system

Abstract

We provide the system. [Solution] A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device, A means for retrieving player information from a storage device in response to a request from a user terminal and providing said player information to the user terminal, A means of analyzing a player's performance using an artificial intelligence model and displaying the analysis results on the user's terminal, A means of communication to facilitate the exchange of opinions with other users, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Existing sports viewing applications have problems such as limited real-time information provision, difficulty for users to easily obtain and analyze data during a game, inability to immediately obtain past data related to competitions and detailed information of players, resulting in a restricted viewing experience and a potential decrease in user engagement. Furthermore, it is difficult to form a community because the exchange of opinions among fans is not smooth.

Means for Solving the Problems

[0005] This invention provides a system that receives competition-related data acquired from a data supply device in real time and provides player information to user terminals in a unified format. This allows users to obtain information immediately during a match. Furthermore, it enhances the user's viewing experience by analyzing player performance using an artificial intelligence model and presenting the analysis results visually. In addition, it includes a communication function that allows users to exchange opinions in real time, thereby strengthening connections among fans and promoting community formation.

[0006] A "data supply device" is a device that acquires competition-related data and provides it in a format that can be used by other systems and devices.

[0007] "Competition-related data" refers to data such as match information, player statistics, team tactics, and past match results related to sports.

[0008] A "storage device" is a device that stores received data and can retrieve it as needed.

[0009] A "user terminal" is an electronic device used by a user to receive data and to view and manipulate information through an interface.

[0010] An "artificial intelligence model" is a computer program that analyzes patterns based on input data and performs predictions and analyses.

[0011] A "communication tool" is a communication function that allows different users to exchange information and share opinions.

[0012] A "graphical display means" is a function that displays graphs and charts on the screen to visually present data to the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention aims to realize a system that provides users with detailed player information and match progress in real time while watching sports. This system begins with a server receiving and processing competition-related data acquired from a data supply device. The server converts the received data into a unified format and stores it in a storage device that functions as a database. This maintains a state where necessary data can be quickly accessed.

[0035] When a user requests specific match information or detailed player data through their device, the device sends the request to a server. The server searches its storage for relevant information and extracts data corresponding to the user's request. The extracted data is further analyzed using artificial intelligence models. For example, performance predictions and tactical evaluations are performed based on players' past performance and current match situations.

[0036] These analysis results are sent to the device and visually displayed on the user interface. For example, users can use their smartphones to view real-time player statistics and predicted match results in graphs and charts. Personalized information based on the user's interests is also provided.

[0037] Furthermore, this system also includes a communication mechanism that allows users to exchange opinions with other sports fans through their devices. This enables real-time information exchange about match impressions and player evaluations, promoting community building.

[0038] These elements work together to provide users with a richer sports viewing experience. This system offers a new viewing style that integrates sports data analysis and communication.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server periodically accesses the data supply device to retrieve the latest competition-related data. This includes match progress, player statistics, and match results.

[0042] Step 2:

[0043] The server converts the acquired data into a unified format, extracts only the necessary data, and saves it to storage. This makes subsequent data retrieval and provision more efficient.

[0044] Step 3:

[0045] Users use their devices to request information about specific matches or players. These requests may include player performance data, match predictions, and current match statistics.

[0046] Step 4:

[0047] The terminal sends the user's request to the server. The request includes specific player names and match IDs.

[0048] Step 5:

[0049] The server searches its storage for the requested information and retrieves the relevant data. The retrieved data is based on the user request.

[0050] Step 6:

[0051] The server analyzes the acquired data using an artificial intelligence model. This analysis includes predicting player performance and evaluating match tactics.

[0052] Step 7:

[0053] The server sends the data along with the analysis results to the terminal. This prepares the terminal to visually display the latest information to the user.

[0054] Step 8:

[0055] The terminal displays data received from the server on the user interface. Users can view the information using graphs and charts, making it easier to understand visually.

[0056] Step 9:

[0057] Users can exchange opinions with other fans through communication channels based on information obtained via their devices. This allows them to deepen their understanding of the match.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] To improve the sports viewing experience, a system is needed that allows users to easily obtain detailed player information and match progress in real time. However, existing systems have problems such as delays in data acquisition and analysis, and fragmented information, which hinders a smooth viewing experience. Furthermore, there is a need for features that provide more interactive elements and promote interaction among users.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving sports-related data acquired from a data supply device, converting the sports-related data into a unified format, and storing it in a storage device; means for searching for athlete information from the storage device in response to a request from a user terminal and providing the athlete information to the user terminal; and means for analyzing the athlete's performance using an automated learning model and displaying the analysis results on the user terminal. This enables users to access information on sports events in real time, quickly and in detail, and further realizes an interactive viewing experience in which they can exchange information with other users.

[0063] A "data supply device" is a device that generates or provides sports-related data and can transmit that data to a server via the internet.

[0064] "Sports-related data" refers to a collection of data that includes information related to sporting events, such as match results, player statistics, and statistical information.

[0065] "Converting to a unified format" refers to the process of standardizing data received in different formats so that it can be stored and used in a consistent manner.

[0066] A "storage device" is a device equipped with computing capabilities for storing digital data, and includes forms such as databases.

[0067] A "user terminal" is a device that a user directly operates to input and output information, and includes personal computers and smartphones.

[0068] "Athlete information" refers to detailed data related to individual athletes, including past performance, profiles, and statistical information.

[0069] An "automated learning model" is a model that uses machine learning algorithms to analyze patterns in data and predict future trends and outcomes.

[0070] "Analysis results" refer to conclusions and insights derived from data processed and analyzed by an automated learning model.

[0071] "Information exchange" refers to the process of sharing information among multiple users and exchanging opinions and data with each other.

[0072] "Communication methods" refer to systems and protocols for sending and receiving information, enabling data transfer over a network.

[0073] The embodiment of this invention relates to a system that provides users with detailed sports information in real time while they are watching a sporting event. Specifically, a server that receives competition-related data from data supply devices via the internet plays a major role. This server converts the received data from JSON format to CSV format and stores it in a MySQL® database to perform unified data management.

[0074] The devices used by users include smartphones and personal computers. These devices can request match information and player information via a user interface. The requests are sent to the server, which searches and extracts the relevant information from the database.

[0075] Next, the server inputs the retrieved data into a generating AI model, which predicts performance based on the players' past results and current match status. This automated learning model is implemented using Python and analyzes the prediction results.

[0076] The analyzed information is visualized on the terminal's user interface and displayed in real time as graphs and charts using visualization libraries such as D3.js. As a concrete example of a prompt statement, a user can make a request to the system in the form of, "Based on player A's performance in recent matches, predict their performance in the next match and visually display the relevant data."

[0077] This system also includes a communication function that allows users to exchange opinions in real time. This enables users to share their evaluations and impressions of matches with other sports fans and form a community.

[0078] By working together, each component plays a role in providing users with a new style and value in watching sports, and enriching the viewing experience.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server receives sports-related data in real time from data supply devices. This data includes real-time match scores, player profile information, and historical performance data. Specifically, the server retrieves data using the HTTP or WebSocket protocol and receives it in JSON format. The received data is converted into a unified format as input, and the necessary information is extracted.

[0082] Step 2:

[0083] The server processes the converted data into CSV format. Next, this standardized data is saved to a MySQL database. The input here is the data converted in step 1, and the output is the data stored in the database in an efficiently indexed state. Data normalization allows subsequent search queries to be processed quickly.

[0084] Step 3:

[0085] The user generates a request to search for specific athlete information or match results through the terminal interface. For example, this might involve entering a specific player's name and match date, and then pressing a submit button. The user's request is formed as a prompt and sent to the server. Here, the user's actions become the input, and the request is sent to the server.

[0086] Step 4:

[0087] The server receives a request from the user and searches the database. The server uses an SQL query to extract data for matching athletes. The input to this operation is the request received in step 3, and the output is the detailed information about the athletes returned by the server. The extracted data is used as input for the next step.

[0088] Step 5:

[0089] The server inputs the extracted athlete data into a generating AI model to analyze the athletes' performance. Using automated learning, it predicts future performance based on past data. Here, the extracted data is input into an analysis tool, and the prediction results are obtained as output. The data produced by this analysis becomes the output desired by the user request.

[0090] Step 6:

[0091] The server sends the analysis results to the terminal. The terminal visualizes the received data using a visualization library such as D3.js. This includes generating graphs and charts. The terminal analyzes this data and displays it in the user interface, providing the user with visual feedback. Based on this information, the user can gain insights into sports matches and players.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] One challenge in watching sports is that it's difficult for spectators to visually check detailed player information and game progress in real time. Furthermore, there's a lack of ways for spectators to exchange opinions with each other in real time and enjoy the game more deeply. There's a need to solve these problems and make the viewing experience richer and more interactive.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for converting competition-related data acquired from a data supply device into a unified format and storing it in a storage device, means for searching for and providing player information in response to requests from user terminals, means for analyzing player performance using an artificial intelligence model and displaying the results, and display means for presenting competition information in real time on a visual display device. As a result, spectators can intuitively obtain detailed information during the match and, by exchanging opinions with other spectators, can have a deeper viewing experience.

[0097] A "data supply device" is a device that acquires and provides competition-related data from external sources.

[0098] "Competition-related data" refers to all information related to sports, such as athletes' performance and match results.

[0099] A "unified format" is a standardized, consistent format created by converting data from different formats into a unified and consistent format.

[0100] A "memory device" is a device or system used to store processed data.

[0101] A "user terminal" refers to a device that allows a user to receive information and interact with it.

[0102] An "artificial intelligence model" is a machine learning technique used to analyze data and generate patterns and predictions.

[0103] "Analysis results" refer to the evaluation or prediction results obtained after data processing.

[0104] A "visual display device" is a device used to present information to a user visually, and includes displays and smart glasses.

[0105] "Real-time" is a term that indicates that processing or reactions occur almost simultaneously.

[0106] "Exchange of opinions" refers to the act of multiple users exchanging information and ideas.

[0107] The system for realizing the application example of this invention consists of a server, a user terminal, a visual display device, and a data supply device. First, the server receives competition-related data acquired from the data supply device. The received data is converted into a unified format and stored in a storage device, enabling efficient data access. The server also uses an artificial intelligence model to analyze the performance of athletes based on their past performance and current match status, and generates analysis results.

[0108] The user terminal communicates with the server in response to user requests and retrieves player information from its storage device. The information provided by the server is displayed in real time through a visual display device, providing users with an environment where they can intuitively obtain the information they need while watching sports. The visual display device includes smart glasses that can interactively display player data and match progress during the game.

[0109] Users can exchange opinions with other spectators based on information obtained through visual displays. This allows for real-time discussions about the match and stimulates communication among fans.

[0110] A concrete example is the ability to use smart glasses during a soccer match to check the performance of specific players or the progress of the game in real time. This provision of visual information significantly improves the viewing experience.

[0111] A concrete example of a prompt using a generative AI model is, "How can I predict a soccer player's performance and display it in real time as optimal information on smart glasses?" Based on this prompt, the AI ​​performs analysis and instantly provides the user with meaningful information.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server acquires competition-related data from the data supply device. It receives player statistics and match status data from the data supply device as input. This data is then converted to a unified format and stored in the storage device. As output, the unified formatted data is recorded in the storage device, enabling efficient access.

[0115] Step 2:

[0116] The user terminal receives user requests and sends them to the server. As input, it receives requests from the user for player information and match data. Based on this, it retrieves the necessary information from the server and sends it to the user terminal. As output, it is ready to provide the information requested by the user.

[0117] Step 3:

[0118] The server searches its storage for player information corresponding to the request. As input, it receives a request from the user's terminal, searches its storage for the relevant data, and compares it. As output, the retrieved player information is sorted and prepared for provision to the user's terminal.

