System

Abstract

A system is provided.SOLUTION: A system comprising: a display unit configured to allow a viewer to input prediction data; a transmission unit configured to transmit the prediction data to a server; a storage unit configured to store the prediction data in the server; an artificial intelligence unit configured to generate a prediction result by artificial intelligence based on game data; a calculation unit configured to calculate a score of the viewer based on the prediction result and the game data; and a reward unit configured to provide a reward to a viewer having a high score.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] In traditional professional baseball broadcasts, viewers simply watch the game, lacking interactive elements, making it difficult to improve the viewing experience and strengthen engagement. Furthermore, it is difficult to keep a specific demographic engaged with the broadcast, preventing an increase in viewership. Given this background, a new system is needed that allows viewers to actively participate in the game and make predictions while having fun. [Means for solving the problem]

[0005] The present invention provides a system including a display means for viewers to input prediction data, a transmission means for transmitting the prediction data to a server, a storage means for the server to save the prediction data, an artificial intelligence means for the server to generate prediction results using artificial intelligence based on game data, a compilation means for compiling viewer performance based on the prediction results and the game data, and a reward means for providing rewards to top performers. This allows viewers to make pitching predictions in real time during the game and receive rewards based on the results, further enhancing the viewing experience and improving viewer engagement. The system also compares the prediction results with those of the artificial intelligence and provides additional rewards if the viewer's performance is superior, further increasing viewers' motivation to participate.

[0006] "Viewers" refers to people who use a dedicated application to watch professional baseball games.

[0007] "Prediction data" refers to information such as the type of pitch and the pitching trajectory that viewers predict will be used during the game.

[0008] "Display means" refers to the device or software that displays the interface or screen for viewers to input prediction data.

[0009] "Transmission means" refers to a communication means having the function of transmitting the prediction data entered by the viewer to the server.

[0010] "Server" refers to a computer system for receiving and storing prediction data and processing match data.

[0011] "Storage means" refers to a function for recording the prediction data received by the server in a database or the like.

[0012] "Artificial Intelligence Means" refers to the AI ​​algorithms and programs used by the Server to generate predicted outcomes based on match data.

[0013] "Counting means" refers to the function by which the server compares the viewer's predicted data with the match results and calculates the viewer's performance.

[0014] "Reward measures" refers to the functions and procedures for providing some kind of reward or benefit to top performers. [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] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a tabulation means for tabulating the performance of viewers, and a reward means for top performers.

[0037] User Registration and Login

[0038] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0039] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[0040] Acquiring and displaying match information

[0041] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[0042] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[0043] Pitching predictions during the game

[0044] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0045] User: Enter and submit prediction data for each at-bat.

[0046] Server: Receives the prediction data, stores it in a database, and aggregates other users' prediction data in real time.

[0047] AI-powered prediction generation

[0048] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[0049] Calculation and display of results after the match

[0050] Server: After the game ends, the predicted data of all users is compared with the actual pitching results, and the accuracy rate of each user is calculated. The top performers are determined based on the calculation results.

[0051] Terminal: Receives the aggregated results and displays the rankings of the top performers and the performance of individual users.

[0052] Providing rewards

[0053] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and notifies them to the device. Compares the AI's predictions with the user's predicted performance and provides additional rewards to users who achieve excellent results.

[0054] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0055] This system allows users to enjoy real-time pitch predictions while watching a game and earn rewards based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their own predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[0059] Step 2:

[0060] The terminal verifies the entered user information and sends it to the server.

[0061] Step 3:

[0062] The server stores the received user information in a database and returns a registration success notice to the terminal.

[0063] Step 4:

[0064] The device notifies the user of successful registration and transitions to the home screen.

[0065] Step 5:

[0066] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[0067] Step 6:

[0068] The server retrieves the match information from the database and sends it to the terminal.

[0069] Step 7:

[0070] The terminal displays the match information to the user and transitions to a prediction screen.

[0071] Step 8:

[0072] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[0073] Step 9:

[0074] The terminal transmits the prediction data to the server.

[0075] Step 10:

[0076] The server receives the prediction data, stores it in a database, and aggregates it with other users' prediction data in real time.

[0077] Step 11:

[0078] The server uses artificial intelligence to generate predictions based on match data and past statistical data.

[0079] Step 12:

[0080] The server stores the generated prediction results in a database.

[0081] Step 13:

[0082] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[0083] Step 14:

[0084] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[0085] Step 15:

[0086] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[0087] Step 16:

[0088] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[0089] Step 17:

[0090] The user completes the procedure to receive the benefit and receives the benefit.

[0091] In this way, viewers can enjoy live broadcasts of professional baseball games interactively.

[0092] Example 1

[0093] 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."

[0094] There is a need for an improved experience for viewers of professional baseball broadcasts, allowing them to predict pitching patterns while watching the game and receive rewards based on the results. However, existing systems do not adequately reflect viewer prediction data and game data in real time, provide highly accurate predictions using artificial intelligence, or provide appropriate evaluations of performance and rewards. Therefore, a system that increases viewer engagement and enhances the viewing experience is needed.

[0095] 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.

[0096] In this invention, the server includes display means for viewers to input predicted data, transmission means for transmitting the predicted data to the server, storage means for the server to save the predicted data, artificial intelligence means for the server to generate predicted results using artificial intelligence based on game data, aggregation means for aggregating viewer performance based on the predicted results and the game data, reward means for providing benefits to top performers, means for acquiring game information and displaying it to viewers, means for comparing the predicted data of all viewers with actual game data after the game ends, and means for displaying the viewers' predicted performance in a ranking format. This allows viewers to make pitching predictions in real time, be instantly evaluated based on the results, and receive rewards.

[0097] - "Viewers" refer to users who use this system to predict pitching patterns while watching live broadcasts of professional baseball games.

[0098] "Prediction data" refers to information about the type of pitch and pitch trajectory that viewers input when making pitching predictions.

[0099] "Display means" refers to a means for providing an interface for a viewer to input prediction data.

[0100] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[0101] "Storage means" refers to a means for the server to store the received prediction data in a database.

[0102] "Artificial Intelligence Means" refers to the artificial intelligence technology used by the Server to generate predicted outcomes based on match data.

[0103] "Counting means" refers to the means by which the server counts the viewer's performance based on the predicted results and match data.

[0104] "Reward measures" refer to measures that provide benefits to top performers.

[0105] "Match information" refers to information about the match on that day, such as team names, start time, and player information.

[0106] "Means for comparison" refers to the means for comparing and matching the predicted data of all viewers with the actual game data after the game has ended.

[0107] "Ranking display means" refers to a means for displaying the predicted performance of viewers in a ranking format.

[0108] The present invention provides a system that allows viewers of live professional baseball games to predict pitch patterns during the game and receive rewards based on the results. This system is configured to include the following various means.

[0109] User Registration and Login

[0110] When a user launches the app for the first time, a registration screen appears on the device. The user enters their email address, password, and username, and the device sends this information to the server. The server stores the received user information in a database. Also, when an existing user enters their email address and password when logging in, the server generates an authentication token and returns it to the device.

[0111] Acquiring and displaying match information

[0112] The server retrieves the game information for the day (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal receives this information, displays it to the user, and prompts them to make a pitching prediction before the game starts.

[0113] Pitching predictions during the game

[0114] When the game begins, a pitching prediction screen will be displayed on the device. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The device then sends this predicted data to the server, which then stores the received data in a database.

[0115] AI-powered prediction generation

[0116] The server uses artificial intelligence to generate pitching predictions based on match data and past statistical data, and stores the results in a database, often using frameworks such as TensorFlow or PyTorch.

[0117] Calculation and display of results after the match

[0118] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates each user's hit rate. Based on this, the server determines the top performers and sends this information to the terminal. The terminal receives the results and displays the rankings of the top performers and the performance of each user.

[0119] Providing rewards

[0120] The server generates rewards (such as digital tickets or discount coupons) for top performers and notifies the device. It also compares the AI's predictions with the user's predicted performance and provides additional rewards to users with outstanding performance. The device notifies the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0121] This system allows viewers to enjoy real-time pitch predictions while watching a game, and they are instantly evaluated and rewarded based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0122] Example prompts to input to a generative AI model:

[0123] 1. Please explain in detail the specific steps a user takes to use the pitching prediction feature of a professional baseball broadcasting app.

[0124] 2. Please explain in detail the program flow and process for this app, from user registration to reward provision.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1: User Registration and Login

[0127] 1-1. User input - When a user launches the app for the first time, they enter their email address, password, and username in the form displayed on the registration screen. Existing users enter their email address and password on the login screen and press the "Login" button.

[0128] 1-2. Terminal processing - The input information is sent to the server. The input data is formatted as string data and sent to the server.

[0129] 1-3. Server processing - The server stores the received user information in a database. If the user is an existing user, it generates an authentication token and sends it to the terminal. The authentication token is generated in a secure format and is used to manage the user's session.

[0130] Example of specific operation: When a user enters the required information into the form and presses the "Submit" button, the terminal formats the information, encrypts it with SSL / TLS, and sends it to the server. The server verifies the received data, stores it in the database, and if the user is registered, generates and returns an authentication token.

[0131] Step 2: Retrieving and displaying match information

[0132] 2-1. Server processing - The server retrieves the match information for the day (team names, start time, player information, etc.) from the database.

[0133] 2-2. Server output - Convert the acquired match information into a data format such as JSON format and send it to the terminal.

[0134] 2-3. Terminal processing - The terminal analyzes the received game information and displays it to the user. It also displays a notification prompting the user to make a pitching prediction before the game starts.

[0135] Example of how it works: The server executes a database query to retrieve today's match information, formats it into JSON format, and sends that data to the device as an HTTP response. The device then analyzes the received data and displays it visually to the user.

[0136] Step 3: Predicting pitching patterns during the game

[0137] 3-1. Device Input - When the game starts, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0138] 3-2. Terminal processing - Format the selected prediction data and send it to the server.

[0139] 3-3. Server Processing - The server receives the prediction data and stores it in a database, which stores all the user's prediction data.

[0140] Specific operation example: When a user inputs a pitching prediction and presses the "Predict" button, the device formats the data and sends it to the server as an HTTP request. The server receives the request and saves it in a database.

[0141] Step 4: Generate predictions with AI

[0142] 4-1. Server Input - The server receives match data and historical statistics as input.

[0143] 4-2. Server Processing - The server uses a generative AI model (e.g., TensorFlow or PyTorch) to run a pitching prediction model, which generates the statistically most likely pitching prediction from the input data.

[0144] 4-3. Server output - The generated prediction results are saved in a database.

[0145] How it works: The server retrieves match and statistical data, invokes the generative AI model, and stores the results in a database.

[0146] Step 5: Calculating and displaying results after the match

[0147] 5-1. Server processing - After the game ends, the predicted data of all viewers is compared with the actual pitching results. The accuracy rate of each user is calculated and the top performers are determined.

[0148] 5-2. Server output - Generate the resulting ranking data and send it to the terminal.

[0149] 5-3. Terminal processing - The terminal displays the received ranking data and notifies the user of their results.

[0150] Specific operation example: The server retrieves match and prediction data from the database, calculates the accuracy rate, generates a ranking, and sends it in JSON format to the device. The device receives the data and displays it visually.

[0151] Step 6: Offer rewards

[0152] 6-1. Server processing - Generate rewards (such as digital tickets or discount coupons) for top performers and notify them to the terminal. Additional rewards may also be provided.

[0153] 6-2. Server output - Generates reward information and sends it to the device.

[0154] 6-3. Terminal processing - The terminal notifies the user of the bonus information and guides them through the collection procedure.

[0155] Specific operation example: The server identifies top performers, generates rewards, and sends the information to the terminal, which displays notifications and procedures to the user.

[0156] (Application example 1)

[0157] 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."

[0158] In today's world, viewers are looking for ways to not only watch content, but also to participate interactively. However, current broadcasting and streaming services limit two-way interaction between viewers and content, making it difficult to improve the viewing experience. Furthermore, there is no mechanism in place for viewers to test their knowledge and prediction skills and receive rewards based on their results, which leads to a decline in viewer engagement.

[0159] 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.

[0160] In this invention, the server includes display means for viewers to input prediction data, communication means for transmitting the prediction data to the server, data storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on competition data, aggregation means for aggregating viewer performance based on the prediction results and the competition data, reward means for providing rewards to top performers, comparison means for the artificial intelligence means to generate prediction results based on past statistical data and compare them with the viewer's prediction results, and user interface means for users using smartphones to participate in predictions in real time. This allows viewers to participate in the content more interactively and test their own prediction skills, improving the viewing experience and increasing engagement.

[0161] "Viewers" refer to users who use content distribution services to watch matches or events.

[0162] "Prediction data" refers to prediction information such as the type of pitch and the course of the pitch entered by the viewer.

[0163] "Display means" refers to a device or software that provides an interface for viewers to input prediction data.

[0164] "Communication means" refers to the internet connection or data communication required to send the prediction data to the server.

[0165] "Data storage means" refers to a database or storage system in which the server stores prediction data.

[0166] "Sports data" refers to various data generated during a game (e.g., number of pitches, batter's performance, etc.).

[0167] "Artificial intelligence means" refers to machine learning models and algorithms used to generate predictive outcomes based on competition data.

[0168] "Prediction result" refers to prediction information generated by the artificial intelligence means based on competition data and past statistical data.

[0169] "Counting Method" means the algorithms or software used to calculate and rank viewers' performance based on predicted results and competition data.

[0170] "Reward measures" are mechanisms for providing rewards and benefits to top performers.

[0171] A "comparison means" is an algorithm or software that compares the viewer's predictions with the artificial intelligence means' predictions.

[0172] "User interface means" refers to an interface that allows viewers to participate in real-time predictions on devices such as smartphones.

[0173] The system of the present invention allows viewers to make predictions in real time and receive rewards based on the results of their predictions. This system has three main roles: a server, a terminal, and a user.

[0174] 1. Server Processing

[0175] The server is configured with the following hardware and software: The hardware used is a high-performance database server and a GPU server for AI processing, and the software used is Python, Flask, SQLAlchemy, SQLite, and machine learning libraries (TensorFlow and PyTorch).

[0176] 1.1 Data storage method

[0177] The server stores the prediction data sent by viewers in a database, which allows all predictions to be managed centrally.

[0178] 1.2 Artificial Intelligence Means

[0179] The server generates prediction results using a generative AI model based on competition data and past statistical data. This artificial intelligence method provides highly accurate prediction results.

[0180] 1.3 Means of comparison

[0181] The prediction results generated by the artificial intelligence means are compared with the prediction results of the viewers, thereby evaluating the viewers' prediction ability.

[0182] 1.4 Aggregation methods

[0183] The server will compile all the viewers' predictions and rank them, which will determine the winners.

[0184] 1.5 Remuneration Means

[0185] Generate and notify information to provide rewards and benefits to top performers.

[0186] 2. Terminal Processing

[0187] The terminal (smartphone) allows viewers to access the system through an application, which provides the following functions:

[0188] 2.1 Display means

[0189] The terminal provides a display means for the viewer to input prediction data, allowing the viewer to easily input predictions.

[0190] 2.2 Communication Methods

[0191] It has a communication means to send forecast data to the server in real time, which allows forecasts to be compiled quickly.

[0192] 2.3 User Interface Methods

[0193] The application on the terminal provides a user interface means, allowing the viewer to make predictions intuitively.

[0194] 3. User Operation

[0195] Users download the application onto their smartphones and register when they first launch it. Then, once the competition begins, they can input their prediction data from their device and make predictions in real time. If their predictions are correct, they are evaluated by the server and reflected in their final scores.

[0196] Specifically, if a user predicts a "low inside straight pitch" and is correct, the prediction will be saved in the system and later tallied, allowing viewers to test their prediction skills and receive rewards accordingly.

[0197] An example of a prompt to input to a generative AI model is as follows:

[0198] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[0199] "Pitch Type: Curve, Pitch Location: High outside"

[0200] This will allow viewers to enjoy a real-time interactive viewing experience, resulting in a system that increases engagement.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1: User registration and login

[0203] When a user launches the app for the first time, the server displays a form on the device for the user to enter their email address, password, and username. When the user enters and submits this information, the server receives the information and stores it in a database. If the user is an existing user, they submit their login information and the server generates an authentication token and returns it to the device.

[0204] Input: Email address, password, username

[0205] Data processing: saving user information, generating authentication tokens when logging in

[0206] Output: Registration completion message, authentication token when logging in

[0207] Step 2: Retrieving and displaying match information

[0208] Before the game starts, the server retrieves the game information (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching pattern before the game starts.

[0209] Input: Match information

[0210] Data processing: Acquisition and transmission of match information

[0211] Output: Display match information

[0212] Step 3: Enter and submit forecast data

[0213] After the game starts, the device displays a pitching prediction screen to the user. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and submits the predicted data. The server receives this predicted data and stores it in a database.

