System

Abstract

A system is provided.SOLUTION: A system includes an explanation setting means, a user setting information transmitting means, a real-time data acquiring means, an explanation generating means, an error detecting means, and an explanation providing 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] One problem with modern sports viewing is the difficulty of providing commentary tailored to the individual needs of casual or inexperienced fans. Traditional commentary provides the same content to all spectators, which is likely to result in low satisfaction for spectators with different areas of expertise or interests. Particularly in the case of major sporting events, there is the issue of low retention rates among fans who only attend temporarily. This makes it difficult for sports team management companies, sponsors, and broadcasting companies to increase fan bases and stabilize revenue over the long term. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system that allows users to individually set their commentary style and provides commentary that matches that style in real time. Specifically, by combining a commentary setting means, a user-set information transmission means, a real-time data acquisition means, a commentary generation means, an error detection means, and a commentary provision means, the system allows users to select their preferred commentary style and specific information, and provides commentary generated using an AI model in real time. This aims to personalize sports viewing, improve fan satisfaction, and increase long-term fan retention.

[0006] The "explanation setting means" is a function that provides a user interface for the user to select their preferred explanation style and specific information.

[0007] The "user setting information transmission means" is a function for transmitting the commentary style and specific information set by the user to the server.

[0008] The "real-time data acquisition means" is a function that acquires real-time data related to a sporting event from an external data source.

[0009] The "explanation generation means" is a function that generates explanations using an AI model based on user setting information and real-time data.

[0010] The "error detection means" is a function that detects errors in the generated commentary content and corrects them as necessary.

[0011] The "explanation providing means" is a function that streams the generated explanation to the user terminal in real time.

[0012] "User terminal" refers to the device used by the user to receive the explanation, such as a PC, smartphone, or tablet.

[0013] A "commentary server" is a central server that performs the primary processing for creating and providing commentary.

[0014] The "data analysis server" is a server that acquires real-time data related to sporting events from an external source and provides it to the commentary server. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention is composed of three main components: a user terminal, a commentary server, and a data analysis server. The role of each component and the specific processing flow are explained below.

[0037] Initial setup on the user device

[0038] A dedicated application is first launched on the user's device. This application displays a login screen for the user, and once authentication is complete, a settings screen is displayed. The settings screen allows the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Once the user has completed their selection, this information is sent from the device to the commentary server.

[0039] Examples:

[0040] The user starts the application and selects "Expert Commentary" and "Emphasis on Player Data." This setting information is immediately sent to the commentary server.

[0041] Sending configuration information

[0042] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0043] Real-time data acquisition

[0044] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0045] Examples:

[0046] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0047] Analysis of explanatory data

[0048] The commentary server uses AI models to generate commentary based on user-submitted settings and real-time data obtained from the data analysis server. This commentary is tailored to the user's preferences and, if necessary, checked by error detection methods. Any inaccuracies are corrected.

[0049] Examples:

[0050] The commentary server generates a professional commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season."

[0051] Providing commentary

[0052] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing the user to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0053] Examples:

[0054] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0055] System Features

[0056] The system's unique feature is that users can customize their commentary style and the information they are interested in. Furthermore, the combination of real-time data acquisition, commentary generation using AI models, and accurate information provision using error detection methods allows for commentary that is perfectly tailored to individual needs. Furthermore, because commentary is provided in real time, users can receive the latest information as the match progresses.

[0057] This system allows casual fans and those with little experience to enjoy watching sports, and can improve fan retention rates.

[0058] ---

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The device launches the application, displays a login screen to the user and prompts them to authenticate, and once the user enters their authentication information and authentication is complete, displays the settings screen.

[0062] Step 2:

[0063] The device presents users with options for commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.), and the user selects their preferred settings from these options.

[0064] Step 3:

[0065] The terminal collects information about the commentary style set by the user and the sporting event being watched, and transmits this setting information to a commentary server.

[0066] Step 4:

[0067] The explanation server stores the received user setting information and prepares to generate an individual explanation for each user.

[0068] Step 5:

[0069] The commentary server sends a request to the data analysis server to obtain real-time data related to the sporting event being watched.

[0070] Step 6:

[0071] The data analysis server obtains real-time data such as the progress of the match, player performance data, and statistical information from external data sources (such as APIs and official databases), and sends this data to the commentary server.

[0072] Step 7:

[0073] The commentary server receives real-time data sent from the data analysis server and generates commentary in combination with user settings. It uses an AI model to create commentary in a style selected by the user.

[0074] Step 8:

[0075] The commentary server checks the generated commentary with an internal error detection function and corrects any inaccuracies, with expert supervision if necessary.

[0076] Step 9:

[0077] The commentary server prepares to stream the generated commentary to the user device in real time.

[0078] Step 10:

[0079] The device receives the streamed commentary from the commentary server and displays or plays audio to the user, who can receive commentary in real time based on their selection.

[0080] ---

[0081] The above is a detailed description of the system's specific processing steps and operations. This process allows users to enjoy commentary tailored to their preferences in real time.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] A problem with conventional sports commentary systems is that they are unable to provide commentary that meets the individual needs of each user. For example, general commentary cannot fully meet the specialized information or interests of individual users, resulting in a lower quality of viewing experience. Furthermore, there are issues with the accuracy and currency of the data acquired in real time, which can lead to the provision of incorrect information. There is a need for a system that can solve these issues and provide commentary with a higher level of user satisfaction.

[0085] 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.

[0086] In this invention, the server includes commentary setting means for allowing a user to select a preferred commentary style and specific information, user setting information transmission means for transmitting setting information from a user terminal to the commentary server, real-time data acquisition means for acquiring real-time data related to a sport event from an external data source to the commentary server, commentary generation means for generating commentary using a generative AI model, error detection means for detecting errors in the generated commentary and correcting inaccurate information, and commentary provision means for providing the generated commentary to the user terminal in real time. This makes it possible to provide professional and accurate commentary tailored to the individual needs of users, as well as to acquire and provide the latest and accurate information in real time.

[0087] The "explanation setting means" is a means for the user to select the preferred explanation style and specific information, and provides a user interface.

[0088] The "user setting information transmission means" is a means for transmitting setting information from the user terminal to the commentary server.

[0089] The "real-time data acquisition means" is a means for acquiring real-time data related to a sporting event from an external data source to the commentary server.

[0090] "Explanation generation means" refers to a means for generating explanations based on user setting information and acquired real-time data using a generative AI model.

[0091] The "error detection means" is a means for detecting errors in the generated explanation and correcting inaccurate information if it is included.

[0092] The "explanation providing means" is a means for providing the generated explanation to the user terminal in real time.

[0093] MODE FOR CARRYING OUT THE INVENTION

[0094] The system of the present invention is mainly composed of three main components: a user terminal, a commentary server, and a data analysis server. A specific embodiment of the system will be described below.

[0095] Initial setup on the user device

[0096] First, the user launches a dedicated application on their device. This application displays a login screen for the user and authenticates them by entering their login information (e.g., user ID and password). Once authentication is complete, a settings screen is displayed, where the user selects their preferred commentary style (e.g., professional, humorous, matter-of-fact) and information of interest (e.g., player data, tactical analysis). This setting information is immediately sent from the user device to the commentary server.

[0097] Examples:

[0098] The user starts the application and selects "Expert commentary" and "Emphasis on player data." This setting information is immediately sent to the commentary server.

[0099] Processing on the commentary server

[0100] The commentary server receives the setting information sent by the user. The server creates a user profile based on this information and prepares to generate commentary tailored to the user's individual needs. The commentary server then sends a request to the data analysis server to obtain real-time data.

[0101] Real-time data acquisition

[0102] The data analysis server acquires real-time match data, player performance data, and statistical information from external sources (e.g., sports data providing services), and transmits the acquired data to the commentary server.

[0103] Examples:

[0104] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0105] Analysis and generation of explanatory data

[0106] The commentary server generates commentary using a generative AI model (e.g., GPT-3) based on the acquired real-time data and the settings information received from the user. The generated commentary is checked by an error detection system, and inaccurate information is corrected if necessary.

[0107] Examples:

[0108] Prompt: "Please explain Player A's goal."

[0109] Generated commentary: "Player A scores a goal. His last pass success rate was 90%, which is above his average success rate this season."

[0110] Providing commentary

[0111] The commentary server then streams the generated commentary to the user's device in real time, allowing the user to enjoy watching the sports game while listening to the commentary.

[0112] Examples:

[0113] The commentary generated by the commentary server is streamed to the user's device in real time, and the user watches the commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0114] This system allows users to receive detailed and interesting commentary tailored to their preferences in real time, significantly improving the sports viewing experience. It is also expected to help casual fans and those with little experience of sports to experience the appeal of sports, thereby improving fan retention rates.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user launches the application. A login screen is displayed, and the user enters their ID and password. Once authentication is complete, the settings screen is displayed. The user selects a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis). The input is the user's selected commentary style and information of interest, and the output is the settings information for these. The settings information is sent from the user's terminal to the commentary server. In concrete terms, the user selects "professional commentary" and "emphasis on player data," and this information is sent to the server.

[0118] Step 2:

[0119] The commentary server receives the setting information sent by the user. The input is the setting information sent from the user terminal, and the output is the user profile stored in the commentary server. Based on this information, the commentary server creates a profile for the user and prepares to generate a dedicated commentary. Specifically, the commentary server creates a profile called "user123" and prepares to generate a commentary.

[0120] Step 3:

[0121] The commentary server sends a request to the data analysis server to obtain real-time data. The input is the request from the commentary server, and the output is a completion log of the request sent to the data analysis server. Specifically, a request such as "Please obtain real-time data for match ID 5678" is sent.

[0122] Step 4:

[0123] The data analysis server obtains real-time data from external data sources. The inputs are match data, player performance data, and statistical information obtained from external data sources, and the output is these data. The data analysis server sends this data to the commentary server. Specifically, data such as "Player A's current pass success rate is 90%" is obtained and sent to the commentary server.

[0124] Step 5:

[0125] The commentary server generates commentary using a generative AI model (for example, GPT-3) based on the acquired real-time data and the setting information received from the user. The input is real-time data and setting information, and the output is the generated commentary. The commentary is generated based on the prompt text. Specifically, based on the prompt text "Please explain Player A's goal," the commentary generated is "Player A has scored a goal. His pass success rate immediately before that was 90%, which is higher than the average success rate for this season."

[0126] Step 6:

[0127] The commentary server checks the generated commentary with an error detection system. The input is the generated commentary, and the output is the corrected commentary. If it contains inaccurate information, it is corrected. Specifically, the generated commentary is checked with the error detection system, and corrections are made if necessary.

[0128] Step 7:

[0129] The commentary server streams the final commentary to the user's device in real time. The input is the completed commentary text, and the output is the commentary delivered to the user's device. The user can enjoy watching the sports game while viewing this. Specifically, the commentary server streams commentary such as "Player A scored a goal..." to the user's device, and the user views it in real time.

[0130] (Application example 1)

[0131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] Conventional sports commentary systems have difficulty providing real-time commentary that meets individual user preferences, and lack the functionality to send appropriate notifications to users when specific events occur, creating a need for an improved user experience.

[0133] 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.

[0134] In this invention, the server includes an explanation setting means, a user setting information transmission means, a real-time data acquisition means, an explanation generation means, an error detection means, an explanation providing means, and a notification means for transmitting a notification to a user terminal, thereby enabling the provision of individual explanations based on the user's preferences and the transmission of notifications in real time.

[0135] The "explanation setting means" is a function that provides an interface that allows the user to select their preferred explanation style and specific information.

[0136] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the server.

[0137] The "real-time data acquisition means" is a function that collects real-time data related to a sporting event from an external data source.

[0138] The "explanation generation means" is a function that generates explanations using a generative AI model based on collected real-time data and user setting information.

[0139] The "error detection means" is a function that checks whether the generated commentary contains any inaccurate information and corrects it if necessary.

[0140] The "explanation providing means" is a function that streams the generated explanation to the user's terminal.

[0141] "Notification means" is a function that sends a push notification to the user's device when a specific trigger event occurs.

[0142] A "generative AI model" is an artificial intelligence model that generates commentary based on user settings and real-time data.

[0143] A "prompt" is an instruction that instructs an AI model to generate an explanation.

[0144] The system for implementing the present invention is configured using the following hardware and software.

[0145] Hardware and software used

[0146] User device: Smartphone

[0147] Explanation server: Cloud-based server (e.g. AWS)

[0148] Data analysis server: Collects real-time data using external APIs (e.g., SportsAPI)

[0149] Generative AI models: For example, OpenAI's GPT-3

[0150] User Device

[0151] First, the user launches a dedicated application on their smartphone. The application displays a login screen and asks the user for authentication. Once authentication is complete, the user proceeds to a settings screen where they can select a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). This setting information is sent to the commentary server by the user setting information sending means.

[0152] Commentary Server

[0153] The commentary server works with the data analysis server to collect data related to sporting events in real time based on the setting information received from the user. The commentary generation means uses a generative AI model to generate commentary based on the collected real-time data and the user's setting information. At this time, an error detection means is used to check whether the content of the commentary is accurate. If necessary, corrections are made automatically.

[0154] The generated commentary is streamed to the user terminal by the commentary providing means, and when a specific trigger event occurs, a push notification is sent to the user via the notification means.

[0155] Data analysis server

[0156] The data analysis server retrieves real-time data about the progress of sporting events and players from external data sources, for example using the SportsAPI, and sends this data to the commentary server, where it is used as input data for generating commentary.

[0157] Specific examples

[0158] The user starts the application, authenticates on the login screen, and then selects "Expert commentary" and "Player data emphasis." This setting information is sent to the commentary server via the user setting information transmission means. The commentary server receives real-time data from the data analysis server and generates commentary based on that data using a generative AI model. After that, a commentary such as "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season" is streamed in real time to the user's device. An example of the prompt sentence used in this generation is as follows:

[0159] "Player A has scored a goal. His pass success rate just before the goal was 90%, which is higher than his average success rate this season. Please provide further commentary from your expert perspective."

[0160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0161] Step 1:

[0162] The user device launches a dedicated application and displays the login screen. When the user enters their authentication information, the application sends the data to the authentication server and the authentication is completed. The input is the user's authentication information, and the output is the authentication completion status.

[0163] Step 2:

[0164] After authentication is complete, the user's device displays a setting screen for selecting a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). The user inputs each selection. The input is the user's commentary style and information of interest, and the output is the user's setting information.

[0165] Step 3:

[0166] When the user completes the setting, the user terminal uses the user setting information transmission means to transmit the setting information to the commentary server. The input is the user setting information, and the output is the setting information transmitted to the server.

[0167] Step 4:

[0168] The commentary server receives user setting information and works with the data analysis server to collect data related to sporting events in real time. This data collection uses APIs of external data sources (e.g., SportsAPI). The input is user setting information and the output is real-time data.

[0169] Step 5:

[0170] The data analysis server sends the collected real-time data to the commentary server. The commentary server generates commentary using a generative AI model based on user setting information and real-time data. The input is user setting information and real-time data, and the output is the generated commentary.

