system

Abstract

We provide the system. [Solution] A means of collecting match data in real time and analyzing the generated data, A means of generating strategic insights into the game based on the analyzed data, A means of providing explanatory information to spectators using visualization tools, A means of providing an automatically generated strategy improvement report after the end of the match, 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, the method 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 as a 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] In the conventional basketball game watching, it has been difficult for spectators and team-related personnel to understand the details and strategic performances of the game in real time, and there has been a problem that strategic improvement points cannot be grasped immediately. For this reason, means for enriching the viewing experience of spectators and contributing to the improvement of the team's strategic awareness have been demanded.

Means for Solving the Problems

[0005] This invention provides a means for collecting match data in real time and analyzing it using a generating AI, thereby constructing a system that instantly visualizes and provides strategic insights and performance data during a match to spectators and teams. Furthermore, it automatically generates a strategic improvement report after the match, which can be used to inform the team's training plan. This makes it possible to simultaneously enhance the spectator experience and improve the team's strategy.

[0006] "Real-time" is a concept that refers to the timing at which events and data occurring during a match are collected, analyzed, and provided.

[0007] "Game data" refers to information related to the progress of a basketball game, such as player movements, ball position, and shot success / failure, collected during the game.

[0008] A "generative AI model" is an artificial intelligence technology used to analyze match strategies and player performance based on collected data.

[0009] A "visualization tool" is software or a function that displays analyzed data in a way that is easy for spectators and teams to understand.

[0010] "Strategic insight" refers to knowledge about a team's tactics and strategies derived from the situation during a match and the performance of the players.

[0011] An "automatically generated strategy improvement report" is a document generated by the system after the end of a match, containing information on areas for improvement in team and player performance, as well as strategic advice. [Brief explanation of the drawing]

[0012] [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] This is a conceptual diagram showing an example of the essential 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] s 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.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention will be described from three perspectives: server, terminal, and user.

[0034] Server-side embodiment

[0035] The server collects real-time data during a match and runs a generative AI model to analyze the data. As a specific example, during a basketball game, it collects data on player movement, shot success / failure, and ball position. Using this collected data, the AI ​​analyzes which strategies are effective in the current situation. This can generate useful strategic insights for players and coaches. This information is then transmitted to a terminal and used in the next step.

[0036] Terminal-side embodiment

[0037] The terminal receives analytical data from the server and presents the information to the user in an easy-to-understand manner using visualization tools. For example, it can visualize how a particular player moves during a match, or which shooting positions are effective, using a 2D or 3D court display. It can also display player performance using a heatmap to provide a more detailed understanding of tactics. As a result, spectators can gain a deep, real-time understanding of the flow of the game and the players' movements.

[0038] User-side embodiment

[0039] Users can interactively manipulate the information provided through the interface on their devices, gaining a deeper understanding of the match. For example, users can click on a specific player to view that player's past performance data and trends. After the match, they can also view automatically generated strategy improvement reports to understand what needs to be improved for the next game. This enhances the viewing experience and deepens the strategic capabilities of the team.

[0040] As described above, this invention functions through the cooperation of the server, terminal, and user, providing a practical and effective solution for deepening understanding of the game.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects real-time situational data from sensors placed on the court and devices worn by players as soon as the match starts. This includes player location and movement, ball position, and the success or failure of each shot.

[0044] Step 2:

[0045] The server instantly saves the collected match data to a database and sends it to the generated AI model. The AI ​​model uses this data to analyze the performance of each player and the strategic movements of the team. For example, it analyzes the movement patterns of players and calculates the success rate at specific positions.

[0046] Step 3:

[0047] The server converts the analysis results into a format that can be visualized. This includes heatmaps, shot charts, and player movement tracking information. This data is then immediately delivered to the terminal.

[0048] Step 4:

[0049] The terminal receives data transmitted from the server and visualizes it in real time through the user interface. The terminal renders the flow of the game using 3D or 2D models, visually displaying the players' movements and performance.

[0050] Step 5:

[0051] Users interact with visualized information through their device's interface to view specific events during a match or detailed performance data for individual players. For example, a user can tap on a specific player to see their past performance trends in matches.

[0052] Step 6:

[0053] After the match ends, the server integrates all match data and creates an automatically generated strategy improvement report. This report summarizes the team's strengths and areas for improvement, and includes specific strategic suggestions for the next match. Users can view this report on their devices and use it to improve their team strategy.

[0054] (Example 1)

[0055] 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."

[0056] In modern sports matches, the information available to spectators and teams in real time is limited, making it difficult to deeply understand the flow of the game and the movements of the players. Therefore, it is difficult to formulate effective strategies during a match or identify areas for improvement afterward. A solution to this problem is needed.

[0057] 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.

[0058] In this invention, the server includes means for collecting match data in real time using an input device and analyzing the data, means for generating strategic insights into the match based on the analyzed data, and means for providing explanatory information to spectators using a visualization device. This allows spectators and teams to gain deep insights in real time during the match and to clearly identify areas for improvement after the match.

[0059] An "input device" is a device used to collect match data in real time.

[0060] An "information processing device" is a device that analyzes data collected in real time and generates analysis results.

[0061] A "terminal device" is a device that receives analysis results provided by an information processing device and presents the information to the user.

[0062] A "visualization device" is a device that presents analyzed data to the user visually, and is capable of displaying data in 2D or 3D.

[0063] A "computational device" is a device used to automatically generate a strategy improvement report after the end of a match.

[0064] A "user" is a person who manipulates and uses the data and information presented from a device.

[0065] This invention is a system that functions through the cooperation of a server, terminal, and user, and aims to provide real-time data analysis of matches and strategic insights.