[0119] Step 4:

[0120] The server analyzes player information using an artificial intelligence model. It receives the searched player information as input and performs data analysis using a generative AI model. This generates player performance predictions and tactical evaluations of matches. The analysis results are generated as output and ready to be sent to the user's terminal.

[0121] Step 5:

[0122] The user terminal sends information and analysis results provided by the server to a visual display device, which displays them visually in real time. As input, it receives analysis results and player information from the server and transfers them to a device such as smart glasses. As output, real-time information is presented in the user's field of vision, improving the viewing experience.

[0123] Step 6:

[0124] Users exchange opinions with other spectators based on information provided through visual display devices. As input, they receive provided data and use it as material for discussion. This enables real-time communication. As output, it fosters active exchange of opinions among users, resulting in a more fulfilling viewing experience.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention provides a system that takes into account the user's emotional state to make the sports viewing experience more personalized. This system involves a server receiving competition-related data from a data supply device, converting it into a unified format, and storing it in a memory device. This basic data processing enables a system that can respond to user requests in real time.

[0127] When a user requests information about a specific match or player through their device, the server searches its storage for relevant data and uses an artificial intelligence model to analyze the player's performance. Based on this, the device displays visually insightful information in the user interface to enhance the viewing experience.

[0128] Furthermore, a key feature of this system is its emotion engine, which recognizes the user's emotions in real time. This recognition of emotional states is achieved, for example, by analyzing the user's facial expressions using a camera or by analyzing the user's text input. This allows the system to adjust the tone and detail of the information it provides according to the user's emotions.

[0129] For example, if a user is fidgeting with their device during a match, the emotion engine can determine that the user is feeling anxious and provide reassuring content such as videos of players performing well. Conversely, if the user is happy, the system can proactively present relevant positive news and data to further enhance their excitement.

[0130] In addition, the emotional information of users recognized by the emotion engine is applied to communication with other users. For example, displaying a user's emotions as icons or statuses within a chat enriches the conversation and stimulates interaction between users.

[0131] Thus, the present invention enables interactive responses based on the user's emotions, tailoring the sports viewing experience to individual sensibilities. This system allows users to enjoy a deeper, more immersive viewing experience.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server periodically retrieves competition-related data from the data supply device. This data includes match progress, player performance data, and team tactical information.

[0135] Step 2:

[0136] The server converts received data into a unified format and integrates information from different data sources in a consistent manner. This allows for efficient storage of data in the storage device.

[0137] Step 3:

[0138] Users use their devices to request information about specific matches or players. These requests may include match statistics or players' past performance records.

[0139] Step 4:

[0140] The terminal sends the user's request to the server. The server searches for and extracts the relevant data from its storage device.

[0141] Step 5:

[0142] The server inputs the extracted information into an artificial intelligence model to analyze player performance and predict match trends. The analysis results include the probability of a player succeeding and which strategies are effective.

[0143] Step 6:

[0144] The terminal receives analysis results from the server and displays them visually on the user interface. Graphs and charts are used to aid user understanding.

[0145] Step 7:

[0146] Devices equipped with an emotion engine can recognize the user's emotional state in real time by analyzing the user's facial expressions and tone of voice.

[0147] Step 8:

[0148] The server selects and provides the most relevant information to the user based on their perceived emotions. For example, if the user is feeling anxious, it will display information that provides reassurance.

[0149] Step 9:

[0150] The device shares the user's emotional information, detected by the emotion engine, with other users during chats. This allows other users to consider the user's emotions while interacting with them.

[0151] Step 10:

[0152] Users can exchange opinions with other fans and gain a deeper viewing experience, along with personalized information tailored to their emotions.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In the sports viewing experience, traditional systems that provide uniform information without considering the emotional state of the user make it difficult to provide appropriate information tailored to the individual needs of each user. Furthermore, if the amount or tone of information does not match the user's emotions, it can lead to an unsatisfactory viewing experience or diminish the excitement. As a result, users are demanding more personalized information and experiences that respond to their emotions, but there is a lack of mechanisms to achieve this.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] This invention includes a server that receives competition-related data acquired from a data supply device, converts the competition-related data into a unified format and stores it in a storage device, retrieves player information from the storage device in response to a request from a user terminal and provides the player information to the user terminal, analyzes the players' performance using an artificial intelligence model and displays the analysis results on the user terminal, recognizes the user's emotional state using an emotion engine and adaptively adjusts the tone and content of the information provided according to the emotional state, and communicates with other users to facilitate the exchange of opinions. This makes it possible to provide an appropriate and personalized sports viewing experience based on the emotional state of each individual user.

[0158] "Data supply device" refers to hardware or software used to provide competition-related information to a server.

[0159] "Competition-related data" refers to information related to sporting events, such as match results, player information, and scores.

[0160] A "unified format" refers to a data structure or protocol used to convert data in different formats into a consistent specification.

[0161] "Storage device" refers to a physical or virtual storage medium that a server uses to store data for extended periods.

[0162] A "user terminal" refers to an electronic device used by a user to access a system and obtain information.

[0163] An "artificial intelligence model" refers to a machine learning algorithm or program used to analyze athlete performance.

[0164] An "emotion engine" refers to a function or system that analyzes a user's emotional state and adjusts the information provided based on the results.

[0165] "Communication methods" refer to technologies such as chat functions and messaging systems used by users to exchange opinions with each other.

[0166] This invention is a system that personalizes the sports viewing experience by taking into account the emotional state of the user. This system begins with a server processing "competition-related data" received from a "data supply device." The server converts the received data into a "unified format" and stores it in a "storage device." This conversion and storage process enables the server to quickly access data in response to user requests.

[0167] When a user requests information about a specific match or player through their "user terminal," the server searches for the relevant player information in its "storage device." Furthermore, the server uses an "artificial intelligence model" to analyze this player information in detail and displays the analysis results on the user interface of the "user terminal." In this process, users can obtain visually interactive information, improving their viewing experience.

[0168] Furthermore, this system features an "emotion engine" that recognizes the user's emotions in real time. Emotion recognition is performed through facial expression analysis using a camera and analysis of the user's text input. This emotion recognition function allows the server to adjust the tone and detail of the information it provides according to the user's emotional state. For example, when the user is feeling anxious, it provides reassuring content, and conversely, when the user is happy, it emphasizes more positive information.

[0169] This emotional information is also used as a "means of communication," enriching interactions with other users. For example, the chat function displays icons and statuses that express a user's emotions, thereby promoting interaction between users. Furthermore, when using the generative AI model, prompts such as "Predict player A's performance in the next match" are utilized. This is an important technique for improving the system's analytical accuracy and meeting user expectations.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The server receives competition-related data from the data supply device. This received data includes match results and player information. After receiving this data, the server converts the data, which may have different formats, into a unified format. This conversion cleanses the data and adds relevant metadata. The converted data is then stored in the "storage device."

[0173] Step 2:

[0174] Users submit requests for information about specific matches or players through their user terminals. The user terminals send these requests to the server. Based on the received requests, the server quickly searches its "storage" for the relevant player information. High-speed database queries are used for the search, and as a result, player data is extracted.

[0175] Step 3:

[0176] The server uses a generative AI model based on player information to perform detailed performance analysis. For example, past performance data of a player is used as input, and a predictive model is used to predict future performance. The output of this process is a detailed analysis result. This allows users to gain deeper insights tailored to their interests.

[0177] Step 4:

[0178] The terminal retrieves analysis results from the server and visualizes and displays them in the user interface. Here, visual elements such as graphs and infographics are used to help users process the information. Users can easily make data-driven decisions through the screen.

[0179] Step 5:

[0180] The server utilizes an "emotion engine" to recognize the user's emotional state. This involves using facial recognition technology with the device's camera data as input. The analysis results in the user's emotional state (e.g., joy, anxiety). Based on this information, the server adjusts the content it provides.

[0181] Step 6:

[0182] The device provides customized content to the user based on the output of the emotion engine. If the user is anxious, it presents reassuring videos to alleviate their anxiety. Conversely, if the user is happy, it provides positive information and news to further enhance that emotion.

[0183] Step 7:

[0184] Users utilize communication tools to exchange opinions with other users. The device supports communication through a chat function and displays the user's emotional state as icons and status indicators. This feature facilitates interaction with other users and enables richer conversations.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In sports viewing, there is a challenge in that users cannot receive personalized information and experiences that respond to their emotions, resulting in a lack of realism and immersion. Furthermore, providing information that responds to real-time emotional changes is difficult.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for receiving competition-related data acquired from a data supply device, converting the data into a unified format, and storing it in a storage device; means for searching for participant information in response to a request from a user terminal and providing the information; means for analyzing the participant's performance using an artificial intelligence model and displaying the analysis results; and image recognition means for analyzing the user's facial expressions. This makes it possible to provide information that is tailored to the user's emotional state.

[0190] A "data supply device" is hardware or software that acquires competition-related data from an external source and provides it to other components within the system.

[0191] A "unified format" is a standard data format that has been converted to ensure consistency between competition-related data obtained in different formats.

[0192] A "storage device" is a computer device or medium used to store data converted into a standardized format for long-term or short-term storage.

[0193] A "user terminal" is an electronic device used by users to display information provided by a server and to request information.

[0194] An "artificial intelligence model" is a machine learning algorithm or data processing method used to analyze participants' performance and evaluate or predict their results.

[0195] "Facial expression analysis" is a process performed to recognize the user's facial movements and expressions and to determine their state.

[0196] "Image recognition means" refers to a technology or system that uses cameras or sensors to acquire image data and identify specific patterns or features.

[0197] "Control mechanisms for dynamic provision" refer to a system that changes the content in real time according to the user's emotional state and presents the most appropriate information.

[0198] The server centrally manages the system for personalizing the sports viewing experience. First, it receives competition-related data from data supply devices, converts data in different formats into a unified format, and stores it in storage. This enables a system that can respond quickly to requests from user terminals. User terminals search for information about athletes and competitions from storage and provide that information. Furthermore, artificial intelligence models are used to analyze athletes' performance from multiple perspectives and display the information visually on the terminal. Technologies such as Google® Cloud AI and OpenCV are utilized for this purpose.

[0199] The user's facial expressions are analyzed in real time using the device's camera. Based on this, an image recognition system determines the user's emotional state, and an emotion engine dynamically provides information and content appropriate to that state. For example, if a user is excited while watching match highlights, positive news or memorable scenes from the past are recommended using prompt messages, providing a deeper sense of immersion.

[0200] For example, if data detects that a user watching a particular soccer match is excited by a goal, the system sends a prompt to the AI ​​model: "Suggest content that recommends appropriate sports news or past highlights when the user is excited." This prompt then displays the most suitable content on the user's screen.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The server receives competition-related data from the data supply device. Since this data is provided in various formats, the server converts it to a unified format and stores it in storage. This conversion process facilitates subsequent data retrieval and analysis. The server converts the input competition-related data and stores the standard format data as output in storage.

[0204] Step 2:

[0205] The terminal searches for information about athletes and competitions stored in its storage device based on requests from the user. When a user requests information about a specific athlete or competition, the terminal receives the request, searches for the relevant data, and provides it to the user's terminal. The input here is the user's request, and the output is a list of related information.

[0206] Step 3:

[0207] The server uses an artificial intelligence model to analyze players' performance. The input is data related to the players, which the AI ​​model processes and outputs analysis results in the form of performance statistics and other information. This is displayed visually on the terminal. In this process, the data is analyzed from multiple perspectives, and insights are provided to the user through a visual interface.

[0208] Step 4:

[0209] The device analyzes the user's facial expressions in real time via its camera. The input is camera footage, and the user's emotions are analyzed using image recognition technology. Based on the analysis results, the server dynamically provides information tailored to the user's emotional state.

[0210] Step 5:

[0211] Depending on the user's emotional state, such as when they are excited, the server uses a generative AI model to send prompts. For example, a prompt such as "Suggest appropriate sports news or memorable moments from the past when the user is excited" is generated and sent to the AI ​​model. Based on this, the AI ​​model selects appropriate content and presents it to the device. The output includes recommended content.

