[2016] The user operates the device and launches the application. The device displays a login screen, where the user enters their email address and password. After completing the entry, the authentication information is sent to the server.

[2017] Input: Email address, password

[2018] Output: Send authentication information

[2019] Step 2:

[2020] The server receives the authentication information and compares it with the registration information in the database. If the comparison is successful, the server generates an authentication token and the user's dashboard data and returns them to the device.

[2021] Input: Credentials

[2022] Output: Authentication token, dashboard data

[2023] Behavior: Database lookup

[2024] Step 3:

[2025] Based on the received authentication token and dashboard data, the user terminal displays the dashboard screen, allowing the user to proceed with game selection.

[2026] Input: Auth token, dashboard data

[2027] Output: Dashboard screen display

[2028] Operation: Screen update

[2029] Step 4:

[2030] The user displays a list of matches from the dashboard on their device and selects the match they want to watch. The selected match ID is sent from the device to the server.

[2031] Input: Match selection

[2032] Output: Match ID

[2033] Action: Match selection operation

[2034] Step 5:

[2035] The server retrieves detailed information about the selected game (player information, team information, game progress, etc.) from the database and sends it to the user's terminal.

[2036] Input: Match ID

[2037] Output: Match details

[2038] Action:Database access

[2039] Step 6:

[2040] The user terminal displays the acquired detailed data of the match, allowing the user to check the detailed information while watching the match.

[2041] Input: Match details

[2042] Output: Match details screen

[2043] Operation: Screen display

[2044] Step 7:

[2045] During the match, the server collects match data in real time and analyzes it using an AI engine, with the analysis results immediately sent to the user's device.

[2046] Input: Real-time match data

[2047] Output: Analysis results

[2048] Operation: Data collection and analysis

[2049] Step 8:

[2050] The user device updates the screen based on the received analysis results and displays the player's performance data in real time.

[2051] Input: Analysis results

[2052] Output: Updated match screen

[2053] Operation: Screen update

[2054] Step 9:

[2055] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server, which retrieves the player's latest profile, performance, and past match data from the database and sends it to the user's device.

[2056] Input: Player Request

[2057] Output: Player details

[2058] Action:Database access

[2059] Step 10:

[2060] The user terminal displays the received detailed player information so that the user can check it.

[2061] Input: Player details

[2062] Output: Player information screen

[2063] Operation: Screen display

[2064] Step 11:

[2065] When a user types a question into the chatbot, the device sends the question to the server, which analyzes the question using a generative AI model and generates the appropriate database query to retrieve the required information.

[2066] Input: Question

[2067] Output: Answer generation

[2068] Behavior: Analysis by generative AI models

[2069] Step 12:

[2070] The server generates an answer and sends it to the user's device, which then displays it on the screen. For example, if you ask, "What is the player's scoring percentage this season?", the server retrieves the relevant data and generates an answer.

[2071] Input: Generated Answer

[2072] Output: Answer displayed on the screen

[2073] Operation: Screen display

[2074] Step 13:

[2075] When a user selects a betting option, the terminal transmits the bet to the server, which processes the bet and updates the user's betting account.

[2076] Input: Bet details

[2077] Output: Bet result

[2078] Action: Bet processing

[2079] Step 14:

[2080] The server performs calculations based on the bet amount and odds, and sends a bet confirmation message to the terminal, which then displays the confirmation message and notifies the user.

[2081] Input: Calculation result

[2082] Output:Confirmation message

[2083] Operation: calculation, notification

[2084] Example prompt: "How do I bet 1000 yen on the next goal?"

[2085] 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.

[2086] The present invention is a system for enhancing sports viewing, allowing users to access the system from their devices and acquire, analyze, and display game information in real time. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, the system provides content optimized for the user. An embodiment of this system is described in detail below.

[2087] User Authentication

[2088] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[2089] Match selection and information provision

[2090] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[2091] Real-time data analysis and provision

[2092] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[2093] Providing individual player information

[2094] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[2095] Chatbot operation

[2096] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[2097] Sports betting features

[2098] (Note: The following applies once sports betting is legalized)

[2099] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[2100] Operating the Emotion Engine

[2101] When a user uses the system, the emotion engine analyzes their emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotion data. For example, if the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is calm, it will provide detailed tactical analysis information and past player data.

[2102] In addition, customizing the interface (for example, changing the color scheme or layout) according to the user's emotional state allows the user to use the system more comfortably.

[2103] Specific examples

[2104] As a specific example, consider a scene in which a user is watching a basketball game.

[2105] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[2106] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[2107] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[2108] As described above, the system of the present invention allows users to analyze the game in real time and receive detailed player information and information based on the user's emotions, greatly improving the enjoyment of watching sports.

[2109] The processing flow will be explained below.

[2110] User Authentication

[2111] User authentication process flow

[2112] Step 1:

[2113] The user starts the device and opens the application's login screen.