[0066] Server-side embodiment

[0067] The server uses sensors and video equipment to collect data in real time during the match. This data includes player movements, shooting success rates, and ball position information. The collected data is processed using software such as Python and Node.js, and preprocessing such as data cleaning and normalization is performed. Next, analysis is performed using generative AI models with AI frameworks such as Tensorflow® and PyTorch. The analysis results are generated in the form of effective strategy suggestions and sent to the terminal in JSON format.

[0068] Terminal-side embodiment

[0069] The terminal receives analysis results from the server and visualizes the information using visualization tools such as "D3.js" and "Three.js". Specifically, it reproduces player movements and positioning in 2D or 3D and effectively visualizes them using heatmaps and court displays. This allows users to gain strategic insights and understand the flow of the game and player movements in detail.

[0070] User-side embodiment

[0071] Users can access multiple functions through the device interface. For example, clicking on a specific player allows them to view detailed performance data about that player. After a match, they can refer to an automatically generated strategy improvement report to identify areas for improvement for the next match. Furthermore, by entering a prompt such as, "What is the optimal strategy for the current basketball game?" into the AI ​​model, users can receive strategic suggestions tailored to the game situation.

[0072] Through the embodiments described above, this invention can provide spectators and teams with deep insights and strategic advantages in the game.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server uses sensors and video equipment to collect real-time data on player movements, shooting success rates, ball position, and more during the match. This collected data is input to the server as raw data. Based on this raw data input, the server performs noise reduction and data cleaning processes to generate an analyzable dataset. This dataset is then used for analysis by an AI model in the next step.

[0076] Step 2:

[0077] The server uses the dataset obtained in Step 1 to perform analysis using a generative AI model. This analysis utilizes "TensorFlow" and "PyTorch". The input dataset is fed into the AI ​​model, which generates output such as player performance and strategic insights. This output includes optimal strategies and predictions based on the current state of the match. The generated analysis results are converted into "JSON" format, ready for use in subsequent steps.

[0078] Step 3:

[0079] The server sends parsed data in JSON format to the terminal. This transmission is done via a REST API or WebSocket, allowing the terminal to receive the parsing results in real time. The input here is the parsed data, and the output is the completion of data transmission to the terminal.

[0080] Step 4:

[0081] The terminal visualizes the analysis data received from the server using "D3.js" or "Three.js". The input for this visualization process is the analysis data obtained from the server, and the output is 2D or 3D graphics of the match situation or player movements presented to the user. This allows the user to understand the actual match movements more intuitively.

[0082] Step 5:

[0083] Users interact with visualized data through an interface on their device. By selecting specific players or situations, they can view detailed data and historical trends for those players. The input to this interaction is the user's interaction, and the output is the displayed information. Users can also directly question the AI ​​model by entering prompts.

[0084] Step 6:

[0085] After the match ends, the server automatically generates a strategy improvement report using computing power. This generation utilizes analysis data from the match and improvement algorithms. The input is all the match data, and the output is a report containing strategic improvements for the next match. Users can review this report and use it to help develop their team's strategy.

[0086] (Application Example 1)

[0087] 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."

[0088] The aim is to address the challenge faced by participants and spectators in local sports events and community leagues, where it is difficult for them to grasp competition performance in real time and gain strategic insights. Currently, real-time data collection and analysis are insufficient, resulting in a limited understanding of the competition and the viewing experience.

[0089] 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.

[0090] In this invention, the server includes means for collecting and analyzing competition data in real time, means for generating strategic insights based on the analyzed data, and means for providing commentary to participants and spectators through visualization means. This allows spectators to gain a more detailed understanding of the athletes' performance and competition highlights in real time through visualized information.

[0091] "Real-time collection of competition data" means instantly acquiring various types of information using sensors and cameras while a match or competition is in progress.

[0092] "Analysis of generated data" refers to the process of using AI and algorithms to derive meaningful information based on collected data.

[0093] "Generating strategic insights" is a method of finding effective tactics and strategies based on analyzed competition data.

[0094] "Visualization methods" refer to technologies and tools used to display analyzed data and insights in an easily understandable way.

[0095] "Providing explanatory content through visualization means" refers to the act of explaining a competition in an easily understandable way for spectators and participants using visualized information.

[0096] "Automatic generation of strategy improvement reports" refers to the process of automatically creating a report in a report format after a match, using a generation AI model or similar tool, to identify areas for strategic improvement based on the data obtained.

[0097] For this invention to be implemented, three main components—a server, a terminal, and a user—work together.

[0098] Server-side embodiment

[0099] The server collects data in real time during the competition and runs a generative AI model on the accumulated data for analysis. Specifically, high-performance servers (e.g., AWS® EC2) are used as hardware. For software, PyTorch or TensorFlow are utilized for data processing and analysis. This analysis derives strategic insights into the current state of the competition. This allows the server to provide real-time insights to terminals.

[0100] Terminal-side embodiment

[0101] The terminal will visualize the analysis data received from the server and present it to the user in an easy-to-understand manner. Specific hardware such as smartphones and tablets will be used. Libraries such as D3.js and Three.js will be used for visualization, displaying player movements and competition highlights in 2D or 3D. This visualization will allow users to understand player performance in real time. Frontend frameworks such as Vue.js and React.js are also being considered as software to use.

[0102] User-side embodiment

[0103] Users can manipulate information provided through the interface on their device to gain a deeper understanding of the sport. For example, a user can select a specific player and examine their movements and past performance data. After the match, they can also view automatically generated strategy improvement reports and review their tactics. This series of operations dramatically improves the spectator's understanding and experience of the sport. An example of a specific prompt would be, "Please use the generating AI to analyze the performance of a specific player during the match in real time and display the visualization results on the device."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects data in real time during the competition. The input is raw data from data acquisition devices such as sensors and cameras. The server receives this data, converts it to a structured data format (e.g., JSON), and stores it in the database.

