System

Abstract

To provide a system.SOLUTION: A system includes prediction means using generative AI, means for utilizing statistical data, low-latency processing means using communication technology, means for converting real-time events into probability, and means for placing bets on the events converted into probability, and provides users with information about betting using the low-latency processing means.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional sports betting services, it was difficult to make real-time probabilities of all unpredictable factors, such as the development of the game and the performance of the players, and to make them the subject of betting. This resulted in the attraction of the user's viewing experience not being fully realized, and an opportunity to support the growth and development of the sports industry was missed. [Means for solving the problem]

[0005] The present invention combines a predictive method using generative AI with a method using statistical data, and uses a low-latency processing method using 5G technology to probabilistically predict all unpredictable elements, such as the development of a game and the performance of players, in real time. By making probabilistic events the subject of betting, it will increase interest in sports through sports betting, make the viewing experience more attractive, and support the growth and development of the sports industry. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The sports betting system of the present invention includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, and a means for probabilistic real-time events.

[0029] , and means for making probabilistic events the subject of betting. Generative AI makes predictions based on all unpredictable factors, such as in-game developments and player performance. Means for utilizing statistical data analyze past match data and player performance data to improve the accuracy of predictions. Low-latency processing means using 5G technology quickly processes and probabilistically processes real-time events. Means for making probabilistic events the subject of betting allow users to place bets in real time.

[0030] "Example 2"

[0031] As a specific embodiment, a soccer match is taken as an example. The generation AI predicts the development during the match (e.g., which team is attacking, which player is performing well, etc.) and the performance of the players (e.g., the distance the players run, the number of shots, the pass success rate, etc.) in real time. The means for utilizing statistical data analyzes past match data and the performance data of the players to improve the accuracy of the prediction. The low-latency processing means using 5G technology processes the development during the match and the performance of the players in real time and probabilizes them. The means for making probabilistic events (e.g., "the probability of the next goal being scored," "the probability of a specific player scoring a goal," etc.) the subject of betting enables users to place bets in real time.

[0032] The process flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: The generative AI starts making predictions based on all sorts of unpredictable factors, including in-game developments and player performance.

[0035] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0036] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0037] Step 4: A means for betting on probabilistic events allows users to place bets in real time.

[0038] "Example 2"

[0039] Step 1: When a soccer match starts, the generative AI predicts in-game developments (e.g., which team is attacking, which players are performing well, etc.) and player performance (e.g., players' running distances, number of shots, pass success rate, etc.) in real time.

[0040] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0041] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0042] Step 4: A means of betting on probabilistic events (e.g., "probability of the next goal being scored," "probability of a particular player scoring a goal," etc.) allows users to place bets in real time.

[0043] Example 1

[0044] Next, a description will be given of Example 1 of the embodiment 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".

[0045] Conventional sports betting systems have difficulty quickly reflecting real-time events during a game, which causes delays when users place bets in real time. In addition, the accuracy of predictions is low, which makes it difficult to increase user satisfaction. Furthermore, there is a lack of means to increase interest in sports and make the viewing experience more attractive.

[0046] 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.

[0047] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events, and a means for making the probabilistic events the subject of betting, thereby enabling a user to select a subject of betting, make a prediction using a generative AI model, analyze the statistical data, transmit real-time data using communication technology, calculate probabilities based on the real-time data, provide the probabilistic events as the subject of betting, and make a bet in real time.

[0048] "Generative AI" is a system that uses artificial intelligence technology to make predictions and generate results from data.

[0049] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[0050] "Statistical Data" means numerical data about past events or performance that is used to improve the accuracy of forecasts.

[0051] "Communications technology" refers to technology for sending and receiving data quickly and efficiently, and in particular technology for achieving low latency.

[0052] A "low-latency processing means" is a method or device for quickly transmitting, receiving, and processing data using communication technology.

[0053] "Real-time events" refers to events or situations that are currently unfolding, specifically during a sports game.

[0054] A "means for probabilizing" is a method or device for calculating the probability of an event occurring based on real-time events.

[0055] "Bet subject" refers to a specific event or outcome upon which a User may place a bet.

[0056] "User" refers to an individual or organization that places bets using the sports betting system.

[0057] This invention relates to a sports betting system, and includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting.

[0058] The server uses a generative AI model (e.g., a general generative AI model) to make predictions based on unpredictable factors such as in-game developments and player performance. The generative AI model receives past match data and player performance data as input, and analyzes this data to generate predictions. Specifically, the following prompt sentence is input to the generative AI model:

[0059] "Predict the probability that Player A will score a goal in the next 10 minutes, taking into account past match data and Player A's performance data."

[0060] The terminal uses communication technology (e.g. 5G technology) to transmit low-latency, real-time data to the server. This makes it possible to quickly process and stochasticize events during a match. For example, data on the moment a player scores a goal is instantly transmitted to the server, and the server uses that data to calculate new betting odds.

[0061] Users can place bets in real time through their terminals. The server provides probabilistic events as betting targets and an interface for users to place bets. As a concrete example, consider a soccer match. A user wants to bet on the probability that a player will score a goal during the match. The server uses a generative AI model to analyze past match data and player performance data to predict the probability of a goal. The terminal uses communication technology to transmit real-time data during the match to the server, and the server calculates odds based on that data. The user places a bet based on the odds through the terminal.

[0062] In this way, the sports betting system combines generative AI, statistical data, and communication technology to allow users to place bets in real time.

[0063] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0064] Step 1:

[0065] The user selects the bet to be made.

[0066] A user opens the app on a smartphone or tablet and selects "Will player A score a goal in the next 10 minutes?" from a list of soccer matches. The user's selection information is sent to the terminal as input. The selected bet target is sent to the server as output.

[0067] Step 2:

[0068] The server makes predictions using the generative AI model.

[0069] The server inputs the following prompt sentence to the generative AI model: "Please predict the probability that player A will score a goal in the next 10 minutes, taking into account past match data and player A's performance data." As input, past match data and player performance data are used. As output, the generative AI model returns the prediction result.

[0070] Step 3:

[0071] The server analyzes the statistical data to improve the accuracy of predictions.

[0072] The server retrieves the number of goals and shooting success rate of Player A from the database for the past 10 games, and integrates them with the prediction results of the generative AI model. Statistical data and the prediction results of the generative AI model are used as inputs. Highly accurate prediction results are obtained as output.

[0073] Step 4:

[0074] The terminal transmits real-time data using communication technology.

[0075] The terminal tracks the movement of Player A during the match in real time and transmits the moment of a shot or goal to the server via communication technology. As input, the terminal acquires real-time data. As output, the data is transmitted to the server.

[0076] Step 5:

[0077] The server calculates the probability based on real-time data.

[0078] The server recalculates the probability of player A scoring a goal based on the real-time data received from the terminal. As input, real-time data and statistical data are used. As output, the latest odds are generated.

[0079] Step 6:

[0080] The server provides probabilistic events as subjects for betting.

[0081] Based on the recalculated probabilities, the server displays information such as "The probability that player A will score a goal in the next 10 minutes is 20%" on the user's device. The recalculated probabilities are used as input. As output, the probability information is displayed on the user's device.

[0082] Step 7:

[0083] Users place bets in real time.

[0084] The user checks the displayed odds, inputs the betting amount through the smartphone app, and confirms the bet. As an input, the user's betting information is entered into the terminal. As an output, the information is sent to the server, and the betting result is updated in real time.

[0085] (Application example 1)

[0086] Next, a description will be given of Application Example 1 of Form 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."

[0087] In conventional sports betting systems, it was difficult to bet in real time, limiting the user experience. It was also difficult to accurately predict the development of the game and the performance of the players, resulting in low betting accuracy. In addition, there was a lack of means to increase interest in sports and make the viewing experience more attractive.

[0088] 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.

[0089] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets. This enables real-time sports betting and improves the user's experience. In addition, it is possible to accurately predict the development of the game and the performance of the players, thereby improving the accuracy of betting. Furthermore, it is possible to support the growth and development of the sports industry by increasing interest in sports and making the spectating experience more attractive.

[0090] A "prediction method using generative AI" is a method that uses a generative AI model to make predictions based on unpredictable factors such as in-game developments and player performance.

[0091] "Means of utilizing statistical data" refers to analyzing statistical information such as past match data and player performance data to improve the accuracy of predictions.

[0092] "Low-latency processing means using 5G technology" refers to a means of quickly processing real-time data by utilizing 5G communications technology.

[0093] A "means of converting real-time events into probability" is a means of converting real-time events during a game into probabilities.

[0094] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[0095] "Means for acquiring real-time data" refers to means for acquiring real-time data during a match.

[0096] "Means for obtaining past data" refers to means for obtaining past match data and player performance data.

[0097] "Means for calculating odds based on prediction results" refers to means for calculating betting odds based on the prediction results by the generating AI.

[0098] "Means for providing a user interface" refers to means for providing an interface for a user to place a bet.

[0099] "Means for processing user bets" refers to means for processing bets entered by users and reflecting the results.

[0100] As an embodiment of the present invention, a real-time sports betting system is constructed. The system includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets.

[0101] The server first uses 5G technology to obtain real-time data, including information on player movements and scores during the match. It then obtains past match data and player performance data to use as statistical data. Based on this data, a generative AI model predicts the development of the match and the performance of the players.

[0102] The generative AI model makes predictions using prompts like this:

[0103] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[0104] Real-time data: {Real-time data}

[0105] Past match data: {Past match data}

[0106] Player performance data: {Player performance data}

[0107] After the generative AI model makes a prediction, it calculates odds based on the prediction. The calculated odds are provided to users through a user interface. Users can place bets in real time using devices such as smartphones. Once a user places a bet, the bet is processed by the server and the results are reflected.

[0108] This system allows users to enjoy real-time sports betting and improves the accuracy of their bets by accurately predicting the development of the game and the performance of the players. It also increases interest in sports and makes the viewing experience more attractive, thereby supporting the growth and development of the sports industry.

[0109] For example, while watching a live stream of a soccer match, users can bet in real time on which team will score the next goal. A generative AI model predicts the probability of the next goal based on past data and the current match situation, and calculates the odds. In this way, users can have a more interactive and exciting viewing experience.

[0110] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0111] Step 1:

[0112] The server uses 5G technology to obtain real-time data. Specifically, it obtains information such as player movements and scores during the game through an API. The input is real-time data, and the output is the data obtained during the game.

[0113] Step 2:

[0114] The server retrieves historical match data and player performance data, again through an API: the input is a request for historical match data, and the output is the retrieved historical data.

[0115] Step 3:

[0116] The server inputs the acquired real-time and past data into the generative AI model to predict the development of the game and the performance of the players. The input is real-time and past data, and the output is the prediction results by the generative AI model. In concrete terms, the server inputs the following prompt sentence into the generative AI model:

[0117] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[0118] Real-time data: {Real-time data}

[0119] Past match data: {Past match data}

[0120] Player performance data: {Player performance data}

[0121] Step 4:

[0122] The server calculates odds based on the prediction results obtained from the generative AI model. The input is the prediction result of the generative AI model, and the output is the calculated odds. In concrete terms, the prediction result is converted into a probability, and the odds are calculated based on that.

[0123] Step 5:

[0124] The server provides the calculated odds to the user through a user interface. The input is the calculated odds, and the output is the odds displayed on the user interface. The specific operation is to display the odds on a smartphone app or web interface.

[0125] Step 6:

[0126] A user places a bet in real time using a device such as a smartphone. The input is the user's betting information, and the output is a betting request. Specifically, the user inputs the betting amount and target, and presses the send button.

[0127] Step 7:

[0128] The server processes the bets entered by the user and reflects the results. The input is the user's betting information, and the output is the betting result. Specifically, the server saves the betting information in a database and calculates the betting result based on the game results.

[0129] This is how the real-time sports betting system operates.

[0130] Example 2

[0131] Next, a second embodiment of the second embodiment will be described. 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."

[0132] In conventional sports betting systems, it was difficult to predict and stochastically predict the development of a game or the performance of players in real time. In addition, there was a lack of means to process data with low latency and provide information to users quickly, which limited the user's betting experience. Furthermore, there were not enough means to increase interest in sports and make the viewing experience more attractive.

[0133] 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.

[0134] In this invention, the server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means, a means for probabilistically converting real-time events, a means for making the probabilistic events the subject of betting, a data collection means, a data analysis means, a prediction model generation means, a real-time processing means, and a means for placing bets through a user terminal. This makes it possible to predict and probabilistically convert the development of a game and the performance of players in real time. In addition, by processing data with low latency and providing information to users quickly, the user's betting experience can be improved. Furthermore, by increasing interest in sports and making the spectating experience more attractive, the growth and development of the sports industry can be supported.

[0135] A "prediction method using generative AI" is a method that uses artificial intelligence technology to predict the development of a match and the performance of players.

[0136] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[0137] "Low latency processing means" refers to a means of quickly processing data and providing information in real time, using 5G technology, etc.

[0138] A "means of probabilistically converting real-time events" is a method for analyzing in-game developments and player performance in real time and expressing them as probabilities.

[0139] An "instrument for betting on probabilistic events" is a means that allows a user to place a bet based on probabilistic events.

[0140] "Data collection means" refers to means for collecting past match data and player performance data.

[0141] "Data analysis means" refers to means for analyzing collected data and extracting necessary statistical information.

[0142] A "predictive model generation means" is a means for using a generative AI to generate a model that predicts developments during a match and the performance of players.

[0143] The "real-time processing means" refers to a means for processing data in real time during a match and generating a predicted result.

[0144] A "means for placing a bet through a user terminal" is a means that allows a user to place a bet in real time using a terminal such as a smartphone or tablet.

[0145] This invention is a sports betting system using a soccer game as an example. The system uses a generative AI model to predict and stochasticize the development of the game and the performance of the players in real time. A specific embodiment of this system will be described below.

[0146] The server first collects past match data and player performance data. To do this, it uses a sports data API and stores the data in a database (e.g., MySQL (registered trademark)). Next, it uses Python libraries (e.g., Pandas and NumPy) to analyze the collected data. This allows it to extract statistical data such as the player's running distance, number of shots, and pass success rate.

[0147] The server uses a generative AI model (e.g., TENSORFLOW (registered trademark), PyTorch) to generate a model that predicts the development of the game and the performance of players. This model learns from past data and improves the accuracy of predictions. The generated model processes data in real time during the game and predicts the probability of the next goal being scored and the probability of a specific player scoring a goal.

[0148] The server uses 5G technology to process data in real time with low latency, allowing for rapid analysis and probability of match developments and player performance. The probability of events is then provided to users as information for making bets in real time.

[0149] Users can place bets in real time using devices such as smartphones and tablets. The user device receives the prediction results provided by the server and displays them to the user. Through the app, users can perform operations such as "betting on the team with the highest probability of scoring the next goal."

[0150] As a concrete example, let's say a user is watching a soccer game on their smartphone. During the game, the server collects data in real time and uses a generative AI model to predict the probability of the next goal being scored. Through the app, the user can "bet on the team most likely to score the next goal."

[0151] An example of a prompt is as follows:

[0152] "Please predict the probability of a goal being scored in the next 10 minutes. Also, what is the probability of a specific player scoring a goal?"

[0153] In this way, a system is realized in which servers, terminals, and users work together to process data in real time and provide prediction results.

[0154] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0155] Step 1: Data collection

[0156] The server collects past match data and player performance data. Specifically, it uses a sports data API to obtain the data and stores it in a database (e.g., MySQL).

[0157] Input: Match and player data from the Sports Data API

[0158] Output: Match and player data stored in a database

[0159] Specific operation: The server periodically calls the API to obtain the latest match data, which is then inserted into the database.

[0160] Step 2: Data analysis

[0161] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the collected data, extracting statistics such as players' distance traveled, number of shots taken, and pass success rate.

[0162] Input: Match and player data read from the database

[0163] Output: Statistical data (e.g. player distance traveled, number of shots taken, pass success rate)

[0164] What happens: The server reads the data from the database, converts it to a data frame using Pandas, and then uses NumPy to perform statistical calculations and calculate the necessary metrics.

[0165] Step 3: Generate a predictive model

[0166] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. This model learns from past data and improves the accuracy of its predictions.

[0167] Input: Statistics and past match data

[0168] Output: A trained predictive model.

[0169] How it works: The server inputs past match data into TensorFlow to train a neural network. Once trained, the model is saved and used for real-time predictions.

[0170] Step 4: Real-time processing

[0171] The server will use 5G technology to process data in real time with low latency, allowing for rapid analysis and probabilistic predictions of in-game developments and player performance.

[0172] Input: Match and player data collected in real time

[0173] Output: Probabilistic predictions (e.g. the probability of the next goal being scored, or the probability of a specific player scoring a goal)

[0174] How it works: The server collects data in real time during a match and feeds it into a generative AI model, which predicts the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0175] Step 5: Odds and offer bets

[0176] The server provides the probabilistic events to the users, who can then place bets in real time via their devices (e.g. smartphones, tablets).

[0177] Input: Probabilistic forecast results

[0178] Output: Betting information provided to the user

[0179] Specific operation: The server sends the prediction results to the terminal via API. The user checks the prediction results through the app and places a bet. The terminal sends the user's betting information to the server, which records it.

[0180] (Application example 2)

[0181] Next, a description will be given of Application Example 2 of Form 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."

[0182] Conventional sports betting systems had the problem that it was difficult for users to predict the development of the game and the performance of the players in real time and place bets immediately. In addition, the user experience was often impaired because data processing was not performed with low latency. Furthermore, the interface for users to place bets was insufficient, and the appeal of watching sports could not be fully realized.

[0183] 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.

[0184] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a real-time data collection means, and a means for providing an interface for users to place bets. This allows the development of a game and the performance of players to be predicted in real time, and enables users to immediately place bets. In addition, low-latency data processing improves the user experience and brings out the appeal of watching sports.

[0185] A "prediction method using generative AI" is a method for predicting in-game developments and player performance using a generative AI model.

[0186] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[0187] A "low-latency processing means using 5G technology" is a means for performing real-time data processing with low latency using 5G technology.

[0188] A "means for probabilistically determining real-time events" is a means for determining the probability of events occurring during a match in real-time and displaying the results.

[0189] A "probabilistic event betting instrument" is one that allows a user to place a bet in real time based on probabilistic events.

[0190] "Real-time data collection means" refers to a means for collecting data during a match in real time.

[0191] "Means for providing an interface for users to place bets" refers to means for providing an interface for users to place bets and facilitating betting operations.

[0192] The present invention is a system that predicts the development of a soccer game and the performance of players in real time, and enables users to place bets immediately. A specific embodiment for realizing this system is described below.

[0193] System Configuration

[0194] The system consists of the following main components:

[0195] 1. Generative AI-based prediction methods

[0196] The server uses generative AI models to predict in-game developments and player performance, using data from past matches and player performance data.

[0197] 2. Ways to use statistical data

[0198] The server analyzes past match data and player performance data to improve the accuracy of predictions. This data is stored in a database and can be accessed as needed.

[0199] 3. Low-latency processing means using 5G technology

[0200] The servers will use 5G technology to process data in real time with low latency, allowing data during matches to be processed quickly and provided to users.

[0201] 4. A means of probabilistic real-time events

[0202] The server calculates the real-time probability of events during a match and displays the results to the user, such as the probability of the next goal being scored or the probability of a particular player scoring a goal.

[0203] 5. Means of betting on probabilistic events

[0204] The server allows users to place bets in real time based on probabilistic events, and an interface is provided for this.

[0205] 6. Real-time data collection methods

[0206] The server collects data in real time during the match, which is retrieved through an API and sent to the server.

[0207] 7. Means of providing an interface for users to place bets

[0208] The terminal provides an interface through which users can place bets, which may be displayed on a device such as a smartphone or a head-mounted display.

[0209] Program processing explanation

[0210] The server uses generative AI models to predict how matches will play out and how well players will perform, using historical match data and player performance data that is stored in a database and accessed as needed.