[2114] Step 2:

[2115] The user enters their email address and password and clicks the "Login" button.

[2116] Step 3:

[2117] The terminal transmits the entered authentication information to the server.

[2118] Step 4:

[2119] The server receives the authentication information and checks it against the user information in its database.

[2120] Step 5:

[2121] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[2122] Step 6:

[2123] The device displays the dashboard screen.

[2124] Match selection and information provision

[2125] Match selection and information provision processing flow

[2126] Step 1:

[2127] A user views a list of matches from the dashboard.

[2128] Step 2:

[2129] The server retrieves the day's match information from the database and sends it to the terminal.

[2130] Step 3:

[2131] The game information acquired by the device is displayed as a list.

[2132] Step 4:

[2133] The user selects the match they wish to view.

[2134] Step 5:

[2135] The device sends the selected match ID to the server.

[2136] Step 6:

[2137] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[2138] Step 7:

[2139] The server sends the detailed information to the terminal.

[2140] Step 8:

[2141] Displays detailed match data acquired by the device.

[2142] Real-time data analysis and provision

[2143] Real-time data analysis and provision process flow

[2144] Step 1:

[2145] The server collects match data in real time.

[2146] Step 2:

[2147] The server obtains data using the real-time API of the match information provider.

[2148] Step 3:

[2149] The server sends the acquired data to the AI ​​engine.

[2150] Step 4:

[2151] The server analyzes the data using an AI engine and generates analysis results.

[2152] Step 5:

[2153] The server sends the analysis results to the device.

[2154] Step 6:

[2155] The device updates the data in real time and displays it on the screen.

[2156] Providing individual player information

[2157] Processing flow for providing individual player information

[2158] Step 1:

[2159] The user clicks on a particular player icon.

[2160] Step 2:

[2161] The device sends an information request including the player ID to the server.

[2162] Step 3:

[2163] The server retrieves the player's latest profile, performance, and past match data from the database.

[2164] Step 4:

[2165] The server transmits the acquired information to the terminal.

[2166] Step 5:

[2167] The terminal displays player information.

[2168] Chatbot operation

[2169] Chatbot operation process flow

[2170] Step 1:

[2171] The user types a question into the chatbot.

[2172] Step 2:

[2173] The terminal sends a question to the server.

[2174] Step 3:

[2175] The server sends the question to a natural language processing (NLP) engine.

[2176] Step 4:

[2177] The server analyzes the question and understands its meaning.

[2178] Step 5:

[2179] The server generates the appropriate database query.

[2180] Step 6:

[2181] The server creates an answer based on information obtained from the database.

[2182] Step 7:

[2183] The server sends the response to the terminal.

[2184] Step 8:

[2185] The device will display the answer.

[2186] Sports betting feature (after lifting the ban)

[2187] Sports betting function processing flow

[2188] Step 1:

[2189] The user selects a betting option (next goal prediction, match result prediction, etc.).

[2190] Step 2:

[2191] The device sends the selection to the server.

[2192] Step 3:

[2193] The server processes the bet and updates the user's betting account.

[2194] Step 4:

[2195] The server performs the calculation based on the bet amount and odds.

[2196] Step 5:

[2197] The server sends a bet confirmation message to the terminal.

[2198] Step 6:

[2199] The device will display a confirmation message.

[2200] Operating the Emotion Engine

[2201] Emotion Engine Operation Process Flow

[2202] Step 1:

[2203] The user begins using the system and performs input or actions (e.g., posting a comment, asking a question to the chatbot, etc.).

[2204] Step 2:

[2205] The server sends user input and behavioral data to the emotion engine.

[2206] Step 3:

[2207] The emotion engine analyzes the received data and recognizes the user's emotional state.

[2208] Step 4:

[2209] The server selects appropriate content and information based on the recognized emotion data.

[2210] Step 5:

[2211] The server sends the selected content and information to the terminal.

[2212] Step 6:

[2213] The device displays an interface and content that corresponds to the user's emotional state.

[2214] Specific examples

[2215] As a specific example, consider a scene in which a user is watching a basketball game.

[2216] Processing flow of specific example

[2217] Step 1:

[2218] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches.

[2219] Step 2:

[2220] The server retrieves the match data from the database and sends it to the user's device.

[2221] Step 3:

[2222] Displays detailed match data acquired by the device.

[2223] Step 4:

[2224] The server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and transmits it to the terminal.

[2225] Step 5:

[2226] When a user clicks on the profile of Player A, the server retrieves detailed information about Player A and displays it on the device.

[2227] Step 6:

[2228] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. The answer is then displayed on the device.

[2229] Step 7:

[2230] The emotion engine analyzes the user's behavior, and if the user is excited, the server will recommend recent highlight videos and notable plays. If the user is relaxed, the server will provide past data and detailed tactical information.