[0171] Step 6:

[0172] The explanation server uses error detection to check the generated explanation for inaccuracies and makes corrections as necessary. The input is the generated explanation, and the output is a verified, accurate explanation.

[0173] Step 7:

[0174] The generated commentary is streamed to the user terminal through the commentary providing means, and the user can view the commentary in real time. The input is the verified commentary, and the output is the commentary streamed on the user terminal.

[0175] Step 8:

[0176] When a specific trigger event occurs (e.g., a goal or an important play), the commentary server sends a push notification to the user via the notification mechanism. The input is the trigger event information, and the output is the push notification sent to the user device.

[0177] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0178] The present invention provides a system that personalizes sports viewing and further provides a richer entertainment experience by recognizing a user's emotions and adjusting commentary accordingly. Specific embodiments of the system of the present invention are described below.

[0179] System Configuration

[0180] This system consists of four main components: a user terminal, a commentary server, a data analysis server, and an emotion engine. The role and processing flow of each component are explained in detail below.

[0181] Initial setup on the user device

[0182] A dedicated application is launched on the user's device, and a login screen is displayed to the user. Once authentication is complete, a settings screen is displayed. On the settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.). The camera and microphone can also be used to recognize the user's emotions. Once the user has completed their selection, this setting information is sent from the device to the commentary server.

[0183] Examples:

[0184] The user launches the application, selects "humorous commentary" and "focus on player data," and allows the camera and microphone. This setting information is immediately sent to the commentary server.

[0185] Sending configuration information

[0186] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0187] Real-time data acquisition

[0188] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0189] Examples:

[0190] The data analysis server obtains game progress and player performance data from external data sources and sends it to the commentary server.

[0191] Emotion Engine Operation

[0192] The emotion engine recognizes the user's emotional state through their facial expressions and voice, and this recognized emotional data is sent to the commentary server in real time.

[0193] Examples:

[0194] If the user is smiling, the emotion engine detects the state of "happiness" and sends the data to the commentary server.

[0195] Analysis of explanatory data

[0196] The commentary server generates commentary using an AI model based on user settings, real-time data, and emotion data from the emotion engine. In particular, the commentary content can be adjusted according to the user's emotional state. Furthermore, the accuracy of the generated commentary content is checked by an error detection means, and corrected if necessary.

[0197] Examples:

[0198] The commentary server generates a professional yet humorous commentary: "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0199] Providing commentary

[0200] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing users to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0201] Examples:

[0202] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0203] System Features

[0204] The system's unique features include the ability for users to individually set their commentary style and the information they are interested in, as well as the ability to recognize users' emotions and provide real-time commentary accordingly, allowing users to receive commentary tailored to their individual needs and providing a more personalized entertainment experience.

[0205] The aim of this system is to provide casual and less experienced fans with the enjoyment of watching sports, and to improve fan retention rates.

[0206] The processing flow will be explained below.

[0207] Step 1:

[0208] The device presents the user with a login screen, waits for the user to enter their credentials, and then waits for the authentication to complete.

[0209] Step 2:

[0210] After logging in, the device displays the commentary settings screen, where the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.).

[0211] Step 3:

[0212] The device collects the user's configuration information and sends it to the commentary server.

[0213] Step 4:

[0214] The commentary server stores the received user setting information and prepares for individual commentary generation.

[0215] Step 5:

[0216] The commentary server sends a request to the data analysis server to obtain real-time data about the sporting event.

[0217] Step 6:

[0218] The data analysis server obtains the progress of the match, player performance data, statistical information, etc. from external data sources and sends the data to the commentary server.

[0219] Step 7:

[0220] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, and the emotion data is sent from the device to the commentary server.

[0221] Step 8:

[0222] The commentary server generates commentary using an AI model based on real-time data sent from the data analysis server, user setting information, and emotion data from the emotion engine. In particular, it adjusts the content of the commentary according to the user's emotions.

[0223] Step 9:

[0224] The commentary server checks the accuracy of the commentary content generated by the commentary server using an error detection means and corrects it if necessary.

[0225] Step 10:

[0226] The commentary server prepares to stream the generated commentary to the user terminal in real time.

[0227] Step 11:

[0228] The device receives the streaming commentary from the commentary server and displays or plays the audio to the user, allowing the user to enjoy commentary tailored to their preferences in real time.

[0229] Specific examples

[0230] For example, suppose a user selects "humorous commentary" and "focus on player data" and allows the camera and microphone. As the match progresses, when Player A scores a goal, the data analysis server collects this information and sends it to the commentary server. At the same time, the emotion engine detects the user's emotional state of "joy" and sends it to the commentary server. The commentary server generates a commentary such as "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season," and provides it with humor depending on the user's emotional state.

[0231] Example 2

[0232] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0233] Conventional sports viewing systems have difficulty providing personalized commentary based on individual user preferences, and are unable to reflect the user's emotional state in real time. As a result, the user's entertainment experience is not improved. The present invention aims to provide commentary tailored to the user's preferences, recognize the user's emotional state in real time, and provide commentary content that corresponds to the user's preferences, thereby realizing a richer entertainment experience.

[0234] 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.

[0235] In this invention, the server includes an explanation setting means, a user setting information transmitting means, a real-time data acquiring means, an emotion recognition means, an explanation generating means, an error detecting means, and an explanation providing means, which make it possible to generate personalized explanations based on the user's setting information, collect real-time data, and adjust the explanation content according to the user's emotional state.

[0236] The "explanation setting means" is a function that provides a user interface for the user to select their preferred explanation style and specific information.

[0237] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the commentary server.

[0238] The "real-time data acquisition means" is a function that acquires real-time data related to a sporting event from an external data source.

[0239] The "emotion recognition means" is a function that analyzes facial expression data and voice data to recognize the user's emotions.

[0240] The "explanation generation means" is a function that generates an explanation using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means.

[0241] The "error detection means" is a function that checks the accuracy of the generated commentary content and corrects it if necessary.

[0242] The "explanation providing means" is a function that provides the generated explanation to the user in real time.

[0243] The present invention provides a sports viewing system that provides commentary tailored to individual user needs and further adjusts the commentary content by recognizing the user's emotions in real time. This system comprises a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means.

[0244] System Configuration

[0245] Initial setup on the user device

[0246] A dedicated application is installed on the user's device. When the application is launched, a login screen is displayed to the user. After the user enters their authentication information and completes the authentication, a settings screen is displayed, allowing the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Furthermore, permission to use the camera and microphone can be selected on the settings screen. This setting information is sent from the user's device to the commentary server.

[0247] Specifically, a user launches the application, selects "humorous commentary" and "focus on player data," and authorizes the use of the camera and microphone. This setting information is immediately sent to the commentary server.

[0248] Sending configuration information

[0249] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0250] Real-time data acquisition

[0251] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, the progress of the match and player performance data can be obtained through the API.

[0252] Emotion Engine Operation

[0253] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0254] Specifically, when the user is smiling, the emotion engine detects a state of "joy" and sends that data to the commentary server.

[0255] Analysis of explanatory data

[0256] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0257] Specifically, the commentary server generates a professional yet humorous commentary such as, "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0258] Providing commentary

[0259] The commentary providing means streams the generated commentary to the user's terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0260] Specifically, the commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0261] Prompt Sentence Examples

[0262] Below are some example prompts to be input to the generative AI model:

[0263] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0264] This system allows users to enjoy watching sports more enjoyably by listening to commentary tailored to their preferences in real time.

[0265] (End)

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] Initial setup on the user device

[0269] When a user launches the dedicated application, a login screen appears. After the user enters their authentication information and completes the authentication, they can access the settings screen. On this settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information they are interested in (player data, tactical analysis, etc.). They can also select whether to allow the use of the camera and microphone. This setting information is sent from the device to the commentary server.

[0270] Input: User credentials, commentary style, interests, camera and microphone permissions

[0271] Output: Send user setting information to the commentary server

[0272] Step 2:

[0273] Sending configuration information

[0274] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0275] Input: User setting information

[0276] Output: Save the configuration information to the server

[0277] Step 3:

[0278] Real-time data acquisition

[0279] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, it obtains match progress and player performance data via an API.

[0280] Input: Real-time data requests from external sources

[0281] Output: Sending real-time data to the commentary server

[0282] Step 4:

[0283] Emotion Engine Operation

[0284] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0285] Input: User's facial expression data, voice data

[0286] Output: Sending emotional state data to the commentary server

[0287] Step 5:

[0288] Analysis of explanatory data

[0289] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0290] Input: User setting information, real-time data, emotion data

[0291] Output: Adjusted commentary

[0292] Step 6:

[0293] Providing commentary

[0294] The commentary providing means streams the generated commentary to the user terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0295] Input: Adjusted commentary

[0296] Output: Commentary on user's device

[0297] As a specific example of how this works, the following prompt sentence is an example of input to the generative AI model.

[0298] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0299] (Application example 2)

[0300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In conventional sports viewing systems, users often receive uniform commentary, making it difficult to provide a personalized entertainment experience based on individual preferences and emotions. As a result, user satisfaction and interest decline, limiting the ability to improve fan retention. Furthermore, if the commentary content does not match the user's emotional state, the viewing experience itself is likely to be interrupted. A new system that solves these issues is needed.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means. This makes it possible to generate and provide a commentary personalized for each user, and further enables real-time commentary adjustment according to the user's emotional state. This is expected to significantly improve the sports viewing experience, and increase user satisfaction and fan retention.

[0303] The "explanation setting means" is a means for providing a user interface for the user to select a preferred explanation style or specific information.

[0304] The "user setting information transmission means" is a means for transmitting information set by the user to the server.

[0305] The "real-time data acquisition means" is a means for acquiring real-time data related to an event from an external data source.

[0306] The "emotion recognition means" is a means for recognizing the emotional state of a user by analyzing their facial expressions and voice.

[0307] The "explanation generating means" is a means for generating an explanation based on user setting information, real-time data, and emotion data.

[0308] The "error detection means" is a means for checking the accuracy of the generated commentary content and correcting it if necessary.

[0309] The "explanation providing means" is a means for providing the generated explanation to the user in real time.

[0310] The system of the present invention personalizes sports viewing, recognizes user emotions, and adjusts commentary accordingly to provide a richer entertainment experience. Specific embodiments of the system are described below.

[0311] System Configuration

[0312] The system consists of the following main components:

[0313] 1. Explanation setting method

[0314] 2. Means of sending user setting information

[0315] 3. Real-time data acquisition methods

[0316] 4. Emotion recognition means

[0317] 5. Explanation Generation Method

[0318] 6. Error Detection Methods

[0319] 7. Means of providing explanations

[0320] program

[0321] The system's programming includes the following processes:

[0322] Explanation setting method

[0323] A dedicated application is launched on the user's device (e.g., a smartphone), and the user authenticates through a login screen. Once authentication is complete, a settings screen is displayed, allowing the user to select a commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). This setting information is sent from the device to the commentary server.

[0324] User setting information transmission method

[0325] The information selected by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0326] Real-time data acquisition method

[0327] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0328] emotion recognition means

[0329] The system recognizes the user's emotional state through their facial expressions and voice. This recognized emotional data is sent to the commentary server in real time. The hardware uses a camera and microphone, and the software uses EmotionRecognizer.

[0330] Explanation generation method

[0331] The commentary server generates commentary using a generative AI model based on user settings, real-time data, and emotion data. The accuracy of the generated commentary is verified by an error detection means.

[0332] Error detection means

[0333] Check the accuracy of the generated commentary and correct it if necessary.

[0334] Means of providing explanations

[0335] The generated commentary is provided to users in real time. The commentary is streamed to the user's device, allowing them to enjoy the game while listening to commentary tailored to their preferences. The emotionally tailored commentary increases user interest and satisfaction.

[0336] Specific examples

[0337] A user launches the application on their smartphone, selects "humorous commentary" and "focus on player data," and allows the use of the camera and microphone. This information is sent to the commentary server, which generates commentary in real time based on the settings. If the user's facial expression is smiling, EmotionRecognizer detects a state of "joy," and the commentary is provided in a more positive tone.

[0338] Example prompt sentence:

[0339] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[0340] In this way, the system can provide a rich sports viewing experience based on the user's emotions and individual commentary requirements.

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] The user launches the dedicated application on their smartphone, and a login screen appears. The user performs login authentication, and if authentication is successful, the device displays the commentary setting screen. On this setting screen, the user selects the commentary style (e.g., professional, humorous, matter-of-fact) and the information of interest (player data, tactical analysis). In addition, the user is asked for permission to use the camera and microphone. Once the user completes the settings and allows use of the camera and microphone, the device sends this information to the server.

[0344] Input: User login information, commentary style, interests, camera and microphone permissions

[0345] Output: User setting information

[0346] Step 2:

[0347] The explanation server receives the user setting information sent from the terminal, saves the setting information, and starts preparations to generate an explanation based on the user's individual settings.

[0348] Input: User setting information

[0349] Output: Save the configuration information

[0350] Step 3:

[0351] The commentary server works in conjunction with the data analysis server to obtain real-time match data, player performance data, and statistical information from external data sources, and the data analysis server provides this data to the commentary server.

[0352] Inputs: Match data, performance data, and statistics from external data sources

[0353] Output: Real-time data

[0354] Step 4:

[0355] The device uses a camera and microphone to capture the user's facial expressions and voice data. These data are then processed into emotion data using the EmotionRecognizer library. The emotion recognition means then transmits this emotion data to the commentary server in real time.

[0356] Input: User's facial expression data, voice data

[0357] Output: Emotion data

[0358] Step 5:

[0359] The commentary server receives user settings, real-time data, and emotion data, and generates commentary using a generative AI model. The generated commentary is checked for accuracy by error detection means and corrected as necessary. For example, the generative AI model is given the following prompt:

[0360] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[0361] Input: User setting information, real-time data, emotion data

[0362] Output: Generated commentary

[0363] Step 6:

[0364] The commentary content confirmed by the error detection means is provided to the user in real time through the commentary providing means, so that the user can enjoy the sporting event while watching the commentary streamed on the terminal.

[0365] Input: Generated commentary

[0366] Output: Streaming commentary provided

[0367] These are the details of the processing steps in the system of the present invention, which allows users to enjoy a rich and personalized entertainment experience.

[0368] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0369] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0370] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0371] [Second embodiment]

[0372] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0373] 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.

[0374] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0375] 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.

[0376] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0377] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0378] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0379] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0380] 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.

[0381] 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.

[0382] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0383] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0384] The system of the present invention is composed of three main components: a user terminal, a commentary server, and a data analysis server. The role of each component and the specific processing flow are explained below.

[0385] Initial setup on the user device

[0386] A dedicated application is first launched on the user's device. This application displays a login screen for the user, and once authentication is complete, a settings screen is displayed. The settings screen allows the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Once the user has completed their selection, this information is sent from the device to the commentary server.

[0387] Examples:

[0388] The user starts the application and selects "Expert Commentary" and "Emphasis on Player Data." This setting information is immediately sent to the commentary server.