[0211] The server uses 5G technology to process data in real time with low latency. This allows data during the match to be processed quickly and provided to users. Events during the match are probabilistic in real time, and the results are displayed to the user. For example, the probability of the next goal being scored or the probability of a specific player scoring a goal is displayed.

[0212] The terminal provides an interface through which users can place bets. The interface is displayed on a device such as a smartphone or a head-mounted display. Through the interface, users can place bets in real time.

[0213] Examples

[0214] For example, while watching a soccer game on a smartphone, a user can place a bet on the moment when the probability of the next goal being scored is predicted. It is also possible to use a head-mounted display to check the performance data of players during the game in real time and make betting decisions.

[0215] Examples of prompt statements

[0216] Predict the probability of the next goal being scored and display it in real time. Also display the probability of a specific player scoring a goal. Provide an interface for users to place bets.

[0217] As described above, this invention is a system that predicts the development of soccer games and the performance of players in real time, and allows users to place bets instantly, improving the viewing experience of the game and supporting the growth and development of the sports industry.

[0218] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0219] Step 1:

[0220] The server retrieves past match data and player performance data from the database. These data are used to train the generative AI model. The input is the past match data and player performance data, and the output is the trained generative AI model.

[0221] Step 2:

[0222] The server uses generative AI models to predict game developments and player performance. The input is game data collected in real time, and the output is a prediction, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0223] Step 3:

[0224] The server uses 5G technology to process data in real time with low latency. The input is match data collected in real time and the prediction results of the generative AI model, and the output is the prediction results processed with low latency. This allows information to be provided to users quickly.

[0225] Step 4:

[0226] The server generates real-time probabilistic predictions for game events. The input is the predictions of the generative AI model, and the output is probabilistic data, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0227] Step 5:

[0228] The server provides an interface that allows users to place bets in real time based on probabilistic events. The input is the probabilistic data and the output is the user interface. Users can place bets through this interface.

[0229] Step 6:

[0230] The terminal displays an interface through which the user can place bets. The input is the user interface provided by the server, and the output is the interface displayed on the terminal's screen. The user uses this interface to place bets.

[0231] Step 7:

[0232] A user places a bet through a terminal. The input is the user's betting information, and the output is the betting information sent to the server. The server processes the bet based on this information.

[0233] Through these steps, the system can predict in-game developments and player performance in real time, allowing users to place bets instantly, improving the viewing experience and supporting the growth and development of the sports industry.

[0234] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0235] "Example 1"

[0236] As one embodiment of the present invention, there is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the subject of betting. Furthermore, the emotion engine recognizes the user's emotions in real time and makes betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine recognizes that excitement and makes betting suggestions that match that excitement. This allows users to make bets based on their own emotions, resulting in a more enjoyable watching experience.

[0237] "Example 2"

[0238] Another embodiment of the present invention is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the target of betting. Furthermore, the emotion engine recognizes the user's emotions and predicts the result of the bet based on the emotion. For example, if a user is excited about a game, the emotion engine recognizes the excitement and predicts the result of the bet according to the excitement. This allows the user to predict the result of the bet based on their own emotions, resulting in a more enjoyable watching experience.

[0239] The process flow of each embodiment will be described below.

[0240] "Example 1"

[0241] Step 1: Generative AI predicts game developments and player performance.

[0242] Step 2: Use statistical data to improve prediction accuracy.

[0243] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[0244] Step 4: Place a bet on the probabilistic event.

[0245] Step 5: The emotion engine recognizes the user's emotions in real time.

[0246] Step 6: The sentiment engine makes betting suggestions based on the sentiment.

[0247] "Example 2"

[0248] Step 1: Generative AI predicts game developments and player performance.

[0249] Step 2: Use statistical data to improve prediction accuracy.

[0250] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[0251] Step 4: Place a bet on the probabilistic event.

[0252] Step 5: The emotion engine recognizes the user's emotion.

[0253] Step 6: The sentiment engine predicts the outcome of the bet based on the sentiment.

[0254] Example 1

[0255] Next, a description will be given of Example 1 of the embodiment 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".

[0256] In conventional sports betting systems, it was difficult to quickly process real-time events during a game and provide appropriate betting suggestions to users. In addition, betting suggestions were not made taking into account the user's emotions, so the user experience was limited. As a result, the appeal of watching sports could not be maximized, and the growth and development of the sports industry could not be fully supported.

[0257] 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.

[0258] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for stochasticizing real-time events, a means for making the stochasticized events the subject of betting, and a means for recognizing the emotions of a user and proposing bets based on the emotions. This makes it possible to quickly process real-time events during a match and to propose bets based on the emotions of the user.

[0259] "Generative AI" is an algorithm that uses artificial intelligence technology to make predictions and generate results from data.

[0260] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[0261] "Statistical data" is a collection of quantified data, such as past match results and player performance.

[0262] "Means for utilizing statistical data" refers to methods and devices for analyzing statistical data and improving the accuracy of predictions.

[0263] "Communication technology" refers to technology for sending and receiving data quickly and efficiently.

[0264] The term "low-latency processing means" refers to a method or device for minimizing data delays and performing processing in real time using communication technology.

[0265] "Real-time events" are instantaneous events that occur during a match, such as goals and fouls.

[0266] A "probabilistic means" is a method or device for calculating the probability of occurrence of a real-time event.

[0267] A "betting instrument" is a method or device that offers probabilistic events as an object of betting by users.

[0268] A "means for recognizing a user's emotions" is a method or device for determining the emotions of a user in real time from the user's facial expressions, tone of voice, etc.

[0269] A "means for providing betting suggestions" is a method or device for presenting appropriate betting options based on a user's sentiment.

[0270] The sports betting system of the present invention includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events into probabilistic events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and making betting suggestions based on those emotions.

[0271] 1. System Overview

[0272] The server uses a generative AI model (e.g., a general generative AI model) to predict game developments and player performance during the game. As a means of utilizing statistical data, past game data and player performance data are obtained from a database (e.g., a general database management system) and analyzed. As a means of low-latency processing using communication technology, a 5G network is used to quickly process real-time events. As a means of probabilizing real-time events, the probability of occurrence of events during the game is calculated. As a means of making the probabilized events the subject of betting, betting options are provided to the user. In addition, an emotion engine (e.g., a general emotion recognition engine) is used as a means of recognizing the user's emotions and making betting suggestions based on those emotions.

[0273] 2. Program Processing

[0274] The server first retrieves past match data and player performance data from a database. Next, this data is input into the generative AI model to predict the game's development and player performance. For example, the server inputs a prompt such as "Please predict the probability that player A will score a goal in the next soccer game" into the generative AI model.

[0275] The server uses data analysis tools (e.g., general data analysis tools) to analyze statistical data and improve the prediction accuracy of the generative AI model. As a low-latency processing means using communication technology, the server receives events during the game in real time via the 5G module and immediately reflects them in the database. This allows users to place bets based on the latest information without delay.

[0276] To stochasticize real-time events, the server calculates the probability of a player scoring a goal or winning or losing a match based on real-time data, and stores the results in a database. To make the stochastic events available for betting, the server uses WebSocket to send betting options to terminals (e.g., smartphone apps) in real time. Users can select the betting options presented through the app and place their bets.

[0277] In addition, the server uses an emotion engine to recognize the user's emotions in real time and make betting suggestions based on those emotions. For example, if a user is excited about a match, the emotion engine will recognize that excitement and make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0278] 3. Specific Examples

[0279] Let's say a user is watching a soccer match. The server uses a generative AI model to predict how the match will unfold and calculate the probability of a goal based on the performance data of the players. It processes this data in real time using 5G technology and makes betting suggestions to the user. The emotion engine recognizes that the user is excited and suggests, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0280] 4. Examples of prompts

[0281] "Predict the probability that Player A will score a goal in the next soccer match, taking into account past match data and player performance data."

[0282] In this way, users can place bets based on their emotions and have a more enjoyable viewing experience.

[0283] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] The server retrieves past match data and player performance data from a database.

[0286] Input: Match and player data obtained from sports data provider's API.

[0287] Data processing: The data obtained through the API is stored in a database.

[0288] Output: Historical match and player data stored in a database.

[0289] How it works: The server periodically calls the API to get the latest match data and stores it in a database, for example using an endpoint such as "https: / / api.sportsdata.io / v3 / soccer / scores / json / Players".

[0290] Step 2:

[0291] The server inputs past match data and player performance data into the generative AI model to predict how the match will unfold and how players will perform.

[0292] Input: Historical match and player data stored in a database.

[0293] Data calculation: Input a prompt sentence into the generative AI model to generate a prediction result.

[0294] Output: The prediction results obtained from the generative AI model.

[0295] How it works: The server runs a Python script and inputs a prompt to the generative AI model, such as "Please predict the probability that player A will score a goal in the next soccer game."

[0296] Step 3:

[0297] The server analyzes the statistical data to improve the predictive accuracy of the generative AI model.

[0298] Input: Predicted outcomes from generative AI models and historical match data.

[0299] Data processing: Use data analysis tools to compare and analyze the forecast results with past data.

[0300] Output: The analysis results are fed back into the generative AI model.

[0301] Specific operation: The server uses Pandas and NumPy to collect past performance data of players and feed it back to the generative AI model. For example, it performs a process such as "collecting the number of goals scored by Player A in the past 10 games and calculating the average value."

[0302] Step 4:

[0303] The server uses communication technology to quickly process real-time events during the match.

[0304] Input: Real-time event data during the match.

[0305] Data processing: Data received via the 5G module is immediately reflected in the database.

[0306] Output: A real-time updated database.

[0307] Specific operation: The server receives real-time events during the game (e.g. goals, fouls) via the 5G module and immediately updates the database.

[0308] Step 5:

[0309] The server randomizes the events that occur in real time.

[0310] Input: Real-time updated database.

[0311] Data calculation: Calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data.

[0312] Output: Probabilistic data.

[0313] Specific operation: The server calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data, and stores the results in a database. For example, it generates data such as "The probability that player A will score a goal in the next 10 minutes is 20%."

[0314] Step 6:

[0315] The server provides users with probabilistic events upon which to place bets.

[0316] Input: Randomized data.

[0317] Data processing: Formatting probabilistic data into betting options.

[0318] Output: The betting options presented to the user.

[0319] Specific operation: The server uses WebSocket to send betting options to a terminal (e.g. a smartphone app) in real time. The user can select the presented betting options and place a bet through the app.

[0320] Step 7:

[0321] The server uses an emotion engine to recognize the user's emotions in real time and makes betting suggestions based on those emotions.

[0322] Input: Emotional data such as the user's facial expressions and tone of voice.

[0323] Data Computation: Determine the user's emotional state using an emotion engine.

[0324] Output: Betting suggestions based on user sentiment.

[0325] Specific operation: The server analyzes the user's facial expressions and tone of voice through the camera and microphone, and uses the emotion engine to determine the user's emotional state. For example, if the user is excited, the server will make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0326] (Application example 1)

[0327] Next, a description will be given of Application Example 1 of Form 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."

[0328] In traditional sports betting systems, real-time betting suggestions were sometimes delayed, making it difficult for users to place bets based on their excitement and emotions. It was also difficult to accurately predict the development of the game and the performance of the players, and there was a lack of means to make the user's viewing experience more attractive. This made it difficult to support the growth and development of the sports industry.

[0329] 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.

[0330] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing the emotions of a user using an emotion engine and proposing bets based on the emotions, and an application installed on a smartphone, which allows a user to place bets based on emotions while enjoying a match in real time.

[0331] "Generative AI" is an artificial intelligence technology that makes predictions based on unpredictable factors such as in-game developments and player performance.

[0332] "Statistical data" refers to data that is used to improve the accuracy of predictions, such as past match data and player performance data.

[0333] "5G technology" is the fifth-generation mobile communications system that enables real-time data communication with low latency.

[0334] "Real-time event probabilization" is a method of processing events during a match in real time and calculating the probability of those events occurring.

[0335] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[0336] The "Emotion Engine" is a technology that recognizes users' emotions in real time and makes betting suggestions based on those emotions.

[0337] An "application installed on a smartphone" is software that enables users to place sports bets via their smartphone.

[0338] As an embodiment of the present invention, a live sports betting application is installed on a smartphone. The server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, and a means for recognizing the user's emotions using an emotion engine and proposing bets based on the emotions.

[0339] The server first uses 5G communications to obtain real-time data on sporting events. It then uses statistical data to analyze past match data and player performance data, and uses generative AI models to predict the course of the match and the performance of players. This allows real-time events to be probabilistic, and these probabilistic events are then used as the subject of betting.

[0340] In addition, the emotion engine is used to recognize the user's emotions in real time and provide betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine will recognize that excitement and provide betting suggestions that match that excitement. This allows users to place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0341] As a concrete example, when a user is watching a soccer game on a smartphone, the user's emotion is recognized as "excitement" at a heated moment in the game. The generation AI predicts the next goal and displays a suggestion, "Now's your chance! Bet on Team A!"

[0342] Here is an example of a prompt to be input to the generative AI model:

[0343] "A soccer match is in progress. The current score is 1-1 and the match time is 75 minutes. Team A players are on the attack. Predict the next goal."

[0344] In this way, users can place emotion-based bets while enjoying the game in real time.

[0345] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0346] Step 1:

[0347] The server uses 5G communications to obtain real-time sporting event data. The input is the sporting event ID, and the output is real-time match data. Specifically, the server obtains data such as match scores, player movements, and match times through the API.

[0348] Step 2:

[0349] The server uses statistical data to obtain past match data and player performance data. The input is the ID of the player or team, and the output is past match data and performance data. Specifically, the server obtains these statistical data through another API and stores them in a database.

[0350] Step 3:

[0351] The server uses a generative AI model to predict the development of the game and the performance of the players. The input is real-time game data and statistical data, and the output is the predicted results. Specifically, the server inputs these data into the generative AI model to predict the next goal and the development of the game.

[0352] Step 4:

[0353] The server probabilistically generates real-time events. The input is the prediction of the generative AI model, and the output is the probabilistic events. Specifically, the server calculates the probability of which team will score the next goal based on the prediction.

[0354] Step 5:

[0355] The server accepts bets on probabilistic events. The input is the probabilistic events and the output is a betting proposal. In particular, the server makes a proposal to the user on which team to bet on.

[0356] Step 6:

[0357] The server uses an emotion engine to recognize the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the recognized emotions. Specifically, the server analyzes the user's facial expressions and voice through a camera and microphone to recognize emotions.

[0358] Step 7:

[0359] The server makes betting suggestions based on the recognized emotions. The inputs are the recognized emotions and probabilistic events, and the output is emotion-based betting suggestions. Specifically, the server suggests riskier bets if the user is excited and less risky bets if the user is calm.

[0360] Step 8:

[0361] A user places a bet through an application installed on a smartphone. The input is a betting proposal from the server, and the output is the user's bet selection. Specifically, the user operates the application to set a stake for the proposed bet and confirm the bet.

[0362] Example 2

[0363] Next, a second embodiment of the second embodiment will be described. 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."

[0364] Conventional sports betting systems have difficulty predicting the development of a game or the performance of players in real time, and cannot predict the results of bets taking into account the user's emotions, so they have not been able to sufficiently improve the user's viewing experience. For this reason, a new system is needed to increase interest in sports and make the viewing experience more attractive.

[0365] 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.

[0366] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a real-time processing means using low-latency communication technology, a means for probabilistically processing real-time events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and predicting the outcome of a bet based on the emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to predict the outcome of a bet based on the user's emotions.

[0367] "Generative AI" is an artificial intelligence technology that uses statistical analysis of past data to predict future events and situations.

[0368] "Statistical data" refers to data that is compiled and analyzed from numerical information, such as past match results and player performance data.

[0369] "Low latency communications technology" refers to communications technology that minimizes delays in data transmission and reception and enables real-time data processing.

[0370] "Real-time processing" is a technology that processes data immediately as it is generated.

[0371] "Probability" means expressing the possibility of a particular event occurring as a number.

[0372] A "bet subject" is a specific event or situation on which a user may place a bet.

[0373] "Emotion recognition" is a technology that analyzes a user's emotions from their facial expressions, voice, etc., and identifies their emotional state.

[0374] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the system's behavior and results based on those emotions.

[0375] This invention is a sports betting system that uses generative AI to analyze soccer matches in real time and predict player performance and game developments, and also combines it with an emotion engine that recognizes user emotions and predicts betting results based on those emotions.

[0376] Hardware and Software Used

[0377] Generative AI models: Analyze past match data and player performance data to predict game developments in real time, using machine learning libraries such as TensorFlow and PyTorch.

[0378] Low-latency communications technology: Using 5G networks, data can be sent and received with low latency.

[0379] Emotion Engine: Recognizes user emotions and predicts betting outcomes based on those emotions, using machine learning models for facial expression recognition and voice analysis.

[0380] Data processing and calculation

[0381] The server collects past match data and player performance data and inputs it into the generative AI model. The server obtains data from sports data providers via API and stores it in a database.

[0382] The generative AI model analyzes this data and predicts game developments and player performance in real time. The server runs Python scripts and uses libraries such as TensorFlow and PyTorch to run the generative AI model.

[0383] The server uses 5G technology to process data with low latency and generate prediction results in real time. The server transmits and receives data through the 5G network and processes the data in real time.

[0384] The server provides betting targets based on probabilistic events predicted by the generative AI model (e.g., "the probability of the next goal being scored," "the probability of a specific player scoring a goal," etc.). The server displays betting options to users through a web application and provides an interface for users to place bets.

[0385] The terminal uses a camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotions. The emotion engine recognizes the user's emotions based on this data and predicts the outcome of the bet based on the emotions. The terminal transmits the data obtained from the camera and microphone to the emotion engine, which analyzes the emotions using a machine learning model.

[0386] Examples

[0387] For example, during a soccer match, a user may want to bet on the probability of a specific player scoring the next goal. In this case, the server inputs past match data and player performance data into the generative AI model to predict the probability of that player scoring a goal in real time. Furthermore, the emotion engine recognizes that the user is excited about the match and predicts the outcome of the bet based on that excitement.

[0388] Examples of prompt statements

[0389] "Predict the probability of the next goal being scored. Also tell us the probability of a specific player scoring a goal. Also, predict the outcome of the bet based on your sentiment."

[0390] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1:

[0393] Data collection

[0394] The server collects past match data and player performance data. Specifically, the server obtains data from sports data providers through APIs and stores it in a database. The input is JSON-formatted data obtained from the API, and the output is structured data stored in the database. The server periodically calls the API to obtain new data.

[0395] Step 2:

[0396] Data Analysis and Prediction

[0397] The server inputs the collected data into a generative AI model to predict how the game will unfold and how the players will perform. Specifically, the server runs a Python script and uses libraries such as TensorFlow and PyTorch to run the generative AI model. The inputs include past match data and player performance data obtained from a database, and the output includes predicted game developments and player performance data. The server inputs this data into the generative AI model to obtain prediction results.

[0398] Step 3:

[0399] Real-time processing

[0400] The server uses 5G technology to process data with low latency and generate prediction results in real time. Specifically, the server transmits and receives data through the 5G network and processes the data in real time. The input is the prediction data from the generative AI model, and the output is the prediction result that is updated in real time. The server uses the 5G network to process data with low latency and generate prediction results instantly.

[0401] Step 4:

[0402] Betting Eligibility

[0403] The server provides the probabilistic events predicted by the generative AI model as betting targets. Specifically, the server displays betting options to the user through a web application and provides an interface for the user to place bets. The input is the probabilistic prediction data from the generative AI model, and the output is the betting options displayed to the user. The server transmits these data to the web application, allowing the user to place bets.

[0404] Step 5:

[0405] Emotion recognition and outcome prediction

[0406] The terminal analyzes the user's facial expressions and voice using a camera and a microphone to recognize the user's emotions. Specifically, the terminal transmits data acquired from the camera and microphone to an emotion engine, which analyzes the emotions using a machine learning model. The input includes the user's facial expressions and voice data acquired from the camera and microphone, and the output includes the analyzed user's emotion data. The terminal transmits these data to the emotion engine, which predicts the outcome of the bet based on the user's emotions.