[2231] Step 8:

[2232] The interface and content are adjusted and displayed according to the user's emotional state.

[2233] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[2234] Example 2

[2235] 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."

[2236] Current sports viewing systems can obtain game information in real time, but they have problems in that they cannot provide optimal content or customize the interface according to the user's emotions and interests. Furthermore, there are limited means to provide detailed information about players or to provide a natural interactive experience in response to user input. As a result, users are unable to efficiently obtain information while watching sports, which can lead to a decrease in satisfaction.

[2237] 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.

[2238] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from the user and providing the player information, and means for analyzing the user's emotional state and providing optimal content and an interface based on the analysis results. This allows the user to obtain match information in real time and be provided with optimal information and content based on their emotions and interests, enabling a more fulfilling sports viewing experience.

[2239] "Authentication information" refers to information such as an email address and password that a user uses to identify themselves.

[2240] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets.

[2241] "Server" refers to a computer system that processes data and communicates with other devices over a network.

[2242] A "database" refers to a computer system or software that organizes and stores specific information.

[2243] "Match data" refers to information generated during a sports match (such as scores, player movements, and the progress of the match).

[2244] "Analysis" refers to the process of analyzing data in detail, including providing information based on the results.

[2245] "Player Information" refers to information related to a specific player, such as profile, performance, and past match data.

[2246] "Emotional state" refers to the user's subjective feelings (excitement, relaxation, etc.), and includes analyzing this to understand the user's current mental state.

[2247] "Content" refers to information and media provided to users (e.g., game highlights, statistics, news articles, etc.).

[2248] "Interface" refers to the screens and operating elements that allow a user to interact with a system.

[2249] A "chatbot" refers to a software program that automatically converses or interacts with a user.

[2250] "Natural language processing" refers to the technology that allows computers to understand human language and generate appropriate responses.

[2251] This invention is a system that allows users to enhance their sports viewing experience, and has the ability to acquire, analyze, and provide game information in real time. In addition, by combining it with an emotion engine that analyzes user emotions, it is possible to provide content optimized for users.

[2252] User Authentication

[2253] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[2254] Match selection and information provision

[2255] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[2256] Real-time data analysis and provision

[2257] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[2258] Providing individual player information

[2259] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[2260] Chatbot operation

[2261] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[2262] Sports betting features

[2263] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[2264] Operating the Emotion Engine

[2265] When a user uses the system, the emotion engine analyzes the user's emotions from their input and actions (for example, comments posted or questions asked to the chatbot). The server then recommends appropriate content and information based on the analyzed emotional data. For example, if the user is excited, the emotion engine will suggest recent highlights and notable plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and past player data. In addition, the system can customize the interface (for example, change the color scheme or layout) according to the user's emotional state, making it easier for users to use the system.

[2266] Specific examples

[2267] As a specific example, consider a scene in which a user is watching a basketball game.

[2268] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[2269] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[2270] Furthermore, the emotion engine analyzes the user's emotional state, and if it determines that the user is excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information.

[2271] Example prompts to input to the generative AI model

[2272] For example, prompts like the one below can be input into a generative AI model to generate content tailored to the user's interests.

[2273] 1. "What are the highlights of the match between Team X and Team Y?"

[2274] 2. "Please give me detailed performance data for Player A this season."

[2275] 3. "What is Player B's scoring percentage this season?"

[2276] 4. "Please explain any notable tactics or plays from recent matches."

[2277] 5. "Use an emotion engine to generate content recommendations for excited users."

[2278] These prompts allow the generative AI model to automatically generate detailed and interesting content tailored to the user's needs.

[2279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2280] User Authentication

[2281] Step 1:

[2282] The user starts up the device and the login screen is displayed. The user enters their email address and password. Based on the input (email address and password), the device generates the data to be sent.

[2283] Step 2:

[2284] The terminal sends the entered authentication information to the server. The server receives this authentication information, compares it with a database based on the input (authentication information), and performs a calculation to determine whether or not authentication is possible.

[2285] Step 3:

[2286] The server checks the registration information in the database to see if authentication is successful. If authentication is successful, the server generates an authentication token and dashboard data for the user. Generates output (authentication token and dashboard data).

[2287] Step 4:

[2288] The server returns the authentication token and dashboard data to the device. The device prepares to display the dashboard screen based on the received data (authentication token and dashboard data).

[2289] Step 5:

[2290] The terminal displays the user's dashboard screen and the user can proceed to select a match. The output (dashboard screen) is displayed.

[2291] Match selection and information provision

[2292] Step 6:

[2293] The user displays the match list from the dashboard. The device sends a request to the server to get the match list. Input (a match list request) is generated.

[2294] Step 7:

[2295] The server retrieves a list of matches from the match database, retrieves data from the database, and outputs it as a list of matches.

[2296] Step 8:

[2297] The server sends a list of matches to the terminal, which displays the list of matches based on the received data (list of matches).