[0212] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0228] This invention aims to realize a system that provides users with detailed player information and match progress in real time while watching sports. This system begins with a server receiving and processing competition-related data acquired from a data supply device. The server converts the received data into a unified format and stores it in a storage device that functions as a database. This maintains a state where necessary data can be quickly accessed.

[0229] When a user requests specific match information or detailed player data through their device, the device sends the request to a server. The server searches its storage for relevant information and extracts data corresponding to the user's request. The extracted data is further analyzed using artificial intelligence models. For example, performance predictions and tactical evaluations are performed based on players' past performance and current match situations.

[0230] These analysis results are sent to the device and visually displayed on the user interface. For example, users can use their smartphones to view real-time player statistics and predicted match results in graphs and charts. Personalized information based on the user's interests is also provided.

[0231] Furthermore, this system also includes a communication mechanism that allows users to exchange opinions with other sports fans through their devices. This enables real-time information exchange about match impressions and player evaluations, promoting community building.

[0232] These elements work together to provide users with a richer sports viewing experience. This system offers a new viewing style that integrates sports data analysis and communication.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server periodically accesses the data supply device to retrieve the latest competition-related data. This includes match progress, player statistics, and match results.

[0236] Step 2:

[0237] The server converts the acquired data into a unified format, extracts only the necessary data, and saves it to storage. This makes subsequent data retrieval and provision more efficient.

[0238] Step 3:

[0239] Users use their devices to request information about specific matches or players. These requests may include player performance data, match predictions, and current match statistics.

[0240] Step 4:

[0241] The terminal sends the user's request to the server. The request includes specific player names and match IDs.

[0242] Step 5:

[0243] The server searches its storage for the requested information and retrieves the relevant data. The retrieved data is based on the user request.

[0244] Step 6:

[0245] The server analyzes the acquired data using an artificial intelligence model. This analysis includes predicting player performance and evaluating match tactics.

[0246] Step 7:

[0247] The server sends the data along with the analysis results to the terminal. This prepares the terminal to visually display the latest information to the user.

[0248] Step 8:

[0249] The terminal displays data received from the server on the user interface. Users can view the information using graphs and charts, making it easier to understand visually.

[0250] Step 9:

[0251] Users can exchange opinions with other fans through communication channels based on information obtained via their devices. This allows them to deepen their understanding of the match.

[0252] (Example 1)

[0253] Next, we will describe Example 1. 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."

[0254] To improve the sports viewing experience, a system is needed that allows users to easily obtain detailed player information and match progress in real time. However, existing systems have problems such as delays in data acquisition and analysis, and fragmented information, which hinders a smooth viewing experience. Furthermore, there is a need for features that provide more interactive elements and promote interaction among users.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for receiving sports-related data acquired from a data supply device, converting the sports-related data into a unified format, and storing it in a storage device; means for searching for athlete information from the storage device in response to a request from a user terminal and providing the athlete information to the user terminal; and means for analyzing the athlete's performance using an automated learning model and displaying the analysis results on the user terminal. This enables users to access information on sports events in real time, quickly and in detail, and further realizes an interactive viewing experience in which they can exchange information with other users.

[0257] A "data supply device" is a device that generates or provides sports-related data and can transmit that data to a server via the internet.

[0258] "Sports-related data" refers to a collection of data that includes information related to sporting events, such as match results, player statistics, and statistical information.

[0259] "Converting to a unified format" refers to the process of standardizing data received in different formats so that it can be stored and used in a consistent manner.

[0260] A "storage device" is a device equipped with computing capabilities for storing digital data, and includes forms such as databases.

[0261] A "user terminal" is a device that a user directly operates to input and output information, and includes personal computers and smartphones.

[0262] "Athlete information" refers to detailed data related to individual athletes, including past performance, profiles, and statistical information.

[0263] An "automated learning model" is a model that uses machine learning algorithms to analyze patterns in data and predict future trends and outcomes.

[0264] "Analysis results" refer to conclusions and insights derived from data processed and analyzed by an automated learning model.

[0265] "Information exchange" refers to the process of sharing information among multiple users and exchanging opinions and data with each other.

[0266] "Communication methods" refer to systems and protocols for sending and receiving information, enabling data transfer over a network.

[0267] The embodiment of this invention relates to a system that provides users with detailed sports information in real time while they are watching a sporting event. Specifically, a server that receives competition-related data from data supply devices via the internet plays a major role. This server converts the received data from JSON format to CSV format and stores it in a MySQL database to perform unified data management.

[0268] The devices used by users include smartphones and personal computers. These devices can request match information and player information via a user interface. The requests are sent to the server, which searches and extracts the relevant information from the database.

[0269] Next, the server inputs the retrieved data into a generating AI model, which predicts performance based on the players' past results and current match status. This automated learning model is implemented using Python and analyzes the prediction results.

[0270] The analyzed information is visualized on the terminal's user interface and displayed in real time as graphs and charts using visualization libraries such as D3.js. As a concrete example of a prompt statement, a user can make a request to the system in the form of, "Based on player A's performance in recent matches, predict their performance in the next match and visually display the relevant data."

[0271] This system also includes a communication function that allows users to exchange opinions in real time. This enables users to share their evaluations and impressions of matches with other sports fans and form a community.

[0272] By working together, each component plays a role in providing users with a new style and value in watching sports, and enriching the viewing experience.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server receives sports-related data in real time from data supply devices. This data includes real-time match scores, player profile information, and historical performance data. Specifically, the server retrieves data using the HTTP or WebSocket protocol and receives it in JSON format. The received data is converted into a unified format as input, and the necessary information is extracted.

[0276] Step 2:

[0277] The server processes the converted data into CSV format. Next, this standardized data is saved to a MySQL database. The input here is the data converted in step 1, and the output is the data stored in the database in an efficiently indexed state. Data normalization allows subsequent search queries to be processed quickly.

[0278] Step 3:

[0279] The user generates a request to search for specific athlete information or match results through the terminal interface. For example, this might involve entering a specific player's name and match date, and then pressing a submit button. The user's request is formed as a prompt and sent to the server. Here, the user's actions become the input, and the request is sent to the server.

[0280] Step 4:

[0281] The server receives a request from the user and searches the database. The server uses an SQL query to extract data for matching athletes. The input to this operation is the request received in step 3, and the output is the detailed information about the athletes returned by the server. The extracted data is used as input for the next step.

[0282] Step 5:

[0283] The server inputs the extracted competitor data into the generative AI model to analyze the performance of the athletes. Using automatic learning, it predicts future results from past data. Here, the extracted data is input into the analysis tool, and the prediction result is obtained as the output. The data produced by this analysis becomes the output desired by the user request.

[0284] Step 6:

[0285] The server sends the analysis result to the terminal. The terminal visualizes the received data using a visualization library such as D3.js. This includes operations to generate graphs and charts. By analyzing this data and displaying it on the user interface, the terminal provides visual feedback to the user. Based on this information, the user can gain insights into sports games and athletes.

[0286] (Application Example 1)

[0287] Next, Application Example will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] During a sports game, there is a problem that it is difficult for spectators to visually confirm detailed athlete information and game trends in real time. In addition, there is a lack of means for spectators to enjoy the game more deeply while exchanging opinions in real time. There is a need to solve such problems and make the viewing experience richer and more interactive.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for converting competition-related data acquired from a data supply device into a unified format and storing it in a storage device, means for searching for and providing player information in response to requests from user terminals, means for analyzing player performance using an artificial intelligence model and displaying the results, and display means for presenting competition information in real time on a visual display device. As a result, spectators can intuitively obtain detailed information during the match and, by exchanging opinions with other spectators, can have a deeper viewing experience.

[0291] A "data supply device" is a device that acquires and provides competition-related data from external sources.

[0292] "Competition-related data" refers to all information related to sports, such as athletes' performance and match results.

[0293] A "unified format" is a standardized, consistent format created by converting data from different formats into a unified and consistent format.

[0294] A "memory device" is a device or system used to store processed data.

[0295] A "user terminal" refers to a device that allows a user to receive information and interact with it.

[0296] An "artificial intelligence model" is a machine learning technique used to analyze data and generate patterns and predictions.

[0297] "Analysis results" refer to the evaluation or prediction results obtained after data processing.

[0298] A "visual display device" is a device used to present information to a user visually, and includes displays and smart glasses.

[0299] "Real-time" is a term that indicates that processing or reactions occur almost simultaneously.

[0300] "Exchange of opinions" refers to the act of multiple users exchanging information and ideas.

[0301] The system for realizing the application example of this invention consists of a server, a user terminal, a visual display device, and a data supply device. First, the server receives competition-related data acquired from the data supply device. The received data is converted into a unified format and stored in a storage device, enabling efficient data access. The server also uses an artificial intelligence model to analyze the performance of athletes based on their past performance and current match status, and generates analysis results.

[0302] The user terminal communicates with the server in response to user requests and retrieves player information from its storage device. The information provided by the server is displayed in real time through a visual display device, providing users with an environment where they can intuitively obtain the information they need while watching sports. The visual display device includes smart glasses that can interactively display player data and match progress during the game.

[0303] Users can exchange opinions with other spectators based on information obtained through visual displays. This allows for real-time discussions about the match and stimulates communication among fans.

[0304] A concrete example is the ability to use smart glasses during a soccer match to check the performance of specific players or the progress of the game in real time. This provision of visual information significantly improves the viewing experience.

[0305] A concrete example of a prompt using a generative AI model is, "How can I predict a soccer player's performance and display it in real time as optimal information on smart glasses?" Based on this prompt, the AI ​​performs analysis and instantly provides the user with meaningful information.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server acquires competition-related data from the data supply device. As input, it receives data on player performance and game status provided from the data supply device. It performs the process of converting this into a unified format and storing it in the storage device. As output, the data in the unified format is recorded in the storage device, enabling efficient access.

[0309] Step 2:

[0310] The user terminal receives the user's request and sends it to the server. As input, it receives requests for player information and game data from the user. Based on this, it acquires the necessary information from the server and sends it to the user terminal. As output, preparations are made to provide the information requested by the user.

[0311] Step 3:

[0312] The server searches for player information corresponding to the request from the storage device. As input, it receives the request from the user terminal, searches for the corresponding data in the storage device, and performs collation. As output, the searched player information is sorted and prepared to be provided to the user terminal.

[0313] Step 4:

[0314] The server analyzes the player information using an artificial intelligence model. As input, it receives the searched player information and performs data analysis using the generated AI model. This generates predictions of player performance and tactical evaluations of the game. As output, the analysis results are generated and prepared to be sent to the user terminal.

[0315] Step 5:

[0316] The user terminal sends information and analysis results provided by the server to a visual display device, which displays them visually in real time. As input, it receives analysis results and player information from the server and transfers them to a device such as smart glasses. As output, real-time information is presented in the user's field of vision, improving the viewing experience.

[0317] Step 6:

[0318] Users exchange opinions with other spectators based on information provided through visual display devices. As input, they receive provided data and use it as material for discussion. This enables real-time communication. As output, it fosters active exchange of opinions among users, resulting in a more fulfilling viewing experience.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention provides a system that takes into account the user's emotional state to make the sports viewing experience more personalized. This system involves a server receiving competition-related data from a data supply device, converting it into a unified format, and storing it in a memory device. This basic data processing enables a system that can respond to user requests in real time.

[0321] When a user requests information about a specific match or player through their device, the server searches its storage for relevant data and uses an artificial intelligence model to analyze the player's performance. Based on this, the device displays visually insightful information in the user interface to enhance the viewing experience.

[0322] Furthermore, a key feature of this system is its emotion engine, which recognizes the user's emotions in real time. This recognition of emotional states is achieved, for example, by analyzing the user's facial expressions using a camera or by analyzing the user's text input. This allows the system to adjust the tone and detail of the information it provides according to the user's emotions.