[0407] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0408] (Application example 2)

[0409] Next, a description will be given of Application Example 2 of Form 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."

[0410] In conventional sports betting systems, it was difficult to predict the development of a game or the performance of players in real time, and betting suggestions were not made that took into account the user's emotions. As a result, users' viewing experience was limited and they were unable to increase their interest in sports. Furthermore, there was a lack of technology to process data in real time with low latency, which resulted in insufficient accuracy and timing of betting.

[0411] 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.

[0412] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing a user's emotions, and a means for making betting suggestions based on the user's emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to make betting suggestions that take the user's emotions into consideration. This improves the user's viewing experience and increases interest in sports.

[0413] "Generative AI" is an artificial intelligence technology used to predict in-game developments and player performance.

[0414] "Statistical data" refers to information including past match data and player performance data, which is used to improve the accuracy of predictions.

[0415] "5G technology" refers to the fifth-generation mobile communications system, a technology that enables high-capacity data communication with low latency.

[0416] "Low latency processing" refers to the sending, receiving, and processing of data in an extremely short time, and is an important technology for ensuring real-time performance.

[0417] "A means of probabilistically converting real-time events" refers to a technology that analyzes events during a game and player performance in real time, and expresses the results as probabilities.

[0418] A "means for betting on probabilistic events" is a technology that allows a user to place a bet based on events during a game that are expressed as probabilities.

[0419] "Means for recognizing a user's emotions" refers to technology that analyzes the user's emotions from facial expressions, voice, etc., and recognizes those emotions.

[0420] The "means for making betting suggestions based on user emotions" refers to a technology that suggests appropriate betting options based on the recognized emotions of a user.

[0421] The system for implementing this invention includes the following main components: a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilizing real-time events, a means for making the probabilistic events the subject of betting, a means for recognizing user emotions, and a means for making betting suggestions based on user emotions.

[0422] 1. System Programming

[0423] The server uses generative AI to predict game developments and player performance. This uses past game data and player performance data as statistical data. Using 5G technology, data is processed in real time with low latency to probabilistically predict events during the game. Probabilistic events are presented to users as targets for betting.

[0424] In addition, the server is equipped with an emotion engine to recognize the user's emotions. It analyzes the user's emotions from facial expressions and voice, and makes betting suggestions based on those emotions. This allows users to receive betting options based on their emotions, resulting in a more engaging viewing experience.

[0425] 2. Hardware and Software

[0426] Hardware used:

[0427] Smartphone

[0428] 5G Communication Module

[0429] Software used:

[0430] EmotionEngine: A library for recognizing user emotions

[0431] PredictionModel: A generative AI model that makes predictions based on match data

[0432] BettingEngine: An engine that generates and suggests betting options

[0433] 3. Data processing and calculation

[0434] The server receives match data in real time via 5G communication and processes it in JSON format. The PredictionModel analyzes the match data and predicts the performance of players and the progress of the match. The BettingEngine generates betting options based on the predicted data and presents them to the user. The EmotionEngine recognizes the user's emotions and makes betting suggestions based on those emotions.

[0435] 4. Specific Examples

[0436] For example, while a user is watching a soccer game on their smartphone, the generative AI predicts the "probability of the next goal being scored" and presents betting options in real time. If the user is excited, the emotion engine will recognize that emotion and suggest betting options such as "the probability of the next goal being scored is high."

[0437] Example of a prompt to be input to a generative AI model:

[0438] Prompt: Given soccer match data, predict the probability of the next goal being scored.

[0439] Input data: {"teamA": {"score": 1, "possession": 60, "shots": 10}, "teamB": {"score": 0, "possession": 40, "shots": 5}}

[0440] In this way, the real-time sports betting system can provide a more engaging experience for users.

[0441] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0442] Step 1:

[0443] The server receives real-time match data through 5G communication. The input is the live match data, and the output is the match data in JSON format. This data includes each team's score, ball possession rate, number of shots, etc.

[0444] Step 2:

[0445] The server inputs the acquired match data into a generative AI model (PredictionModel). The input is match data in JSON format, and the output is predicted data on the progress of the match and the performance of the players. The generative AI model makes predictions based on past match data and player performance data.

[0446] Step 3:

[0447] The server inputs the prediction data obtained from the generative AI model into the BettingEngine. The input is the prediction data, and the output is the betting options. The BettingEngine generates options for users to place bets based on the prediction data.

[0448] Step 4:

[0449] The server acquires emotion data from the user's device. The input is the user's facial expression and voice data, and the output is the emotion recognition result by the emotion engine (EmotionEngine). The EmotionEngine analyzes the user's emotion and recognizes it.

[0450] Step 5:

[0451] The server inputs the emotion recognition results obtained from the emotion engine to the BettingEngine. The input is the emotion recognition results, and the output is emotion-based betting suggestions. The BettingEngine then suggests appropriate betting options based on the user's emotions.

[0452] Step 6:

[0453] The server transmits the generated betting options and sentiment-based suggestions to the user's terminal, where the input is the betting options and sentiment-based suggestions, and the output is the betting options displayed on the user's terminal, based on which the user can place a bet.

[0454] Step 7:

[0455] The user places a bet based on the betting options presented to them. The inputs are the betting options sent by the server and the output is the user's bet selection. The user's selection is sent to the server and the bet is settled.

[0456] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0458] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0459] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 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.

[0463] 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 wide area network (WAN) and / or a local area network (LAN).

[0464] 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.

[0465] 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 the voice according to instructions from the processor 46.

[0466] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0467] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0468] Fig. 4 shows an example of 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0473] "Example 1"

[0474] The sports betting system of the present invention includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting. The generation AI makes predictions based on all unpredictable factors, such as the development of the game and the performance of the players. The means for utilizing statistical data analyzes past game data and player performance data, etc., to improve the accuracy of predictions. The low-latency processing means using 5G technology quickly processes and probabilistically processes real-time events. The means for making the probabilistic events the subject of betting allows users to make bets in real time.

[0475] "Example 2"

[0476] As a specific embodiment, a soccer match is taken as an example. The generation AI predicts the development during the match (e.g., which team is attacking, which player is performing well, etc.) and the performance of the players (e.g., the distance the players run, the number of shots, the pass success rate, etc.) in real time. The means for utilizing statistical data analyzes past match data and the performance data of the players to improve the accuracy of the prediction. The low-latency processing means using 5G technology processes the development during the match and the performance of the players in real time and probabilizes them. The means for making probabilistic events (e.g., "the probability that the next goal will be scored," "the probability that a specific player will score a goal," etc.) the subject of betting enables users to place bets in real time.

[0477] The process flow of each embodiment will be described below.

[0478] "Example 1"

[0479] Step 1: The generative AI starts making predictions based on all sorts of unpredictable factors, including in-game developments and player performance.

[0480] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0481] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0482] Step 4: A means for betting on probabilistic events allows users to place bets in real time.

[0483] "Example 2"

[0484] Step 1: When a soccer match starts, the generative AI predicts in-game developments (e.g., which team is attacking, which players are performing well, etc.) and player performance (e.g., players' running distances, number of shots, pass success rate, etc.) in real time.

[0485] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0486] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0487] Step 4: A means of betting on probabilistic events (e.g., "probability of the next goal being scored," "probability of a particular player scoring a goal," etc.) allows users to place bets in real time.

[0488] Example 1

[0489] Next, a description will be given of Example 1 of Form 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".

[0490] Conventional sports betting systems have difficulty quickly reflecting real-time events during a game, which causes delays when users place bets in real time. In addition, the accuracy of predictions is low, which makes it difficult to increase user satisfaction. Furthermore, there is a lack of means to increase interest in sports and make the viewing experience more attractive.

[0491] 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.

[0492] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events, and a means for making the probabilistic events the subject of betting, thereby enabling a user to select a subject of betting, make a prediction using a generative AI model, analyze the statistical data, transmit real-time data using communication technology, calculate probabilities based on the real-time data, provide the probabilistic events as the subject of betting, and make a bet in real time.

[0493] "Generative AI" is a system that uses artificial intelligence technology to make predictions and generate results from data.

[0494] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[0495] "Statistical Data" means numerical data about past events or performance that is used to improve the accuracy of forecasts.

[0496] "Communications technology" refers to technology for sending and receiving data quickly and efficiently, and in particular technology for achieving low latency.

[0497] A "low-latency processing means" is a method or device for quickly transmitting, receiving, and processing data using communication technology.

[0498] "Real-time events" refers to events or situations that are currently unfolding, specifically during a sports game.

[0499] A "means for probabilizing" is a method or device for calculating the probability of an event occurring based on real-time events.

[0500] "Bet subject" refers to a specific event or outcome upon which a User may place a bet.

[0501] "User" refers to an individual or organization that places bets using the sports betting system.

[0502] This invention relates to a sports betting system, and includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting.

[0503] The server uses a generative AI model (e.g., a general generative AI model) to make predictions based on unpredictable factors such as in-game developments and player performance. The generative AI model receives past match data and player performance data as input, and analyzes this data to generate predictions. Specifically, the following prompt sentence is input to the generative AI model:

[0504] "Predict the probability that Player A will score a goal in the next 10 minutes, taking into account past match data and Player A's performance data."

[0505] The terminal uses communication technology (e.g. 5G technology) to transmit low-latency, real-time data to the server. This makes it possible to quickly process and stochasticize events during a match. For example, data on the moment a player scores a goal is instantly transmitted to the server, and the server uses that data to calculate new betting odds.

[0506] Users can place bets in real time through their terminals. The server provides probabilistic events as betting targets and an interface for users to place bets. As a concrete example, consider a soccer match. A user wants to bet on the probability that a player will score a goal during the match. The server uses a generative AI model to analyze past match data and player performance data to predict the probability of a goal. The terminal uses communication technology to transmit real-time data during the match to the server, and the server calculates odds based on that data. The user places a bet based on the odds through the terminal.

[0507] In this way, the sports betting system combines generative AI, statistical data, and communication technology to allow users to place bets in real time.

[0508] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0509] Step 1:

[0510] The user selects the bet to be made.

[0511] A user opens the app on a smartphone or tablet and selects "Will player A score a goal in the next 10 minutes?" from a list of soccer matches. The user's selection information is sent to the terminal as input. The selected bet target is sent to the server as output.

[0512] Step 2:

[0513] The server makes predictions using the generative AI model.

[0514] The server inputs the following prompt sentence to the generative AI model: "Please predict the probability that player A will score a goal in the next 10 minutes, taking into account past match data and player A's performance data." As input, past match data and player performance data are used. As output, the generative AI model returns the prediction result.

[0515] Step 3:

[0516] The server analyzes the statistical data to improve the accuracy of predictions.

[0517] The server retrieves the number of goals and shooting success rate of Player A from the database for the past 10 games, and integrates them with the prediction results of the generative AI model. Statistical data and the prediction results of the generative AI model are used as inputs. Highly accurate prediction results are obtained as output.

[0518] Step 4:

[0519] The terminal transmits real-time data using communication technology.

[0520] The terminal tracks the movement of Player A during the match in real time and transmits the moment of a shot or goal to the server via communication technology. As input, the terminal acquires real-time data. As output, the data is transmitted to the server.

[0521] Step 5:

[0522] The server calculates the probability based on real-time data.

[0523] The server recalculates the probability of player A scoring a goal based on the real-time data received from the terminal. As input, real-time data and statistical data are used. As output, the latest odds are generated.

[0524] Step 6:

[0525] The server provides probabilistic events as subjects for betting.

[0526] Based on the recalculated probabilities, the server displays information such as "The probability that player A will score a goal in the next 10 minutes is 20%" on the user's device. The recalculated probabilities are used as input. As output, the probability information is displayed on the user's device.

[0527] Step 7:

[0528] Users place bets in real time.

[0529] The user checks the displayed odds, inputs the betting amount through the smartphone app, and confirms the bet. As an input, the user's betting information is entered into the terminal. As an output, the information is sent to the server, and the betting result is updated in real time.

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1 of Form 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".

[0532] In conventional sports betting systems, it was difficult to bet in real time, limiting the user experience. It was also difficult to accurately predict the development of the game and the performance of the players, resulting in low betting accuracy. In addition, there was a lack of means to increase interest in sports and make the viewing experience more attractive.

[0533] 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.

[0534] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets. This enables real-time sports betting and improves the user's experience. In addition, it is possible to accurately predict the development of the game and the performance of the players, thereby improving the accuracy of betting. Furthermore, it is possible to support the growth and development of the sports industry by increasing interest in sports and making the spectating experience more attractive.

[0535] A "prediction method using generative AI" is a method that uses a generative AI model to make predictions based on unpredictable factors such as in-game developments and player performance.

[0536] "Means of utilizing statistical data" refers to analyzing statistical information such as past match data and player performance data to improve the accuracy of predictions.

[0537] "Low-latency processing means using 5G technology" refers to a means of quickly processing real-time data by utilizing 5G communications technology.

[0538] A "means of converting real-time events into probability" is a means of converting real-time events during a game into probabilities.

[0539] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[0540] "Means for acquiring real-time data" refers to means for acquiring real-time data during a match.

[0541] "Means for obtaining past data" refers to means for obtaining past match data and player performance data.

[0542] "Means for calculating odds based on prediction results" refers to means for calculating betting odds based on the prediction results by the generating AI.

[0543] "Means for providing a user interface" refers to means for providing an interface for a user to place a bet.

[0544] "Means for processing user bets" refers to means for processing bets entered by users and reflecting the results.

[0545] As an embodiment of the present invention, a real-time sports betting system is constructed. The system includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets.

[0546] The server first uses 5G technology to obtain real-time data, including information on player movements and scores during the match. It then obtains past match data and player performance data to use as statistical data. Based on this data, a generative AI model predicts the development of the match and the performance of the players.

[0547] The generative AI model makes predictions using prompts like this:

[0548] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[0549] Real-time data: {Real-time data}

[0550] Past match data: {Past match data}

[0551] Player performance data: {Player performance data}

[0552] After the generative AI model makes a prediction, it calculates odds based on the prediction. The calculated odds are provided to users through a user interface. Users can place bets in real time using devices such as smartphones. Once a user places a bet, the bet is processed by the server and the results are reflected.

[0553] This system allows users to enjoy real-time sports betting and improves the accuracy of their bets by accurately predicting the development of the game and the performance of the players. It also increases interest in sports and makes the viewing experience more attractive, thereby supporting the growth and development of the sports industry.

[0554] For example, while watching a live stream of a soccer match, users can bet in real time on which team will score the next goal. A generative AI model predicts the probability of the next goal based on past data and the current match situation, and calculates the odds. In this way, users can have a more interactive and exciting viewing experience.

[0555] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0556] Step 1:

[0557] The server uses 5G technology to obtain real-time data. Specifically, it obtains information such as player movements and scores during the game through an API. The input is real-time data, and the output is the data obtained during the game.

[0558] Step 2:

[0559] The server retrieves historical match data and player performance data, again through an API: the input is a request for historical match data, and the output is the retrieved historical data.

[0560] Step 3:

[0561] The server inputs the acquired real-time and past data into the generative AI model to predict the development of the game and the performance of the players. The input is real-time and past data, and the output is the prediction results by the generative AI model. In concrete terms, the server inputs the following prompt sentence into the generative AI model:

[0562] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[0563] Real-time data: {Real-time data}

[0564] Past match data: {Past match data}

[0565] Player performance data: {Player performance data}

[0566] Step 4:

[0567] The server calculates odds based on the prediction results obtained from the generative AI model. The input is the prediction result of the generative AI model, and the output is the calculated odds. In concrete terms, the prediction result is converted into a probability, and the odds are calculated based on that.

[0568] Step 5:

[0569] The server provides the calculated odds to the user through a user interface. The input is the calculated odds, and the output is the odds displayed on the user interface. The specific operation is to display the odds on a smartphone app or web interface.

[0570] Step 6:

[0571] A user places a bet in real time using a device such as a smartphone. The input is the user's betting information, and the output is a betting request. Specifically, the user inputs the betting amount and target, and presses the send button.

[0572] Step 7:

[0573] The server processes the bets entered by the user and reflects the results. The input is the user's betting information, and the output is the betting result. Specifically, the server saves the betting information in a database and calculates the betting result based on the game results.

[0574] This is how the real-time sports betting system operates.

[0575] Example 2

[0576] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0577] In conventional sports betting systems, it was difficult to predict and stochastically predict the development of a game or the performance of players in real time. In addition, there was a lack of means to process data with low latency and provide information to users quickly, which limited the user's betting experience. Furthermore, there were not enough means to increase interest in sports and make the viewing experience more attractive.

[0578] 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.

[0579] In this invention, the server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means, a means for probabilistically converting real-time events, a means for making the probabilistic events the subject of betting, a data collection means, a data analysis means, a prediction model generation means, a real-time processing means, and a means for placing bets through a user terminal. This makes it possible to predict and probabilistically convert the development of a game and the performance of players in real time. In addition, by processing data with low latency and providing information to users quickly, the user's betting experience can be improved. Furthermore, by increasing interest in sports and making the spectating experience more attractive, the growth and development of the sports industry can be supported.

[0580] A "prediction method using generative AI" is a method that uses artificial intelligence technology to predict the development of a match and the performance of players.

[0581] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[0582] "Low latency processing means" refers to a means of quickly processing data and providing information in real time, using 5G technology, etc.

[0583] A "means of probabilistically converting real-time events" is a method for analyzing in-game developments and player performance in real time and expressing them as probabilities.

[0584] An "instrument for betting on probabilistic events" is a means that allows a user to place a bet based on probabilistic events.

[0585] "Data collection means" refers to means for collecting past match data and player performance data.

[0586] "Data analysis means" refers to means for analyzing collected data and extracting necessary statistical information.

[0587] A "predictive model generation means" is a means for using a generative AI to generate a model that predicts developments during a match and the performance of players.

[0588] The "real-time processing means" refers to a means for processing data in real time during a match and generating a predicted result.

[0589] A "means for placing a bet through a user terminal" is a means that allows a user to place a bet in real time using a terminal such as a smartphone or tablet.

[0590] This invention is a sports betting system using a soccer game as an example. The system uses a generative AI model to predict and stochasticize the development of the game and the performance of the players in real time. A specific embodiment of this system will be described below.

[0591] The server first collects past match data and player performance data. To do this, it uses a sports data API and stores it in a database (e.g., MySQL). Next, it uses Python libraries (e.g., Pandas and NumPy) to analyze the collected data. This allows it to extract statistical data such as the player's running distance, number of shots, and pass success rate.

[0592] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. The model learns from past data and improves the accuracy of the prediction. The generated model processes data in real time during the game and predicts the probability of the next goal being scored and the probability of a specific player scoring a goal.

[0593] The server uses 5G technology to process data in real time with low latency, allowing for rapid analysis and probability of match developments and player performance. The probability of events is then provided to users as information for making bets in real time.

[0594] Users can place bets in real time using devices such as smartphones and tablets. The user device receives the prediction results provided by the server and displays them to the user. Through the app, users can perform operations such as "betting on the team with the highest probability of scoring the next goal."

[0595] As a concrete example, let's say a user is watching a soccer game on their smartphone. During the game, the server collects data in real time and uses a generative AI model to predict the probability of the next goal being scored. Through the app, the user can "bet on the team most likely to score the next goal."

[0596] An example of a prompt is as follows:

[0597] "Please predict the probability of a goal being scored in the next 10 minutes. Also, what is the probability of a specific player scoring a goal?"

[0598] In this way, a system is realized in which servers, terminals, and users work together to process data in real time and provide prediction results.

[0599] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0600] Step 1: Data collection

[0601] The server collects past match data and player performance data. Specifically, it uses a sports data API to obtain the data and stores it in a database (e.g., MySQL).

[0602] Input: Match and player data from the Sports Data API

[0603] Output: Match and player data stored in a database

[0604] Specific operation: The server periodically calls the API to obtain the latest match data, which is then inserted into the database.

[0605] Step 2: Data analysis

[0606] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the collected data, extracting statistics such as players' distance traveled, number of shots taken, and pass success rate.