[2298] Step 9:

[2299] The user selects the match they want to view. The device sends the selected match ID to the server. An input (request for match ID) is generated.

[2300] Step 10:

[2301] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database. The server retrieves the data from the database and outputs it as detailed information about the match.

[2302] Step 11:

[2303] The server sends detailed match data to the terminal, which then displays the match details screen based on the received data (detailed match data).

[2304] Real-time data analysis and provision

[2305] Step 12:

[2306] While the match is in progress, the server collects match data in real time from match information providers. Data collection is performed based on input (real-time match data).

[2307] Step 13:

[2308] The server analyzes the collected match data using an AI engine. Data analysis is performed based on the input (collected data) and analysis results are generated.

[2309] Step 14:

[2310] The server sends the analysis results to the user's terminal. The terminal generates output (updated screen data) that updates the screen in real time based on the received data (analysis results).

[2311] Step 15:

[2312] The user terminal updates the screen in real time based on the analysis data and displays the analysis results. Output (display of analysis results) is performed.

[2313] Providing individual player information

[2314] Step 16:

[2315] The user clicks on a specific player icon. The device sends a request for player information to the server. An input (request for player information) is generated.

[2316] Step 17:

[2317] The server retrieves the player's latest profile, performance, and past match data from the database, and outputs the data as player information.

[2318] Step 18:

[2319] The server sends player information to the terminal. The terminal prepares to display the player details screen based on the received data (player information).

[2320] Step 19:

[2321] The terminal displays detailed player information to the user. Output (player details screen) is performed.

[2322] Chatbot operation

[2323] Step 20:

[2324] The user inputs a question to the chatbot. The device sends the question to the server. A request is generated based on the input (question data).

[2325] Step 21:

[2326] The server sends the question to a natural language processing (NLP) engine, which analyzes the question. Data analysis is performed based on the input (question data), and analysis results are generated.

[2327] Step 22:

[2328] The server generates an appropriate database query based on the analysis results. A query is generated based on the input (analysis results).

[2329] Step 23:

[2330] The server retrieves the necessary information from the database, retrieves data from the database, and generates response data.

[2331] Step 24:

[2332] The server generates a response based on the information it has acquired and sends it to the terminal. It generates output (response data).

[2333] Step 25:

[2334] The device displays the generated answer to the user. Output (answer screen) is generated.

[2335] Sports betting features

[2336] Step 26:

[2337] User selects betting options (e.g. next goal prediction, match outcome prediction, etc.) Device sends bet to server Generates input (bet request)

[2338] Step 27:

[2339] The server processes the bet details and updates the user's betting account. The account is updated based on the input (bet details).

[2340] Step 28:

[2341] The server performs calculations based on the bet amount and odds. It performs calculations based on the input (bet amount and odds) and generates a result.

[2342] Step 29:

[2343] The server sends a bet confirmation message to the terminal. Generates an output (confirmation message).

[2344] Step 30:

[2345] The terminal displays a confirmation message and notifies the user. Output (display of confirmation message) is performed.

[2346] Operating the Emotion Engine

[2347] Step 31:

[2348] When a user uses the system, the emotion engine analyzes emotions from the user's input and behavior. Emotion analysis is performed based on the input (user behavior data).

[2349] Step 32:

[2350] The server recommends appropriate content and information based on the analyzed emotional data. Data recommendations are made based on the input (emotional data) and recommended content is generated.

[2351] Step 33:

[2352] If the user is excited, the emotion engine will suggest recent highlights and noteworthy plays. Conversely, if the user is relaxed, it will provide detailed tactical analysis information and historical player data. An output (recommended content) is generated.

[2353] Step 34:

[2354] The interface is customized (e.g., color scheme or layout changes) according to the user's emotional state, and an output (customized interface) is generated.

[2355] Specific examples

[2356] Step 35:

[2357] As a concrete example, consider a scenario where a user is watching a basketball game. The user opens the app, logs in, and selects the "Team X vs Team Y" game from the list of games. The server retrieves game data from the database and sends it to the user's device. Data is retrieved based on input (a request for game data). During the game, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[2358] Step 36:

[2359] When a user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. An output (answer data) is generated, and the answer is displayed to the user.

[2360] Step 37:

[2361] The emotion engine analyzes the user's emotional state. For example, if the user is determined to be excited, it will recommend recent highlight videos or notable play scenes. Conversely, if the user is relaxed, it will provide past data and detailed tactical information. The output (recommended content) is generated.

[2362] (Application example 2)

[2363] 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."

[2364] Conventional sports viewing systems only provide game information and display analysis results, without customizing the content based on the user's emotions. As a result, the same information is provided regardless of whether the user is excited or relaxed. It is necessary to resolve this shortcoming and enhance the sports viewing experience by recommending optimal content based on the user's emotional state.