[0323] For example, if a user is fidgeting with their device during a match, the emotion engine can determine that the user is feeling anxious and provide reassuring content such as videos of players performing well. Conversely, if the user is happy, the system can proactively present relevant positive news and data to further enhance their excitement.

[0324] In addition, the emotional information of users recognized by the emotion engine is applied to communication with other users. For example, displaying a user's emotions as icons or statuses within a chat enriches the conversation and stimulates interaction between users.

[0325] Thus, the present invention enables interactive responses based on the user's emotions, tailoring the sports viewing experience to individual sensibilities. This system allows users to enjoy a deeper, more immersive viewing experience.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The server periodically retrieves competition-related data from the data supply device. This data includes match progress, player performance data, and team tactical information.

[0329] Step 2:

[0330] The server converts received data into a unified format and integrates information from different data sources in a consistent manner. This allows for efficient storage of data in the storage device.

[0331] Step 3:

[0332] Users use their devices to request information about specific matches or players. These requests may include match statistics or players' past performance records.

[0333] Step 4:

[0334] The terminal sends the user's request to the server. The server searches for and extracts the relevant data from its storage device.

[0335] Step 5:

[0336] The server inputs the extracted information into an artificial intelligence model to analyze player performance and predict match trends. The analysis results include the probability of a player succeeding and which strategies are effective.

[0337] Step 6:

[0338] The terminal receives analysis results from the server and displays them visually on the user interface. Graphs and charts are used to aid user understanding.

[0339] Step 7:

[0340] Devices equipped with an emotion engine can recognize the user's emotional state in real time by analyzing the user's facial expressions and tone of voice.

[0341] Step 8:

[0342] The server selects and provides the most relevant information to the user based on their perceived emotions. For example, if the user is feeling anxious, it will display information that provides reassurance.

[0343] Step 9:

[0344] The device shares the user's emotional information, detected by the emotion engine, with other users during chats. This allows other users to consider the user's emotions while interacting with them.

[0345] Step 10:

[0346] Users can exchange opinions with other fans and gain a deeper viewing experience, along with personalized information tailored to their emotions.

[0347] (Example 2)

[0348] Next, we will describe Example 2. 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".

[0349] In the sports viewing experience, traditional systems that provide uniform information without considering the emotional state of the user make it difficult to provide appropriate information tailored to the individual needs of each user. Furthermore, if the amount or tone of information does not match the user's emotions, it can lead to an unsatisfactory viewing experience or diminish the excitement. As a result, users are demanding more personalized information and experiences that respond to their emotions, but there is a lack of mechanisms to achieve this.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] This invention includes a server that receives competition-related data acquired from a data supply device, converts the competition-related data into a unified format and stores it in a storage device, retrieves player information from the storage device in response to a request from a user terminal and provides the player information to the user terminal, analyzes the players' performance using an artificial intelligence model and displays the analysis results on the user terminal, recognizes the user's emotional state using an emotion engine and adaptively adjusts the tone and content of the information provided according to the emotional state, and communicates with other users to facilitate the exchange of opinions. This makes it possible to provide an appropriate and personalized sports viewing experience based on the emotional state of each individual user.

[0352] "Data supply device" refers to hardware or software used to provide competition-related information to a server.

[0353] "Competition-related data" refers to information related to sporting events, such as match results, player information, and scores.

[0354] A "unified format" refers to a data structure or protocol used to convert data in different formats into a consistent specification.

[0355] "Storage device" refers to a physical or virtual storage medium that a server uses to store data for extended periods.

[0356] A "user terminal" refers to an electronic device used by a user to access a system and obtain information.

[0357] An "artificial intelligence model" refers to a machine learning algorithm or program used to analyze athlete performance.

[0358] An "emotion engine" refers to a function or system that analyzes a user's emotional state and adjusts the information provided based on the results.

[0359] "Communication methods" refer to technologies such as chat functions and messaging systems used by users to exchange opinions with each other.

[0360] This invention is a system that personalizes the sports viewing experience by taking into account the emotional state of the user. This system begins with a server processing "competition-related data" received from a "data supply device." The server converts the received data into a "unified format" and stores it in a "storage device." This conversion and storage process enables the server to quickly access data in response to user requests.

[0361] When a user requests information about a specific match or player through their "user terminal," the server searches for the relevant player information in its "storage device." Furthermore, the server uses an "artificial intelligence model" to analyze this player information in detail and displays the analysis results on the user interface of the "user terminal." In this process, users can obtain visually interactive information, improving their viewing experience.

[0362] Furthermore, this system features an "emotion engine" that recognizes the user's emotions in real time. Emotion recognition is performed through facial expression analysis using a camera and analysis of the user's text input. This emotion recognition function allows the server to adjust the tone and detail of the information it provides according to the user's emotional state. For example, when the user is feeling anxious, it provides reassuring content, and conversely, when the user is happy, it emphasizes more positive information.

[0363] This emotional information is also used as a "means of communication," enriching interactions with other users. For example, the chat function displays icons and statuses that express a user's emotions, thereby promoting interaction between users. Furthermore, when using the generative AI model, prompts such as "Predict player A's performance in the next match" are utilized. This is an important technique for improving the system's analytical accuracy and meeting user expectations.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The server receives competition-related data from the data supply device. This received data includes match results and player information. After receiving this data, the server converts the data, which may have different formats, into a unified format. This conversion cleanses the data and adds relevant metadata. The converted data is then stored in the "storage device."

[0367] Step 2:

[0368] Users submit requests for information about specific matches or players through their user terminals. The user terminals send these requests to the server. Based on the received requests, the server quickly searches its "storage" for the relevant player information. High-speed database queries are used for the search, and as a result, player data is extracted.

[0369] Step 3:

[0370] The server uses a generative AI model based on player information to perform detailed performance analysis. For example, past performance data of a player is used as input, and a predictive model is used to predict future performance. The output of this process is a detailed analysis result. This allows users to gain deeper insights tailored to their interests.

[0371] Step 4:

[0372] The terminal retrieves analysis results from the server and visualizes and displays them in the user interface. Here, visual elements such as graphs and infographics are used to help users process the information. Users can easily make data-driven decisions through the screen.

[0373] Step 5:

[0374] The server utilizes an "emotion engine" to recognize the user's emotional state. This involves using facial recognition technology with the device's camera data as input. The analysis results in the user's emotional state (e.g., joy, anxiety). Based on this information, the server adjusts the content it provides.

[0375] Step 6:

[0376] The device provides customized content to the user based on the output of the emotion engine. If the user is anxious, it presents reassuring videos to alleviate their anxiety. Conversely, if the user is happy, it provides positive information and news to further enhance that emotion.

[0377] Step 7:

[0378] Users utilize communication tools to exchange opinions with other users. The device supports communication through a chat function and displays the user's emotional state as icons and status indicators. This feature facilitates interaction with other users and enables richer conversations.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] In sports viewing, there is a challenge in that users cannot receive personalized information and experiences that respond to their emotions, resulting in a lack of realism and immersion. Furthermore, providing information that responds to real-time emotional changes is difficult.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for receiving competition-related data acquired from a data supply device, converting the data into a unified format, and storing it in a storage device; means for searching for participant information in response to a request from a user terminal and providing the information; means for analyzing the participant's performance using an artificial intelligence model and displaying the analysis results; and image recognition means for analyzing the user's facial expressions. This makes it possible to provide information that is tailored to the user's emotional state.

[0384] A "data supply device" is hardware or software that acquires competition-related data from an external source and provides it to other components within the system.

[0385] A "unified format" is a standard data format that has been converted to ensure consistency between competition-related data obtained in different formats.

[0386] A "storage device" is a computer device or medium used to store data converted into a standardized format for long-term or short-term storage.

[0387] A "user terminal" is an electronic device used by users to display information provided by a server and to request information.

[0388] An "artificial intelligence model" is a machine learning algorithm or data processing method used to analyze participants' performance and evaluate or predict their results.

[0389] "Facial expression analysis" is a process performed to recognize the user's facial movements and expressions and to determine their state.

[0390] "Image recognition means" refers to a technology or system that uses cameras or sensors to acquire image data and identify specific patterns or features.

[0391] "Control mechanisms for dynamic provision" refer to a system that changes the content in real time according to the user's emotional state and presents the most appropriate information.

[0392] The server centrally manages the system for personalizing the sports viewing experience. First, it receives competition-related data from data supply devices, converts data in different formats into a unified format, and stores it in storage. This enables a system that can quickly respond to requests from user terminals. User terminals search for information about athletes and competitions from storage and provide that information. Furthermore, artificial intelligence models are used to analyze athletes' performance from multiple perspectives and display the information visually on the terminal. Technologies such as Google Cloud AI and OpenCV are utilized for this purpose.

[0393] The user's facial expressions are analyzed in real time using the device's camera. Based on this, an image recognition system determines the user's emotional state, and an emotion engine dynamically provides information and content appropriate to that state. For example, if a user is excited while watching match highlights, positive news or memorable scenes from the past are recommended using prompt messages, providing a deeper sense of immersion.

[0394] For example, if data detects that a user watching a particular soccer match is excited by a goal, the system sends a prompt to the AI ​​model: "Suggest content that recommends appropriate sports news or past highlights when the user is excited." This prompt then displays the most suitable content on the user's screen.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The server receives competition-related data from the data supply device. Since this data is provided in various formats, the server converts it to a unified format and stores it in storage. This conversion process facilitates subsequent data retrieval and analysis. The server converts the input competition-related data and stores the standard format data as output in storage.

[0398] Step 2:

[0399] The terminal searches for information about athletes and competitions stored in its storage device based on requests from the user. When a user requests information about a specific athlete or competition, the terminal receives the request, searches for the relevant data, and provides it to the user's terminal. The input here is the user's request, and the output is a list of related information.

[0400] Step 3:

[0401] The server uses an artificial intelligence model to analyze players' performance. The input is data related to the players, which the AI ​​model processes and outputs analysis results in the form of performance statistics and other information. This is displayed visually on the terminal. In this process, the data is analyzed from multiple perspectives, and insights are provided to the user through a visual interface.

[0402] Step 4:

[0403] The device analyzes the user's facial expressions in real time via its camera. The input is camera footage, and the user's emotions are analyzed using image recognition technology. Based on the analysis results, the server dynamically provides information tailored to the user's emotional state.

[0404] Step 5:

[0405] Depending on the user's emotional state, such as when they are excited, the server uses a generative AI model to send prompts. For example, a prompt such as "Suggest appropriate sports news or memorable moments from the past when the user is excited" is generated and sent to the AI ​​model. Based on this, the AI ​​model selects appropriate content and presents it to the device. The output includes recommended content.

[0406] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention aims to realize a system that provides users with detailed player information and match progress in real time while watching sports. This system begins with a server receiving and processing competition-related data acquired from a data supply device. The server converts the received data into a unified format and stores it in a storage device that functions as a database. This maintains a state where necessary data can be quickly accessed.

[0423] When a user requests specific match information or detailed player data through their device, the device sends the request to a server. The server searches its storage for relevant information and extracts data corresponding to the user's request. The extracted data is further analyzed using artificial intelligence models. For example, performance predictions and tactical evaluations are performed based on players' past performance and current match situations.

[0424] These analysis results are sent to the device and visually displayed on the user interface. For example, users can use their smartphones to view real-time player statistics and predicted match results in graphs and charts. Personalized information based on the user's interests is also provided.

[0425] Furthermore, this system also includes a communication mechanism that allows users to exchange opinions with other sports fans through their devices. This enables real-time information exchange about match impressions and player evaluations, promoting community building.

[0426] These elements work together to provide users with a richer sports viewing experience. This system offers a new viewing style that integrates sports data analysis and communication.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The server periodically accesses the data supply device to retrieve the latest competition-related data. This includes match progress, player statistics, and match results.