[0107] Step 2:

[0108] The server collects data and analyzes it using an AI model. The input is the structured data stored in Step 1. The server passes this data to an AI model built with TensorFlow or PyTorch to generate strategic insights. The output is an analysis of strategies and player performance based on the current state of the competition.

[0109] Step 3:

[0110] The server sends the analysis results to the terminal. The input is the analysis results obtained in step 2. The server sends data to the terminal using WebSocket or REST API to send the analysis results in real time. The output is a data stream containing the analysis results.

[0111] Step 4:

[0112] The terminal visualizes the analysis results it receives. The input is the data stream sent in step 3. The terminal uses a visualization library such as D3.js or Three.js to display this data in 2D or 3D. The output is visualized competition information, which the user can view through the interface.

[0113] Step 5:

[0114] The user interacts with the visualized information. The input is the information visualized in step 4. Through the terminal interface, the user can examine detailed performance data and past trends of specific players. The output is a detailed analysis result based on the specific information selected by the user.

[0115] Step 6:

[0116] After the competition ends, the server automatically generates a strategy improvement report. The input consists of the entire competition data history and prompt messages. The server uses a generation AI model to automatically generate improvement points in report format. The output is a strategy improvement report that shows points that should be improved for the next competition.

[0117] 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.

[0118] This invention provides a more personalized match viewing experience by combining an emotion engine that recognizes user emotions with an existing real-time data analysis system. The following details the embodiments of the invention from three perspectives: server, terminal, and user.

[0119] Server-side embodiment

[0120] The server collects real-time data during the match and analyzes it using a generative AI model, as well as collecting user emotion data. This emotion data includes changes in the user's facial expressions and voice, captured through the device's camera and microphone. The server analyzes this emotion data with an emotion engine and provides the user with commentary on the match based on the results. For example, if the emotion engine determines that the user is highly excited, it adjusts the analysis results to provide more detailed commentary, especially on the most intense battle zones.

[0121] Terminal-side embodiment

[0122] The device combines match data and analysis results received from the server, utilizes feedback from the emotion engine, and presents individually optimized information to the user using visualization tools. For example, if the user shows excitement over a successful shot, the device can provide a more detailed explanation of the importance of that shot and the player's backstory.

[0123] User-side embodiment

[0124] Users can receive match data and commentary in real time through their devices and enjoy content tailored to their emotions. For example, if a user expresses stress during a match, the system will display information that highlights the positive aspects of the match, adjusting the viewing experience. Furthermore, after the match, a detailed strategy improvement report, including changes in the user's emotions, is provided to help them prepare for future matches.

[0125] Thus, the present invention utilizes an emotion engine to provide flexible match commentary and strategic information tailored to the user's emotional state, thereby creating a groundbreaking system that can offer individual spectators a deeper experience and greater satisfaction.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server collects real-time information on movement, shot success / failure, and ball position from sensors on the court and from players at the start of the match. This data is sent to a generative AI model and used to analyze in-game strategies and player performance.

[0129] Step 2:

[0130] The server collects emotional data from the user's facial expressions and changes in voice through the device's camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state in real time. For example, if there are signs of excitement in the user's voice, the engine recognizes that emotion as "excitement."

[0131] Step 3:

[0132] Based on the user's emotional state, the server customizes the analyzed match data. For example, if the user is excited, the system will emphasize match highlights and commentary on important plays.

[0133] Step 4:

[0134] The terminal receives customized match data and sentiment analysis results sent from the server, and presents the information in an interactive user interface using visualization tools. This makes it easier for users to visually understand the flow of the match and the movements of the players.

[0135] Step 5:

[0136] Users can watch match commentary provided through their devices in real time and obtain information that matches their interests and feelings. If a user shows particular interest in a specific play, the device will display detailed commentary and player profiles related to that play.

[0137] Step 6:

[0138] After the match ends, the server automatically generates a detailed strategy improvement report based on the data collected during the match and the history of emotional changes. This report includes key moments from the match and insights based on the user's emotional shifts, which users can use to inform their next match viewing and team strategy planning.

[0139] (Example 2)

[0140] 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".

[0141] Traditional match viewing systems have struggled to provide viewers with information tailored to their individual emotions and the dynamic flow of the game, failing to deliver a highly satisfying and personalized experience. Furthermore, the lack of strategic information based on real-time emotional states made it difficult to maintain viewers' interest.

[0142] 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.

[0143] This invention includes a server that collects information related to the match in real time and analyzes the data using a generative AI model; a server that combines the analyzed information with collected sentiment-based insights to generate strategic information about the match; and a server that provides individually optimized commentary information to spectators using visualization technology. This enables spectators to receive personalized commentary information based on their own sentiments in real time.

[0144] "Real-time" refers to processing or using data almost simultaneously with the event occurring.

[0145] "Information related to the match" includes various data related to the detailed content of the match, such as the progress of the match, the score, player movements, and statistical data.

[0146] A "generative AI model" refers to an artificial intelligence algorithm that uses previously trained data to understand new data and perform predictions and analyses.

[0147] "Emotion-based insights" refer to knowledge gained by analyzing a user's emotional state and generating analysis and information based on that analysis.

[0148] "Visualization technology" refers to techniques that visually represent data and information to make them easier for users to understand.

[0149] "Personalized explanatory information" refers to explanatory content that is customized to the individual user's preferences and circumstances.

[0150] Modes for carrying out the invention

[0151] Server Embodiment

[0152] The server is connected to an external database that supplies match data, serving as a means of collecting match-related information in real time. This allows for the acquisition of details such as scores and player performance. The server utilizes a "generative AI model" to analyze match data and user emotion data transmitted from the terminal. Emotion data is acquired through facial recognition technology using the terminal's camera and voice analysis technology using the microphone. An "artificial intelligence algorithm" is used for this analysis to identify the user's emotional state in real time. For example, if an increase in the user's heart rate is detected during a tense match, the emotion analysis engine analyzes this state as excitement.