[2365] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2366] In this invention, the server includes means for receiving authentication information input by a user from a terminal, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving data for providing detailed match information from a database, means for analyzing match data in real time and displaying the analysis results, means for receiving requests for player information from users and providing player information, and means for analyzing user emotions and recommending content based on the analysis results, thereby making it possible to provide optimal content according to the user's emotional state.

[2367] "User authentication" is the process of verifying a user's identity based on authentication information entered by the user from a terminal.

[2368] "Detailed match information" refers to data related to the match, such as player information, team information, and match progress.

[2369] "Real-time analysis" is the process of collecting data in a timely manner while the game is in progress and analyzing that data immediately.

[2370] "Player information" refers to detailed data about a specific player, such as the player's profile, performance, and past match data.

[2371] "Emotion analysis" is the process of identifying an emotional state from a user's input and behavior and generating emotional data.

[2372] "Content recommendation" is a function that suggests the best content to watch based on data such as the user's emotional state.

[2373] A "chatbot" is a dialogue system that automatically responds to text entered by a user.

[2374] "Natural language processing (NLP)" is a technology that uses computers to understand, analyze, and generate human language.

[2375] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[2376] The present invention is a system for enhancing the real-time viewing of sports. Specific embodiments of the present invention will be described below.

[2377] Overall system overview

[2378] The system includes a user device (smartphone, computer, etc.), a server, a database, an artificial intelligence (AI) engine, and a natural language processing (NLP) engine. Users log in from their devices and the system acquires, analyzes, and displays match information in real time. It also uses an emotion analysis engine to analyze the user's emotional state and provide optimal content.

[2379] User Authentication

[2380] When a user starts up a device and enters their authentication information (email address and password), the device sends this information to the server. The server receives the authentication information and checks it against the registration information in its database. If authentication is successful, the server sends an authentication token and the user's dashboard data back to the device, which displays it and allows the user to proceed to the next step.

[2381] Match selection and information provision

[2382] The user displays a list of matches from the dashboard and selects the match they wish to watch. The selected match ID is sent from the device to the server, which retrieves detailed information about the match (player information, team information, match progress, etc.) from the database. The retrieved information is sent to the device and displayed to the user.

[2383] Real-time data analysis and provision

[2384] The server collects game data in real time during the game and analyzes it using an AI engine. The analysis results (e.g., players' shooting success rates, number of rebounds, etc.) are immediately sent to the user's device and updated in real time.

[2385] Providing individual player information

[2386] When a user clicks on a specific player's icon, the device sends a request for player information to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device, allowing the user to view detailed player information.

[2387] Chatbot operation

[2388] When a user types a question into the chatbot, the device sends it to the server, which uses an NLP engine to analyze the question, generate the appropriate database query, and retrieve the necessary information. The generated answer is then sent to the device and displayed to the user.

[2389] Content recommendation using emotion engine

[2390] While the user is using the system, the emotion engine analyzes the user's input and behavior to generate emotion data. Based on the analysis results, the server recommends appropriate content (such as highlight videos or detailed tactical information).

[2391] Specific examples

[2392] Consider a scenario in which a user is watching a basketball game. The user opens the app, logs in, and selects the "Team A vs Team B" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes players' performance data in real time and provides it to the user's device. If the user inputs a question into the chatbot, such as "What is Player X's scoring percentage this season?", the server uses an NLP engine to analyze the question, retrieves the necessary information from the database, and generates an answer. If the collected emotional data indicates that the user is excited, the server will recommend recent highlight videos or notable play scenes.

[2393] Prompt Sentence Examples

[2394] "If a user types, 'I'm really excited about this match!', generate a sentiment analysis result like this. The analysis result should follow this format:

[2395] {

[2396] "happiness": 0.7,

[2397] "anger": 0.1,

[2398] "surprise": 0.2

[2399] }

[2400] "

[2401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2402] Step 1:

[2403] The user turns on the device and provides authentication information by entering an email address and password.

[2404] Input: User credentials (email address and password)

[2405] Output: Sending authentication information to the server

[2406] Action: The device sends the entered authentication information to the server.

[2407] Step 2:

[2408] The server receives the authentication information and compares it with the registration information in the database. If authentication is successful, it generates an authentication token and the user's dashboard data and returns them to the device.

[2409] Input: The authentication information sent to the server

[2410] Output: Authentication token and user dashboard data

[2411] How it works: The server checks against its database and generates an authentication result.

[2412] Step 3:

[2413] The device receives the authentication token and displays a list of matches on the user's dashboard screen.

[2414] Input: Authentication token and dashboard data sent from the server

[2415] Output: Display of dashboard screen

[2416] What it does: Your device will refresh the dashboard screen and display a list of matches.

[2417] Step 4:

[2418] The user selects the match they want to watch from the dashboard and sends the match ID to the server.