[0430] Step 2:

[0431] The server converts the acquired data into a unified format, extracts only the necessary data, and saves it to storage. This makes subsequent data retrieval and provision more efficient.

[0432] Step 3:

[0433] Users use their devices to request information about specific matches or players. These requests may include player performance data, match predictions, and current match statistics.

[0434] Step 4:

[0435] The terminal sends the user's request to the server. The request includes specific player names and match IDs.

[0436] Step 5:

[0437] The server searches its storage for the requested information and retrieves the relevant data. The retrieved data is based on the user request.

[0438] Step 6:

[0439] The server analyzes the acquired data using an artificial intelligence model. This analysis includes predicting player performance and evaluating match tactics.

[0440] Step 7:

[0441] The server sends the data along with the analysis results to the terminal. This prepares the terminal to visually display the latest information to the user.

[0442] Step 8:

[0443] The terminal displays data received from the server on the user interface. Users can view the information using graphs and charts, making it easier to understand visually.

[0444] Step 9:

[0445] Users can exchange opinions with other fans through communication channels based on information obtained via their devices. This allows them to deepen their understanding of the match.

[0446] (Example 1)

[0447] Next, we will describe Example 1. 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."

[0448] To improve the sports viewing experience, a system is needed that allows users to easily obtain detailed player information and match progress in real time. However, existing systems have problems such as delays in data acquisition and analysis, and fragmented information, which hinders a smooth viewing experience. Furthermore, there is a need for features that provide more interactive elements and promote interaction among users.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for receiving sports-related data acquired from a data supply device, converting the sports-related data into a unified format, and storing it in a storage device; means for searching for athlete information from the storage device in response to a request from a user terminal and providing the athlete information to the user terminal; and means for analyzing the athlete's performance using an automated learning model and displaying the analysis results on the user terminal. This enables users to access information on sports events in real time, quickly and in detail, and further realizes an interactive viewing experience in which they can exchange information with other users.

[0451] A "data supply device" is a device that generates or provides sports-related data and can transmit that data to a server via the internet.

[0452] "Sports-related data" refers to a collection of data that includes information related to sporting events, such as match results, player statistics, and statistical information.

[0453] "Converting to a unified format" refers to the process of standardizing data received in different formats so that it can be stored and used in a consistent manner.

[0454] A "storage device" is a device equipped with computing capabilities for storing digital data, and includes forms such as databases.

[0455] A "user terminal" is a device that a user directly operates to input and output information, and includes personal computers and smartphones.

[0456] "Athlete information" refers to detailed data related to individual athletes, including past performance, profiles, and statistical information.

[0457] An "automated learning model" is a model that uses machine learning algorithms to analyze patterns in data and predict future trends and outcomes.

[0458] "Analysis results" refer to conclusions and insights derived from data processed and analyzed by an automated learning model.

[0459] "Information exchange" refers to the process of sharing information among multiple users and exchanging opinions and data with each other.

[0460] "Communication methods" refer to systems and protocols for sending and receiving information, enabling data transfer over a network.

[0461] The embodiment of this invention relates to a system that provides users with detailed sports information in real time while they are watching a sporting event. Specifically, a server that receives competition-related data from data supply devices via the internet plays a major role. This server converts the received data from JSON format to CSV format and stores it in a MySQL database to perform unified data management.

[0462] The devices used by users include smartphones and personal computers. These devices can request match information and player information via a user interface. The requests are sent to the server, which searches and extracts the relevant information from the database.

[0463] Next, the server inputs the retrieved data into a generating AI model, which predicts performance based on the players' past results and current match status. This automated learning model is implemented using Python and analyzes the prediction results.

[0464] The analyzed information is visualized on the terminal's user interface and displayed in real time as graphs and charts using visualization libraries such as D3.js. As a concrete example of a prompt statement, a user can make a request to the system in the form of, "Based on player A's performance in recent matches, predict their performance in the next match and visually display the relevant data."

[0465] This system also includes a communication function that allows users to exchange opinions in real time. This enables users to share their evaluations and impressions of matches with other sports fans and form a community.

[0466] By working together, each component plays a role in providing users with a new style and value in watching sports, and enriching the viewing experience.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server receives sports-related data in real time from data supply devices. This data includes real-time match scores, player profile information, and historical performance data. Specifically, the server retrieves data using the HTTP or WebSocket protocol and receives it in JSON format. The received data is converted into a unified format as input, and the necessary information is extracted.

[0470] Step 2:

[0471] The server processes the converted data into CSV format. Next, this standardized data is saved to a MySQL database. The input here is the data converted in step 1, and the output is the data stored in the database in an efficiently indexed state. Data normalization allows subsequent search queries to be processed quickly.

[0472] Step 3:

[0473] The user generates a request to search for specific athlete information or match results through the terminal interface. For example, this might involve entering a specific player's name and match date, and then pressing a submit button. The user's request is formed as a prompt and sent to the server. Here, the user's actions become the input, and the request is sent to the server.

[0474] Step 4:

[0475] The server receives a request from the user and searches the database. The server uses an SQL query to extract data for matching athletes. The input to this operation is the request received in step 3, and the output is the detailed information about the athletes returned by the server. The extracted data is used as input for the next step.

[0476] Step 5:

[0477] The server inputs the extracted athlete data into a generating AI model to analyze the athletes' performance. Using automated learning, it predicts future performance based on past data. Here, the extracted data is input into an analysis tool, and the prediction results are obtained as output. The data produced by this analysis becomes the output desired by the user request.

[0478] Step 6:

[0479] The server sends the analysis results to the terminal. The terminal visualizes the received data using a visualization library such as D3.js. This includes generating graphs and charts. The terminal analyzes this data and displays it in the user interface, providing the user with visual feedback. Based on this information, the user can gain insights into sports matches and players.

[0480] (Application Example 1)

[0481] Next, we will explain Application Example 1. In the following explanation, 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."

[0482] One challenge in watching sports is that it's difficult for spectators to visually check detailed player information and game progress in real time. Furthermore, there's a lack of ways for spectators to exchange opinions with each other in real time and enjoy the game more deeply. There's a need to solve these problems and make the viewing experience richer and more interactive.

[0483] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0484] In this invention, the server includes means for converting competition-related data acquired from a data supply device into a unified format and storing it in a storage device, means for searching for and providing player information in response to requests from user terminals, means for analyzing player performance using an artificial intelligence model and displaying the results, and display means for presenting competition information in real time on a visual display device. As a result, spectators can intuitively obtain detailed information during the match and, by exchanging opinions with other spectators, can have a deeper viewing experience.

[0485] A "data supply device" is a device that acquires and provides competition-related data from external sources.

[0486] "Competition-related data" refers to all information related to sports, such as athletes' performance and match results.

[0487] A "unified format" is a standardized, consistent format created by converting data from different formats into a unified and consistent format.

[0488] A "memory device" is a device or system used to store processed data.

[0489] A "user terminal" refers to a device that allows a user to receive information and interact with it.

[0490] An "artificial intelligence model" is a machine learning technique used to analyze data and generate patterns and predictions.

[0491] "Analysis results" refer to the evaluation or prediction results obtained after data processing.

[0492] A "visual display device" is a device used to present information to a user visually, and includes displays and smart glasses.

[0493] "Real-time" is a term that indicates that processing or reactions occur almost simultaneously.

[0494] "Exchange of opinions" refers to the act of multiple users exchanging information and ideas.

[0495] The system for realizing the application example of this invention consists of a server, a user terminal, a visual display device, and a data supply device. First, the server receives competition-related data acquired from the data supply device. The received data is converted into a unified format and stored in a storage device, enabling efficient data access. The server also uses an artificial intelligence model to analyze the performance of athletes based on their past performance and current match status, and generates analysis results.

[0496] The user terminal communicates with the server in response to user requests and retrieves player information from its storage device. The information provided by the server is displayed in real time through a visual display device, providing users with an environment where they can intuitively obtain the information they need while watching sports. The visual display device includes smart glasses that can interactively display player data and match progress during the game.

[0497] Users can exchange opinions with other spectators based on information obtained through visual displays. This allows for real-time discussions about the match and stimulates communication among fans.

[0498] A concrete example is the ability to use smart glasses during a soccer match to check the performance of specific players or the progress of the game in real time. This provision of visual information significantly improves the viewing experience.

[0499] A concrete example of a prompt using a generative AI model is, "How can I predict a soccer player's performance and display it in real time as optimal information on smart glasses?" Based on this prompt, the AI ​​performs analysis and instantly provides the user with meaningful information.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server acquires competition-related data from the data supply device. It receives player statistics and match status data from the data supply device as input. This data is then converted to a unified format and stored in the storage device. As output, the unified formatted data is recorded in the storage device, enabling efficient access.

[0503] Step 2:

[0504] The user terminal receives user requests and sends them to the server. As input, it receives requests from the user for player information and match data. Based on this, it retrieves the necessary information from the server and sends it to the user terminal. As output, it is ready to provide the information requested by the user.

[0505] Step 3:

[0506] The server searches its storage for player information corresponding to the request. As input, it receives a request from the user's terminal, searches its storage for the relevant data, and compares it. As output, the retrieved player information is sorted and prepared for provision to the user's terminal.

[0507] Step 4:

[0508] The server analyzes player information using an artificial intelligence model. It receives the searched player information as input and performs data analysis using a generative AI model. This generates player performance predictions and tactical evaluations of matches. The analysis results are generated as output and ready to be sent to the user's terminal.

[0509] Step 5:

[0510] The user terminal sends information and analysis results provided by the server to a visual display device, which displays them visually in real time. As input, it receives analysis results and player information from the server and transfers them to a device such as smart glasses. As output, real-time information is presented in the user's field of vision, improving the viewing experience.

[0511] Step 6:

[0512] Users exchange opinions with other spectators based on information provided through visual display devices. As input, they receive provided data and use it as material for discussion. This enables real-time communication. As output, it fosters active exchange of opinions among users, resulting in a more fulfilling viewing experience.

[0513] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0514] This invention provides a system that takes into account the user's emotional state to make the sports viewing experience more personalized. This system involves a server receiving competition-related data from a data supply device, converting it into a unified format, and storing it in a memory device. This basic data processing enables a system that can respond to user requests in real time.

[0515] When a user requests information about a specific match or player through their device, the server searches its storage for relevant data and uses an artificial intelligence model to analyze the player's performance. Based on this, the device displays visually insightful information in the user interface to enhance the viewing experience.

[0516] Furthermore, a key feature of this system is its emotion engine, which recognizes the user's emotions in real time. This recognition of emotional states is achieved, for example, by analyzing the user's facial expressions using a camera or by analyzing the user's text input. This allows the system to adjust the tone and detail of the information it provides according to the user's emotions.

[0517] For example, if a user is fidgeting with their device during a match, the emotion engine can determine that the user is feeling anxious and provide reassuring content such as videos of players performing well. Conversely, if the user is happy, the system can proactively present relevant positive news and data to further enhance their excitement.

[0518] In addition, the emotional information of users recognized by the emotion engine is applied to communication with other users. For example, displaying a user's emotions as icons or statuses within a chat enriches the conversation and stimulates interaction between users.

[0519] Thus, the present invention enables interactive responses based on the user's emotions, tailoring the sports viewing experience to individual sensibilities. This system allows users to enjoy a deeper, more immersive viewing experience.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The server periodically retrieves competition-related data from the data supply device. This data includes match progress, player performance data, and team tactical information.

[0523] Step 2:

[0524] The server converts received data into a unified format and integrates information from different data sources in a consistent manner. This allows for efficient storage of data in the storage device.

[0525] Step 3:

[0526] Users use their devices to request information about specific matches or players. These requests may include match statistics or players' past performance records.

[0527] Step 4:

[0528] The terminal sends the user's request to the server. The server searches for and extracts the relevant data from its storage device.