[0153] Terminal embodiment

[0154] The terminal displays information using "visualization technology" based on analyzed data received from the server. This utilizes "data visualization software" that dynamically updates information in accordance with the progress of the match. For example, visualization technology can be used to highlight important moments during the match or to present detailed information about specific players. The terminal customizes the interface according to the user's emotional state and adjusts the priority of the information displayed.

[0155] User Embodiment

[0156] Users can enjoy watching the game based on match data and personalized commentary presented through their device. After the game ends, the system provides users with "strategy improvement information" based on their emotional changes. This includes advice to further enhance the viewing experience and can be applied to future viewings. A specific example of a prompt is, "Generate detailed commentary on highlight scenes for users who are in an excited state."

[0157] This invention allows users to take advantage of richly personalized information when watching matches in real time, resulting in a deeper level of satisfaction.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server collects match-related information in real time from external data providers. The input is formatted match data, including scores, player statistics, and match progress. The server stores this data in a database, preparing it for later analysis. During this process, data processing is performed to supplement and standardize any incomplete match data.

[0161] Step 2:

[0162] The device collects user emotion data using its built-in camera and microphone. The input consists of user facial expression data and voice data. This data is preprocessed using image processing algorithms and voice analysis techniques before being sent to the server. For example, face detection and landmark detection are performed to extract facial features, and voice recognition and feature extraction are performed on voice data.

[0163] Step 3:

[0164] The server uses a generative AI model to analyze collected match data and user emotion data. The inputs are match data and emotion data. The AI ​​model estimates the user's emotional state in real time and generates necessary insights according to the flow of the match. In this process, emotional features are extracted, analyzed, and emotion determination is made using a classification model. The output is the analysis result regarding the user's emotional state.

[0165] Step 4:

[0166] The server generates prompts based on the analysis results and uses them to generate personalized commentary information tailored to the user's emotional state. The input consists of AI analysis results and predefined prompts. The AI ​​generates commentary that matches the user's specific emotions, creating match commentary content. The output is custom commentary information based on those emotions.

[0167] Step 5:

[0168] The terminal presents custom explanatory information received from the server to the user using visualization technology. The input is the custom explanatory information. Based on this information, the terminal uses visualization tools to display the information on the screen in the most optimal format. For example, for highly excited users, important scenes can be displayed as large video clips. The output is an interactive explanatory interface presented to the user.

[0169] Step 6:

[0170] Users watch the match based on information provided through their device, and after the match ends, the system generates and provides an improvement report based on the user's emotional changes. The input is all emotional data collected during the match. The system analyzes this data and generates strategic improvement information to enhance the viewing experience. The output is an emotional strategic improvement report for the next match.

[0171] (Application Example 2)

[0172] 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".

[0173] Existing event viewing systems lack the ability to provide information that takes into account the individual emotional states of spectators, limiting personalized viewing experiences. Therefore, there is a need for a system that can flexibly deliver content tailored to users' interests and emotions.

[0174] 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.

[0175] In this invention, the server includes means for acquiring event data in real time and analyzing the generated information, means for generating strategic knowledge based on the analyzed information, and means for providing personalized content according to the emotions, equipped with an emotion engine that recognizes and analyzes the emotions of the user. This enables a more engaging and personalized viewing experience for the user.

[0176] "Acquiring event data in real time" is a technology that processes information without delay by promptly obtaining event information at the current moment.

[0177] "Analyzing generated information" refers to the process of conducting a detailed analysis of acquired data in order to derive useful knowledge and insights from it.

[0178] "Generating strategic knowledge" means creating planned information to support decision-making based on analytical results.

[0179] "Providing explanatory information using visualization devices" refers to the technique of using visual tools to deliver event-related information in a way that is easy for the audience to understand.

[0180] "Providing automatically generated strategy improvement reports after the event ends" means that after an event has concluded, it analyzes its content and automatically creates and provides a report document that includes improvement measures.

[0181] An "emotion engine that recognizes and analyzes user emotions" is a technology that uses camera and audio data to analyze changes in the user's facial expressions and voice, and to determine their emotional state.

[0182] "Providing personalized content based on emotions" means selecting and delivering the most suitable information and entertainment based on analyzed emotional data of the user.

[0183] This invention aims to provide personalized content that responds to the user's emotional state and is based on a system that acquires and analyzes event data and user emotional data in real time.

[0184] The server collects data in real time as the event begins. This data includes information about the event's progress and important events. The server also integrates emotion data sent from client terminals. Emotion data is acquired using the terminal's camera and microphone, facial expressions are analyzed using OpenCV, and emotions are determined from the audio using TensorFlow. The information generated through this process reflects the user's psychological state.

[0185] The terminal provides users with customized explanatory information through a visualization device, based on analysis results received from the server. This information is accompanied by data delivered via Flask, and the terminal delivers it to the user using visual and auditory means. This allows users to receive detailed explanations and related stories about specific moments during the event, enriching their viewing experience.

[0186] To give a concrete example, if a user shows a high level of excitement while watching a sporting event, the system will provide content that includes a video detailing the highlights of that moment, as well as backstories of the players. A generative AI model assists with this task, supplementing the information with highly relevant prompts. By utilizing prompts such as, "The user's current emotion is excitement. Please suggest specific commentary and stories to provide during the match," the system can deliver content that is more accurately optimized for the user.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server acquires event data in real time as the event begins. The input consists of various event data feeds, stored in a database. The server analyzes the acquired data and extracts important information. This analysis uses algorithms to identify the progress of the match and key moments. The output is immediately usable strategic insights.

[0190] Step 2:

[0191] The terminal receives the results analyzed from the server. The data the terminal receives as input contains important information about the event. In addition, the terminal acquires user emotion data through the camera and microphone. This emotion data is used as input to analyze facial expressions using OpenCV and to estimate emotions from speech using a TensorFlow model. The output is the user's real-time emotional state.