[0607] Input: Match and player data read from the database

[0608] Output: Statistical data (e.g. player distance traveled, number of shots taken, pass success rate)

[0609] What happens: The server reads the data from the database, converts it to a data frame using Pandas, and then uses NumPy to perform statistical calculations and calculate the necessary metrics.

[0610] Step 3: Generate a predictive model

[0611] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. This model learns from past data and improves the accuracy of its predictions.

[0612] Input: Statistics and past match data

[0613] Output: A trained predictive model.

[0614] How it works: The server inputs past match data into TensorFlow to train a neural network. Once trained, the model is saved and used for real-time predictions.

[0615] Step 4: Real-time processing

[0616] The server will use 5G technology to process data in real time with low latency, allowing for rapid analysis and probabilistic predictions of in-game developments and player performance.

[0617] Input: Match and player data collected in real time

[0618] Output: Probabilistic predictions (e.g. the probability of the next goal being scored, or the probability of a specific player scoring a goal)

[0619] How it works: The server collects data in real time during a match and feeds it into a generative AI model, which then predicts the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0620] Step 5: Odds and offer bets

[0621] The server provides the probabilistic events to the users, who can then place bets in real time via their devices (e.g. smartphones, tablets).

[0622] Input: Probabilistic forecast results

[0623] Output: Betting information provided to the user

[0624] Specific operation: The server sends the prediction results to the terminal via API. The user checks the prediction results through the app and places a bet. The terminal sends the user's betting information to the server, which records it.

[0625] (Application example 2)

[0626] Next, a description will be given of Application Example 2 of Form 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".

[0627] Conventional sports betting systems had the problem that it was difficult for users to predict the development of the game and the performance of the players in real time and place bets immediately. In addition, the user experience was often impaired because data processing was not performed with low latency. Furthermore, the interface for users to place bets was insufficient, and the appeal of watching sports could not be fully realized.

[0628] 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.

[0629] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a real-time data collection means, and a means for providing an interface for users to place bets. This allows the development of a game and the performance of players to be predicted in real time, and enables users to immediately place bets. In addition, low-latency data processing improves the user experience and brings out the appeal of watching sports.

[0630] A "prediction method using generative AI" is a method for predicting in-game developments and player performance using a generative AI model.

[0631] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[0632] A "low-latency processing means using 5G technology" is a means for performing real-time data processing with low latency using 5G technology.

[0633] A "means for probabilistically determining real-time events" is a means for determining the probability of events occurring during a match in real-time and displaying the results.

[0634] A "probabilistic event betting instrument" is one that allows a user to place a bet in real time based on probabilistic events.

[0635] "Real-time data collection means" refers to a means for collecting data during a match in real time.

[0636] "Means for providing an interface for users to place bets" refers to means for providing an interface for users to place bets and facilitating betting operations.

[0637] The present invention is a system that predicts the development of a soccer game and the performance of players in real time, and enables users to place bets immediately. A specific embodiment for realizing this system is described below.

[0638] System Configuration

[0639] The system consists of the following main components:

[0640] 1. Generative AI-based prediction methods

[0641] The server uses generative AI models to predict in-game developments and player performance, using data from past matches and player performance data.

[0642] 2. Ways to use statistical data

[0643] The server analyzes past match data and player performance data to improve the accuracy of predictions. This data is stored in a database and can be accessed as needed.

[0644] 3. Low-latency processing means using 5G technology

[0645] The servers will use 5G technology to process data in real time with low latency, allowing data during matches to be processed quickly and provided to users.

[0646] 4. A means of probabilistic real-time events

[0647] The server calculates the real-time probability of events during a match and displays the results to the user, such as the probability of the next goal being scored or the probability of a particular player scoring a goal.

[0648] 5. Means of betting on probabilistic events

[0649] The server allows users to place bets in real time based on probabilistic events, and an interface is provided for this.

[0650] 6. Real-time data collection methods

[0651] The server collects data in real time during the match, which is retrieved through an API and sent to the server.

[0652] 7. Means of providing an interface for users to place bets

[0653] The terminal provides an interface through which users can place bets, which may be displayed on a device such as a smartphone or a head-mounted display.

[0654] Program processing explanation

[0655] The server uses generative AI models to predict how matches will play out and how well players will perform, using historical match data and player performance data that is stored in a database and accessed as needed.

[0656] The server uses 5G technology to process data in real time with low latency. This allows data during the match to be processed quickly and provided to users. Events during the match are probabilistic in real time, and the results are displayed to the user. For example, the probability of the next goal being scored or the probability of a specific player scoring a goal is displayed.

[0657] The terminal provides an interface for users to place bets, which may be displayed on a device such as a smartphone or a head-mounted display, through which users may place bets in real time.

[0658] Examples

[0659] For example, while watching a soccer game on a smartphone, a user can place a bet on the moment when the probability of the next goal being scored is predicted. It is also possible to use a head-mounted display to check the performance data of players during the game in real time and make betting decisions.

[0660] Examples of prompt statements

[0661] Predict the probability of the next goal being scored and display it in real time. Also display the probability of a specific player scoring a goal. Provide an interface for users to place bets.

[0662] As described above, this invention is a system that predicts the development of soccer games and the performance of players in real time, and allows users to place bets instantly, improving the viewing experience of the game and supporting the growth and development of the sports industry.

[0663] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0664] Step 1:

[0665] The server retrieves past match data and player performance data from the database. These data are used to train the generative AI model. The input is the past match data and player performance data, and the output is the trained generative AI model.

[0666] Step 2:

[0667] The server uses generative AI models to predict game developments and player performance. The input is game data collected in real time, and the output is a prediction, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0668] Step 3:

[0669] The server uses 5G technology to process data in real time with low latency. The input is match data collected in real time and the prediction results of the generative AI model, and the output is the prediction results processed with low latency. This allows information to be provided to users quickly.

[0670] Step 4:

[0671] The server generates real-time probabilistic predictions for game events. The input is the predictions of the generative AI model, and the output is probabilistic data, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[0672] Step 5:

[0673] The server provides an interface that allows users to place bets in real time based on probabilistic events. The input is the probabilistic data and the output is the user interface. Users can place bets through this interface.

[0674] Step 6:

[0675] The terminal displays an interface through which the user can place bets. The input is the user interface provided by the server, and the output is the interface displayed on the terminal's screen. The user uses this interface to place bets.

[0676] Step 7:

[0677] A user places a bet through a terminal. The input is the user's betting information, and the output is the betting information sent to the server. The server processes the bet based on this information.

[0678] Through these steps, the system can predict in-game developments and player performance in real time, allowing users to place bets instantly, improving the viewing experience and supporting the growth and development of the sports industry.

[0679] In addition, 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.

[0680] "Example 1"

[0681] As one embodiment of the present invention, there is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the subject of betting. Furthermore, the emotion engine recognizes the user's emotions in real time and makes betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine recognizes that excitement and makes betting suggestions that match that excitement. This allows users to make bets based on their own emotions, resulting in a more enjoyable watching experience.

[0682] "Example 2"

[0683] Another embodiment of the present invention is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the target of betting. Furthermore, the emotion engine recognizes the user's emotions and predicts the result of the bet based on the emotion. For example, if a user is excited about a game, the emotion engine recognizes the excitement and predicts the result of the bet according to the excitement. This allows the user to predict the result of the bet based on their own emotions, resulting in a more enjoyable watching experience.

[0684] The process flow of each embodiment will be described below.

[0685] "Example 1"

[0686] Step 1: Generative AI predicts game developments and player performance.

[0687] Step 2: Use statistical data to improve prediction accuracy.

[0688] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[0689] Step 4: Place a bet on the probabilistic event.

[0690] Step 5: The emotion engine recognizes the user's emotions in real time.

[0691] Step 6: The sentiment engine makes betting suggestions based on the sentiment.

[0692] "Example 2"

[0693] Step 1: Generative AI predicts game developments and player performance.

[0694] Step 2: Use statistical data to improve prediction accuracy.

[0695] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[0696] Step 4: Place a bet on the probabilistic event.

[0697] Step 5: The emotion engine recognizes the user's emotion.

[0698] Step 6: The sentiment engine predicts the outcome of the bet based on the sentiment.

[0699] Example 1

[0700] Next, a description will be given of Example 1 of Form 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".

[0701] In conventional sports betting systems, it was difficult to quickly process real-time events during a game and provide appropriate betting suggestions to users. In addition, betting suggestions were not made taking into account the user's emotions, so the user experience was limited. As a result, the appeal of watching sports could not be maximized, and the growth and development of the sports industry could not be fully supported.

[0702] 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.

[0703] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for stochasticizing real-time events, a means for making the stochasticized events the subject of betting, and a means for recognizing the emotions of a user and proposing bets based on the emotions. This makes it possible to quickly process real-time events during a match and to propose bets based on the emotions of the user.

[0704] "Generative AI" is an algorithm that uses artificial intelligence technology to make predictions and generate results from data.

[0705] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[0706] "Statistical data" is a collection of quantified data, such as past match results and player performance.

[0707] "Means for utilizing statistical data" refers to methods and devices for analyzing statistical data and improving the accuracy of predictions.

[0708] "Communication technology" refers to technology for sending and receiving data quickly and efficiently.

[0709] The term "low-latency processing means" refers to a method or device for minimizing data delays and performing processing in real time using communication technology.

[0710] "Real-time events" are instantaneous events that occur during a match, such as goals and fouls.

[0711] A "probabilistic means" is a method or device for calculating the probability of occurrence of a real-time event.

[0712] A "betting instrument" is a method or device that offers probabilistic events as an object of betting by users.

[0713] A "means for recognizing a user's emotions" is a method or device for determining the emotions of a user in real time from the user's facial expressions, tone of voice, etc.

[0714] A "means for providing betting suggestions" is a method or device for presenting appropriate betting options based on a user's sentiment.

[0715] The sports betting system of the present invention includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events into probabilistic events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and making betting suggestions based on those emotions.

[0716] 1. System Overview

[0717] The server uses a generative AI model (e.g., a general generative AI model) to predict game developments and player performance during the game. As a means of utilizing statistical data, past game data and player performance data are obtained from a database (e.g., a general database management system) and analyzed. As a means of low-latency processing using communication technology, a 5G network is used to quickly process real-time events. As a means of probabilizing real-time events, the probability of occurrence of events during the game is calculated. As a means of making the probabilized events the subject of betting, betting options are provided to the user. In addition, an emotion engine (e.g., a general emotion recognition engine) is used as a means of recognizing the user's emotions and making betting suggestions based on those emotions.

[0718] 2. Program Processing

[0719] The server first retrieves past match data and player performance data from a database. Next, this data is input into the generative AI model to predict the game's development and player performance. For example, the server inputs a prompt such as "Please predict the probability that player A will score a goal in the next soccer game" into the generative AI model.

[0720] The server uses data analysis tools (e.g., general data analysis tools) to analyze statistical data and improve the prediction accuracy of the generative AI model. As a low-latency processing means using communication technology, the server receives events during the game in real time via the 5G module and immediately reflects them in the database. This allows users to place bets based on the latest information without delay.

[0721] To stochasticize real-time events, the server calculates the probability of a player scoring a goal or winning or losing a match based on real-time data, and stores the results in a database. To make the stochastic events available for betting, the server uses WebSocket to send betting options to terminals (e.g., smartphone apps) in real time. Users can select the betting options presented through the app and place their bets.

[0722] In addition, the server uses an emotion engine to recognize the user's emotions in real time and make betting suggestions based on those emotions. For example, if a user is excited about a match, the emotion engine will recognize that excitement and make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0723] 3. Specific Examples

[0724] Let's say a user is watching a soccer match. The server uses a generative AI model to predict how the match will unfold and calculate the probability of a goal based on the performance data of the players. It processes this data in real time using 5G technology and makes betting suggestions to the user. The emotion engine recognizes that the user is excited and suggests, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0725] 4. Examples of prompts

[0726] "Predict the probability that Player A will score a goal in the next soccer match, taking into account past match data and player performance data."

[0727] In this way, users can place bets based on their emotions and have a more enjoyable viewing experience.

[0728] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] The server retrieves past match data and player performance data from a database.

[0731] Input: Match and player data obtained from sports data provider's API.

[0732] Data processing: The data obtained through the API is stored in a database.

[0733] Output: Historical match and player data stored in a database.

[0734] How it works: The server periodically calls the API to get the latest match data and stores it in a database, for example using an endpoint such as "https: / / api.sportsdata.io / v3 / soccer / scores / json / Players".

[0735] Step 2:

[0736] The server inputs past match data and player performance data into the generative AI model to predict how the match will unfold and how players will perform.

[0737] Input: Historical match and player data stored in a database.

[0738] Data calculation: Input a prompt sentence into the generative AI model to generate a prediction result.

[0739] Output: The prediction results obtained from the generative AI model.

[0740] How it works: The server runs a Python script and inputs a prompt to the generative AI model, such as "Please predict the probability that player A will score a goal in the next soccer game."

[0741] Step 3:

[0742] The server analyzes the statistical data to improve the predictive accuracy of the generative AI model.

[0743] Input: Predicted outcomes from generative AI models and historical match data.

[0744] Data processing: Use data analysis tools to compare and analyze the forecast results with past data.

[0745] Output: The analysis results are fed back into the generative AI model.

[0746] Specific operation: The server uses Pandas and NumPy to collect past performance data of players and feed it back to the generative AI model. For example, it performs a process such as "collecting the number of goals scored by Player A in the past 10 games and calculating the average value."

[0747] Step 4:

[0748] The server uses communication technology to quickly process real-time events during the match.

[0749] Input: Real-time event data during the match.

[0750] Data processing: Data received via the 5G module is immediately reflected in the database.

[0751] Output: A real-time updated database.

[0752] Specific operation: The server receives real-time events during the game (e.g. goals, fouls) via the 5G module and immediately updates the database.

[0753] Step 5:

[0754] The server randomizes the events that occur in real time.

[0755] Input: Real-time updated database.

[0756] Data calculation: Calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data.

[0757] Output: Probabilistic data.

[0758] Specific operation: The server calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data, and stores the results in a database. For example, it generates data such as "The probability that player A will score a goal in the next 10 minutes is 20%."

[0759] Step 6:

[0760] The server provides users with probabilistic events upon which to place bets.

[0761] Input: Randomized data.

[0762] Data processing: Formatting probabilistic data into betting options.

[0763] Output: The betting options presented to the user.

[0764] Specific operation: The server uses WebSocket to send betting options to a terminal (e.g. a smartphone app) in real time. The user can select the presented betting options and place a bet through the app.

[0765] Step 7:

[0766] The server uses an emotion engine to recognize the user's emotions in real time and makes betting suggestions based on those emotions.

[0767] Input: Emotional data such as the user's facial expressions and tone of voice.

[0768] Data Computation: Determine the user's emotional state using an emotion engine.

[0769] Output: Betting suggestions based on user sentiment.

[0770] Specific operation: The server analyzes the user's facial expressions and tone of voice through the camera and microphone, and uses the emotion engine to determine the user's emotional state. For example, if the user is excited, the server will make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[0771] (Application example 1)

[0772] Next, a description will be given of Application Example 1 of Form 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".

[0773] In traditional sports betting systems, real-time betting suggestions were sometimes delayed, making it difficult for users to place bets based on their excitement and emotions. It was also difficult to accurately predict the development of the game and the performance of the players, and there was a lack of means to make the user's viewing experience more attractive. This made it difficult to support the growth and development of the sports industry.

[0774] 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.

[0775] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing the emotions of a user using an emotion engine and proposing bets based on the emotions, and an application installed on a smartphone, which allows a user to place bets based on emotions while enjoying a match in real time.

[0776] "Generative AI" is an artificial intelligence technology that makes predictions based on unpredictable factors such as in-game developments and player performance.

[0777] "Statistical data" refers to data that is used to improve the accuracy of predictions, such as past match data and player performance data.

[0778] "5G technology" is the fifth-generation mobile communications system that enables real-time data communication with low latency.

[0779] "Real-time event probabilization" is a method of processing events during a match in real time and calculating the probability of those events occurring.

[0780] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[0781] The "Emotion Engine" is a technology that recognizes users' emotions in real time and makes betting suggestions based on those emotions.

[0782] An "application installed on a smartphone" is software that enables users to place sports bets via their smartphone.

[0783] As an embodiment of the present invention, a live sports betting application is installed on a smartphone. The server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, and a means for recognizing the user's emotions using an emotion engine and proposing bets based on the emotions.

[0784] The server first uses 5G communications to obtain real-time data on sporting events. It then uses statistical data to analyze past match data and player performance data, and uses generative AI models to predict the course of the match and the performance of players. This allows real-time events to be probabilistic, and these probabilistic events are then used as the subject of betting.

[0785] In addition, the emotion engine is used to recognize the user's emotions in real time and provide betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine will recognize that excitement and provide betting suggestions that match that excitement. This allows users to place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0786] As a concrete example, when a user is watching a soccer game on a smartphone, the user's emotion is recognized as "excitement" at a heated moment in the game. The generation AI predicts the next goal and displays a suggestion, "Now's your chance! Bet on Team A!"

[0787] Here is an example of a prompt to be input to the generative AI model:

[0788] "A soccer match is in progress. The current score is 1-1 and the match time is 75 minutes. Team A players are on the attack. Predict the next goal."

[0789] In this way, users can place emotion-based bets while enjoying the game in real time.

[0790] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0791] Step 1:

[0792] The server uses 5G communications to obtain real-time sporting event data. The input is the sporting event ID, and the output is real-time match data. Specifically, the server obtains data such as match scores, player movements, and match times through the API.

[0793] Step 2:

[0794] The server uses statistical data to obtain past match data and player performance data. The input is the ID of the player or team, and the output is past match data and performance data. Specifically, the server obtains these statistical data through another API and stores them in a database.

[0795] Step 3:

[0796] The server uses a generative AI model to predict the development of the game and the performance of the players. The input is real-time game data and statistical data, and the output is the predicted results. Specifically, the server inputs these data into the generative AI model to predict the next goal and the development of the game.

[0797] Step 4:

[0798] The server probabilistically generates real-time events. The input is the prediction of the generative AI model, and the output is the probabilistic events. Specifically, the server calculates the probability of which team will score the next goal based on the prediction.

[0799] Step 5:

[0800] The server accepts bets on probabilistic events. The input is the probabilistic events and the output is a betting proposal. In particular, the server makes a proposal to the user on which team to bet on.

[0801] Step 6:

[0802] The server uses an emotion engine to recognize the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the recognized emotions. Specifically, the server analyzes the user's facial expressions and voice through a camera and microphone to recognize emotions.

[0803] Step 7:

[0804] The server makes betting suggestions based on the recognized emotions. The inputs are the recognized emotions and probabilistic events, and the output is emotion-based betting suggestions. Specifically, the server suggests riskier bets if the user is excited and less risky bets if the user is calm.

[0805] Step 8:

[0806] A user places a bet through an application installed on a smartphone. The input is a betting proposal from the server, and the output is the user's bet selection. Specifically, the user operates the application to set a stake for the proposed bet and confirm the bet.

[0807] Example 2

[0808] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0809] Conventional sports betting systems have difficulty predicting the development of a game or the performance of players in real time, and cannot predict the results of bets taking into account the user's emotions, so they have not been able to sufficiently improve the user's viewing experience. For this reason, a new system is needed to increase interest in sports and make the viewing experience more attractive.

[0810] 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.

[0811] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a real-time processing means using low-latency communication technology, a means for probabilistically processing real-time events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and predicting the outcome of a bet based on the emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to predict the outcome of a bet based on the user's emotions.

[0812] "Generative AI" is an artificial intelligence technology that uses statistical analysis of past data to predict future events and situations.

[0813] "Statistical data" refers to data that is compiled and analyzed from numerical information, such as past match results and player performance data.

[0814] "Low latency communications technology" refers to communications technology that minimizes delays in data transmission and reception and enables real-time data processing.

[0815] "Real-time processing" is a technology that processes data immediately as it is generated.