[0389] Sending configuration information

[0390] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0391] Real-time data acquisition

[0392] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0393] Examples:

[0394] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0395] Analysis of explanatory data

[0396] The commentary server uses AI models to generate commentary based on user-submitted settings and real-time data obtained from the data analysis server. This commentary is tailored to the user's preferences and, if necessary, checked by error detection methods. Any inaccuracies are corrected.

[0397] Examples:

[0398] The commentary server generates a professional commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season."

[0399] Providing commentary

[0400] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing the user to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0401] Examples:

[0402] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0403] System Features

[0404] The system's unique feature is that users can customize their commentary style and the information they are interested in. Furthermore, the combination of real-time data acquisition, commentary generation using AI models, and accurate information provision using error detection methods allows for commentary that is perfectly tailored to individual needs. Furthermore, because commentary is provided in real time, users can receive the latest information as the match progresses.

[0405] This system allows casual fans and those with little experience to enjoy watching sports, and can improve fan retention rates.

[0406] ---

[0407] The processing flow will be explained below.

[0408] Step 1:

[0409] The device launches the application, displays a login screen to the user and prompts them to authenticate, and once the user enters their authentication information and authentication is complete, displays the settings screen.

[0410] Step 2:

[0411] The device presents users with options for commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.), and the user selects their preferred settings from these options.

[0412] Step 3:

[0413] The terminal collects information about the commentary style set by the user and the sporting event being watched, and transmits this setting information to a commentary server.

[0414] Step 4:

[0415] The explanation server stores the received user setting information and prepares to generate an individual explanation for each user.

[0416] Step 5:

[0417] The commentary server sends a request to the data analysis server to obtain real-time data related to the sporting event being watched.

[0418] Step 6:

[0419] The data analysis server obtains real-time data such as the progress of the match, player performance data, and statistical information from external data sources (such as APIs and official databases), and sends this data to the commentary server.

[0420] Step 7:

[0421] The commentary server receives real-time data sent from the data analysis server and generates commentary in combination with user settings. It uses an AI model to create commentary in a style selected by the user.

[0422] Step 8:

[0423] The commentary server checks the generated commentary with an internal error detection function and corrects any inaccuracies, with expert supervision if necessary.

[0424] Step 9:

[0425] The commentary server prepares to stream the generated commentary to the user device in real time.

[0426] Step 10:

[0427] The device receives the streamed commentary from the commentary server and displays or plays audio to the user, who can receive commentary in real time based on their selection.

[0428] ---

[0429] The above is a detailed description of the system's specific processing steps and operations. This process allows users to enjoy commentary tailored to their preferences in real time.

[0430] Example 1

[0431] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0432] A problem with conventional sports commentary systems is that they are unable to provide commentary that meets the individual needs of each user. For example, general commentary cannot fully meet the specialized information or interests of individual users, resulting in a lower quality of viewing experience. Furthermore, there are issues with the accuracy and currency of the data acquired in real time, which can lead to the provision of incorrect information. There is a need for a system that can solve these issues and provide commentary with a higher level of user satisfaction.

[0433] 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.

[0434] In this invention, the server includes commentary setting means for allowing a user to select a preferred commentary style and specific information, user setting information transmission means for transmitting setting information from a user terminal to the commentary server, real-time data acquisition means for acquiring real-time data related to a sport event from an external data source to the commentary server, commentary generation means for generating commentary using a generative AI model, error detection means for detecting errors in the generated commentary and correcting inaccurate information, and commentary provision means for providing the generated commentary to the user terminal in real time. This makes it possible to provide professional and accurate commentary tailored to the individual needs of users, as well as to acquire and provide the latest and accurate information in real time.

[0435] The "explanation setting means" is a means for the user to select the preferred explanation style and specific information, and provides a user interface.

[0436] The "user setting information transmission means" is a means for transmitting setting information from the user terminal to the commentary server.

[0437] The "real-time data acquisition means" is a means for acquiring real-time data related to a sporting event from an external data source to the commentary server.

[0438] "Explanation generation means" refers to a means for generating explanations based on user setting information and acquired real-time data using a generative AI model.

[0439] The "error detection means" is a means for detecting errors in the generated explanation and correcting inaccurate information if it is included.

[0440] The "explanation providing means" is a means for providing the generated explanation to the user terminal in real time.

[0441] MODE FOR CARRYING OUT THE INVENTION

[0442] The system of the present invention is mainly composed of three main components: a user terminal, a commentary server, and a data analysis server. A specific embodiment of the system will be described below.

[0443] Initial setup on the user device

[0444] First, the user launches a dedicated application on their device. This application displays a login screen for the user and authenticates them by entering their login information (e.g., user ID and password). Once authentication is complete, a settings screen is displayed, where the user selects their preferred commentary style (e.g., professional, humorous, matter-of-fact) and information of interest (e.g., player data, tactical analysis). This setting information is immediately sent from the user device to the commentary server.

[0445] Examples:

[0446] The user starts the application and selects "Expert commentary" and "Emphasis on player data." This setting information is immediately sent to the commentary server.

[0447] Processing on the commentary server

[0448] The commentary server receives the setting information sent by the user. The server creates a user profile based on this information and prepares to generate commentary tailored to the user's individual needs. The commentary server then sends a request to the data analysis server to obtain real-time data.

[0449] Real-time data acquisition

[0450] The data analysis server acquires real-time match data, player performance data, and statistical information from external sources (e.g., sports data providing services), and transmits the acquired data to the commentary server.

[0451] Examples:

[0452] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0453] Analysis and generation of explanatory data

[0454] The commentary server generates commentary using a generative AI model (e.g., GPT-3) based on the acquired real-time data and the settings information received from the user. The generated commentary is checked by an error detection system, and inaccurate information is corrected if necessary.

[0455] Examples:

[0456] Prompt: "Please explain Player A's goal."

[0457] Generated commentary: "Player A scores a goal. His last pass success rate was 90%, which is above his average success rate this season."

[0458] Providing commentary

[0459] The commentary server then streams the generated commentary to the user's device in real time, allowing the user to enjoy watching the sports game while listening to the commentary.

[0460] Examples:

[0461] The commentary generated by the commentary server is streamed to the user's device in real time, and the user watches the commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0462] This system allows users to receive detailed and interesting commentary tailored to their preferences in real time, significantly improving the sports viewing experience. It is also expected to help casual fans and those with little experience of sports to experience the appeal of sports, thereby improving fan retention rates.

[0463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] The user launches the application. A login screen is displayed, and the user enters their ID and password. Once authentication is complete, the settings screen is displayed. The user selects a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis). The input is the user's selected commentary style and information of interest, and the output is the settings information for these. The settings information is sent from the user's terminal to the commentary server. In concrete terms, the user selects "professional commentary" and "emphasis on player data," and this information is sent to the server.

[0466] Step 2:

[0467] The commentary server receives the setting information sent by the user. The input is the setting information sent from the user terminal, and the output is the user profile stored in the commentary server. Based on this information, the commentary server creates a profile for the user and prepares to generate a dedicated commentary. Specifically, the commentary server creates a profile called "user123" and prepares to generate a commentary.

[0468] Step 3:

[0469] The commentary server sends a request to the data analysis server to obtain real-time data. The input is the request from the commentary server, and the output is a completion log of the request sent to the data analysis server. Specifically, a request such as "Please obtain real-time data for match ID 5678" is sent.

[0470] Step 4:

[0471] The data analysis server obtains real-time data from external data sources. The inputs are match data, player performance data, and statistical information obtained from external data sources, and the output is these data. The data analysis server sends this data to the commentary server. Specifically, data such as "Player A's current pass success rate is 90%" is obtained and sent to the commentary server.

[0472] Step 5:

[0473] The commentary server generates commentary using a generative AI model (for example, GPT-3) based on the acquired real-time data and the setting information received from the user. The input is real-time data and setting information, and the output is the generated commentary. The commentary is generated based on the prompt text. Specifically, based on the prompt text "Please explain Player A's goal," the commentary generated is "Player A has scored a goal. His pass success rate immediately before that was 90%, which is higher than the average success rate for this season."

[0474] Step 6:

[0475] The commentary server checks the generated commentary with an error detection system. The input is the generated commentary, and the output is the corrected commentary. If it contains inaccurate information, it is corrected. Specifically, the generated commentary is checked with the error detection system, and corrections are made if necessary.

[0476] Step 7:

[0477] The commentary server streams the final commentary to the user's device in real time. The input is the completed commentary text, and the output is the commentary delivered to the user's device. The user can enjoy watching the sports game while viewing this. Specifically, the commentary server streams commentary such as "Player A scored a goal..." to the user's device, and the user views it in real time.

[0478] (Application example 1)

[0479] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0480] Conventional sports commentary systems have difficulty providing real-time commentary that meets individual user preferences, and lack the functionality to send appropriate notifications to users when specific events occur, creating a need for an improved user experience.

[0481] 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.

[0482] In this invention, the server includes an explanation setting means, a user setting information transmission means, a real-time data acquisition means, an explanation generation means, an error detection means, an explanation providing means, and a notification means for transmitting a notification to a user terminal, thereby enabling the provision of individual explanations based on the user's preferences and the transmission of notifications in real time.

[0483] The "explanation setting means" is a function that provides an interface that allows the user to select their preferred explanation style and specific information.

[0484] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the server.

[0485] The "real-time data acquisition means" is a function that collects real-time data related to a sporting event from an external data source.

[0486] The "explanation generation means" is a function that generates explanations using a generative AI model based on collected real-time data and user setting information.

[0487] The "error detection means" is a function that checks whether the generated commentary contains any inaccurate information and corrects it if necessary.

[0488] The "explanation providing means" is a function that streams the generated explanation to the user's terminal.

[0489] "Notification means" is a function that sends a push notification to the user's device when a specific trigger event occurs.

[0490] A "generative AI model" is an artificial intelligence model that generates commentary based on user settings and real-time data.

[0491] A "prompt" is an instruction that instructs an AI model to generate an explanation.

[0492] The system for implementing the present invention is configured using the following hardware and software.

[0493] Hardware and software used

[0494] User device: Smartphone

[0495] Explanation server: Cloud-based server (e.g. AWS)

[0496] Data analysis server: Collects real-time data using external APIs (e.g., SportsAPI)

[0497] Generative AI models: For example, OpenAI's GPT-3

[0498] User Device

[0499] First, the user launches a dedicated application on their smartphone. The application displays a login screen and asks the user for authentication. Once authentication is complete, the user proceeds to a settings screen where they can select a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). This setting information is sent to the commentary server by the user setting information sending means.

[0500] Commentary Server

[0501] The commentary server works with the data analysis server to collect data related to sporting events in real time based on the setting information received from the user. The commentary generation means uses a generative AI model to generate commentary based on the collected real-time data and the user's setting information. At this time, an error detection means is used to check whether the content of the commentary is accurate. If necessary, corrections are made automatically.

[0502] The generated commentary is streamed to the user terminal by the commentary providing means, and when a specific trigger event occurs, a push notification is sent to the user via the notification means.

[0503] Data analysis server

[0504] The data analysis server retrieves real-time data about the progress of sporting events and players from external data sources, for example using the SportsAPI, and sends this data to the commentary server, where it is used as input data for generating commentary.

[0505] Specific examples

[0506] The user starts the application, authenticates on the login screen, and then selects "Expert commentary" and "Player data emphasis." This setting information is sent to the commentary server via the user setting information transmission means. The commentary server receives real-time data from the data analysis server and generates commentary based on that data using a generative AI model. After that, a commentary such as "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season" is streamed in real time to the user's device. An example of the prompt sentence used in this generation is as follows:

[0507] "Player A has scored a goal. His pass success rate just before the goal was 90%, which is higher than his average success rate this season. Please provide further commentary from your expert perspective."

[0508] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0509] Step 1:

[0510] The user device launches a dedicated application and displays the login screen. When the user enters their authentication information, the application sends the data to the authentication server and the authentication is completed. The input is the user's authentication information, and the output is the authentication completion status.

[0511] Step 2:

[0512] After authentication is complete, the user's device displays a setting screen for selecting a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). The user inputs each selection. The input is the user's commentary style and information of interest, and the output is the user's setting information.

[0513] Step 3:

[0514] When the user completes the setting, the user terminal uses the user setting information transmission means to transmit the setting information to the commentary server. The input is the user setting information, and the output is the setting information transmitted to the server.

[0515] Step 4:

[0516] The commentary server receives user setting information and works with the data analysis server to collect data related to sporting events in real time. This data collection uses APIs of external data sources (e.g., SportsAPI). The input is user setting information and the output is real-time data.

[0517] Step 5:

[0518] The data analysis server sends the collected real-time data to the commentary server. The commentary server generates commentary using a generative AI model based on user setting information and real-time data. The input is user setting information and real-time data, and the output is the generated commentary.

[0519] Step 6:

[0520] The explanation server uses error detection to check the generated explanation for inaccuracies and makes corrections as necessary. The input is the generated explanation, and the output is a verified, accurate explanation.

[0521] Step 7:

[0522] The generated commentary is streamed to the user terminal through the commentary providing means, and the user can view the commentary in real time. The input is the verified commentary, and the output is the commentary streamed on the user terminal.

[0523] Step 8:

[0524] When a specific trigger event occurs (e.g., a goal or an important play), the commentary server sends a push notification to the user via the notification mechanism. The input is the trigger event information, and the output is the push notification sent to the user device.

[0525] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0526] The present invention provides a system that personalizes sports viewing and further provides a richer entertainment experience by recognizing a user's emotions and adjusting commentary accordingly. Specific embodiments of the system of the present invention are described below.

[0527] System Configuration

[0528] This system consists of four main components: a user terminal, a commentary server, a data analysis server, and an emotion engine. The role and processing flow of each component are explained in detail below.

[0529] Initial setup on the user device

[0530] A dedicated application is launched on the user's device, and a login screen is displayed to the user. Once authentication is complete, a settings screen is displayed. On the settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.). The camera and microphone can also be used to recognize the user's emotions. Once the user has completed their selection, this setting information is sent from the device to the commentary server.

[0531] Examples:

[0532] The user launches the application, selects "humorous commentary" and "focus on player data," and allows the camera and microphone. This setting information is immediately sent to the commentary server.

[0533] Sending configuration information

[0534] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0535] Real-time data acquisition

[0536] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0537] Examples:

[0538] The data analysis server obtains game progress and player performance data from external data sources and sends it to the commentary server.

[0539] Emotion Engine Operation

[0540] The emotion engine recognizes the user's emotional state through their facial expressions and voice, and this recognized emotional data is sent to the commentary server in real time.

[0541] Examples:

[0542] If the user is smiling, the emotion engine detects the state of "happiness" and sends the data to the commentary server.

[0543] Analysis of explanatory data

[0544] The commentary server generates commentary using an AI model based on user settings, real-time data, and emotion data from the emotion engine. In particular, the commentary content can be adjusted according to the user's emotional state. Furthermore, the accuracy of the generated commentary content is checked by an error detection means, and corrected if necessary.