[0192] Step 3:

[0193] The server re-analyzes the user's sentiment data sent from the terminal. This input sentiment data is then used by a generating AI model to create prompts that determine what information is appropriate. The output is instructions regarding the most relevant and customized content for the user.

[0194] Step 4:

[0195] The terminal, based on instructions from the server, presents personalized information to the user through a visualization device. Input consists of strategic knowledge and content tailored to the user's emotional state, which the terminal integrates to form the displayed data. Output is personalized video and audio content for the viewer. Specific actions include playing match highlights or displaying player backstories.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention will be described from three perspectives: server, terminal, and user.

[0213] Server-side embodiment

[0214] The server collects real-time data during a match and runs a generative AI model to analyze the data. As a specific example, during a basketball game, it collects data on player movement, shot success / failure, and ball position. Using this collected data, the AI ​​analyzes which strategies are effective in the current situation. This can generate useful strategic insights for players and coaches. This information is then transmitted to a terminal and used in the next step.

[0215] Terminal-side embodiment

[0216] The terminal receives analytical data from the server and presents the information to the user in an easy-to-understand manner using visualization tools. For example, it can visualize how a particular player moves during a match, or which shooting positions are effective, using a 2D or 3D court display. It can also display player performance using a heatmap to provide a more detailed understanding of tactics. As a result, spectators can gain a deep, real-time understanding of the flow of the game and the players' movements.

[0217] User-side embodiment

[0218] Users can interactively manipulate the information provided through the interface on their devices, gaining a deeper understanding of the match. For example, users can click on a specific player to view that player's past performance data and trends. After the match, they can also view automatically generated strategy improvement reports to understand what needs to be improved for the next game. This enhances the viewing experience and deepens the strategic capabilities of the team.

[0219] As described above, this invention functions through the cooperation of the server, terminal, and user, providing a practical and effective solution for deepening understanding of the game.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server collects real-time situational data from sensors placed on the court and devices worn by players as soon as the match starts. This includes player location and movement, ball position, and the success or failure of each shot.

[0223] Step 2:

[0224] The server instantly saves the collected match data to a database and sends it to the generated AI model. The AI ​​model uses this data to analyze the performance of each player and the strategic movements of the team. For example, it analyzes the movement patterns of players and calculates the success rate at specific positions.

[0225] Step 3:

[0226] The server converts the analysis results into a format that can be visualized. This includes heatmaps, shot charts, and player movement tracking information. This data is then immediately delivered to the terminal.

[0227] Step 4:

[0228] The terminal receives data transmitted from the server and visualizes it in real time through the user interface. The terminal renders the flow of the game using 3D or 2D models, visually displaying the players' movements and performance.

[0229] Step 5:

[0230] Users interact with visualized information through their device's interface to view specific events during a match or detailed performance data for individual players. For example, a user can tap on a specific player to see their past performance trends in matches.

[0231] Step 6:

[0232] After the match ends, the server integrates all match data and creates an automatically generated strategy improvement report. This report summarizes the team's strengths and areas for improvement, and includes specific strategic suggestions for the next match. Users can view this report on their devices and use it to improve their team strategy.

[0233] (Example 1)

[0234] 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."

[0235] In modern sports matches, the information available to spectators and teams in real time is limited, making it difficult to deeply understand the flow of the game and the movements of the players. Therefore, it is difficult to formulate effective strategies during a match or identify areas for improvement afterward. A solution to this problem is needed.

[0236] 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.

[0237] In this invention, the server includes means for collecting match data in real time using an input device and analyzing the data, means for generating strategic insights into the match based on the analyzed data, and means for providing explanatory information to spectators using a visualization device. This allows spectators and teams to gain deep insights in real time during the match and to clearly identify areas for improvement after the match.

[0238] An "input device" is a device used to collect match data in real time.

[0239] An "information processing device" is a device that analyzes data collected in real time and generates analysis results.

[0240] A "terminal device" is a device that receives analysis results provided by an information processing device and presents the information to the user.

[0241] A "visualization device" is a device that presents analyzed data to the user visually, and is capable of displaying data in 2D or 3D.

[0242] A "computational device" is a device used to automatically generate a strategy improvement report after the end of a match.

[0243] A "user" is a person who manipulates and uses the data and information presented from a device.

[0244] This invention is a system that functions through the cooperation of a server, terminal, and user, and aims to provide real-time data analysis of matches and strategic insights.

[0245] Server-side embodiment

[0246] The server uses sensors and video equipment to collect data in real time during the match. This data includes player movements, shooting success rates, and ball position information. The collected data is processed using software such as Python and Node.js, and preprocessing such as data cleaning and normalization is performed. Next, analysis is performed using generative AI models with AI frameworks such as TensorFlow and PyTorch. The analysis results are generated in the form of effective strategy suggestions and sent to the terminal in JSON format.

[0247] Terminal-side embodiment

[0248] The terminal receives analysis results from the server and visualizes the information using visualization tools such as "D3.js" and "Three.js". Specifically, it reproduces player movements and positioning in 2D or 3D and effectively visualizes them using heatmaps and court displays. This allows users to gain strategic insights and understand the flow of the game and player movements in detail.

[0249] User-side embodiment

[0250] Users can access multiple functions through the device interface. For example, clicking on a specific player allows them to view detailed performance data about that player. After a match, they can refer to an automatically generated strategy improvement report to identify areas for improvement for the next match. Furthermore, by entering a prompt such as, "What is the optimal strategy for the current basketball game?" into the AI ​​model, users can receive strategic suggestions tailored to the game situation.

