System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and storing users' information in a database; means for training a generative AI model using the stored information to generate prediction results; means for displaying the generated prediction results on the users' terminals; means for receiving and storing wagering information from the terminals in the database; means for updating game results in real time and delivering the information to the users' terminals; and means for supporting message exchange between users.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] Existing sports betting services have issues such as not being able to be enjoyed in areas where gambling is illegal, fantasy sports being predictable and lacking in novelty, difficulty for users to earn profits with a high winning rate, and a lack of real-time information while the competition is in progress. [Means for solving the problem]

[0005] This invention provides a sports betting system using a generative AI model. The system includes means for receiving user information and storing it in a database, means for acquiring data about sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's device, means for receiving user betting information and storing it in a database, means for updating game results in real time and delivering that information to the user's device, and means for supporting message exchanges between users. This allows users to enjoy sports betting legally even in jurisdictions where it is illegal, and allows users to enjoy a fresh experience and earn profits with a high win rate based on the provision of real-time information and the highly accurate predictions made by the generative AI.

[0006] "User" refers to an individual or group that uses this system.

[0007] "Database" refers to an information management system for storing and managing user information, sports match data, betting information, and predicted results.

[0008] "Sports Match Data" means statistical data and information relating to a sporting event, such as player and team performance, match results, and match details.

[0009] "Generative AI model" refers to a mathematical model that uses machine learning and artificial intelligence techniques to predict the outcome of a sports match.

[0010] "Predicted results" refers to information about the outcome of a match, such as the probability of winning or losing a match and score predictions, generated by a generative AI model.

[0011] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access this system.

[0012] "Interface" refers to the screens and operating means through which a user operates the system and selects betting amounts and games.

[0013] "Real-time information delivery" refers to the function of instantly displaying match results and progress data on the user's device as the match progresses.

[0014] "Message exchange" refers to the functionality that allows users to communicate with each other and share and discuss information. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

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

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention relates to a sports betting system that uses a generative AI model. This system aims to enable users to legally enjoy sports betting and provide them with a fresh experience and a high rate of profits through the provision of highly accurate predictions and real-time information by generative AI.

[0037] Program processing overview

[0038] User registration and login process

[0039] The server receives a new user registration request and saves it in the database. It compares it with existing user information to ensure that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[0040] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[0041] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[0042] Sports data collection and analysis

[0043] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[0044] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[0045] Users can check the latest updated match data and use it as a reference for betting.

[0046] AI-powered predictions

[0047] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0048] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[0049] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[0050] Simulation game progress management

[0051] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0052] The terminal displays the match results and simulation progress received from the server in real time.

[0053] Users can check the progress of their bets in real time and wait for the results.

[0054] View real-time results

[0055] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[0056] The terminal receives updates on the match results and updates the user interface in real time.

[0057] Users can check the latest results as the game progresses and keep track of their betting progress.

[0058] User-to-user community function

[0059] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[0060] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[0061] Users can exchange messages with other users to share information and discuss strategies.

[0062] Specific examples

[0063] For example, a user may wish to bet on a basketball game.

[0064] 1. User Registration and Login:

[0065] A user enters their name, email address, and password into the new registration form and submits it.

[0066] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[0067] The user enters their email address and password in the login form and clicks the login button.

[0068] The server verifies the login information and issues a session ID.

[0069] The device displays a dashboard specific to the user.

[0070] 2. Sports data collection and analysis:

[0071] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[0072] The terminal notifies the user that the latest match data has been updated.

[0073] 3. AI prediction:

[0074] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0075] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0076] Users check the AI's predictions and place their bets.

[0077] 4. Simulation game progress management:

[0078] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0079] The terminal displays the match results and the progress of the simulation in real time.

[0080] Users can check the progress of their bets.

[0081] 5. View real-time results:

[0082] The server updates the match results in real time and sends them to the user's device.

[0083] The terminal receives the updates and updates the user interface in real time.

[0084] Users can check the results of matches and keep track of their betting progress.

[0085] 6. User-to-user community features:

[0086] The server provides message boards and chat functions to support user communication.

[0087] The terminal provides an interface through which users can post messages and start chats.

[0088] Users can exchange messages with other users to share information and discuss strategies.

[0089] In this way, the system provides users with a wealth of data and highly accurate predictions, enabling a legitimate and fresh sports betting experience.

[0090] The processing flow will be explained below.

[0091] User registration and login process

[0092] Step 1:

[0093] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[0094] Step 2:

[0095] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[0096] Step 3:

[0097] The server receives a new registration request and checks its database to see if the email address is already registered.

[0098] Step 4:

[0099] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[0100] Step 5:

[0101] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[0102] Step 6:

[0103] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[0104] Step 7:

[0105] The terminal sends the entered login information to the server.

[0106] Step 8:

[0107] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[0108] Step 9:

[0109] The device stores the session ID and displays a dashboard specific to the user.

[0110] Sports data collection and analysis

[0111] Step 1:

[0112] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[0113] Step 2:

[0114] The server formats the data it receives and stores it in a database.

[0115] Step 3:

[0116] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[0117] Step 4:

[0118] The server stores the cleaned, analyzable data as training data for the generated AI model.

[0119] Step 5:

[0120] Once the device has completed collecting and analyzing the data, it will display an update notification to the user regarding the latest sports information.

[0121] AI-powered predictions

[0122] Step 1:

[0123] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[0124] Step 2:

[0125] The server generates new parameters and updates the latest model.

[0126] Step 3:

[0127] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[0128] Step 4:

[0129] The server stores the generated prediction results in a database and provides them to the user.

[0130] Step 5:

[0131] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0132] Step 6:

[0133] Users decide on their bets based on the AI's predictions and place their bets.

[0134] Simulation game progress management

[0135] Step 1:

[0136] The server receives betting information from users and stores it in a database.

[0137] Step 2:

[0138] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[0139] Step 3:

[0140] The server stores the updated match results in a database and distributes them to the device.

[0141] Step 4:

[0142] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[0143] Step 5:

[0144] Users check their bets and game progress and consider their next actions.

[0145] View real-time results

[0146] Step 1:

[0147] The server periodically checks the database to see if new data is available as the match progresses in real time.

[0148] Step 2:

[0149] If the server has new data, it sends updated information to the terminal.

[0150] Step 3:

[0151] The terminal processes the received update information and updates the user interface in real time.

[0152] Step 4:

[0153] Users can check the latest results while the match is in progress and decide what to do next.

[0154] User-to-user community function

[0155] Step 1:

[0156] The server provides a message board and chat function to support message exchanges between users.

[0157] Step 2:

[0158] A user visits a community page and posts a message.

[0159] Step 3:

[0160] The terminal sends the input message to the server, which stores the message in a database.

[0161] Step 4:

[0162] The server distributes the stored messages to other users.

[0163] Step 5:

[0164] Users can exchange messages with other users to share information and discuss strategies.

[0165] Example 1

[0166] 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."

[0167] In conventional sports betting systems, it was difficult for users to obtain reliable prediction data, and they did not provide sufficient real-time updates on game results or communication support between users. As a result, user experience and satisfaction were low, and it was difficult to acquire new users and retain existing users.

[0168] 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.

[0169] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for cleaning the acquired data and arranging it into an analyzable format, means for training a generative AI model using the cleaned data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time as the game progresses and delivering that information to the user's terminal, and means for supporting message exchange between users. This allows users to place sports bets based on reliable prediction data and check the progress and results of games in real time. Furthermore, communication with other users makes it easy to share and discuss betting strategies.

[0170] "Means for receiving user information and storing it in a database" refers to a mechanism for receiving personal information and authentication information provided by users and storing it in a database for safe management.

[0171] The "means for acquiring data related to sports matches specified by the user" is a function for acquiring data related to sports matches or events in which the user is interested from an external sports API or the like.

[0172] "Means for cleaning acquired data and preparing it in an analyzable format" refers to the process of removing unnecessary information and noise from acquired raw data and converting it into a format suitable for data analysis.

[0173] The "means for training a generative AI model and generating predicted results" refers to a system that uses historical and real-time data to train an AI model and, as a result, predicts future match outcomes.

[0174] "Means for displaying the generated prediction results on the user's device" refers to a function for displaying the generated AI prediction results on the user's device through a user interface.

[0175] The "means for receiving betting information from the terminal and storing it in a database" is a mechanism for receiving betting information entered by a user through a terminal and recording it in a database.

[0176] "Means of updating match results in real time as the match progresses and delivering that information to the user's device" refers to a function that constantly checks the database while the match is in progress and instantly delivers new match results to the user's device.

[0177] The "means for supporting message exchange between users" is a function that provides a chat or message board for users to exchange messages with other users in real time.

[0178] "Methods for training generative AI models using cleaned data" refers to techniques that effectively train AI models using cleaned data that has been stripped of unnecessary data.

[0179] "Means for updating match results" refers to the process of collecting the latest information each time the match results change and reflecting that information in the database and on the user's terminal.

[0180] This invention relates to a sports betting system that uses generative AI models. This system allows users to enjoy legal sports betting and provides users with a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information by generative AI.

[0181] Overall system configuration

[0182] The system includes the following main elements:

[0183] 1. Database server: A system for managing user information, betting information, and sports match data.

[0184] 2. Sports API: An external data source for retrieving real-time data on sports matches.

[0185] 3. Generative AI model: An algorithm that uses past and real-time match data to predict future match outcomes.

[0186] 4. User device: The interface through which the user accesses the system. This includes smartphones and PCs.

[0187] System Components and Processing

[0188] User Registration and Login

[0189] The server receives the information the user entered into the new registration form, such as name, email address, and password, and stores it in a database. It compares this with existing users to ensure there are no duplicates. Once registration is complete, it sends authentication information to the user. When logging in, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[0190] The device sends the registration information entered by the user to the server, displays a confirmation message from the server, and if login is successful, displays the user's personal dashboard.

[0191] The user enters their name, email address, and password in the new registration form and submits it.The user enters their email address and password in the login form and clicks the login button.

[0192] Sports data collection and analysis

[0193] The server periodically calls the sports API to retrieve the latest match data and stores it in a database. The data is then cleaned and formatted for analysis. This analyzed data is used as training data for the generative AI model.

[0194] Upon receiving the notification from the server, the terminal notifies the user that the latest match data has been updated.

[0195] Users receive notifications from their devices and can refer to the latest updated match data to help them make bets.

[0196] AI-powered predictions

[0197] The server feeds the collected sports data to the generative AI model, which trains it and generates predictions. These predictions are stored in a database and made accessible to users.

[0198] The terminal receives the latest AI prediction results from the server and displays them to the user, who then uses an interface to view the results and place their bets.

[0199] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[0200] Simulation game progress management

[0201] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0202] The terminal displays real-time match results and simulation progress from the server.

[0203] Users can see the progress of their bets in real time.

[0204] View real-time results

[0205] The server periodically checks the database and updates the new match results in real time, and the updated information is sent to the user's device.

[0206] The terminal updates the user interface in real time based on the received update information.

[0207] Users can check the latest results as the game progresses and keep track of their betting progress.

[0208] User-to-user community function

[0209] The server provides message boards and chat functions to support real-time communication between users, and also manages and publishes user profile information and rankings.

[0210] The device provides an interface for posting messages and using chat functions.

[0211] Users exchange messages with other users and share information and strategies about sports betting.

[0212] Specific examples

[0213] For example, if a user wants to bet on a basketball game:

[0214] 1. User Registration and Login:

[0215] A user enters their name, email address, and password into the new registration form and submits it.

[0216] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[0217] The user enters their email address and password in the login form and clicks the login button.

[0218] The server verifies the login information and issues a session ID.

[0219] The device displays a dashboard specific to the user.

[0220] 2. Sports data collection and analysis:

[0221] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[0222] The terminal notifies the user that the latest match data has been updated.

[0223] 3. AI prediction:

[0224] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0225] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0226] Users check the AI's predictions and place their bets.

[0227] 4. Simulation game progress management:

[0228] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0229] The terminal displays the match results and the progress of the simulation in real time.

[0230] Users can check the progress of their bets.

[0231] 5. View real-time results:

[0232] The server updates the match results in real time and sends them to the user's device.

[0233] The terminal receives the updates and updates the user interface in real time.

[0234] Users can check the results of matches and keep track of their betting progress.

[0235] 6. User-to-user community features:

[0236] The server provides message boards and chat functions to support user communication.

[0237] The terminal provides an interface through which users can post messages and start chats.

[0238] Users can exchange messages with other users to share information and discuss strategies.

[0239] Prompt Sentence Examples

[0240] Here are some example prompts to get predictions from a generative AI model:

[0241] "What are your predictions for the winner and scoreline of the next NBA game?"

[0242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0243] Step 1: User Registration and Login

[0244] Input:

[0245] The user enters their name, email address, and password in the new registration form and clicks the submit button.

[0246] 1.2. Data Processing:

[0247] The terminal encodes the registration information entered by the user and transmits it to the server.

[0248] Processing:

[0249] The server receives a new registration request and validates the information entered, comparing it with existing information to ensure there are no duplicates.

[0250] Output:

[0251] The server stores the information in a database and then sends the user an email containing the authentication information.

[0252] Input:

[0253] The user enters their email address and password into the login form and clicks the submit button.

[0254] 1.6. Data Processing:

[0255] The terminal transmits the login information to the server.

[0256] Processing:

[0257] The server verifies the email address and password, and if authentication is successful, issues a session ID.

[0258] Output:

[0259] The device displays a personalized dashboard for the user, which includes their profile information and available features.

[0260] Step 2: Collecting and analyzing sports data

[0261] Input:

[0262] The server periodically calls the sports API to retrieve the latest match data.

[0263] 2.2. Data Processing:

[0264] The server stores the received match data in a database.

[0265] Processing:

[0266] The server cleans the data and converts it into a format that can be parsed.

[0267] Output:

[0268] The server uses the cleaned data as training data for the generative AI model.

[0269] Input:

[0270] The terminal receives the notification from the server and notifies the user that the sports data has been updated.

[0271] Output:

[0272] The user can check the updated game data through the terminal.

[0273] Step 3: AI prediction

[0274] Input:

[0275] The server feeds the collected sports data into the generative AI model.

[0276] 3.2. Data Processing:

[0277] The server trains the generative AI model and builds the predictive model.

[0278] Processing:

[0279] The server generates prediction results using a trained generative AI model.

[0280] Output:

[0281] The server stores the prediction results in a database and provides them to the user.

[0282] Input:

[0283] The device receives the latest AI prediction results from the server.

[0284] Output:

[0285] The terminal displays the prediction results on an interface and provides information for the user to place bets.

[0286] Input:

[0287] Users check the AI's predictions, select the amount to bet and the game to bet on, and click the "Bet" button.

[0288] Step 4: Managing the progress of the simulation game

[0289] Input:

[0290] The server receives betting information from the user.

[0291] 4.2. Data Processing:

[0292] The server stores the received betting information in a database.

[0293] Processing:

[0294] The server monitors the progress of the match and records progress information in real time.

[0295] Output:

[0296] The server sends progress data to the terminal.

[0297] Input:

[0298] The device receives real-time match results and progress information from the server.

[0299] Output:

[0300] The terminal displays this information on the user interface.

[0301] 4.7. Users can see the progress of their bets in real time.

[0302] Step 5: View real-time results

[0303] Input:

[0304] The server periodically checks the database while a match is in progress.

[0305] 5.2. Data Processing:

[0306] The server updates new match results information in real time.

[0307] Processing:

[0308] The server sends the updated information to the user's terminal.

[0309] Output:

[0310] The terminal reflects the received information in real time on the user interface.

[0311] 5.5. Users can check the latest results as the match progresses and keep track of their betting progress.

[0312] Step 6: User-to-user community function

[0313] Input:

[0314] Users use chats and message boards to exchange messages with other users.

[0315] 6.2. Data Processing:

[0316] The server receives messages between users and stores them in a database.

[0317] Processing:

[0318] The server manages and publishes user profile information and rankings.

[0319] Output:

[0320] The terminal displays messages sent by the user to other users.

[0321] 6.5. Users communicate with other Users and share information and strategies regarding betting.

[0322] (Application example 1)

[0323] 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."

[0324] Current sports betting systems lack high-precision prediction functions, real-time game result updates, and electronic payment integration to improve users' betting experience. As a result, users cannot quickly check their betting results or smoothly manage their bets. In addition, the community functions are insufficient, making it difficult to smoothly exchange information with other users.

[0325] 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.

[0326] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data on sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for depositing and refunding bets via an electronic payment system, and means for supporting message exchange between users. This allows users to place bets based on highly accurate prediction results, and allows them to smoothly manage bets and exchange information with other users while checking game results in real time.

[0327] "User information" refers to the personal information, betting history, authentication information, etc. of system users.

[0328] A "database" is a system for efficiently storing and managing collected information and data, and enabling them to be quickly searched and used as needed.

[0329] "Sports Match Data" means all data relating to a sporting event, including but not limited to match results, player statistics, and match schedules.

[0330] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to train itself based on collected data and predict the outcome of a match.

[0331] "Predicted Outcomes" means predicted information about future match outcomes and other relevant data calculated by a generative AI model.

[0332] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to access the system and place bets or check information.

[0333] "Bet information" refers to information such as the bet amount, target game, betting options, etc. that a user enters when placing a bet.

[0334] "Real-time match result updates" is the process of instantly updating the system with match results and progress as the real-world match progresses.

[0335] "Electronic payment systems" refers to the platforms and technologies used to transfer money online (such as credit card payments, digital wallets, and bank transfers).

[0336] "Means supporting message exchange" are communication tools such as message boards and chat functions that allow users to share information, opinions, and strategies.

[0337] This invention is a system that allows users to legally enjoy sports betting and gain a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information using generative AI models. To realize this application example, the following elements and processes are required.

[0338] System Components

[0339] server

[0340] Hardware: Cloud services with high-performance data processing capabilities (e.g., AWS, Google Cloud, Azure).

[0341] Software: Databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., TensorFlow, PyTorch), and API integration capabilities.

[0342] Terminal

[0343] Hardware: The device the user uses to access the app (smartphone, computer, tablet, etc.).

[0344] Software: The application that implements the user interface (e.g., React Native, Flutter).

[0345] User

[0346] Users of sports betting systems.

[0347] Program processing overview

[0348] 1. User Registration and Login

[0349] A user enters their name, email address, and password into the new registration form and submits it.

[0350] The server saves the registration information in a database and sends the authentication information to the user, who completes the authentication process by entering their email address and password in the login form and clicking the login button.

[0351] 2. Sports data collection and analysis

[0352] The server retrieves the latest match data from the sports API and stores it in a database.

[0353] The acquired data is cleaned and put into an analyzable format.

[0354] 3. Prediction using generative AI models

[0355] The server uses the cleaned data to train a generative AI model to predict match outcomes.

[0356] The prediction results are stored in a database and sent to the device.