[0816] "Probability" means expressing the possibility of a particular event occurring as a number.

[0817] A "bet subject" is a specific event or situation on which a user may place a bet.

[0818] "Emotion recognition" is a technology that analyzes a user's emotions from their facial expressions, voice, etc., and identifies their emotional state.

[0819] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the system's behavior and results based on those emotions.

[0820] This invention is a sports betting system that uses generative AI to analyze soccer matches in real time and predict player performance and game developments, and also combines an emotion engine that recognizes user emotions and predicts betting results based on those emotions.

[0821] Hardware and Software Used

[0822] Generative AI models: Analyze past match data and player performance data to predict game developments in real time, using machine learning libraries such as TensorFlow and PyTorch.

[0823] Low-latency communications technology: Using 5G networks, data can be sent and received with low latency.

[0824] Emotion Engine: Recognizes user emotions and predicts betting outcomes based on those emotions, using machine learning models for facial expression recognition and voice analysis.

[0825] Data processing and calculation

[0826] The server collects past match data and player performance data and inputs it into the generative AI model. The server obtains data from sports data providers via API and stores it in a database.

[0827] The generative AI model analyzes this data and predicts game developments and player performance in real time. The server runs Python scripts and uses libraries such as TensorFlow and PyTorch to run the generative AI model.

[0828] The server uses 5G technology to process data with low latency and generate prediction results in real time. The server transmits and receives data through the 5G network and processes the data in real time.

[0829] The server provides betting targets based on probabilistic events predicted by the generative AI model (e.g., "the probability of the next goal being scored," "the probability of a specific player scoring a goal," etc.). The server displays betting options to users through a web application and provides an interface for users to place bets.

[0830] The terminal uses a camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotions. The emotion engine recognizes the user's emotions based on this data and predicts the outcome of the bet based on the emotions. The terminal transmits the data obtained from the camera and microphone to the emotion engine, which analyzes the emotions using a machine learning model.

[0831] Examples

[0832] For example, during a soccer match, a user may want to bet on the probability of a specific player scoring the next goal. In this case, the server inputs past match data and player performance data into the generative AI model to predict the probability of that player scoring a goal in real time. Furthermore, the emotion engine recognizes that the user is excited about the match and predicts the outcome of the bet based on that excitement.

[0833] Examples of prompt statements

[0834] "Predict the probability of the next goal being scored. Also tell us the probability of a specific player scoring a goal. Also, predict the outcome of the bet based on your sentiment."

[0835] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] Data collection

[0839] The server collects past match data and player performance data. Specifically, the server obtains data from sports data providers through APIs and stores it in a database. The input is JSON-formatted data obtained from the API, and the output is structured data stored in the database. The server periodically calls the API to obtain new data.

[0840] Step 2:

[0841] Data Analysis and Prediction

[0842] The server inputs the collected data into a generative AI model to predict how the game will unfold and how the players will perform. Specifically, the server runs a Python script and uses libraries such as TensorFlow and PyTorch to run the generative AI model. The inputs include past match data and player performance data obtained from a database, and the output includes predicted game developments and player performance data. The server inputs this data into the generative AI model to obtain prediction results.

[0843] Step 3:

[0844] Real-time processing

[0845] The server uses 5G technology to process data with low latency and generate prediction results in real time. Specifically, the server transmits and receives data through the 5G network and processes the data in real time. The input is the prediction data from the generative AI model, and the output is the prediction result that is updated in real time. The server uses the 5G network to process data with low latency and generate prediction results instantly.

[0846] Step 4:

[0847] Betting Eligibility

[0848] The server provides the probabilistic events predicted by the generative AI model as betting targets. Specifically, the server displays betting options to the user through a web application and provides an interface for the user to place bets. The input is the probabilistic prediction data from the generative AI model, and the output is the betting options displayed to the user. The server transmits these data to the web application, allowing the user to place bets.

[0849] Step 5:

[0850] Emotion recognition and outcome prediction

[0851] The terminal analyzes the user's facial expressions and voice using a camera and a microphone to recognize the user's emotions. Specifically, the terminal transmits data acquired from the camera and microphone to an emotion engine, which analyzes the emotions using a machine learning model. The input includes the user's facial expressions and voice data acquired from the camera and microphone, and the output includes the analyzed user's emotion data. The terminal transmits these data to the emotion engine, which predicts the outcome of the bet based on the user's emotions.

[0852] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[0853] (Application example 2)

[0854] Next, a description will be given of Application Example 2 of Form 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".

[0855] In conventional sports betting systems, it was difficult to predict the development of a game or the performance of players in real time, and betting suggestions were not made that took into account the user's emotions. As a result, users' viewing experience was limited and they were unable to increase their interest in sports. Furthermore, there was a lack of technology to process data in real time with low latency, which resulted in insufficient accuracy and timing of betting.

[0856] 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.

[0857] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing a user's emotions, and a means for making betting suggestions based on the user's emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to make betting suggestions that take the user's emotions into consideration. This improves the user's viewing experience and increases interest in sports.

[0858] "Generative AI" is an artificial intelligence technology used to predict in-game developments and player performance.

[0859] "Statistical data" refers to information including past match data and player performance data, which is used to improve the accuracy of predictions.

[0860] "5G technology" refers to the fifth-generation mobile communications system, a technology that enables high-capacity data communication with low latency.

[0861] "Low latency processing" refers to the sending, receiving, and processing of data in an extremely short time, and is an important technology for ensuring real-time performance.

[0862] "A means of probabilistically converting real-time events" refers to a technology that analyzes events during a game and player performance in real time, and expresses the results as probabilities.

[0863] A "means for betting on probabilistic events" is a technology that allows a user to place a bet based on events during a game that are expressed as probabilities.

[0864] "Means for recognizing a user's emotions" refers to technology that analyzes the user's emotions from facial expressions, voice, etc., and recognizes those emotions.

[0865] The "means for making betting suggestions based on user emotions" is a technology for suggesting appropriate betting options based on the recognized emotions of a user.

[0866] The system for implementing this invention includes the following main components: a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilizing real-time events, a means for making the probabilistic events the subject of betting, a means for recognizing user emotions, and a means for making betting suggestions based on user emotions.

[0867] 1. System Programming

[0868] The server uses generative AI to predict game developments and player performance. This uses past game data and player performance data as statistical data. Using 5G technology, data is processed in real time with low latency to probabilistically predict events during the game. Probabilistic events are presented to users as targets for betting.

[0869] In addition, the server is equipped with an emotion engine to recognize the user's emotions. It analyzes the user's emotions from facial expressions and voice, and makes betting suggestions based on those emotions. This allows users to receive betting options based on their emotions, resulting in a more engaging viewing experience.

[0870] 2. Hardware and Software

[0871] Hardware used:

[0872] Smartphone

[0873] 5G Communication Module

[0874] Software used:

[0875] EmotionEngine: A library for recognizing user emotions

[0876] PredictionModel: A generative AI model that makes predictions based on match data

[0877] BettingEngine: An engine that generates and suggests betting options

[0878] 3. Data processing and calculation

[0879] The server receives match data in real time via 5G communication and processes it in JSON format. The PredictionModel analyzes the match data and predicts the performance of players and the progress of the match. The BettingEngine generates betting options based on the predicted data and presents them to the user. The EmotionEngine recognizes the user's emotions and makes betting suggestions based on those emotions.

[0880] 4. Specific Examples

[0881] For example, while a user is watching a soccer game on their smartphone, the generative AI predicts the "probability of the next goal being scored" and presents betting options in real time. If the user is excited, the emotion engine will recognize that emotion and suggest betting options such as "the probability of the next goal being scored is high."

[0882] Example of a prompt to be input to a generative AI model:

[0883] Prompt: Given soccer match data, predict the probability of the next goal being scored.

[0884] Input data: {"teamA": {"score": 1, "possession": 60, "shots": 10}, "teamB": {"score": 0, "possession": 40, "shots": 5}}

[0885] In this way, the real-time sports betting system can provide a more engaging experience for users.

[0886] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0887] Step 1:

[0888] The server receives real-time match data through 5G communication. The input is the live match data, and the output is the match data in JSON format. This data includes each team's score, ball possession rate, number of shots, etc.

[0889] Step 2:

[0890] The server inputs the acquired match data into a generative AI model (PredictionModel). The input is match data in JSON format, and the output is predicted data on the progress of the match and the performance of the players. The generative AI model makes predictions based on past match data and player performance data.

[0891] Step 3:

[0892] The server inputs the prediction data obtained from the generative AI model into the BettingEngine. The input is the prediction data, and the output is the betting options. The BettingEngine generates options for users to place bets based on the prediction data.

[0893] Step 4:

[0894] The server acquires emotion data from the user's device. The input is the user's facial expression and voice data, and the output is the emotion recognition result by the emotion engine (EmotionEngine). The EmotionEngine analyzes the user's emotion and recognizes it.

[0895] Step 5:

[0896] The server inputs the emotion recognition results obtained from the emotion engine to the BettingEngine. The input is the emotion recognition results, and the output is emotion-based betting suggestions. The BettingEngine then suggests appropriate betting options based on the user's emotions.

[0897] Step 6:

[0898] The server transmits the generated betting options and sentiment-based suggestions to the user's terminal, where the input is the betting options and sentiment-based suggestions, and the output is the betting options displayed on the user's terminal, based on which the user can place a bet.

[0899] Step 7:

[0900] The user places a bet based on the betting options presented to them. The inputs are the betting options sent by the server and the output is the user's bet selection. The user's selection is sent to the server and the bet is settled.

[0901] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0902] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0903] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0904] 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 smart glasses 214.

[0905] [Third embodiment]

[0906] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0907] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0908] 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 wide area network (WAN) and / or a local area network (LAN).

[0909] 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.

[0910] 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 the voice according to instructions from the processor 46.

[0911] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0912] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0913] Fig. 6 shows an example of 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.

[0914] 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.

[0915] 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.

[0916] In the headset type terminal 314, 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.

[0917] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0918] "Example 1"

[0919] The sports betting system of the present invention includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting. The generation AI makes predictions based on all unpredictable factors, such as the development of the game and the performance of the players. The means for utilizing statistical data analyzes past game data and player performance data, etc., to improve the accuracy of predictions. The low-latency processing means using 5G technology quickly processes and probabilistically processes real-time events. The means for making the probabilistic events the subject of betting allows users to make bets in real time.

[0920] "Example 2"

[0921] As a specific embodiment, a soccer match is taken as an example. The generation AI predicts the development during the match (e.g., which team is attacking, which player is performing well, etc.) and the performance of the players (e.g., the distance the players run, the number of shots, the pass success rate, etc.) in real time. The means for utilizing statistical data analyzes past match data and the performance data of the players to improve the accuracy of the prediction. The low-latency processing means using 5G technology processes the development during the match and the performance of the players in real time and probabilizes them. The means for making probabilistic events (e.g., "the probability that the next goal will be scored," "the probability that a specific player will score a goal," etc.) the subject of betting enables users to place bets in real time.

[0922] The process flow of each embodiment will be described below.

[0923] "Example 1"

[0924] Step 1: The generative AI starts making predictions based on all sorts of unpredictable factors, including in-game developments and player performance.

[0925] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0926] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0927] Step 4: A means for betting on probabilistic events allows users to place bets in real time.

[0928] "Example 2"

[0929] Step 1: When a soccer match starts, the generative AI predicts in-game developments (e.g., which team is attacking, which players are performing well, etc.) and player performance (e.g., players' running distances, number of shots, pass success rate, etc.) in real time.

[0930] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[0931] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[0932] Step 4: A means of betting on probabilistic events (e.g., "probability of the next goal being scored," "probability of a particular player scoring a goal," etc.) allows users to place bets in real time.

[0933] Example 1

[0934] Next, a description will be given of Example 1 of the embodiment 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".

[0935] Conventional sports betting systems have difficulty quickly reflecting real-time events during a game, which causes delays when users place bets in real time. In addition, the accuracy of predictions is low, which makes it difficult to increase user satisfaction. Furthermore, there is a lack of means to increase interest in sports and make the viewing experience more attractive.

[0936] 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.

[0937] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events, and a means for making the probabilistic events the subject of betting, thereby enabling a user to select a subject of betting, make a prediction using a generative AI model, analyze the statistical data, transmit real-time data using communication technology, calculate probabilities based on the real-time data, provide the probabilistic events as the subject of betting, and make a bet in real time.

[0938] "Generative AI" is a system that uses artificial intelligence technology to make predictions and generate results from data.

[0939] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[0940] "Statistical Data" means numerical data about past events or performance that is used to improve the accuracy of forecasts.

[0941] "Communications technology" refers to technology for sending and receiving data quickly and efficiently, and in particular technology for achieving low latency.

[0942] A "low-latency processing means" is a method or device for quickly transmitting, receiving, and processing data using communication technology.

[0943] "Real-time events" refers to events or situations that are currently unfolding, specifically during a sports game.

[0944] A "means for probabilizing" is a method or device for calculating the probability of an event occurring based on real-time events.

[0945] "Bet subject" refers to a specific event or outcome upon which a User may place a bet.

[0946] "User" refers to an individual or organization that places bets using the sports betting system.

[0947] This invention relates to a sports betting system, and includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting.

[0948] The server uses a generative AI model (e.g., a general generative AI model) to make predictions based on unpredictable factors such as in-game developments and player performance. The generative AI model receives past match data and player performance data as input, and analyzes this data to generate predictions. Specifically, the following prompt sentence is input to the generative AI model:

[0949] "Predict the probability that Player A will score a goal in the next 10 minutes, taking into account past match data and Player A's performance data."

[0950] The terminal uses communication technology (e.g. 5G technology) to transmit low-latency, real-time data to the server. This makes it possible to quickly process and stochasticize events during a match. For example, data on the moment a player scores a goal is instantly transmitted to the server, and the server uses that data to calculate new betting odds.

[0951] Users can place bets in real time through their terminals. The server provides probabilistic events as betting targets and an interface for users to place bets. As a concrete example, consider a soccer match. A user wants to bet on the probability that a player will score a goal during the match. The server uses a generative AI model to analyze past match data and player performance data to predict the probability of a goal. The terminal uses communication technology to transmit real-time data during the match to the server, and the server calculates odds based on that data. The user places a bet based on the odds through the terminal.

[0952] In this way, the sports betting system combines generative AI, statistical data, and communication technology to allow users to place bets in real time.

[0953] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0954] Step 1:

[0955] The user selects the bet to be made.

[0956] A user opens the app on a smartphone or tablet and selects "Will player A score a goal in the next 10 minutes?" from a list of soccer matches. The user's selection information is sent to the terminal as input. The selected bet target is sent to the server as output.

[0957] Step 2:

[0958] The server makes predictions using the generative AI model.

[0959] The server inputs the following prompt sentence into the generative AI model: "Please predict the probability that player A will score a goal in the next 10 minutes, taking into account past match data and player A's performance data." As input, past match data and player performance data are used. As output, the generative AI model returns the prediction result.

[0960] Step 3:

[0961] The server analyzes the statistical data to improve the accuracy of predictions.

[0962] The server retrieves the number of goals and shooting success rate of Player A from the database in the past 10 games and integrates them with the prediction results of the generative AI model. Statistical data and the prediction results of the generative AI model are used as inputs. Highly accurate prediction results are obtained as output.

[0963] Step 4:

[0964] The terminal transmits real-time data using communication technology.

[0965] The terminal tracks the movement of Player A during the match in real time and transmits the moment of a shot or goal to the server via communication technology. As input, the real-time data is acquired by the terminal. As output, the data is transmitted to the server.

[0966] Step 5:

[0967] The server calculates the probability based on real-time data.

[0968] The server recalculates the probability of player A scoring a goal based on the real-time data received from the terminal. As input, real-time data and statistical data are used. As output, the latest odds are generated.

[0969] Step 6:

[0970] The server provides probabilistic events as subjects for betting.

[0971] Based on the recalculated probabilities, the server displays information on the user's device, such as "The probability that player A will score a goal in the next 10 minutes is 20%." The recalculated probabilities are used as input. As output, the probability information is displayed on the user's device.

[0972] Step 7:

[0973] Users place bets in real time.

[0974] The user checks the displayed odds, inputs the betting amount through the smartphone app, and confirms the bet. As an input, the user's betting information is entered into the terminal. As an output, the information is sent to the server, and the betting result is updated in real time.

[0975] (Application example 1)

[0976] Next, a description will be given of Application Example 1 of Form 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".

[0977] In conventional sports betting systems, it was difficult to bet in real time, limiting the user experience. It was also difficult to accurately predict the development of the game and the performance of the players, resulting in low betting accuracy. In addition, there was a lack of means to increase interest in sports and make the viewing experience more attractive.

[0978] 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.

[0979] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets. This enables real-time sports betting and improves the user's experience. In addition, it is possible to accurately predict the development of the game and the performance of the players, thereby improving the accuracy of betting. Furthermore, it is possible to support the growth and development of the sports industry by increasing interest in sports and making the spectating experience more attractive.

[0980] A "prediction method using generative AI" is a method that uses a generative AI model to make predictions based on unpredictable factors such as in-game developments and player performance.

[0981] "Means of utilizing statistical data" refers to analyzing statistical information such as past match data and player performance data to improve the accuracy of predictions.

[0982] "Low-latency processing means using 5G technology" refers to a means of quickly processing real-time data by utilizing 5G communications technology.

[0983] A "means of converting real-time events into probability" is a means of converting real-time events during a game into probabilities.

[0984] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[0985] "Means for acquiring real-time data" refers to means for acquiring real-time data during a match.

[0986] "Means for obtaining past data" refers to means for obtaining past match data and player performance data.

[0987] "Means for calculating odds based on prediction results" refers to means for calculating betting odds based on the prediction results by the generating AI.

[0988] "Means for providing a user interface" refers to means for providing an interface for a user to place a bet.

[0989] "Means for processing user bets" refers to means for processing bets entered by users and reflecting the results.

[0990] As an embodiment of the present invention, a real-time sports betting system is constructed. The system includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets.

[0991] The server first uses 5G technology to obtain real-time data, including information on player movements and scores during the match. It then obtains past match data and player performance data to use as statistical data. Based on this data, a generative AI model predicts the development of the match and the performance of the players.

[0992] The generative AI model makes predictions using prompts like this:

[0993] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[0994] Real-time data: {Real-time data}

[0995] Past match data: {Past match data}

[0996] Player performance data: {Player performance data}

[0997] After the generative AI model makes a prediction, it calculates odds based on the prediction. The calculated odds are provided to users through a user interface. Users can place bets in real time using devices such as smartphones. Once a user places a bet, the bet is processed by the server and the results are reflected.

[0998] This system allows users to enjoy real-time sports betting and improves the accuracy of their bets by accurately predicting the development of the game and the performance of the players. It also increases interest in sports and makes the viewing experience more attractive, thereby supporting the growth and development of the sports industry.

[0999] For example, while watching a live stream of a soccer match, users can bet in real time on which team will score the next goal. A generative AI model predicts the probability of the next goal based on past data and the current match situation, and calculates the odds. In this way, users can have a more interactive and exciting viewing experience.

[1000] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1001] Step 1:

[1002] The server uses 5G technology to obtain real-time data. Specifically, it obtains information such as player movements and scores during the game through an API. The input is real-time data, and the output is the data obtained during the game.

[1003] Step 2:

[1004] The server retrieves historical match data and player performance data, again through an API: the input is a request for historical match data, and the output is the retrieved historical data.

[1005] Step 3:

[1006] The server inputs the acquired real-time and past data into the generative AI model to predict the development of the game and the performance of the players. The input is real-time and past data, and the output is the prediction results by the generative AI model. In concrete terms, the server inputs the following prompt sentence into the generative AI model:

[1007] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[1008] Real-time data: {Real-time data}

[1009] Past match data: {Past match data}

[1010] Player performance data: {Player performance data}

[1011] Step 4:

[1012] The server calculates odds based on the prediction results obtained from the generative AI model. The input is the prediction result of the generative AI model, and the output is the calculated odds. In concrete terms, the prediction result is converted into a probability, and the odds are calculated based on that.