[0251] Through the embodiments described above, this invention can provide spectators and teams with deep insights and strategic advantages in the game.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The server uses sensors and video equipment to collect real-time data on player movements, shooting success rates, ball position, and more during the match. This collected data is input to the server as raw data. Based on this raw data input, the server performs noise reduction and data cleaning processes to generate an analyzable dataset. This dataset is then used for analysis by an AI model in the next step.

[0255] Step 2:

[0256] The server uses the dataset obtained in Step 1 to perform analysis using a generative AI model. This analysis utilizes "TensorFlow" and "PyTorch". The input dataset is fed into the AI ​​model, which generates output such as player performance and strategic insights. This output includes optimal strategies and predictions based on the current state of the match. The generated analysis results are converted into "JSON" format, ready for use in subsequent steps.

[0257] Step 3:

[0258] The server sends parsed data in JSON format to the terminal. This transmission is done via a REST API or WebSocket, allowing the terminal to receive the parsing results in real time. The input here is the parsed data, and the output is the completion of data transmission to the terminal.

[0259] Step 4:

[0260] The terminal visualizes the analysis data received from the server using "D3.js" or "Three.js". The input for this visualization process is the analysis data obtained from the server, and the output is 2D or 3D graphics of the match situation or player movements presented to the user. This allows the user to understand the actual match movements more intuitively.

[0261] Step 5:

[0262] Users interact with visualized data through an interface on their device. By selecting specific players or situations, they can view detailed data and historical trends for those players. The input to this interaction is the user's interaction, and the output is the displayed information. Users can also directly question the AI ​​model by entering prompts.

[0263] Step 6:

[0264] After the match ends, the server automatically generates a strategy improvement report using computing power. This generation utilizes analysis data from the match and improvement algorithms. The input is all the match data, and the output is a report containing strategic improvements for the next match. Users can review this report and use it to help develop their team's strategy.

[0265] (Application Example 1)

[0266] 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 glasses 214 will be referred to as the "terminal."

[0267] The aim is to address the challenge faced by participants and spectators in local sports events and community leagues, where it is difficult for them to grasp competition performance in real time and gain strategic insights. Currently, real-time data collection and analysis are insufficient, resulting in a limited understanding of the competition and the viewing experience.

[0268] 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.

[0269] In this invention, the server includes means for collecting and analyzing competition data in real time, means for generating strategic insights based on the analyzed data, and means for providing commentary to participants and spectators through visualization means. This allows spectators to gain a more detailed understanding of the athletes' performance and competition highlights in real time through visualized information.

[0270] "Real-time collection of competition data" means instantly acquiring various types of information using sensors and cameras while a match or competition is in progress.

[0271] "Analysis of generated data" refers to the process of using AI and algorithms to derive meaningful information based on collected data.

[0272] "Generating strategic insights" is a method of finding effective tactics and strategies based on analyzed competition data.

[0273] "Visualization methods" refer to technologies and tools used to display analyzed data and insights in an easily understandable way.

[0274] "Providing explanatory content through visualization means" refers to the act of explaining a competition in an easily understandable way for spectators and participants using visualized information.

[0275] "Automatic generation of strategy improvement reports" refers to the process of automatically creating a report in a report format after a match, using a generation AI model or similar tool, to identify areas for strategic improvement based on the data obtained.

[0276] For this invention to be implemented, three main components—a server, a terminal, and a user—work together.

[0277] Server-side embodiment

[0278] The server collects data in real time during the competition and runs a generative AI model on the accumulated data for analysis. Specifically, high-performance servers (e.g., AWS EC2) are used. For software, PyTorch or TensorFlow are utilized for data processing and analysis. This analysis derives strategic insights into the current state of the competition. This allows the server to provide real-time insights to terminals.

[0279] Terminal-side embodiment

[0280] The terminal is responsible for visualizing the analysis data received from the server and presenting it to the user in an understandable manner. As specific hardware, smartphones and tablets are used. For visualization, libraries such as D3.js and Three.js are used to display the movements of players and highlights of competitions in 2D or 3D. Through this visualization, users can grasp the performance of players in real time. As the software to be used, it is conceivable to use front-end frameworks such as Vue.js and React.js.

[0281] Embodiments on the user side

[0282] The user can operate the information provided through the interface on the terminal and gain a deep understanding of the competition. For example, the user can select a specific player and examine their movements and past performance data. Also, after the competition, the user can view the automatically generated strategic improvement report and review the tactics. Through this series of operations, the viewer's understanding and experience of the competition are significantly improved. An example of a specific prompt sentence is "Use generative AI to analyze the performance of a specific player during the competition in real time and display the visualization results on the terminal."

[0283] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0284] Step 1:

[0285] The server collects data during the competition in real time. The input is raw data from data collection devices such as sensors and cameras. The server receives this data, converts it into a structured data format (e.g., JSON), and stores it in a database.

[0286] Step 2:

[0287] The server collects data and analyzes it using an AI model. The input is the structured data stored in Step 1. The server passes this data to an AI model built with TensorFlow or PyTorch to generate strategic insights. The output is an analysis of strategies and player performance based on the current state of the competition.

[0288] Step 3:

[0289] The server sends the analysis results to the terminal. The input is the analysis results obtained in step 2. The server sends data to the terminal using WebSocket or REST API to send the analysis results in real time. The output is a data stream containing the analysis results.

[0290] Step 4:

[0291] The terminal visualizes the analysis results it receives. The input is the data stream sent in step 3. The terminal uses a visualization library such as D3.js or Three.js to display this data in 2D or 3D. The output is visualized competition information, which the user can view through the interface.

[0292] Step 5:

[0293] The user interacts with the visualized information. The input is the information visualized in step 4. Through the terminal interface, the user can examine detailed performance data and past trends of specific players. The output is a detailed analysis result based on the specific information selected by the user.

[0294] Step 6:

[0295] After the competition ends, the server automatically generates a strategy improvement report. The input consists of the entire competition data history and prompt messages. The server uses a generation AI model to automatically generate improvement points in report format. The output is a strategy improvement report that shows points that should be improved for the next competition.