[0545] Examples:

[0546] The commentary server generates a professional yet humorous commentary: "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0547] Providing commentary

[0548] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing users to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0549] Examples:

[0550] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0551] System Features

[0552] The system's unique features include the ability for users to individually set their commentary style and the information they are interested in, as well as the ability to recognize users' emotions and provide real-time commentary accordingly, allowing users to receive commentary tailored to their individual needs and providing a more personalized entertainment experience.

[0553] The aim of this system is to provide casual and less experienced fans with the enjoyment of watching sports, and to improve fan retention rates.

[0554] The processing flow will be explained below.

[0555] Step 1:

[0556] The device presents the user with a login screen, waits for the user to enter their credentials, and then waits for the authentication to complete.

[0557] Step 2:

[0558] After logging in, the device displays the commentary settings screen, where the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.).

[0559] Step 3:

[0560] The device collects the user's configuration information and sends it to the commentary server.

[0561] Step 4:

[0562] The commentary server stores the received user setting information and prepares for individual commentary generation.

[0563] Step 5:

[0564] The commentary server sends a request to the data analysis server to obtain real-time data about the sporting event.

[0565] Step 6:

[0566] The data analysis server obtains the progress of the match, player performance data, statistical information, etc. from external data sources and sends the data to the commentary server.

[0567] Step 7:

[0568] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, and the emotion data is sent from the device to the commentary server.

[0569] Step 8:

[0570] The commentary server generates commentary using an AI model based on real-time data sent from the data analysis server, user setting information, and emotion data from the emotion engine. In particular, it adjusts the content of the commentary according to the user's emotions.

[0571] Step 9:

[0572] The commentary server checks the accuracy of the commentary content generated by the commentary server using an error detection means and corrects it if necessary.

[0573] Step 10:

[0574] The commentary server prepares to stream the generated commentary to the user terminal in real time.

[0575] Step 11:

[0576] The device receives the streaming commentary from the commentary server and displays or plays the audio to the user, allowing the user to enjoy commentary tailored to their preferences in real time.

[0577] Specific examples

[0578] For example, suppose a user selects "humorous commentary" and "focus on player data" and allows the camera and microphone. As the match progresses, when Player A scores a goal, the data analysis server collects this information and sends it to the commentary server. At the same time, the emotion engine detects the user's emotional state of "joy" and sends it to the commentary server. The commentary server generates a commentary such as "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season," and provides it with humor depending on the user's emotional state.

[0579] Example 2

[0580] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0581] Conventional sports viewing systems have difficulty providing personalized commentary based on individual user preferences, and are unable to reflect the user's emotional state in real time. As a result, the user's entertainment experience is not improved. The present invention aims to provide commentary tailored to the user's preferences, recognize the user's emotional state in real time, and provide commentary content that corresponds to the user's preferences, thereby realizing a richer entertainment experience.

[0582] 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.

[0583] In this invention, the server includes an explanation setting means, a user setting information transmitting means, a real-time data acquiring means, an emotion recognition means, an explanation generating means, an error detecting means, and an explanation providing means, which make it possible to generate personalized explanations based on the user's setting information, collect real-time data, and adjust the explanation content according to the user's emotional state.

[0584] The "explanation setting means" is a function that provides a user interface for the user to select their preferred explanation style and specific information.

[0585] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the commentary server.

[0586] The "real-time data acquisition means" is a function that acquires real-time data related to a sporting event from an external data source.

[0587] The "emotion recognition means" is a function that analyzes facial expression data and voice data to recognize the user's emotions.

[0588] The "explanation generation means" is a function that generates an explanation using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means.

[0589] The "error detection means" is a function that checks the accuracy of the generated commentary content and corrects it if necessary.

[0590] The "explanation providing means" is a function that provides the generated explanation to the user in real time.

[0591] The present invention provides a sports viewing system that provides commentary tailored to individual user needs and further adjusts the commentary content by recognizing the user's emotions in real time. This system comprises a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means.

[0592] System Configuration

[0593] Initial setup on the user device

[0594] A dedicated application is installed on the user's device. When the application is launched, a login screen is displayed to the user. After the user enters their authentication information and completes the authentication, a settings screen is displayed, allowing the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Furthermore, permission to use the camera and microphone can be selected on the settings screen. This setting information is sent from the user's device to the commentary server.

[0595] Specifically, a user launches the application, selects "humorous commentary" and "focus on player data," and authorizes the use of the camera and microphone. This setting information is immediately sent to the commentary server.

[0596] Sending configuration information

[0597] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0598] Real-time data acquisition

[0599] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, the progress of the match and player performance data can be obtained through the API.

[0600] Emotion Engine Operation

[0601] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0602] Specifically, when the user is smiling, the emotion engine detects a state of "joy" and sends that data to the commentary server.

[0603] Analysis of explanatory data

[0604] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0605] Specifically, the commentary server generates a professional yet humorous commentary such as, "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0606] Providing commentary

[0607] The commentary providing means streams the generated commentary to the user's terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0608] Specifically, the commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0609] Prompt Sentence Examples

[0610] Below are some example prompts to be input to the generative AI model:

[0611] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0612] This system allows users to enjoy watching sports more enjoyably by listening to commentary tailored to their preferences in real time.

[0613] (End)

[0614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] Initial setup on the user device

[0617] When a user launches the dedicated application, a login screen appears. After the user enters their authentication information and completes the authentication, they can access the settings screen. On this settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information they are interested in (player data, tactical analysis, etc.). They can also select whether to allow the use of the camera and microphone. This setting information is sent from the device to the commentary server.

[0618] Input: User credentials, commentary style, interests, camera and microphone permissions

[0619] Output: Send user setting information to the commentary server

[0620] Step 2:

[0621] Sending configuration information

[0622] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0623] Input: User setting information

[0624] Output: Save the configuration information to the server

[0625] Step 3:

[0626] Real-time data acquisition

[0627] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, it obtains match progress and player performance data via an API.

[0628] Input: Real-time data requests from external sources

[0629] Output: Sending real-time data to the commentary server

[0630] Step 4:

[0631] Emotion Engine Operation

[0632] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0633] Input: User's facial expression data, voice data

[0634] Output: Sending emotional state data to the commentary server

[0635] Step 5:

[0636] Analysis of explanatory data

[0637] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0638] Input: User setting information, real-time data, emotion data

[0639] Output: Adjusted commentary

[0640] Step 6:

[0641] Providing commentary

[0642] The commentary providing means streams the generated commentary to the user terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0643] Input: Adjusted commentary

[0644] Output: Commentary on user's device

[0645] As a specific example of how this works, the following prompt sentence is an example of input to the generative AI model.

[0646] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0647] (Application example 2)

[0648] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] In conventional sports viewing systems, users often receive uniform commentary, making it difficult to provide a personalized entertainment experience based on individual preferences and emotions. As a result, user satisfaction and interest decline, limiting the ability to improve fan retention. Furthermore, if the commentary content does not match the user's emotional state, the viewing experience itself is likely to be interrupted. A new system that solves these issues is needed.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means. This makes it possible to generate and provide a commentary personalized for each user, and further enables real-time commentary adjustment according to the user's emotional state. This is expected to significantly improve the sports viewing experience, and increase user satisfaction and fan retention.

[0651] The "explanation setting means" is a means for providing a user interface for the user to select a preferred explanation style or specific information.

[0652] The "user setting information transmission means" is a means for transmitting information set by the user to the server.

[0653] The "real-time data acquisition means" is a means for acquiring real-time data related to an event from an external data source.

[0654] The "emotion recognition means" is a means for recognizing the emotional state of a user by analyzing their facial expressions and voice.

[0655] The "explanation generating means" is a means for generating an explanation based on user setting information, real-time data, and emotion data.

[0656] The "error detection means" is a means for checking the accuracy of the generated commentary content and correcting it if necessary.

[0657] The "explanation providing means" is a means for providing the generated explanation to the user in real time.

[0658] The system of the present invention personalizes sports viewing, recognizes user emotions, and adjusts commentary accordingly to provide a richer entertainment experience. Specific embodiments of the system are described below.

[0659] System Configuration

[0660] The system consists of the following main components:

[0661] 1. Explanation setting method

[0662] 2. Means of sending user setting information

[0663] 3. Real-time data acquisition methods

[0664] 4. Emotion recognition means

[0665] 5. Explanation Generation Method

[0666] 6. Error Detection Methods

[0667] 7. Means of providing explanations

[0668] program

[0669] The system's programming includes the following processes:

[0670] Explanation setting method

[0671] A dedicated application is launched on the user's device (e.g., a smartphone), and the user authenticates through a login screen. Once authentication is complete, a settings screen is displayed, allowing the user to select a commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). This setting information is sent from the device to the commentary server.

[0672] User setting information transmission method

[0673] The information selected by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0674] Real-time data acquisition method

[0675] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0676] emotion recognition means

[0677] The system recognizes the user's emotional state through their facial expressions and voice. This recognized emotional data is sent to the commentary server in real time. The hardware uses a camera and microphone, and the software uses EmotionRecognizer.

[0678] Explanation generation method

[0679] The commentary server generates commentary using a generative AI model based on user settings, real-time data, and emotion data. The accuracy of the generated commentary is verified by an error detection means.

[0680] Error detection means

[0681] Check the accuracy of the generated commentary and correct it if necessary.

[0682] Means of providing explanations

[0683] The generated commentary is provided to users in real time. The commentary is streamed to the user's device, allowing them to enjoy the game while listening to commentary tailored to their preferences. The emotionally tailored commentary increases user interest and satisfaction.

[0684] Specific examples

[0685] A user launches the application on their smartphone, selects "humorous commentary" and "focus on player data," and allows the use of the camera and microphone. This information is sent to the commentary server, which generates commentary in real time based on the settings. If the user's facial expression is smiling, EmotionRecognizer detects a state of "joy," and the commentary is provided in a more positive tone.

[0686] Example prompt sentence:

[0687] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[0688] In this way, the system can provide a rich sports viewing experience based on the user's emotions and individual commentary requirements.

[0689] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0690] Step 1:

[0691] The user launches the dedicated application on their smartphone, and a login screen appears. The user performs login authentication, and if authentication is successful, the device displays the commentary setting screen. On this setting screen, the user selects the commentary style (e.g., professional, humorous, matter-of-fact) and the information of interest (player data, tactical analysis). In addition, the user is asked for permission to use the camera and microphone. Once the user completes the settings and allows use of the camera and microphone, the device sends this information to the server.

[0692] Input: User login information, commentary style, interests, camera and microphone permissions

[0693] Output: User setting information

[0694] Step 2:

[0695] The explanation server receives the user setting information sent from the terminal, saves the setting information, and starts preparations to generate an explanation based on the user's individual settings.

[0696] Input: User setting information

[0697] Output: Save the configuration information

[0698] Step 3:

[0699] The commentary server works in conjunction with the data analysis server to obtain real-time match data, player performance data, and statistical information from external data sources, and the data analysis server provides this data to the commentary server.

[0700] Inputs: Match data, performance data, and statistics from external data sources

[0701] Output: Real-time data

[0702] Step 4:

[0703] The device uses a camera and microphone to capture the user's facial expressions and voice data. These data are then processed into emotion data using the EmotionRecognizer library. The emotion recognition means then transmits this emotion data to the commentary server in real time.

[0704] Input: User's facial expression data, voice data

[0705] Output: Emotion data

[0706] Step 5:

[0707] The commentary server receives user settings, real-time data, and emotion data, and generates commentary using a generative AI model. The generated commentary is checked for accuracy by error detection means and corrected as necessary. For example, the generative AI model is given the following prompt:

[0708] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[0709] Input: User setting information, real-time data, emotion data

[0710] Output: Generated commentary

[0711] Step 6:

[0712] The commentary content confirmed by the error detection means is provided to the user in real time through the commentary providing means, so that the user can enjoy the sporting event while watching the commentary streamed on the terminal.

[0713] Input: Generated commentary

[0714] Output: Streaming commentary provided

[0715] These are the details of the processing steps in the system of the present invention, which allows users to enjoy a rich and personalized entertainment experience.

[0716] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0717] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0718] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0719] [Third embodiment]

[0720] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0721] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0722] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0723] 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.

[0724] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0725] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0726] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0727] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0728] 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.

[0729] 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.

[0730] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0731] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0732] The system of the present invention is composed of three main components: a user terminal, a commentary server, and a data analysis server. The role of each component and the specific processing flow are explained below.

[0733] Initial setup on the user device

[0734] A dedicated application is first launched on the user's device. This application displays a login screen for the user, and once authentication is complete, a settings screen is displayed. The settings screen allows the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Once the user has completed their selection, this information is sent from the device to the commentary server.

[0735] Examples:

[0736] The user starts the application and selects "Expert Commentary" and "Emphasis on Player Data." This setting information is immediately sent to the commentary server.

[0737] Sending configuration information

[0738] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0739] Real-time data acquisition

[0740] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0741] Examples:

[0742] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0743] Analysis of explanatory data

[0744] The commentary server uses AI models to generate commentary based on user-submitted settings and real-time data obtained from the data analysis server. This commentary is tailored to the user's preferences and, if necessary, checked by error detection methods. Any inaccuracies are corrected.

[0745] Examples:

[0746] The commentary server generates a professional commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season."

[0747] Providing commentary

[0748] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing the user to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0749] Examples:

[0750] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0751] System Features

[0752] The system's unique feature is that users can customize their commentary style and the information they are interested in. Furthermore, the combination of real-time data acquisition, commentary generation using AI models, and accurate information provision using error detection methods allows for commentary that is perfectly tailored to individual needs. Furthermore, because commentary is provided in real time, users can receive the latest information as the match progresses.

[0753] This system allows casual fans and those with little experience to enjoy watching sports, and can improve fan retention rates.

[0754] ---

[0755] The processing flow will be explained below.

[0756] Step 1:

[0757] The device launches the application, displays a login screen to the user and prompts them to authenticate, and once the user enters their authentication information and authentication is complete, displays the settings screen.

[0758] Step 2:

[0759] The device presents users with options for commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.), and the user selects their preferred settings from these options.

[0760] Step 3:

[0761] The terminal collects information about the commentary style set by the user and the sporting event being watched, and transmits this setting information to a commentary server.

[0762] Step 4:

[0763] The explanation server stores the received user setting information and prepares to generate an individual explanation for each user.

[0764] Step 5:

[0765] The commentary server sends a request to the data analysis server to obtain real-time data related to the sporting event being watched.

[0766] Step 6:

[0767] The data analysis server obtains real-time data such as the progress of the match, player performance data, and statistical information from external data sources (such as APIs and official databases), and sends this data to the commentary server.

[0768] Step 7:

[0769] The commentary server receives real-time data sent from the data analysis server and generates commentary in combination with user settings. It uses an AI model to create commentary in a style selected by the user.

[0770] Step 8:

[0771] The commentary server checks the generated commentary with an internal error detection function and corrects any inaccuracies, with expert supervision if necessary.