[0214] Input: pitch type, pitching trajectory

[0215] Data processing: Saving predicted data

[0216] Output: Message that forecast data has been saved

[0217] Step 4: Generate predictions with AI

[0218] The server generates prediction results using a generative AI model based on competition data and past statistical data. The results are saved in a database. The following prompt is used:

[0219] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[0220] "Pitch Type: Curve, Pitch Location: High outside"

[0221] Input: Competition data, past statistical data

[0222] Data processing: Generate predictions using AI models and save the results

[0223] Output: Prediction results

[0224] Step 5: Compare your predictions with your audience forecasts

[0225] The server compares the AI's predictions with the viewers' predictions, and if the viewers' predictions match the AI's predictions, they are added to the aggregate data.

[0226] Input: AI prediction results, viewer prediction results

[0227] Data processing: Comparing prediction results and adding them to aggregated data

[0228] Output: Comparison results

[0229] Step 6: Counting grades

[0230] The server compares all viewers' predictions with the actual competition results and calculates the accuracy of each prediction. Based on the results, the top performers are determined and a ranking is generated.

[0231] Input: Viewer predictions, actual competition results

[0232] Data processing: Calculating hit rates and generating rankings

[0233] Output: List of top performers

[0234] Step 7: Offer Rewards

[0235] The server generates reward information for top-performing users and notifies the terminal, which then guides the user through the procedure for receiving the reward.

[0236] Input: Top performers list

[0237] Data processing: Reward information generation, reward notification

[0238] Output: Reward notification message, procedure guide

[0239] 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.

[0240] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results and the user's emotional data. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a compilation means for compiling viewer performance, a reward means for top performers, and an emotion engine that recognizes viewer emotions.

[0241] User Registration and Login

[0242] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0243] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[0244] Acquiring and displaying match information

[0245] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[0246] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[0247] Pitching predictions during the game

[0248] Device: When the game begins, a pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. After all selections are complete, the emotion engine analyzes emotional data from the user's facial expressions and voice.

[0249] User: Enter and submit prediction data for each at-bat.

[0250] Device: Emotion data is sent to the server along with the prediction data.

[0251] AI-powered prediction generation

[0252] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[0253] Emotional data analysis and reflection

[0254] Server: Analyzes the received emotional data and provides feedback on prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized tips and advice may be provided to the user.

[0255] Calculation and display of results after the match

[0256] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top performers are determined based on the results.

[0257] Device: Receives the aggregated results and displays the rankings of top performers and the performance of individual users. It also displays feedback based on emotional data.

[0258] Providing rewards

[0259] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[0260] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0261] Specific examples

[0262] For example, if a user predicts a "straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[0263] This system allows users to enjoy watching games in a more interactive way, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched. Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[0267] Step 2:

[0268] The terminal verifies the entered user information and sends it to the server.

[0269] Step 3:

[0270] The server stores the received user information in a database and returns a registration success notice to the terminal.

[0271] Step 4:

[0272] The device notifies the user of successful registration and transitions to the home screen.

[0273] Step 5:

[0274] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[0275] Step 6:

[0276] The server retrieves the match information from the database and sends it to the terminal.

[0277] Step 7:

[0278] The terminal displays the match information to the user and transitions to a prediction screen.

[0279] Step 8:

[0280] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[0281] Step 9:

[0282] Immediately after the user presses the "predict" button, the terminal acquires the user's facial expression and voice data using an emotion engine and generates emotion data.

[0283] Step 10:

[0284] The device sends the prediction data and emotion data to the server.

[0285] Step 11:

[0286] The server receives the prediction data and emotion data, stores them in a database, and aggregates them with other users' prediction data in real time.

[0287] Step 12:

[0288] The server generates pitching predictions using artificial intelligence means based on match data and past statistical data.

[0289] Step 13:

[0290] The server stores the generated prediction results in a database.

[0291] Step 14:

[0292] The server analyzes the received emotional data in real time and provides feedback to the prediction results based on the user's emotional state. For example, if high concentration is detected, it provides a prediction hint.

[0293] Step 15:

[0294] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[0295] Step 16:

[0296] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[0297] Step 17:

[0298] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[0299] Step 18:

[0300] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[0301] Step 19:

[0302] The user completes the procedure to receive the benefit and receives the benefit.

[0303] In this way, viewers can enjoy professional baseball broadcasts interactively, and analysis of emotional data can provide personalized feedback to viewers, increasing their motivation to participate.

[0304] Example 2

[0305] 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."

[0306] Conventional professional baseball broadcasting systems have limited means to encourage viewer interactive participation, making it difficult to maintain interest. Furthermore, there is no feedback or reward system that takes into account viewer emotional data, meaning the viewing experience is not sufficiently personalized. This leads to a decrease in viewer satisfaction and motivation to participate, making it difficult to improve viewer ratings.

[0307] 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.

[0308] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means by which the server stores the prediction data, artificial intelligence means by which the server generates prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, emotion recognition means for analyzing viewer emotion data, and feedback means for providing feedback to predictions based on the emotion data. This makes it possible to promote interactive participation by viewers and provide a personalized viewing experience.

[0309] The "display means" is an interface that allows the viewer to check and manipulate input data.

[0310] The "transmission means" is a function for transmitting data input by the viewer to the server.

[0311] "Storage" refers to a database or storage system for recording and storing data received by the server.

[0312] "Artificial intelligence means" refers to algorithms or models that the server uses to generate predicted results based on match data and past statistical data.

[0313] The "aggregation method" is a function that calculates results based on viewers' predictions and match data, and generates rankings.

[0314] "Reward mechanism" refers to a function or system for providing rewards to viewers who perform well.

[0315] "Emotion recognition means" refers to tools and algorithms for analyzing viewer emotional data.

[0316] "Feedback measures" is a function that provides hints and advice on predictions based on viewer emotional data.

[0317] This invention is a system that allows viewers to enjoy professional baseball broadcasts in a more interactive way. This system allows viewers to predict pitching patterns during the game and receive rewards based on the results and the viewer's emotional data. Specific embodiments of this system are described below.

[0318] User Registration and Login

[0319] Device: When a user launches the app for the first time, the device displays the user registration screen. The user enters their email address, password, and username and presses the "Register" button. Existing users enter their email address and password on the login screen and press the "Login" button.

[0320] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device.

[0321] Acquiring and displaying match information

[0322] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database.

[0323] Terminal: The received game information is displayed to the user, and the user is prompted to make pitching predictions before the game begins.

[0324] Pitching predictions during the game

[0325] Device: At the start of the game, a pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0326] User: Enter and submit predicted data for each at-bat.

[0327] Device: The emotion data analyzed by the emotion engine is sent to the server along with the prediction data.

[0328] AI-powered prediction generation

[0329] Server: The server generates pitching predictions using artificial intelligence based on match data and past statistical data. The generated predictions are also stored in a database.

[0330] Emotional data analysis and reflection

[0331] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, the server will provide personalized tips and advice to the user.

[0332] Calculation and display of results after the match

[0333] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top players are determined.

[0334] Device: Receives the aggregated results and displays the rankings of top players and their individual performances. It also displays feedback based on emotional data.

[0335] Providing rewards

[0336] Server: Generates rewards (digital tickets or discount coupons) for top performers and users who exceed the predicted results, and notifies the device.

[0337] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[0338] Examples and prompts

[0339] For example, if a user predicts a "straight ball, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will receive advice based on past data as a "hint for the next prediction." Additionally, after the game, the user with the highest accuracy rate will receive a pop-up notification on their digital ticket, along with feedback based on their emotion data, such as "points to improve your prediction skills."

[0340] This system allows viewers to enjoy watching the game interactively, and improves the viewing experience by receiving rewards and feedback based on the results. Analysis of emotional data can increase user participation, which is expected to increase the number of viewers.

[0341] Example prompt sentence:

[0342] "Predict the next pitch in today's game. Choose the type and trajectory of the pitch."

[0343] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0344] Step 1: Display the user registration screen

[0345] Device: When a user launches the app for the first time, the device displays a user registration screen. Input fields include email address, password, and username. Input: App launch. Output: User registration screen loads.

[0346] Step 2: Enter your user information

[0347] User: The user enters their email address, password, and username on the registration screen and presses the "Register" button. Input: User's personal information. Output: "Register" button press event.

[0348] Step 3: Submit user information

[0349] Terminal: When the "Register" button is pressed, the terminal sends the entered user information to the server. Input: User's personal information. Output: Sends information to the server.

[0350] Step 4: Storing and authenticating user information

[0351] Server: The server saves the received user information, and if the user is an existing user, generates an authentication token and returns it to the device. Input: Sent user information. Output: Saves new user information, and if the user is an existing user, generates an authentication token.

[0352] Step 5: Get match information

[0353] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database. Input: The current day's request. Output: Match information.

[0354] Step 6: Send and view match information

[0355] Server: The server sends the acquired game information to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts. Input: Game information. Output: Sending and displaying game information.

[0356] Step 7: Input your pitching predictions

[0357] Device: When the game starts, the pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button. Input: User's prediction data. Output: Preparation for sending the prediction data.

[0358] Step 8: Analyze the sentiment data

[0359] Device: After the user presses the "Predict" button, the device's emotion engine analyzes emotion data from the user's facial expressions and voice. Input: User's facial expressions and voice data. Output: Generation of emotion data.

[0360] Step 9: Send prediction and sentiment data

[0361] Terminal: Sends prediction data and emotion data to the server. Input: Prediction data, emotion data. Output: Sends data to the server.

[0362] Step 10: Generate prediction results

[0363] Server: The server uses AI to generate pitching predictions based on match data and past statistical data, and stores them in a database. Input: Match data, statistical data. Output: Prediction results.

[0364] Step 11: Emotional Data Analysis and Feedback

[0365] Server: The server analyzes the received emotion data and provides hints and advice based on the emotional state. Input: Emotion data. Output: Personalized feedback.

[0366] Step 12: Collating and calculating grades

[0367] Server: After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the results. Input: User's predicted data, actual pitching results. Output: Calculated results.

[0368] Step 13: Displaying the results

[0369] Terminal: Receives the aggregated results from the server and displays the rankings and individual scores. It also displays feedback based on emotion data. Input: Aggregated results. Output: Display of scores and feedback.

[0370] Step 14: Offer and notify rewards

[0371] Server: The server generates rewards for top performers and users who exceed predicted results, and notifies the device. Input: Performance results. Output: Reward notification.

[0372] Step 15: Reward Collection Instructions

[0373] Terminal: Notifies the user of reward information and how to receive it, and guides them through the process of receiving the reward. Input: Reward notification. Output: Reward receipt instructions.

[0374] (Application example 2)

[0375] 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."

[0376] Systems already exist that evaluate and reward viewers' prediction skills as a way to make professional baseball broadcasts more interactive for viewers. However, these systems do not take into account viewers' emotions and are unable to provide more personalized feedback or hints based on viewers' levels of concentration and excitement. This has resulted in insufficient viewer engagement and limited improvements to the viewing experience.

[0377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0378] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, and emotion analysis means for analyzing viewer emotional information and providing feedback. This makes it possible to provide personalized feedback and hints based on the viewer's emotional state, increasing viewer engagement and providing a richer viewing experience.

[0379] "Display means" refers to a device or interface that displays the information necessary for the viewer to input prediction data.

[0380] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[0381] "Storage means" refers to a database or storage system for storing the prediction data received by the server.

[0382] "Artificial Intelligence Means" refers to the artificial intelligence algorithms or models used by the Server to generate predicted outcomes based on Match Data.

[0383] "Counting means" refers to the calculation process or system used to compile the viewer's performance based on the predicted results and match data.

[0384] "Reward means" refers to a device or system for providing rewards to top performers.

[0385] "Emotion analysis means" refers to an analytical engine or algorithm that analyzes viewers' emotional information and reflects the results in feedback.

[0386] "Generative AI Model" refers to an artificial intelligence model used to provide viewers with personalized tips and advice.

[0387] "Feedback mechanism" refers to a system or method for providing personalized advice or tips to a viewer based on the analyzed emotional state.

[0388] The system for implementing this invention is a system that allows viewers to predict pitching patterns while watching live professional baseball games and receive rewards based on the results. This system is composed of the following main components:

[0389] User Registration and Login

[0390] On the device: When a user launches the application for the first time, they are prompted to enter their email address, password, and username. The user creates an account by entering this information. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0391] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device. This process uses Firebase Authentication and MySQL.

[0392] Acquiring and displaying match information

[0393] Server: The server retrieves information about the day of the match (team names, start time, player information, etc.) from the database. This information is sent to the device.

[0394] Terminal: The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts.

[0395] Pitching predictions during the game

[0396] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0397] User: Enter prediction data for each at-bat and submit it.

[0398] Device: Along with the prediction data, emotion data analyzed from the user's facial expressions and voice is also sent to the server. Emotion analysis is performed using Google Cloud Vision API and AWS Comprehend.

[0399] AI-powered prediction generation

[0400] Server: The server generates pitching predictions using artificial intelligence (TensorFlow) based on match data and past statistical data. The generated predictions are stored in a database.

[0401] Emotional data analysis and reflection

[0402] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized hints and advice will be provided to the user.

[0403] Calculation and display of results after the match

[0404] Server: After the game ends, the server compares all users' prediction data with the actual pitching results, calculates the results based on the accuracy rate and emotion data, and determines the top performers based on the calculation results.

[0405] Device: The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on emotion data.

[0406] Providing rewards

[0407] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[0408] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[0409] Specific examples

[0410] For example, if a user predicts "a straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[0411] Prompt Sentence Examples

[0412] "Please predict the pitching strategy for the current game. Do you think the next pitch will be a straight pitch, high and outside?"

[0413] "High concentration has been detected. Would you like to continue with your predictions?"

[0414] "Your prediction was correct! Tap here to receive your digital ticket."

[0415] This allows users to enjoy watching the game more interactively, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched.Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] When a user launches the application for the first time, the server displays a screen for entering an email address, password, and username. The user enters this information to create an account. The entered information is authenticated using Firebase Authentication, and if successful, the server generates an authentication token and saves the entered information in a MySQL database. As a result, the user's account information is saved on the server.

[0419] Step 2:

[0420] The server retrieves information about the day of the game (team names, starting time, player information, etc.) from the database and sends it to the terminal. The terminal displays this information to the user and prompts them to make a pitching prediction before the game starts. This allows the user to check the specific details of the game and prepare to make a pitching prediction.

[0421] Step 3:

[0422] When the game begins, the device displays a pitching prediction screen. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The entered prediction data is sent from the device to the server. At the same time, the device collects emotional data from the user's facial expressions and voice and sends it to the server.

[0423] Step 4:

[0424] The server stores the received prediction data and emotion data. The emotion data is analyzed using Google Cloud Vision API and AWS Comprehend. This allows the user's emotional state to be analyzed and metrics such as concentration and excitement levels to be obtained.

[0425] Step 5:

[0426] The server generates pitching predictions using artificial intelligence (TensorFlow) based on game data and past statistical data. The generated prediction results are then saved in the server's database. This ensures the generation and storage of prediction results.

[0427] Step 6:

[0428] The server analyzes the received emotional data and provides feedback to the prediction results based on the results. For example, if high concentration is recognized, personalized hints and advice will be provided to the user. This feedback information is generated using a generative AI model.

[0429] Step 7:

[0430] After the game ends, the server compares all users' prediction data with their actual pitching results, and calculates their scores based on their accuracy and emotional data. Based on the results of the calculation, the top performers are determined and this information is sent to the terminal.

[0431] Step 8:

[0432] The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on the user's emotional data.

[0433] Step 9:

[0434] The server generates rewards (e.g., digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the terminal of the reward information, thereby providing the reward information to the user.

[0435] Step 10:

[0436] The terminal notifies the user of the benefit information and how to receive it, and guides the user through the procedure for receiving the benefit. The user can receive the benefit by following the instructions.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Second embodiment]

[0441] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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."

[0453] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a tabulation means for tabulating the performance of viewers, and a reward means for top performers.

[0454] User Registration and Login

[0455] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0456] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[0457] Acquiring and displaying match information

[0458] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[0459] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[0460] Pitching predictions during the game

[0461] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0462] User: Enter and submit prediction data for each at-bat.

[0463] Server: Receives the prediction data, stores it in a database, and aggregates other users' prediction data in real time.

[0464] AI-powered prediction generation

[0465] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[0466] Calculation and display of results after the match

[0467] Server: After the game ends, the predicted data of all users is compared with the actual pitching results, and the accuracy rate of each user is calculated. The top performers are determined based on the calculation results.

[0468] Terminal: Receives the aggregated results and displays the rankings of the top performers and the performance of individual users.

[0469] Providing rewards

[0470] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and notifies them to the device. Compares the AI's predictions with the user's predicted performance and provides additional rewards to users who achieve excellent results.