[0296] 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.

[0297] This invention provides a more personalized match viewing experience by combining an emotion engine that recognizes user emotions with an existing real-time data analysis system. The following details the embodiments of the invention from three perspectives: server, terminal, and user.

[0298] Server-side embodiment

[0299] The server collects real-time data during the match and analyzes it using a generative AI model, as well as collecting user emotion data. This emotion data includes changes in the user's facial expressions and voice, captured through the device's camera and microphone. The server analyzes this emotion data with an emotion engine and provides the user with commentary on the match based on the results. For example, if the emotion engine determines that the user is highly excited, it adjusts the analysis results to provide more detailed commentary, especially on the most intense battle zones.

[0300] Terminal-side embodiment

[0301] The device combines match data and analysis results received from the server, utilizes feedback from the emotion engine, and presents individually optimized information to the user using visualization tools. For example, if the user shows excitement over a successful shot, the device can provide a more detailed explanation of the importance of that shot and the player's backstory.

[0302] User-side embodiment

[0303] Users can receive real-time game data and commentary information provided through the terminal and enjoy content tailored to their emotions. For example, during the progress of the game, if a user shows an emotion indicating stress, the system displays information highlighting the positive aspects of the game to adjust the viewing experience. Furthermore, after the game, a detailed strategic improvement report including the changes in the user's emotions can be provided and used for watching the next game.

[0304] In this way, the present invention utilizes an emotion engine to implement flexible game commentary and strategic information provision according to the user's emotional state, constructing an epoch-making system that can provide a deeper experience and satisfaction to individual viewers.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The server collects in real time the movements, shooting success or failure, and ball position information obtained from the sensors on the court and the players at the start of the game. This data is sent to the generative AI model and used to analyze the strategies and player performances during the game.

[0308] Step 2:

[0309] The server collects the changes in the user's expressions and voices as emotion data through the camera and microphone of the terminal. The emotion engine analyzes these data and determines the user's current emotional state in real time. For example, if there are signs of excitement in the user's voice, the engine recognizes that emotion as "excitement".

[0310] Step 3:

[0311] Based on the emotional state, the server customizes the analyzed game data. For example, when the user is excited, the system emphasizes the highlights of the game and the explanations of important plays.

[0312] Step 4:

[0313] The terminal receives customized match data and sentiment analysis results sent from the server, and presents the information in an interactive user interface using visualization tools. This makes it easier for users to visually understand the flow of the match and the movements of the players.

[0314] Step 5:

[0315] Users can watch match commentary provided through their devices in real time and obtain information that matches their interests and feelings. If a user shows particular interest in a specific play, the device will display detailed commentary and player profiles related to that play.

[0316] Step 6:

[0317] After the match ends, the server automatically generates a detailed strategy improvement report based on the data collected during the match and the history of emotional changes. This report includes key moments from the match and insights based on the user's emotional shifts, which users can use to inform their next match viewing and team strategy planning.

[0318] (Example 2)

[0319] 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".

[0320] Traditional match viewing systems have struggled to provide viewers with information tailored to their individual emotions and the dynamic flow of the game, failing to deliver a highly satisfying and personalized experience. Furthermore, the lack of strategic information based on real-time emotional states made it difficult to maintain viewers' interest.

[0321] 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.

[0322] This invention includes a server that collects information related to the match in real time and analyzes the data using a generative AI model; a server that combines the analyzed information with collected sentiment-based insights to generate strategic information about the match; and a server that provides individually optimized commentary information to spectators using visualization technology. This enables spectators to receive personalized commentary information based on their own sentiments in real time.

[0323] "Real-time" refers to processing or using data almost simultaneously with the event occurring.

[0324] "Information related to the match" includes various data related to the detailed content of the match, such as the progress of the match, the score, player movements, and statistical data.

[0325] A "generative AI model" refers to an artificial intelligence algorithm that uses previously trained data to understand new data and perform predictions and analyses.

[0326] "Emotion-based insights" refer to knowledge gained by analyzing a user's emotional state and generating analysis and information based on that analysis.

[0327] "Visualization technology" refers to techniques that visually represent data and information to make them easier for users to understand.

[0328] "Personalized explanatory information" refers to explanatory content that is customized to the individual user's preferences and circumstances.

[0329] Modes for carrying out the invention

[0330] Server Embodiment

[0331] The server is connected to an external database that supplies match data, serving as a means of collecting match-related information in real time. This allows for the acquisition of details such as scores and player performance. The server utilizes a "generative AI model" to analyze match data and user emotion data transmitted from the terminal. Emotion data is acquired through facial recognition technology using the terminal's camera and voice analysis technology using the microphone. An "artificial intelligence algorithm" is used for this analysis to identify the user's emotional state in real time. For example, if an increase in the user's heart rate is detected during a tense match, the emotion analysis engine analyzes this state as excitement.

[0332] Terminal embodiment

[0333] The terminal displays information using "visualization technology" based on analyzed data received from the server. This utilizes "data visualization software" that dynamically updates information in accordance with the progress of the match. For example, visualization technology can be used to highlight important moments during the match or to present detailed information about specific players. The terminal customizes the interface according to the user's emotional state and adjusts the priority of the information displayed.

[0334] User Embodiment

[0335] Users can enjoy watching the game based on match data and personalized commentary presented through their device. After the game ends, the system provides users with "strategy improvement information" based on their emotional changes. This includes advice to further enhance the viewing experience and can be applied to future viewings. A specific example of a prompt is, "Generate detailed commentary on highlight scenes for users who are in an excited state."

[0336] This invention allows users to take advantage of richly personalized information when watching matches in real time, resulting in a deeper level of satisfaction.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The server collects match-related information in real time from external data providers. The input is formatted match data, including scores, player statistics, and match progress. The server stores this data in a database, preparing it for later analysis. During this process, data processing is performed to supplement and standardize any incomplete match data.