[0772] Step 9:

[0773] The commentary server prepares to stream the generated commentary to the user device in real time.

[0774] Step 10:

[0775] The device receives the streamed commentary from the commentary server and displays or plays audio to the user, who can receive commentary in real time based on their selection.

[0776] ---

[0777] The above is a detailed description of the system's specific processing steps and operations. This process allows users to enjoy commentary tailored to their preferences in real time.

[0778] Example 1

[0779] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0780] A problem with conventional sports commentary systems is that they are unable to provide commentary that meets the individual needs of each user. For example, general commentary cannot fully meet the specialized information or interests of individual users, resulting in a lower quality of viewing experience. Furthermore, there are issues with the accuracy and currency of the data acquired in real time, which can lead to the provision of incorrect information. There is a need for a system that can solve these issues and provide commentary with a higher level of user satisfaction.

[0781] 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.

[0782] In this invention, the server includes commentary setting means for allowing a user to select a preferred commentary style and specific information, user setting information transmission means for transmitting setting information from a user terminal to the commentary server, real-time data acquisition means for acquiring real-time data related to a sport event from an external data source to the commentary server, commentary generation means for generating commentary using a generative AI model, error detection means for detecting errors in the generated commentary and correcting inaccurate information, and commentary provision means for providing the generated commentary to the user terminal in real time. This makes it possible to provide professional and accurate commentary tailored to the individual needs of users, as well as to acquire and provide the latest and accurate information in real time.

[0783] The "explanation setting means" is a means for the user to select the preferred explanation style and specific information, and provides a user interface.

[0784] The "user setting information transmission means" is a means for transmitting setting information from the user terminal to the commentary server.

[0785] The "real-time data acquisition means" is a means for acquiring real-time data related to a sporting event from an external data source to the commentary server.

[0786] "Explanation generation means" refers to a means for generating explanations based on user setting information and acquired real-time data using a generative AI model.

[0787] The "error detection means" is a means for detecting errors in the generated explanation and correcting inaccurate information if it is included.

[0788] The "explanation providing means" is a means for providing the generated explanation to the user terminal in real time.

[0789] MODE FOR CARRYING OUT THE INVENTION

[0790] The system of the present invention is mainly composed of three main components: a user terminal, a commentary server, and a data analysis server. A specific embodiment of the system will be described below.

[0791] Initial setup on the user device

[0792] First, the user launches a dedicated application on their device. This application displays a login screen for the user and authenticates them by entering their login information (e.g., user ID and password). Once authentication is complete, a settings screen is displayed, where the user selects their preferred commentary style (e.g., professional, humorous, matter-of-fact) and information of interest (e.g., player data, tactical analysis). This setting information is immediately sent from the user device to the commentary server.

[0793] Examples:

[0794] The user starts the application and selects "Expert commentary" and "Emphasis on player data." This setting information is immediately sent to the commentary server.

[0795] Processing on the commentary server

[0796] The commentary server receives the setting information sent by the user. The server creates a user profile based on this information and prepares to generate commentary tailored to the user's individual needs. The commentary server then sends a request to the data analysis server to obtain real-time data.

[0797] Real-time data acquisition

[0798] The data analysis server acquires real-time match data, player performance data, and statistical information from external sources (e.g., sports data providing services), and transmits the acquired data to the commentary server.

[0799] Examples:

[0800] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[0801] Analysis and generation of explanatory data

[0802] The commentary server generates commentary using a generative AI model (e.g., GPT-3) based on the acquired real-time data and the settings information received from the user. The generated commentary is checked by an error detection system, and inaccurate information is corrected if necessary.

[0803] Examples:

[0804] Prompt: "Please explain Player A's goal."

[0805] Generated commentary: "Player A scores a goal. His last pass success rate was 90%, which is above his average success rate this season."

[0806] Providing commentary

[0807] The commentary server then streams the generated commentary to the user's device in real time, allowing the user to enjoy watching the sports game while listening to the commentary.

[0808] Examples:

[0809] The commentary generated by the commentary server is streamed to the user's device in real time, and the user watches the commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[0810] This system allows users to receive detailed and interesting commentary tailored to their preferences in real time, significantly improving the sports viewing experience. It is also expected to help casual fans and those with little experience of sports to experience the appeal of sports, thereby improving fan retention rates.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1:

[0813] The user launches the application. A login screen is displayed, and the user enters their ID and password. Once authentication is complete, the settings screen is displayed. The user selects a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis). The input is the user's selected commentary style and information of interest, and the output is the settings information for these. The settings information is sent from the user's terminal to the commentary server. In concrete terms, the user selects "professional commentary" and "emphasis on player data," and this information is sent to the server.

[0814] Step 2:

[0815] The commentary server receives the setting information sent by the user. The input is the setting information sent from the user terminal, and the output is the user profile stored in the commentary server. Based on this information, the commentary server creates a profile for the user and prepares to generate a dedicated commentary. Specifically, the commentary server creates a profile called "user123" and prepares to generate a commentary.

[0816] Step 3:

[0817] The commentary server sends a request to the data analysis server to obtain real-time data. The input is the request from the commentary server, and the output is a completion log of the request sent to the data analysis server. Specifically, a request such as "Please obtain real-time data for match ID 5678" is sent.

[0818] Step 4:

[0819] The data analysis server obtains real-time data from external data sources. The inputs are match data, player performance data, and statistical information obtained from external data sources, and the output is these data. The data analysis server sends this data to the commentary server. Specifically, data such as "Player A's current pass success rate is 90%" is obtained and sent to the commentary server.

[0820] Step 5:

[0821] The commentary server generates commentary using a generative AI model (for example, GPT-3) based on the acquired real-time data and the setting information received from the user. The input is real-time data and setting information, and the output is the generated commentary. The commentary is generated based on the prompt text. Specifically, based on the prompt text "Please explain Player A's goal," the commentary generated is "Player A has scored a goal. His pass success rate immediately before that was 90%, which is higher than the average success rate for this season."

[0822] Step 6:

[0823] The commentary server checks the generated commentary with an error detection system. The input is the generated commentary, and the output is the corrected commentary. If it contains inaccurate information, it is corrected. Specifically, the generated commentary is checked with the error detection system, and corrections are made if necessary.

[0824] Step 7:

[0825] The commentary server streams the final commentary to the user's device in real time. The input is the completed commentary text, and the output is the commentary delivered to the user's device. The user can enjoy watching the sports game while viewing this. Specifically, the commentary server streams commentary such as "Player A scored a goal..." to the user's device, and the user views it in real time.

[0826] (Application example 1)

[0827] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0828] Conventional sports commentary systems have difficulty providing real-time commentary that meets individual user preferences, and lack the functionality to send appropriate notifications to users when specific events occur, creating a need for an improved user experience.

[0829] 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.

[0830] In this invention, the server includes an explanation setting means, a user setting information transmission means, a real-time data acquisition means, an explanation generation means, an error detection means, an explanation providing means, and a notification means for transmitting a notification to a user terminal, thereby enabling the provision of individual explanations based on the user's preferences and the transmission of notifications in real time.

[0831] The "explanation setting means" is a function that provides an interface that allows the user to select their preferred explanation style and specific information.

[0832] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the server.

[0833] The "real-time data acquisition means" is a function that collects real-time data related to a sporting event from an external data source.

[0834] The "explanation generation means" is a function that generates explanations using a generative AI model based on collected real-time data and user setting information.

[0835] The "error detection means" is a function that checks whether the generated commentary contains any inaccurate information and corrects it if necessary.

[0836] The "explanation providing means" is a function that streams the generated explanation to the user's terminal.

[0837] "Notification means" is a function that sends a push notification to the user's device when a specific trigger event occurs.

[0838] A "generative AI model" is an artificial intelligence model that generates commentary based on user settings and real-time data.

[0839] A "prompt" is an instruction that instructs an AI model to generate an explanation.

[0840] The system for implementing the present invention is configured using the following hardware and software.

[0841] Hardware and software used

[0842] User device: Smartphone

[0843] Explanation server: Cloud-based server (e.g. AWS)

[0844] Data analysis server: Collects real-time data using external APIs (e.g., SportsAPI)

[0845] Generative AI models: For example, OpenAI's GPT-3

[0846] User Device

[0847] First, the user launches a dedicated application on their smartphone. The application displays a login screen and asks the user for authentication. Once authentication is complete, the user proceeds to a settings screen where they can select a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). This setting information is sent to the commentary server by the user setting information sending means.

[0848] Commentary Server

[0849] The commentary server works with the data analysis server to collect data related to sporting events in real time based on the setting information received from the user. The commentary generation means uses a generative AI model to generate commentary based on the collected real-time data and the user's setting information. At this time, an error detection means is used to check whether the content of the commentary is accurate. If necessary, corrections are made automatically.

[0850] The generated commentary is streamed to the user terminal by the commentary providing means, and when a specific trigger event occurs, a push notification is sent to the user via the notification means.

[0851] Data analysis server

[0852] The data analysis server retrieves real-time data about the progress of sporting events and players from external data sources, for example using the SportsAPI, and sends this data to the commentary server, where it is used as input data for generating commentary.

[0853] Specific examples

[0854] The user starts the application, authenticates on the login screen, and then selects "Expert commentary" and "Player data emphasis." This setting information is sent to the commentary server via the user setting information transmission means. The commentary server receives real-time data from the data analysis server and generates commentary based on that data using a generative AI model. After that, a commentary such as "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season" is streamed in real time to the user's device. An example of the prompt sentence used in this generation is as follows:

[0855] "Player A has scored a goal. His pass success rate just before the goal was 90%, which is higher than his average success rate this season. Please provide further commentary from your expert perspective."

[0856] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0857] Step 1:

[0858] The user device launches a dedicated application and displays the login screen. When the user enters their authentication information, the application sends the data to the authentication server and the authentication is completed. The input is the user's authentication information, and the output is the authentication completion status.

[0859] Step 2:

[0860] After authentication is complete, the user's device displays a setting screen for selecting a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). The user inputs each selection. The input is the user's commentary style and information of interest, and the output is the user's setting information.

[0861] Step 3:

[0862] When the user completes the setting, the user terminal uses the user setting information transmission means to transmit the setting information to the commentary server. The input is the user setting information, and the output is the setting information transmitted to the server.

[0863] Step 4:

[0864] The commentary server receives user setting information and works with the data analysis server to collect data related to sporting events in real time. This data collection uses APIs of external data sources (e.g., SportsAPI). The input is user setting information and the output is real-time data.

[0865] Step 5:

[0866] The data analysis server sends the collected real-time data to the commentary server. The commentary server generates commentary using a generative AI model based on user setting information and real-time data. The input is user setting information and real-time data, and the output is the generated commentary.

[0867] Step 6:

[0868] The explanation server uses error detection to check the generated explanation for inaccuracies and makes corrections as necessary. The input is the generated explanation, and the output is a verified, accurate explanation.

[0869] Step 7:

[0870] The generated commentary is streamed to the user terminal through the commentary providing means, and the user can view the commentary in real time. The input is the verified commentary, and the output is the commentary streamed on the user terminal.

[0871] Step 8:

[0872] When a specific trigger event occurs (e.g., a goal or an important play), the commentary server sends a push notification to the user via the notification mechanism. The input is the trigger event information, and the output is the push notification sent to the user device.

[0873] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0874] The present invention provides a system that personalizes sports viewing and further provides a richer entertainment experience by recognizing a user's emotions and adjusting commentary accordingly. Specific embodiments of the system of the present invention are described below.

[0875] System Configuration

[0876] This system consists of four main components: a user terminal, a commentary server, a data analysis server, and an emotion engine. The role and processing flow of each component are explained in detail below.

[0877] Initial setup on the user device

[0878] A dedicated application is launched on the user's device, and a login screen is displayed to the user. Once authentication is complete, a settings screen is displayed. On the settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.). The camera and microphone can also be used to recognize the user's emotions. Once the user has completed their selection, this setting information is sent from the device to the commentary server.

[0879] Examples:

[0880] The user launches the application, selects "humorous commentary" and "focus on player data," and allows the camera and microphone. This setting information is immediately sent to the commentary server.

[0881] Sending configuration information

[0882] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[0883] Real-time data acquisition

[0884] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[0885] Examples:

[0886] The data analysis server obtains game progress and player performance data from external data sources and sends it to the commentary server.

[0887] Emotion Engine Operation

[0888] The emotion engine recognizes the user's emotional state through their facial expressions and voice, and this recognized emotional data is sent to the commentary server in real time.

[0889] Examples:

[0890] If the user is smiling, the emotion engine detects the state of "happiness" and sends the data to the commentary server.

[0891] Analysis of explanatory data

[0892] The commentary server generates commentary using an AI model based on user settings, real-time data, and emotion data from the emotion engine. In particular, the commentary content can be adjusted according to the user's emotional state. Furthermore, the accuracy of the generated commentary content is checked by an error detection means, and corrected if necessary.

[0893] Examples:

[0894] The commentary server generates a professional yet humorous commentary: "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0895] Providing commentary

[0896] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing users to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[0897] Examples:

[0898] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0899] System Features

[0900] The system's unique features include the ability for users to individually set their commentary style and the information they are interested in, as well as the ability to recognize users' emotions and provide real-time commentary accordingly, allowing users to receive commentary tailored to their individual needs and providing a more personalized entertainment experience.

[0901] The aim of this system is to provide casual and less experienced fans with the enjoyment of watching sports, and to improve fan retention rates.

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] The device presents the user with a login screen, waits for the user to enter their credentials, and then waits for the authentication to complete.

[0905] Step 2:

[0906] After logging in, the device displays the commentary setting screen, where the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.).

[0907] Step 3:

[0908] The device collects the user's configuration information and sends it to the commentary server.

[0909] Step 4:

[0910] The commentary server stores the received user setting information and prepares for individual commentary generation.

[0911] Step 5:

[0912] The commentary server sends a request to the data analysis server to obtain real-time data about the sporting event.

[0913] Step 6:

[0914] The data analysis server obtains the progress of the match, player performance data, statistical information, etc. from external data sources and sends the data to the commentary server.

[0915] Step 7:

[0916] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, and the emotion data is sent from the device to the commentary server.

[0917] Step 8:

[0918] The commentary server generates commentary using an AI model based on real-time data sent from the data analysis server, user setting information, and emotion data from the emotion engine. In particular, it adjusts the content of the commentary according to the user's emotions.

[0919] Step 9:

[0920] The commentary server checks the accuracy of the commentary content generated by the commentary server using an error detection means and corrects it if necessary.

[0921] Step 10:

[0922] The commentary server prepares to stream the generated commentary to the user terminal in real time.

[0923] Step 11:

[0924] The device receives the streaming commentary from the commentary server and displays or plays the audio to the user, allowing the user to enjoy commentary tailored to their preferences in real time.

[0925] Specific examples

[0926] For example, suppose a user selects "humorous commentary" and "focus on player data" and allows the camera and microphone. As the match progresses, when Player A scores a goal, the data analysis server collects this information and sends it to the commentary server. At the same time, the emotion engine detects the user's emotional state of "joy" and sends it to the commentary server. The commentary server generates a commentary such as "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season," and provides it with humor depending on the user's emotional state.