[0471] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0472] This system allows users to enjoy real-time pitch predictions while watching a game and earn rewards based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their own predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0473] The processing flow will be explained below.

[0474] Step 1:

[0475] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[0476] Step 2:

[0477] The terminal verifies the entered user information and sends it to the server.

[0478] Step 3:

[0479] The server stores the received user information in a database and returns a registration success notice to the terminal.

[0480] Step 4:

[0481] The device notifies the user of successful registration and transitions to the home screen.

[0482] Step 5:

[0483] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[0484] Step 6:

[0485] The server retrieves the match information from the database and sends it to the terminal.

[0486] Step 7:

[0487] The terminal displays the match information to the user and transitions to a prediction screen.

[0488] Step 8:

[0489] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[0490] Step 9:

[0491] The terminal transmits the prediction data to the server.

[0492] Step 10:

[0493] The server receives the prediction data, stores it in a database, and aggregates it with other users' prediction data in real time.

[0494] Step 11:

[0495] The server uses artificial intelligence to generate predictions based on match data and past statistical data.

[0496] Step 12:

[0497] The server stores the generated prediction results in a database.

[0498] Step 13:

[0499] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[0500] Step 14:

[0501] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[0502] Step 15:

[0503] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[0504] Step 16:

[0505] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[0506] Step 17:

[0507] The user completes the procedure to receive the benefit and receives the benefit.

[0508] In this way, viewers can enjoy live broadcasts of professional baseball games interactively.

[0509] Example 1

[0510] 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."

[0511] There is a need for an improved experience for viewers of professional baseball broadcasts, allowing them to predict pitching patterns while watching the game and receive rewards based on the results. However, existing systems do not adequately reflect viewer prediction data and game data in real time, provide highly accurate predictions using artificial intelligence, or provide appropriate evaluations of performance and rewards. Therefore, a system that increases viewer engagement and enhances the viewing experience is needed.

[0512] 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.

[0513] In this invention, the server includes display means for viewers to input predicted data, transmission means for transmitting the predicted data to the server, storage means for the server to save the predicted data, artificial intelligence means for the server to generate predicted results using artificial intelligence based on game data, aggregation means for aggregating viewer performance based on the predicted results and the game data, reward means for providing benefits to top performers, means for acquiring game information and displaying it to viewers, means for comparing the predicted data of all viewers with actual game data after the game ends, and means for displaying the viewers' predicted performance in a ranking format. This allows viewers to make pitching predictions in real time, be instantly evaluated based on the results, and receive rewards.

[0514] - "Viewers" refer to users who use this system to predict pitching patterns while watching live broadcasts of professional baseball games.

[0515] "Prediction data" refers to information about the type of pitch and pitch trajectory that viewers input when making pitching predictions.

[0516] "Display means" refers to a means for providing an interface for a viewer to input prediction data.

[0517] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[0518] "Storage means" refers to a means for the server to store the received prediction data in a database.

[0519] "Artificial Intelligence Means" refers to the artificial intelligence technology used by the Server to generate predicted outcomes based on match data.

[0520] "Counting means" refers to the means by which the server counts the viewer's performance based on the predicted results and match data.

[0521] "Reward measures" refer to measures that provide benefits to top performers.

[0522] "Match information" refers to information about the match on that day, such as team names, start time, and player information.

[0523] "Means for comparison" refers to the means for comparing and matching the predicted data of all viewers with the actual game data after the game has ended.

[0524] "Ranking display means" refers to a means for displaying the predicted performance of viewers in a ranking format.

[0525] The present invention provides a system that allows viewers of live professional baseball games to predict pitch patterns during the game and receive rewards based on the results. This system is configured to include the following various means.

[0526] User Registration and Login

[0527] When a user launches the app for the first time, a registration screen appears on the device. The user enters their email address, password, and username, and the device sends this information to the server. The server stores the received user information in a database. Also, when an existing user enters their email address and password when logging in, the server generates an authentication token and returns it to the device.

[0528] Acquiring and displaying match information

[0529] The server retrieves the game information for the day (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal receives this information, displays it to the user, and prompts them to make a pitching prediction before the game starts.

[0530] Pitching predictions during the game

[0531] When the game begins, a pitching prediction screen will be displayed on the device. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The device then sends this predicted data to the server, which then stores the received data in a database.

[0532] AI-powered prediction generation

[0533] The server uses artificial intelligence to generate pitching predictions based on match data and past statistical data, and stores the results in a database, often using frameworks such as TensorFlow or PyTorch.

[0534] Calculation and display of results after the match

[0535] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates each user's hit rate. Based on this, the server determines the top performers and sends this information to the terminal. The terminal receives the results and displays the rankings of the top performers and the performance of each user.

[0536] Providing rewards

[0537] The server generates rewards (such as digital tickets or discount coupons) for top performers and notifies the device. It also compares the AI's predictions with the user's predicted performance and provides additional rewards to users with outstanding performance. The device notifies the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0538] This system allows viewers to enjoy real-time pitch predictions while watching a game, and they are instantly evaluated and rewarded based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0539] Example prompts to input to a generative AI model:

[0540] 1. Please explain in detail the specific steps a user takes to use the pitching prediction feature of a professional baseball broadcasting app.

[0541] 2. Please explain in detail the program flow and process for this app, from user registration to reward provision.

[0542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0543] Step 1: User Registration and Login

[0544] 1-1. User input - When a user launches the app for the first time, they enter their email address, password, and username in the form displayed on the registration screen. Existing users enter their email address and password on the login screen and press the "Login" button.

[0545] 1-2. Terminal processing - The input information is sent to the server. The input data is formatted as string data and sent to the server.

[0546] 1-3. Server processing - The server stores the received user information in a database. If the user is an existing user, it generates an authentication token and sends it to the terminal. The authentication token is generated in a secure format and is used to manage the user's session.

[0547] Example of specific operation: When a user enters the required information into the form and presses the "Submit" button, the terminal formats the information, encrypts it with SSL / TLS, and sends it to the server. The server verifies the received data, stores it in the database, and if the user is registered, generates and returns an authentication token.

[0548] Step 2: Retrieving and displaying match information

[0549] 2-1. Server processing - The server retrieves the match information for the day (team names, start time, player information, etc.) from the database.

[0550] 2-2. Server output - Convert the acquired match information into a data format such as JSON format and send it to the terminal.

[0551] 2-3. Terminal processing - The terminal analyzes the received game information and displays it to the user. It also displays a notification prompting the user to make a pitching prediction before the game starts.

[0552] Example of how it works: The server executes a database query to retrieve today's match information, formats it into JSON format, and sends that data to the device as an HTTP response. The device then analyzes the received data and displays it visually to the user.

[0553] Step 3: Predicting pitching patterns during the game

[0554] 3-1. Device Input - When the game starts, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0555] 3-2. Terminal processing - Format the selected prediction data and send it to the server.

[0556] 3-3. Server Processing - The server receives the prediction data and stores it in a database, which stores all the user's prediction data.

[0557] Specific operation example: When a user inputs a pitching prediction and presses the "Predict" button, the device formats the data and sends it to the server as an HTTP request. The server receives the request and saves it in a database.

[0558] Step 4: Generate predictions with AI

[0559] 4-1. Server Input - The server receives match data and historical statistics as input.

[0560] 4-2. Server Processing - The server uses a generative AI model (e.g., TensorFlow or PyTorch) to run a pitching prediction model, which generates the statistically most likely pitching prediction from the input data.

[0561] 4-3. Server output - The generated prediction results are saved in a database.

[0562] How it works: The server retrieves match and statistical data, invokes the generative AI model, and stores the results in a database.

[0563] Step 5: Calculating and displaying results after the match

[0564] 5-1. Server processing - After the game ends, the predicted data of all viewers is compared with the actual pitching results. The accuracy rate of each user is calculated and the top performers are determined.

[0565] 5-2. Server output - Generate the resulting ranking data and send it to the terminal.

[0566] 5-3. Terminal processing - The terminal displays the received ranking data and notifies the user of their results.

[0567] Specific operation example: The server retrieves match and prediction data from the database, calculates the accuracy rate, generates a ranking, and sends it in JSON format to the device. The device receives the data and displays it visually.

[0568] Step 6: Offer rewards

[0569] 6-1. Server processing - Generate rewards (such as digital tickets or discount coupons) for top performers and notify them to the terminal. Additional rewards may also be provided.

[0570] 6-2. Server output - Generates reward information and sends it to the device.

[0571] 6-3. Terminal processing - The terminal notifies the user of the bonus information and guides them through the collection procedure.

[0572] Specific operation example: The server identifies top performers, generates rewards, and sends the information to the terminal, which displays notifications and procedures to the user.

[0573] (Application example 1)

[0574] 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."

[0575] In today's world, viewers are looking for ways to not only watch content, but also to participate interactively. However, current broadcasting and streaming services limit two-way interaction between viewers and content, making it difficult to improve the viewing experience. Furthermore, there is no mechanism in place for viewers to test their knowledge and prediction skills and receive rewards based on their results, which leads to a decline in viewer engagement.

[0576] 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.

[0577] In this invention, the server includes display means for viewers to input prediction data, communication means for transmitting the prediction data to the server, data storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on competition data, aggregation means for aggregating viewer performance based on the prediction results and the competition data, reward means for providing rewards to top performers, comparison means for the artificial intelligence means to generate prediction results based on past statistical data and compare them with the viewer's prediction results, and user interface means for users using smartphones to participate in predictions in real time. This allows viewers to participate in the content more interactively and test their own prediction skills, improving the viewing experience and increasing engagement.

[0578] "Viewers" refer to users who use content distribution services to watch matches or events.

[0579] "Prediction data" refers to prediction information such as the type of pitch and the course of the pitch entered by the viewer.

[0580] "Display means" refers to a device or software that provides an interface for viewers to input prediction data.

[0581] "Communication means" refers to the internet connection or data communication required to send the prediction data to the server.

[0582] "Data storage means" refers to a database or storage system in which the server stores prediction data.

[0583] "Sports data" refers to various data generated during a game (e.g., number of pitches, batter's performance, etc.).

[0584] "Artificial intelligence means" refers to machine learning models and algorithms used to generate predictive outcomes based on competition data.

[0585] "Prediction result" refers to prediction information generated by the artificial intelligence means based on competition data and past statistical data.

[0586] "Counting Method" means the algorithms or software used to calculate and rank viewers' performance based on predicted results and competition data.

[0587] "Reward measures" are mechanisms for providing rewards and benefits to top performers.

[0588] A "comparison means" is an algorithm or software that compares the viewer's predictions with the artificial intelligence means' predictions.

[0589] "User interface means" refers to an interface that allows viewers to participate in real-time predictions on devices such as smartphones.

[0590] The system of the present invention allows viewers to make predictions in real time and receive rewards based on the results of their predictions. This system has three main roles: a server, a terminal, and a user.

[0591] 1. Server Processing

[0592] The server is configured with the following hardware and software: The hardware used is a high-performance database server and a GPU server for AI processing, and the software used is Python, Flask, SQLAlchemy, SQLite, and machine learning libraries (TensorFlow and PyTorch).

[0593] 1.1 Data storage method

[0594] The server stores the prediction data sent by viewers in a database, which allows all predictions to be managed centrally.

[0595] 1.2 Artificial Intelligence Means

[0596] The server generates prediction results using a generative AI model based on competition data and past statistical data. This artificial intelligence method provides highly accurate prediction results.

[0597] 1.3 Means of comparison

[0598] The prediction results generated by the artificial intelligence means are compared with the prediction results of the viewers, thereby evaluating the viewers' prediction ability.

[0599] 1.4 Aggregation methods

[0600] The server will compile all the viewers' predictions and rank them, which will determine the winners.

[0601] 1.5 Remuneration Means

[0602] Generate and notify information to provide rewards and benefits to top performers.

[0603] 2. Terminal Processing

[0604] The terminal (smartphone) allows viewers to access the system through an application, which provides the following functions:

[0605] 2.1 Display means

[0606] The terminal provides a display means for the viewer to input prediction data, allowing the viewer to easily input predictions.

[0607] 2.2 Communication Methods

[0608] It has a communication means to send forecast data to the server in real time, which allows forecasts to be compiled quickly.

[0609] 2.3 User Interface Methods

[0610] The application on the terminal provides a user interface means, allowing the viewer to make predictions intuitively.

[0611] 3. User Operation

[0612] Users download the application onto their smartphones and register when they first launch it. Then, once the competition begins, they can input their prediction data from their device and make predictions in real time. If their predictions are correct, they are evaluated by the server and reflected in their final scores.

[0613] Specifically, if a user predicts a "low inside straight pitch" and is correct, the prediction will be saved in the system and later tallied, allowing viewers to test their prediction skills and receive rewards accordingly.

[0614] An example of a prompt to input to a generative AI model is as follows:

[0615] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[0616] "Pitch Type: Curve, Pitch Location: High outside"

[0617] This will allow viewers to enjoy a real-time interactive viewing experience, resulting in a system that increases engagement.

[0618] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0619] Step 1: User registration and login

[0620] When a user launches the app for the first time, the server displays a form on the device for the user to enter their email address, password, and username. When the user enters and submits this information, the server receives the information and stores it in a database. If the user is an existing user, they submit their login information and the server generates an authentication token and returns it to the device.

[0621] Input: Email address, password, username

[0622] Data processing: saving user information, generating authentication tokens when logging in

[0623] Output: Registration completion message, authentication token when logging in

[0624] Step 2: Retrieving and displaying match information

[0625] Before the game starts, the server retrieves the game information (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching pattern before the game starts.

[0626] Input: Match information

[0627] Data processing: Acquisition and transmission of match information

[0628] Output: Display match information

[0629] Step 3: Enter and submit forecast data

[0630] After the game starts, the device displays a pitching prediction screen to the user. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and submits the predicted data. The server receives this predicted data and stores it in a database.

[0631] Input: pitch type, pitching trajectory

[0632] Data processing: Saving predicted data

[0633] Output: Message that forecast data has been saved

[0634] Step 4: Generate predictions with AI

[0635] The server generates prediction results using a generative AI model based on competition data and past statistical data. The results are saved in a database. The following prompt is used:

[0636] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[0637] "Pitch Type: Curve, Pitch Location: High outside"

[0638] Input: Competition data, past statistical data

[0639] Data processing: Generate predictions using AI models and save the results

[0640] Output: Prediction results

[0641] Step 5: Compare your predictions with your audience forecasts

[0642] The server compares the AI's predictions with the viewers' predictions, and if the viewers' predictions match the AI's predictions, they are added to the aggregate data.

[0643] Input: AI prediction results, viewer prediction results

[0644] Data processing: Comparing prediction results and adding them to aggregated data

[0645] Output: Comparison results

[0646] Step 6: Counting grades

[0647] The server compares all viewers' predictions with the actual competition results and calculates the accuracy of each prediction. Based on the results, the top performers are determined and a ranking is generated.

[0648] Input: Viewer predictions, actual competition results

[0649] Data processing: Calculating hit rates and generating rankings

[0650] Output: List of top performers

[0651] Step 7: Offer Rewards

[0652] The server generates reward information for top-performing users and notifies the terminal, which then guides the user through the procedure for receiving the reward.

[0653] Input: Top performers list

[0654] Data processing: Reward information generation, reward notification

[0655] Output: Reward notification message, procedure guide

[0656] 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.

[0657] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results and the user's emotional data. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a compilation means for compiling viewer performance, a reward means for top performers, and an emotion engine that recognizes viewer emotions.

[0658] User Registration and Login

[0659] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0660] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[0661] Acquiring and displaying match information

[0662] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[0663] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[0664] Pitching predictions during the game

[0665] Device: When the game begins, a pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. After all selections are complete, the emotion engine analyzes emotional data from the user's facial expressions and voice.

[0666] User: Enter and submit prediction data for each at-bat.

[0667] Device: Emotion data is sent to the server along with the prediction data.

[0668] AI-powered prediction generation

[0669] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[0670] Emotional data analysis and reflection

[0671] Server: Analyzes the received emotional data and provides feedback on prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized tips and advice may be provided to the user.

[0672] Calculation and display of results after the match

[0673] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top performers are determined based on the results.

[0674] Device: Receives the aggregated results and displays the rankings of top performers and the performance of individual users. It also displays feedback based on emotional data.

[0675] Providing rewards

[0676] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[0677] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0678] Specific examples

[0679] For example, if a user predicts a "straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[0680] This system allows users to enjoy watching games in a more interactive way, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched. Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[0681] The processing flow will be explained below.

[0682] Step 1:

[0683] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[0684] Step 2:

[0685] The terminal verifies the entered user information and sends it to the server.

[0686] Step 3:

[0687] The server stores the received user information in a database and returns a registration success notice to the terminal.

[0688] Step 4:

[0689] The device notifies the user of successful registration and transitions to the home screen.