[2419] Input: Match ID selected by user

[2420] Output: Match ID sent to server

[2421] Action: The device sends the selected match ID to the server.

[2422] Step 5:

[2423] The server retrieves detailed information about the match from the database and sends that information to the terminal.

[2424] Input: Match ID sent to the server

[2425] Output: Detailed information about the match

[2426] Operation: The server retrieves data from the database and sends it to the terminal.

[2427] Step 6:

[2428] The terminal receives the detailed information about the match and displays it to the user.

[2429] Input: Match details sent from the server

[2430] Output: Display match information

[2431] What it does: The device displays the match information on the screen.

[2432] Step 7:

[2433] The server collects match data in real time while the match is in progress, analyzes it using an AI engine, and sends the analysis results to the device.

[2434] Input: Match data collected in real time

[2435] Output: Sending analysis results to the terminal

[2436] How it works: The server uses an AI engine to analyze the data and sends the results to the device.

[2437] Step 8:

[2438] The terminal receives the analysis results in real time and displays them to the user.

[2439] Input: Analysis results sent from the server

[2440] Output: Display of analysis results

[2441] Operation: The device reflects and displays the analysis results on the screen.

[2442] Step 9:

[2443] When a user clicks on a particular player icon, the terminal sends a request for player information to the server.

[2444] Input: The player icon clicked by the user

[2445] Output: Request to server for player information

[2446] Operation: The device sends a request for player information to the server.

[2447] Step 10:

[2448] The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the terminal.

[2449] Input: Player information request sent from the device

[2450] Output: Detailed information about the player

[2451] Operation: The server retrieves player information from the database and returns it to the terminal.

[2452] Step 11:

[2453] The terminal receives the detailed information of the player and displays it to the user.

[2454] Input: Player details sent from the server

[2455] Output: Display player information

[2456] Action: The device displays player information on the screen.

[2457] Step 12:

[2458] When a user inputs a question to the chatbot, the terminal sends the question to the server.

[2459] Input: The question entered by the user

[2460] Output: Sending the question to the server

[2461] Operation: The device sends a query to the server.

[2462] Step 13:

[2463] The server analyzes the question using an NLP engine, generates the appropriate database query to retrieve the required information, and sends the generated answer to the device.

[2464] Input: Question sent from terminal

[2465] Output: The generated answer

[2466] How it works: The server uses an NLP engine to analyze the question, generate an answer, and send it to the device.

[2467] Step 14:

[2468] The terminal receives the response and displays it to the user.

[2469] Input: The answer sent by the server

[2470] Output: Show answer

[2471] Action: The device displays the answer on the screen.

[2472] Step 15:

[2473] The emotion engine analyzes user input and behavior, generates emotion data, and provides content appropriate for the device from the server.

[2474] Input: User input and behavioral data

[2475] Output: Parsed emotion data and corresponding content

[2476] Operation: The server uses the emotion engine to analyze the emotion data and sends the corresponding content to the device.

[2477] 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.

[2478] 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.

[2479] 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.

[2480] [Fourth embodiment]

[2481] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2482] 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.

[2483] 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).

[2484] 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.

[2485] 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.

[2486] 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).

[2487] 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.

[2488] 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.

[2489] 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.

[2490] 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.

[2491] 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.

[2492] 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.

[2493] 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."

[2494] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from their terminals and acquire, analyze, and display game information in real time. An embodiment of the system will be described in detail below.

[2495] User Authentication

[2496] The user starts up the device and enters their email address and password on the login screen. The device sends this authentication information to the server. The server receives the authentication information and compares it with the registered information in its database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the device. The device displays the dashboard screen, and the user can proceed to select a match.

[2497] Match selection and information provision

[2498] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the device. The device displays the retrieved detailed data about the match.

[2499] Real-time data analysis and provision

[2500] While the game is in progress, the server collects game data in real time. This data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (for example, Player A's shooting success rate and number of rebounds) are immediately sent to the user's device, and the screen is updated in real time.

[2501] Providing individual player information

[2502] When a user clicks on a specific player's icon, a request for player information is sent from the device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the device. The user can then view detailed player information.

[2503] Chatbot operation

[2504] When a user enters a question into the chatbot, the device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the device and displayed on the screen.

[2505] Sports betting features

[2506] (Note: The following applies once sports betting is legalized)

[2507] Once the user selects a betting option (e.g., next goal prediction, match result prediction, etc.), the terminal sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the terminal. The terminal displays the confirmation message and notifies the user.

[2508] Specific examples

[2509] As a specific example, consider a scene in which a user is watching a basketball game.

[2510] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves the match data from the database and sends it to the user's device. During the match, the server analyzes Player A's performance data (e.g., shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the device.

[2511] For example, if a user asks the chatbot, "What is Player B's scoring percentage this season?", the server will analyze the question using the NLP engine, retrieve the necessary information from the database, and generate an answer, which will be displayed to the user.