[0927] Example 2

[0928] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0929] Conventional sports viewing systems have difficulty providing personalized commentary based on individual user preferences, and are unable to reflect the user's emotional state in real time. As a result, the user's entertainment experience is not improved. The present invention aims to provide commentary tailored to the user's preferences, recognize the user's emotional state in real time, and provide commentary content that corresponds to the user's preferences, thereby realizing a richer entertainment experience.

[0930] 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.

[0931] In this invention, the server includes an explanation setting means, a user setting information transmitting means, a real-time data acquiring means, an emotion recognition means, an explanation generating means, an error detecting means, and an explanation providing means, which make it possible to generate personalized explanations based on the user's setting information, collect real-time data, and adjust the explanation content according to the user's emotional state.

[0932] The "explanation setting means" is a function that provides a user interface for the user to select their preferred explanation style and specific information.

[0933] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the commentary server.

[0934] The "real-time data acquisition means" is a function that acquires real-time data related to a sporting event from an external data source.

[0935] The "emotion recognition means" is a function that analyzes facial expression data and voice data to recognize the user's emotions.

[0936] The "explanation generation means" is a function that generates an explanation using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means.

[0937] The "error detection means" is a function that checks the accuracy of the generated commentary content and corrects it if necessary.

[0938] The "explanation providing means" is a function that provides the generated explanation to the user in real time.

[0939] The present invention provides a sports viewing system that provides commentary tailored to individual user needs and further adjusts the commentary content by recognizing the user's emotions in real time. This system comprises a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means.

[0940] System Configuration

[0941] Initial setup on the user device

[0942] A dedicated application is installed on the user's device. When the application is launched, a login screen is displayed to the user. After the user enters their authentication information and completes the authentication, a settings screen is displayed, allowing the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Furthermore, permission to use the camera and microphone can be selected on the settings screen. This setting information is sent from the user's device to the commentary server.

[0943] Specifically, a user launches the application, selects "humorous commentary" and "focus on player data," and authorizes the use of the camera and microphone. This setting information is immediately sent to the commentary server.

[0944] Sending configuration information

[0945] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0946] Real-time data acquisition

[0947] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, the progress of the match and player performance data can be obtained through the API.

[0948] Emotion Engine Operation

[0949] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0950] Specifically, when the user is smiling, the emotion engine detects a state of "joy" and sends that data to the commentary server.

[0951] Analysis of explanatory data

[0952] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0953] Specifically, the commentary server generates a professional yet humorous commentary such as, "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[0954] Providing commentary

[0955] The commentary providing means streams the generated commentary to the user's terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0956] Specifically, the commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[0957] Prompt Sentence Examples

[0958] Below are some example prompts to be input to the generative AI model:

[0959] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0960] This system allows users to enjoy watching sports more enjoyably by listening to commentary tailored to their preferences in real time.

[0961] (End)

[0962] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] Initial setup on the user device

[0965] When a user launches the dedicated application, a login screen appears. After the user enters their authentication information and completes the authentication, they can access the settings screen. On this settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information they are interested in (player data, tactical analysis, etc.). They can also select whether to allow the use of the camera and microphone. This setting information is sent from the device to the commentary server.

[0966] Input: User credentials, commentary style, interests, camera and microphone permissions

[0967] Output: Send user setting information to the commentary server

[0968] Step 2:

[0969] Sending configuration information

[0970] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[0971] Input: User setting information

[0972] Output: Save the configuration information to the server

[0973] Step 3:

[0974] Real-time data acquisition

[0975] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, it obtains match progress and player performance data via an API.

[0976] Input: Real-time data requests from external sources

[0977] Output: Sending real-time data to the commentary server

[0978] Step 4:

[0979] Emotion Engine Operation

[0980] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[0981] Input: User's facial expression data, voice data

[0982] Output: Sending emotional state data to the commentary server

[0983] Step 5:

[0984] Analysis of explanatory data

[0985] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[0986] Input: User setting information, real-time data, emotion data

[0987] Output: Adjusted commentary

[0988] Step 6:

[0989] Providing commentary

[0990] The commentary providing means streams the generated commentary to the user terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[0991] Input: Adjusted commentary

[0992] Output: Commentary on user's device

[0993] As a specific example of how this works, the following prompt sentence is an example of input to the generative AI model.

[0994] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[0995] (Application example 2)

[0996] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0997] In conventional sports viewing systems, users often receive uniform commentary, making it difficult to provide a personalized entertainment experience based on individual preferences and emotions. As a result, user satisfaction and interest decline, limiting the ability to improve fan retention. Furthermore, if the commentary content does not match the user's emotional state, the viewing experience itself is likely to be interrupted. A new system that solves these issues is needed.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means. This makes it possible to generate and provide a commentary personalized for each user, and further enables real-time commentary adjustment according to the user's emotional state. This is expected to significantly improve the sports viewing experience, and increase user satisfaction and fan retention.

[0999] The "explanation setting means" is a means for providing a user interface for the user to select a preferred explanation style or specific information.

[1000] The "user setting information transmission means" is a means for transmitting information set by the user to the server.

[1001] The "real-time data acquisition means" is a means for acquiring real-time data related to an event from an external data source.

[1002] The "emotion recognition means" is a means for recognizing the emotional state of a user by analyzing their facial expressions and voice.

[1003] The "explanation generating means" is a means for generating an explanation based on user setting information, real-time data, and emotion data.

[1004] The "error detection means" is a means for checking the accuracy of the generated commentary content and correcting it if necessary.

[1005] The "explanation providing means" is a means for providing the generated explanation to the user in real time.

[1006] The system of the present invention personalizes sports viewing, recognizes user emotions, and adjusts commentary accordingly to provide a richer entertainment experience. Specific embodiments of the system are described below.

[1007] System Configuration

[1008] The system consists of the following main components:

[1009] 1. Explanation setting method

[1010] 2. Means of sending user setting information

[1011] 3. Real-time data acquisition methods

[1012] 4. Emotion recognition means

[1013] 5. Explanation Generation Method

[1014] 6. Error Detection Methods

[1015] 7. Means of providing explanations

[1016] program

[1017] The system's programming includes the following processes:

[1018] Explanation setting method

[1019] A dedicated application is launched on the user's device (e.g., a smartphone), and the user authenticates through a login screen. Once authentication is complete, a settings screen is displayed, allowing the user to select a commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). This setting information is sent from the device to the commentary server.

[1020] User setting information transmission method

[1021] The information selected by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[1022] Real-time data acquisition method

[1023] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[1024] emotion recognition means

[1025] The system recognizes the user's emotional state through their facial expressions and voice. This recognized emotional data is sent to the commentary server in real time. The hardware uses a camera and microphone, and the software uses EmotionRecognizer.

[1026] Explanation generation method

[1027] The commentary server generates commentary using a generative AI model based on user settings, real-time data, and emotion data. The accuracy of the generated commentary is verified by an error detection means.

[1028] Error detection means

[1029] Check the accuracy of the generated commentary and correct it if necessary.

[1030] Means of providing explanations

[1031] The generated commentary is provided to users in real time. The commentary is streamed to the user's device, allowing them to enjoy the game while listening to commentary tailored to their preferences. The emotionally tailored commentary increases user interest and satisfaction.

[1032] Specific examples

[1033] A user launches the application on their smartphone, selects "humorous commentary" and "focus on player data," and allows the use of the camera and microphone. This information is sent to the commentary server, which generates commentary in real time based on the settings. If the user's facial expression is smiling, EmotionRecognizer detects a state of "joy," and the commentary is provided in a more positive tone.

[1034] Example prompt sentence:

[1035] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[1036] In this way, the system can provide a rich sports viewing experience based on the user's emotions and individual commentary requirements.

[1037] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1038] Step 1:

[1039] The user launches the dedicated application on their smartphone, and a login screen appears. The user performs login authentication, and if authentication is successful, the device displays the commentary setting screen. On this setting screen, the user selects the commentary style (e.g., professional, humorous, matter-of-fact) and the information of interest (player data, tactical analysis). In addition, the user is asked for permission to use the camera and microphone. Once the user completes the settings and allows use of the camera and microphone, the device sends this information to the server.

[1040] Input: User login information, commentary style, interests, camera and microphone permissions

[1041] Output: User setting information

[1042] Step 2:

[1043] The explanation server receives the user setting information sent from the terminal, saves the setting information, and starts preparations to generate an explanation based on the user's individual settings.

[1044] Input: User setting information

[1045] Output: Save the configuration information

[1046] Step 3:

[1047] The commentary server works in conjunction with the data analysis server to obtain real-time match data, player performance data, and statistical information from external data sources, and the data analysis server provides this data to the commentary server.

[1048] Inputs: Match data, performance data, and statistics from external data sources

[1049] Output: Real-time data

[1050] Step 4:

[1051] The device uses a camera and microphone to capture the user's facial expressions and voice data. These data are then processed into emotion data using the EmotionRecognizer library. The emotion recognition means then transmits this emotion data to the commentary server in real time.

[1052] Input: User's facial expression data, voice data

[1053] Output: Emotion data

[1054] Step 5:

[1055] The commentary server receives user settings, real-time data, and emotion data, and generates commentary using a generative AI model. The generated commentary is checked for accuracy by error detection means and corrected as necessary. For example, the generative AI model is given the following prompt:

[1056] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[1057] Input: User setting information, real-time data, emotion data

[1058] Output: Generated commentary

[1059] Step 6:

[1060] The commentary content confirmed by the error detection means is provided to the user in real time through the commentary providing means, so that the user can enjoy the sporting event while watching the commentary streamed on the terminal.

[1061] Input: Generated commentary

[1062] Output: Streaming commentary provided

[1063] These are the details of the processing steps in the system of the present invention, which allows users to enjoy a rich and personalized entertainment experience.

[1064] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1065] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1066] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1067] [Fourth embodiment]

[1068] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1069] 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.

[1070] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1071] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1072] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1073] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1074] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1075] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1076] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1077] 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.

[1078] 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.

[1079] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1080] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1081] The system of the present invention is composed of three main components: a user terminal, a commentary server, and a data analysis server. The role of each component and the specific processing flow are explained below.

[1082] Initial setup on the user device

[1083] A dedicated application is first launched on the user's device. This application displays a login screen for the user, and once authentication is complete, a settings screen is displayed. The settings screen allows the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Once the user has completed their selection, this information is sent from the device to the commentary server.

[1084] Examples:

[1085] The user starts the application and selects "Expert Commentary" and "Emphasis on Player Data." This setting information is immediately sent to the commentary server.

[1086] Sending configuration information

[1087] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[1088] Real-time data acquisition

[1089] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[1090] Examples:

[1091] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[1092] Analysis of explanatory data

[1093] The commentary server uses AI models to generate commentary based on user-submitted settings and real-time data obtained from the data analysis server. This commentary is tailored to the user's preferences and, if necessary, checked by error detection methods. Any inaccuracies are corrected.

[1094] Examples:

[1095] The commentary server generates a professional commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season."

[1096] Providing commentary

[1097] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing the user to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[1098] Examples:

[1099] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[1100] System Features

[1101] The system's unique feature is that users can customize their commentary style and the information they are interested in. Furthermore, the combination of real-time data acquisition, commentary generation using AI models, and accurate information provision using error detection methods allows for commentary that is perfectly tailored to individual needs. Furthermore, because commentary is provided in real time, users can receive the latest information as the match progresses.

[1102] This system allows casual fans and those with little experience to enjoy watching sports, and can improve fan retention rates.

[1103] ---

[1104] The processing flow will be explained below.

[1105] Step 1:

[1106] The device launches the application, displays a login screen to the user and prompts them to authenticate, and once the user enters their authentication information and authentication is complete, displays the settings screen.

[1107] Step 2:

[1108] The device presents users with options for commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.), and the user selects their preferred settings from these options.

[1109] Step 3:

[1110] The terminal collects information about the commentary style set by the user and the sporting event being watched, and transmits this setting information to a commentary server.

[1111] Step 4:

[1112] The explanation server stores the received user setting information and prepares to generate an individual explanation for each user.

[1113] Step 5:

[1114] The commentary server sends a request to the data analysis server to obtain real-time data related to the sporting event being watched.

[1115] Step 6:

[1116] The data analysis server obtains real-time data such as the progress of the match, player performance data, and statistical information from external data sources (such as APIs and official databases), and sends this data to the commentary server.

[1117] Step 7:

[1118] The commentary server receives real-time data sent from the data analysis server and generates commentary in combination with user settings. It uses an AI model to create commentary in a style selected by the user.

[1119] Step 8:

[1120] The commentary server checks the generated commentary with an internal error detection function and corrects any inaccuracies, with expert supervision if necessary.

[1121] Step 9:

[1122] The commentary server prepares to stream the generated commentary to the user device in real time.

[1123] Step 10:

[1124] The device receives the streamed commentary from the commentary server and displays or plays audio to the user, who can receive commentary in real time based on their selection.

[1125] ---

[1126] The above is a detailed description of the system's specific processing steps and operations. This process allows users to enjoy commentary tailored to their preferences in real time.

[1127] Example 1

[1128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1129] A problem with conventional sports commentary systems is that they are unable to provide commentary that meets the individual needs of each user. For example, general commentary cannot fully meet the specialized information or interests of individual users, resulting in a lower quality of viewing experience. Furthermore, there are issues with the accuracy and currency of the data acquired in real time, which can lead to the provision of incorrect information. There is a need for a system that can solve these issues and provide commentary with a higher level of user satisfaction.

[1130] 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.

[1131] In this invention, the server includes commentary setting means for allowing a user to select a preferred commentary style and specific information, user setting information transmission means for transmitting setting information from a user terminal to the commentary server, real-time data acquisition means for acquiring real-time data related to a sport event from an external data source to the commentary server, commentary generation means for generating commentary using a generative AI model, error detection means for detecting errors in the generated commentary and correcting inaccurate information, and commentary provision means for providing the generated commentary to the user terminal in real time. This makes it possible to provide professional and accurate commentary tailored to the individual needs of users, as well as to acquire and provide the latest and accurate information in real time.

[1132] The "explanation setting means" is a means for the user to select the preferred explanation style and specific information, and provides a user interface.

[1133] The "user setting information transmission means" is a means for transmitting setting information from the user terminal to the commentary server.

[1134] The "real-time data acquisition means" is a means for acquiring real-time data related to a sporting event from an external data source to the commentary server.

[1135] "Explanation generation means" refers to a means for generating explanations based on user setting information and acquired real-time data using a generative AI model.

[1136] The "error detection means" is a means for detecting errors in the generated explanation and correcting inaccurate information if it is included.

[1137] The "explanation providing means" is a means for providing the generated explanation to the user terminal in real time.

[1138] MODE FOR CARRYING OUT THE INVENTION

[1139] The system of the present invention is mainly composed of three main components: a user terminal, a commentary server, and a data analysis server. A specific embodiment of the system will be described below.