[0690] Step 5:

[0691] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[0692] Step 6:

[0693] The server retrieves the match information from the database and sends it to the terminal.

[0694] Step 7:

[0695] The terminal displays the match information to the user and transitions to a prediction screen.

[0696] Step 8:

[0697] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[0698] Step 9:

[0699] Immediately after the user presses the "predict" button, the terminal acquires the user's facial expression and voice data using an emotion engine and generates emotion data.

[0700] Step 10:

[0701] The device sends the prediction data and emotion data to the server.

[0702] Step 11:

[0703] The server receives the prediction data and emotion data, stores them in a database, and aggregates them with other users' prediction data in real time.

[0704] Step 12:

[0705] The server generates pitching predictions using artificial intelligence means based on match data and past statistical data.

[0706] Step 13:

[0707] The server stores the generated prediction results in a database.

[0708] Step 14:

[0709] The server analyzes the received emotional data in real time and provides feedback to the prediction results based on the user's emotional state. For example, if high concentration is detected, it provides a prediction hint.

[0710] Step 15:

[0711] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[0712] Step 16:

[0713] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[0714] Step 17:

[0715] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[0716] Step 18:

[0717] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[0718] Step 19:

[0719] The user completes the procedure to receive the benefit and receives the benefit.

[0720] In this way, viewers can enjoy professional baseball broadcasts interactively, and analysis of emotional data can provide personalized feedback to viewers, increasing their motivation to participate.

[0721] Example 2

[0722] 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."

[0723] Conventional professional baseball broadcasting systems have limited means to encourage viewer interactive participation, making it difficult to maintain interest. Furthermore, there is no feedback or reward system that takes into account viewer emotional data, meaning the viewing experience is not sufficiently personalized. This leads to a decrease in viewer satisfaction and motivation to participate, making it difficult to improve viewer ratings.

[0724] 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.

[0725] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means by which the server stores the prediction data, artificial intelligence means by which the server generates prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, emotion recognition means for analyzing viewer emotion data, and feedback means for providing feedback to predictions based on the emotion data. This makes it possible to promote interactive participation by viewers and provide a personalized viewing experience.

[0726] The "display means" is an interface that allows the viewer to check and manipulate input data.

[0727] The "transmission means" is a function for transmitting data input by the viewer to the server.

[0728] "Storage" refers to a database or storage system for recording and storing data received by the server.

[0729] "Artificial intelligence means" refers to algorithms or models that the server uses to generate predicted results based on match data and past statistical data.

[0730] The "aggregation method" is a function that calculates results based on viewers' predictions and match data, and generates rankings.

[0731] "Reward mechanism" refers to a function or system for providing rewards to viewers who perform well.

[0732] "Emotion recognition means" refers to tools and algorithms for analyzing viewer emotional data.

[0733] "Feedback measures" is a function that provides hints and advice on predictions based on viewer emotional data.

[0734] This invention is a system that allows viewers to enjoy professional baseball broadcasts in a more interactive way. This system allows viewers to predict pitching patterns during the game and receive rewards based on the results and the viewer's emotional data. Specific embodiments of this system are described below.

[0735] User Registration and Login

[0736] Device: When a user launches the app for the first time, the device displays the user registration screen. The user enters their email address, password, and username and presses the "Register" button. Existing users enter their email address and password on the login screen and press the "Login" button.

[0737] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device.

[0738] Acquiring and displaying match information

[0739] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database.

[0740] Terminal: The received game information is displayed to the user, and the user is prompted to make pitching predictions before the game begins.

[0741] Pitching predictions during the game

[0742] Device: At the start of the game, a pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0743] User: Enter and submit predicted data for each at-bat.

[0744] Device: The emotion data analyzed by the emotion engine is sent to the server along with the prediction data.

[0745] AI-powered prediction generation

[0746] Server: The server generates pitching predictions using artificial intelligence based on match data and past statistical data. The generated predictions are also stored in a database.

[0747] Emotional data analysis and reflection

[0748] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, the server will provide personalized tips and advice to the user.

[0749] Calculation and display of results after the match

[0750] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top players are determined.

[0751] Device: Receives the aggregated results and displays the rankings of top players and their individual performances. It also displays feedback based on emotional data.

[0752] Providing rewards

[0753] Server: Generates rewards (digital tickets or discount coupons) for top performers and users who exceed the predicted results, and notifies the device.

[0754] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[0755] Examples and prompts

[0756] For example, if a user predicts a "straight ball, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will receive advice based on past data as a "hint for the next prediction." Additionally, after the game, the user with the highest accuracy rate will receive a pop-up notification on their digital ticket, along with feedback based on their emotion data, such as "points to improve your prediction skills."

[0757] This system allows viewers to enjoy watching the game interactively, and improves the viewing experience by receiving rewards and feedback based on the results. Analysis of emotional data can increase user participation, which is expected to increase the number of viewers.

[0758] Example prompt sentence:

[0759] "Predict the next pitch in today's game. Choose the type and trajectory of the pitch."

[0760] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0761] Step 1: Display the user registration screen

[0762] Device: When a user launches the app for the first time, the device displays a user registration screen. Input fields include email address, password, and username. Input: App launch. Output: User registration screen loads.

[0763] Step 2: Enter your user information

[0764] User: The user enters their email address, password, and username on the registration screen and presses the "Register" button. Input: User's personal information. Output: "Register" button press event.

[0765] Step 3: Submit user information

[0766] Terminal: When the "Register" button is pressed, the terminal sends the entered user information to the server. Input: User's personal information. Output: Sends information to the server.

[0767] Step 4: Storing and authenticating user information

[0768] Server: The server saves the received user information, and if the user is an existing user, generates an authentication token and returns it to the device. Input: Sent user information. Output: Saves new user information, and if the user is an existing user, generates an authentication token.

[0769] Step 5: Get match information

[0770] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database. Input: The current day's request. Output: Match information.

[0771] Step 6: Send and view match information

[0772] Server: The server sends the acquired game information to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts. Input: Game information. Output: Sending and displaying game information.

[0773] Step 7: Input your pitching predictions

[0774] Device: When the game starts, the pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button. Input: User's prediction data. Output: Preparation for sending the prediction data.

[0775] Step 8: Analyze the sentiment data

[0776] Device: After the user presses the "Predict" button, the device's emotion engine analyzes emotion data from the user's facial expressions and voice. Input: User's facial expressions and voice data. Output: Generation of emotion data.

[0777] Step 9: Send prediction and sentiment data

[0778] Terminal: Sends prediction data and emotion data to the server. Input: Prediction data, emotion data. Output: Sends data to the server.

[0779] Step 10: Generate prediction results

[0780] Server: The server uses AI to generate pitching predictions based on match data and past statistical data, and stores them in a database. Input: Match data, statistical data. Output: Prediction results.

[0781] Step 11: Emotional Data Analysis and Feedback

[0782] Server: The server analyzes the received emotion data and provides hints and advice based on the emotional state. Input: Emotion data. Output: Personalized feedback.

[0783] Step 12: Collating and calculating grades

[0784] Server: After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the results. Input: User's predicted data, actual pitching results. Output: Calculated results.

[0785] Step 13: Displaying the results

[0786] Terminal: Receives the aggregated results from the server and displays the rankings and individual scores. It also displays feedback based on emotion data. Input: Aggregated results. Output: Display of scores and feedback.

[0787] Step 14: Offer and notify rewards

[0788] Server: The server generates rewards for top performers and users who exceed predicted results, and notifies the device. Input: Performance results. Output: Reward notification.

[0789] Step 15: Reward Collection Instructions

[0790] Terminal: Notifies the user of reward information and how to receive it, and guides them through the process of receiving the reward. Input: Reward notification. Output: Reward receipt instructions.

[0791] (Application example 2)

[0792] 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."

[0793] Systems already exist that evaluate and reward viewers' prediction skills as a way to make professional baseball broadcasts more interactive for viewers. However, these systems do not take into account viewers' emotions and are unable to provide more personalized feedback or hints based on viewers' levels of concentration and excitement. This has resulted in insufficient viewer engagement and limited improvements to the viewing experience.

[0794] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0795] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, and emotion analysis means for analyzing viewer emotional information and providing feedback. This makes it possible to provide personalized feedback and hints based on the viewer's emotional state, increasing viewer engagement and providing a richer viewing experience.

[0796] "Display means" refers to a device or interface that displays the information necessary for the viewer to input prediction data.

[0797] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[0798] "Storage means" refers to a database or storage system for storing the prediction data received by the server.

[0799] "Artificial Intelligence Means" refers to the artificial intelligence algorithms or models used by the Server to generate predicted outcomes based on Match Data.

[0800] "Counting means" refers to the calculation process or system used to compile the viewer's performance based on the predicted results and match data.

[0801] "Reward means" refers to a device or system for providing rewards to top performers.

[0802] "Emotion analysis means" refers to an analytical engine or algorithm that analyzes viewers' emotional information and reflects the results in feedback.

[0803] "Generative AI Model" refers to an artificial intelligence model used to provide viewers with personalized tips and advice.

[0804] "Feedback mechanism" refers to a system or method for providing personalized advice or tips to a viewer based on the analyzed emotional state.

[0805] The system for implementing this invention is a system that allows viewers to predict pitching patterns while watching live professional baseball games and receive rewards based on the results. This system is composed of the following main components:

[0806] User Registration and Login

[0807] On the device: When a user launches the application for the first time, they are prompted to enter their email address, password, and username. The user creates an account by entering this information. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0808] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device. This process uses Firebase Authentication and MySQL.

[0809] Acquiring and displaying match information

[0810] Server: The server retrieves information about the day of the match (team names, start time, player information, etc.) from the database. This information is sent to the device.

[0811] Terminal: The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts.

[0812] Pitching predictions during the game

[0813] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0814] User: Enter prediction data for each at-bat and submit it.

[0815] Device: Along with the prediction data, emotion data analyzed from the user's facial expressions and voice is also sent to the server. Emotion analysis is performed using Google Cloud Vision API and AWS Comprehend.

[0816] AI-powered prediction generation

[0817] Server: The server generates pitching predictions using artificial intelligence (TensorFlow) based on match data and past statistical data. The generated predictions are stored in a database.

[0818] Emotional data analysis and reflection

[0819] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized hints and advice will be provided to the user.

[0820] Calculation and display of results after the match

[0821] Server: After the game ends, the server compares all users' prediction data with the actual pitching results, calculates the results based on the accuracy rate and emotion data, and determines the top performers based on the calculation results.

[0822] Device: The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on emotion data.

[0823] Providing rewards

[0824] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[0825] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[0826] Specific examples

[0827] For example, if a user predicts "a straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[0828] Prompt Sentence Examples

[0829] "Please predict the pitching strategy for the current game. Do you think the next pitch will be a straight pitch, high and outside?"

[0830] "High concentration has been detected. Would you like to continue with your predictions?"

[0831] "Your prediction was correct! Tap here to receive your digital ticket."

[0832] This allows users to enjoy watching the game more interactively, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched.Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[0833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0834] Step 1:

[0835] When a user launches the application for the first time, the server displays a screen for entering an email address, password, and username. The user enters this information to create an account. The entered information is authenticated using Firebase Authentication, and if successful, the server generates an authentication token and saves the entered information in a MySQL database. As a result, the user's account information is saved on the server.

[0836] Step 2:

[0837] The server retrieves information about the day of the game (team names, starting time, player information, etc.) from the database and sends it to the terminal. The terminal displays this information to the user and prompts them to make a pitching prediction before the game starts. This allows the user to check the specific details of the game and prepare to make a pitching prediction.

[0838] Step 3:

[0839] When the game begins, the device displays a pitching prediction screen. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The entered prediction data is sent from the device to the server. At the same time, the device collects emotional data from the user's facial expressions and voice and sends it to the server.

[0840] Step 4:

[0841] The server stores the received prediction data and emotion data. The emotion data is analyzed using Google Cloud Vision API and AWS Comprehend. This allows the user's emotional state to be analyzed and metrics such as concentration and excitement levels to be obtained.

[0842] Step 5:

[0843] The server generates pitching predictions using artificial intelligence (TensorFlow) based on game data and past statistical data. The generated prediction results are then saved in the server's database. This ensures the generation and storage of prediction results.

[0844] Step 6:

[0845] The server analyzes the received emotional data and provides feedback to the prediction results based on the results. For example, if high concentration is recognized, personalized hints and advice will be provided to the user. This feedback information is generated using a generative AI model.

[0846] Step 7:

[0847] After the game ends, the server compares all users' prediction data with their actual pitching results, and calculates their scores based on their accuracy and emotional data. Based on the results of the calculation, the top performers are determined and this information is sent to the terminal.

[0848] Step 8:

[0849] The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on the user's emotional data.

[0850] Step 9:

[0851] The server generates rewards (e.g., digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the terminal of the reward information, thereby providing the reward information to the user.

[0852] Step 10:

[0853] The terminal notifies the user of the benefit information and how to receive it, and guides the user through the procedure for receiving the benefit. The user can receive the benefit by following the instructions.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] [Third embodiment]

[0858] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0859] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0860] 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).

[0861] 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.

[0862] 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.

[0863] 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).

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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."

[0870] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a tabulation means for tabulating the performance of viewers, and a reward means for top performers.

[0871] User Registration and Login

[0872] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[0873] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[0874] Acquiring and displaying match information

[0875] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[0876] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[0877] Pitching predictions during the game

[0878] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0879] User: Enter and submit prediction data for each at-bat.

[0880] Server: Receives the prediction data, stores it in a database, and aggregates other users' prediction data in real time.

[0881] AI-powered prediction generation

[0882] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[0883] Calculation and display of results after the match

[0884] Server: After the game ends, the predicted data of all users is compared with the actual pitching results, and the accuracy rate of each user is calculated. The top performers are determined based on the calculation results.

[0885] Terminal: Receives the aggregated results and displays the rankings of the top performers and the performance of individual users.

[0886] Providing rewards

[0887] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and notifies them to the device. Compares the AI's predictions with the user's predicted performance and provides additional rewards to users who achieve excellent results.

[0888] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0889] This system allows users to enjoy real-time pitch predictions while watching a game and earn rewards based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their own predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0890] The processing flow will be explained below.

[0891] Step 1:

[0892] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[0893] Step 2:

[0894] The terminal verifies the entered user information and sends it to the server.

[0895] Step 3:

[0896] The server stores the received user information in a database and returns a registration success notice to the terminal.

[0897] Step 4:

[0898] The device notifies the user of successful registration and transitions to the home screen.

[0899] Step 5:

[0900] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[0901] Step 6:

[0902] The server retrieves the match information from the database and sends it to the terminal.

[0903] Step 7:

[0904] The terminal displays the match information to the user and transitions to a prediction screen.

[0905] Step 8:

[0906] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[0907] Step 9:

[0908] The terminal transmits the prediction data to the server.

[0909] Step 10:

[0910] The server receives the prediction data, stores it in a database, and aggregates it with other users' prediction data in real time.

[0911] Step 11:

[0912] The server uses artificial intelligence to generate predictions based on match data and past statistical data.

[0913] Step 12:

[0914] The server stores the generated prediction results in a database.

[0915] Step 13:

[0916] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[0917] Step 14:

[0918] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[0919] Step 15:

[0920] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[0921] Step 16:

[0922] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[0923] Step 17:

[0924] The user completes the procedure to receive the benefit and receives the benefit.

[0925] In this way, viewers can enjoy live broadcasts of professional baseball games interactively.

[0926] Example 1

[0927] 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."

[0928] There is a need for an improved experience for viewers of professional baseball broadcasts, allowing them to predict pitching patterns while watching the game and receive rewards based on the results. However, existing systems do not adequately reflect viewer prediction data and game data in real time, provide highly accurate predictions using artificial intelligence, or provide appropriate evaluations of performance and rewards. Therefore, a system that increases viewer engagement and enhances the viewing experience is needed.

[0929] 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.

[0930] In this invention, the server includes display means for viewers to input predicted data, transmission means for transmitting the predicted data to the server, storage means for the server to save the predicted data, artificial intelligence means for the server to generate predicted results using artificial intelligence based on game data, aggregation means for aggregating viewer performance based on the predicted results and the game data, reward means for providing benefits to top performers, means for acquiring game information and displaying it to viewers, means for comparing the predicted data of all viewers with actual game data after the game ends, and means for displaying the viewers' predicted performance in a ranking format. This allows viewers to make pitching predictions in real time, be instantly evaluated based on the results, and receive rewards.

[0931] - "Viewers" refer to users who use this system to predict pitching patterns while watching live broadcasts of professional baseball games.

[0932] "Prediction data" refers to information about the type of pitch and pitch trajectory that viewers input when making pitching predictions.

[0933] "Display means" refers to a means for providing an interface for a viewer to input prediction data.

[0934] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[0935] "Storage means" refers to a means for the server to store the received prediction data in a database.