[0357] 4. Betting information management and real-time results display

[0358] The user inputs betting information through the terminal and transmits it to the server.

[0359] The server updates the match results in real time and delivers the information to the terminal.

[0360] 5. Electronic payment function

[0361] Users deposit their bets via an electronic payment system.

[0362] If the bet is won, the server immediately refunds the bet amount to the user's account.

[0363] 6. Messaging and Communication

[0364] The server provides message boards and chat facilities to support message exchange between users.

[0365] Users can exchange information and share strategies with other users.

[0366] Examples and prompts

[0367] As a specific example, consider a case where a user places a bet on a basketball game.

[0368] 1. User Registration and Login

[0369] New Registration

[0370] ~~~~~~~~~

[0371] Name: Taro Yamada

[0372] Email: example@mail.com

[0373] password:

[0374] Log in

[0375] ~~~~~~~~

[0376] Email: example@mail.com

[0377] password:

[0378] 2. Sports data collection and analysis

[0379] Capture and analyze data from new basketball games.

[0380] 3. Prediction using generative AI models

[0381] Predict the outcome of the match based on the latest match data.

[0382] 4. Betting and Electronic Payments

[0383] Use the PayPal API to settle your users' bets instantly.

[0384] In this way, a system is realized that allows users to place bets based on highly accurate prediction results, check match results in real time, smoothly manage bets, and exchange information with other users.

[0385] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0386] Step 1:

[0387] User Registration and Authentication

[0388] Input: A user enters their name, email address, and password into a new registration form and submits it.

[0389] Data processing: Before storing the received user information in the database, the server checks for duplication with existing data.

[0390] Output: If there are no duplicates, authentication information is generated and sent to the user. When logging in, the user enters their email address and password, which the server verifies and authenticates.

[0391] Specific operation: The server saves the new registration information in the database and sends a verification email to the user. The login process also verifies the user's authentication information, and if successful, issues a session ID and displays the dashboard.

[0392] Step 2:

[0393] Sports data collection and analysis

[0394] Input: The server requests and retrieves the latest match data from the sports API.

[0395] Data processing: The server cleans the acquired data and prepares it into an analyzable format (e.g., removing unnecessary data and standardizing the format).

[0396] Output: Save the cleaned data to the database.

[0397] Specific operation: The server normalizes the match data obtained from the sports API, removes inconsistent data, and prepares it for use as training data for the generative AI model.

[0398] Step 3:

[0399] Predictions from generative AI models

[0400] Input: Cleaned match data.

[0401] Data computation: The server uses generative AI models (e.g., TensorFlow, PyTorch) to train and predict match outcomes.

[0402] Output: The prediction results are stored in a database and sent to the device.

[0403] Specific operation: The server inputs data into the generative AI model to train it, calculates prediction results, stores them in a database, and then distributes them to the device.

[0404] Step 4:

[0405] Betting information management and real-time results display

[0406] Input: The user inputs betting information (betting amount, game, options) through the terminal and sends it to the server.

[0407] Data processing: The server stores the betting information in a database and monitors the progress of the matches.

[0408] Output: Deliver real-time updated match results to your device.

[0409] Specific operation: The server stores the received betting information in a database, updates the results as the game progresses, and delivers them to the terminal in real time.

[0410] Step 5:

[0411] Electronic payment function

[0412] Input: The user inputs information to deposit a bet through an electronic payment system.

[0413] Data Processing: The server receives the payment information and outsources the payment process to a third-party electronic payment system.

[0414] Output: If the payment is successful, a confirmation is sent to the user's device.

[0415] Specific operation: The server executes the payment process using an electronic payment API (e.g., PayPal, Stripe), and if successful, records the result in a database and notifies the terminal.

[0416] Step 6:

[0417] Messaging and Communication

[0418] Input: A user posts a message using a message board or chat function.

[0419] Data processing: The server stores the received messages in a database and distributes them to other users in real time.

[0420] Output: Other users receive and view the message.

[0421] Specific operation: The server manages message exchanges between users through message boards and chat functions, and distributes them instantly to facilitate communication.

[0422] 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.

[0423] This invention combines a sports betting system using a generative AI model with an emotion engine that recognizes user emotions. This system allows users to legally enjoy sports betting, and aims to provide highly accurate predictions and real-time information using generative AI, as well as customize the interface and suggest bets based on the user's emotional state.

[0424] Program processing overview

[0425] User registration and login process

[0426] The server receives a new user registration request and saves it in the database. It compares the information with existing users and verifies that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[0427] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[0428] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[0429] Sports data collection and analysis

[0430] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[0431] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[0432] Users can check the latest updated match data and use it as a reference for betting.

[0433] AI-powered predictions

[0434] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0435] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[0436] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[0437] Simulation game progress management

[0438] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0439] The terminal displays the match results and simulation progress received from the server in real time.

[0440] Users can check the progress of their bets in real time and wait for the results.

[0441] View real-time results

[0442] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[0443] The terminal receives updates on the match results and updates the user interface in real time.

[0444] Users can check the latest results as the game progresses and keep track of their betting progress.

[0445] User-to-user community function

[0446] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[0447] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[0448] Users can exchange messages with other users to share information and discuss strategies.

[0449] Overview of Emotion Engine Operation

[0450] User Emotion Recognition

[0451] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[0452] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[0453] The user uses the system as usual, and their operations are used for emotion recognition.

[0454] Emotion-based interface customization

[0455] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[0456] The terminal updates the user interface based on the customization information provided by the server.

[0457] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[0458] Emotion-based betting suggestions

[0459] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[0460] The terminal displays betting suggestions to the user and assists in selection.

[0461] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[0462] Specific examples

[0463] For example, a user may want to bet on a soccer match.

[0464] 1. User Registration and Login:

[0465] A user enters their name, email address, and password into the new registration form and submits it.

[0466] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[0467] The user enters their email address and password in the login form and clicks the Login button.

[0468] The server verifies the login information and issues a session ID.

[0469] The device displays a dashboard specific to the user.

[0470] 2. Sports data collection and analysis:

[0471] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[0472] The terminal notifies the user that the latest game data has been updated.

[0473] 3. AI prediction:

[0474] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0475] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0476] Users check the AI's predictions and place their bets.

[0477] 4. Simulation game progress management:

[0478] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0479] The device displays the match results and the progress of the simulation in real time.

[0480] Users can check the progress of their bets.

[0481] 5. View real-time results:

[0482] The server updates the match results in real time and sends them to the user's device.

[0483] The terminal receives the update information and updates the user interface in real time.

[0484] Users can check the results of matches and keep track of their betting progress.

[0485] 6. User-to-user community features:

[0486] The server provides message boards and chat functions to support user communication.

[0487] The terminal provides an interface through which users can post messages and start chats.

[0488] Users can exchange messages with other users to share information and discuss strategies.

[0489] 7. Emotion Engine in Action:

[0490] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[0491] The device sends the necessary input data and displays feedback of the emotional state.

[0492] The user uses the system as usual, and their operations are used for emotion recognition.

[0493] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[0494] The terminal displays customization information and betting suggestions.

[0495] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[0496] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[0497] The processing flow will be explained below.

[0498] User registration and login process

[0499] Step 1:

[0500] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[0501] Step 2:

[0502] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[0503] Step 3:

[0504] The server receives a new registration request and checks its database to see if the email address is already registered.

[0505] Step 4:

[0506] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[0507] Step 5:

[0508] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[0509] Step 6:

[0510] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[0511] Step 7:

[0512] The terminal sends the entered login information to the server.

[0513] Step 8:

[0514] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[0515] Step 9:

[0516] The device stores the session ID and displays a dashboard specific to the user.

[0517] Sports data collection and analysis

[0518] Step 1:

[0519] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[0520] Step 2:

[0521] The server formats the data it receives and stores it in a database.

[0522] Step 3:

[0523] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[0524] Step 4:

[0525] The server stores the cleaned, analyzable data as training data for the generated AI model.

[0526] Step 5:

[0527] When the device has completed data collection and analysis, it will notify the user that the latest sports information has been updated.

[0528] AI-powered predictions

[0529] Step 1:

[0530] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[0531] Step 2:

[0532] The server generates new parameters and updates the latest model.

[0533] Step 3:

[0534] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[0535] Step 4:

[0536] The server stores the generated prediction results in a database and provides them to the user.

[0537] Step 5:

[0538] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0539] Step 6:

[0540] Users decide on their bets based on the AI's predictions and place their bets.

[0541] Simulation game progress management

[0542] Step 1:

[0543] The server receives betting information from users and stores it in a database.

[0544] Step 2:

[0545] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[0546] Step 3:

[0547] The server stores the updated match results in a database and distributes them to the device.

[0548] Step 4:

[0549] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[0550] Step 5:

[0551] Users check their bets and game progress and consider their next actions.

[0552] View real-time results

[0553] Step 1:

[0554] The server periodically checks the database to see if new data is available as the match progresses in real time.

[0555] Step 2:

[0556] If the server has new data, it sends updated information to the terminal.

[0557] Step 3:

[0558] The terminal processes the received update information and updates the user interface in real time.

[0559] Step 4:

[0560] Users can check the latest results while the match is in progress and decide what to do next.

[0561] User-to-user community function

[0562] Step 1:

[0563] The server provides a message board and chat function to support message exchanges between users.

[0564] Step 2:

[0565] A user visits a community page and posts a message.

[0566] Step 3:

[0567] The terminal sends the input message to the server, which stores the message in a database.

[0568] Step 4:

[0569] The server distributes the stored messages to other users.

[0570] Step 5:

[0571] Users can exchange messages with other users to share information and discuss strategies.

[0572] User Emotion Recognition

[0573] Step 1:

[0574] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history.

[0575] Step 2:

[0576] The server uses an emotion engine to estimate the user's emotional state.

[0577] Step 3:

[0578] The terminal transmits the user's input data required for emotion recognition to the server.

[0579] Step 4:

[0580] The device displays emotional state feedback to the user.

[0581] Step 5:

[0582] The user uses the system as usual, and their operations are used for emotion recognition.

[0583] Emotion-based interface customization

[0584] Step 1:

[0585] The server dynamically customizes the interface display based on the user's emotional state.

[0586] Step 2:

[0587] The server transmits the customization information to the terminal.

[0588] Step 3:

[0589] The terminal receives the customization information and updates the user interface.

[0590] Step 4:

[0591] Users will have an improved experience using an interface that is optimized according to their emotional state.

[0592] Emotion-based betting suggestions

[0593] Step 1:

[0594] The server dynamically adjusts the betting suggestions based on the user's emotional state analyzed by the emotion engine.

[0595] Step 2:

[0596] The server sends dynamically adjusted betting proposals to the terminal.

[0597] Step 3:

[0598] The terminal displays betting suggestions to the user.

[0599] Step 4:

[0600] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[0601] Specific examples

[0602] For example, a user may want to bet on a soccer match.

[0603] User registration and login:

[0604] Step 1:

[0605] A user enters their name, email address, and password into the new registration form and submits it.

[0606] Step 2:

[0607] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[0608] Step 3:

[0609] The user enters their email address and password in the login form and clicks the Login button.

[0610] Step 4:

[0611] The server verifies the login information and issues a session ID.

[0612] Step 5:

[0613] The device displays a dashboard specific to the user.

[0614] Sports data collection and analysis:

[0615] Step 1:

[0616] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[0617] Step 2:

[0618] The terminal notifies the user that the latest game data has been updated.

[0619] AI-powered predictions:

[0620] Step 1:

[0621] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0622] Step 2:

[0623] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0624] Step 3:

[0625] Users check the AI's predictions and place their bets.

[0626] Simulation game progress management:

[0627] Step 1:

[0628] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0629] Step 2:

[0630] The device displays the match results and the progress of the simulation in real time.

[0631] Step 3:

[0632] Users can check the progress of their bets.

[0633] View real-time results:

[0634] Step 1:

[0635] The server updates the match results in real time and sends them to the user's device.

[0636] Step 2:

[0637] The terminal receives the update information and updates the user interface in real time.

[0638] Step 3:

[0639] Users can check the results of matches and keep track of their betting progress.

[0640] User-to-user community features:

[0641] Step 1:

[0642] The server provides message boards and chat functions to support user communication.

[0643] Step 2:

[0644] The terminal provides an interface through which users can post messages and start chats.

[0645] Step 3:

[0646] Users can exchange messages with other users to share information and discuss strategies.

[0647] Emotion Engine in action:

[0648] Step 1:

[0649] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[0650] Step 2:

[0651] The device sends the necessary input data and displays feedback of the emotional state.

[0652] Step 3:

[0653] The user uses the system as usual, and their operations are used for emotion recognition.

[0654] Step 4:

[0655] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[0656] Step 5:

[0657] The terminal displays customization information and betting suggestions.

[0658] Step 6:

[0659] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[0660] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[0661] Example 2

[0662] 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."

[0663] Conventional sports betting systems do not provide services that take into account the user's emotional state, limiting the improvement of user experience. Furthermore, in addition to highly accurate predictions and real-time information provision, they lack effective communication between users and interface customization. This has led to issues such as reduced user satisfaction and engagement.

[0664] 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.

[0665] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for supporting message exchange between users, means for analyzing the user's input data and behavioral history to estimate the user's emotional state, means for dynamically customizing the interface based on the user's emotional state, and means for dynamically adjusting betting suggestions based on the user's emotional state. This enables the sports betting system to provide services that take the user's emotional state into consideration, thereby improving the user experience and strengthening engagement.

[0666] "Means for receiving user information and storing it in a database" refers to a method in which the server collects registration information entered by the user, such as name, email address, and password, and stores it in a database.

[0667] "Means for obtaining data on sports games specified by users" refers to a method for obtaining information on specific sports games that users have specified they wish to bet on via external data sources or APIs, and incorporating that data into the system.

[0668] "Means for training a generative AI model using acquired data to generate prediction results" refers to a method for training an artificial intelligence (AI) algorithm based on collected sports match data and predicting match results from the training results.

[0669] "Means for displaying the generated prediction results on the user's device" refers to a method for displaying the prediction result data generated by the AI ​​model on the device used by the user through a user interface.

[0670] "Means for receiving betting information from the terminal and storing it in a database" refers to a method in which the server receives the betting amount and betting details entered by the user through the terminal and stores that information in a database.

[0671] "Means for updating match results in real time and delivering that information to the user's device" refers to a method for periodically collecting new match results as the match progresses and immediately sending that information to the user's device.

[0672] "Means supporting the exchange of messages between users" means communication features that allow users to text message or chat with other users through the system.

[0673] "Means for analyzing a user's input data and behavioral history to infer their emotional state" refers to technology that analyzes the text data and usage history that a user inputs into the system to infer the user's emotional state.

[0674] The "means for dynamically customizing an interface based on an emotional state" is a method for optimizing the screen display and content provided in real time according to the estimated emotional state of the user.

[0675] A "means for dynamically adjusting betting suggestions based on emotional state" is a method for automatically adjusting betting options and risk levels to suggest betting strategies and content appropriate to a user's current emotional state.

[0676] This invention is a sports betting system that uses a generative AI model and an emotion engine to enable users to bet legally and enjoyably on sports. This system not only provides highly accurate predictions and real-time information using generative AI, but also customizes the interface and suggests bets based on the user's emotional state.

[0677] System Overview

[0678] The system includes the following main components: a server, a terminal, and a user.

[0679] 1. User registration and login process

[0680] The server receives a new user registration request and saves it in the database. The database used here is "MySQL." The information is compared with existing users to ensure that the email address is not a duplicate. Once registration is complete, authentication information is sent to the user. When a login request is made, the entered email address and password are compared, and if authentication is successful, a session ID is issued.

[0681] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if the login is successful, displays a dashboard dedicated to the user using "React."

[0682] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[0683] Example: A user enters their name, email address, and password into a new registration form and submits it. The server receives the registration information and stores it in a database. The authentication information is sent to the user. The user enters their email address and password into a login form and clicks the login button. The server verifies the login information and issues a session ID. The device displays a dashboard specific to the user.

[0684] 2. Sports data collection and analysis

[0685] The server periodically retrieves the latest match data from a sports API (e.g., "SportsRadar") and stores it in a database. This data is then cleaned and formatted for analysis, using the "Pandas" library in Python. The analyzed data is then used as training data for the generative AI model.

[0686] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[0687] Users can check the latest updated match data and use it as a reference for betting.

[0688] 3. AI-based predictions

[0689] The server uses the collected data to train a generative AI model and generate prediction results using Python and TensorFlow. The generated prediction results are stored in a database and provided to users.

[0690] The terminal uses "D3.js" to display the latest AI prediction results received from the server to the user, and provides an interface for users to place bets.

[0691] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[0692] 4. Simulation game progress management

[0693] The server manages the betting information received from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0694] The terminal displays the match results and simulation progress received from the server in real time.

[0695] Users can check the progress of their bets in real time and wait for the results.

[0696] 5. View real-time results

[0697] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[0698] The terminal receives updates on the match results and updates the user interface in real time.

[0699] Users can check the latest results as the game progresses and keep track of their betting progress.

[0700] 6. User-to-user community function

[0701] The server provides message boards and chat functions to support message exchanges between users, and also manages and publishes user profiles and rankings.

[0702] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[0703] Users can exchange messages with other users to share information and discuss strategies.

[0704] 7. Emotion Engine Operation

[0705] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[0706] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[0707] The user uses the system as usual, and their operations are used for emotion recognition.

[0708] Emotion-based interface customization

[0709] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[0710] The terminal updates the user interface based on the customization information provided by the server.

[0711] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[0712] Emotion-based betting suggestions

[0713] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[0714] The terminal displays betting suggestions to the user and assists in selection.

[0715] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[0716] Example prompt:

[0717] The "User" enters their name, email address, and password into the new registration form and clicks the "Submit" button. The "Server" receives this information, saves it in the "MySQL" database, and sends a registration completion email. The "User" then enters their email address and password into the login form and clicks the "Login" button. The "Server" checks the login information, and if it is correct, issues a session ID and displays the dashboard created with "React" on the "Terminal."

[0718] The system aims to provide users with highly accurate sports betting predictions and an optimal experience based on their emotional state.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] The flow of this system's program processing

[0721] Step 1: User registration and login process

[0722] server

[0723] Input: Name, email address, and password entered by the user in the sign-up form.

[0724] Data processing: Before storing this information in the MySQL database, we check whether the email address is a duplicate of existing data.

[0725] Output: Generates authentication information and sends it to the user.

[0726] Specific operation: The server saves the new user information in the MySQL database and sends a registration completion email.

[0727] Terminal

[0728] Input: Registration information entered by the user.

[0729] Data processing: Send the input information to the server.

[0730] Output: The status message returned by the server.

[0731] Specific behavior: The device displays status messages in a "React" interface.

[0732] User

[0733] Input: Enter your name, email address, and password in the new registration form and click the submit button.