[0529] Step 5:

[0530] The server inputs the extracted information into an artificial intelligence model to analyze player performance and predict match trends. The analysis results include the probability of a player succeeding and which strategies are effective.

[0531] Step 6:

[0532] The terminal receives analysis results from the server and displays them visually on the user interface. Graphs and charts are used to aid user understanding.

[0533] Step 7:

[0534] Devices equipped with an emotion engine can recognize the user's emotional state in real time by analyzing the user's facial expressions and tone of voice.

[0535] Step 8:

[0536] The server selects and provides the most relevant information to the user based on their perceived emotions. For example, if the user is feeling anxious, it will display information that provides reassurance.

[0537] Step 9:

[0538] The device shares the user's emotional information, detected by the emotion engine, with other users during chats. This allows other users to consider the user's emotions while interacting with them.

[0539] Step 10:

[0540] Users can exchange opinions with other fans and gain a deeper viewing experience, along with personalized information tailored to their emotions.

[0541] (Example 2)

[0542] Next, we will describe Example 2. 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."

[0543] In the sports viewing experience, traditional systems that provide uniform information without considering the emotional state of the user make it difficult to provide appropriate information tailored to the individual needs of each user. Furthermore, if the amount or tone of information does not match the user's emotions, it can lead to an unsatisfactory viewing experience or diminish the excitement. As a result, users are demanding more personalized information and experiences that respond to their emotions, but there is a lack of mechanisms to achieve this.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0545] This invention includes a server that receives competition-related data acquired from a data supply device, converts the competition-related data into a unified format and stores it in a storage device, retrieves player information from the storage device in response to a request from a user terminal and provides the player information to the user terminal, analyzes the players' performance using an artificial intelligence model and displays the analysis results on the user terminal, recognizes the user's emotional state using an emotion engine and adaptively adjusts the tone and content of the information provided according to the emotional state, and communicates with other users to facilitate the exchange of opinions. This makes it possible to provide an appropriate and personalized sports viewing experience based on the emotional state of each individual user.

[0546] "Data supply device" refers to hardware or software used to provide competition-related information to a server.

[0547] "Competition-related data" refers to information related to sporting events, such as match results, player information, and scores.

[0548] A "unified format" refers to a data structure or protocol used to convert data in different formats into a consistent specification.

[0549] "Storage device" refers to a physical or virtual storage medium that a server uses to store data for extended periods.

[0550] A "user terminal" refers to an electronic device used by a user to access a system and obtain information.

[0551] An "artificial intelligence model" refers to a machine learning algorithm or program used to analyze athlete performance.

[0552] An "emotion engine" refers to a function or system that analyzes a user's emotional state and adjusts the information provided based on the results.

[0553] "Communication methods" refer to technologies such as chat functions and messaging systems used by users to exchange opinions with each other.

[0554] This invention is a system that personalizes the sports viewing experience by taking into account the emotional state of the user. This system begins with a server processing "competition-related data" received from a "data supply device." The server converts the received data into a "unified format" and stores it in a "storage device." This conversion and storage process enables the server to quickly access data in response to user requests.

[0555] When a user requests information about a specific match or player through their "user terminal," the server searches for the relevant player information in its "storage device." Furthermore, the server uses an "artificial intelligence model" to analyze this player information in detail and displays the analysis results on the user interface of the "user terminal." In this process, users can obtain visually interactive information, improving their viewing experience.

[0556] Furthermore, this system features an "emotion engine" that recognizes the user's emotions in real time. Emotion recognition is performed through facial expression analysis using a camera and analysis of the user's text input. This emotion recognition function allows the server to adjust the tone and detail of the information it provides according to the user's emotional state. For example, when the user is feeling anxious, it provides reassuring content, and conversely, when the user is happy, it emphasizes more positive information.

[0557] This emotional information is also used as a "means of communication," enriching interactions with other users. For example, the chat function displays icons and statuses that express a user's emotions, thereby promoting interaction between users. Furthermore, when using the generative AI model, prompts such as "Predict player A's performance in the next match" are utilized. This is an important technique for improving the system's analytical accuracy and meeting user expectations.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The server receives competition-related data from the data supply device. This received data includes match results and player information. After receiving this data, the server converts the data, which may have different formats, into a unified format. This conversion cleanses the data and adds relevant metadata. The converted data is then stored in the "storage device."

[0561] Step 2:

[0562] Users submit requests for information about specific matches or players through their user terminals. The user terminals send these requests to the server. Based on the received requests, the server quickly searches its "storage" for the relevant player information. High-speed database queries are used for the search, and as a result, player data is extracted.

[0563] Step 3:

[0564] The server uses a generative AI model based on player information to perform detailed performance analysis. For example, past performance data of a player is used as input, and a predictive model is used to predict future performance. The output of this process is a detailed analysis result. This allows users to gain deeper insights tailored to their interests.

[0565] Step 4:

[0566] The terminal retrieves analysis results from the server and visualizes and displays them in the user interface. Here, visual elements such as graphs and infographics are used to help users process the information. Users can easily make data-driven decisions through the screen.

[0567] Step 5:

[0568] The server utilizes an "emotion engine" to recognize the user's emotional state. This involves using facial recognition technology with the device's camera data as input. The analysis results in the user's emotional state (e.g., joy, anxiety). Based on this information, the server adjusts the content it provides.

[0569] Step 6:

[0570] The device provides customized content to the user based on the output of the emotion engine. If the user is anxious, it presents reassuring videos to alleviate their anxiety. Conversely, if the user is happy, it provides positive information and news to further enhance that emotion.

[0571] Step 7:

[0572] Users utilize communication tools to exchange opinions with other users. The device supports communication through a chat function and displays the user's emotional state as icons and status indicators. This feature facilitates interaction with other users and enables richer conversations.

[0573] (Application Example 2)

[0574] Next, we will explain application example 2. In the following explanation, 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."

[0575] In sports viewing, there is a challenge in that users cannot receive personalized information and experiences that respond to their emotions, resulting in a lack of realism and immersion. Furthermore, providing information that responds to real-time emotional changes is difficult.

[0576] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0577] In this invention, the server includes means for receiving competition-related data acquired from a data supply device, converting the data into a unified format, and storing it in a storage device; means for searching for participant information in response to a request from a user terminal and providing the information; means for analyzing the participant's performance using an artificial intelligence model and displaying the analysis results; and image recognition means for analyzing the user's facial expressions. This makes it possible to provide information that is tailored to the user's emotional state.

[0578] A "data supply device" is hardware or software that acquires competition-related data from an external source and provides it to other components within the system.

[0579] A "unified format" is a standard data format that has been converted to ensure consistency between competition-related data obtained in different formats.

[0580] A "storage device" is a computer device or medium used to store data converted into a standardized format for long-term or short-term storage.

[0581] A "user terminal" is an electronic device used by users to display information provided by a server and to request information.

[0582] An "artificial intelligence model" is a machine learning algorithm or data processing method used to analyze participants' performance and evaluate or predict their results.

[0583] "Facial expression analysis" is a process performed to recognize the user's facial movements and expressions and to determine their state.

[0584] "Image recognition means" refers to a technology or system that uses cameras or sensors to acquire image data and identify specific patterns or features.

[0585] "Control mechanisms for dynamic provision" refer to a system that changes the content in real time according to the user's emotional state and presents the most appropriate information.

[0586] The server centrally manages the system for personalizing the sports viewing experience. First, it receives competition-related data from data supply devices, converts data in different formats into a unified format, and stores it in storage. This enables a system that can quickly respond to requests from user terminals. User terminals search for information about athletes and competitions from storage and provide that information. Furthermore, artificial intelligence models are used to analyze athletes' performance from multiple perspectives and display the information visually on the terminal. Technologies such as Google Cloud AI and OpenCV are utilized for this purpose.

[0587] The user's facial expressions are analyzed in real time using the device's camera. Based on this, an image recognition system determines the user's emotional state, and an emotion engine dynamically provides information and content appropriate to that state. For example, if a user is excited while watching match highlights, positive news or memorable scenes from the past are recommended using prompt messages, providing a deeper sense of immersion.

[0588] For example, if data detects that a user watching a particular soccer match is excited by a goal, the system sends a prompt to the AI ​​model: "Suggest content that recommends appropriate sports news or past highlights when the user is excited." This prompt then displays the most suitable content on the user's screen.

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The server receives competition-related data from the data supply device. Since this data is provided in various formats, the server converts it to a unified format and stores it in storage. This conversion process facilitates subsequent data retrieval and analysis. The server converts the input competition-related data and stores the standard format data as output in storage.

[0592] Step 2:

[0593] The terminal searches for information about athletes and competitions stored in its storage device based on requests from the user. When a user requests information about a specific athlete or competition, the terminal receives the request, searches for the relevant data, and provides it to the user's terminal. The input here is the user's request, and the output is a list of related information.

[0594] Step 3:

[0595] The server uses an artificial intelligence model to analyze players' performance. The input is data related to the players, which the AI ​​model processes and outputs analysis results in the form of performance statistics and other information. This is displayed visually on the terminal. In this process, the data is analyzed from multiple perspectives, and insights are provided to the user through a visual interface.

[0596] Step 4:

[0597] The device analyzes the user's facial expressions in real time via its camera. The input is camera footage, and the user's emotions are analyzed using image recognition technology. Based on the analysis results, the server dynamically provides information tailored to the user's emotional state.

[0598] Step 5:

[0599] Depending on the user's emotional state, such as when they are excited, the server uses a generative AI model to send prompts. For example, a prompt such as "Suggest appropriate sports news or memorable moments from the past when the user is excited" is generated and sent to the AI ​​model. Based on this, the AI ​​model selects appropriate content and presents it to the device. The output includes recommended content.

[0600] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] As shown in Figure 7, the 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.

[0606] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0607] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0608] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0609] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0610] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0611] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0612] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0613] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0614] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0615] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0616] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] This invention aims to realize a system that provides users with detailed player information and match progress in real time while watching sports. This system begins with a server receiving and processing competition-related data acquired from a data supply device. The server converts the received data into a unified format and stores it in a storage device that functions as a database. This maintains a state where necessary data can be quickly accessed.

[0618] When a user requests specific match information or detailed player data through their device, the device sends the request to a server. The server searches its storage for relevant information and extracts data corresponding to the user's request. The extracted data is further analyzed using artificial intelligence models. For example, performance predictions and tactical evaluations are performed based on players' past performance and current match situations.

[0619] These analysis results are sent to the device and visually displayed on the user interface. For example, users can use their smartphones to view real-time player statistics and predicted match results in graphs and charts. Personalized information based on the user's interests is also provided.

[0620] Furthermore, this system also includes a communication mechanism that allows users to exchange opinions with other sports fans through their devices. This enables real-time information exchange about match impressions and player evaluations, promoting community building.

[0621] These elements work together to provide users with a richer sports viewing experience. This system offers a new viewing style that integrates sports data analysis and communication.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The server periodically accesses the data supply device to retrieve the latest competition-related data. This includes match progress, player statistics, and match results.

[0625] Step 2:

[0626] The server converts the acquired data into a unified format, extracts only the necessary data, and saves it to storage. This makes subsequent data retrieval and provision more efficient.

[0627] Step 3:

[0628] Users use their devices to request information about specific matches or players. These requests may include player performance data, match predictions, and current match statistics.

[0629] Step 4:

[0630] The terminal sends the user's request to the server. The request includes specific player names and match IDs.

[0631] Step 5:

[0632] The server searches its storage for the requested information and retrieves the relevant data. The retrieved data is based on the user request.

[0633] Step 6:

[0634] The server analyzes the acquired data using an artificial intelligence model. This analysis includes predicting player performance and evaluating match tactics.

[0635] Step 7:

[0636] The server sends the data along with the analysis results to the terminal. This prepares the terminal to visually display the latest information to the user.