[1140] Initial setup on the user device

[1141] First, the user launches a dedicated application on their device. This application displays a login screen for the user and authenticates them by entering their login information (e.g., user ID and password). Once authentication is complete, a settings screen is displayed, where the user selects their preferred commentary style (e.g., professional, humorous, matter-of-fact) and information of interest (e.g., player data, tactical analysis). This setting information is immediately sent from the user device to the commentary server.

[1142] Examples:

[1143] The user starts the application and selects "Expert commentary" and "Emphasis on player data." This setting information is immediately sent to the commentary server.

[1144] Processing on the commentary server

[1145] The commentary server receives the setting information sent by the user. The server creates a user profile based on this information and prepares to generate commentary tailored to the user's individual needs. The commentary server then sends a request to the data analysis server to obtain real-time data.

[1146] Real-time data acquisition

[1147] The data analysis server acquires real-time match data, player performance data, and statistical information from external sources (e.g., sports data providing services), and transmits the acquired data to the commentary server.

[1148] Examples:

[1149] The data analysis server obtains the progress of the game and data on the players' plays from external data sources and sends it to the commentary server.

[1150] Analysis and generation of explanatory data

[1151] The commentary server generates commentary using a generative AI model (e.g., GPT-3) based on the acquired real-time data and the settings information received from the user. The generated commentary is checked by an error detection system, and inaccurate information is corrected if necessary.

[1152] Examples:

[1153] Prompt: "Please explain Player A's goal."

[1154] Generated commentary: "Player A scores a goal. His last pass success rate was 90%, which is above his average success rate this season."

[1155] Providing commentary

[1156] The commentary server then streams the generated commentary to the user's device in real time, allowing the user to enjoy watching the sports game while listening to the commentary.

[1157] Examples:

[1158] The commentary generated by the commentary server is streamed to the user's device in real time, and the user watches the commentary such as, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than the average success rate this season."

[1159] This system allows users to receive detailed and interesting commentary tailored to their preferences in real time, significantly improving the sports viewing experience. It is also expected to help casual fans and those with little experience of sports to experience the appeal of sports, thereby improving fan retention rates.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user launches the application. A login screen is displayed, and the user enters their ID and password. Once authentication is complete, the settings screen is displayed. The user selects a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis). The input is the user's selected commentary style and information of interest, and the output is the settings information for these. The settings information is sent from the user's terminal to the commentary server. In concrete terms, the user selects "professional commentary" and "emphasis on player data," and this information is sent to the server.

[1163] Step 2:

[1164] The commentary server receives the setting information sent by the user. The input is the setting information sent from the user terminal, and the output is the user profile stored in the commentary server. Based on this information, the commentary server creates a profile for the user and prepares to generate a dedicated commentary. Specifically, the commentary server creates a profile called "user123" and prepares to generate a commentary.

[1165] Step 3:

[1166] The commentary server sends a request to the data analysis server to obtain real-time data. The input is the request from the commentary server, and the output is a completion log of the request sent to the data analysis server. Specifically, a request such as "Please obtain real-time data for match ID 5678" is sent.

[1167] Step 4:

[1168] The data analysis server obtains real-time data from external data sources. The inputs are match data, player performance data, and statistical information obtained from external data sources, and the output is these data. The data analysis server sends this data to the commentary server. Specifically, data such as "Player A's current pass success rate is 90%" is obtained and sent to the commentary server.

[1169] Step 5:

[1170] The commentary server generates commentary using a generative AI model (for example, GPT-3) based on the acquired real-time data and the setting information received from the user. The input is real-time data and setting information, and the output is the generated commentary. The commentary is generated based on the prompt text. Specifically, based on the prompt text "Please explain Player A's goal," the commentary generated is "Player A has scored a goal. His pass success rate immediately before that was 90%, which is higher than the average success rate for this season."

[1171] Step 6:

[1172] The commentary server checks the generated commentary with an error detection system. The input is the generated commentary, and the output is the corrected commentary. If it contains inaccurate information, it is corrected. Specifically, the generated commentary is checked with the error detection system, and corrections are made if necessary.

[1173] Step 7:

[1174] The commentary server streams the final commentary to the user's device in real time. The input is the completed commentary text, and the output is the commentary delivered to the user's device. The user can enjoy watching the sports game while viewing this. Specifically, the commentary server streams commentary such as "Player A scored a goal..." to the user's device, and the user views it in real time.

[1175] (Application example 1)

[1176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1177] Conventional sports commentary systems have difficulty providing real-time commentary that meets individual user preferences, and lack the functionality to send appropriate notifications to users when specific events occur, creating a need for an improved user experience.

[1178] 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.

[1179] In this invention, the server includes an explanation setting means, a user setting information transmission means, a real-time data acquisition means, an explanation generation means, an error detection means, an explanation providing means, and a notification means for transmitting a notification to a user terminal, thereby enabling the provision of individual explanations based on the user's preferences and the transmission of notifications in real time.

[1180] The "explanation setting means" is a function that provides an interface that allows the user to select their preferred explanation style and specific information.

[1181] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the server.

[1182] The "real-time data acquisition means" is a function that collects real-time data related to a sporting event from an external data source.

[1183] The "explanation generation means" is a function that generates explanations using a generative AI model based on collected real-time data and user setting information.

[1184] The "error detection means" is a function that checks whether the generated commentary contains any inaccurate information and corrects it if necessary.

[1185] The "explanation providing means" is a function that streams the generated explanation to the user's terminal.

[1186] "Notification means" is a function that sends a push notification to the user's device when a specific trigger event occurs.

[1187] A "generative AI model" is an artificial intelligence model that generates commentary based on user settings and real-time data.

[1188] A "prompt" is an instruction that instructs an AI model to generate an explanation.

[1189] The system for implementing the present invention is configured using the following hardware and software.

[1190] Hardware and software used

[1191] User device: Smartphone

[1192] Explanation server: Cloud-based server (e.g. AWS)

[1193] Data analysis server: Collects real-time data using external APIs (e.g., SportsAPI)

[1194] Generative AI models: For example, OpenAI's GPT-3

[1195] User Device

[1196] First, the user launches a dedicated application on their smartphone. The application displays a login screen and asks the user for authentication. Once authentication is complete, the user proceeds to a settings screen where they can select a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). This setting information is sent to the commentary server by the user setting information sending means.

[1197] Commentary Server

[1198] The commentary server works with the data analysis server to collect data related to sporting events in real time based on the setting information received from the user. The commentary generation means uses a generative AI model to generate commentary based on the collected real-time data and the user's setting information. At this time, an error detection means is used to check whether the content of the commentary is accurate. If necessary, corrections are made automatically.

[1199] The generated commentary is streamed to the user terminal by the commentary providing means, and when a specific trigger event occurs, a push notification is sent to the user via the notification means.

[1200] Data analysis server

[1201] The data analysis server retrieves real-time data about the progress of sporting events and players from external data sources, for example using the SportsAPI, and sends this data to the commentary server, where it is used as input data for generating commentary.

[1202] Specific examples

[1203] The user starts the application, authenticates on the login screen, and then selects "Expert commentary" and "Player data emphasis." This setting information is sent to the commentary server via the user setting information transmission means. The commentary server receives real-time data from the data analysis server and generates commentary based on that data using a generative AI model. After that, a commentary such as "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season" is streamed in real time to the user's device. An example of the prompt sentence used in this generation is as follows:

[1204] "Player A has scored a goal. His pass success rate just before the goal was 90%, which is higher than his average success rate this season. Please provide further commentary from your expert perspective."

[1205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1206] Step 1:

[1207] The user device launches a dedicated application and displays the login screen. When the user enters their authentication information, the application sends the data to the authentication server and the authentication is completed. The input is the user's authentication information, and the output is the authentication completion status.

[1208] Step 2:

[1209] After authentication is complete, the user's device displays a setting screen for selecting a commentary style (professional, humorous, matter-of-fact) and information of interest (player data, tactical analysis, etc.). The user inputs each selection. The input is the user's commentary style and information of interest, and the output is the user's setting information.

[1210] Step 3:

[1211] When the user completes the setting, the user terminal uses the user setting information transmission means to transmit the setting information to the commentary server. The input is the user setting information, and the output is the setting information transmitted to the server.

[1212] Step 4:

[1213] The commentary server receives user setting information and works with the data analysis server to collect data related to sporting events in real time. This data collection uses APIs of external data sources (e.g., SportsAPI). The input is user setting information and the output is real-time data.

[1214] Step 5:

[1215] The data analysis server sends the collected real-time data to the commentary server. The commentary server generates commentary using a generative AI model based on user setting information and real-time data. The input is user setting information and real-time data, and the output is the generated commentary.

[1216] Step 6:

[1217] The explanation server uses error detection to check the generated explanation for inaccuracies and makes corrections as necessary. The input is the generated explanation, and the output is a verified, accurate explanation.

[1218] Step 7:

[1219] The generated commentary is streamed to the user terminal through the commentary providing means, and the user can view the commentary in real time. The input is the verified commentary, and the output is the commentary streamed on the user terminal.

[1220] Step 8:

[1221] When a specific trigger event occurs (e.g., a goal or an important play), the commentary server sends a push notification to the user via the notification mechanism. The input is the trigger event information, and the output is the push notification sent to the user device.

[1222] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1223] The present invention provides a system that personalizes sports viewing and further provides a richer entertainment experience by recognizing a user's emotions and adjusting commentary accordingly. Specific embodiments of the system of the present invention are described below.

[1224] System Configuration

[1225] This system consists of four main components: a user terminal, a commentary server, a data analysis server, and an emotion engine. The role and processing flow of each component are explained in detail below.

[1226] Initial setup on the user device

[1227] A dedicated application is launched on the user's device, and a login screen is displayed to the user. Once authentication is complete, a settings screen is displayed. On the settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.). The camera and microphone can also be used to recognize the user's emotions. Once the user has completed their selection, this setting information is sent from the device to the commentary server.

[1228] Examples:

[1229] The user launches the application, selects "humorous commentary" and "focus on player data," and allows the camera and microphone. This setting information is immediately sent to the commentary server.

[1230] Sending configuration information

[1231] The information set by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[1232] Real-time data acquisition

[1233] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[1234] Examples:

[1235] The data analysis server obtains game progress and player performance data from external data sources and sends it to the commentary server.

[1236] Emotion Engine Operation

[1237] The emotion engine recognizes the user's emotional state through their facial expressions and voice, and this recognized emotional data is sent to the commentary server in real time.

[1238] Examples:

[1239] If the user is smiling, the emotion engine detects the state of "happiness" and sends the data to the commentary server.

[1240] Analysis of explanatory data

[1241] The commentary server generates commentary using an AI model based on user settings, real-time data, and emotion data from the emotion engine. In particular, the commentary content can be adjusted according to the user's emotional state. Furthermore, the accuracy of the generated commentary content is checked by an error detection means, and corrected if necessary.

[1242] Examples:

[1243] The commentary server generates a professional yet humorous commentary: "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[1244] Providing commentary

[1245] The generated commentary is provided to users in real time. The commentary server streams the generated commentary to the user's device, allowing users to watch and listen to the commentary in real time. This allows users to enjoy watching sports while listening to detailed and interesting commentary tailored to their preferences.

[1246] Examples:

[1247] The commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[1248] System Features

[1249] The system's unique features include the ability for users to individually set their commentary style and the information they are interested in, as well as the ability to recognize users' emotions and provide real-time commentary accordingly, allowing users to receive commentary tailored to their individual needs and providing a more personalized entertainment experience.

[1250] The aim of this system is to provide casual and less experienced fans with the enjoyment of watching sports, and to improve fan retention rates.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] The device presents the user with a login screen, waits for the user to enter their credentials, and then waits for the authentication to complete.

[1254] Step 2:

[1255] After logging in, the device displays the commentary settings screen, where the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information of interest (player data, tactical analysis, etc.).

[1256] Step 3:

[1257] The device collects the user's configuration information and sends it to the commentary server.

[1258] Step 4:

[1259] The commentary server stores the received user setting information and prepares for individual commentary generation.

[1260] Step 5:

[1261] The commentary server sends a request to the data analysis server to obtain real-time data about the sporting event.

[1262] Step 6:

[1263] The data analysis server obtains the progress of the match, player performance data, statistical information, etc. from external data sources and sends the data to the commentary server.

[1264] Step 7:

[1265] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state, and the emotion data is sent from the device to the commentary server.

[1266] Step 8:

[1267] The commentary server generates commentary using an AI model based on real-time data sent from the data analysis server, user setting information, and emotion data from the emotion engine. In particular, it adjusts the content of the commentary according to the user's emotions.

[1268] Step 9:

[1269] The commentary server checks the accuracy of the commentary content generated by the commentary server using an error detection means and corrects it if necessary.

[1270] Step 10:

[1271] The commentary server prepares to stream the generated commentary to the user terminal in real time.

[1272] Step 11:

[1273] The device receives the streaming commentary from the commentary server and displays or plays the audio to the user, allowing the user to enjoy commentary tailored to their preferences in real time.

[1274] Specific examples

[1275] For example, suppose a user selects "humorous commentary" and "focus on player data" and allows the camera and microphone. As the match progresses, when Player A scores a goal, the data analysis server collects this information and sends it to the commentary server. At the same time, the emotion engine detects the user's emotional state of "joy" and sends it to the commentary server. The commentary server generates a commentary such as "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season," and provides it with humor depending on the user's emotional state.

[1276] Example 2

[1277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1278] Conventional sports viewing systems have difficulty providing personalized commentary based on individual user preferences, and are unable to reflect the user's emotional state in real time. As a result, the user's entertainment experience is not improved. The present invention aims to provide commentary tailored to the user's preferences, recognize the user's emotional state in real time, and provide commentary content that corresponds to the user's preferences, thereby realizing a richer entertainment experience.

[1279] 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.

[1280] In this invention, the server includes an explanation setting means, a user setting information transmitting means, a real-time data acquiring means, an emotion recognition means, an explanation generating means, an error detecting means, and an explanation providing means, which make it possible to generate personalized explanations based on the user's setting information, collect real-time data, and adjust the explanation content according to the user's emotional state.

[1281] The "explanation setting means" is a function that provides a user interface for the user to select their preferred explanation style and specific information.

[1282] The "user setting information transmission means" is a function that transmits the commentary style and information of interest set by the user to the commentary server.

[1283] The "real-time data acquisition means" is a function that acquires real-time data related to a sporting event from an external data source.

[1284] The "emotion recognition means" is a function that analyzes facial expression data and voice data to recognize the user's emotions.

[1285] The "explanation generation means" is a function that generates an explanation using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means.

[1286] The "error detection means" is a function that checks the accuracy of the generated commentary content and corrects it if necessary.

[1287] The "explanation providing means" is a function that provides the generated explanation to the user in real time.

[1288] The present invention provides a sports viewing system that provides commentary tailored to individual user needs and further adjusts the commentary content by recognizing the user's emotions in real time. This system comprises a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means.