[0734] Output: You will receive a registration confirmation email.

[0735] Specific actions: Fill in the required information in the form, submit it, and check the verification email.

[0736] Step 2: Collecting and analyzing sports data

[0737] server

[0738] Input: Match data obtained via a sports API (e.g. "SportsRadar").

[0739] Data processing: The acquired data was cleaned using the Python Pandas library and converted into an analyzable format.

[0740] Output: Cleaned match data stored in a MySQL database.

[0741] What it does: The server periodically sends requests to the sports API, retrieves the data, cleans it, and stores it in the database.

[0742] Terminal

[0743] Input: Data update notification from the server.

[0744] Data processing: Display a notification to the user.

[0745] Output: Latest sports data updates.

[0746] Specific operation: The device will display a notification of the latest data update.

[0747] User

[0748] Input: Check update notifications and interact with the system.

[0749] Output: Show new match data.

[0750] Specific actions: Check the updated match information on the device.

[0751] Step 3: AI prediction

[0752] server

[0753] Input: Cleaned match data.

[0754] Data processing: Train the data using a generative AI model (Python, TensorFlow).

[0755] Output: Generate prediction results and store them in a MySQL database.

[0756] Specific operation: Trains an AI model, generates prediction results, and stores them.

[0757] Terminal

[0758] Input: Prediction result data from the server.

[0759] Data processing: Visualize the prediction results using "D3.js".

[0760] Output: Prediction results are visualized and displayed to the user.

[0761] Specific behavior: Prediction results are displayed visually on the device.

[0762] User

[0763] Input: Check the prediction results.

[0764] Output: Place a bet.

[0765] Specific operations: Determine the betting amount and target game based on the prediction result and complete the operation.

[0766] Step 4: Managing the progress of the simulation game

[0767] server

[0768] Input: Betting information from the user.

[0769] Data processing: Store betting information in a database and update match results in real time.

[0770] Output: Real-time match results.

[0771] Specific operation: The database is updated continuously while the match is in progress and the results are sent to the user.

[0772] Terminal

[0773] Input: Real-time match results.

[0774] Data processing: Display result data in real time.

[0775] Output: Updated match results and progress.

[0776] Specific operation: Display match results in real time.

[0777] User

[0778] Input: Check real-time match results.

[0779] Output: Betting progress.

[0780] Specific actions: Monitor the progress of the match and wait for the results.

[0781] Step 5: View real-time results

[0782] server

[0783] Input: Match result data.

[0784] Data processing: Result data is updated in real time.

[0785] Output: Updated results are delivered to the user's device.

[0786] Specific operation: New match results are periodically updated in the database and sent to users.

[0787] Terminal

[0788] Input: Updated data from the server.

[0789] Data Processing: Interface update.

[0790] Output: Display the latest match results.

[0791] Specific operation: The device displays the match results in real time.

[0792] User

[0793] Input: Updated match results.

[0794] Output: The progress of your bet.

[0795] Specific actions: Check match results and understand progress.

[0796] Step 6: User-to-user community function

[0797] server

[0798] Input: The user's message data.

[0799] Data processing: Messages are stored in a database and distributed to other users.

[0800] Output: Providing message boards and chat functionality.

[0801] What it does: Manage and deliver messages in real time.

[0802] Terminal

[0803] Input: User message input.

[0804] Data processing: Display of messages.

[0805] Output: Display of new messages.

[0806] Specific behavior: Provides a message input interface and implements chat functionality.

[0807] User

[0808] Input: Type a message to exchange with other users.

[0809] Output: Communication information.

[0810] Specific action: Type a message and communicate with other users.

[0811] Step 7: Emotion Engine in Action

[0812] server

[0813] Input: User input data and behavioral history.

[0814] Data processing: Analyze using an emotion engine to estimate emotional state.

[0815] Output: Interface customization information based on emotional state.

[0816] Specific behavior: Analyze the user's state using the emotion engine and use the results to customize the interface.

[0817] Terminal

[0818] Input: Customization information from the server.

[0819] Data processing: Interface update.

[0820] Output: Optimized user interface.

[0821] Specific behavior: Update the user interface in real time according to the emotional state.

[0822] User

[0823] Input: Normal operation.

[0824] Output: Customized interface.

[0825] Specific behavior: Operate an interface optimized for your emotional state and enjoy a better user experience.

[0826] These steps enable the system of the present invention to provide users with highly accurate predictions and a customized emotion-based interface.

[0827] (Application example 2)

[0828] 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."

[0829] Conventional systems simply predict events and provide information without taking into account the user's emotional state and its fluctuations. This results in a uniform user experience, resulting in insufficient optimal support and customization for individual users. Furthermore, when it comes to managing users' spending, they lack real-time feedback and advice based on their emotional state.

[0830] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and saving it in a database, means for acquiring data related to events specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving information from the terminal and saving it in a database, means for updating event results in real time and delivering the information to the user's terminal, means for supporting information exchange between users, and means for analyzing the user's emotional state and dynamically customizing the display interface and proposal content based on the state. This makes it possible to provide the user with optimal information and advice on expense management that takes into account the user's emotional state.

[0831] "User Information" refers to data relating to a user's personal information, behavioral data, and emotional state.

[0832] A "database" is an information system for efficiently storing and searching information, particularly for storing user information, event data, prediction results, etc.

[0833] "Server" refers to a computer system that processes, stores, distributes data, and fulfills user requests.

[0834] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence techniques to make predictions and suggestions from collected data.

[0835] "Prediction Results" refers to predictive data about future events or situations generated by a generative AI model.

[0836] "Terminal" refers to an electronic device (smartphone, smart glasses, etc.) that a user operates.

[0837] "Real-time updates" refers to a process in which information is immediately reflected in the current situation and provided to the user immediately.

[0838] "Information exchange between users" refers to multiple users of the system sharing messages and data.

[0839] "Emotional state" refers to the user's current psychological state (e.g., happy, sad, excited, relaxed, etc.).

[0840] "Display interface" refers to a screen that allows a user to visually check and operate information.

[0841] "Suggestion content" refers to information that the system provides to the user with specific actions and options to be taken.

[0842] "Dynamic customization" refers to changing the interface and suggestions in real time according to the user's emotional state and behavior.

[0843] The system embodying this invention collects user information and provides real-time predictions and advice based on the collected information according to the user's emotional state. The main components are as follows:

[0844] Hardware and Software

[0845] This system uses a general server and user devices (smartphones, smart glasses, etc.) and utilizes the following software:

[0846] Emotion recognition engine (e.g. Microsoft Azure Cognitive Services)

[0847] Database (e.g. MySQL)

[0848] Generative AI models (e.g., GPT-4)

[0849] User information collection and storage

[0850] The server receives and stores in a database the user's registration information (personal information, behavioral data, and data on emotional state) that the user enters through the interface and processes in real time.

[0851] Event data acquisition and analysis

[0852] The server acquires data related to events specified by the user and stores it in a database. The acquired data is cleaned and formatted for analysis. A generative AI model is then used to generate predictions. These predictions are stored in the database and displayed on the user's device.

[0853] Real-time updates and information distribution

[0854] The server updates the event results in real time and delivers the information to the user's device, allowing the user to always obtain the latest information.

[0855] Information exchange between users

[0856] The server provides a message board and chat function to support information exchange between users, allowing users to share information and discuss strategies.

[0857] Emotional state analysis and customization

[0858] The server analyzes the user's input data and behavioral history, and uses an emotion recognition engine to estimate their emotional state. Based on their emotional state, the displayed interface and suggestions are dynamically customized. For example, if the user is tired, suggestions to reduce spending are made, and if the user is excited, suggestions to reduce risk are made.

[0859] Specific examples

[0860] For example, if the emotion engine detects that a user is tired while shopping at a supermarket, the app will display a notification saying, "Maybe you should take a little rest today. You have 3,000 yen left in your budget this week." In this way, users can optimally manage their spending based on their emotional state.

[0861] Prompt Sentence Examples

[0862] "Describe a system that analyzes your emotional state in real time and provides spending management advice. It can analyze your spending history and behavioral history from a database and generate optimal spending suggestions based on your emotions. The system uses a smartphone or smart glasses and includes a generative AI model, an emotion recognition engine, and real-time updates. For example, it provides savings suggestions when the user is tired, and risk-reducing suggestions when the user is excited."

[0863] In this way, the system of the present invention provides the user with optimal information and advice on expense management that takes into account their emotional state.

[0864] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0865] Step 1:

[0866] The server receives the user's information and stores it in a database. This information includes the user's personal information, behavioral data, and emotional state data. As input, it receives the information entered by the user through the interface and processes it to store it in the database. As output, it obtains the stored user information.

[0867] Step 2:

[0868] The server acquires data about events specified by the user and stores it in a database. As input, it receives information about the type of event and date and time selected by the user. The database processes the data acquired from an external event information system and stores it. As output, it obtains the stored event data.

[0869] Step 3:

[0870] The server uses the acquired data to train a generative AI model and generate predictions. As input, it receives stored event data. It analyzes this data and performs data calculations that feed it into the generative AI model to generate predictions. As output, it obtains the generated predictions.

[0871] Step 4:

[0872] The terminal displays the generated prediction results on the user's device. As input, it receives the prediction results received from the server. It processes the data to display them in a user interface in a visually easy-to-understand format. As output, the user can check the prediction results.

[0873] Step 5:

[0874] The terminal receives information from the user and sends it to the server. As input, it receives the user's actions (information input and selection). It processes this information and sends it to the server. As output, it obtains the user's action data that the server receives.

[0875] Step 6:

[0876] The server stores information from the device in a database. As input, it receives the user's action data sent from the device. It processes the action data and stores it in the database. As output, it obtains the stored user's action data.

[0877] Step 7:

[0878] The server updates the event results in real time and delivers the information to the user's device. As input, it receives the latest event results obtained from an external event result information providing system. It analyzes these and performs data calculations to deliver them to the user's device. As output, the latest event results are delivered to the device.

[0879] Step 8:

[0880] The terminal displays the latest event results delivered to the user's terminal. As input, it receives the latest event result information from the server. It processes this data to display it in a visually easy-to-understand format on the user interface. As output, the user can check the latest event results.

[0881] Step 9:

[0882] The server provides message board and chat functions to support information exchange between users. As input, it receives messages and chat information from users. It processes this data to distribute it to other users as needed. As output, it enables messages and chat between users.

[0883] Step 10:

[0884] The server analyzes the user's input data and behavioral history and uses an emotion recognition engine to estimate the user's emotional state. It receives the user's operation data and behavioral data as input. It supplies this data to the emotion recognition engine, which then performs a data calculation to estimate the user's emotional state. The output is the user's emotional state.

[0885] Step 11:

[0886] The server dynamically customizes the display interface and suggestions based on the user's emotional state. It receives the user's estimated emotional state as input, performs data calculations to change the color of the interface and adjust the suggestions based on the user's emotional state, and delivers the customized interface and suggestions to the user's device as output.

[0887] Step 12:

[0888] The terminal displays the customized interface and suggested content to the user. As input, it receives customization information distributed from the server. It processes this data to display it on the user interface in a visually easy-to-understand format. As output, it displays the interface and suggested content optimized for the user.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] [Second embodiment]

[0893] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0894] 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.

[0895] 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).

[0896] 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.

[0897] 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.

[0898] 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).

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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."

[0905] This invention relates to a sports betting system that uses a generative AI model. This system aims to enable users to legally enjoy sports betting and provide them with a fresh experience and a high rate of profits through the provision of highly accurate predictions and real-time information by generative AI.

[0906] Program processing overview

[0907] User registration and login process

[0908] The server receives a new user registration request and saves it in the database. It compares it with existing user information to ensure that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[0909] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[0910] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[0911] Sports data collection and analysis

[0912] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[0913] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[0914] Users can check the latest updated match data and use it as a reference for betting.

[0915] AI-powered predictions

[0916] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0917] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[0918] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[0919] Simulation game progress management

[0920] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0921] The terminal displays the match results and simulation progress received from the server in real time.

[0922] Users can check the progress of their bets in real time and wait for the results.

[0923] View real-time results

[0924] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[0925] The terminal receives updates on the match results and updates the user interface in real time.

[0926] Users can check the latest results as the game progresses and keep track of their betting progress.

[0927] User-to-user community function

[0928] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[0929] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[0930] Users can exchange messages with other users to share information and discuss strategies.

[0931] Specific examples

[0932] For example, a user may wish to bet on a basketball game.

[0933] 1. User Registration and Login:

[0934] A user enters their name, email address, and password into the new registration form and submits it.

[0935] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[0936] The user enters their email address and password in the login form and clicks the login button.

[0937] The server verifies the login information and issues a session ID.

[0938] The device displays a dashboard specific to the user.

[0939] 2. Sports data collection and analysis:

[0940] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[0941] The terminal notifies the user that the latest match data has been updated.

[0942] 3. AI prediction:

[0943] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[0944] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[0945] Users check the AI's predictions and place their bets.

[0946] 4. Simulation game progress management:

[0947] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[0948] The terminal displays the match results and the progress of the simulation in real time.

[0949] Users can check the progress of their bets.

[0950] 5. View real-time results:

[0951] The server updates the match results in real time and sends them to the user's device.

[0952] The terminal receives the updates and updates the user interface in real time.

[0953] Users can check the results of matches and keep track of their betting progress.

[0954] 6. User-to-user community features:

[0955] The server provides message boards and chat functions to support user communication.

[0956] The terminal provides an interface through which users can post messages and start chats.

[0957] Users can exchange messages with other users to share information and discuss strategies.

[0958] In this way, the system provides users with a wealth of data and highly accurate predictions, enabling a legitimate and fresh sports betting experience.

[0959] The processing flow will be explained below.

[0960] User registration and login process

[0961] Step 1:

[0962] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[0963] Step 2:

[0964] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[0965] Step 3:

[0966] The server receives a new registration request and checks its database to see if the email address is already registered.

[0967] Step 4:

[0968] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[0969] Step 5:

[0970] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[0971] Step 6:

[0972] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[0973] Step 7:

[0974] The terminal sends the entered login information to the server.

[0975] Step 8:

[0976] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[0977] Step 9:

[0978] The device stores the session ID and displays a dashboard specific to the user.

[0979] Sports data collection and analysis

[0980] Step 1:

[0981] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[0982] Step 2:

[0983] The server formats the data it receives and stores it in a database.

[0984] Step 3:

[0985] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[0986] Step 4:

[0987] The server stores the cleaned, analyzable data as training data for the generated AI model.

[0988] Step 5:

[0989] Once the device has completed collecting and analyzing the data, it will display an update notification to the user regarding the latest sports information.

[0990] AI-powered predictions

[0991] Step 1:

[0992] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[0993] Step 2:

[0994] The server generates new parameters and updates the latest model.

[0995] Step 3:

[0996] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[0997] Step 4:

[0998] The server stores the generated prediction results in a database and provides them to the user.

[0999] Step 5:

[1000] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1001] Step 6:

[1002] Users decide on their bets based on the AI's predictions and place their bets.

[1003] Simulation game progress management

[1004] Step 1:

[1005] The server receives betting information from users and stores it in a database.

[1006] Step 2:

[1007] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[1008] Step 3:

[1009] The server stores the updated match results in a database and distributes them to the device.

[1010] Step 4:

[1011] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[1012] Step 5:

[1013] Users check their bets and game progress and consider their next actions.

[1014] View real-time results

[1015] Step 1:

[1016] The server periodically checks the database to see if new data is available as the match progresses in real time.

[1017] Step 2:

[1018] If the server has new data, it sends updated information to the terminal.

[1019] Step 3:

[1020] The terminal processes the received update information and updates the user interface in real time.

[1021] Step 4:

[1022] Users can check the latest results while the match is in progress and decide what to do next.

[1023] User-to-user community function

[1024] Step 1:

[1025] The server provides a message board and chat function to support message exchanges between users.

[1026] Step 2:

[1027] A user visits a community page and posts a message.

[1028] Step 3:

[1029] The terminal sends the input message to the server, which stores the message in a database.

[1030] Step 4:

[1031] The server distributes the stored messages to other users.

[1032] Step 5:

[1033] Users can exchange messages with other users to share information and discuss strategies.

[1034] Example 1

[1035] 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."

[1036] In conventional sports betting systems, it was difficult for users to obtain reliable prediction data, and they did not provide sufficient real-time updates on game results or communication support between users. As a result, user experience and satisfaction were low, and it was difficult to acquire new users and retain existing users.

[1037] 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.

[1038] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for cleaning the acquired data and arranging it into an analyzable format, means for training a generative AI model using the cleaned data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time as the game progresses and delivering that information to the user's terminal, and means for supporting message exchange between users. This allows users to place sports bets based on reliable prediction data and check the progress and results of games in real time. Furthermore, communication with other users makes it easy to share and discuss betting strategies.

[1039] "Means for receiving user information and storing it in a database" refers to a mechanism for receiving personal information and authentication information provided by users and storing it in a database for safe management.

[1040] The "means for acquiring data related to sports matches specified by the user" is a function for acquiring data related to sports matches or events in which the user is interested from an external sports API or the like.

[1041] "Means for cleaning acquired data and preparing it in an analyzable format" refers to the process of removing unnecessary information and noise from acquired raw data and converting it into a format suitable for data analysis.

[1042] The "means for training a generative AI model and generating predicted results" refers to a system that uses historical and real-time data to train an AI model and, as a result, predicts future match outcomes.

[1043] "Means for displaying the generated prediction results on the user's device" refers to a function for displaying the generated AI prediction results on the user's device through a user interface.

[1044] The "means for receiving betting information from the terminal and storing it in a database" is a mechanism for receiving betting information entered by a user through a terminal and recording it in a database.

[1045] "Means of updating match results in real time as the match progresses and delivering that information to the user's device" refers to a function that constantly checks the database while the match is in progress and instantly delivers new match results to the user's device.

[1046] The "means for supporting message exchange between users" is a function that provides a chat or message board for users to exchange messages with other users in real time.

[1047] "Methods for training generative AI models using cleaned data" refers to techniques that effectively train AI models using cleaned data that has been stripped of unnecessary data.

[1048] "Means for updating match results" refers to the process of collecting the latest information each time the match results change and reflecting that information in the database and on the user's terminal.

[1049] This invention relates to a sports betting system that uses generative AI models. This system allows users to enjoy legal sports betting and provides users with a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information by generative AI.

[1050] Overall system configuration

[1051] The system includes the following main elements:

[1052] 1. Database server: A system for managing user information, betting information, and sports match data.

[1053] 2. Sports API: An external data source for retrieving real-time data on sports matches.

[1054] 3. Generative AI model: An algorithm that uses past and real-time match data to predict future match outcomes.

[1055] 4. User device: The interface through which the user accesses the system. This includes smartphones and PCs.