[1013] Step 5:

[1014] The server provides the calculated odds to the user through a user interface. The input is the calculated odds, and the output is the odds displayed on the user interface. The specific operation is to display the odds on a smartphone app or web interface.

[1015] Step 6:

[1016] A user places a bet in real time using a device such as a smartphone. The input is the user's betting information, and the output is a betting request. Specifically, the user inputs the betting amount and target, and presses the send button.

[1017] Step 7:

[1018] The server processes the bets entered by the user and reflects the results. The input is the user's betting information, and the output is the betting result. Specifically, the server saves the betting information in a database and calculates the betting result based on the game results.

[1019] This is how the real-time sports betting system operates.

[1020] Example 2

[1021] Next, a second embodiment of the second form will be described. 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".

[1022] In conventional sports betting systems, it was difficult to predict and stochastically predict the development of a game or the performance of players in real time. In addition, there was a lack of means to process data with low latency and provide information to users quickly, which limited the user's betting experience. Furthermore, there were not enough means to increase interest in sports and make the viewing experience more attractive.

[1023] 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.

[1024] In this invention, the server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means, a means for probabilistically converting real-time events, a means for making the probabilistic events the subject of betting, a data collection means, a data analysis means, a prediction model generation means, a real-time processing means, and a means for placing bets through a user terminal. This makes it possible to predict and probabilistically convert the development of a game and the performance of players in real time. In addition, by processing data with low latency and providing information to users quickly, the user's betting experience can be improved. Furthermore, by increasing interest in sports and making the spectating experience more attractive, the growth and development of the sports industry can be supported.

[1025] A "prediction method using generative AI" is a method that uses artificial intelligence technology to predict the development of a match and the performance of players.

[1026] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[1027] "Low latency processing means" refers to a means of quickly processing data and providing information in real time, using 5G technology, etc.

[1028] A "means of probabilistically converting real-time events" is a method for analyzing in-game developments and player performance in real time and expressing them as probabilities.

[1029] An "instrument for betting on probabilistic events" is a means that allows a user to place a bet based on probabilistic events.

[1030] "Data collection means" refers to means for collecting past match data and player performance data.

[1031] "Data analysis means" refers to means for analyzing collected data and extracting necessary statistical information.

[1032] A "predictive model generation means" is a means for using a generative AI to generate a model that predicts developments during a match and the performance of players.

[1033] The "real-time processing means" refers to a means for processing data in real time during a match and generating a predicted result.

[1034] A "means for placing a bet through a user terminal" is a means that allows a user to place a bet in real time using a terminal such as a smartphone or tablet.

[1035] This invention is a sports betting system using a soccer game as an example. The system uses a generative AI model to predict and stochasticize the development of the game and the performance of the players in real time. A specific embodiment of this system will be described below.

[1036] The server first collects past match data and player performance data. To do this, it uses a sports data API and stores it in a database (e.g., MySQL). Next, it uses Python libraries (e.g., Pandas and NumPy) to analyze the collected data. This allows it to extract statistical data such as the player's running distance, number of shots, and pass success rate.

[1037] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. The model learns from past data and improves the accuracy of the prediction. The generated model processes data in real time during the game and predicts the probability of the next goal being scored and the probability of a specific player scoring a goal.

[1038] The server uses 5G technology to process data in real time with low latency, allowing for rapid analysis and probability of match developments and player performance. The probability of events is then provided to users as information for making bets in real time.

[1039] Users can place bets in real time using devices such as smartphones and tablets. The user device receives the prediction results provided by the server and displays them to the user. Through the app, users can perform operations such as "betting on the team with the highest probability of scoring the next goal."

[1040] As a concrete example, let's say a user is watching a soccer game on their smartphone. During the game, the server collects data in real time and uses a generative AI model to predict the probability of the next goal being scored. Through the app, the user can "bet on the team most likely to score the next goal."

[1041] An example of a prompt is as follows:

[1042] "Please predict the probability of a goal being scored in the next 10 minutes. Also, what is the probability of a specific player scoring a goal?"

[1043] In this way, a system is realized in which servers, terminals, and users work together to process data in real time and provide prediction results.

[1044] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1045] Step 1: Data collection

[1046] The server collects past match data and player performance data. Specifically, it uses a sports data API to obtain the data and stores it in a database (e.g., MySQL).

[1047] Input: Match and player data from the Sports Data API

[1048] Output: Match and player data stored in a database

[1049] Specific operation: The server periodically calls the API to obtain the latest match data, which is then inserted into the database.

[1050] Step 2: Data analysis

[1051] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the collected data, extracting statistics such as players' distance traveled, number of shots taken, and pass success rate.

[1052] Input: Match and player data read from the database

[1053] Output: Statistical data (e.g. player distance traveled, number of shots taken, pass success rate)

[1054] What happens: The server reads the data from the database, converts it to a data frame using Pandas, and then uses NumPy to perform statistical calculations and calculate the necessary metrics.

[1055] Step 3: Generate a predictive model

[1056] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. This model learns from past data and improves the accuracy of its predictions.

[1057] Input: Statistics and past match data

[1058] Output: A trained predictive model.

[1059] How it works: The server inputs past match data into TensorFlow to train a neural network. Once trained, the model is saved and used for real-time predictions.

[1060] Step 4: Real-time processing

[1061] The server will use 5G technology to process data in real time with low latency, allowing for rapid analysis and probabilistic predictions of in-game developments and player performance.

[1062] Input: Match and player data collected in real time

[1063] Output: Probabilistic predictions (e.g. the probability of the next goal being scored, or the probability of a specific player scoring a goal)

[1064] How it works: The server collects data in real time during a match and feeds it into a generative AI model, which predicts the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1065] Step 5: Odds and offer bets

[1066] The server provides the probabilistic events to the users, who can then place bets in real time via their devices (e.g. smartphones, tablets).

[1067] Input: Probabilistic forecast results

[1068] Output: Betting information provided to the user

[1069] Specific operation: The server sends the prediction results to the terminal via API. The user checks the prediction results through the app and places a bet. The terminal sends the user's betting information to the server, which records it.

[1070] (Application example 2)

[1071] Next, a description will be given of Application Example 2 of Form 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".

[1072] Conventional sports betting systems had the problem that it was difficult for users to predict the development of the game and the performance of the players in real time and place bets immediately. In addition, the user experience was often impaired because data processing was not performed with low latency. Furthermore, the interface for users to place bets was insufficient, and the appeal of watching sports could not be fully realized.

[1073] 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.

[1074] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a real-time data collection means, and a means for providing an interface for users to place bets. This allows the development of a game and the performance of players to be predicted in real time, and enables users to immediately place bets. In addition, low-latency data processing improves the user experience and brings out the appeal of watching sports.

[1075] A "prediction method using generative AI" is a method for predicting in-game developments and player performance using a generative AI model.

[1076] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[1077] A "low-latency processing means using 5G technology" is a means for performing real-time data processing with low latency using 5G technology.

[1078] A "means for probabilistically determining real-time events" is a means for determining the probability of events occurring during a match in real-time and displaying the results.

[1079] A "probabilistic event betting instrument" is one that allows a user to place a bet in real time based on probabilistic events.

[1080] "Real-time data collection means" refers to a means for collecting data during a match in real time.

[1081] "Means for providing an interface for users to place bets" refers to means for providing an interface for users to place bets and facilitating betting operations.

[1082] The present invention is a system that predicts the development of a soccer game and the performance of players in real time, and enables users to place bets immediately. A specific embodiment for realizing this system is described below.

[1083] System Configuration

[1084] The system consists of the following main components:

[1085] 1. Generative AI-based prediction methods

[1086] The server uses generative AI models to predict in-game developments and player performance, using data from past matches and player performance data.

[1087] 2. Ways to use statistical data

[1088] The server analyzes past match data and player performance data to improve the accuracy of predictions. This data is stored in a database and can be accessed as needed.

[1089] 3. Low-latency processing means using 5G technology

[1090] The servers will use 5G technology to process data in real time with low latency, allowing data during matches to be processed quickly and provided to users.

[1091] 4. A means of probabilistic real-time events

[1092] The server calculates the real-time probability of events during a match and displays the results to the user, such as the probability of the next goal being scored or the probability of a particular player scoring a goal.

[1093] 5. Means of betting on probabilistic events

[1094] The server allows users to place bets in real time based on probabilistic events, and an interface is provided for this.

[1095] 6. Real-time data collection methods

[1096] The server collects data in real time during the match, which is retrieved through an API and sent to the server.

[1097] 7. Means of providing an interface for users to place bets

[1098] The terminal provides an interface through which users can place bets, which may be displayed on a device such as a smartphone or a head-mounted display.

[1099] Program processing explanation

[1100] The server uses generative AI models to predict how matches will play out and how well players will perform, using historical match data and player performance data that is stored in a database and accessed as needed.

[1101] The server uses 5G technology to process data in real time with low latency. This allows data during the match to be processed quickly and provided to users. Events during the match are probabilistic in real time, and the results are displayed to the user. For example, the probability of the next goal being scored or the probability of a specific player scoring a goal is displayed.

[1102] The terminal provides an interface through which users can place bets. The interface is displayed on a device such as a smartphone or a head-mounted display. Through the interface, users can place bets in real time.

[1103] Examples

[1104] For example, while watching a soccer game on a smartphone, a user can place a bet on the moment when the probability of the next goal being scored is predicted. It is also possible to use a head-mounted display to check the performance data of players during the game in real time and make betting decisions.

[1105] Examples of prompt statements

[1106] Predict the probability of the next goal being scored and display it in real time. Also display the probability of a specific player scoring a goal. Provide an interface for users to place bets.

[1107] As described above, this invention is a system that predicts the development of soccer games and the performance of players in real time, and allows users to place bets instantly, improving the viewing experience of the game and supporting the growth and development of the sports industry.

[1108] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1109] Step 1:

[1110] The server retrieves past match data and player performance data from the database. These data are used to train the generative AI model. The input is the past match data and player performance data, and the output is the trained generative AI model.

[1111] Step 2:

[1112] The server uses generative AI models to predict game developments and player performance. The input is game data collected in real time, and the output is a prediction, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1113] Step 3:

[1114] The server uses 5G technology to process data in real time with low latency. The input is match data collected in real time and the prediction results of the generative AI model, and the output is the prediction results processed with low latency. This allows information to be provided to users quickly.

[1115] Step 4:

[1116] The server generates real-time probabilistic predictions for game events. The input is the predictions of the generative AI model, and the output is probabilistic data, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1117] Step 5:

[1118] The server provides an interface that allows users to place bets in real time based on probabilistic events. The input is the probabilistic data and the output is the user interface. Users can place bets through this interface.

[1119] Step 6:

[1120] The terminal displays an interface through which the user can place bets. The input is the user interface provided by the server, and the output is the interface displayed on the terminal's screen. The user uses this interface to place bets.

[1121] Step 7:

[1122] A user places a bet through a terminal. The input is the user's betting information, and the output is the betting information sent to the server. The server processes the bet based on this information.

[1123] Through these steps, the system can predict in-game developments and player performance in real time, allowing users to place bets instantly, improving the viewing experience and supporting the growth and development of the sports industry.

[1124] In addition, 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.

[1125] "Example 1"

[1126] As one embodiment of the present invention, there is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the subject of betting. Furthermore, the emotion engine recognizes the user's emotions in real time and makes betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine recognizes that excitement and makes betting suggestions that match that excitement. This allows users to make bets based on their own emotions, resulting in a more enjoyable watching experience.

[1127] "Example 2"

[1128] Another embodiment of the present invention is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the target of betting. Furthermore, the emotion engine recognizes the user's emotions and predicts the result of the bet based on the emotion. For example, if a user is excited about a game, the emotion engine recognizes the excitement and predicts the result of the bet according to the excitement. This allows the user to predict the result of the bet based on their own emotions, resulting in a more enjoyable watching experience.

[1129] The process flow of each embodiment will be described below.

[1130] "Example 1"

[1131] Step 1: Generative AI predicts game developments and player performance.

[1132] Step 2: Use statistical data to improve prediction accuracy.

[1133] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[1134] Step 4: Place a bet on the probabilistic event.

[1135] Step 5: The emotion engine recognizes the user's emotions in real time.

[1136] Step 6: The sentiment engine makes betting suggestions based on the sentiment.

[1137] "Example 2"

[1138] Step 1: Generative AI predicts game developments and player performance.

[1139] Step 2: Use statistical data to improve prediction accuracy.

[1140] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in real time and converted into probability data.

[1141] Step 4: Place a bet on the probabilistic event.

[1142] Step 5: The emotion engine recognizes the user's emotion.

[1143] Step 6: The sentiment engine predicts the outcome of the bet based on the sentiment.

[1144] Example 1

[1145] Next, a description will be given of Example 1 of the embodiment 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".

[1146] In conventional sports betting systems, it was difficult to quickly process real-time events during a game and provide appropriate betting suggestions to users. In addition, betting suggestions were not made taking into account the user's emotions, so the user experience was limited. As a result, the appeal of watching sports could not be maximized, and the growth and development of the sports industry could not be fully supported.

[1147] 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.

[1148] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for stochasticizing real-time events, a means for making the stochasticized events the subject of betting, and a means for recognizing the emotions of a user and proposing bets based on the emotions. This makes it possible to quickly process real-time events during a match and to propose bets based on the emotions of the user.

[1149] "Generative AI" is an algorithm that uses artificial intelligence technology to make predictions and generate results from data.

[1150] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[1151] "Statistical data" is a collection of quantified data, such as past match results and player performance.

[1152] "Means for utilizing statistical data" refers to methods and devices for analyzing statistical data and improving the accuracy of predictions.

[1153] "Communication technology" refers to technology for sending and receiving data quickly and efficiently.

[1154] The term "low-latency processing means" refers to a method or device for minimizing data delays and performing processing in real time using communication technology.

[1155] "Real-time events" are instantaneous events that occur during a match, such as goals and fouls.

[1156] A "probabilistic means" is a method or device for calculating the probability of occurrence of a real-time event.

[1157] A "betting instrument" is a method or device that offers probabilistic events as an object of betting by users.

[1158] A "means for recognizing a user's emotions" is a method or device for determining the emotions of a user in real time from the user's facial expressions, tone of voice, etc.

[1159] A "means for providing betting suggestions" is a method or device for presenting appropriate betting options based on a user's sentiment.

[1160] The sports betting system of the present invention includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events into probabilistic events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and making betting suggestions based on those emotions.

[1161] 1. System Overview

[1162] The server uses a generative AI model (e.g., a general generative AI model) to predict game developments and player performance during the game. As a means of utilizing statistical data, past game data and player performance data are obtained from a database (e.g., a general database management system) and analyzed. As a means of low-latency processing using communication technology, a 5G network is used to quickly process real-time events. As a means of probabilizing real-time events, the probability of occurrence of events during the game is calculated. As a means of making the probabilized events the subject of betting, betting options are provided to the user. In addition, an emotion engine (e.g., a general emotion recognition engine) is used as a means of recognizing the user's emotions and making betting suggestions based on those emotions.

[1163] 2. Program Processing

[1164] The server first retrieves past match data and player performance data from a database. Next, this data is input into the generative AI model to predict the game's development and player performance. For example, the server inputs a prompt such as "Please predict the probability that player A will score a goal in the next soccer game" into the generative AI model.

[1165] The server uses data analysis tools (e.g., general data analysis tools) to analyze statistical data and improve the prediction accuracy of the generative AI model. As a low-latency processing means using communication technology, the server receives events during the game in real time via the 5G module and immediately reflects them in the database. This allows users to place bets based on the latest information without delay.

[1166] To stochasticize real-time events, the server calculates the probability of a player scoring a goal or winning or losing a match based on real-time data, and stores the results in a database. To make the stochastic events available for betting, the server uses WebSocket to send betting options to terminals (e.g., smartphone apps) in real time. Users can select the betting options presented through the app and place their bets.

[1167] In addition, the server uses an emotion engine to recognize the user's emotions in real time and make betting suggestions based on those emotions. For example, if a user is excited about a match, the emotion engine will recognize that excitement and make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[1168] 3. Specific Examples

[1169] Let's say a user is watching a soccer match. The server uses a generative AI model to predict how the match will unfold and calculate the probability of a goal based on the performance data of the players. It processes this data in real time using 5G technology and makes betting suggestions to the user. The emotion engine recognizes that the user is excited and suggests, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[1170] 4. Examples of prompts

[1171] "Predict the probability that Player A will score a goal in the next soccer match, taking into account past match data and player performance data."

[1172] In this way, users can place bets based on their emotions and have a more enjoyable viewing experience.

[1173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] The server retrieves past match data and player performance data from a database.

[1176] Input: Match and player data obtained from sports data provider's API.

[1177] Data processing: The data obtained through the API is stored in a database.

[1178] Output: Historical match and player data stored in a database.

[1179] How it works: The server periodically calls the API to get the latest match data and stores it in a database, for example using an endpoint such as "https: / / api.sportsdata.io / v3 / soccer / scores / json / Players".

[1180] Step 2:

[1181] The server inputs past match data and player performance data into the generative AI model to predict how the match will unfold and how players will perform.

[1182] Input: Historical match and player data stored in a database.

[1183] Data calculation: Input a prompt sentence into the generative AI model to generate a prediction result.

[1184] Output: The prediction results obtained from the generative AI model.

[1185] How it works: The server runs a Python script and inputs a prompt to the generative AI model, such as "Please predict the probability that player A will score a goal in the next soccer game."

[1186] Step 3:

[1187] The server analyzes the statistical data to improve the predictive accuracy of the generative AI model.

[1188] Input: Predicted outcomes from generative AI models and historical match data.

[1189] Data processing: Use data analysis tools to compare and analyze the forecast results with past data.

[1190] Output: The analysis results are fed back into the generative AI model.

[1191] Specific operation: The server uses Pandas and NumPy to collect past performance data of players and feed it back to the generative AI model. For example, it performs a process such as "collecting the number of goals scored by Player A in the past 10 games and calculating the average value."

[1192] Step 4:

[1193] The server uses communication technology to quickly process real-time events during the match.

[1194] Input: Real-time event data during the match.

[1195] Data processing: Data received via the 5G module is immediately reflected in the database.

[1196] Output: A real-time updated database.

[1197] Specific operation: The server receives real-time events during the game (e.g. goals, fouls) via the 5G module and immediately updates the database.

[1198] Step 5:

[1199] The server randomizes the events that occur in real time.

[1200] Input: Real-time updated database.

[1201] Data calculation: Calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data.

[1202] Output: Probabilistic data.

[1203] Specific operation: The server calculates the probability of a player scoring a goal and the probability of winning or losing a match based on real-time data, and stores the results in a database. For example, it generates data such as "The probability that player A will score a goal in the next 10 minutes is 20%."

[1204] Step 6:

[1205] The server provides users with probabilistic events upon which to place bets.

[1206] Input: Randomized data.

[1207] Data processing: Formatting probabilistic data into betting options.

[1208] Output: The betting options presented to the user.

[1209] Specific operation: The server uses WebSocket to send betting options to a terminal (e.g. a smartphone app) in real time. The user can select the presented betting options and place a bet through the app.

[1210] Step 7:

[1211] The server uses an emotion engine to recognize the user's emotions in real time and makes betting suggestions based on those emotions.

[1212] Input: Emotional data such as the user's facial expressions and tone of voice.

[1213] Data Computation: Determine the user's emotional state using an emotion engine.

[1214] Output: Betting suggestions based on user sentiment.

[1215] Specific operation: The server analyzes the user's facial expressions and tone of voice through the camera and microphone, and uses the emotion engine to determine the user's emotional state. For example, if the user is excited, the server will make a suggestion such as, "There is a high probability that a goal will be scored in the next 10 minutes. Would you like to bet?"

[1216] (Application example 1)

[1217] Next, a description will be given of Application Example 1 of Form 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".