[0637] Step 8:

[0638] The terminal displays data received from the server on the user interface. Users can view the information using graphs and charts, making it easier to understand visually.

[0639] Step 9:

[0640] Users can exchange opinions with other fans through communication channels based on information obtained via their devices. This allows them to deepen their understanding of the match.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] To improve the sports viewing experience, a system is needed that allows users to easily obtain detailed player information and match progress in real time. However, existing systems have problems such as delays in data acquisition and analysis, and fragmented information, which hinders a smooth viewing experience. Furthermore, there is a need for features that provide more interactive elements and promote interaction among users.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes means for receiving sports-related data acquired from a data supply device, converting the sports-related data into a unified format, and storing it in a storage device; means for searching for athlete information from the storage device in response to a request from a user terminal and providing the athlete information to the user terminal; and means for analyzing the athlete's performance using an automated learning model and displaying the analysis results on the user terminal. This enables users to access information on sports events in real time, quickly and in detail, and further realizes an interactive viewing experience in which they can exchange information with other users.

[0646] A "data supply device" is a device that generates or provides sports-related data and can transmit that data to a server via the internet.

[0647] "Sports-related data" refers to a collection of data that includes information related to sporting events, such as match results, player statistics, and statistical information.

[0648] "Converting to a unified format" refers to the process of standardizing data received in different formats so that it can be stored and used in a consistent manner.

[0649] A "storage device" is a device equipped with computing capabilities for storing digital data, and includes forms such as databases.

[0650] A "user terminal" is a device that a user directly operates to input and output information, and includes personal computers and smartphones.

[0651] "Athlete information" refers to detailed data related to individual athletes, including past performance, profiles, and statistical information.

[0652] An "automated learning model" is a model that uses machine learning algorithms to analyze patterns in data and predict future trends and outcomes.

[0653] "Analysis results" refer to conclusions and insights derived from data processed and analyzed by an automated learning model.

[0654] "Information exchange" refers to the process of sharing information among multiple users and exchanging opinions and data with each other.

[0655] "Communication methods" refer to systems and protocols for sending and receiving information, enabling data transfer over a network.

[0656] The embodiment of this invention relates to a system that provides users with detailed sports information in real time while they are watching a sporting event. Specifically, a server that receives competition-related data from data supply devices via the internet plays a major role. This server converts the received data from JSON format to CSV format and stores it in a MySQL database to perform unified data management.

[0657] The devices used by users include smartphones and personal computers. These devices can request match information and player information via a user interface. The requests are sent to the server, which searches and extracts the relevant information from the database.

[0658] Next, the server inputs the retrieved data into a generating AI model, which predicts performance based on the players' past results and current match status. This automated learning model is implemented using Python and analyzes the prediction results.

[0659] The analyzed information is visualized on the terminal's user interface and displayed in real time as graphs and charts using visualization libraries such as D3.js. As a concrete example of a prompt statement, a user can make a request to the system in the form of, "Based on player A's performance in recent matches, predict their performance in the next match and visually display the relevant data."

[0660] This system also includes a communication function that allows users to exchange opinions in real time. This enables users to share their evaluations and impressions of matches with other sports fans and form a community.

[0661] By working together, each component plays a role in providing users with a new style and value in watching sports, and enriching the viewing experience.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] The server receives sports-related data in real time from data supply devices. This data includes real-time match scores, player profile information, and historical performance data. Specifically, the server retrieves data using the HTTP or WebSocket protocol and receives it in JSON format. The received data is converted into a unified format as input, and the necessary information is extracted.

[0665] Step 2:

[0666] The server processes the converted data into CSV format. Next, this standardized data is saved to a MySQL database. The input here is the data converted in step 1, and the output is the data stored in the database in an efficiently indexed state. Data normalization allows subsequent search queries to be processed quickly.

[0667] Step 3:

[0668] The user generates a request to search for specific athlete information or match results through the terminal interface. For example, this might involve entering a specific player's name and match date, and then pressing a submit button. The user's request is formed as a prompt and sent to the server. Here, the user's actions become the input, and the request is sent to the server.

[0669] Step 4:

[0670] The server receives a request from the user and searches the database. The server uses an SQL query to extract data for matching athletes. The input to this operation is the request received in step 3, and the output is the detailed information about the athletes returned by the server. The extracted data is used as input for the next step.

[0671] Step 5:

[0672] The server inputs the extracted athlete data into a generating AI model to analyze the athletes' performance. Using automated learning, it predicts future performance based on past data. Here, the extracted data is input into an analysis tool, and the prediction results are obtained as output. The data produced by this analysis becomes the output desired by the user request.

[0673] Step 6:

[0674] The server sends the analysis results to the terminal. The terminal visualizes the received data using a visualization library such as D3.js. This includes generating graphs and charts. The terminal analyzes this data and displays it in the user interface, providing the user with visual feedback. Based on this information, the user can gain insights into sports matches and players.

[0675] (Application Example 1)

[0676] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] One challenge in watching sports is that it's difficult for spectators to visually check detailed player information and game progress in real time. Furthermore, there's a lack of ways for spectators to exchange opinions with each other in real time and enjoy the game more deeply. There's a need to solve these problems and make the viewing experience richer and more interactive.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0679] In this invention, the server includes means for converting competition-related data acquired from a data supply device into a unified format and storing it in a storage device, means for searching for and providing player information in response to requests from user terminals, means for analyzing player performance using an artificial intelligence model and displaying the results, and display means for presenting competition information in real time on a visual display device. As a result, spectators can intuitively obtain detailed information during the match and, by exchanging opinions with other spectators, can have a deeper viewing experience.

[0680] A "data supply device" is a device that acquires and provides competition-related data from external sources.

[0681] "Competition-related data" refers to all information related to sports, such as athletes' performance and match results.

[0682] A "unified format" is a standardized, consistent format created by converting data from different formats into a unified and consistent format.

[0683] A "memory device" is a device or system used to store processed data.

[0684] A "user terminal" refers to a device that allows a user to receive information and interact with it.

[0685] An "artificial intelligence model" is a machine learning technique used to analyze data and generate patterns and predictions.

[0686] "Analysis results" refer to the evaluation or prediction results obtained after data processing.

[0687] A "visual display device" is a device used to present information to a user visually, and includes displays and smart glasses.

[0688] "Real-time" is a term that indicates that processing or reactions occur almost simultaneously.

[0689] "Exchange of opinions" refers to the act of multiple users exchanging information and ideas.

[0690] The system for realizing the application example of this invention consists of a server, a user terminal, a visual display device, and a data supply device. First, the server receives competition-related data acquired from the data supply device. The received data is converted into a unified format and stored in a storage device, enabling efficient data access. The server also uses an artificial intelligence model to analyze the performance of athletes based on their past performance and current match status, and generates analysis results.

[0691] The user terminal communicates with the server in response to user requests and retrieves player information from its storage device. The information provided by the server is displayed in real time through a visual display device, providing users with an environment where they can intuitively obtain the information they need while watching sports. The visual display device includes smart glasses that can interactively display player data and match progress during the game.

[0692] Users can exchange opinions with other spectators based on information obtained through visual displays. This allows for real-time discussions about the match and stimulates communication among fans.

[0693] A concrete example is the ability to use smart glasses during a soccer match to check the performance of specific players or the progress of the game in real time. This provision of visual information significantly improves the viewing experience.

[0694] A concrete example of a prompt using a generative AI model is, "How can I predict a soccer player's performance and display it in real time as optimal information on smart glasses?" Based on this prompt, the AI ​​performs analysis and instantly provides the user with meaningful information.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The server acquires competition-related data from the data supply device. It receives player statistics and match status data from the data supply device as input. This data is then converted to a unified format and stored in the storage device. As output, the unified formatted data is recorded in the storage device, enabling efficient access.

[0698] Step 2:

[0699] The user terminal receives user requests and sends them to the server. As input, it receives requests from the user for player information and match data. Based on this, it retrieves the necessary information from the server and sends it to the user terminal. As output, it is ready to provide the information requested by the user.

[0700] Step 3:

[0701] The server searches its storage for player information corresponding to the request. As input, it receives a request from the user's terminal, searches its storage for the relevant data, and compares it. As output, the retrieved player information is sorted and prepared for provision to the user's terminal.

[0702] Step 4:

[0703] The server analyzes player information using an artificial intelligence model. It receives the searched player information as input and performs data analysis using a generative AI model. This generates player performance predictions and tactical evaluations of matches. The analysis results are generated as output and ready to be sent to the user's terminal.

[0704] Step 5:

[0705] The user terminal sends information and analysis results provided by the server to a visual display device, which displays them visually in real time. As input, it receives analysis results and player information from the server and transfers them to a device such as smart glasses. As output, real-time information is presented in the user's field of vision, improving the viewing experience.

[0706] Step 6:

[0707] Users exchange opinions with other spectators based on information provided through visual display devices. As input, they receive provided data and use it as material for discussion. This enables real-time communication. As output, it fosters active exchange of opinions among users, resulting in a more fulfilling viewing experience.

[0708] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0709] This invention provides a system that takes into account the user's emotional state to make the sports viewing experience more personalized. This system involves a server receiving competition-related data from a data supply device, converting it into a unified format, and storing it in a memory device. This basic data processing enables a system that can respond to user requests in real time.

[0710] When a user requests information about a specific match or player through their device, the server searches its storage for relevant data and uses an artificial intelligence model to analyze the player's performance. Based on this, the device displays visually insightful information in the user interface to enhance the viewing experience.

[0711] Furthermore, a key feature of this system is its emotion engine, which recognizes the user's emotions in real time. This recognition of emotional states is achieved, for example, by analyzing the user's facial expressions using a camera or by analyzing the user's text input. This allows the system to adjust the tone and detail of the information it provides according to the user's emotions.

[0712] For example, if a user is fidgeting with their device during a match, the emotion engine can determine that the user is feeling anxious and provide reassuring content such as videos of players performing well. Conversely, if the user is happy, the system can proactively present relevant positive news and data to further enhance their excitement.

[0713] In addition, the emotional information of users recognized by the emotion engine is applied to communication with other users. For example, displaying a user's emotions as icons or statuses within a chat enriches the conversation and stimulates interaction between users.

[0714] Thus, the present invention enables interactive responses based on the user's emotions, tailoring the sports viewing experience to individual sensibilities. This system allows users to enjoy a deeper, more immersive viewing experience.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The server periodically retrieves competition-related data from the data supply device. This data includes match progress, player performance data, and team tactical information.

[0718] Step 2:

[0719] The server converts received data into a unified format and integrates information from different data sources in a consistent manner. This allows for efficient storage of data in the storage device.

[0720] Step 3:

[0721] Users use their devices to request information about specific matches or players. These requests may include match statistics or players' past performance records.

[0722] Step 4:

[0723] The terminal sends the user's request to the server. The server searches for and extracts the relevant data from its storage device.

[0724] Step 5:

[0725] The server inputs the extracted information into an artificial intelligence model to analyze player performance and predict match trends. The analysis results include the probability of a player succeeding and which strategies are effective.

[0726] Step 6:

[0727] The terminal receives analysis results from the server and displays them visually on the user interface. Graphs and charts are used to aid user understanding.

[0728] Step 7:

[0729] Devices equipped with an emotion engine can recognize the user's emotional state in real time by analyzing the user's facial expressions and tone of voice.

[0730] Step 8:

[0731] The server selects and provides the most relevant information to the user based on their perceived emotions. For example, if the user is feeling anxious, it will display information that provides reassurance.

[0732] Step 9:

[0733] The device shares the user's emotional information, detected by the emotion engine, with other users during chats. This allows other users to consider the user's emotions while interacting with them.

[0734] Step 10:

[0735] Users can exchange opinions with other fans and gain a deeper viewing experience, along with personalized information tailored to their emotions.