[1056] System Components and Processing

[1057] User Registration and Login

[1058] The server receives the information the user entered into the new registration form, such as name, email address, and password, and stores it in a database. It compares this with existing users to ensure there are no duplicates. Once registration is complete, it sends authentication information to the user. When logging in, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[1059] The device sends the registration information entered by the user to the server, displays a confirmation message from the server, and if login is successful, displays the user's personal dashboard.

[1060] The user enters their name, email address, and password in the new registration form and submits it.The user enters their email address and password in the login form and clicks the login button.

[1061] Sports data collection and analysis

[1062] The server periodically calls the sports API to retrieve the latest match data and stores it in a database. The data is then cleaned and formatted for analysis. This analyzed data is used as training data for the generative AI model.

[1063] Upon receiving the notification from the server, the terminal notifies the user that the latest match data has been updated.

[1064] Users receive notifications from their devices and can refer to the latest updated match data to help them make bets.

[1065] AI-powered predictions

[1066] The server feeds the collected sports data to the generative AI model, which trains it and generates predictions. These predictions are stored in a database and made accessible to users.

[1067] The terminal receives the latest AI prediction results from the server and displays them to the user, who then uses an interface to view the results and place their bets.

[1068] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[1069] Simulation game progress management

[1070] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1071] The terminal displays real-time match results and simulation progress from the server.

[1072] Users can see the progress of their bets in real time.

[1073] View real-time results

[1074] The server periodically checks the database and updates the new match results in real time, and the updated information is sent to the user's device.

[1075] The terminal updates the user interface in real time based on the received update information.

[1076] Users can check the latest results as the game progresses and keep track of their betting progress.

[1077] User-to-user community function

[1078] The server provides message boards and chat functions to support real-time communication between users, and also manages and publishes user profile information and rankings.

[1079] The device provides an interface for posting messages and using chat functions.

[1080] Users exchange messages with other users and share information and strategies about sports betting.

[1081] Specific examples

[1082] For example, if a user wants to bet on a basketball game:

[1083] 1. User Registration and Login:

[1084] A user enters their name, email address, and password into the new registration form and submits it.

[1085] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[1086] The user enters their email address and password in the login form and clicks the login button.

[1087] The server verifies the login information and issues a session ID.

[1088] The device displays a dashboard specific to the user.

[1089] 2. Sports data collection and analysis:

[1090] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[1091] The terminal notifies the user that the latest match data has been updated.

[1092] 3. AI prediction:

[1093] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1094] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1095] Users check the AI's predictions and place their bets.

[1096] 4. Simulation game progress management:

[1097] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1098] The terminal displays the match results and the progress of the simulation in real time.

[1099] Users can check the progress of their bets.

[1100] 5. View real-time results:

[1101] The server updates the match results in real time and sends them to the user's device.

[1102] The terminal receives the updates and updates the user interface in real time.

[1103] Users can check the results of matches and keep track of their betting progress.

[1104] 6. User-to-user community features:

[1105] The server provides message boards and chat functions to support user communication.

[1106] The terminal provides an interface through which users can post messages and start chats.

[1107] Users can exchange messages with other users to share information and discuss strategies.

[1108] Prompt Sentence Examples

[1109] Here are some example prompts to get predictions from a generative AI model:

[1110] "What are your predictions for the winner and scoreline of the next NBA game?"

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Step 1: User Registration and Login

[1113] Input:

[1114] The user enters their name, email address, and password in the new registration form and clicks the submit button.

[1115] 1.2. Data Processing:

[1116] The terminal encodes the registration information entered by the user and transmits it to the server.

[1117] Processing:

[1118] The server receives a new registration request and validates the information entered, comparing it with existing information to ensure there are no duplicates.

[1119] Output:

[1120] The server stores the information in a database and then sends the user an email containing the authentication information.

[1121] Input:

[1122] The user enters their email address and password into the login form and clicks the submit button.

[1123] 1.6. Data Processing:

[1124] The terminal transmits the login information to the server.

[1125] Processing:

[1126] The server verifies the email address and password, and if authentication is successful, issues a session ID.

[1127] Output:

[1128] The device displays a personalized dashboard for the user, which includes their profile information and available features.

[1129] Step 2: Collecting and analyzing sports data

[1130] Input:

[1131] The server periodically calls the sports API to retrieve the latest match data.

[1132] 2.2. Data Processing:

[1133] The server stores the received match data in a database.

[1134] Processing:

[1135] The server cleans the data and converts it into a format that can be parsed.

[1136] Output:

[1137] The server uses the cleaned data as training data for the generative AI model.

[1138] Input:

[1139] The terminal receives the notification from the server and notifies the user that the sports data has been updated.

[1140] Output:

[1141] The user can check the updated game data through the terminal.

[1142] Step 3: AI prediction

[1143] Input:

[1144] The server feeds the collected sports data into the generative AI model.

[1145] 3.2. Data Processing:

[1146] The server trains the generative AI model and builds the predictive model.

[1147] Processing:

[1148] The server generates prediction results using a trained generative AI model.

[1149] Output:

[1150] The server stores the prediction results in a database and provides them to the user.

[1151] Input:

[1152] The device receives the latest AI prediction results from the server.

[1153] Output:

[1154] The terminal displays the prediction results on an interface and provides information for the user to place bets.

[1155] Input:

[1156] Users check the AI's predictions, select the amount to bet and the game to bet on, and click the "Bet" button.

[1157] Step 4: Managing the progress of the simulation game

[1158] Input:

[1159] The server receives betting information from the user.

[1160] 4.2. Data Processing:

[1161] The server stores the received betting information in a database.

[1162] Processing:

[1163] The server monitors the progress of the match and records progress information in real time.

[1164] Output:

[1165] The server sends progress data to the terminal.

[1166] Input:

[1167] The device receives real-time match results and progress information from the server.

[1168] Output:

[1169] The terminal displays this information on the user interface.

[1170] 4.7. Users can see the progress of their bets in real time.

[1171] Step 5: View real-time results

[1172] Input:

[1173] The server periodically checks the database while a match is in progress.

[1174] 5.2. Data Processing:

[1175] The server updates new match results information in real time.

[1176] Processing:

[1177] The server sends the updated information to the user's terminal.

[1178] Output:

[1179] The terminal reflects the received information in real time on the user interface.

[1180] 5.5. Users can check the latest results as the match progresses and keep track of their betting progress.

[1181] Step 6: User-to-user community function

[1182] Input:

[1183] Users use chats and message boards to exchange messages with other users.

[1184] 6.2. Data Processing:

[1185] The server receives messages between users and stores them in a database.

[1186] Processing:

[1187] The server manages and publishes user profile information and rankings.

[1188] Output:

[1189] The terminal displays messages sent by the user to other users.

[1190] 6.5. Users communicate with other Users and share information and strategies regarding betting.

[1191] (Application example 1)

[1192] 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."

[1193] Current sports betting systems lack high-precision prediction functions, real-time game result updates, and electronic payment integration to improve users' betting experience. As a result, users cannot quickly check their betting results or smoothly manage their bets. In addition, the community functions are insufficient, making it difficult to smoothly exchange information with other users.

[1194] 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.

[1195] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data on sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for depositing and refunding bets via an electronic payment system, and means for supporting message exchange between users. This allows users to place bets based on highly accurate prediction results, and allows them to smoothly manage bets and exchange information with other users while checking game results in real time.

[1196] "User information" refers to the personal information, betting history, authentication information, etc. of system users.

[1197] A "database" is a system for efficiently storing and managing collected information and data, and enabling them to be quickly searched and used as needed.

[1198] "Sports Match Data" means all data relating to a sporting event, including but not limited to match results, player statistics, and match schedules.

[1199] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to train itself based on collected data and predict the outcome of a match.

[1200] "Predicted Outcomes" means predicted information about future match outcomes and other relevant data calculated by a generative AI model.

[1201] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to access the system and place bets or check information.

[1202] "Bet information" refers to information such as the bet amount, target game, betting options, etc. that a user enters when placing a bet.

[1203] "Real-time match result updates" is the process of instantly updating the system with match results and progress as the real-world match progresses.

[1204] "Electronic payment systems" refers to the platforms and technologies used to transfer money online (such as credit card payments, digital wallets, and bank transfers).

[1205] "Means supporting message exchange" are communication tools such as message boards and chat functions that allow users to share information, opinions, and strategies.

[1206] This invention is a system that allows users to legally enjoy sports betting and gain a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information using generative AI models. To realize this application example, the following elements and processes are required.

[1207] System Components

[1208] server

[1209] Hardware: Cloud services with high-performance data processing capabilities (e.g., AWS, Google Cloud, Azure).

[1210] Software: Databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., TensorFlow, PyTorch), and API integration capabilities.

[1211] Terminal

[1212] Hardware: The device the user uses to access the app (smartphone, computer, tablet, etc.).

[1213] Software: The application that implements the user interface (e.g., React Native, Flutter).

[1214] User

[1215] Users of sports betting systems.

[1216] Program processing overview

[1217] 1. User Registration and Login

[1218] A user enters their name, email address, and password into the new registration form and submits it.

[1219] The server saves the registration information in a database and sends the authentication information to the user, who completes the authentication process by entering their email address and password in the login form and clicking the login button.

[1220] 2. Sports data collection and analysis

[1221] The server retrieves the latest match data from the sports API and stores it in a database.

[1222] The acquired data is cleaned and put into an analyzable format.

[1223] 3. Prediction using generative AI models

[1224] The server uses the cleaned data to train a generative AI model to predict match outcomes.

[1225] The prediction results are stored in a database and sent to the device.

[1226] 4. Betting information management and real-time results display

[1227] The user inputs betting information through the terminal and transmits it to the server.

[1228] The server updates the match results in real time and delivers the information to the terminal.

[1229] 5. Electronic payment function

[1230] Users deposit their bets via an electronic payment system.

[1231] If the bet is won, the server immediately refunds the bet amount to the user's account.

[1232] 6. Messaging and Communication

[1233] The server provides message boards and chat facilities to support message exchange between users.

[1234] Users can exchange information and share strategies with other users.

[1235] Examples and prompts

[1236] As a specific example, consider a case where a user places a bet on a basketball game.

[1237] 1. User Registration and Login

[1238] New Registration

[1239] ~~~~~~~~~

[1240] Name: Taro Yamada

[1241] Email: example@mail.com

[1242] password:

[1243] Log in

[1244] ~~~~~~~~

[1245] Email: example@mail.com

[1246] password:

[1247] 2. Sports data collection and analysis

[1248] Capture and analyze data from new basketball games.

[1249] 3. Prediction using generative AI models

[1250] Predict the outcome of the match based on the latest match data.

[1251] 4. Betting and Electronic Payments

[1252] Use the PayPal API to settle your users' bets instantly.

[1253] In this way, a system is realized that allows users to place bets based on highly accurate prediction results, check match results in real time, smoothly manage bets, and exchange information with other users.

[1254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1255] Step 1:

[1256] User Registration and Authentication

[1257] Input: A user enters their name, email address, and password into a new registration form and submits it.

[1258] Data processing: Before storing the received user information in the database, the server checks for duplication with existing data.

[1259] Output: If there are no duplicates, authentication information is generated and sent to the user. When logging in, the user enters their email address and password, which the server verifies and authenticates.

[1260] Specific operation: The server saves the new registration information in the database and sends a verification email to the user. The login process also verifies the user's authentication information, and if successful, issues a session ID and displays the dashboard.

[1261] Step 2:

[1262] Sports data collection and analysis

[1263] Input: The server requests and retrieves the latest match data from the sports API.

[1264] Data processing: The server cleans the acquired data and prepares it into an analyzable format (e.g., removing unnecessary data and standardizing the format).

[1265] Output: Save the cleaned data to the database.

[1266] Specific operation: The server normalizes the match data obtained from the sports API, removes inconsistent data, and prepares it for use as training data for the generative AI model.

[1267] Step 3:

[1268] Predictions from generative AI models

[1269] Input: Cleaned match data.

[1270] Data computation: The server uses generative AI models (e.g., TensorFlow, PyTorch) to train and predict match outcomes.

[1271] Output: The prediction results are stored in a database and sent to the device.

[1272] Specific operation: The server inputs data into the generative AI model to train it, calculates prediction results, stores them in a database, and then distributes them to the device.

[1273] Step 4:

[1274] Betting information management and real-time results display

[1275] Input: The user inputs betting information (betting amount, game, options) through the terminal and sends it to the server.

[1276] Data processing: The server stores the betting information in a database and monitors the progress of the matches.

[1277] Output: Deliver real-time updated match results to your device.

[1278] Specific operation: The server stores the received betting information in a database, updates the results as the game progresses, and delivers them to the terminal in real time.

[1279] Step 5:

[1280] Electronic payment function

[1281] Input: The user inputs information to deposit a bet through an electronic payment system.

[1282] Data Processing: The server receives the payment information and outsources the payment process to a third-party electronic payment system.

[1283] Output: If the payment is successful, a confirmation is sent to the user's device.

[1284] Specific operation: The server executes the payment process using an electronic payment API (e.g., PayPal, Stripe), and if successful, records the result in a database and notifies the terminal.

[1285] Step 6:

[1286] Messaging and Communication

[1287] Input: A user posts a message using a message board or chat function.

[1288] Data processing: The server stores the received messages in a database and distributes them to other users in real time.

[1289] Output: Other users receive and view the message.

[1290] Specific operation: The server manages message exchanges between users through message boards and chat functions, and distributes them instantly to facilitate communication.

[1291] 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.

[1292] This invention combines a sports betting system using a generative AI model with an emotion engine that recognizes user emotions. This system allows users to legally enjoy sports betting, and aims to provide highly accurate predictions and real-time information using generative AI, as well as customize the interface and suggest bets based on the user's emotional state.

[1293] Program processing overview

[1294] User registration and login process

[1295] The server receives a new user registration request and saves it in the database. It compares the information with existing users and verifies that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[1296] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[1297] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[1298] Sports data collection and analysis

[1299] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[1300] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[1301] Users can check the latest updated match data and use it as a reference for betting.

[1302] AI-powered predictions

[1303] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1304] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[1305] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[1306] Simulation game progress management

[1307] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1308] The terminal displays the match results and simulation progress received from the server in real time.

[1309] Users can check the progress of their bets in real time and wait for the results.

[1310] View real-time results

[1311] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[1312] The terminal receives updates on the match results and updates the user interface in real time.

[1313] Users can check the latest results as the game progresses and keep track of their betting progress.

[1314] User-to-user community function

[1315] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[1316] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[1317] Users can exchange messages with other users to share information and discuss strategies.

[1318] Overview of Emotion Engine Operation

[1319] User Emotion Recognition

[1320] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[1321] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[1322] The user uses the system as usual, and their operations are used for emotion recognition.

[1323] Emotion-based interface customization

[1324] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[1325] The terminal updates the user interface based on the customization information provided by the server.

[1326] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[1327] Emotion-based betting suggestions

[1328] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[1329] The terminal displays betting suggestions to the user and assists in selection.

[1330] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[1331] Specific examples

[1332] For example, a user may want to bet on a soccer match.

[1333] 1. User Registration and Login:

[1334] A user enters their name, email address, and password into the new registration form and submits it.

[1335] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[1336] The user enters their email address and password in the login form and clicks the Login button.

[1337] The server verifies the login information and issues a session ID.

[1338] The device displays a dashboard specific to the user.

[1339] 2. Sports data collection and analysis:

[1340] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[1341] The terminal notifies the user that the latest game data has been updated.

[1342] 3. AI prediction:

[1343] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1344] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1345] Users check the AI's predictions and place their bets.

[1346] 4. Simulation game progress management:

[1347] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1348] The device displays the match results and the progress of the simulation in real time.

[1349] Users can check the progress of their bets.

[1350] 5. View real-time results:

[1351] The server updates the match results in real time and sends them to the user's device.

[1352] The terminal receives the update information and updates the user interface in real time.

[1353] Users can check the results of matches and keep track of their betting progress.

[1354] 6. User-to-user community features:

[1355] The server provides message boards and chat functions to support user communication.

[1356] The terminal provides an interface through which users can post messages and start chats.

[1357] Users can exchange messages with other users to share information and discuss strategies.

[1358] 7. Emotion Engine in Action:

[1359] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[1360] The device sends the necessary input data and displays feedback of the emotional state.

[1361] The user uses the system as usual, and their operations are used for emotion recognition.

[1362] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[1363] The terminal displays customization information and betting suggestions.

[1364] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[1365] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[1366] The processing flow will be explained below.

[1367] User registration and login process

[1368] Step 1:

[1369] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[1370] Step 2:

[1371] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[1372] Step 3:

[1373] The server receives a new registration request and checks its database to see if the email address is already registered.

[1374] Step 4:

[1375] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[1376] Step 5:

[1377] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[1378] Step 6:

[1379] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[1380] Step 7:

[1381] The terminal sends the entered login information to the server.

[1382] Step 8:

[1383] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[1384] Step 9:

[1385] The device stores the session ID and displays a dashboard specific to the user.

[1386] Sports data collection and analysis

[1387] Step 1:

[1388] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[1389] Step 2:

[1390] The server formats the data it receives and stores it in a database.

[1391] Step 3:

[1392] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[1393] Step 4:

[1394] The server stores the cleaned, analyzable data as training data for the generated AI model.

[1395] Step 5:

[1396] When the device has completed data collection and analysis, it will notify the user that the latest sports information has been updated.

[1397] AI-powered predictions

[1398] Step 1:

[1399] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[1400] Step 2:

[1401] The server generates new parameters and updates the latest model.

[1402] Step 3:

[1403] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[1404] Step 4:

[1405] The server stores the generated prediction results in a database and provides them to the user.

[1406] Step 5:

[1407] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1408] Step 6:

[1409] Users decide on their bets based on the AI's predictions and place their bets.

[1410] Simulation game progress management

[1411] Step 1:

[1412] The server receives betting information from users and stores it in a database.

[1413] Step 2:

[1414] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[1415] Step 3:

[1416] The server stores the updated match results in a database and distributes them to the device.

[1417] Step 4:

[1418] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[1419] Step 5:

[1420] Users check their bets and game progress and consider their next actions.

[1421] View real-time results

[1422] Step 1:

[1423] The server periodically checks the database to see if new data is available as the match progresses in real time.

[1424] Step 2:

[1425] If the server has new data, it sends updated information to the terminal.

[1426] Step 3:

[1427] The terminal processes the received update information and updates the user interface in real time.

[1428] Step 4:

[1429] Users can check the latest results while the match is in progress and decide what to do next.

[1430] User-to-user community function

[1431] Step 1:

[1432] The server provides a message board and chat function to support message exchanges between users.

[1433] Step 2:

[1434] A user visits a community page and posts a message.

[1435] Step 3:

[1436] The terminal sends the input message to the server, which stores the message in a database.

[1437] Step 4:

[1438] The server distributes the stored messages to other users.