[1289] System Configuration

[1290] Initial setup on the user device

[1291] A dedicated application is installed on the user's device. When the application is launched, a login screen is displayed to the user. After the user enters their authentication information and completes the authentication, a settings screen is displayed, allowing the user to select a commentary style (e.g., professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). Furthermore, permission to use the camera and microphone can be selected on the settings screen. This setting information is sent from the user's device to the commentary server.

[1292] Specifically, a user launches the application, selects "humorous commentary" and "focus on player data," and authorizes the use of the camera and microphone. This setting information is immediately sent to the commentary server.

[1293] Sending configuration information

[1294] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[1295] Real-time data acquisition

[1296] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, the progress of the match and player performance data can be obtained through the API.

[1297] Emotion Engine Operation

[1298] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[1299] Specifically, when the user is smiling, the emotion engine detects a state of "joy" and sends that data to the commentary server.

[1300] Analysis of explanatory data

[1301] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[1302] Specifically, the commentary server generates a professional yet humorous commentary such as, "Player A has scored a goal. His last pass success rate was 90%, which is higher than his average success rate this season." Because the user is in the "joy" emotional state, the commentary is provided in a more positive tone.

[1303] Providing commentary

[1304] The commentary providing means streams the generated commentary to the user's terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[1305] Specifically, the commentary generated by the commentary server is streamed to the user's device in real time, and the user hears the commentary, "Player A has scored a goal. His pass success rate just before that was 90%, which is higher than his average success rate this season." Depending on the user's emotional state, the commentary is provided in a more dynamic tone.

[1306] Prompt Sentence Examples

[1307] Below are some example prompts to be input to the generative AI model:

[1308] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[1309] This system allows users to enjoy watching sports more enjoyably by listening to commentary tailored to their preferences in real time.

[1310] (End)

[1311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1312] Step 1:

[1313] Initial setup on the user device

[1314] When a user launches the dedicated application, a login screen appears. After the user enters their authentication information and completes the authentication, they can access the settings screen. On this settings screen, the user can select the commentary style (e.g., professional, humorous, matter-of-fact, etc.) and the information they are interested in (player data, tactical analysis, etc.). They can also select whether to allow the use of the camera and microphone. This setting information is sent from the device to the commentary server.

[1315] Input: User credentials, commentary style, interests, camera and microphone permissions

[1316] Output: Send user setting information to the commentary server

[1317] Step 2:

[1318] Sending configuration information

[1319] The user device sends the configured information to the commentary server. The commentary server receives this information and prepares to generate commentary according to the user's needs. The received configuration information is stored in an internal database and used for subsequent processing.

[1320] Input: User setting information

[1321] Output: Save the configuration information to the server

[1322] Step 3:

[1323] Real-time data acquisition

[1324] The commentary server works in conjunction with the data analysis server to collect real-time data related to sporting events. The data analysis server obtains match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server. Specifically, it obtains match progress and player performance data via an API.

[1325] Input: Real-time data requests from external sources

[1326] Output: Sending real-time data to the commentary server

[1327] Step 4:

[1328] Emotion Engine Operation

[1329] The emotion recognition means analyzes facial expression data and voice data sent from the user terminal to recognize the emotional state of the user. This emotion data is sent to the commentary server in real time and used in the commentary generation process.

[1330] Input: User's facial expression data, voice data

[1331] Output: Sending emotional state data to the commentary server

[1332] Step 5:

[1333] Analysis of explanatory data

[1334] The commentary generation means generates commentary using a generative AI model based on user setting information, real-time data, and emotion data from the emotion recognition means. The AI ​​model performs integrated analysis based on this data and can adjust the commentary content according to the user's emotional state. The accuracy of the generated commentary content is checked using an error detection means, and it is corrected if necessary.

[1335] Input: User setting information, real-time data, emotion data

[1336] Output: Adjusted commentary

[1337] Step 6:

[1338] Providing commentary

[1339] The commentary providing means streams the generated commentary to the user terminal in real time. The user can enjoy watching the sports game while watching the commentary received on the terminal. The commentary is provided according to the style and interests set by the user, which improves user satisfaction.

[1340] Input: Adjusted commentary

[1341] Output: Commentary on user's device

[1342] As a specific example of how this works, the following prompt sentence is an example of input to the generative AI model.

[1343] "Please provide commentary on the last moments of the current match. The commentary style should be humorous and include player data. The user's emotion should be joy."

[1344] (Application example 2)

[1345] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1346] In conventional sports viewing systems, users often receive uniform commentary, making it difficult to provide a personalized entertainment experience based on individual preferences and emotions. As a result, user satisfaction and interest decline, limiting the ability to improve fan retention. Furthermore, if the commentary content does not match the user's emotional state, the viewing experience itself is likely to be interrupted. A new system that solves these issues is needed.

[1347] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a commentary setting means, a user setting information transmission means, a real-time data acquisition means, an emotion recognition means, a commentary generation means, an error detection means, and a commentary provision means. This makes it possible to generate and provide a commentary personalized for each user, and further enables real-time commentary adjustment according to the user's emotional state. This is expected to significantly improve the sports viewing experience, and increase user satisfaction and fan retention.

[1348] The "explanation setting means" is a means for providing a user interface for the user to select a preferred explanation style or specific information.

[1349] The "user setting information transmission means" is a means for transmitting information set by the user to the server.

[1350] The "real-time data acquisition means" is a means for acquiring real-time data related to an event from an external data source.

[1351] The "emotion recognition means" is a means for recognizing the emotional state of a user by analyzing their facial expressions and voice.

[1352] The "explanation generating means" is a means for generating an explanation based on user setting information, real-time data, and emotion data.

[1353] The "error detection means" is a means for checking the accuracy of the generated commentary content and correcting it if necessary.

[1354] The "explanation providing means" is a means for providing the generated explanation to the user in real time.

[1355] The system of the present invention personalizes sports viewing, recognizes user emotions, and adjusts commentary accordingly to provide a richer entertainment experience. Specific embodiments of the system are described below.

[1356] System Configuration

[1357] The system consists of the following main components:

[1358] 1. Explanation setting method

[1359] 2. Means of sending user setting information

[1360] 3. Real-time data acquisition methods

[1361] 4. Emotion recognition means

[1362] 5. Explanation Generation Method

[1363] 6. Error Detection Methods

[1364] 7. Means of providing explanations

[1365] program

[1366] The system's programming includes the following processes:

[1367] Explanation setting method

[1368] A dedicated application is launched on the user's device (e.g., a smartphone), and the user authenticates through a login screen. Once authentication is complete, a settings screen is displayed, allowing the user to select a commentary style (professional, humorous, matter-of-fact, etc.) and information of interest (player data, tactical analysis, etc.). This setting information is sent from the device to the commentary server.

[1369] User setting information transmission method

[1370] The information selected by the user is sent from the user terminal to the commentary server, which receives this setting information and prepares to generate commentary according to the user's individual needs.

[1371] Real-time data acquisition method

[1372] The commentary server works in conjunction with the data analysis server to collect real-time data related to the sporting event being watched. The data analysis server obtains real-time match data, player performance data, statistical information, etc. from external sources and provides it to the commentary server.

[1373] emotion recognition means

[1374] The system recognizes the user's emotional state through their facial expressions and voice. This recognized emotional data is sent to the commentary server in real time. The hardware uses a camera and microphone, and the software uses EmotionRecognizer.

[1375] Explanation generation method

[1376] The commentary server generates commentary using a generative AI model based on user settings, real-time data, and emotion data. The accuracy of the generated commentary is verified by an error detection means.

[1377] Error detection means

[1378] Check the accuracy of the generated commentary and correct it if necessary.

[1379] Means of providing explanations

[1380] The generated commentary is provided to users in real time. The commentary is streamed to the user's device, allowing them to enjoy the game while listening to commentary tailored to their preferences. The emotionally tailored commentary increases user interest and satisfaction.

[1381] Specific examples

[1382] A user launches the application on their smartphone, selects "humorous commentary" and "focus on player data," and allows the use of the camera and microphone. This information is sent to the commentary server, which generates commentary in real time based on the settings. If the user's facial expression is smiling, EmotionRecognizer detects a state of "joy," and the commentary is provided in a more positive tone.

[1383] Example prompt sentence:

[1384] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[1385] In this way, the system can provide a rich sports viewing experience based on the user's emotions and individual commentary requirements.

[1386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1387] Step 1:

[1388] The user launches the dedicated application on their smartphone, and a login screen appears. The user performs login authentication, and if authentication is successful, the device displays the commentary setting screen. On this setting screen, the user selects the commentary style (e.g., professional, humorous, matter-of-fact) and the information of interest (player data, tactical analysis). In addition, the user is asked for permission to use the camera and microphone. Once the user completes the settings and allows use of the camera and microphone, the device sends this information to the server.

[1389] Input: User login information, commentary style, interests, camera and microphone permissions

[1390] Output: User setting information

[1391] Step 2:

[1392] The explanation server receives the user setting information sent from the terminal, saves the setting information, and starts preparations to generate an explanation based on the user's individual settings.

[1393] Input: User setting information

[1394] Output: Save the configuration information

[1395] Step 3:

[1396] The commentary server works in conjunction with the data analysis server to obtain real-time match data, player performance data, and statistical information from external data sources, and the data analysis server provides this data to the commentary server.

[1397] Inputs: Match data, performance data, and statistics from external data sources

[1398] Output: Real-time data

[1399] Step 4:

[1400] The device uses a camera and microphone to capture the user's facial expressions and voice data. These data are then processed into emotion data using the EmotionRecognizer library. The emotion recognition means then transmits this emotion data to the commentary server in real time.

[1401] Input: User's facial expression data, voice data

[1402] Output: Emotion data

[1403] Step 5:

[1404] The commentary server receives user settings, real-time data, and emotion data, and generates commentary using a generative AI model. The generated commentary is checked for accuracy by error detection means and corrected as necessary. For example, the generative AI model is given the following prompt:

[1405] "User is in a happy emotional state. Player A scores a goal. Commentary style should be humorous with emphasis on player data."

[1406] Input: User setting information, real-time data, emotion data

[1407] Output: Generated commentary

[1408] Step 6:

[1409] The commentary content confirmed by the error detection means is provided to the user in real time through the commentary providing means, so that the user can enjoy the sporting event while watching the commentary streamed on the terminal.

[1410] Input: Generated commentary

[1411] Output: Streaming commentary provided

[1412] These are the details of the processing steps in the system of the present invention, which allows users to enjoy a rich and personalized entertainment experience.

[1413] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1414] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1415] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1416] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1417] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1418] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1419] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1420] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1421] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1422] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1423] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1424] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1425] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1426] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1427] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1428] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1429] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1430] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1431] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1432] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1433] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1434] The following is further disclosed regarding the above embodiment.

[1435] Below are draft claims that can be used in filing a patent application.

[1436] (Claim 1)

[1437] Explanation setting means;

[1438] user setting information transmission means;

[1439] A real-time data acquisition means;

[1440] Explanation generation means;

[1441] error detection means;

[1442] a means for providing commentary;

[1443] A system including:

[1444] (Claim 2)

[1445] 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

[1446] (Claim 3)

[1447] 10. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the sporting event from an external data source.

[1448] (Claim 4)

[1449] The system of claim 1, wherein the explanation generation means generates explanations using an AI model based on user setting information and real-time data.

[1450] (Claim 5)

[1451] 2. The system according to claim 1, wherein the error detection means detects errors in the generated commentary content and corrects them as necessary.

[1452] (Claim 6)

[1453] 2. The system of claim 1, wherein the commentary providing means streams the generated commentary to the user terminal in real time.

[1454] "Example 1"

[1455] (Claim 1)

[1456] a commentary setting means for allowing a user to select a preferred commentary style and specific information;

[1457] A user setting information transmission means for transmitting setting information from the user terminal to the commentary server;

[1458] real-time data acquisition means for acquiring real-time data relating to the sporting event from an external data source to a commentary server;

[1459] An explanation generation means for generating an explanation using a generative AI model;

[1460] an error detection means for detecting errors in the generated commentary and correcting inaccurate information;

[1461] an explanation providing means for providing the generated explanation to a user terminal in real time;

[1462] A system including:

[1463] (Claim 2)

[1464] 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

[1465] (Claim 3)

[1466] 2. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the sporting event from an external data source.

[1467] "Application Example 1"

[1468] (Claim 1)

[1469] Explanation setting means;

[1470] user setting information transmission means;

[1471] A real-time data acquisition means;

[1472] Explanation generation means;

[1473] error detection means;

[1474] a means for providing commentary;

[1475] a notification means for sending a notification to a user terminal;

[1476] A system including:

[1477] (Claim 2)

[1478] 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

[1479] (Claim 3)

[1480] 10. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the sporting event from an external data source.

[1481] (Claim 4)

[1482] The system of claim 1, wherein the explanation generation means uses a generative AI model to create a prompt sentence that generates an explanation based on user setting information and real-time data.

[1483] (Claim 5)

[1484] 10. The system of claim 1, wherein the notification means sends a push notification to the user upon a specific trigger event.

[1485] "Example 2: Combining Emotion Engines"

[1486] (Claim 1)

[1487] Explanation setting means;

[1488] user setting information transmission means;

[1489] A real-time data acquisition means;

[1490] An emotion recognition means;

[1491] Explanation generation means;

[1492] error detection means;

[1493] a means for providing commentary;

[1494] A system including:

[1495] (Claim 2)

[1496] 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

[1497] (Claim 3)

[1498] 10. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the sporting event from an external data source.

[1499] (Claim 4)

[1500] 2. The system of claim 1, wherein the emotion recognition means analyzes facial expression data and voice data to recognize the user's emotions.

[1501] (Claim 5)

[1502] The system described in claim 1, characterized in that the explanation generation means generates explanations using a generative AI model based on user setting information, real-time data, and emotional data from the emotion recognition means, and adjusts the content of the explanation according to the user's emotional state.

[1503] "Application example 2 when combining emotion engines"

[1504] (Claim 1)

[1505] Explanation setting means;

[1506] user setting information transmission means;

[1507] A real-time data acquisition means;

[1508] An emotion recognition means;

[1509] Explanation generation means;

[1510] error detection means;

[1511] a means for providing commentary;

[1512] A system including:

[1513] (Claim 2)

[1514] 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

[1515] (Claim 3)

[1516] 2. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the event from an external data source. [Explanation of symbols]

[1517] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. Explanation setting means; user setting information transmission means; A real-time data acquisition means; Explanation generation means; error detection means; a means for providing commentary; A system including:

2. 2. The system according to claim 1, wherein the explanation setting means provides a user interface for the user to select a preferred explanation style and specific information.

3. 2. The system of claim 1, wherein the real-time data acquisition means acquires real-time data related to the sporting event from an external data source.

4. The system according to claim 1, wherein the explanation generation means generates explanations using an AI model based on user setting information and real-time data.

5. 2. The system according to claim 1, wherein the error detection means detects errors in the generated commentary content and corrects them as necessary.

6. 2. The system according to claim 1, wherein the commentary providing means streams the generated commentary to the user terminal in real time.

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