[1218] In traditional sports betting systems, real-time betting suggestions were sometimes delayed, making it difficult for users to place bets based on their excitement and emotions. It was also difficult to accurately predict the development of the game and the performance of the players, and there was a lack of means to make the user's viewing experience more attractive. This made it difficult to support the growth and development of the sports industry.

[1219] 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.

[1220] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing the emotions of a user using an emotion engine and proposing bets based on the emotions, and an application installed on a smartphone, which allows a user to place bets based on emotions while enjoying a match in real time.

[1221] "Generative AI" is an artificial intelligence technology that makes predictions based on unpredictable factors such as in-game developments and player performance.

[1222] "Statistical data" refers to data that is used to improve the accuracy of predictions, such as past match data and player performance data.

[1223] "5G technology" is the fifth-generation mobile communications system that enables real-time data communication with low latency.

[1224] "Real-time event probabilization" is a method of processing events during a match in real time and calculating the probability of those events occurring.

[1225] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[1226] The "Emotion Engine" is a technology that recognizes users' emotions in real time and makes betting suggestions based on those emotions.

[1227] An "application installed on a smartphone" is software that enables users to place sports bets via their smartphone.

[1228] As an embodiment of the present invention, a live sports betting application is installed on a smartphone. The server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, and a means for recognizing the user's emotions using an emotion engine and proposing bets based on the emotions.

[1229] The server first uses 5G communications to obtain real-time data on sporting events. It then uses statistical data to analyze past match data and player performance data, and uses generative AI models to predict the course of the match and the performance of players. This allows real-time events to be probabilistic, and these probabilistic events are then used as the subject of betting.

[1230] In addition, the emotion engine is used to recognize the user's emotions in real time and provide betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine will recognize that excitement and provide betting suggestions that match that excitement. This allows users to place bets based on their emotions, resulting in a more enjoyable viewing experience.

[1231] As a concrete example, when a user is watching a soccer game on a smartphone, the user's emotion is recognized as "excitement" at a heated moment in the game. The generation AI predicts the next goal and displays a suggestion, "Now's your chance! Bet on Team A!"

[1232] Here is an example of a prompt to be input to the generative AI model:

[1233] "A soccer match is in progress. The current score is 1-1 and the match time is 75 minutes. Team A players are on the attack. Predict the next goal."

[1234] In this way, users can place emotion-based bets while enjoying the game in real time.

[1235] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1236] Step 1:

[1237] The server uses 5G communications to obtain real-time sporting event data. The input is the sporting event ID, and the output is real-time match data. Specifically, the server obtains data such as match scores, player movements, and match times through the API.

[1238] Step 2:

[1239] The server uses statistical data to obtain past match data and player performance data. The input is the ID of the player or team, and the output is past match data and performance data. Specifically, the server obtains these statistical data through another API and stores them in a database.

[1240] Step 3:

[1241] The server uses a generative AI model to predict the development of the game and the performance of the players. The input is real-time game data and statistical data, and the output is the predicted results. Specifically, the server inputs these data into the generative AI model to predict the next goal and the development of the game.

[1242] Step 4:

[1243] The server probabilistically generates real-time events. The input is the prediction of the generative AI model, and the output is the probabilistic events. Specifically, the server calculates the probability of which team will score the next goal based on the prediction.

[1244] Step 5:

[1245] The server accepts bets on probabilistic events. The input is the probabilistic events and the output is a betting proposal. In particular, the server makes a proposal to the user on which team to bet on.

[1246] Step 6:

[1247] The server uses an emotion engine to recognize the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the recognized emotions. Specifically, the server analyzes the user's facial expressions and voice through a camera and microphone to recognize emotions.

[1248] Step 7:

[1249] The server makes betting suggestions based on the recognized emotions. The inputs are the recognized emotions and probabilistic events, and the output is emotion-based betting suggestions. Specifically, the server suggests riskier bets if the user is excited and less risky bets if the user is calm.

[1250] Step 8:

[1251] A user places a bet through an application installed on a smartphone. The input is a betting proposal from the server, and the output is the user's bet selection. Specifically, the user operates the application to set a stake for the proposed bet and confirm the bet.

[1252] Example 2

[1253] Next, a second embodiment of the second form will be described. 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".

[1254] Conventional sports betting systems have difficulty predicting the development of a game or the performance of players in real time, and cannot predict the results of bets taking into account the user's emotions, so they have not been able to sufficiently improve the user's viewing experience. For this reason, a new system is needed to increase interest in sports and make the viewing experience more attractive.

[1255] 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.

[1256] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a real-time processing means using low-latency communication technology, a means for probabilistically processing real-time events, a means for making the probabilistic events the subject of betting, and a means for recognizing a user's emotions and predicting the outcome of a bet based on the emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to predict the outcome of a bet based on the user's emotions.

[1257] "Generative AI" is an artificial intelligence technology that uses statistical analysis of past data to predict future events and situations.

[1258] "Statistical data" refers to data that is compiled and analyzed from numerical information, such as past match results and player performance data.

[1259] "Low latency communications technology" refers to communications technology that minimizes delays in data transmission and reception and enables real-time data processing.

[1260] "Real-time processing" is a technology that processes data immediately as it is generated.

[1261] "Probability" means expressing the possibility of a particular event occurring as a number.

[1262] A "bet subject" is a specific event or situation on which a user may place a bet.

[1263] "Emotion recognition" is a technology that analyzes a user's emotions from their facial expressions, voice, etc., and identifies their emotional state.

[1264] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the system's behavior and results based on those emotions.

[1265] This invention is a sports betting system that uses generative AI to analyze soccer matches in real time and predict player performance and game developments, and also combines an emotion engine that recognizes user emotions and predicts betting results based on those emotions.

[1266] Hardware and Software Used

[1267] Generative AI models: Analyze past match data and player performance data to predict game developments in real time, using machine learning libraries such as TensorFlow and PyTorch.

[1268] Low-latency communications technology: Using 5G networks, data can be sent and received with low latency.

[1269] Emotion Engine: Recognizes user emotions and predicts betting outcomes based on those emotions, using machine learning models for facial expression recognition and voice analysis.

[1270] Data processing and calculation

[1271] The server collects past match data and player performance data and inputs it into the generative AI model. The server obtains data from sports data providers via API and stores it in a database.

[1272] The generative AI model analyzes this data and predicts game developments and player performance in real time. The server runs Python scripts and uses libraries such as TensorFlow and PyTorch to run the generative AI model.

[1273] The server uses 5G technology to process data with low latency and generate prediction results in real time. The server transmits and receives data through the 5G network and processes the data in real time.

[1274] The server provides betting targets based on probabilistic events predicted by the generative AI model (e.g., "the probability of the next goal being scored," "the probability of a specific player scoring a goal," etc.). The server displays betting options to users through a web application and provides an interface for users to place bets.

[1275] The terminal uses a camera and microphone to analyze the user's facial expressions and voice to recognize the user's emotions. The emotion engine recognizes the user's emotions based on this data and predicts the outcome of the bet based on the emotions. The terminal transmits the data obtained from the camera and microphone to the emotion engine, which analyzes the emotions using a machine learning model.

[1276] Examples

[1277] For example, during a soccer match, a user may want to bet on the probability of a specific player scoring the next goal. In this case, the server inputs past match data and player performance data into the generative AI model to predict the probability of that player scoring a goal in real time. Furthermore, the emotion engine recognizes that the user is excited about the match and predicts the outcome of the bet based on that excitement.

[1278] Examples of prompt statements

[1279] "Predict the probability of the next goal being scored. Also tell us the probability of a specific player scoring a goal. Also, predict the outcome of the bet based on your sentiment."

[1280] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[1281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1282] Step 1:

[1283] Data collection

[1284] The server collects past match data and player performance data. Specifically, the server obtains data from sports data providers through APIs and stores it in a database. The input is JSON-formatted data obtained from the API, and the output is structured data stored in the database. The server periodically calls the API to obtain new data.

[1285] Step 2:

[1286] Data Analysis and Prediction

[1287] The server inputs the collected data into a generative AI model to predict how the game will unfold and how the players will perform. Specifically, the server runs a Python script and uses libraries such as TensorFlow and PyTorch to run the generative AI model. The inputs include past match data and player performance data obtained from a database, and the output includes predicted game developments and player performance data. The server inputs this data into the generative AI model to obtain prediction results.

[1288] Step 3:

[1289] Real-time processing

[1290] The server uses 5G technology to process data with low latency and generate prediction results in real time. Specifically, the server transmits and receives data through the 5G network and processes the data in real time. The input is the prediction data from the generative AI model, and the output is the prediction result that is updated in real time. The server uses the 5G network to process data with low latency and generate prediction results instantly.

[1291] Step 4:

[1292] Betting Eligibility

[1293] The server provides the probabilistic events predicted by the generative AI model as betting targets. Specifically, the server displays betting options to the user through a web application and provides an interface for the user to place bets. The input is the probabilistic prediction data from the generative AI model, and the output is the betting options displayed to the user. The server transmits these data to the web application, allowing the user to place bets.

[1294] Step 5:

[1295] Emotion recognition and outcome prediction

[1296] The terminal analyzes the user's facial expressions and voice using a camera and a microphone to recognize the user's emotions. Specifically, the terminal transmits data acquired from the camera and microphone to an emotion engine, which analyzes the emotions using a machine learning model. The input includes the user's facial expressions and voice data acquired from the camera and microphone, and the output includes the analyzed user's emotion data. The terminal transmits these data to the emotion engine, which predicts the outcome of the bet based on the user's emotions.

[1297] The system allows users to predict the development of the game in real time and place bets based on their emotions, resulting in a more enjoyable viewing experience.

[1298] (Application example 2)

[1299] Next, a description will be given of Application Example 2 of Form 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".

[1300] In conventional sports betting systems, it was difficult to predict the development of a game or the performance of players in real time, and betting suggestions were not made that took into account the user's emotions. As a result, users' viewing experience was limited and they were unable to increase their interest in sports. Furthermore, there was a lack of technology to process data in real time with low latency, which resulted in insufficient accuracy and timing of betting.

[1301] 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.

[1302] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for stochasticizing real-time events, a means for making the stochastic events the subject of betting, a means for recognizing a user's emotions, and a means for making betting suggestions based on the user's emotions. This makes it possible to predict the development of a game and the performance of players in real time, and to make betting suggestions that take the user's emotions into consideration. This improves the user's viewing experience and increases interest in sports.

[1303] "Generative AI" is an artificial intelligence technology used to predict in-game developments and player performance.

[1304] "Statistical data" refers to information including past match data and player performance data, which is used to improve the accuracy of predictions.

[1305] "5G technology" refers to the fifth-generation mobile communications system, a technology that enables high-capacity data communication with low latency.

[1306] "Low latency processing" refers to the sending, receiving, and processing of data in an extremely short time, and is an important technology for ensuring real-time performance.

[1307] "A means of probabilistically converting real-time events" refers to a technology that analyzes events during a game and player performance in real time, and expresses the results as probabilities.

[1308] A "means for betting on probabilistic events" is a technology that allows a user to place a bet based on events during a game that are expressed as probabilities.

[1309] "Means for recognizing a user's emotions" refers to technology that analyzes the user's emotions from facial expressions, voice, etc., and recognizes those emotions.

[1310] The "means for making betting suggestions based on user emotions" is a technology for suggesting appropriate betting options based on the recognized emotions of a user.

[1311] The system for implementing this invention includes the following main components: a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilizing real-time events, a means for making the probabilistic events the subject of betting, a means for recognizing user emotions, and a means for making betting suggestions based on user emotions.

[1312] 1. System Programming

[1313] The server uses generative AI to predict game developments and player performance. This uses past game data and player performance data as statistical data. Using 5G technology, data is processed in real time with low latency to probabilistically predict events during the game. Probabilistic events are presented to users as targets for betting.

[1314] In addition, the server is equipped with an emotion engine to recognize the user's emotions. It analyzes the user's emotions from facial expressions and voice, and makes betting suggestions based on those emotions. This allows users to receive betting options based on their emotions, resulting in a more engaging viewing experience.

[1315] 2. Hardware and Software

[1316] Hardware used:

[1317] Smartphone

[1318] 5G Communication Module

[1319] Software used:

[1320] EmotionEngine: A library for recognizing user emotions

[1321] PredictionModel: A generative AI model that makes predictions based on match data

[1322] BettingEngine: An engine that generates and suggests betting options

[1323] 3. Data processing and calculation

[1324] The server receives match data in real time via 5G communication and processes it in JSON format. The PredictionModel analyzes the match data and predicts the performance of players and the progress of the match. The BettingEngine generates betting options based on the predicted data and presents them to the user. The EmotionEngine recognizes the user's emotions and makes betting suggestions based on those emotions.

[1325] 4. Specific Examples

[1326] For example, while a user is watching a soccer game on their smartphone, the generative AI predicts the "probability of the next goal being scored" and presents betting options in real time. If the user is excited, the emotion engine will recognize that emotion and suggest betting options such as "the probability of the next goal being scored is high."

[1327] Example of a prompt to be input to a generative AI model:

[1328] Prompt: Given soccer match data, predict the probability of the next goal being scored.

[1329] Input data: {"teamA": {"score": 1, "possession": 60, "shots": 10}, "teamB": {"score": 0, "possession": 40, "shots": 5}}

[1330] In this way, the real-time sports betting system can provide a more engaging experience for users.

[1331] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1332] Step 1:

[1333] The server receives real-time match data through 5G communication. The input is the live match data, and the output is the match data in JSON format. This data includes each team's score, ball possession rate, number of shots, etc.

[1334] Step 2:

[1335] The server inputs the acquired match data into a generative AI model (PredictionModel). The input is match data in JSON format, and the output is predicted data on the progress of the match and the performance of the players. The generative AI model makes predictions based on past match data and player performance data.

[1336] Step 3:

[1337] The server inputs the prediction data obtained from the generative AI model into the BettingEngine. The input is the prediction data, and the output is the betting options. The BettingEngine generates options for users to place bets based on the prediction data.

[1338] Step 4:

[1339] The server acquires emotion data from the user's device. The input is the user's facial expression and voice data, and the output is the emotion recognition result by the emotion engine (EmotionEngine). The EmotionEngine analyzes the user's emotion and recognizes it.

[1340] Step 5:

[1341] The server inputs the emotion recognition results obtained from the emotion engine to the BettingEngine. The input is the emotion recognition results, and the output is emotion-based betting suggestions. The BettingEngine then suggests appropriate betting options based on the user's emotions.

[1342] Step 6:

[1343] The server transmits the generated betting options and sentiment-based suggestions to the user's terminal, where the input is the betting options and sentiment-based suggestions, and the output is the betting options displayed on the user's terminal, based on which the user can place a bet.

[1344] Step 7:

[1345] The user places a bet based on the betting options presented to them. The inputs are the betting options sent by the server and the output is the user's bet selection. The user's selection is sent to the server and the bet is settled.

[1346] 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 voice indicating a user input for 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.

[1347] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1348] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1349] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given.

[1350] However, the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1351] [Fourth embodiment]

[1352] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1353] 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.

[1354] 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 wide area network (WAN) and / or a local area network (LAN).

[1355] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1356] 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 the voice according to instructions from the processor 46.

[1357] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1358] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1359] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[1360] Fig. 8 shows an example of 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.

[1361] 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.

[1362] 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.

[1363] In the robot 414, 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.

[1364] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1365] "Example 1"

[1366] The sports betting system of the present invention includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting. The generation AI makes predictions based on all unpredictable factors, such as the development of the game and the performance of the players. The means for utilizing statistical data analyzes past game data and player performance data, etc., to improve the accuracy of predictions. The low-latency processing means using 5G technology quickly processes and probabilistically processes real-time events. The means for making the probabilistic events the subject of betting allows users to make bets in real time.

[1367] "Example 2"

[1368] As a specific embodiment, a soccer match is taken as an example. The generation AI predicts the development during the match (e.g., which team is attacking, which player is performing well, etc.) and the performance of the players (e.g., the distance the players run, the number of shots, the pass success rate, etc.) in real time. The means for utilizing statistical data analyzes past match data and the performance data of the players to improve the accuracy of the prediction. The low-latency processing means using 5G technology processes the development during the match and the performance of the players in real time and probabilizes them. The means for making probabilistic events (e.g., "the probability of the next goal being scored," "the probability of a specific player scoring a goal," etc.) the subject of betting enables users to place bets in real time.

[1369] The process flow of each embodiment will be described below.

[1370] "Example 1"

[1371] Step 1: The generative AI starts making predictions based on all sorts of unpredictable factors, including in-game developments and player performance.

[1372] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[1373] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[1374] Step 4: A means for betting on probabilistic events allows users to place bets in real time.

[1375] "Example 2"

[1376] Step 1: When a soccer match starts, the generative AI predicts in-game developments (e.g., which team is attacking, which players are performing well, etc.) and player performance (e.g., players' running distances, number of shots, pass success rate, etc.) in real time.

[1377] Step 2: Utilizing statistical data involves analyzing past match data and player performance data to improve the accuracy of predictions.

[1378] Step 3: Low-latency processing means using 5G technology will process and probabilistic data on in-game developments and player performance in real time.

[1379] Step 4: A means of betting on probabilistic events (e.g., "probability of the next goal being scored," "probability of a particular player scoring a goal," etc.) allows users to place bets in real time.

[1380] Example 1

[1381] Next, a description will be given of Example 1 of the embodiment 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".

[1382] Conventional sports betting systems have difficulty quickly reflecting real-time events during a game, which causes delays when users place bets in real time. In addition, the accuracy of predictions is low, which makes it difficult to increase user satisfaction. Furthermore, there is a lack of means to increase interest in sports and make the viewing experience more attractive.

[1383] 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.

[1384] In this invention, the server includes a prediction means using a generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically converting real-time events, and a means for making the probabilistic events the subject of betting, thereby enabling a user to select a subject of betting, make a prediction using a generative AI model, analyze the statistical data, transmit real-time data using communication technology, calculate probabilities based on the real-time data, provide the probabilistic events as the subject of betting, and make a bet in real time.

[1385] "Generative AI" is a system that uses artificial intelligence technology to make predictions and generate results from data.

[1386] A “prediction tool” is a method or device for using generative AI to predict future events or outcomes.

[1387] "Statistical Data" means numerical data about past events or performance that is used to improve the accuracy of forecasts.

[1388] "Communications technology" refers to technology for sending and receiving data quickly and efficiently, and in particular technology for achieving low latency.

[1389] A "low-latency processing means" is a method or device for quickly transmitting, receiving, and processing data using communication technology.

[1390] "Real-time events" refers to events or situations that are currently unfolding, specifically during a sports game.

[1391] A "means for probabilizing" is a method or device for calculating the probability of an event occurring based on real-time events.

[1392] "Bet subject" refers to a specific event or outcome upon which a User may place a bet.

[1393] "User" refers to an individual or organization that places bets using the sports betting system.

[1394] This invention relates to a sports betting system, and includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using communication technology, a means for probabilistically processing real-time events, and a means for making the probabilistic events the subject of betting.

[1395] The server uses a generative AI model (e.g., a general generative AI model) to make predictions based on unpredictable factors such as in-game developments and player performance. The generative AI model receives past match data and player performance data as input, and analyzes this data to generate predictions. Specifically, the following prompt sentence is input to the generative AI model:

[1396] "Predict the probability that Player A will score a goal in the next 10 minutes, taking into account past match data and Player A's performance data."

[1397] The terminal uses communication technology (e.g. 5G technology) to transmit low-latency, real-time data to the server. This makes it possible to quickly process and stochasticize events during a match. For example, data on the moment a player scores a goal is instantly transmitted to the server, and the server uses that data to calculate new betting odds.

[1398] Users can place bets in real time through their terminals. The server provides probabilistic events as betting targets and an interface for users to place bets. As a concrete example, consider a soccer match. A user wants to bet on the probability that a player will score a goal during the match. The server uses a generative AI model to analyze past match data and player performance data to predict the probability of a goal. The terminal uses communication technology to transmit real-time data during the match to the server, and the server calculates odds based on that data. The user places a bet based on the odds through the terminal.