[1439] Step 5:

[1440] Users can exchange messages with other users to share information and discuss strategies.

[1441] User Emotion Recognition

[1442] Step 1:

[1443] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history.

[1444] Step 2:

[1445] The server uses an emotion engine to estimate the user's emotional state.

[1446] Step 3:

[1447] The terminal transmits the user's input data required for emotion recognition to the server.

[1448] Step 4:

[1449] The device displays emotional state feedback to the user.

[1450] Step 5:

[1451] The user uses the system as usual, and their operations are used for emotion recognition.

[1452] Emotion-based interface customization

[1453] Step 1:

[1454] The server dynamically customizes the interface display based on the user's emotional state.

[1455] Step 2:

[1456] The server transmits the customization information to the terminal.

[1457] Step 3:

[1458] The terminal receives the customization information and updates the user interface.

[1459] Step 4:

[1460] Users will have an improved experience using an interface that is optimized according to their emotional state.

[1461] Emotion-based betting suggestions

[1462] Step 1:

[1463] The server dynamically adjusts the betting suggestions based on the user's emotional state analyzed by the emotion engine.

[1464] Step 2:

[1465] The server sends dynamically adjusted betting proposals to the terminal.

[1466] Step 3:

[1467] The terminal displays betting suggestions to the user.

[1468] Step 4:

[1469] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[1470] Specific examples

[1471] For example, a user may want to bet on a soccer match.

[1472] User registration and login:

[1473] Step 1:

[1474] A user enters their name, email address, and password into the new registration form and submits it.

[1475] Step 2:

[1476] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[1477] Step 3:

[1478] The user enters their email address and password in the login form and clicks the Login button.

[1479] Step 4:

[1480] The server verifies the login information and issues a session ID.

[1481] Step 5:

[1482] The device displays a dashboard specific to the user.

[1483] Sports data collection and analysis:

[1484] Step 1:

[1485] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[1486] Step 2:

[1487] The terminal notifies the user that the latest game data has been updated.

[1488] AI-powered predictions:

[1489] Step 1:

[1490] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1491] Step 2:

[1492] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1493] Step 3:

[1494] Users check the AI's predictions and place their bets.

[1495] Simulation game progress management:

[1496] Step 1:

[1497] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1498] Step 2:

[1499] The device displays the match results and the progress of the simulation in real time.

[1500] Step 3:

[1501] Users can check the progress of their bets.

[1502] View real-time results:

[1503] Step 1:

[1504] The server updates the match results in real time and sends them to the user's device.

[1505] Step 2:

[1506] The terminal receives the update information and updates the user interface in real time.

[1507] Step 3:

[1508] Users can check the results of matches and keep track of their betting progress.

[1509] User-to-user community features:

[1510] Step 1:

[1511] The server provides message boards and chat functions to support user communication.

[1512] Step 2:

[1513] The terminal provides an interface through which users can post messages and start chats.

[1514] Step 3:

[1515] Users can exchange messages with other users to share information and discuss strategies.

[1516] Emotion Engine in action:

[1517] Step 1:

[1518] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[1519] Step 2:

[1520] The device sends the necessary input data and displays feedback of the emotional state.

[1521] Step 3:

[1522] The user uses the system as usual, and their operations are used for emotion recognition.

[1523] Step 4:

[1524] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[1525] Step 5:

[1526] The terminal displays customization information and betting suggestions.

[1527] Step 6:

[1528] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[1529] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[1530] Example 2

[1531] 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."

[1532] Conventional sports betting systems do not provide services that take into account the user's emotional state, limiting the improvement of user experience. Furthermore, in addition to highly accurate predictions and real-time information provision, they lack effective communication between users and interface customization. This has led to issues such as reduced user satisfaction and engagement.

[1533] 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.

[1534] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for supporting message exchange between users, means for analyzing the user's input data and behavioral history to estimate the user's emotional state, means for dynamically customizing the interface based on the user's emotional state, and means for dynamically adjusting betting suggestions based on the user's emotional state. This enables the sports betting system to provide services that take the user's emotional state into consideration, thereby improving the user experience and strengthening engagement.

[1535] "Means for receiving user information and storing it in a database" refers to a method in which the server collects registration information entered by the user, such as name, email address, and password, and stores it in a database.

[1536] "Means for obtaining data on sports games specified by users" refers to a method for obtaining information on specific sports games that users have specified they wish to bet on via external data sources or APIs, and incorporating that data into the system.

[1537] "Means for training a generative AI model using acquired data to generate prediction results" refers to a method for training an artificial intelligence (AI) algorithm based on collected sports match data and predicting match results from the training results.

[1538] "Means for displaying the generated prediction results on the user's device" refers to a method for displaying the prediction result data generated by the AI ​​model on the device used by the user through a user interface.

[1539] "Means for receiving betting information from the terminal and storing it in a database" refers to a method in which the server receives the betting amount and betting details entered by the user through the terminal and stores that information in a database.

[1540] "Means for updating match results in real time and delivering that information to the user's device" refers to a method for periodically collecting new match results as the match progresses and immediately sending that information to the user's device.

[1541] "Means supporting the exchange of messages between users" means communication features that allow users to text message or chat with other users through the system.

[1542] "Means for analyzing a user's input data and behavioral history to infer their emotional state" refers to technology that analyzes the text data and usage history that a user inputs into the system to infer the user's emotional state.

[1543] The "means for dynamically customizing an interface based on an emotional state" is a method for optimizing the screen display and content provided in real time according to the estimated emotional state of the user.

[1544] A "means for dynamically adjusting betting suggestions based on emotional state" is a method for automatically adjusting betting options and risk levels to suggest betting strategies and content appropriate to a user's current emotional state.

[1545] This invention is a sports betting system that uses a generative AI model and an emotion engine to enable users to bet legally and enjoyably on sports. This system not only provides highly accurate predictions and real-time information using generative AI, but also customizes the interface and suggests bets based on the user's emotional state.

[1546] System Overview

[1547] The system includes the following main components: a server, a terminal, and a user.

[1548] 1. User registration and login process

[1549] The server receives a new user registration request and saves it in the database. The database used here is "MySQL." The information is compared with existing users to ensure that the email address is not a duplicate. Once registration is complete, authentication information is sent to the user. When a login request is made, the entered email address and password are compared, and if authentication is successful, a session ID is issued.

[1550] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if the login is successful, displays a dashboard dedicated to the user using "React."

[1551] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[1552] Example: A user enters their name, email address, and password into a new registration form and submits it. The server receives the registration information and stores it in a database. The authentication information is sent to the user. The user enters their email address and password into a login form and clicks the login button. The server verifies the login information and issues a session ID. The device displays a dashboard specific to the user.

[1553] 2. Sports data collection and analysis

[1554] The server periodically retrieves the latest match data from a sports API (e.g., "SportsRadar") and stores it in a database. This data is then cleaned and formatted for analysis, using the "Pandas" library in Python. The analyzed data is then used as training data for the generative AI model.

[1555] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[1556] Users can check the latest updated match data and use it as a reference for betting.

[1557] 3. AI-based predictions

[1558] The server uses the collected data to train a generative AI model and generate prediction results using Python and TensorFlow. The generated prediction results are stored in a database and provided to users.

[1559] The terminal uses "D3.js" to display the latest AI prediction results received from the server to the user, and provides an interface for users to place bets.

[1560] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[1561] 4. Simulation game progress management

[1562] The server manages the betting information received from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1563] The terminal displays the match results and simulation progress received from the server in real time.

[1564] Users can check the progress of their bets in real time and wait for the results.

[1565] 5. View real-time results

[1566] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[1567] The terminal receives updates on the match results and updates the user interface in real time.

[1568] Users can check the latest results as the game progresses and keep track of their betting progress.

[1569] 6. User-to-user community function

[1570] The server provides message boards and chat functions to support message exchanges between users, and also manages and publishes user profiles and rankings.

[1571] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[1572] Users can exchange messages with other users to share information and discuss strategies.

[1573] 7. Emotion Engine Operation

[1574] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[1575] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[1576] The user uses the system as usual, and their operations are used for emotion recognition.

[1577] Emotion-based interface customization

[1578] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[1579] The terminal updates the user interface based on the customization information provided by the server.

[1580] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[1581] Emotion-based betting suggestions

[1582] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[1583] The terminal displays betting suggestions to the user and assists in selection.

[1584] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[1585] Example prompt:

[1586] The "User" enters their name, email address, and password into the new registration form and clicks the "Submit" button. The "Server" receives this information, saves it in the "MySQL" database, and sends a registration completion email. The "User" then enters their email address and password into the login form and clicks the "Login" button. The "Server" checks the login information, and if it is correct, issues a session ID and displays the dashboard created with "React" on the "Terminal."

[1587] The system aims to provide users with highly accurate sports betting predictions and an optimal experience based on their emotional state.

[1588] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1589] The flow of this system's program processing

[1590] Step 1: User registration and login process

[1591] server

[1592] Input: Name, email address, and password entered by the user in the sign-up form.

[1593] Data processing: Before storing this information in the MySQL database, we check whether the email address is a duplicate of existing data.

[1594] Output: Generates authentication information and sends it to the user.

[1595] Specific operation: The server saves the new user information in the MySQL database and sends a registration completion email.

[1596] Terminal

[1597] Input: Registration information entered by the user.

[1598] Data processing: Send the input information to the server.

[1599] Output: The status message returned by the server.

[1600] Specific behavior: The device displays status messages in a "React" interface.

[1601] User

[1602] Input: Enter your name, email address, and password in the new registration form and click the submit button.

[1603] Output: You will receive a registration confirmation email.

[1604] Specific actions: Fill in the required information in the form, submit it, and check the verification email.

[1605] Step 2: Collecting and analyzing sports data

[1606] server

[1607] Input: Match data obtained via a sports API (e.g. "SportsRadar").

[1608] Data processing: The acquired data was cleaned using the Python Pandas library and converted into an analyzable format.

[1609] Output: Cleaned match data stored in a MySQL database.

[1610] What it does: The server periodically sends requests to the sports API, retrieves the data, cleans it, and stores it in the database.

[1611] Terminal

[1612] Input: Data update notification from the server.

[1613] Data processing: Display a notification to the user.

[1614] Output: Latest sports data updates.

[1615] Specific operation: The device will display a notification of the latest data update.

[1616] User

[1617] Input: Check update notifications and interact with the system.

[1618] Output: Show new match data.

[1619] Specific actions: Check the updated match information on the device.

[1620] Step 3: AI prediction

[1621] server

[1622] Input: Cleaned match data.

[1623] Data processing: Train the data using a generative AI model (Python, TensorFlow).

[1624] Output: Generate prediction results and store them in a MySQL database.

[1625] Specific operation: Trains an AI model, generates prediction results, and stores them.

[1626] Terminal

[1627] Input: Prediction result data from the server.

[1628] Data processing: Visualize the prediction results using "D3.js".

[1629] Output: Prediction results are visualized and displayed to the user.

[1630] Specific behavior: Prediction results are displayed visually on the device.

[1631] User

[1632] Input: Check the prediction results.

[1633] Output: Place a bet.

[1634] Specific operations: Determine the betting amount and target game based on the prediction result and complete the operation.

[1635] Step 4: Managing the progress of the simulation game

[1636] server

[1637] Input: Betting information from the user.

[1638] Data processing: Store betting information in a database and update match results in real time.

[1639] Output: Real-time match results.

[1640] Specific operation: The database is updated continuously while the match is in progress and the results are sent to the user.

[1641] Terminal

[1642] Input: Real-time match results.

[1643] Data processing: Display result data in real time.

[1644] Output: Updated match results and progress.

[1645] Specific operation: Display match results in real time.

[1646] User

[1647] Input: Check real-time match results.

[1648] Output: Betting progress.

[1649] Specific actions: Monitor the progress of the match and wait for the results.

[1650] Step 5: View real-time results

[1651] server

[1652] Input: Match result data.

[1653] Data processing: Result data is updated in real time.

[1654] Output: Updated results are delivered to the user's device.

[1655] Specific operation: New match results are periodically updated in the database and sent to users.

[1656] Terminal

[1657] Input: Updated data from the server.

[1658] Data Processing: Interface update.

[1659] Output: Display the latest match results.

[1660] Specific operation: The device displays the match results in real time.

[1661] User

[1662] Input: Updated match results.

[1663] Output: The progress of your bet.

[1664] Specific actions: Check match results and understand progress.

[1665] Step 6: User-to-user community function

[1666] server

[1667] Input: The user's message data.

[1668] Data processing: Messages are stored in a database and distributed to other users.

[1669] Output: Providing message boards and chat functionality.

[1670] What it does: Manage and deliver messages in real time.

[1671] Terminal

[1672] Input: User message input.

[1673] Data processing: Display of messages.

[1674] Output: Display of new messages.

[1675] Specific behavior: Provides a message input interface and implements chat functionality.

[1676] User

[1677] Input: Type a message to exchange with other users.

[1678] Output: Communication information.

[1679] Specific action: Type a message and communicate with other users.

[1680] Step 7: Emotion Engine in Action

[1681] server

[1682] Input: User input data and behavioral history.

[1683] Data processing: Analyze using an emotion engine to estimate emotional state.

[1684] Output: Interface customization information based on emotional state.

[1685] Specific behavior: Analyze the user's state using the emotion engine and use the results to customize the interface.

[1686] Terminal

[1687] Input: Customization information from the server.

[1688] Data processing: Interface update.

[1689] Output: Optimized user interface.

[1690] Specific behavior: Update the user interface in real time according to the emotional state.

[1691] User

[1692] Input: Normal operation.

[1693] Output: Customized interface.

[1694] Specific behavior: Operate an interface optimized for your emotional state and enjoy a better user experience.

[1695] These steps enable the system of the present invention to provide users with highly accurate predictions and a customized emotion-based interface.

[1696] (Application example 2)

[1697] 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."

[1698] Conventional systems simply predict events and provide information without taking into account the user's emotional state and its fluctuations. This results in a uniform user experience, resulting in insufficient optimal support and customization for individual users. Furthermore, when it comes to managing users' spending, they lack real-time feedback and advice based on their emotional state.

[1699] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and saving it in a database, means for acquiring data related to events specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving information from the terminal and saving it in a database, means for updating event results in real time and delivering the information to the user's terminal, means for supporting information exchange between users, and means for analyzing the user's emotional state and dynamically customizing the display interface and proposal content based on the state. This makes it possible to provide the user with optimal information and advice on expense management that takes into account the user's emotional state.

[1700] "User Information" refers to data relating to a user's personal information, behavioral data, and emotional state.

[1701] A "database" is an information system for efficiently storing and searching information, particularly for storing user information, event data, prediction results, etc.

[1702] "Server" refers to a computer system that processes, stores, distributes data, and fulfills user requests.

[1703] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence techniques to make predictions and suggestions from collected data.

[1704] "Prediction Results" refers to predictive data about future events or situations generated by a generative AI model.

[1705] "Terminal" refers to an electronic device (smartphone, smart glasses, etc.) that a user operates.

[1706] "Real-time updates" refers to a process in which information is immediately reflected in the current situation and provided to the user immediately.

[1707] "Information exchange between users" refers to multiple users of the system sharing messages and data.

[1708] "Emotional state" refers to the user's current psychological state (e.g., happy, sad, excited, relaxed, etc.).

[1709] "Display interface" refers to a screen that allows a user to visually check and operate information.

[1710] "Suggestion content" refers to information that the system provides to the user with specific actions and options to be taken.

[1711] "Dynamic customization" refers to changing the interface and suggestions in real time according to the user's emotional state and behavior.

[1712] The system embodying this invention collects user information and provides real-time predictions and advice based on the collected information according to the user's emotional state. The main components are as follows:

[1713] Hardware and Software

[1714] This system uses a general server and user devices (smartphones, smart glasses, etc.) and utilizes the following software:

[1715] Emotion recognition engine (e.g. Microsoft Azure Cognitive Services)

[1716] Database (e.g. MySQL)

[1717] Generative AI models (e.g., GPT-4)

[1718] User information collection and storage

[1719] The server receives and stores in a database the user's registration information (personal information, behavioral data, and data on emotional state) that the user enters through the interface and processes in real time.

[1720] Event data acquisition and analysis

[1721] The server acquires data related to events specified by the user and stores it in a database. The acquired data is cleaned and formatted for analysis. A generative AI model is then used to generate predictions. These predictions are stored in the database and displayed on the user's device.

[1722] Real-time updates and information distribution

[1723] The server updates the event results in real time and delivers the information to the user's device, allowing the user to always obtain the latest information.

[1724] Information exchange between users

[1725] The server provides a message board and chat function to support information exchange between users, allowing users to share information and discuss strategies.

[1726] Emotional state analysis and customization

[1727] The server analyzes the user's input data and behavioral history, and uses an emotion recognition engine to estimate their emotional state. Based on their emotional state, the displayed interface and suggestions are dynamically customized. For example, if the user is tired, suggestions to reduce spending are made, and if the user is excited, suggestions to reduce risk are made.

[1728] Specific examples

[1729] For example, if the emotion engine detects that a user is tired while shopping at a supermarket, the app will display a notification saying, "Maybe you should take a little rest today. You have 3,000 yen left in your budget this week." In this way, users can optimally manage their spending based on their emotional state.

[1730] Prompt Sentence Examples

[1731] "Describe a system that analyzes your emotional state in real time and provides spending management advice. It can analyze your spending history and behavioral history from a database and generate optimal spending suggestions based on your emotions. The system uses a smartphone or smart glasses and includes a generative AI model, an emotion recognition engine, and real-time updates. For example, it provides savings suggestions when the user is tired, and risk-reducing suggestions when the user is excited."

[1732] In this way, the system of the present invention provides the user with optimal information and advice on expense management that takes into account their emotional state.

[1733] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1734] Step 1:

[1735] The server receives the user's information and stores it in a database. This information includes the user's personal information, behavioral data, and emotional state data. As input, it receives the information entered by the user through the interface and processes it to store it in the database. As output, it obtains the stored user information.

[1736] Step 2:

[1737] The server acquires data about events specified by the user and stores it in a database. As input, it receives information about the type of event and date and time selected by the user. The database processes the data acquired from an external event information system and stores it. As output, it obtains the stored event data.

[1738] Step 3:

[1739] The server uses the acquired data to train a generative AI model and generate predictions. As input, it receives stored event data. It analyzes this data and performs data calculations that feed it into the generative AI model to generate predictions. As output, it obtains the generated predictions.

[1740] Step 4:

[1741] The terminal displays the generated prediction results on the user's device. As input, it receives the prediction results received from the server. It processes the data to display them in a user interface in a visually easy-to-understand format. As output, the user can check the prediction results.

[1742] Step 5:

[1743] The terminal receives information from the user and sends it to the server. As input, it receives the user's actions (information input and selection). It processes this information and sends it to the server. As output, it obtains the user's action data that the server receives.