[0936] "Artificial Intelligence Means" refers to the artificial intelligence technology used by the Server to generate predicted outcomes based on match data.

[0937] "Counting means" refers to the means by which the server counts the viewer's performance based on the predicted results and match data.

[0938] "Reward measures" refer to measures that provide benefits to top performers.

[0939] "Match information" refers to information about the match on that day, such as team names, start time, and player information.

[0940] "Means for comparison" refers to the means for comparing and matching the predicted data of all viewers with the actual game data after the game has ended.

[0941] "Ranking display means" refers to a means for displaying the predicted performance of viewers in a ranking format.

[0942] The present invention provides a system that allows viewers of live professional baseball games to predict pitch patterns during the game and receive rewards based on the results. This system is configured to include the following various means.

[0943] User Registration and Login

[0944] When a user launches the app for the first time, a registration screen appears on the device. The user enters their email address, password, and username, and the device sends this information to the server. The server stores the received user information in a database. Also, when an existing user enters their email address and password when logging in, the server generates an authentication token and returns it to the device.

[0945] Acquiring and displaying match information

[0946] The server retrieves the game information for the day (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal receives this information, displays it to the user, and prompts them to make a pitching prediction before the game starts.

[0947] Pitching predictions during the game

[0948] When the game begins, a pitching prediction screen will be displayed on the device. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The device then sends this predicted data to the server, which then stores the received data in a database.

[0949] AI-powered prediction generation

[0950] The server uses artificial intelligence to generate pitching predictions based on match data and past statistical data, and stores the results in a database, often using frameworks such as TensorFlow or PyTorch.

[0951] Calculation and display of results after the match

[0952] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates each user's hit rate. Based on this, the server determines the top performers and sends this information to the terminal. The terminal receives the results and displays the rankings of the top performers and the performance of each user.

[0953] Providing rewards

[0954] The server generates rewards (such as digital tickets or discount coupons) for top performers and notifies the device. It also compares the AI's predictions with the user's predicted performance and provides additional rewards to users with outstanding performance. The device notifies the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[0955] This system allows viewers to enjoy real-time pitch predictions while watching a game, and they are instantly evaluated and rewarded based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[0956] Example prompts to input to a generative AI model:

[0957] 1. Please explain in detail the specific steps a user takes to use the pitching prediction feature of a professional baseball broadcasting app.

[0958] 2. Please explain in detail the program flow and process for this app, from user registration to reward provision.

[0959] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0960] Step 1: User Registration and Login

[0961] 1-1. User input - When a user launches the app for the first time, they enter their email address, password, and username in the form displayed on the registration screen. Existing users enter their email address and password on the login screen and press the "Login" button.

[0962] 1-2. Terminal processing - The input information is sent to the server. The input data is formatted as string data and sent to the server.

[0963] 1-3. Server processing - The server stores the received user information in a database. If the user is an existing user, it generates an authentication token and sends it to the terminal. The authentication token is generated in a secure format and is used to manage the user's session.

[0964] Example of specific operation: When a user enters the required information into the form and presses the "Submit" button, the terminal formats the information, encrypts it with SSL / TLS, and sends it to the server. The server verifies the received data, stores it in the database, and if the user is registered, generates and returns an authentication token.

[0965] Step 2: Retrieving and displaying match information

[0966] 2-1. Server processing - The server retrieves the match information for the day (team names, start time, player information, etc.) from the database.

[0967] 2-2. Server output - Convert the acquired match information into a data format such as JSON format and send it to the terminal.

[0968] 2-3. Terminal processing - The terminal analyzes the received game information and displays it to the user. It also displays a notification prompting the user to make a pitching prediction before the game starts.

[0969] Example of how it works: The server executes a database query to retrieve today's match information, formats it into JSON format, and sends that data to the device as an HTTP response. The device then analyzes the received data and displays it visually to the user.

[0970] Step 3: Predicting pitching patterns during the game

[0971] 3-1. Device Input - When the game starts, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[0972] 3-2. Terminal processing - Format the selected prediction data and send it to the server.

[0973] 3-3. Server Processing - The server receives the prediction data and stores it in a database, which stores all the user's prediction data.

[0974] Specific operation example: When a user inputs a pitching prediction and presses the "Predict" button, the device formats the data and sends it to the server as an HTTP request. The server receives the request and saves it in a database.

[0975] Step 4: Generate predictions with AI

[0976] 4-1. Server Input - The server receives match data and historical statistics as input.

[0977] 4-2. Server Processing - The server uses a generative AI model (e.g., TensorFlow or PyTorch) to run a pitching prediction model, which generates the statistically most likely pitching prediction from the input data.

[0978] 4-3. Server output - The generated prediction results are saved in a database.

[0979] How it works: The server retrieves match and statistical data, invokes the generative AI model, and stores the results in a database.

[0980] Step 5: Calculating and displaying results after the match

[0981] 5-1. Server processing - After the game ends, the predicted data of all viewers is compared with the actual pitching results. The accuracy rate of each user is calculated and the top performers are determined.

[0982] 5-2. Server output - Generate the resulting ranking data and send it to the terminal.

[0983] 5-3. Terminal processing - The terminal displays the received ranking data and notifies the user of their results.

[0984] Specific operation example: The server retrieves match and prediction data from the database, calculates the accuracy rate, generates a ranking, and sends it in JSON format to the device. The device receives the data and displays it visually.

[0985] Step 6: Offer rewards

[0986] 6-1. Server processing - Generate rewards (such as digital tickets or discount coupons) for top performers and notify them to the terminal. Additional rewards may also be provided.

[0987] 6-2. Server output - Generates reward information and sends it to the device.

[0988] 6-3. Terminal processing - The terminal notifies the user of the bonus information and guides them through the collection procedure.

[0989] Specific operation example: The server identifies top performers, generates rewards, and sends the information to the terminal, which displays notifications and procedures to the user.

[0990] (Application example 1)

[0991] 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."

[0992] In today's world, viewers are looking for ways to not only watch content, but also to participate interactively. However, current broadcasting and streaming services limit two-way interaction between viewers and content, making it difficult to improve the viewing experience. Furthermore, there is no mechanism in place for viewers to test their knowledge and prediction skills and receive rewards based on their results, which leads to a decline in viewer engagement.

[0993] 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.

[0994] In this invention, the server includes display means for viewers to input prediction data, communication means for transmitting the prediction data to the server, data storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on competition data, aggregation means for aggregating viewer performance based on the prediction results and the competition data, reward means for providing rewards to top performers, comparison means for the artificial intelligence means to generate prediction results based on past statistical data and compare them with the viewer's prediction results, and user interface means for users using smartphones to participate in predictions in real time. This allows viewers to participate in the content more interactively and test their own prediction skills, improving the viewing experience and increasing engagement.

[0995] "Viewers" refer to users who use content distribution services to watch matches or events.

[0996] "Prediction data" refers to prediction information such as the type of pitch and the course of the pitch entered by the viewer.

[0997] "Display means" refers to a device or software that provides an interface for viewers to input prediction data.

[0998] "Communication means" refers to the internet connection or data communication required to send the prediction data to the server.

[0999] "Data storage means" refers to a database or storage system in which the server stores prediction data.

[1000] "Sports data" refers to various data generated during a game (e.g., number of pitches, batter's performance, etc.).

[1001] "Artificial intelligence means" refers to machine learning models and algorithms used to generate predictive outcomes based on competition data.

[1002] "Prediction result" refers to prediction information generated by the artificial intelligence means based on competition data and past statistical data.

[1003] "Counting Method" means the algorithms or software used to calculate and rank viewers' performance based on predicted results and competition data.

[1004] "Reward measures" are mechanisms for providing rewards and benefits to top performers.

[1005] A "comparison means" is an algorithm or software that compares the viewer's predictions with the artificial intelligence means' predictions.

[1006] "User interface means" refers to an interface that allows viewers to participate in real-time predictions on devices such as smartphones.

[1007] The system of the present invention allows viewers to make predictions in real time and receive rewards based on the results of their predictions. This system has three main roles: a server, a terminal, and a user.

[1008] 1. Server Processing

[1009] The server is configured with the following hardware and software: The hardware used is a high-performance database server and a GPU server for AI processing, and the software used is Python, Flask, SQLAlchemy, SQLite, and machine learning libraries (TensorFlow and PyTorch).

[1010] 1.1 Data storage method

[1011] The server stores the prediction data sent by viewers in a database, which allows all predictions to be managed centrally.

[1012] 1.2 Artificial Intelligence Means

[1013] The server generates prediction results using a generative AI model based on competition data and past statistical data. This artificial intelligence method provides highly accurate prediction results.

[1014] 1.3 Means of comparison

[1015] The prediction results generated by the artificial intelligence means are compared with the prediction results of the viewers, thereby evaluating the viewers' prediction ability.

[1016] 1.4 Aggregation methods

[1017] The server will compile all the viewers' predictions and rank them, which will determine the winners.

[1018] 1.5 Remuneration Means

[1019] Generate and notify information to provide rewards and benefits to top performers.

[1020] 2. Terminal Processing

[1021] The terminal (smartphone) allows viewers to access the system through an application, which provides the following functions:

[1022] 2.1 Display means

[1023] The terminal provides a display means for the viewer to input prediction data, allowing the viewer to easily input predictions.

[1024] 2.2 Communication Methods

[1025] It has a communication means to send forecast data to the server in real time, which allows forecasts to be compiled quickly.

[1026] 2.3 User Interface Methods

[1027] The application on the terminal provides a user interface means, allowing the viewer to make predictions intuitively.

[1028] 3. User Operation

[1029] Users download the application onto their smartphones and register when they first launch it. Then, once the competition begins, they can input their prediction data from their device and make predictions in real time. If their predictions are correct, they are evaluated by the server and reflected in their final scores.

[1030] Specifically, if a user predicts a "low inside straight pitch" and is correct, the prediction will be saved in the system and later tallied, allowing viewers to test their prediction skills and receive rewards accordingly.

[1031] An example of a prompt to input to a generative AI model is as follows:

[1032] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[1033] "Pitch Type: Curve, Pitch Location: High outside"

[1034] This will allow viewers to enjoy a real-time interactive viewing experience, resulting in a system that increases engagement.

[1035] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1036] Step 1: User registration and login

[1037] When a user launches the app for the first time, the server displays a form on the device for the user to enter their email address, password, and username. When the user enters and submits this information, the server receives the information and stores it in a database. If the user is an existing user, they submit their login information and the server generates an authentication token and returns it to the device.

[1038] Input: Email address, password, username

[1039] Data processing: saving user information, generating authentication tokens when logging in

[1040] Output: Registration completion message, authentication token when logging in

[1041] Step 2: Retrieving and displaying match information

[1042] Before the game starts, the server retrieves the game information (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching pattern before the game starts.

[1043] Input: Match information

[1044] Data processing: Acquisition and transmission of match information

[1045] Output: Display match information

[1046] Step 3: Enter and submit forecast data

[1047] After the game starts, the device displays a pitching prediction screen to the user. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and submits the predicted data. The server receives this predicted data and stores it in a database.

[1048] Input: pitch type, pitching trajectory

[1049] Data processing: Saving predicted data

[1050] Output: Message that forecast data has been saved

[1051] Step 4: Generate predictions with AI

[1052] The server generates prediction results using a generative AI model based on competition data and past statistical data. The results are saved in a database. The following prompt is used:

[1053] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[1054] "Pitch Type: Curve, Pitch Location: High outside"

[1055] Input: Competition data, past statistical data

[1056] Data processing: Generate predictions using AI models and save the results

[1057] Output: Prediction results

[1058] Step 5: Compare your predictions with your audience forecasts

[1059] The server compares the AI's predictions with the viewers' predictions, and if the viewers' predictions match the AI's predictions, they are added to the aggregate data.

[1060] Input: AI prediction results, viewer prediction results

[1061] Data processing: Comparing prediction results and adding them to aggregated data

[1062] Output: Comparison results

[1063] Step 6: Counting grades

[1064] The server compares all viewers' predictions with the actual competition results and calculates the accuracy of each prediction. Based on the results, the top performers are determined and a ranking is generated.

[1065] Input: Viewer predictions, actual competition results

[1066] Data processing: Calculating hit rates and generating rankings

[1067] Output: List of top performers

[1068] Step 7: Offer Rewards

[1069] The server generates reward information for top-performing users and notifies the terminal, which then guides the user through the procedure for receiving the reward.

[1070] Input: Top performers list

[1071] Data processing: Reward information generation, reward notification

[1072] Output: Reward notification message, procedure guide

[1073] 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.

[1074] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results and the user's emotional data. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a compilation means for compiling viewer performance, a reward means for top performers, and an emotion engine that recognizes viewer emotions.

[1075] User Registration and Login

[1076] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[1077] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[1078] Acquiring and displaying match information

[1079] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[1080] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[1081] Pitching predictions during the game

[1082] Device: When the game begins, a pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. After all selections are complete, the emotion engine analyzes emotional data from the user's facial expressions and voice.

[1083] User: Enter and submit prediction data for each at-bat.

[1084] Device: Emotion data is sent to the server along with the prediction data.

[1085] AI-powered prediction generation

[1086] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[1087] Emotional data analysis and reflection

[1088] Server: Analyzes the received emotional data and provides feedback on prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized tips and advice may be provided to the user.

[1089] Calculation and display of results after the match

[1090] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top performers are determined based on the results.

[1091] Device: Receives the aggregated results and displays the rankings of top performers and the performance of individual users. It also displays feedback based on emotional data.

[1092] Providing rewards

[1093] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[1094] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[1095] Specific examples

[1096] For example, if a user predicts a "straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[1097] This system allows users to enjoy watching games in a more interactive way, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched. Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[1098] The processing flow will be explained below.

[1099] Step 1:

[1100] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[1101] Step 2:

[1102] The terminal verifies the entered user information and sends it to the server.

[1103] Step 3:

[1104] The server stores the received user information in a database and returns a registration success notice to the terminal.

[1105] Step 4:

[1106] The device notifies the user of successful registration and transitions to the home screen.

[1107] Step 5:

[1108] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[1109] Step 6:

[1110] The server retrieves the match information from the database and sends it to the terminal.

[1111] Step 7:

[1112] The terminal displays the match information to the user and transitions to a prediction screen.

[1113] Step 8:

[1114] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[1115] Step 9:

[1116] Immediately after the user presses the "predict" button, the terminal acquires the user's facial expression and voice data using an emotion engine and generates emotion data.

[1117] Step 10:

[1118] The device sends the prediction data and emotion data to the server.

[1119] Step 11:

[1120] The server receives the prediction data and emotion data, stores them in a database, and aggregates them with other users' prediction data in real time.

[1121] Step 12:

[1122] The server generates pitching predictions using artificial intelligence means based on match data and past statistical data.

[1123] Step 13:

[1124] The server stores the generated prediction results in a database.

[1125] Step 14:

[1126] The server analyzes the received emotional data in real time and provides feedback to the prediction results based on the user's emotional state. For example, if high concentration is detected, it provides a prediction hint.

[1127] Step 15:

[1128] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[1129] Step 16:

[1130] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[1131] Step 17:

[1132] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[1133] Step 18:

[1134] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[1135] Step 19:

[1136] The user completes the procedure to receive the benefit and receives the benefit.

[1137] In this way, viewers can enjoy professional baseball broadcasts interactively, and analysis of emotional data can provide personalized feedback to viewers, increasing their motivation to participate.

[1138] Example 2

[1139] 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."

[1140] Conventional professional baseball broadcasting systems have limited means to encourage viewer interactive participation, making it difficult to maintain interest. Furthermore, there is no feedback or reward system that takes into account viewer emotional data, meaning the viewing experience is not sufficiently personalized. This leads to a decrease in viewer satisfaction and motivation to participate, making it difficult to improve viewer ratings.

[1141] 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.

[1142] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means by which the server stores the prediction data, artificial intelligence means by which the server generates prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, emotion recognition means for analyzing viewer emotion data, and feedback means for providing feedback to predictions based on the emotion data. This makes it possible to promote interactive participation by viewers and provide a personalized viewing experience.

[1143] The "display means" is an interface that allows the viewer to check and manipulate input data.

[1144] The "transmission means" is a function for transmitting data input by the viewer to the server.

[1145] "Storage" refers to a database or storage system for recording and storing data received by the server.

[1146] "Artificial intelligence means" refers to algorithms or models that the server uses to generate predicted results based on match data and past statistical data.

[1147] The "aggregation method" is a function that calculates results based on viewers' predictions and match data, and generates rankings.

[1148] "Reward mechanism" refers to a function or system for providing rewards to viewers who perform well.

[1149] "Emotion recognition means" refers to tools and algorithms for analyzing viewer emotional data.

[1150] "Feedback measures" is a function that provides hints and advice on predictions based on viewer emotional data.