[1399] In this way, the sports betting system combines generative AI, statistical data, and communication technology to allow users to place bets in real time.

[1400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] The user selects the bet to be made.

[1403] A user opens the app on a smartphone or tablet and selects "Will player A score a goal in the next 10 minutes?" from a list of soccer matches. The user's selection information is sent to the terminal as input. The selected bet target is sent to the server as output.

[1404] Step 2:

[1405] The server makes predictions using the generative AI model.

[1406] The server inputs the following prompt sentence into the generative AI model: "Please predict the probability that player A will score a goal in the next 10 minutes, taking into account past match data and player A's performance data." As input, past match data and player performance data are used. As output, the generative AI model returns the prediction result.

[1407] Step 3:

[1408] The server analyzes the statistical data to improve the accuracy of predictions.

[1409] The server retrieves the number of goals and shooting success rate of Player A from the database in the past 10 games and integrates them with the prediction results of the generative AI model. Statistical data and the prediction results of the generative AI model are used as inputs. Highly accurate prediction results are obtained as output.

[1410] Step 4:

[1411] The terminal transmits real-time data using communication technology.

[1412] The terminal tracks the movement of Player A during the match in real time and transmits the moment of a shot or goal to the server via communication technology. As input, the real-time data is acquired by the terminal. As output, the data is transmitted to the server.

[1413] Step 5:

[1414] The server calculates the probability based on real-time data.

[1415] The server recalculates the probability of player A scoring a goal based on the real-time data received from the terminal. As input, real-time data and statistical data are used. As output, the latest odds are generated.

[1416] Step 6:

[1417] The server provides probabilistic events as subjects for betting.

[1418] Based on the recalculated probabilities, the server displays information on the user's device, such as "The probability that player A will score a goal in the next 10 minutes is 20%." The recalculated probabilities are used as input. As output, the probability information is displayed on the user's device.

[1419] Step 7:

[1420] Users place bets in real time.

[1421] The user checks the displayed odds, inputs the betting amount through the smartphone app, and confirms the bet. As an input, the user's betting information is entered into the terminal. As an output, the information is sent to the server, and the betting result is updated in real time.

[1422] (Application example 1)

[1423] Next, a description will be given of Application Example 1 of Form 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".

[1424] In conventional sports betting systems, it was difficult to bet in real time, limiting the user experience. It was also difficult to accurately predict the development of the game and the performance of the players, resulting in low betting accuracy. In addition, there was a lack of means to increase interest in sports and make the viewing experience more attractive.

[1425] 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.

[1426] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets. This enables real-time sports betting and improves the user's experience. In addition, it is possible to accurately predict the development of the game and the performance of the players, thereby improving the accuracy of betting. Furthermore, it is possible to support the growth and development of the sports industry by increasing interest in sports and making the spectating experience more attractive.

[1427] A "prediction method using generative AI" is a method that uses a generative AI model to make predictions based on unpredictable factors such as in-game developments and player performance.

[1428] "Means of utilizing statistical data" refers to analyzing statistical information such as past match data and player performance data to improve the accuracy of predictions.

[1429] "Low-latency processing means using 5G technology" refers to a means of quickly processing real-time data by utilizing 5G communications technology.

[1430] A "means of converting real-time events into probability" is a means of converting real-time events during a game into probabilities.

[1431] An "instrument for betting on probabilistic events" is a means that allows a user to place bets based on probabilistic events.

[1432] "Means for acquiring real-time data" refers to means for acquiring real-time data during a match.

[1433] "Means for obtaining past data" refers to means for obtaining past match data and player performance data.

[1434] "Means for calculating odds based on prediction results" refers to means for calculating betting odds based on the prediction results by the generating AI.

[1435] "Means for providing a user interface" refers to means for providing an interface for a user to place a bet.

[1436] "Means for processing user bets" refers to means for processing bets entered by users and reflecting the results.

[1437] As an embodiment of the present invention, a real-time sports betting system is constructed. The system includes a prediction means using generative AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a means for acquiring real-time data, a means for acquiring past data, a means for calculating odds based on prediction results, a means for providing a user interface, and a means for processing user bets.

[1438] The server first uses 5G technology to obtain real-time data, including information on player movements and scores during the match. It then obtains past match data and player performance data to use as statistical data. Based on this data, a generative AI model predicts the development of the match and the performance of the players.

[1439] The generative AI model makes predictions using prompts like this:

[1440] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[1441] Real-time data: {Real-time data}

[1442] Past match data: {Past match data}

[1443] Player performance data: {Player performance data}

[1444] After the generative AI model makes a prediction, it calculates odds based on the prediction. The calculated odds are provided to users through a user interface. Users can place bets in real time using devices such as smartphones. Once a user places a bet, the bet is processed by the server and the results are reflected.

[1445] This system allows users to enjoy real-time sports betting and improves the accuracy of their bets by accurately predicting the development of the game and the performance of the players. It also increases interest in sports and makes the viewing experience more attractive, thereby supporting the growth and development of the sports industry.

[1446] For example, while watching a live stream of a soccer match, users can bet in real time on which team will score the next goal. A generative AI model predicts the probability of the next goal based on past data and the current match situation, and calculates the odds. In this way, users can have a more interactive and exciting viewing experience.

[1447] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] The server uses 5G technology to obtain real-time data. Specifically, it obtains information such as player movements and scores during the game through an API. The input is real-time data, and the output is the data obtained during the game.

[1450] Step 2:

[1451] The server retrieves historical match data and player performance data, again through an API: the input is a request for historical match data, and the output is the retrieved historical data.

[1452] Step 3:

[1453] The server inputs the acquired real-time and past data into the generative AI model to predict the development of the game and the performance of the players. The input is real-time and past data, and the output is the prediction results by the generative AI model. In concrete terms, the server inputs the following prompt sentence into the generative AI model:

[1454] Predict which team will score the next goal in the next soccer match. Make your prediction based on the data below.

[1455] Real-time data: {Real-time data}

[1456] Past match data: {Past match data}

[1457] Player performance data: {Player performance data}

[1458] Step 4:

[1459] The server calculates odds based on the prediction results obtained from the generative AI model. The input is the prediction result of the generative AI model, and the output is the calculated odds. In concrete terms, the prediction result is converted into a probability, and the odds are calculated based on that.

[1460] Step 5:

[1461] The server provides the calculated odds to the user through a user interface. The input is the calculated odds, and the output is the odds displayed on the user interface. The specific operation is to display the odds on a smartphone app or web interface.

[1462] Step 6:

[1463] A user places a bet in real time using a device such as a smartphone. The input is the user's betting information, and the output is a betting request. Specifically, the user inputs the betting amount and target, and presses the send button.

[1464] Step 7:

[1465] The server processes the bets entered by the user and reflects the results. The input is the user's betting information, and the output is the betting result. Specifically, the server saves the betting information in a database and calculates the betting result based on the game results.

[1466] This is how the real-time sports betting system operates.

[1467] Example 2

[1468] Next, a second embodiment of the second embodiment will be described. 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".

[1469] In conventional sports betting systems, it was difficult to predict and stochastically predict the development of a game or the performance of players in real time. In addition, there was a lack of means to process data with low latency and provide information to users quickly, which limited the user's betting experience. Furthermore, there were not enough means to increase interest in sports and make the viewing experience more attractive.

[1470] 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.

[1471] In this invention, the server includes a prediction means using a generation AI, a means for utilizing statistical data, a low-latency processing means, a means for probabilistically converting real-time events, a means for making the probabilistic events the subject of betting, a data collection means, a data analysis means, a prediction model generation means, a real-time processing means, and a means for placing bets through a user terminal. This makes it possible to predict and probabilistically convert the development of a game and the performance of players in real time. In addition, by processing data with low latency and providing information to users quickly, the user's betting experience can be improved. Furthermore, by increasing interest in sports and making the spectating experience more attractive, the growth and development of the sports industry can be supported.

[1472] A "prediction method using generative AI" is a method that uses artificial intelligence technology to predict the development of a match and the performance of players.

[1473] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[1474] "Low latency processing means" refers to a means of quickly processing data and providing information in real time, using 5G technology, etc.

[1475] A "means of probabilistically converting real-time events" is a method for analyzing in-game developments and player performance in real time and expressing them as probabilities.

[1476] An "instrument for betting on probabilistic events" is a means that allows a user to place a bet based on probabilistic events.

[1477] "Data collection means" refers to means for collecting past match data and player performance data.

[1478] "Data analysis means" refers to means for analyzing collected data and extracting necessary statistical information.

[1479] A "predictive model generation means" is a means for using a generative AI to generate a model that predicts developments during a match and the performance of players.

[1480] The "real-time processing means" refers to a means for processing data in real time during a match and generating a predicted result.

[1481] A "means for placing a bet through a user terminal" is a means that allows a user to place a bet in real time using a terminal such as a smartphone or tablet.

[1482] This invention is a sports betting system using a soccer game as an example. The system uses a generative AI model to predict and stochasticize the development of the game and the performance of the players in real time. A specific embodiment of this system will be described below.

[1483] The server first collects past match data and player performance data. To do this, it uses a sports data API and stores it in a database (e.g., MySQL). Next, it uses Python libraries (e.g., Pandas and NumPy) to analyze the collected data. This allows it to extract statistical data such as the player's running distance, number of shots, and pass success rate.

[1484] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. The model learns from past data and improves the accuracy of the prediction. The generated model processes data in real time during the game and predicts the probability of the next goal being scored and the probability of a specific player scoring a goal.

[1485] The server uses 5G technology to process data in real time with low latency, allowing for rapid analysis and probability of match developments and player performance. The probability of events is then provided to users as information for making bets in real time.

[1486] Users can place bets in real time using devices such as smartphones and tablets. The user device receives the prediction results provided by the server and displays them to the user. Through the app, users can perform operations such as "betting on the team with the highest probability of scoring the next goal."

[1487] As a concrete example, let's say a user is watching a soccer game on their smartphone. During the game, the server collects data in real time and uses a generative AI model to predict the probability of the next goal being scored. Through the app, the user can "bet on the team most likely to score the next goal."

[1488] An example of a prompt is as follows:

[1489] "Please predict the probability of a goal being scored in the next 10 minutes. Also, what is the probability of a specific player scoring a goal?"

[1490] In this way, a system is realized in which servers, terminals, and users work together to process data in real time and provide prediction results.

[1491] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1492] Step 1: Data collection

[1493] The server collects past match data and player performance data. Specifically, it uses a sports data API to obtain the data and stores it in a database (e.g., MySQL).

[1494] Input: Match and player data from the Sports Data API

[1495] Output: Match and player data stored in a database

[1496] Specific operation: The server periodically calls the API to obtain the latest match data, which is then inserted into the database.

[1497] Step 2: Data analysis

[1498] The server uses Python libraries (e.g., Pandas, NumPy) to analyze the collected data, extracting statistics such as players' distance traveled, number of shots taken, and pass success rate.

[1499] Input: Match and player data read from the database

[1500] Output: Statistical data (e.g. player distance traveled, number of shots taken, pass success rate)

[1501] What happens: The server reads the data from the database, converts it to a data frame using Pandas, and then uses NumPy to perform statistical calculations and calculate the necessary metrics.

[1502] Step 3: Generate a predictive model

[1503] The server uses a generative AI model (e.g., TensorFlow, PyTorch) to generate a model that predicts the development of the game and the performance of the players. This model learns from past data and improves the accuracy of its predictions.

[1504] Input: Statistics and past match data

[1505] Output: A trained predictive model.

[1506] How it works: The server inputs past match data into TensorFlow to train a neural network. Once trained, the model is saved and used for real-time predictions.

[1507] Step 4: Real-time processing

[1508] The server will use 5G technology to process data in real time with low latency, allowing for rapid analysis and probabilistic predictions of in-game developments and player performance.

[1509] Input: Match and player data collected in real time

[1510] Output: Probabilistic predictions (e.g. the probability of the next goal being scored, or the probability of a specific player scoring a goal)

[1511] How it works: The server collects data in real time during a match and feeds it into a generative AI model, which predicts the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1512] Step 5: Odds and offer bets

[1513] The server provides the probabilistic events to the users, who can then place bets in real time via their devices (e.g. smartphones, tablets).

[1514] Input: Probabilistic forecast results

[1515] Output: Betting information provided to the user

[1516] Specific operation: The server sends the prediction results to the terminal via API. The user checks the prediction results through the app and places a bet. The terminal sends the user's betting information to the server, which records it.

[1517] (Application example 2)

[1518] Next, a description will be given of Application Example 2 of Form 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".

[1519] Conventional sports betting systems had the problem that it was difficult for users to predict the development of the game and the performance of the players in real time and place bets immediately. In addition, the user experience was often impaired because data processing was not performed with low latency. Furthermore, the interface for users to place bets was insufficient, and the appeal of watching sports could not be fully realized.

[1520] 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.

[1521] In this invention, the server includes a prediction means using a generating AI, a means for utilizing statistical data, a low-latency processing means using 5G technology, a means for probabilistically predicting real-time events, a means for making the probabilistic events the subject of betting, a real-time data collection means, and a means for providing an interface for users to place bets. This allows the development of a game and the performance of players to be predicted in real time, and enables users to immediately place bets. In addition, low-latency data processing improves the user experience and brings out the appeal of watching sports.

[1522] A "prediction method using generative AI" is a method for predicting in-game developments and player performance using a generative AI model.

[1523] "Means of utilizing statistical data" refers to analyzing past match data and player performance data to improve the accuracy of predictions.

[1524] A "low-latency processing means using 5G technology" is a means for performing real-time data processing with low latency using 5G technology.

[1525] A "means for probabilistically determining real-time events" is a means for determining the probability of events occurring during a match in real-time and displaying the results.

[1526] A "probabilistic event betting instrument" is one that allows a user to place a bet in real time based on probabilistic events.

[1527] "Real-time data collection means" refers to a means for collecting data during a match in real time.

[1528] "Means for providing an interface for users to place bets" refers to means for providing an interface for users to place bets and facilitating betting operations.

[1529] The present invention is a system that predicts the development of a soccer game and the performance of players in real time, and enables users to place bets immediately. A specific embodiment for realizing this system is described below.

[1530] System Configuration

[1531] The system consists of the following main components:

[1532] 1. Generative AI-based prediction methods

[1533] The server uses generative AI models to predict in-game developments and player performance, using data from past matches and player performance data.

[1534] 2. Ways to use statistical data

[1535] The server analyzes past match data and player performance data to improve the accuracy of predictions. This data is stored in a database and can be accessed as needed.

[1536] 3. Low-latency processing means using 5G technology

[1537] The servers will use 5G technology to process data in real time with low latency, allowing data during matches to be processed quickly and provided to users.

[1538] 4. A means of probabilistic real-time events

[1539] The server calculates the real-time probability of events during a match and displays the results to the user, such as the probability of the next goal being scored or the probability of a particular player scoring a goal.

[1540] 5. Means of betting on probabilistic events

[1541] The server allows users to place bets in real time based on probabilistic events, and an interface is provided for this.

[1542] 6. Real-time data collection methods

[1543] The server collects data in real time during the match, which is retrieved through an API and sent to the server.

[1544] 7. Means of providing an interface for users to place bets

[1545] The terminal provides an interface through which users can place bets, which may be displayed on a device such as a smartphone or a head-mounted display.

[1546] Program processing explanation

[1547] The server uses generative AI models to predict how matches will play out and how well players will perform, using historical match data and player performance data that is stored in a database and accessed as needed.

[1548] The server uses 5G technology to process data in real time with low latency. This allows data during the match to be processed quickly and provided to users. Events during the match are probabilistic in real time, and the results are displayed to the user. For example, the probability of the next goal being scored or the probability of a specific player scoring a goal is displayed.

[1549] The terminal provides an interface through which users can place bets. The interface is displayed on a device such as a smartphone or a head-mounted display. Through the interface, users can place bets in real time.

[1550] Examples

[1551] For example, while watching a soccer game on a smartphone, a user can place a bet on the moment when the probability of the next goal being scored is predicted. It is also possible to use a head-mounted display to check the performance data of players during the game in real time and make betting decisions.

[1552] Examples of prompt statements

[1553] Predict the probability of the next goal being scored and display it in real time. Also display the probability of a specific player scoring a goal. Provide an interface for users to place bets.

[1554] As described above, this invention is a system that predicts the development of soccer games and the performance of players in real time and allows users to place bets instantly, thereby improving the viewing experience of the game and supporting the growth and development of the sports industry.

[1555] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1556] Step 1:

[1557] The server retrieves past match data and player performance data from the database. These data are used to train the generative AI model. The input is the past match data and player performance data, and the output is the trained generative AI model.

[1558] Step 2:

[1559] The server uses generative AI models to predict game developments and player performance. The input is game data collected in real time, and the output is a prediction, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1560] Step 3:

[1561] The server uses 5G technology to process data in real time with low latency. The input is match data collected in real time and the prediction results of the generative AI model, and the output is the prediction results processed with low latency. This allows information to be provided to users quickly.

[1562] Step 4:

[1563] The server generates real-time probabilistic predictions for game events. The input is the predictions of the generative AI model, and the output is probabilistic data, such as the probability of the next goal being scored or the probability of a specific player scoring a goal.

[1564] Step 5:

[1565] The server provides an interface that allows users to place bets in real time based on probabilistic events. The input is the probabilistic data and the output is the user interface. Users can place bets through this interface.

[1566] Step 6:

[1567] The terminal displays an interface through which the user can place bets. The input is the user interface provided by the server, and the output is the interface displayed on the terminal's screen. The user uses this interface to place bets.

[1568] Step 7:

[1569] A user places a bet through a terminal. The input is the user's betting information, and the output is the betting information sent to the server. The server processes the bet based on this information.

[1570] Through these steps, the system can predict in-game developments and player performance in real time, allowing users to place bets instantly, improving the viewing experience and supporting the growth and development of the sports industry.

[1571] In addition, 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.

[1572] "Example 1"

[1573] As one embodiment of the present invention, there is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the subject of betting. Furthermore, the emotion engine recognizes the user's emotions in real time and makes betting suggestions based on those emotions. For example, if a user is excited about a game, the emotion engine recognizes that excitement and makes betting suggestions that match that excitement. This allows users to make bets based on their own emotions, resulting in a more enjoyable watching experience.

[1574] "Example 2"

[1575] Another embodiment of the present invention is a sports betting system that combines an emotion engine. This system combines elements of generation AI, statistical data, 5G technology, real-time event probabilization, probabilistic events as betting targets, and recognition of user emotions. Specifically, the generation AI predicts the development of the game and the performance of the players, and improves the accuracy of the prediction by utilizing statistical data. Low-latency processing using 5G technology processes the development of the game and the performance of the players in real time and probabilizes them. Then, the probabilistic events are the target of betting. Furthermore, the emotion engine recognizes the user's emotions and predicts the result of the bet based on the emotion. For example, if a user is excited about a game, the emotion engine recognizes the excitement and predicts the result of the bet according to the excitement. This allows the user to predict the result of the bet based on their own emotions, resulting in a more enjoyable watching experience.

[1576] The process flow of each embodiment will be described below.

[1577] "Example 1"

[1578] Step 1: Generative AI predicts game developments and player performance.

[1579] Step 2: Use statistical data to improve prediction accuracy.

[1580] Step 3: Using low-latency processing with 5G technology, in-game developments and player performance are processed in r...

Claims

1. A prediction method using generative AI, Means of using statistical data; A low-latency processing means using communication technology; A means of probabilistically predicting real-time events; A means for placing bets on probabilistic events; Including, A system for providing betting information to a user using said low latency processing means.

2. The system according to claim 1 , wherein the prediction means using the generating AI makes predictions based on the development of the game, the performance of players, and other factors.

3. 2. The system of claim 1, wherein the system enables a user to select an object to bet on, make predictions using the generative AI model, analyze statistical data, transmit real-time data using communications technology, calculate probabilities based on the real-time data, provide the probabilistic events as objects to bet on, and allow the user to place bets in real time.

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