[1744] Step 6:

[1745] The server stores information from the device in a database. As input, it receives the user's action data sent from the device. It processes the action data and stores it in the database. As output, it obtains the stored user's action data.

[1746] Step 7:

[1747] The server updates the event results in real time and delivers the information to the user's device. As input, it receives the latest event results obtained from an external event result information providing system. It analyzes these and performs data calculations to deliver them to the user's device. As output, the latest event results are delivered to the device.

[1748] Step 8:

[1749] The terminal displays the latest event results delivered to the user's terminal. As input, it receives the latest event result information from the server. It processes this data to display it in a visually easy-to-understand format on the user interface. As output, the user can check the latest event results.

[1750] Step 9:

[1751] The server provides message board and chat functions to support information exchange between users. As input, it receives messages and chat information from users. It processes this data to distribute it to other users as needed. As output, it enables messages and chat between users.

[1752] Step 10:

[1753] The server analyzes the user's input data and behavioral history and uses an emotion recognition engine to estimate the user's emotional state. It receives the user's operation data and behavioral data as input. It supplies this data to the emotion recognition engine, which then performs a data calculation to estimate the user's emotional state. The output is the user's emotional state.

[1754] Step 11:

[1755] The server dynamically customizes the display interface and suggestions based on the user's emotional state. It receives the user's estimated emotional state as input, performs data calculations to change the color of the interface and adjust the suggestions based on the user's emotional state, and delivers the customized interface and suggestions to the user's device as output.

[1756] Step 12:

[1757] The terminal displays the customized interface and suggested content to the user. As input, it receives customization information distributed from the server. It processes this data to display it on the user interface in a visually easy-to-understand format. As output, it displays the interface and suggested content optimized for the user.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] [Third embodiment]

[1762] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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.

[1767] 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).

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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."

[1774] This invention relates to a sports betting system that uses a generative AI model. This system aims to enable users to legally enjoy sports betting and provide them with a fresh experience and a high rate of profits through the provision of highly accurate predictions and real-time information by generative AI.

[1775] Program processing overview

[1776] User registration and login process

[1777] The server receives a new user registration request and saves it in the database. It compares it with existing user information to ensure that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[1778] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[1779] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[1780] Sports data collection and analysis

[1781] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[1782] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[1783] Users can check the latest updated match data and use it as a reference for betting.

[1784] AI-powered predictions

[1785] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1786] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[1787] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[1788] Simulation game progress management

[1789] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1790] The terminal displays the match results and simulation progress received from the server in real time.

[1791] Users can check the progress of their bets in real time and wait for the results.

[1792] View real-time results

[1793] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[1794] The terminal receives updates on the match results and updates the user interface in real time.

[1795] Users can check the latest results as the game progresses and keep track of their betting progress.

[1796] User-to-user community function

[1797] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[1798] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[1799] Users can exchange messages with other users to share information and discuss strategies.

[1800] Specific examples

[1801] For example, a user may wish to bet on a basketball game.

[1802] 1. User Registration and Login:

[1803] A user enters their name, email address, and password into the new registration form and submits it.

[1804] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[1805] The user enters their email address and password in the login form and clicks the login button.

[1806] The server verifies the login information and issues a session ID.

[1807] The device displays a dashboard specific to the user.

[1808] 2. Sports data collection and analysis:

[1809] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[1810] The terminal notifies the user that the latest match data has been updated.

[1811] 3. AI prediction:

[1812] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1813] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1814] Users check the AI's predictions and place their bets.

[1815] 4. Simulation game progress management:

[1816] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1817] The terminal displays the match results and the progress of the simulation in real time.

[1818] Users can check the progress of their bets.

[1819] 5. View real-time results:

[1820] The server updates the match results in real time and sends them to the user's device.

[1821] The terminal receives the updates and updates the user interface in real time.

[1822] Users can check the results of matches and keep track of their betting progress.

[1823] 6. User-to-user community features:

[1824] The server provides message boards and chat functions to support user communication.

[1825] The terminal provides an interface through which users can post messages and start chats.

[1826] Users can exchange messages with other users to share information and discuss strategies.

[1827] In this way, the system provides users with a wealth of data and highly accurate predictions, enabling a legitimate and fresh sports betting experience.

[1828] The processing flow will be explained below.

[1829] User registration and login process

[1830] Step 1:

[1831] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[1832] Step 2:

[1833] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[1834] Step 3:

[1835] The server receives a new registration request and checks its database to see if the email address is already registered.

[1836] Step 4:

[1837] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[1838] Step 5:

[1839] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[1840] Step 6:

[1841] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[1842] Step 7:

[1843] The terminal sends the entered login information to the server.

[1844] Step 8:

[1845] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[1846] Step 9:

[1847] The device stores the session ID and displays a dashboard specific to the user.

[1848] Sports data collection and analysis

[1849] Step 1:

[1850] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[1851] Step 2:

[1852] The server formats the data it receives and stores it in a database.

[1853] Step 3:

[1854] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[1855] Step 4:

[1856] The server stores the cleaned, analyzable data as training data for the generated AI model.

[1857] Step 5:

[1858] Once the device has completed collecting and analyzing the data, it will display an update notification to the user regarding the latest sports information.

[1859] AI-powered predictions

[1860] Step 1:

[1861] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[1862] Step 2:

[1863] The server generates new parameters and updates the latest model.

[1864] Step 3:

[1865] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[1866] Step 4:

[1867] The server stores the generated prediction results in a database and provides them to the user.

[1868] Step 5:

[1869] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1870] Step 6:

[1871] Users decide on their bets based on the AI's predictions and place their bets.

[1872] Simulation game progress management

[1873] Step 1:

[1874] The server receives betting information from users and stores it in a database.

[1875] Step 2:

[1876] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[1877] Step 3:

[1878] The server stores the updated match results in a database and distributes them to the device.

[1879] Step 4:

[1880] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[1881] Step 5:

[1882] Users check their bets and game progress and consider their next actions.

[1883] View real-time results

[1884] Step 1:

[1885] The server periodically checks the database to see if new data is available as the match progresses in real time.

[1886] Step 2:

[1887] If the server has new data, it sends updated information to the terminal.

[1888] Step 3:

[1889] The terminal processes the received update information and updates the user interface in real time.

[1890] Step 4:

[1891] Users can check the latest results while the match is in progress and decide what to do next.

[1892] User-to-user community function

[1893] Step 1:

[1894] The server provides a message board and chat function to support message exchanges between users.

[1895] Step 2:

[1896] A user visits a community page and posts a message.

[1897] Step 3:

[1898] The terminal sends the input message to the server, which stores the message in a database.

[1899] Step 4:

[1900] The server distributes the stored messages to other users.

[1901] Step 5:

[1902] Users can exchange messages with other users to share information and discuss strategies.

[1903] Example 1

[1904] 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."

[1905] In conventional sports betting systems, it was difficult for users to obtain reliable prediction data, and they did not provide sufficient real-time updates on game results or communication support between users. As a result, user experience and satisfaction were low, and it was difficult to acquire new users and retain existing users.

[1906] 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.

[1907] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for cleaning the acquired data and arranging it into an analyzable format, means for training a generative AI model using the cleaned data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time as the game progresses and delivering that information to the user's terminal, and means for supporting message exchange between users. This allows users to place sports bets based on reliable prediction data and check the progress and results of games in real time. Furthermore, communication with other users makes it easy to share and discuss betting strategies.

[1908] "Means for receiving user information and storing it in a database" refers to a mechanism for receiving personal information and authentication information provided by users and storing it in a database for safe management.

[1909] The "means for acquiring data related to sports matches specified by the user" is a function for acquiring data related to sports matches or events in which the user is interested from an external sports API or the like.

[1910] "Means for cleaning acquired data and preparing it in an analyzable format" refers to the process of removing unnecessary information and noise from acquired raw data and converting it into a format suitable for data analysis.

[1911] The "means for training a generative AI model and generating predicted results" refers to a system that uses historical and real-time data to train an AI model and, as a result, predicts future match outcomes.

[1912] "Means for displaying the generated prediction results on the user's device" refers to a function for displaying the generated AI prediction results on the user's device through a user interface.

[1913] The "means for receiving betting information from the terminal and storing it in a database" is a mechanism for receiving betting information entered by a user through a terminal and recording it in a database.

[1914] "Means of updating match results in real time as the match progresses and delivering that information to the user's device" refers to a function that constantly checks the database while the match is in progress and instantly delivers new match results to the user's device.

[1915] The "means for supporting message exchange between users" is a function that provides a chat or message board for users to exchange messages with other users in real time.

[1916] "Methods for training generative AI models using cleaned data" refers to techniques that effectively train AI models using cleaned data that has been stripped of unnecessary data.

[1917] "Means for updating match results" refers to the process of collecting the latest information each time the match results change and reflecting that information in the database and on the user's terminal.

[1918] This invention relates to a sports betting system that uses generative AI models. This system allows users to enjoy legal sports betting and provides users with a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information by generative AI.

[1919] Overall system configuration

[1920] The system includes the following main elements:

[1921] 1. Database server: A system for managing user information, betting information, and sports match data.

[1922] 2. Sports API: An external data source for retrieving real-time data on sports matches.

[1923] 3. Generative AI model: An algorithm that uses past and real-time match data to predict future match outcomes.

[1924] 4. User device: The interface through which the user accesses the system. This includes smartphones and PCs.

[1925] System Components and Processing

[1926] User Registration and Login

[1927] The server receives the information the user entered into the new registration form, such as name, email address, and password, and stores it in a database. It compares this with existing users to ensure there are no duplicates. Once registration is complete, it sends authentication information to the user. When logging in, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[1928] The device sends the registration information entered by the user to the server, displays a confirmation message from the server, and if login is successful, displays the user's personal dashboard.

[1929] The user enters their name, email address, and password in the new registration form and submits it.The user enters their email address and password in the login form and clicks the login button.

[1930] Sports data collection and analysis

[1931] The server periodically calls the sports API to retrieve the latest match data and stores it in a database. The data is then cleaned and formatted for analysis. This analyzed data is used as training data for the generative AI model.

[1932] Upon receiving the notification from the server, the terminal notifies the user that the latest match data has been updated.

[1933] Users receive notifications from their devices and can refer to the latest updated match data to help them make bets.

[1934] AI-powered predictions

[1935] The server feeds the collected sports data to the generative AI model, which trains it and generates predictions. These predictions are stored in a database and made accessible to users.

[1936] The terminal receives the latest AI prediction results from the server and displays them to the user, who then uses an interface to view the results and place their bets.

[1937] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[1938] Simulation game progress management

[1939] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1940] The terminal displays real-time match results and simulation progress from the server.

[1941] Users can see the progress of their bets in real time.

[1942] View real-time results

[1943] The server periodically checks the database and updates the new match results in real time, and the updated information is sent to the user's device.

[1944] The terminal updates the user interface in real time based on the received update information.

[1945] Users can check the latest results as the game progresses and keep track of their betting progress.

[1946] User-to-user community function

[1947] The server provides message boards and chat functions to support real-time communication between users, and also manages and publishes user profile information and rankings.

[1948] The device provides an interface for posting messages and using chat functions.

[1949] Users exchange messages with other users and share information and strategies about sports betting.

[1950] Specific examples

[1951] For example, if a user wants to bet on a basketball game:

[1952] 1. User Registration and Login:

[1953] A user enters their name, email address, and password into the new registration form and submits it.

[1954] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[1955] The user enters their email address and password in the login form and clicks the login button.

[1956] The server verifies the login information and issues a session ID.

[1957] The device displays a dashboard specific to the user.

[1958] 2. Sports data collection and analysis:

[1959] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[1960] The terminal notifies the user that the latest match data has been updated.

[1961] 3. AI prediction:

[1962] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[1963] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[1964] Users check the AI's predictions and place their bets.

[1965] 4. Simulation game progress management:

[1966] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[1967] The terminal displays the match results and the progress of the simulation in real time.

[1968] Users can check the progress of their bets.

[1969] 5. View real-time results:

[1970] The server updates the match results in real time and sends them to the user's device.

[1971] The terminal receives the updates and updates the user interface in real time.

[1972] Users can check the results of matches and keep track of their betting progress.

[1973] 6. User-to-user community features:

[1974] The server provides message boards and chat functions to support user communication.

[1975] The terminal provides an interface through which users can post messages and start chats.

[1976] Users can exchange messages with other users to share information and discuss strategies.

[1977] Prompt Sentence Examples

[1978] Here are some example prompts to get predictions from a generative AI model:

[1979] "What are your predictions for the winner and scoreline of the next NBA game?"

[1980] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1981] Step 1: User Registration and Login

[1982] Input:

[1983] The user enters their name, email address, and password in the new registration form and clicks the submit button.

[1984] 1.2. Data Processing:

[1985] The terminal encodes the registration information entered by the user and transmits it to the server.

[1986] Processing:

[1987] The server receives a new registration request and validates the information entered, comparing it with existing information to ensure there are no duplicates.

[1988] Output:

[1989] The server stores the information in a database and then sends the user an email containing the authentication information.

[1990] Input:

[1991] The user enters their email address and password into the login form and clicks the submit button.

[1992] 1.6. Data Processing:

[1993] The terminal transmits the login information to the server.

[1994] Processing:

[1995] The server verifies the email address and password, and if authentication is successful, issues a session ID.

[1996] Output:

[1997] The device displays a personalized dashboard for the user, which includes their profile information and available features.

[1998] Step 2: Collecting and analyzing sports data

[1999] Input:

[2000] The server periodically calls the sports API to retrieve the latest match data.

[2001] 2.2. Data Processing:

[2002] The server stores the received match data in a database.

[2003] Processing:

[2004] The server cleans the data and converts it into a format that can be parsed.

[2005] Output:

[2006] The server uses the cleaned data as training data for the generative AI model.

[2007] Input:

[2008] The terminal receives the notification from the server and notifies the user that the sports data has been updated.

[2009] Output:

[2010] The user can check the updated game data through the terminal.

[2011] Step 3: AI prediction

[2012] Input:

[2013] The server feeds the collected sports data into the generative AI model.

[2014] 3.2. Data Processing:

[2015] The server trains the generative AI model and builds the predictive model.

[2016] Processing:

[2017] The server generates prediction results using a trained generative AI model.

[2018] Output:

[2019] The server stores the prediction results in a database and provides them to the user.

[2020] Input:

[2021] The device receives the latest AI prediction results from the server.

[2022] Output:

[2023] The terminal displays the prediction results on an interface and provides information for the user to place bets.

[2024] Input:

[2025] Users check the AI's predictions, select the amount to bet and the game to bet on, and click the "Bet" button.

[2026] Step 4: Managing the progress of the simulation game

[2027] Input:

[2028] The server receives betting information from the user.

[2029] 4.2. Data Processing:

[2030] The server stores the received betting information in a database.

[2031] Processing:

[2032] The server monitors the progress of the match and records progress information in real time.

[2033] Output:

[2034] The server sends progress data to the terminal.

[2035] Input:

[2036] The device receives real-time match results and progress information from the server.

[2037] Output:

[2038] The terminal displays this information on the user interface.

[2039] 4.7. Users can see the progress of their bets in real time.

[2040] Step 5: View real-time results

[2041] Input:

[2042] The server periodically checks the database while a match is in progress.

[2043] 5.2. Data Processing:

[2044] The server updates new match results information in real time.

[2045] Processing:

[2046] The server sends the updated information to the user's terminal.

[2047] Output:

[2048] The terminal reflects the received information in real time on the user interface.

[2049] 5.5. Users can check the latest results as the match progresses and keep track of their betting progress.

[2050] Step 6: User-to-user community function

[2051] Input:

[2052] Users use chats and message boards to exchange messages with other users.

[2053] 6.2. Data Processing:

[2054] The server receives messages between users and stores them in a database.

[2055] Processing:

[2056] The server manages and publishes user profile information and rankings.

[2057] Output:

[2058] The terminal displays messages sent by the user to other users.

[2059] 6.5. Users communicate with other Users and share information and strategies regarding betting.

[2060] (Application example 1)

[2061] 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."

[2062] Current sports betting systems lack high-precision prediction functions, real-time game result updates, and electronic payment integration to improve users' betting experience. As a result, users cannot quickly check their betting results or smoothly manage their bets. In addition, the community functions are insufficient, making it difficult to smoothly exchange information with other users.

[2063] 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.

[2064] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data on sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for depositing and refunding bets via an electronic payment system, and means for supporting message exchange between users. This allows users to place bets based on highly accurate prediction results, and allows them to smoothly manage bets and exchange information with other users while checking game results in real time.

[2065] "User information" refers to the personal information, betting history, authentication information, etc. of system users.

[2066] A "database" is a system for efficiently storing and managing collected information and data, and enabling them to be quickly searched and used as needed.

[2067] "Sports Match Data" means all data relating to a sporting event, including but not limited to match results, player statistics, and match schedules.

[2068] A "generative AI model" is an algorithm that uses machine learning and deep learning techniques to train itself based on collected data and predict the outcome of a match.

[2069] "Predicted Outcomes" means predicted information about future match outcomes and other relevant data calculated by a generative AI model.

[2070] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to access the system and place bets or check information.

[2071] "Bet information" refers to information such as the bet amount, target game, betting options, etc. that a user enters when placing a bet.

[2072] "Real-time match result updates" is the process of instantly updating the system with match results and progress as the real-world match progresses.

[2073] "Electronic payment systems" refers to the platforms and technologies used to transfer money online (such as credit card payments, digital wallets, and bank transfers).

[2074] "Means supporting message exchange" are communication tools such as message boards and chat functions that allow users to share information, opinions, and strategies.

[2075] This invention is a system that allows users to legally enjoy sports betting and gain a fresh experience and a high probability of profits through the provision of highly accurate predictions and real-time information using generative AI models. To realize this application example, the following elements and processes are required.

[2076] System Components

[2077] server

[2078] Hardware: Cloud services with high-performance data processing capabilities (e.g., AWS, Google Cloud, Azure).

[2079] Software: Databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., TensorFlow, PyTorch), and API integration capabilities.

[2080] Terminal

[2081] Hardware: The device the user uses to access the app (smartphone, computer, tablet, etc.).

[2082] Software: The application that implements the user interface (e.g., React Native, Flutter).

[2083] User

[2084] Users of sports betting systems.

[2085] Program processing overview

[2086] 1. User Registration and Login

[2087] A user enters their name, email address, and password into the new registration form and submits it.

[2088] The server saves the registration information in a database and sends the authentication information to the user, who completes the authentication process by entering their email address and password in the login form and clicking the login button.

[2089] 2. Sports data collection and analysis

[2090] The server retrieves the latest match data from the sports API and stores it in a database.

[2091] The acquired data is cleaned and put into an analyzable format.

[2092] 3. Prediction using generative AI models

[2093] The server uses the cleaned data to train a generative AI model to predict match outcomes.