[1151] This invention is a system that allows viewers to enjoy professional baseball broadcasts in a more interactive way. This system allows viewers to predict pitching patterns during the game and receive rewards based on the results and the viewer's emotional data. Specific embodiments of this system are described below.

[1152] User Registration and Login

[1153] Device: When a user launches the app for the first time, the device displays the user registration screen. The user enters their email address, password, and username and presses the "Register" button. Existing users enter their email address and password on the login screen and press the "Login" button.

[1154] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device.

[1155] Acquiring and displaying match information

[1156] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database.

[1157] Terminal: The received game information is displayed to the user, and the user is prompted to make pitching predictions before the game begins.

[1158] Pitching predictions during the game

[1159] Device: At the start of the game, a pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1160] User: Enter and submit predicted data for each at-bat.

[1161] Device: The emotion data analyzed by the emotion engine is sent to the server along with the prediction data.

[1162] AI-powered prediction generation

[1163] Server: The server generates pitching predictions using artificial intelligence based on match data and past statistical data. The generated predictions are also stored in a database.

[1164] Emotional data analysis and reflection

[1165] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, the server will provide personalized tips and advice to the user.

[1166] Calculation and display of results after the match

[1167] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top players are determined.

[1168] Device: Receives the aggregated results and displays the rankings of top players and their individual performances. It also displays feedback based on emotional data.

[1169] Providing rewards

[1170] Server: Generates rewards (digital tickets or discount coupons) for top performers and users who exceed the predicted results, and notifies the device.

[1171] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[1172] Examples and prompts

[1173] For example, if a user predicts a "straight ball, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will receive advice based on past data as a "hint for the next prediction." Additionally, after the game, the user with the highest accuracy rate will receive a pop-up notification on their digital ticket, along with feedback based on their emotion data, such as "points to improve your prediction skills."

[1174] This system allows viewers to enjoy watching the game interactively, and improves the viewing experience by receiving rewards and feedback based on the results. Analysis of emotional data can increase user participation, which is expected to increase the number of viewers.

[1175] Example prompt sentence:

[1176] "Predict the next pitch in today's game. Choose the type and trajectory of the pitch."

[1177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1178] Step 1: Display the user registration screen

[1179] Device: When a user launches the app for the first time, the device displays a user registration screen. Input fields include email address, password, and username. Input: App launch. Output: User registration screen loads.

[1180] Step 2: Enter your user information

[1181] User: The user enters their email address, password, and username on the registration screen and presses the "Register" button. Input: User's personal information. Output: "Register" button press event.

[1182] Step 3: Submit user information

[1183] Terminal: When the "Register" button is pressed, the terminal sends the entered user information to the server. Input: User's personal information. Output: Sends information to the server.

[1184] Step 4: Storing and authenticating user information

[1185] Server: The server saves the received user information, and if the user is an existing user, generates an authentication token and returns it to the device. Input: Sent user information. Output: Saves new user information, and if the user is an existing user, generates an authentication token.

[1186] Step 5: Get match information

[1187] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database. Input: The current day's request. Output: Match information.

[1188] Step 6: Send and view match information

[1189] Server: The server sends the acquired game information to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts. Input: Game information. Output: Sending and displaying game information.

[1190] Step 7: Input your pitching predictions

[1191] Device: When the game starts, the pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button. Input: User's prediction data. Output: Preparation for sending the prediction data.

[1192] Step 8: Analyze the sentiment data

[1193] Device: After the user presses the "Predict" button, the device's emotion engine analyzes emotion data from the user's facial expressions and voice. Input: User's facial expressions and voice data. Output: Generation of emotion data.

[1194] Step 9: Send prediction and sentiment data

[1195] Terminal: Sends prediction data and emotion data to the server. Input: Prediction data, emotion data. Output: Sends data to the server.

[1196] Step 10: Generate prediction results

[1197] Server: The server uses AI to generate pitching predictions based on match data and past statistical data, and stores them in a database. Input: Match data, statistical data. Output: Prediction results.

[1198] Step 11: Emotional Data Analysis and Feedback

[1199] Server: The server analyzes the received emotion data and provides hints and advice based on the emotional state. Input: Emotion data. Output: Personalized feedback.

[1200] Step 12: Collating and calculating grades

[1201] Server: After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the results. Input: User's predicted data, actual pitching results. Output: Calculated results.

[1202] Step 13: Displaying the results

[1203] Terminal: Receives the aggregated results from the server and displays the rankings and individual scores. It also displays feedback based on emotion data. Input: Aggregated results. Output: Display of scores and feedback.

[1204] Step 14: Offer and notify rewards

[1205] Server: The server generates rewards for top performers and users who exceed predicted results, and notifies the device. Input: Performance results. Output: Reward notification.

[1206] Step 15: Reward Collection Instructions

[1207] Terminal: Notifies the user of reward information and how to receive it, and guides them through the process of receiving the reward. Input: Reward notification. Output: Reward receipt instructions.

[1208] (Application example 2)

[1209] 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."

[1210] Systems already exist that evaluate and reward viewers' prediction skills as a way to make professional baseball broadcasts more interactive for viewers. However, these systems do not take into account viewers' emotions and are unable to provide more personalized feedback or hints based on viewers' levels of concentration and excitement. This has resulted in insufficient viewer engagement and limited improvements to the viewing experience.

[1211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1212] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, and emotion analysis means for analyzing viewer emotional information and providing feedback. This makes it possible to provide personalized feedback and hints based on the viewer's emotional state, increasing viewer engagement and providing a richer viewing experience.

[1213] "Display means" refers to a device or interface that displays the information necessary for the viewer to input prediction data.

[1214] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[1215] "Storage means" refers to a database or storage system for storing the prediction data received by the server.

[1216] "Artificial Intelligence Means" refers to the artificial intelligence algorithms or models used by the Server to generate predicted outcomes based on Match Data.

[1217] "Counting means" refers to the calculation process or system used to compile the viewer's performance based on the predicted results and match data.

[1218] "Reward means" refers to a device or system for providing rewards to top performers.

[1219] "Emotion analysis means" refers to an analytical engine or algorithm that analyzes viewers' emotional information and reflects the results in feedback.

[1220] "Generative AI Model" refers to an artificial intelligence model used to provide viewers with personalized tips and advice.

[1221] "Feedback mechanism" refers to a system or method for providing personalized advice or tips to a viewer based on the analyzed emotional state.

[1222] The system for implementing this invention is a system that allows viewers to predict pitching patterns while watching live professional baseball games and receive rewards based on the results. This system is composed of the following main components:

[1223] User Registration and Login

[1224] On the device: When a user launches the application for the first time, they are prompted to enter their email address, password, and username. The user creates an account by entering this information. Existing users can enter their email address and password on the login screen and press the "Login" button.

[1225] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device. This process uses Firebase Authentication and MySQL.

[1226] Acquiring and displaying match information

[1227] Server: The server retrieves information about the day of the match (team names, start time, player information, etc.) from the database. This information is sent to the device.

[1228] Terminal: The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts.

[1229] Pitching predictions during the game

[1230] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1231] User: Enter prediction data for each at-bat and submit it.

[1232] Device: Along with the prediction data, emotion data analyzed from the user's facial expressions and voice is also sent to the server. Emotion analysis is performed using Google Cloud Vision API and AWS Comprehend.

[1233] AI-powered prediction generation

[1234] Server: The server generates pitching predictions using artificial intelligence (TensorFlow) based on match data and past statistical data. The generated predictions are stored in a database.

[1235] Emotional data analysis and reflection

[1236] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized hints and advice will be provided to the user.

[1237] Calculation and display of results after the match

[1238] Server: After the game ends, the server compares all users' prediction data with the actual pitching results, calculates the results based on the accuracy rate and emotion data, and determines the top performers based on the calculation results.

[1239] Device: The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on emotion data.

[1240] Providing rewards

[1241] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[1242] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[1243] Specific examples

[1244] For example, if a user predicts "a straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[1245] Prompt Sentence Examples

[1246] "Please predict the pitching strategy for the current game. Do you think the next pitch will be a straight pitch, high and outside?"

[1247] "High concentration has been detected. Would you like to continue with your predictions?"

[1248] "Your prediction was correct! Tap here to receive your digital ticket."

[1249] This allows users to enjoy watching the game more interactively, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched.Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[1250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1251] Step 1:

[1252] When a user launches the application for the first time, the server displays a screen for entering an email address, password, and username. The user enters this information to create an account. The entered information is authenticated using Firebase Authentication, and if successful, the server generates an authentication token and saves the entered information in a MySQL database. As a result, the user's account information is saved on the server.

[1253] Step 2:

[1254] The server retrieves information about the day of the game (team names, starting time, player information, etc.) from the database and sends it to the terminal. The terminal displays this information to the user and prompts them to make a pitching prediction before the game starts. This allows the user to check the specific details of the game and prepare to make a pitching prediction.

[1255] Step 3:

[1256] When the game begins, the device displays a pitching prediction screen. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The entered prediction data is sent from the device to the server. At the same time, the device collects emotional data from the user's facial expressions and voice and sends it to the server.

[1257] Step 4:

[1258] The server stores the received prediction data and emotion data. The emotion data is analyzed using Google Cloud Vision API and AWS Comprehend. This allows the user's emotional state to be analyzed and metrics such as concentration and excitement levels to be obtained.

[1259] Step 5:

[1260] The server generates pitching predictions using artificial intelligence (TensorFlow) based on game data and past statistical data. The generated prediction results are then saved in the server's database. This ensures the generation and storage of prediction results.

[1261] Step 6:

[1262] The server analyzes the received emotional data and provides feedback to the prediction results based on the results. For example, if high concentration is recognized, personalized hints and advice will be provided to the user. This feedback information is generated using a generative AI model.

[1263] Step 7:

[1264] After the game ends, the server compares all users' prediction data with their actual pitching results, and calculates their scores based on their accuracy and emotional data. Based on the results of the calculation, the top performers are determined and this information is sent to the terminal.

[1265] Step 8:

[1266] The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on the user's emotional data.

[1267] Step 9:

[1268] The server generates rewards (e.g., digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the terminal of the reward information, thereby providing the reward information to the user.

[1269] Step 10:

[1270] The terminal notifies the user of the benefit information and how to receive it, and guides the user through the procedure for receiving the benefit. The user can receive the benefit by following the instructions.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] [Fourth embodiment]

[1275] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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."

[1288] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a tabulation means for tabulating the performance of viewers, and a reward means for top performers.

[1289] User Registration and Login

[1290] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[1291] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[1292] Acquiring and displaying match information

[1293] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[1294] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[1295] Pitching predictions during the game

[1296] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1297] User: Enter and submit prediction data for each at-bat.

[1298] Server: Receives the prediction data, stores it in a database, and aggregates other users' prediction data in real time.

[1299] AI-powered prediction generation

[1300] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[1301] Calculation and display of results after the match

[1302] Server: After the game ends, the predicted data of all users is compared with the actual pitching results, and the accuracy rate of each user is calculated. The top performers are determined based on the calculation results.

[1303] Terminal: Receives the aggregated results and displays the rankings of the top performers and the performance of individual users.

[1304] Providing rewards

[1305] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and notifies them to the device. Compares the AI's predictions with the user's predicted performance and provides additional rewards to users who achieve excellent results.

[1306] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[1307] This system allows users to enjoy real-time pitch predictions while watching a game and earn rewards based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their own predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[1308] The processing flow will be explained below.

[1309] Step 1:

[1310] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[1311] Step 2:

[1312] The terminal verifies the entered user information and sends it to the server.

[1313] Step 3:

[1314] The server stores the received user information in a database and returns a registration success notice to the terminal.

[1315] Step 4:

[1316] The device notifies the user of successful registration and transitions to the home screen.

[1317] Step 5:

[1318] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[1319] Step 6:

[1320] The server retrieves the match information from the database and sends it to the terminal.

[1321] Step 7:

[1322] The terminal displays the match information to the user and transitions to a prediction screen.

[1323] Step 8:

[1324] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[1325] Step 9:

[1326] The terminal transmits the prediction data to the server.

[1327] Step 10:

[1328] The server receives the prediction data, stores it in a database, and aggregates it with other users' prediction data in real time.

[1329] Step 11:

[1330] The server uses artificial intelligence to generate predictions based on match data and past statistical data.

[1331] Step 12:

[1332] The server stores the generated prediction results in a database.

[1333] Step 13:

[1334] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[1335] Step 14:

[1336] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[1337] Step 15:

[1338] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[1339] Step 16:

[1340] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[1341] Step 17:

[1342] The user completes the procedure to receive the benefit and receives the benefit.

[1343] In this way, viewers can enjoy live broadcasts of professional baseball games interactively.

[1344] Example 1

[1345] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1346] There is a need for an improved experience for viewers of professional baseball broadcasts, allowing them to predict pitching patterns while watching the game and receive rewards based on the results. However, existing systems do not adequately reflect viewer prediction data and game data in real time, provide highly accurate predictions using artificial intelligence, or provide appropriate evaluations of performance and rewards. Therefore, a system that increases viewer engagement and enhances the viewing experience is needed.

[1347] 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.

[1348] In this invention, the server includes display means for viewers to input predicted data, transmission means for transmitting the predicted data to the server, storage means for the server to save the predicted data, artificial intelligence means for the server to generate predicted results using artificial intelligence based on game data, aggregation means for aggregating viewer performance based on the predicted results and the game data, reward means for providing benefits to top performers, means for acquiring game information and displaying it to viewers, means for comparing the predicted data of all viewers with actual game data after the game ends, and means for displaying the viewers' predicted performance in a ranking format. This allows viewers to make pitching predictions in real time, be instantly evaluated based on the results, and receive rewards.

[1349] - "Viewers" refer to users who use this system to predict pitching patterns while watching live broadcasts of professional baseball games.

[1350] "Prediction data" refers to information about the type of pitch and pitch trajectory that viewers input when making pitching predictions.

[1351] "Display means" refers to a means for providing an interface for a viewer to input prediction data.

[1352] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[1353] "Storage means" refers to a means for the server to store the received prediction data in a database.

[1354] "Artificial Intelligence Means" refers to the artificial intelligence technology used by the Server to generate predicted outcomes based on match data.

[1355] "Counting means" refers to the means by which the server counts the viewer's performance based on the predicted results and match data.

[1356] "Reward measures" refer to measures that provide benefits to top performers.

[1357] "Match information" refers to information about the match on that day, such as team names, start time, and player information.

[1358] "Means for comparison" refers to the means for comparing and matching the predicted data of all viewers with the actual game data after the game has ended.

[1359] "Ranking display means" refers to a means for displaying the predicted performance of viewers in a ranking format.

[1360] The present invention provides a system that allows viewers of live professional baseball games to predict pitch patterns during the game and receive rewards based on the results. This system is configured to include the following various means.

[1361] User Registration and Login

[1362] When a user launches the app for the first time, a registration screen appears on the device. The user enters their email address, password, and username, and the device sends this information to the server. The server stores the received user information in a database. Also, when an existing user enters their email address and password when logging in, the server generates an authentication token and returns it to the device.

[1363] Acquiring and displaying match information

[1364] The server retrieves the game information for the day (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal receives this information, displays it to the user, and prompts them to make a pitching prediction before the game starts.

[1365] Pitching predictions during the game

[1366] When the game begins, a pitching prediction screen will be displayed on the device. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The device then sends this predicted data to the server, which then stores the received data in a database.

[1367] AI-powered prediction generation

[1368] The server uses artificial intelligence to generate pitching predictions based on match data and past statistical data, and stores the results in a database, often using frameworks such as TensorFlow or PyTorch.

[1369] Calculation and display of results after the match

[1370] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates each user's hit rate. Based on this, the server determines the top performers and sends this information to the terminal. The terminal receives the results and displays the rankings of the top performers and the performance of each user.

[1371] Providing rewards

[1372] The server generates rewards (such as digital tickets or discount coupons) for top performers and notifies the device. It also compares the AI's predictions with the user's predicted performance and provides additional rewards to users with outstanding performance. The device notifies the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[1373] This system allows viewers to enjoy real-time pitch predictions while watching a game, and they are instantly evaluated and rewarded based on the results. This increases viewer engagement and further enhances the viewing experience. Furthermore, by comparing their predictions with those made by AI, viewers are more motivated to participate, which is expected to lead to an increase in viewership.

[1374] Example prompts to input to a generative AI model:

[1375] 1. Please explain in detail the specific steps a user takes to use the pitching prediction feature of a professional baseball broadcasting app.

[1376] 2. Please explain in detail the program flow and process for this app, from user registration to reward provision.

[1377] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1378] Step 1: User Registration and Login

[1379] 1-1. User input - When a user launches the app for the first time, they enter their email address, password, and username in the form displayed on the registration screen. Existing users enter their email address and password on the login screen and press the "Login" button.

[1380] 1-2. Terminal processing - The input information is sent to the server. The input data is formatted as string data and sent to the server.