[2512] As described above, the system of the present invention allows users to analyze a game in real time and intuitively obtain detailed player information, greatly improving the enjoyment of watching sports.

[2513] The processing flow will be explained below.

[2514] User Authentication

[2515] Step 1:

[2516] The user starts the device and opens the application's login screen.

[2517] Step 2:

[2518] The user enters their email address and password and clicks the "Login" button.

[2519] Step 3:

[2520] The terminal transmits the entered authentication information to the server.

[2521] Step 4:

[2522] The server receives the authentication information and checks it against the user information in its database.

[2523] Step 5:

[2524] If the server is successful in authentication, it generates an authentication token and returns the user's dashboard data to the terminal.

[2525] Step 6:

[2526] The device displays the dashboard screen.

[2527] Match selection and information provision

[2528] Step 1:

[2529] A user views a list of matches from the dashboard.

[2530] Step 2:

[2531] The server retrieves the day's match information from the database and sends it to the terminal.

[2532] Step 3:

[2533] The game information acquired by the device is displayed as a list.

[2534] Step 4:

[2535] The user selects the match they wish to view.

[2536] Step 5:

[2537] The device sends the selected match ID to the server.

[2538] Step 6:

[2539] The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database.

[2540] Step 7:

[2541] The server sends the detailed information to the terminal.

[2542] Step 8:

[2543] Displays detailed match data acquired by the device.

[2544] Real-time data analysis and provision

[2545] Step 1:

[2546] The server collects match data in real time.

[2547] Step 2:

[2548] The server obtains data using the real-time API of the match information provider.

[2549] Step 3:

[2550] The server sends the acquired data to the AI ​​engine.

[2551] Step 4:

[2552] The server analyzes the data using an AI engine and generates analysis results.

[2553] Step 5:

[2554] The server sends the analysis results to the device.

[2555] Step 6:

[2556] The device updates the data in real time and displays it on the screen.

[2557] Providing individual player information

[2558] Step 1:

[2559] The user clicks on a particular player icon.

[2560] Step 2:

[2561] The device sends an information request including the player ID to the server.

[2562] Step 3:

[2563] The server retrieves the player's latest profile, performance, and past match data from the database.

[2564] Step 4:

[2565] The server transmits the acquired information to the terminal.

[2566] Step 5:

[2567] The terminal displays player information.

[2568] Chatbot operation

[2569] Step 1:

[2570] The user types a question into the chatbot.

[2571] Step 2:

[2572] The terminal sends a question to the server.

[2573] Step 3:

[2574] The server sends the question to a natural language processing (NLP) engine.

[2575] Step 4:

[2576] The server analyzes the question and understands its meaning.

[2577] Step 5:

[2578] The server generates the appropriate database query.

[2579] Step 6:

[2580] The server creates an answer based on information obtained from the database.

[2581] Step 7:

[2582] The server sends the response to the terminal.

[2583] Step 8:

[2584] The device will display the answer.

[2585] Sports betting feature (after lifting the ban)

[2586] Step 1:

[2587] The user selects a betting option (next goal prediction, match result prediction, etc.).

[2588] Step 2:

[2589] The device sends the selection to the server.

[2590] Step 3:

[2591] The server processes the bet and updates the user's betting account.

[2592] Step 4:

[2593] The server performs the calculation based on the bet amount and odds.

[2594] Step 5:

[2595] The server sends a bet confirmation message to the terminal.

[2596] Step 6:

[2597] The device will display a confirmation message.

[2598] The above are the specific processing steps and operations of the "Sports Spectator Master" system.

[2599] Example 1

[2600] 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."

[2601] Conventional sports viewing systems have the problem of making it difficult to easily obtain and analyze detailed match data and player information in real time. They also lack the functionality to quickly and accurately respond when users request detailed information about specific players or matches. Furthermore, they lack the ability to respond to questions via chatbots or incorporate AI engines for real-time analysis, creating a need for an improved user experience.

[2602] 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.

[2603] In this invention, the server includes means for receiving authentication information input by a user from a communication device, means for authenticating the user based on the authentication information, means for the user to select detailed match information, means for retrieving information for providing detailed match information from an information repository, means for analyzing the match information in real time and displaying the analysis results, means for receiving requests for player information from users and providing the player information, and means for instantly collecting data during the match and analyzing it with an AI engine. This allows users to easily obtain and analyze detailed match data and player information in real time, enabling quick and accurate responses using chatbots and instant analysis of data during the match.

[2604] A "user" is someone who uses the system to obtain match information and player data to enhance the viewing experience.

[2605] A "communication device" is a device, such as a smartphone, tablet, or personal computer, that allows a user to access the system over the Internet.

[2606] "Authentication information" refers to the identification information a user enters to log in to a system, typically a combination of an email address and a password.

[2607] An "information repository" is a database or data storage system where the system stores and manages match information, player data, etc.