[2094] The prediction results are stored in a database and sent to the device.

[2095] 4. Betting information management and real-time results display

[2096] The user inputs betting information through the terminal and transmits it to the server.

[2097] The server updates the match results in real time and delivers the information to the terminal.

[2098] 5. Electronic payment function

[2099] Users deposit their bets via an electronic payment system.

[2100] If the bet is won, the server immediately refunds the bet amount to the user's account.

[2101] 6. Messaging and Communication

[2102] The server provides message boards and chat facilities to support message exchange between users.

[2103] Users can exchange information and share strategies with other users.

[2104] Examples and prompts

[2105] As a specific example, consider a case where a user places a bet on a basketball game.

[2106] 1. User Registration and Login

[2107] New Registration

[2108] ~~~~~~~~~

[2109] Name: Taro Yamada

[2110] Email: example@mail.com

[2111] password:

[2112] Log in

[2113] ~~~~~~~~

[2114] Email: example@mail.com

[2115] password:

[2116] 2. Sports data collection and analysis

[2117] Capture and analyze data from new basketball games.

[2118] 3. Prediction using generative AI models

[2119] Predict the outcome of the match based on the latest match data.

[2120] 4. Betting and Electronic Payments

[2121] Use the PayPal API to settle your users' bets instantly.

[2122] In this way, a system is realized that allows users to place bets based on highly accurate prediction results, check match results in real time, smoothly manage bets, and exchange information with other users.

[2123] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2124] Step 1:

[2125] User Registration and Authentication

[2126] Input: A user enters their name, email address, and password into a new registration form and submits it.

[2127] Data processing: Before storing the received user information in the database, the server checks for duplication with existing data.

[2128] Output: If there are no duplicates, authentication information is generated and sent to the user. When logging in, the user enters their email address and password, which the server verifies and authenticates.

[2129] Specific operation: The server saves the new registration information in the database and sends a verification email to the user. The login process also verifies the user's authentication information, and if successful, issues a session ID and displays the dashboard.

[2130] Step 2:

[2131] Sports data collection and analysis

[2132] Input: The server requests and retrieves the latest match data from the sports API.

[2133] Data processing: The server cleans the acquired data and prepares it into an analyzable format (e.g., removing unnecessary data and standardizing the format).

[2134] Output: Save the cleaned data to the database.

[2135] Specific operation: The server normalizes the match data obtained from the sports API, removes inconsistent data, and prepares it for use as training data for the generative AI model.

[2136] Step 3:

[2137] Predictions from generative AI models

[2138] Input: Cleaned match data.

[2139] Data computation: The server uses generative AI models (e.g., TensorFlow, PyTorch) to train and predict match outcomes.

[2140] Output: The prediction results are stored in a database and sent to the device.

[2141] Specific operation: The server inputs data into the generative AI model to train it, calculates prediction results, stores them in a database, and then distributes them to the device.

[2142] Step 4:

[2143] Betting information management and real-time results display

[2144] Input: The user inputs betting information (betting amount, game, options) through the terminal and sends it to the server.

[2145] Data processing: The server stores the betting information in a database and monitors the progress of the matches.

[2146] Output: Deliver real-time updated match results to your device.

[2147] Specific operation: The server stores the received betting information in a database, updates the results as the game progresses, and delivers them to the terminal in real time.

[2148] Step 5:

[2149] Electronic payment function

[2150] Input: The user inputs information to deposit a bet through an electronic payment system.

[2151] Data Processing: The server receives the payment information and outsources the payment process to a third-party electronic payment system.

[2152] Output: If the payment is successful, a confirmation is sent to the user's device.

[2153] Specific operation: The server executes the payment process using an electronic payment API (e.g., PayPal, Stripe), and if successful, records the result in a database and notifies the terminal.

[2154] Step 6:

[2155] Messaging and Communication

[2156] Input: A user posts a message using a message board or chat function.

[2157] Data processing: The server stores the received messages in a database and distributes them to other users in real time.

[2158] Output: Other users receive and view the message.

[2159] Specific operation: The server manages message exchanges between users through message boards and chat functions, and distributes them instantly to facilitate communication.

[2160] 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.

[2161] This invention combines a sports betting system using a generative AI model with an emotion engine that recognizes user emotions. This system allows users to legally enjoy sports betting, and aims to provide highly accurate predictions and real-time information using generative AI, as well as customize the interface and suggest bets based on the user's emotional state.

[2162] Program processing overview

[2163] User registration and login process

[2164] The server receives a new user registration request and saves it in the database. It compares the information with existing users and verifies that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[2165] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[2166] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[2167] Sports data collection and analysis

[2168] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[2169] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[2170] Users can check the latest updated match data and use it as a reference for betting.

[2171] AI-powered predictions

[2172] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[2173] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[2174] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[2175] Simulation game progress management

[2176] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2177] The terminal displays the match results and simulation progress received from the server in real time.

[2178] Users can check the progress of their bets in real time and wait for the results.

[2179] View real-time results

[2180] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[2181] The terminal receives updates on the match results and updates the user interface in real time.

[2182] Users can check the latest results as the game progresses and keep track of their betting progress.

[2183] User-to-user community function

[2184] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[2185] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[2186] Users can exchange messages with other users to share information and discuss strategies.

[2187] Overview of Emotion Engine Operation

[2188] User Emotion Recognition

[2189] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[2190] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[2191] The user uses the system as usual, and their operations are used for emotion recognition.

[2192] Emotion-based interface customization

[2193] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[2194] The terminal updates the user interface based on the customization information provided by the server.

[2195] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[2196] Emotion-based betting suggestions

[2197] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[2198] The terminal displays betting suggestions to the user and assists in selection.

[2199] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[2200] Specific examples

[2201] For example, a user may want to bet on a soccer match.

[2202] 1. User Registration and Login:

[2203] A user enters their name, email address, and password into the new registration form and submits it.

[2204] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[2205] The user enters their email address and password in the login form and clicks the Login button.

[2206] The server verifies the login information and issues a session ID.

[2207] The device displays a dashboard specific to the user.

[2208] 2. Sports data collection and analysis:

[2209] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[2210] The terminal notifies the user that the latest game data has been updated.

[2211] 3. AI prediction:

[2212] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[2213] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[2214] Users check the AI's predictions and place their bets.

[2215] 4. Simulation game progress management:

[2216] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2217] The device displays the match results and the progress of the simulation in real time.

[2218] Users can check the progress of their bets.

[2219] 5. View real-time results:

[2220] The server updates the match results in real time and sends them to the user's device.

[2221] The terminal receives the update information and updates the user interface in real time.

[2222] Users can check the results of matches and keep track of their betting progress.

[2223] 6. User-to-user community features:

[2224] The server provides message boards and chat functions to support user communication.

[2225] The terminal provides an interface through which users can post messages and start chats.

[2226] Users can exchange messages with other users to share information and discuss strategies.

[2227] 7. Emotion Engine in Action:

[2228] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[2229] The device sends the necessary input data and displays feedback of the emotional state.

[2230] The user uses the system as usual, and their operations are used for emotion recognition.

[2231] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[2232] The terminal displays customization information and betting suggestions.

[2233] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[2234] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[2235] The processing flow will be explained below.

[2236] User registration and login process

[2237] Step 1:

[2238] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[2239] Step 2:

[2240] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[2241] Step 3:

[2242] The server receives a new registration request and checks its database to see if the email address is already registered.

[2243] Step 4:

[2244] The server hashes the password and stores it, and when registration is complete, it generates a success message and sends it to the terminal.

[2245] Step 5:

[2246] The terminal receives the message from the server and displays a notification to the user that registration is complete.

[2247] Step 6:

[2248] The user accesses the login page, enters their email address and password, and clicks the "Login" button.

[2249] Step 7:

[2250] The terminal sends the entered login information to the server.

[2251] Step 8:

[2252] The server checks whether the email address and password combination is correct. If authentication is successful, it issues a session ID and returns it to the device.

[2253] Step 9:

[2254] The device stores the session ID and displays a dashboard specific to the user.

[2255] Sports data collection and analysis

[2256] Step 1:

[2257] The server periodically (for example, every day at midnight) retrieves the latest match data from a sports API on the Internet.

[2258] Step 2:

[2259] The server formats the data it receives and stores it in a database.

[2260] Step 3:

[2261] The server cleans the data, removing unnecessary data and missing values, and prepares it in an analyzable format.

[2262] Step 4:

[2263] The server stores the cleaned, analyzable data as training data for the generated AI model.

[2264] Step 5:

[2265] When the device has completed data collection and analysis, it will notify the user that the latest sports information has been updated.

[2266] AI-powered predictions

[2267] Step 1:

[2268] The server periodically (e.g., every Sunday) uses the collected data to retrain the generative AI model.

[2269] Step 2:

[2270] The server generates new parameters and updates the latest model.

[2271] Step 3:

[2272] The server runs the generative AI model for each match or event and generates predicted results (e.g., win probability, score predictions, etc.).

[2273] Step 4:

[2274] The server stores the generated prediction results in a database and provides them to the user.

[2275] Step 5:

[2276] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[2277] Step 6:

[2278] Users decide on their bets based on the AI's predictions and place their bets.

[2279] Simulation game progress management

[2280] Step 1:

[2281] The server receives betting information from users and stores it in a database.

[2282] Step 2:

[2283] The server monitors the progress of the simulated game and updates the results in real time as the match progresses.

[2284] Step 3:

[2285] The server stores the updated match results in a database and distributes them to the device.

[2286] Step 4:

[2287] The device displays the progress of the match, the user's points, and the results of the simulation in real time.

[2288] Step 5:

[2289] Users check their bets and game progress and consider their next actions.

[2290] View real-time results

[2291] Step 1:

[2292] The server periodically checks the database to see if new data is available as the match progresses in real time.

[2293] Step 2:

[2294] If the server has new data, it sends updated information to the terminal.

[2295] Step 3:

[2296] The terminal processes the received update information and updates the user interface in real time.

[2297] Step 4:

[2298] Users can check the latest results while the match is in progress and decide what to do next.

[2299] User-to-user community function

[2300] Step 1:

[2301] The server provides a message board and chat function to support message exchanges between users.

[2302] Step 2:

[2303] A user visits a community page and posts a message.

[2304] Step 3:

[2305] The terminal sends the input message to the server, which stores the message in a database.

[2306] Step 4:

[2307] The server distributes the stored messages to other users.

[2308] Step 5:

[2309] Users can exchange messages with other users to share information and discuss strategies.

[2310] User Emotion Recognition

[2311] Step 1:

[2312] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history.

[2313] Step 2:

[2314] The server uses an emotion engine to estimate the user's emotional state.

[2315] Step 3:

[2316] The terminal transmits the user's input data required for emotion recognition to the server.

[2317] Step 4:

[2318] The device displays emotional state feedback to the user.

[2319] Step 5:

[2320] The user uses the system as usual, and their operations are used for emotion recognition.

[2321] Emotion-based interface customization

[2322] Step 1:

[2323] The server dynamically customizes the interface display based on the user's emotional state.

[2324] Step 2:

[2325] The server transmits the customization information to the terminal.

[2326] Step 3:

[2327] The terminal receives the customization information and updates the user interface.

[2328] Step 4:

[2329] Users will have an improved experience using an interface that is optimized according to their emotional state.

[2330] Emotion-based betting suggestions

[2331] Step 1:

[2332] The server dynamically adjusts the betting suggestions based on the user's emotional state analyzed by the emotion engine.

[2333] Step 2:

[2334] The server sends dynamically adjusted betting proposals to the terminal.

[2335] Step 3:

[2336] The terminal displays betting suggestions to the user.

[2337] Step 4:

[2338] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[2339] Specific examples

[2340] For example, a user may want to bet on a soccer match.

[2341] User registration and login:

[2342] Step 1:

[2343] A user enters their name, email address, and password into the new registration form and submits it.

[2344] Step 2:

[2345] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[2346] Step 3:

[2347] The user enters their email address and password in the login form and clicks the Login button.

[2348] Step 4:

[2349] The server verifies the login information and issues a session ID.

[2350] Step 5:

[2351] The device displays a dashboard specific to the user.

[2352] Sports data collection and analysis:

[2353] Step 1:

[2354] The server retrieves the latest soccer match data from the sports API and stores it in a database. The data is then cleaned and formatted for analysis.

[2355] Step 2:

[2356] The terminal notifies the user that the latest game data has been updated.

[2357] AI-powered predictions:

[2358] Step 1:

[2359] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[2360] Step 2:

[2361] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[2362] Step 3:

[2363] Users check the AI's predictions and place their bets.

[2364] Simulation game progress management:

[2365] Step 1:

[2366] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2367] Step 2:

[2368] The device displays the match results and the progress of the simulation in real time.

[2369] Step 3:

[2370] Users can check the progress of their bets.

[2371] View real-time results:

[2372] Step 1:

[2373] The server updates the match results in real time and sends them to the user's device.

[2374] Step 2:

[2375] The terminal receives the update information and updates the user interface in real time.

[2376] Step 3:

[2377] Users can check the results of matches and keep track of their betting progress.

[2378] User-to-user community features:

[2379] Step 1:

[2380] The server provides message boards and chat functions to support user communication.

[2381] Step 2:

[2382] The terminal provides an interface through which users can post messages and start chats.

[2383] Step 3:

[2384] Users can exchange messages with other users to share information and discuss strategies.

[2385] Emotion Engine in action:

[2386] Step 1:

[2387] The server analyzes the user's input data and behavioral history and uses an emotion engine to estimate the user's emotional state.

[2388] Step 2:

[2389] The device sends the necessary input data and displays feedback of the emotional state.

[2390] Step 3:

[2391] The user uses the system as usual, and their operations are used for emotion recognition.

[2392] Step 4:

[2393] The server customizes the interface display based on the emotional state and dynamically adjusts the suggestions.

[2394] Step 5:

[2395] The terminal displays customization information and betting suggestions.

[2396] Step 6:

[2397] The user receives and utilizes an interface and betting suggestions optimized for their emotional state.

[2398] In this way, the system of the present invention provides users with rich data and highly accurate predictions, and further enhances the experience through various customizations and suggestions based on the user's emotional state.

[2399] Example 2

[2400] 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."

[2401] Conventional sports betting systems do not provide services that take into account the user's emotional state, limiting the improvement of user experience. Furthermore, in addition to highly accurate predictions and real-time information provision, they lack effective communication between users and interface customization. This has led to issues such as reduced user satisfaction and engagement.

[2402] 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.

[2403] In this invention, the server includes means for receiving user information and storing it in a database, means for acquiring data related to sports games specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving betting information from the terminal and storing it in a database, means for updating game results in real time and distributing the information to the user's terminal, means for supporting message exchange between users, means for analyzing the user's input data and behavioral history to estimate the user's emotional state, means for dynamically customizing the interface based on the user's emotional state, and means for dynamically adjusting betting suggestions based on the user's emotional state. This enables the sports betting system to provide services that take the user's emotional state into consideration, thereby improving the user experience and strengthening engagement.

[2404] "Means for receiving user information and storing it in a database" refers to a method in which the server collects registration information entered by the user, such as name, email address, and password, and stores it in a database.

[2405] "Means for obtaining data on sports games specified by users" refers to a method for obtaining information on specific sports games that users have specified they wish to bet on via external data sources or APIs, and incorporating that data into the system.

[2406] "Means for training a generative AI model using acquired data to generate prediction results" refers to a method for training an artificial intelligence (AI) algorithm based on collected sports match data and predicting match results from the training results.

[2407] "Means for displaying the generated prediction results on the user's device" refers to a method for displaying the prediction result data generated by the AI ​​model on the device used by the user through a user interface.

[2408] "Means for receiving betting information from the terminal and storing it in a database" refers to a method in which the server receives the betting amount and betting details entered by the user through the terminal and stores that information in a database.

[2409] "Means for updating match results in real time and delivering that information to the user's device" refers to a method for periodically collecting new match results as the match progresses and immediately sending that information to the user's device.

[2410] "Means supporting the exchange of messages between users" means communication features that allow users to text message or chat with other users through the system.

[2411] "Means for analyzing a user's input data and behavioral history to infer their emotional state" refers to technology that analyzes the text data and usage history that a user inputs into the system to infer the user's emotional state.

[2412] The "means for dynamically customizing an interface based on an emotional state" is a method for optimizing the screen display and content provided in real time according to the estimated emotional state of the user.

[2413] A "means for dynamically adjusting betting suggestions based on emotional state" is a method for automatically adjusting betting options and risk levels to suggest betting strategies and content appropriate to a user's current emotional state.

[2414] This invention is a sports betting system that uses a generative AI model and an emotion engine to enable users to bet legally and enjoyably on sports. This system not only provides highly accurate predictions and real-time information using generative AI, but also customizes the interface and suggests bets based on the user's emotional state.

[2415] System Overview

[2416] The system includes the following main components: a server, a terminal, and a user.

[2417] 1. User registration and login process

[2418] The server receives a new user registration request and saves it in the database. The database used here is "MySQL." The information is compared with existing users to ensure that the email address is not a duplicate. Once registration is complete, authentication information is sent to the user. When a login request is made, the entered email address and password are compared, and if authentication is successful, a session ID is issued.

[2419] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if the login is successful, displays a dashboard dedicated to the user using "React."

[2420] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[2421] Example: A user enters their name, email address, and password into a new registration form and submits it. The server receives the registration information and stores it in a database. The authentication information is sent to the user. The user enters their email address and password into a login form and clicks the login button. The server verifies the login information and issues a session ID. The device displays a dashboard specific to the user.

[2422] 2. Sports data collection and analysis

[2423] The server periodically retrieves the latest match data from a sports API (e.g., "SportsRadar") and stores it in a database. This data is then cleaned and formatted for analysis, using the "Pandas" library in Python. The analyzed data is then used as training data for the generative AI model.

[2424] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[2425] Users can check the latest updated match data and use it as a reference for betting.

[2426] 3. AI-based predictions

[2427] The server uses the collected data to train a generative AI model and generate prediction results using Python and TensorFlow. The generated prediction results are stored in a database and provided to users.

[2428] The terminal uses "D3.js" to display the latest AI prediction results received from the server to the user, and provides an interface for users to place bets.

[2429] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[2430] 4. Simulation game progress management

[2431] The server manages the betting information received from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2432] The terminal displays the match results and simulation progress received from the server in real time.

[2433] Users can check the progress of their bets in real time and wait for the results.

[2434] 5. View real-time results

[2435] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[2436] The terminal receives updates on the match results and updates the user interface in real time.

[2437] Users can check the latest results as the game progresses and keep track of their betting progress.

[2438] 6. User-to-user community function

[2439] The server provides message boards and chat functions to support message exchanges between users, and also manages and publishes user profiles and rankings.

[2440] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[2441] Users can exchange messages with other users to share information and discuss strategies.