[1381] 1-3. Server processing - The server stores the received user information in a database. If the user is an existing user, it generates an authentication token and sends it to the terminal. The authentication token is generated in a secure format and is used to manage the user's session.

[1382] Example of specific operation: When a user enters the required information into the form and presses the "Submit" button, the terminal formats the information, encrypts it with SSL / TLS, and sends it to the server. The server verifies the received data, stores it in the database, and if the user is registered, generates and returns an authentication token.

[1383] Step 2: Retrieving and displaying match information

[1384] 2-1. Server processing - The server retrieves the match information for the day (team names, start time, player information, etc.) from the database.

[1385] 2-2. Server output - Convert the acquired match information into a data format such as JSON format and send it to the terminal.

[1386] 2-3. Terminal processing - The terminal analyzes the received game information and displays it to the user. It also displays a notification prompting the user to make a pitching prediction before the game starts.

[1387] Example of how it works: The server executes a database query to retrieve today's match information, formats it into JSON format, and sends that data to the device as an HTTP response. The device then analyzes the received data and displays it visually to the user.

[1388] Step 3: Predicting pitching patterns during the game

[1389] 3-1. Device Input - When the game starts, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1390] 3-2. Terminal processing - Format the selected prediction data and send it to the server.

[1391] 3-3. Server Processing - The server receives the prediction data and stores it in a database, which stores all the user's prediction data.

[1392] Specific operation example: When a user inputs a pitching prediction and presses the "Predict" button, the device formats the data and sends it to the server as an HTTP request. The server receives the request and saves it in a database.

[1393] Step 4: Generate predictions with AI

[1394] 4-1. Server Input - The server receives match data and historical statistics as input.

[1395] 4-2. Server Processing - The server uses a generative AI model (e.g., TensorFlow or PyTorch) to run a pitching prediction model, which generates the statistically most likely pitching prediction from the input data.

[1396] 4-3. Server output - The generated prediction results are saved in a database.

[1397] How it works: The server retrieves match and statistical data, invokes the generative AI model, and stores the results in a database.

[1398] Step 5: Calculating and displaying results after the match

[1399] 5-1. Server processing - After the game ends, the predicted data of all viewers is compared with the actual pitching results. The accuracy rate of each user is calculated and the top performers are determined.

[1400] 5-2. Server output - Generate the resulting ranking data and send it to the terminal.

[1401] 5-3. Terminal processing - The terminal displays the received ranking data and notifies the user of their results.

[1402] Specific operation example: The server retrieves match and prediction data from the database, calculates the accuracy rate, generates a ranking, and sends it in JSON format to the device. The device receives the data and displays it visually.

[1403] Step 6: Offer rewards

[1404] 6-1. Server processing - Generate rewards (such as digital tickets or discount coupons) for top performers and notify them to the terminal. Additional rewards may also be provided.

[1405] 6-2. Server output - Generates reward information and sends it to the device.

[1406] 6-3. Terminal processing - The terminal notifies the user of the bonus information and guides them through the collection procedure.

[1407] Specific operation example: The server identifies top performers, generates rewards, and sends the information to the terminal, which displays notifications and procedures to the user.

[1408] (Application example 1)

[1409] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1410] In today's world, viewers are looking for ways to not only watch content, but also to participate interactively. However, current broadcasting and streaming services limit two-way interaction between viewers and content, making it difficult to improve the viewing experience. Furthermore, there is no mechanism in place for viewers to test their knowledge and prediction skills and receive rewards based on their results, which leads to a decline in viewer engagement.

[1411] 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.

[1412] In this invention, the server includes display means for viewers to input prediction data, communication means for transmitting the prediction data to the server, data storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on competition data, aggregation means for aggregating viewer performance based on the prediction results and the competition data, reward means for providing rewards to top performers, comparison means for the artificial intelligence means to generate prediction results based on past statistical data and compare them with the viewer's prediction results, and user interface means for users using smartphones to participate in predictions in real time. This allows viewers to participate in the content more interactively and test their own prediction skills, improving the viewing experience and increasing engagement.

[1413] "Viewers" refer to users who use content distribution services to watch matches or events.

[1414] "Prediction data" refers to prediction information such as the type of pitch and the course of the pitch entered by the viewer.

[1415] "Display means" refers to a device or software that provides an interface for viewers to input prediction data.

[1416] "Communication means" refers to the internet connection or data communication required to send the prediction data to the server.

[1417] "Data storage means" refers to a database or storage system in which the server stores prediction data.

[1418] "Sports data" refers to various data generated during a game (e.g., number of pitches, batter's performance, etc.).

[1419] "Artificial intelligence means" refers to machine learning models and algorithms used to generate predictive outcomes based on competition data.

[1420] "Prediction result" refers to prediction information generated by the artificial intelligence means based on competition data and past statistical data.

[1421] "Counting Method" means the algorithms or software used to calculate and rank viewers' performance based on predicted results and competition data.

[1422] "Reward measures" are mechanisms for providing rewards and benefits to top performers.

[1423] A "comparison means" is an algorithm or software that compares the viewer's predictions with the artificial intelligence means' predictions.

[1424] "User interface means" refers to an interface that allows viewers to participate in real-time predictions on devices such as smartphones.

[1425] The system of the present invention allows viewers to make predictions in real time and receive rewards based on the results of their predictions. This system has three main roles: a server, a terminal, and a user.

[1426] 1. Server Processing

[1427] The server is configured with the following hardware and software: The hardware used is a high-performance database server and a GPU server for AI processing, and the software used is Python, Flask, SQLAlchemy, SQLite, and machine learning libraries (TensorFlow and PyTorch).

[1428] 1.1 Data storage method

[1429] The server stores the prediction data sent by viewers in a database, which allows all predictions to be managed centrally.

[1430] 1.2 Artificial Intelligence Means

[1431] The server generates prediction results using a generative AI model based on competition data and past statistical data. This artificial intelligence method provides highly accurate prediction results.

[1432] 1.3 Means of comparison

[1433] The prediction results generated by the artificial intelligence means are compared with the prediction results of the viewers, thereby evaluating the viewers' prediction ability.

[1434] 1.4 Aggregation methods

[1435] The server will compile all the viewers' predictions and rank them, which will determine the winners.

[1436] 1.5 Remuneration Means

[1437] Generate and notify information to provide rewards and benefits to top performers.

[1438] 2. Terminal Processing

[1439] The terminal (smartphone) allows viewers to access the system through an application, which provides the following functions:

[1440] 2.1 Display means

[1441] The terminal provides a display means for the viewer to input prediction data, allowing the viewer to easily input predictions.

[1442] 2.2 Communication Methods

[1443] It has a communication means to send forecast data to the server in real time, which allows forecasts to be compiled quickly.

[1444] 2.3 User Interface Methods

[1445] The application on the terminal provides a user interface means, allowing the viewer to make predictions intuitively.

[1446] 3. User Operation

[1447] Users download the application onto their smartphones and register when they first launch it. Then, once the competition begins, they can input their prediction data from their device and make predictions in real time. If their predictions are correct, they are evaluated by the server and reflected in their final scores.

[1448] Specifically, if a user predicts a "low inside straight pitch" and is correct, the prediction will be saved in the system and later tallied, allowing viewers to test their prediction skills and receive rewards accordingly.

[1449] An example of a prompt to input to a generative AI model is as follows:

[1450] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[1451] "Pitch Type: Curve, Pitch Location: High outside"

[1452] This will allow viewers to enjoy a real-time interactive viewing experience, resulting in a system that increases engagement.

[1453] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1454] Step 1: User registration and login

[1455] When a user launches the app for the first time, the server displays a form on the device for the user to enter their email address, password, and username. When the user enters and submits this information, the server receives the information and stores it in a database. If the user is an existing user, they submit their login information and the server generates an authentication token and returns it to the device.

[1456] Input: Email address, password, username

[1457] Data processing: saving user information, generating authentication tokens when logging in

[1458] Output: Registration completion message, authentication token when logging in

[1459] Step 2: Retrieving and displaying match information

[1460] Before the game starts, the server retrieves the game information (team names, start time, player information, etc.) from the database and sends it to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching pattern before the game starts.

[1461] Input: Match information

[1462] Data processing: Acquisition and transmission of match information

[1463] Output: Display match information

[1464] Step 3: Enter and submit forecast data

[1465] After the game starts, the device displays a pitching prediction screen to the user. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and submits the predicted data. The server receives this predicted data and stores it in a database.

[1466] Input: pitch type, pitching trajectory

[1467] Data processing: Saving predicted data

[1468] Output: Message that forecast data has been saved

[1469] Step 4: Generate predictions with AI

[1470] The server generates prediction results using a generative AI model based on competition data and past statistical data. The results are saved in a database. The following prompt is used:

[1471] Based on the pitching data from the past season, please predict the pitch that the pitcher will use against the next batter. Please output the results in the following format:

[1472] "Pitch Type: Curve, Pitch Location: High outside"

[1473] Input: Competition data, past statistical data

[1474] Data processing: Generate predictions using AI models and save the results

[1475] Output: Prediction results

[1476] Step 5: Compare your predictions with your audience forecasts

[1477] The server compares the AI's predictions with the viewers' predictions, and if the viewers' predictions match the AI's predictions, they are added to the aggregate data.

[1478] Input: AI prediction results, viewer prediction results

[1479] Data processing: Comparing prediction results and adding them to aggregated data

[1480] Output: Comparison results

[1481] Step 6: Counting grades

[1482] The server compares all viewers' predictions with the actual competition results and calculates the accuracy of each prediction. Based on the results, the top performers are determined and a ranking is generated.

[1483] Input: Viewer predictions, actual competition results

[1484] Data processing: Calculating hit rates and generating rankings

[1485] Output: List of top performers

[1486] Step 7: Offer Rewards

[1487] The server generates reward information for top-performing users and notifies the terminal, which then guides the user through the procedure for receiving the reward.

[1488] Input: Top performers list

[1489] Data processing: Reward information generation, reward notification

[1490] Output: Reward notification message, procedure guide

[1491] 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.

[1492] The present invention provides a system that allows users watching live professional baseball games to predict pitching patterns during the game and receive rewards based on the results and the user's emotional data. This system includes a display means for inputting predicted data, a transmission means for transmitting the data to a server, a storage means for the predicted data by the server, an artificial intelligence means based on game data, a compilation means for compiling viewer performance, a reward means for top performers, and an emotion engine that recognizes viewer emotions.

[1493] User Registration and Login

[1494] On the device: When a user launches the app for the first time, a registration screen appears. The registration screen contains a form for entering an email address, password, and username. Existing users can enter their email address and password on the login screen and press the "Login" button.

[1495] Server: Receives the entered user information and stores it in the database. If the user information already exists, generates an authentication token and returns it to the device.

[1496] Acquiring and displaying match information

[1497] Server: Retrieves the current day's match information (team names, start time, player information, etc.) from the database. Sends this information to the device.

[1498] Terminal: The received game information is displayed to the user, and the user is prompted to make a pitching prediction before the game starts.

[1499] Pitching predictions during the game

[1500] Device: When the game begins, a pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. After all selections are complete, the emotion engine analyzes emotional data from the user's facial expressions and voice.

[1501] User: Enter and submit prediction data for each at-bat.

[1502] Device: Emotion data is sent to the server along with the prediction data.

[1503] AI-powered prediction generation

[1504] Server: Based on match data and past statistical data, pitching predictions are generated using artificial intelligence. The generated prediction results are also stored in a database.

[1505] Emotional data analysis and reflection

[1506] Server: Analyzes the received emotional data and provides feedback on prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized tips and advice may be provided to the user.

[1507] Calculation and display of results after the match

[1508] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top performers are determined based on the results.

[1509] Device: Receives the aggregated results and displays the rankings of top performers and the performance of individual users. It also displays feedback based on emotional data.

[1510] Providing rewards

[1511] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[1512] Terminal: Informs the user of the reward information and how to receive it, and guides them through the process of actually receiving the reward.

[1513] Specific examples

[1514] For example, if a user predicts a "straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[1515] This system allows users to enjoy watching games in a more interactive way, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched. Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[1516] The processing flow will be explained below.

[1517] Step 1:

[1518] When the user launches the app for the first time, they enter their email address, password, and username on the registration screen and press the "Register" button.

[1519] Step 2:

[1520] The terminal verifies the entered user information and sends it to the server.

[1521] Step 3:

[1522] The server stores the received user information in a database and returns a registration success notice to the terminal.

[1523] Step 4:

[1524] The device notifies the user of successful registration and transitions to the home screen.

[1525] Step 5:

[1526] On the day of the match, the terminal displays match information (for example, team names, start time, player information) to encourage the user to prepare for predictions.

[1527] Step 6:

[1528] The server retrieves the match information from the database and sends it to the terminal.

[1529] Step 7:

[1530] The terminal displays the match information to the user and transitions to a prediction screen.

[1531] Step 8:

[1532] During a game, the user inputs predicted data (such as pitch type and pitch trajectory) for each turn at bat and presses the "Predict" button.

[1533] Step 9:

[1534] Immediately after the user presses the "predict" button, the terminal acquires the user's facial expression and voice data using an emotion engine and generates emotion data.

[1535] Step 10:

[1536] The device sends the prediction data and emotion data to the server.

[1537] Step 11:

[1538] The server receives the prediction data and emotion data, stores them in a database, and aggregates them with other users' prediction data in real time.

[1539] Step 12:

[1540] The server generates pitching predictions using artificial intelligence means based on match data and past statistical data.

[1541] Step 13:

[1542] The server stores the generated prediction results in a database.

[1543] Step 14:

[1544] The server analyzes the received emotional data in real time and provides feedback to the prediction results based on the user's emotional state. For example, if high concentration is detected, it provides a prediction hint.

[1545] Step 15:

[1546] After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the accuracy rate for each.

[1547] Step 16:

[1548] The server determines the top performers based on the calculation results, and also compares them with the predictions of the artificial intelligence.

[1549] Step 17:

[1550] The server generates rewards for top performers and users who exceed the artificial intelligence's predictions, and notifies the terminal.

[1551] Step 18:

[1552] The terminal displays the benefit information and the method of receiving it to the user, and guides the user through the procedure for receiving the benefit.

[1553] Step 19:

[1554] The user completes the procedure to receive the benefit and receives the benefit.

[1555] In this way, viewers can enjoy professional baseball broadcasts interactively, and analysis of emotional data can provide personalized feedback to viewers, increasing their motivation to participate.

[1556] Example 2

[1557] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1558] Conventional professional baseball broadcasting systems have limited means to encourage viewer interactive participation, making it difficult to maintain interest. Furthermore, there is no feedback or reward system that takes into account viewer emotional data, meaning the viewing experience is not sufficiently personalized. This leads to a decrease in viewer satisfaction and motivation to participate, making it difficult to improve viewer ratings.

[1559] 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.

[1560] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means by which the server stores the prediction data, artificial intelligence means by which the server generates prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, emotion recognition means for analyzing viewer emotion data, and feedback means for providing feedback to predictions based on the emotion data. This makes it possible to promote interactive participation by viewers and provide a personalized viewing experience.

[1561] The "display means" is an interface that allows the viewer to check and manipulate input data.

[1562] The "transmission means" is a function for transmitting data input by the viewer to the server.

[1563] "Storage" refers to a database or storage system for recording and storing data received by the server.

[1564] "Artificial intelligence means" refers to algorithms or models that the server uses to generate predicted results based on match data and past statistical data.

[1565] The "aggregation method" is a function that calculates results based on viewers' predictions and match data, and generates rankings.

[1566] "Reward mechanism" refers to a function or system for providing rewards to viewers who perform well.

[1567] "Emotion recognition means" refers to tools and algorithms for analyzing viewer emotional data.

[1568] "Feedback measures" is a function that provides hints and advice on predictions based on viewer emotional data.

[1569] This invention is a system that allows viewers to enjoy professional baseball broadcasts in a more interactive way. This system allows viewers to predict pitching patterns during the game and receive rewards based on the results and the viewer's emotional data. Specific embodiments of this system are described below.

[1570] User Registration and Login

[1571] Device: When a user launches the app for the first time, the device displays the user registration screen. The user enters their email address, password, and username and presses the "Register" button. Existing users enter their email address and password on the login screen and press the "Login" button.

[1572] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device.

[1573] Acquiring and displaying match information

[1574] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database.

[1575] Terminal: The received game information is displayed to the user, and the user is prompted to make pitching predictions before the game begins.

[1576] Pitching predictions during the game

[1577] Device: At the start of the game, a pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1578] User: Enter and submit predicted data for each at-bat.

[1579] Device: The emotion data analyzed by the emotion engine is sent to the server along with the prediction data.

[1580] AI-powered prediction generation

[1581] Server: The server generates pitching predictions using artificial intelligence based on match data and past statistical data. The generated predictions are also stored in a database.