[2608] "Analysis results" are the output of match data and player performance data processed by the AI ​​engine.

[2609] "Real-time" refers to the immediacy of obtaining data instantly in accordance with the progress of the game and providing analytical results.

[2610] A "request" is an action in which a user requests specific information (for example, data about a specific player) from the system.

[2611] The "AI engine" is a component that uses artificial intelligence technology to analyze match data and generate performance and statistical information.

[2612] A "chatbot" is an interactive program that receives natural language input from a user, analyzes the question, and generates an appropriate response.

[2613] The present invention provides a system for enhancing the sports viewing experience, allowing users to access the system from a communication device and acquire, analyze, and display game information in real time. The following describes an embodiment of the present invention in detail.

[2614] User Authentication

[2615] The user starts up the communication device and enters their email address and password on the login screen. The communication device sends this authentication information to the server. The server receives the authentication information and compares it with the registration information stored in a database. If authentication is successful, the server returns an authentication token and the user's dashboard data to the communication device. The communication device displays the dashboard screen, and the user can proceed with game selection.

[2616] Match selection and information provision

[2617] The user displays a list of matches from the dashboard and selects the match they wish to view. The selected match ID is sent from the communication device to the server. The server retrieves detailed information about the selected match (player information, team information, match progress, etc.) from the database and sends it to the communication device. The communication device then displays the retrieved detailed data about the match.

[2618] Real-time data analysis and provision

[2619] While the game is in progress, the server collects game data in real time. The real-time data is acquired from the game information provider and analyzed by the AI ​​engine. The analysis results (e.g., Player A's shooting success rate and number of rebounds) are immediately sent to the user's communication device, and the screen is updated in real time.

[2620] Providing individual player information

[2621] When a user clicks on a specific player's icon, a request for player information is sent from the communication device to the server. The server retrieves the player's latest profile, performance, and past match data from the database and sends them to the communication device. The user can then view detailed player information.

[2622] Chatbot operation

[2623] When a user enters a question into the chatbot, the communication device sends the question to the server. The server then sends the question to a natural language processing (NLP) engine, which analyzes the question. Based on the analysis results, the server generates an appropriate database query to retrieve the required information. For example, in response to the question, "What is Player B's scoring percentage this season?", the server retrieves Player B's scoring percentage from the database and generates an answer. The generated answer is sent to the communication device and displayed on the screen.

[2624] Sports betting features

[2625] (Note: The following applies once sports betting is legalized)

[2626] Once the user selects a betting option (e.g., next goal prediction, match outcome prediction, etc.), the communication device sends the bet details to the server. The server processes the bet details and updates the user's betting account. It performs calculations based on the bet amount and odds and sends a bet confirmation message to the communication device. The communication device displays the confirmation message and notifies the user.

[2627] Specific examples

[2628] As a specific example, consider a scene in which a user is watching a basketball game.

[2629] A user opens the app, logs in, and selects the "Team X vs Team Y" match from the list of matches. The server retrieves match data from the database and sends it to the user's communication device. During the match, the server analyzes Player A's performance data (for example, shooting success rate and number of assists) in real time and provides it to the user. Furthermore, when the user clicks on Player A's profile, the server retrieves Player A's detailed information and displays it on the communication device. When the user asks the chatbot, "What is Player B's scoring percentage this season?", the server analyzes the question using an NLP engine, retrieves the necessary information from the database, and generates an answer. The answer is displayed to the user.

[2630] Example prompts for generative AI models

[2631] 1. "Log in with your account to view today's matches."

[2632] 2. "Analyze and display real-time performance data for Player A in Match X."

[2633] 3. "What is Player B's profile and what is his scoring percentage this season?"

[2634] 4. "What are the betting options for predicting the next goal?"

[2635] By inputting the above prompt sentences into a generative AI model, ideal response examples that will help the user are generated.

[2636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2637] Step 1:

[2638] A user starts up a communication device and runs an application.

[2639] What happens: A user taps an app icon to open it.

[2640] Input: User action.

[2641] Output: Launches the app and displays the login screen.

[2642] Step 2:

[2643] The user enters their email address and password on the login screen.

[2644] Specific behavior: The user enters their email address and password into the login form and clicks the login button.

[2645] Enter your email address and password.

[2646] Output: Sends authentication information to the server. 【2...

Claims

1. means for receiving authentication information input by a user from a terminal; means for authenticating a user based on the authentication information; a means for a user to select match details; a means for retrieving data from a database to provide match details; A means of analyzing match data in real time and displaying the analysis results; means for receiving a request for player information from a user and providing the player information; A system including:

2. The system of claim 1 , further comprising means for the chatbot to receive user input, analyze the question using natural language processing, and generate and provide an appropriate answer.

3. 2. The system of claim 1, further comprising an AI engine for continuously acquiring and analyzing game data in real time.

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