[0736] (Example 2)

[0737] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] In the sports viewing experience, traditional systems that provide uniform information without considering the emotional state of the user make it difficult to provide appropriate information tailored to the individual needs of each user. Furthermore, if the amount or tone of information does not match the user's emotions, it can lead to an unsatisfactory viewing experience or diminish the excitement. As a result, users are demanding more personalized information and experiences that respond to their emotions, but there is a lack of mechanisms to achieve this.

[0739] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0740] This invention includes a server that receives competition-related data acquired from a data supply device, converts the competition-related data into a unified format and stores it in a storage device, retrieves player information from the storage device in response to a request from a user terminal and provides the player information to the user terminal, analyzes the players' performance using an artificial intelligence model and displays the analysis results on the user terminal, recognizes the user's emotional state using an emotion engine and adaptively adjusts the tone and content of the information provided according to the emotional state, and communicates with other users to facilitate the exchange of opinions. This makes it possible to provide an appropriate and personalized sports viewing experience based on the emotional state of each individual user.

[0741] "Data supply device" refers to hardware or software used to provide competition-related information to a server.

[0742] "Competition-related data" refers to information related to sporting events, such as match results, player information, and scores.

[0743] A "unified format" refers to a data structure or protocol used to convert data in different formats into a consistent specification.

[0744] "Storage device" refers to a physical or virtual storage medium that a server uses to store data for extended periods.

[0745] A "user terminal" refers to an electronic device used by a user to access a system and obtain information.

[0746] An "artificial intelligence model" refers to a machine learning algorithm or program used to analyze athlete performance.

[0747] An "emotion engine" refers to a function or system that analyzes a user's emotional state and adjusts the information provided based on the results.

[0748] "Communication methods" refer to technologies such as chat functions and messaging systems used by users to exchange opinions with each other.

[0749] This invention is a system that personalizes the sports viewing experience by taking into account the emotional state of the user. This system begins with a server processing "competition-related data" received from a "data supply device." The server converts the received data into a "unified format" and stores it in a "storage device." This conversion and storage process enables the server to quickly access data in response to user requests.

[0750] When a user requests information about a specific match or player through their "user terminal," the server searches for the relevant player information in its "storage device." Furthermore, the server uses an "artificial intelligence model" to analyze this player information in detail and displays the analysis results on the user interface of the "user terminal." In this process, users can obtain visually interactive information, improving their viewing experience.

[0751] Furthermore, this system features an "emotion engine" that recognizes the user's emotions in real time. Emotion recognition is performed through facial expression analysis using a camera and analysis of the user's text input. This emotion recognition function allows the server to adjust the tone and detail of the information it provides according to the user's emotional state. For example, when the user is feeling anxious, it provides reassuring content, and conversely, when the user is happy, it emphasizes more positive information.

[0752] This emotional information is also used as a "means of communication," enriching interactions with other users. For example, the chat function displays icons and statuses that express a user's emotions, thereby promoting interaction between users. Furthermore, when using the generative AI model, prompts such as "Predict player A's performance in the next match" are utilized. This is an important technique for improving the system's analytical accuracy and meeting user expectations.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The server receives competition-related data from the data supply device. This received data includes match results and player information. After receiving this data, the server converts the data, which may have different formats, into a unified format. This conversion cleanses the data and adds relevant metadata. The converted data is then stored in the "storage device."

[0756] Step 2:

[0757] Users submit requests for information about specific matches or players through their user terminals. The user terminals send these requests to the server. Based on the received requests, the server quickly searches its "storage" for the relevant player information. High-speed database queries are used for the search, and as a result, player data is extracted.

[0758] Step 3:

[0759] The server uses a generative AI model based on player information to perform detailed performance analysis. For example, past performance data of a player is used as input, and a predictive model is used to predict future performance. The output of this process is a detailed analysis result. This allows users to gain deeper insights tailored to their interests.

[0760] Step 4:

[0761] The terminal retrieves analysis results from the server and visualizes and displays them in the user interface. Here, visual elements such as graphs and infographics are used to help users process the information. Users can easily make data-driven decisions through the screen.

[0762] Step 5:

[0763] The server utilizes an "emotion engine" to recognize the user's emotional state. This involves using facial recognition technology with the device's camera data as input. The analysis results in the user's emotional state (e.g., joy, anxiety). Based on this information, the server adjusts the content it provides.

[0764] Step 6:

[0765] The device provides customized content to the user based on the output of the emotion engine. If the user is anxious, it presents reassuring videos to alleviate their anxiety. Conversely, if the user is happy, it provides positive information and news to further enhance that emotion.

[0766] Step 7:

[0767] Users utilize communication tools to exchange opinions with other users. The device supports communication through a chat function and displays the user's emotional state as icons and status indicators. This feature facilitates interaction with other users and enables richer conversations.

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] In sports viewing, there is a challenge in that users cannot receive personalized information and experiences that respond to their emotions, resulting in a lack of realism and immersion. Furthermore, providing information that responds to real-time emotional changes is difficult.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0772] In this invention, the server includes means for receiving competition-related data acquired from a data supply device, converting the data into a unified format, and storing it in a storage device; means for searching for participant information in response to a request from a user terminal and providing the information; means for analyzing the participant's performance using an artificial intelligence model and displaying the analysis results; and image recognition means for analyzing the user's facial expressions. This makes it possible to provide information that is tailored to the user's emotional state.

[0773] A "data supply device" is hardware or software that acquires competition-related data from an external source and provides it to other components within the system.

[0774] A "unified format" is a standard data format that has been converted to ensure consistency between competition-related data obtained in different formats.

[0775] A "storage device" is a computer device or medium used to store data converted into a standardized format for long-term or short-term storage.

[0776] A "user terminal" is an electronic device used by users to display information provided by a server and to request information.

[0777] An "artificial intelligence model" is a machine learning algorithm or data processing method used to analyze participants' performance and evaluate or predict their results.

[0778] "Facial expression analysis" is a process performed to recognize the user's facial movements and expressions and to determine their state.

[0779] "Image recognition means" refers to a technology or system that uses cameras or sensors to acquire image data and identify specific patterns or features.

[0780] "Control mechanisms for dynamic provision" refer to a system that changes the content in real time according to the user's emotional state and presents the most appropriate information.

[0781] The server centrally manages the system for personalizing the sports viewing experience. First, it receives competition-related data from data supply devices, converts data in different formats into a unified format, and stores it in storage. This enables a system that can quickly respond to requests from user terminals. User terminals search for information about athletes and competitions from storage and provide that information. Furthermore, artificial intelligence models are used to analyze athletes' performance from multiple perspectives and display the information visually on the terminal. Technologies such as Google Cloud AI and OpenCV are utilized for this purpose.

[0782] The user's facial expressions are analyzed in real time using the device's camera. Based on this, an image recognition system determines the user's emotional state, and an emotion engine dynamically provides information and content appropriate to that state. For example, if a user is excited while watching match highlights, positive news or memorable scenes from the past are recommended using prompt messages, providing a deeper sense of immersion.

[0783] For example, if data detects that a user watching a particular soccer match is excited by a goal, the system sends a prompt to the AI ​​model: "Suggest content that recommends appropriate sports news or past highlights when the user is excited." This prompt then displays the most suitable content on the user's screen.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] The server receives competition-related data from the data supply device. Since this data is provided in various formats, the server converts it to a unified format and stores it in storage. This conversion process facilitates subsequent data retrieval and analysis. The server converts the input competition-related data and stores the standard format data as output in storage.

[0787] Step 2:

[0788] The terminal searches for information about athletes and competitions stored in its storage device based on requests from the user. When a user requests information about a specific athlete or competition, the terminal receives the request, searches for the relevant data, and provides it to the user's terminal. The input here is the user's request, and the output is a list of related information.

[0789] Step 3:

[0790] The server uses an artificial intelligence model to analyze players' performance. The input is data related to the players, which the AI ​​model processes and outputs analysis results in the form of performance statistics and other information. This is displayed visually on the terminal. In this process, the data is analyzed from multiple perspectives, and insights are provided to the user through a visual interface.

[0791] Step 4:

[0792] The device analyzes the user's facial expressions in real time via its camera. The input is camera footage, and the user's emotions are analyzed using image recognition technology. Based on the analysis results, the server dynamically provides information tailored to the user's emotional state.

[0793] Step 5:

[0794] Depending on the user's emotional state, such as when they are excited, the server uses a generative AI model to send prompts. For example, a prompt such as "Suggest appropriate sports news or memorable moments from the past when the user is excited" is generated and sent to the AI ​​model. Based on this, the AI ​​model selects appropriate content and presents it to the device. The output includes recommended content.

[0795] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0798] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0799] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0800] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0801] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0802] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0803] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0804] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0805] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0806] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0807] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0808] 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.

[0809] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0810] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0811] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0812] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0813] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0814] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0815] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device,

[0819] A means for retrieving player information from a storage device in response to a request from a user terminal and providing said player information to the user terminal,

[0820] A means of analyzing a player's performance using an artificial intelligence model and displaying the analysis results on the user's terminal,

[0821] A means of communication to facilitate the exchange of opinions with other users,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which periodically updates the aforementioned competition-related data.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising graphical display means for visually presenting information on the interface of the user terminal.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means for receiving sports-related data acquired from a data supply device, converting the sports-related data into a unified format, and storing it in a storage device,

[0830] A means for retrieving athlete information from a storage device in response to a request from a user terminal and providing said athlete information to the user terminal,

[0831] A means for analyzing athletes' performance using an automated learning model and displaying the analysis results on the user's terminal,

[0832] A means of communication for exchanging information and facilitating the exchange of opinions with other users,

[0833] A means of dynamically updating information by predicting based on indicators using data acquired in real time,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which securely performs the acquisition of the aforementioned sports-related data via an encrypted connection.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising dynamic visualization means for visually presenting information on the interface of the user terminal.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device,

[0842] A means for retrieving player information from a storage device in response to a request from a user terminal and providing said player information to the user terminal,

[0843] A means of analyzing a player's performance using an artificial intelligence model and displaying the analysis results on the user's terminal,

[0844] A means of communication to facilitate the exchange of opinions with other users,

[0845] A display means for presenting competition information in real time on a visual display device,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which periodically updates the aforementioned competition-related data.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising a graphical display means for presenting information on the interface of the visual display device.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device,

[0854] A means for retrieving player information from a storage device in response to a request from a user terminal and providing said player information to the user terminal,

[0855] A means of analyzing a player's performance using an artificial intelligence model and displaying the analysis results on the user's terminal,

[0856] A means for recognizing the user's emotional state using an emotion engine and adaptively adjusting the tone and content of the information provided according to that emotional state,

[0857] A means of communication to facilitate the exchange of opinions with other users,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which periodically updates the aforementioned competition-related data.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising graphical display means for visually presenting information on the interface of the user terminal.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device,

[0866] A means for retrieving participant information from a storage device in response to a request from a user terminal and providing said participant information to the user terminal,

[0867] A means of analyzing participants' performance using an artificial intelligence model and displaying the analysis results on the user's terminal,

[0868] A means of communication to facilitate the exchange of information and opinions with other users,

[0869] The system includes image recognition means for analyzing the user's facial expressions, and control means for dynamically providing information corresponding to the user's emotional state based on the facial expression analysis.

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, which periodically updates the aforementioned competition-related data.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising visual representation means for visually presenting information on the interface of the user terminal. [Explanation of Symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving competition-related data acquired from a data supply device, converting said competition-related data into a unified format, and storing it in a storage device, A means for retrieving player information from a storage device in response to a request from a user terminal and providing said player information to the user terminal, A means of analyzing a player's performance using an artificial intelligence model and displaying the analysis results on the user's terminal, A means of communication to facilitate the exchange of opinions with other users, A system that includes this.

2. The system according to claim 1, which periodically updates the aforementioned competition-related data.

3. The system according to claim 1, further comprising graphical display means for visually presenting information on the interface of the user terminal.

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