[1582] Emotional data analysis and reflection

[1583] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, the server will provide personalized tips and advice to the user.

[1584] Calculation and display of results after the match

[1585] Server: After the game ends, all users' prediction data is compared with the actual pitching results, and the results are calculated based on their accuracy and emotional data. The top players are determined.

[1586] Device: Receives the aggregated results and displays the rankings of top players and their individual performances. It also displays feedback based on emotional data.

[1587] Providing rewards

[1588] Server: Generates rewards (digital tickets or discount coupons) for top performers and users who exceed the predicted results, and notifies the device.

[1589] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[1590] Examples and prompts

[1591] For example, if a user predicts a "straight ball, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will receive advice based on past data as a "hint for the next prediction." Additionally, after the game, the user with the highest accuracy rate will receive a pop-up notification on their digital ticket, along with feedback based on their emotion data, such as "points to improve your prediction skills."

[1592] This system allows viewers to enjoy watching the game interactively, and improves the viewing experience by receiving rewards and feedback based on the results. Analysis of emotional data can increase user participation, which is expected to increase the number of viewers.

[1593] Example prompt sentence:

[1594] "Predict the next pitch in today's game. Choose the type and trajectory of the pitch."

[1595] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1596] Step 1: Display the user registration screen

[1597] Device: When a user launches the app for the first time, the device displays a user registration screen. Input fields include email address, password, and username. Input: App launch. Output: User registration screen loads.

[1598] Step 2: Enter your user information

[1599] User: The user enters their email address, password, and username on the registration screen and presses the "Register" button. Input: User's personal information. Output: "Register" button press event.

[1600] Step 3: Submit user information

[1601] Terminal: When the "Register" button is pressed, the terminal sends the entered user information to the server. Input: User's personal information. Output: Sends information to the server.

[1602] Step 4: Storing and authenticating user information

[1603] Server: The server saves the received user information, and if the user is an existing user, generates an authentication token and returns it to the device. Input: Sent user information. Output: Saves new user information, and if the user is an existing user, generates an authentication token.

[1604] Step 5: Get match information

[1605] Server: The server retrieves the current day's match information (team names, start time, player information, etc.) from the database. Input: The current day's request. Output: Match information.

[1606] Step 6: Send and view match information

[1607] Server: The server sends the acquired game information to the terminal. The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts. Input: Game information. Output: Sending and displaying game information.

[1608] Step 7: Input your pitching predictions

[1609] Device: When the game starts, the pitching prediction screen is displayed. The user selects the pitch type (straight, curve, etc.) and pitching trajectory (high outside, low inside, etc.) and presses the "Predict" button. Input: User's prediction data. Output: Preparation for sending the prediction data.

[1610] Step 8: Analyze the sentiment data

[1611] Device: After the user presses the "Predict" button, the device's emotion engine analyzes emotion data from the user's facial expressions and voice. Input: User's facial expressions and voice data. Output: Generation of emotion data.

[1612] Step 9: Send prediction and sentiment data

[1613] Terminal: Sends prediction data and emotion data to the server. Input: Prediction data, emotion data. Output: Sends data to the server.

[1614] Step 10: Generate prediction results

[1615] Server: The server uses AI to generate pitching predictions based on match data and past statistical data, and stores them in a database. Input: Match data, statistical data. Output: Prediction results.

[1616] Step 11: Emotional Data Analysis and Feedback

[1617] Server: The server analyzes the received emotion data and provides hints and advice based on the emotional state. Input: Emotion data. Output: Personalized feedback.

[1618] Step 12: Collating and calculating grades

[1619] Server: After the game ends, the server compares all users' predicted data with the actual pitching results and calculates the results. Input: User's predicted data, actual pitching results. Output: Calculated results.

[1620] Step 13: Displaying the results

[1621] Terminal: Receives the aggregated results from the server and displays the rankings and individual scores. It also displays feedback based on emotion data. Input: Aggregated results. Output: Display of scores and feedback.

[1622] Step 14: Offer and notify rewards

[1623] Server: The server generates rewards for top performers and users who exceed predicted results, and notifies the device. Input: Performance results. Output: Reward notification.

[1624] Step 15: Reward Collection Instructions

[1625] Terminal: Notifies the user of reward information and how to receive it, and guides them through the process of receiving the reward. Input: Reward notification. Output: Reward receipt instructions.

[1626] (Application example 2)

[1627] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1628] Systems already exist that evaluate and reward viewers' prediction skills as a way to make professional baseball broadcasts more interactive for viewers. However, these systems do not take into account viewers' emotions and are unable to provide more personalized feedback or hints based on viewers' levels of concentration and excitement. This has resulted in insufficient viewer engagement and limited improvements to the viewing experience.

[1629] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1630] In this invention, the server includes display means for viewers to input prediction data, transmission means for transmitting the prediction data to the server, storage means for the server to save the prediction data, artificial intelligence means for the server to generate prediction results using artificial intelligence based on match data, aggregation means for aggregating viewer performance based on the prediction results and the match data, reward means for providing rewards to top performers, and emotion analysis means for analyzing viewer emotional information and providing feedback. This makes it possible to provide personalized feedback and hints based on the viewer's emotional state, increasing viewer engagement and providing a richer viewing experience.

[1631] "Display means" refers to a device or interface that displays the information necessary for the viewer to input prediction data.

[1632] "Transmission means" refers to a communication means for transmitting the prediction data entered by the viewer to the server.

[1633] "Storage means" refers to a database or storage system for storing the prediction data received by the server.

[1634] "Artificial Intelligence Means" refers to the artificial intelligence algorithms or models used by the Server to generate predicted outcomes based on Match Data.

[1635] "Counting means" refers to the calculation process or system used to compile the viewer's performance based on the predicted results and match data.

[1636] "Reward means" refers to a device or system for providing rewards to top performers.

[1637] "Emotion analysis means" refers to an analytical engine or algorithm that analyzes viewers' emotional information and reflects the results in feedback.

[1638] "Generative AI Model" refers to an artificial intelligence model used to provide viewers with personalized tips and advice.

[1639] "Feedback mechanism" refers to a system or method for providing personalized advice or tips to a viewer based on the analyzed emotional state.

[1640] The system for implementing this invention is a system that allows viewers to predict pitching patterns while watching live professional baseball games and receive rewards based on the results. This system is composed of the following main components:

[1641] User Registration and Login

[1642] On the device: When a user launches the application for the first time, they are prompted to enter their email address, password, and username. The user creates an account by entering this information. Existing users can enter their email address and password on the login screen and press the "Login" button.

[1643] Server: The server receives the entered user information and stores it in a database. If the user is already registered, it generates an authentication token and returns it to the device. This process uses Firebase Authentication and MySQL.

[1644] Acquiring and displaying match information

[1645] Server: The server retrieves information about the day of the match (team names, start time, player information, etc.) from the database. This information is sent to the device.

[1646] Terminal: The terminal displays the received game information to the user and prompts them to predict the pitching before the game starts.

[1647] Pitching predictions during the game

[1648] Device: When the game begins, the pitching prediction screen will be displayed. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button.

[1649] User: Enter prediction data for each at-bat and submit it.

[1650] Device: Along with the prediction data, emotion data analyzed from the user's facial expressions and voice is also sent to the server. Emotion analysis is performed using Google Cloud Vision API and AWS Comprehend.

[1651] AI-powered prediction generation

[1652] Server: The server generates pitching predictions using artificial intelligence (TensorFlow) based on match data and past statistical data. The generated predictions are stored in a database.

[1653] Emotional data analysis and reflection

[1654] Server: The server analyzes the received emotional data and provides feedback on the prediction results based on the viewer's emotional state. For example, if high concentration is recognized, personalized hints and advice will be provided to the user.

[1655] Calculation and display of results after the match

[1656] Server: After the game ends, the server compares all users' prediction data with the actual pitching results, calculates the results based on the accuracy rate and emotion data, and determines the top performers based on the calculation results.

[1657] Device: The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on emotion data.

[1658] Providing rewards

[1659] Server: Generates rewards (such as digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the device.

[1660] Terminal: Notifies the user of the reward information and how to receive it, and guides them through the process of receiving the reward.

[1661] Specific examples

[1662] For example, if a user predicts "a straight pitch, high and outside" during a game and the emotion engine recognizes their high level of concentration, the user will be provided with further prediction hints. After the game, the user with the highest accuracy rate will be awarded a digital ticket and will also receive feedback on how to improve their prediction skills based on emotion data.

[1663] Prompt Sentence Examples

[1664] "Please predict the pitching strategy for the current game. Do you think the next pitch will be a straight pitch, high and outside?"

[1665] "High concentration has been detected. Would you like to continue with your predictions?"

[1666] "Your prediction was correct! Tap here to receive your digital ticket."

[1667] This allows users to enjoy watching the game more interactively, and by receiving rewards and feedback based on the results, the viewing experience will be further enriched.Analysis of emotional data can increase users' motivation to participate, which is expected to increase the number of viewers.

[1668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1669] Step 1:

[1670] When a user launches the application for the first time, the server displays a screen for entering an email address, password, and username. The user enters this information to create an account. The entered information is authenticated using Firebase Authentication, and if successful, the server generates an authentication token and saves the entered information in a MySQL database. As a result, the user's account information is saved on the server.

[1671] Step 2:

[1672] The server retrieves information about the day of the game (team names, starting time, player information, etc.) from the database and sends it to the terminal. The terminal displays this information to the user and prompts them to make a pitching prediction before the game starts. This allows the user to check the specific details of the game and prepare to make a pitching prediction.

[1673] Step 3:

[1674] When the game begins, the device displays a pitching prediction screen. The user selects the pitch type (straight, curve, etc.) and pitch trajectory (high outside, low inside, etc.) and presses the "Predict" button. The entered prediction data is sent from the device to the server. At the same time, the device collects emotional data from the user's facial expressions and voice and sends it to the server.

[1675] Step 4:

[1676] The server stores the received prediction data and emotion data. The emotion data is analyzed using Google Cloud Vision API and AWS Comprehend. This allows the user's emotional state to be analyzed and metrics such as concentration and excitement levels to be obtained.

[1677] Step 5:

[1678] The server generates pitching predictions using artificial intelligence (TensorFlow) based on game data and past statistical data. The generated prediction results are then saved in the server's database. This ensures the generation and storage of prediction results.

[1679] Step 6:

[1680] The server analyzes the received emotional data and provides feedback to the prediction results based on the results. For example, if high concentration is recognized, personalized hints and advice will be provided to the user. This feedback information is generated using a generative AI model.

[1681] Step 7:

[1682] After the game ends, the server compares all users' prediction data with their actual pitching results, and calculates their scores based on their accuracy and emotional data. Based on the results of the calculation, the top performers are determined and this information is sent to the terminal.

[1683] Step 8:

[1684] The device displays the aggregated results received from the server to the user, including the ranking of top performers and the performance of each individual user, as well as feedback based on the user's emotional data.

[1685] Step 9:

[1686] The server generates rewards (e.g., digital tickets or discount coupons) for top performers and users who exceed the AI ​​predictions, and notifies the terminal of the reward information, thereby providing the reward information to the user.

[1687] Step 10:

[1688] The terminal notifies the user of the benefit information and how to receive it, and guides the user through the procedure for receiving the benefit. The user can receive the benefit by following the instructions.

[1689] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1690] 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.

[1691] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1692] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1693] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1694] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1695] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1696] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1697] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1698] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1699] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1700] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1701] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1702] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1703] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1704] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1705] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1706] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1707] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1708] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1709] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1710] The following is further disclosed regarding the above embodiment.

[1711] (Claim 1)

[1712] display means for a viewer to input prediction data;

[1713] a transmitting means for transmitting the prediction data to a server;

[1714] a storage means for storing the prediction data in the server;

[1715] an artificial intelligence means for generating a prediction result by artificial intelligence based on the match data;

[1716] a counting means for counting the performance of viewers based on the prediction results and the match data;

[1717] a reward mechanism for providing rewards to top performers;

[1718] A system including:

[1719] (Claim 2)

[1720] 10. The system of claim 1, further comprising means for comparing the prediction data with the prediction results of the artificial intelligence and providing additional rewards if the viewer performs well.

[1721] (Claim 3)

[1722] 2. The system of claim 1, further comprising means for updating the prediction data in real time during a match, allowing viewers to make predictions on an ongoing basis.

[1723] "Example 1"

[1724] (Claim 1)

[1725] display means for a viewer to input prediction data;

[1726] a transmitting means for transmitting the prediction data to a server;

[1727] a storage means for storing the prediction data in the server;

[1728] an artificial intelligence means for generating a prediction result by artificial intelligence based on the match data;

[1729] a counting means for counting the performance of viewers based on the prediction results and the match data;

[1730] a reward mechanism that provides perks to top performers;

[1731] a means for obtaining match information and displaying it to viewers;

[1732] A means of comparing the predicted data of all viewers with the actual match data after the match has finished;

[1733] A means for displaying the viewer's predicted performance in a ranking format;

[1734] A system including:

[1735] (Claim 2)

[1736] The system of claim 1, further comprising means for comparing the prediction data with the prediction results of the artificial intelligence and providing additional rewards to viewers who perform better.

[1737] (Claim 3)

[1738] 2. The system of claim 1, further comprising means for updating the prediction data in real time during a match, allowing viewers to make predictions on an ongoing basis.

[1739] "Application Example 1"

[1740] (Claim 1)

[1741] a display means for a viewer to input prediction data;

[1742] a communication means for transmitting the prediction data to a server;

[1743] a data storage means for storing the prediction data in the server;

[1744] an artificial intelligence means for generating a predicted result by artificial intelligence based on the competition data;

[1745] a counting means for counting the results of viewers based on the prediction results and the competition data;

[1746] a reward mechanism for providing rewards to top performers;

[1747] a comparison means for generating a prediction result based on past statistical data by the artificial intelligence means and comparing the result with the viewer's prediction result;

[1748] a user interface means that allows users using smartphones to participate in real-time predictions;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, further comprising means for providing additional rewards to viewers for superior performance.

[1752] (Claim 3)

[1753] 2. The system of claim 1, further comprising means for updating the prediction data in real time during a competition, allowing viewers to make predictions on an ongoing basis.

[1754] "Example 2: Combining Emotion Engines"

[1755] (Claim 1)

[1756] display means for a viewer to input prediction data;

[1757] a transmitting means for transmitting the prediction data to a server;

[1758] a storage means for storing the prediction data in the server;

[1759] an artificial intelligence means for generating a prediction result by artificial intelligence based on the match data;

[1760] a counting means for counting the performance of viewers based on the prediction results and the match data;

[1761] a reward mechanism for providing rewards to top performers;

[1762] emotion recognition means for analyzing viewer emotion data;

[1763] a feedback means for providing feedback to the prediction based on the emotion data;

[1764] A system including:

[1765] (Claim 2)

[1766] 10. The system of claim 1, further comprising means for comparing the prediction data with the prediction results of the artificial intelligence and providing additional rewards if the viewer performs well.

[1767] (Claim 3)

[1768] 2. The system of claim 1, further comprising means for updating the prediction data in real time during a match, allowing viewers to make predictions on an ongoing basis.

[1769] "Application example 2 when combining emotion engines"

[1770] (Claim 1)

[1771] display means for a viewer to input prediction data;

[1772] a transmitting means for transmitting the prediction data to a server;

[1773] a storage means for storing the prediction data in the server;

[1774] an artificial intelligence means for generating a prediction result by artificial intelligence based on the match data;

[1775] a counting means for counting the performance of viewers based on the prediction results and the match data;

[1776] a reward mechanism for providing rewards to top performers;

[1777] An emotion analysis means for analyzing the emotion information of the viewer and providing feedback;

[1778] A system including:

[1779] (Claim 2)

[1780] 10. The system of claim 1, further comprising: means for comparing the prediction data with the artificial intelligence predictions and providing additional rewards if the viewer performs well; and means for providing personalized tips and advice to the viewer through a generative AI model.

[1781] (Claim 3)

[1782] The system according to claim 1, further comprising: a means for updating the prediction data in real time during a match, allowing viewers to make predictions on an ongoing basis; and a means for collecting and analyzing viewers' emotional information in real time using an emotion analysis means. [Explanation of symbols]

[1783] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. display means for a viewer to input prediction data; a transmitting means for transmitting the prediction data to a server; a storage means for storing the prediction data in the server; an artificial intelligence means for generating a prediction result by artificial intelligence based on the match data; a counting means for counting the performance of viewers based on the prediction results and the match data; a reward mechanism for providing rewards to top performers; A system including:

2. The system of claim 1 , further comprising means for comparing the prediction data with the prediction results of the artificial intelligence and providing additional rewards if the viewer performs well.

3. 2. The system of claim 1, further comprising means for updating said prediction data in real time during a match, allowing viewers to make predictions on an ongoing basis.

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