[2442] 7. Emotion Engine Operation

[2443] The server analyzes the user's input data (e.g., chat content, betting frequency, win / loss status, etc.) and behavioral history, and uses an emotion engine to estimate the user's emotional state.

[2444] The device sends the user's input data required for emotion recognition to the server and also displays feedback of the user's emotional state.

[2445] The user uses the system as usual, and their operations are used for emotion recognition.

[2446] Emotion-based interface customization

[2447] The server dynamically customizes the interface display based on the user's emotional state, for example, displaying bright colors and encouraging messages in response to a positive emotional state.

[2448] The terminal updates the user interface based on the customization information provided by the server.

[2449] Users will have an interface that is optimized according to their emotional state, enhancing their experience.

[2450] Emotion-based betting suggestions

[2451] The server dynamically adjusts betting suggestions based on the user's emotional state as analyzed by the emotion engine, for example suggesting risky bets to an excited user and safe bets to a calm user.

[2452] The terminal displays betting suggestions to the user and assists in selection.

[2453] The user receives betting suggestions optimized for their emotional state and places a bet based on them.

[2454] Example prompt:

[2455] The "User" enters their name, email address, and password into the new registration form and clicks the "Submit" button. The "Server" receives this information, saves it in the "MySQL" database, and sends a registration completion email. The "User" then enters their email address and password into the login form and clicks the "Login" button. The "Server" checks the login information, and if it is correct, issues a session ID and displays the dashboard created with "React" on the "Terminal."

[2456] The system aims to provide users with highly accurate sports betting predictions and an optimal experience based on their emotional state.

[2457] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2458] The flow of this system's program processing

[2459] Step 1: User registration and login process

[2460] server

[2461] Input: Name, email address, and password entered by the user in the sign-up form.

[2462] Data processing: Before storing this information in the MySQL database, we check whether the email address is a duplicate of existing data.

[2463] Output: Generates authentication information and sends it to the user.

[2464] Specific operation: The server saves the new user information in the MySQL database and sends a registration completion email.

[2465] Terminal

[2466] Input: Registration information entered by the user.

[2467] Data processing: Send the input information to the server.

[2468] Output: The status message returned by the server.

[2469] Specific behavior: The device displays status messages in a "React" interface.

[2470] User

[2471] Input: Enter your name, email address, and password in the new registration form and click the submit button.

[2472] Output: You will receive a registration confirmation email.

[2473] Specific actions: Fill in the required information in the form, submit it, and check the verification email.

[2474] Step 2: Collecting and analyzing sports data

[2475] server

[2476] Input: Match data obtained via a sports API (e.g. "SportsRadar").

[2477] Data processing: The acquired data was cleaned using the Python Pandas library and converted into an analyzable format.

[2478] Output: Cleaned match data stored in a MySQL database.

[2479] What it does: The server periodically sends requests to the sports API, retrieves the data, cleans it, and stores it in the database.

[2480] Terminal

[2481] Input: Data update notification from the server.

[2482] Data processing: Display a notification to the user.

[2483] Output: Latest sports data updates.

[2484] Specific operation: The device will display a notification of the latest data update.

[2485] User

[2486] Input: Check update notifications and interact with the system.

[2487] Output: Show new match data.

[2488] Specific actions: Check the updated match information on the device.

[2489] Step 3: AI prediction

[2490] server

[2491] Input: Cleaned match data.

[2492] Data processing: Train the data using a generative AI model (Python, TensorFlow).

[2493] Output: Generate prediction results and store them in a MySQL database.

[2494] Specific operation: Trains an AI model, generates prediction results, and stores them.

[2495] Terminal

[2496] Input: Prediction result data from the server.

[2497] Data processing: Visualize the prediction results using "D3.js".

[2498] Output: Prediction results are visualized and displayed to the user.

[2499] Specific behavior: Prediction results are displayed visually on the device.

[2500] User

[2501] Input: Check the prediction results.

[2502] Output: Place a bet.

[2503] Specific operations: Determine the betting amount and target game based on the prediction result and complete the operation.

[2504] Step 4: Managing the progress of the simulation game

[2505] server

[2506] Input: Betting information from the user.

[2507] Data processing: Store betting information in a database and update match results in real time.

[2508] Output: Real-time match results.

[2509] Specific operation: The database is updated continuously while the match is in progress and the results are sent to the user.

[2510] Terminal

[2511] Input: Real-time match results.

[2512] Data processing: Display result data in real time.

[2513] Output: Updated match results and progress.

[2514] Specific operation: Display match results in real time.

[2515] User

[2516] Input: Check real-time match results.

[2517] Output: Betting progress.

[2518] Specific actions: Monitor the progress of the match and wait for the results.

[2519] Step 5: View real-time results

[2520] server

[2521] Input: Match result data.

[2522] Data processing: Result data is updated in real time.

[2523] Output: Updated results are delivered to the user's device.

[2524] Specific operation: New match results are periodically updated in the database and sent to users.

[2525] Terminal

[2526] Input: Updated data from the server.

[2527] Data Processing: Interface update.

[2528] Output: Display the latest match results.

[2529] Specific operation: The device displays the match results in real time.

[2530] User

[2531] Input: Updated match results.

[2532] Output: The progress of your bet.

[2533] Specific actions: Check match results and understand progress.

[2534] Step 6: User-to-user community function

[2535] server

[2536] Input: The user's message data.

[2537] Data processing: Messages are stored in a database and distributed to other users.

[2538] Output: Providing message boards and chat functionality.

[2539] What it does: Manage and deliver messages in real time.

[2540] Terminal

[2541] Input: User message input.

[2542] Data processing: Display of messages.

[2543] Output: Display of new messages.

[2544] Specific behavior: Provides a message input interface and implements chat functionality.

[2545] User

[2546] Input: Type a message to exchange with other users.

[2547] Output: Communication information.

[2548] Specific action: Type a message and communicate with other users.

[2549] Step 7: Emotion Engine in Action

[2550] server

[2551] Input: User input data and behavioral history.

[2552] Data processing: Analyze using an emotion engine to estimate emotional state.

[2553] Output: Interface customization information based on emotional state.

[2554] Specific behavior: Analyze the user's state using the emotion engine and use the results to customize the interface.

[2555] Terminal

[2556] Input: Customization information from the server.

[2557] Data processing: Interface update.

[2558] Output: Optimized user interface.

[2559] Specific behavior: Update the user interface in real time according to the emotional state.

[2560] User

[2561] Input: Normal operation.

[2562] Output: Customized interface.

[2563] Specific behavior: Operate an interface optimized for your emotional state and enjoy a better user experience.

[2564] These steps enable the system of the present invention to provide users with highly accurate predictions and a customized emotion-based interface.

[2565] (Application example 2)

[2566] 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."

[2567] Conventional systems simply predict events and provide information without taking into account the user's emotional state and its fluctuations. This results in a uniform user experience, resulting in insufficient optimal support and customization for individual users. Furthermore, when it comes to managing users' spending, they lack real-time feedback and advice based on their emotional state.

[2568] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and saving it in a database, means for acquiring data related to events specified by the user, means for training a generative AI model using the acquired data to generate prediction results, means for displaying the generated prediction results on the user's terminal, means for receiving information from the terminal and saving it in a database, means for updating event results in real time and delivering the information to the user's terminal, means for supporting information exchange between users, and means for analyzing the user's emotional state and dynamically customizing the display interface and proposal content based on the state. This makes it possible to provide the user with optimal information and advice on expense management that takes into account the user's emotional state.

[2569] "User Information" refers to data relating to a user's personal information, behavioral data, and emotional state.

[2570] A "database" is an information system for efficiently storing and searching information, particularly for storing user information, event data, prediction results, etc.

[2571] "Server" refers to a computer system that processes, stores, distributes data, and fulfills user requests.

[2572] A "generative AI model" refers to an algorithm that uses machine learning and artificial intelligence techniques to make predictions and suggestions from collected data.

[2573] "Prediction Results" refers to predictive data about future events or situations generated by a generative AI model.

[2574] "Terminal" refers to an electronic device (smartphone, smart glasses, etc.) that a user operates.

[2575] "Real-time updates" refers to a process in which information is immediately reflected in the current situation and provided to the user immediately.

[2576] "Information exchange between users" refers to multiple users of the system sharing messages and data.

[2577] "Emotional state" refers to the user's current psychological state (e.g., happy, sad, excited, relaxed, etc.).

[2578] "Display interface" refers to a screen that allows a user to visually check and operate information.

[2579] "Suggestion content" refers to information that the system provides to the user with specific actions and options to be taken.

[2580] "Dynamic customization" refers to changing the interface and suggestions in real time according to the user's emotional state and behavior.

[2581] The system embodying this invention collects user information and provides real-time predictions and advice based on the collected information according to the user's emotional state. The main components are as follows:

[2582] Hardware and Software

[2583] This system uses a general server and user devices (smartphones, smart glasses, etc.) and utilizes the following software:

[2584] Emotion recognition engine (e.g. Microsoft Azure Cognitive Services)

[2585] Database (e.g. MySQL)

[2586] Generative AI models (e.g., GPT-4)

[2587] User information collection and storage

[2588] The server receives and stores in a database the user's registration information (personal information, behavioral data, and data on emotional state) that the user enters through the interface and processes in real time.

[2589] Event data acquisition and analysis

[2590] The server acquires data related to events specified by the user and stores it in a database. The acquired data is cleaned and formatted for analysis. A generative AI model is then used to generate predictions. These predictions are stored in the database and displayed on the user's device.

[2591] Real-time updates and information distribution

[2592] The server updates the event results in real time and delivers the information to the user's device, allowing the user to always obtain the latest information.

[2593] Information exchange between users

[2594] The server provides a message board and chat function to support information exchange between users, allowing users to share information and discuss strategies.

[2595] Emotional state analysis and customization

[2596] The server analyzes the user's input data and behavioral history, and uses an emotion recognition engine to estimate their emotional state. Based on their emotional state, the displayed interface and suggestions are dynamically customized. For example, if the user is tired, suggestions to reduce spending are made, and if the user is excited, suggestions to reduce risk are made.

[2597] Specific examples

[2598] For example, if the emotion engine detects that a user is tired while shopping at a supermarket, the app will display a notification saying, "Maybe you should take a little rest today. You have 3,000 yen left in your budget this week." In this way, users can optimally manage their spending based on their emotional state.

[2599] Prompt Sentence Examples

[2600] "Describe a system that analyzes your emotional state in real time and provides spending management advice. It can analyze your spending history and behavioral history from a database and generate optimal spending suggestions based on your emotions. The system uses a smartphone or smart glasses and includes a generative AI model, an emotion recognition engine, and real-time updates. For example, it provides savings suggestions when the user is tired, and risk-reducing suggestions when the user is excited."

[2601] In this way, the system of the present invention provides the user with optimal information and advice on expense management that takes into account their emotional state.

[2602] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2603] Step 1:

[2604] The server receives the user's information and stores it in a database. This information includes the user's personal information, behavioral data, and emotional state data. As input, it receives the information entered by the user through the interface and processes it to store it in the database. As output, it obtains the stored user information.

[2605] Step 2:

[2606] The server acquires data about events specified by the user and stores it in a database. As input, it receives information about the type of event and date and time selected by the user. The database processes the data acquired from an external event information system and stores it. As output, it obtains the stored event data.

[2607] Step 3:

[2608] The server uses the acquired data to train a generative AI model and generate predictions. As input, it receives stored event data. It analyzes this data and performs data calculations that feed it into the generative AI model to generate predictions. As output, it obtains the generated predictions.

[2609] Step 4:

[2610] The terminal displays the generated prediction results on the user's device. As input, it receives the prediction results received from the server. It processes the data to display them in a user interface in a visually easy-to-understand format. As output, the user can check the prediction results.

[2611] Step 5:

[2612] The terminal receives information from the user and sends it to the server. As input, it receives the user's actions (information input and selection). It processes this information and sends it to the server. As output, it obtains the user's action data that the server receives.

[2613] Step 6:

[2614] The server stores information from the device in a database. As input, it receives the user's action data sent from the device. It processes the action data and stores it in the database. As output, it obtains the stored user's action data.

[2615] Step 7:

[2616] The server updates the event results in real time and delivers the information to the user's device. As input, it receives the latest event results obtained from an external event result information providing system. It analyzes these and performs data calculations to deliver them to the user's device. As output, the latest event results are delivered to the device.

[2617] Step 8:

[2618] The terminal displays the latest event results delivered to the user's terminal. As input, it receives the latest event result information from the server. It processes this data to display it in a visually easy-to-understand format on the user interface. As output, the user can check the latest event results.

[2619] Step 9:

[2620] The server provides message board and chat functions to support information exchange between users. As input, it receives messages and chat information from users. It processes this data to distribute it to other users as needed. As output, it enables messages and chat between users.

[2621] Step 10:

[2622] The server analyzes the user's input data and behavioral history and uses an emotion recognition engine to estimate the user's emotional state. It receives the user's operation data and behavioral data as input. It supplies this data to the emotion recognition engine, which then performs a data calculation to estimate the user's emotional state. The output is the user's emotional state.

[2623] Step 11:

[2624] The server dynamically customizes the display interface and suggestions based on the user's emotional state. It receives the user's estimated emotional state as input, performs data calculations to change the color of the interface and adjust the suggestions based on the user's emotional state, and delivers the customized interface and suggestions to the user's device as output.

[2625] Step 12:

[2626] The terminal displays the customized interface and suggested content to the user. As input, it receives customization information distributed from the server. It processes this data to display it on the user interface in a visually easy-to-understand format. As output, it displays the interface and suggested content optimized for the user.

[2627] 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.

[2628] 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.

[2629] 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.

[2630] [Fourth embodiment]

[2631] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2632] 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.

[2633] 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).

[2634] 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.

[2635] 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.

[2636] 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).

[2637] 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.

[2638] 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.

[2639] 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.

[2640] 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.

[2641] 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.

[2642] 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.

[2643] 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."

[2644] This invention relates to a sports betting system that uses a generative AI model. This system aims to enable users to legally enjoy sports betting and provide them with a fresh experience and a high rate of profits through the provision of highly accurate predictions and real-time information by generative AI.

[2645] Program processing overview

[2646] User registration and login process

[2647] The server receives a new user registration request and saves it in the database. It compares it with existing user information to ensure that the email address is not a duplicate. Once registration is complete, it sends authentication information to the user. When a login request is made, it compares the entered email address and password, and if authentication is successful, it issues a session ID.

[2648] The device sends the information entered by the user to the server, receives and displays status messages from the server, and if login is successful, displays the user's personal dashboard.

[2649] The user enters the required information into the new registration form and submits it. Also, the user enters the username and password into the login form and clicks the login button.

[2650] Sports data collection and analysis

[2651] The server periodically retrieves the latest match data from the sports API and stores it in a database. This data is then cleaned and formatted for analysis. The analyzed data is then used as training data for the generative AI model.

[2652] Once the data collection and analysis is complete, the device will notify the user that the latest sports information has been updated.

[2653] Users can check the latest updated match data and use it as a reference for betting.

[2654] AI-powered predictions

[2655] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[2656] The terminal displays the latest AI prediction results received from the server to the user, who then places their bet based on the displayed prediction results.

[2657] Users check the AI's predictions, select their bet amount and the game they want to bet on, and click the "Bet" button.

[2658] Simulation game progress management

[2659] The server manages the betting information received from users and monitors the progress of the simulation game. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2660] The terminal displays the match results and simulation progress received from the server in real time.

[2661] Users can check the progress of their bets in real time and wait for the results.

[2662] View real-time results

[2663] The server periodically checks the database as the match progresses and updates the new match results in real time, which are then sent to the user's device.

[2664] The terminal receives updates on the match results and updates the user interface in real time.

[2665] Users can check the latest results as the game progresses and keep track of their betting progress.

[2666] User-to-user community function

[2667] The server provides a message board and chat function to support message exchanges between users, and also manages and publishes user profiles and rankings.

[2668] The terminal provides an interface for smooth communication between users, and supports operations such as posting messages and starting chats.

[2669] Users can exchange messages with other users to share information and discuss strategies.

[2670] Specific examples

[2671] For example, a user may wish to bet on a basketball game.

[2672] 1. User Registration and Login:

[2673] A user enters their name, email address, and password into the new registration form and submits it.

[2674] The server receives the registration information, stores it in a database, and sends the authentication information to the user.

[2675] The user enters their email address and password in the login form and clicks the login button.

[2676] The server verifies the login information and issues a session ID.

[2677] The device displays a dashboard specific to the user.

[2678] 2. Sports data collection and analysis:

[2679] The server retrieves the latest basketball game data from a sports API, stores it in a database, and formats it in a parseable format to be used as training data for the generative AI model.

[2680] The terminal notifies the user that the latest match data has been updated.

[2681] 3. AI prediction:

[2682] The server uses the collected data to train the generative AI model and generate prediction results, which are stored in a database and provided to users.

[2683] The terminal displays the latest AI prediction results and provides an interface for users to place bets.

[2684] Users check the AI's predictions and place their bets.

[2685] 4. Simulation game progress management:

[2686] The server receives betting information from users and stores it in a database. As the game progresses, it updates the game results in real time and sends them to the terminal.

[2687] The terminal displays the match results and the progress of the simulation in real time.

[2688] Users can check the progress of their bets.

[2689] 5. View real-time results:

[2690] The server updates the match results in real time and sends them to the user's device.

[2691] The terminal receives the updates and updates the user interface in real time.

[2692] Users can check the results of matches and keep track of their betting progress.

[2693] 6. User-to-user community features:

[2694] The server provides message boards and chat functions to support user communication.

[2695] The terminal provides an interface through which users can post messages and start chats.

[2696] Users can exchange messages with other users to share information and discuss strategies.

[2697] In this way, the system provides users with a wealth of data and highly accurate predictions, enabling a legitimate and fresh sports betting experience.

[2698] The processing flow will be explained below.

[2699] User registration and login process

[2700] Step 1:

[2701] The user accesses the new registration page, enters their name, email address, and password, and then clicks the "Register" button.

[2702] Step 2:

[2703] The terminal checks the entered information to ensure it is in the correct format, then sends a registration request to the server.

[2704] Step 3:

[2705] The server receives a new registration request and checks its database to see if the email address is already registered. ...

Claims

1. means for receiving and storing user information in a database; means for obtaining data relating to a sports match specified by a user; a means for training a generative AI model using the acquired data to generate predictions; means for displaying the generated prediction results on a user's terminal; means for receiving betting information from the terminal and storing it in a database; A means for updating match results in real time and delivering that information to users' devices; A system that includes a means for supporting message exchange between users.

2. 10. The system of claim 1, wherein the acquired sports game data is cleaned and formatted into an analyzable format.

3. 10. The system of claim 1, wherein the system provides an interface for a user to select a game or event on which to bet and to input a bet amount.

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