[0340] Step 2:

[0341] The device collects user emotion data using its built-in camera and microphone. The input consists of user facial expression data and voice data. This data is preprocessed using image processing algorithms and voice analysis techniques before being sent to the server. For example, face detection and landmark detection are performed to extract facial features, and voice recognition and feature extraction are performed on voice data.

[0342] Step 3:

[0343] The server uses a generative AI model to analyze collected match data and user emotion data. The inputs are match data and emotion data. The AI ​​model estimates the user's emotional state in real time and generates necessary insights according to the flow of the match. In this process, emotional features are extracted, analyzed, and emotion determination is made using a classification model. The output is the analysis result regarding the user's emotional state.

[0344] Step 4:

[0345] The server generates prompts based on the analysis results and uses them to generate personalized commentary information tailored to the user's emotional state. The input consists of AI analysis results and predefined prompts. The AI ​​generates commentary that matches the user's specific emotions, creating match commentary content. The output is custom commentary information based on those emotions.

[0346] Step 5:

[0347] The terminal presents custom explanatory information received from the server to the user using visualization technology. The input is the custom explanatory information. Based on this information, the terminal uses visualization tools to display the information on the screen in the most optimal format. For example, for highly excited users, important scenes can be displayed as large video clips. The output is an interactive explanatory interface presented to the user.

[0348] Step 6:

[0349] Users watch the match based on information provided through their device, and after the match ends, the system generates and provides an improvement report based on the user's emotional changes. The input is all emotional data collected during the match. The system analyzes this data and generates strategic improvement information to enhance the viewing experience. The output is an emotional strategic improvement report for the next match.

[0350] (Application Example 2)

[0351] 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."

[0352] Existing event viewing systems lack the ability to provide information that takes into account the individual emotional states of spectators, limiting personalized viewing experiences. Therefore, there is a need for a system that can flexibly deliver content tailored to users' interests and emotions.

[0353] 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.

[0354] In this invention, the server includes means for acquiring event data in real time and analyzing the generated information, means for generating strategic knowledge based on the analyzed information, and means for providing personalized content according to the emotions, equipped with an emotion engine that recognizes and analyzes the emotions of the user. This enables a more engaging and personalized viewing experience for the user.

[0355] "Acquiring event data in real time" is a technology that processes information without delay by promptly obtaining event information at the current moment.

[0356] "Analyzing generated information" refers to the process of conducting a detailed analysis of acquired data in order to derive useful knowledge and insights from it.

[0357] "Generating strategic knowledge" means creating planned information to support decision-making based on analytical results.

[0358] "Providing explanatory information using visualization devices" refers to the technique of using visual tools to deliver event-related information in a way that is easy for the audience to understand.

[0359] "Providing automatically generated strategy improvement reports after the event ends" means that after an event has concluded, it analyzes its content and automatically creates and provides a report document that includes improvement measures.

[0360] An "emotion engine that recognizes and analyzes user emotions" is a technology that uses camera and audio data to analyze changes in the user's facial expressions and voice, and to determine their emotional state.

[0361] "Providing personalized content based on emotions" means selecting and delivering the most suitable information and entertainment based on analyzed emotional data of the user.

[0362] This invention aims to provide personalized content that responds to the user's emotional state and is based on a system that acquires and analyzes event data and user emotional data in real time.

[0363] The server collects data in real time as the event begins. This data includes information about the event's progress and important events. The server also integrates emotion data sent from client terminals. Emotion data is acquired using the terminal's camera and microphone, facial expressions are analyzed using OpenCV, and emotions are determined from the audio using TensorFlow. The information generated through this process reflects the user's psychological state.

[0364] The terminal provides users with customized explanatory information through a visualization device, based on analysis results received from the server. This information is accompanied by data delivered via Flask, and the terminal delivers it to the user using visual and auditory means. This allows users to receive detailed explanations and related stories about specific moments during the event, enriching their viewing experience.

[0365] To give a concrete example, if a user shows a high level of excitement while watching a sporting event, the system will provide content that includes a video detailing the highlights of that moment, as well as backstories of the players. A generative AI model assists with this task, supplementing the information with highly relevant prompts. By utilizing prompts such as, "The user's current emotion is excitement. Please suggest specific commentary and stories to provide during the match," the system can deliver content that is more accurately optimized for the user.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The server acquires event data in real time as the event begins. The input consists of various event data feeds, stored in a database. The server analyzes the acquired data and extracts important information. This analysis uses algorithms to identify the progress of the match and key moments. The output is immediately usable strategic insights.

[0369] Step 2:

[0370] The terminal receives the results analyzed from the server. The data the terminal receives as input contains important information about the event. In addition, the terminal acquires user emotion data through the camera and microphone. This emotion data is used as input to analyze facial expressions using OpenCV and to estimate emotions from speech using a TensorFlow model. The output is the user's real-time emotional state.

[0371] Step 3:

[0372] The server re-analyzes the user's sentiment data sent from the terminal. This input sentiment data is then used by a generating AI model to create prompts that determine what information is appropriate. The output is instructions regarding the most relevant and customized content for the user.

[0373] Step 4:

[0374] The terminal, based on instructions from the server, presents personalized information to the user through a visualization device. Input consists of strategic knowledge and content tailored to the user's emotional state, which the terminal integrates to form the displayed data. Output is personalized video and audio content for the viewer. Specific actions include playing match highlights or displaying player backstories.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] This invention will be described from three perspectives: server, terminal, and user.

[0392] Server-side embodiment

[0393] The server collects real-time data during a match and runs a generative AI model to analyze the data. As a specific example, during a basketball game, it collects data on player movement, shot success / failure, and ball position. Using this collected data, the AI ​​analyzes which strategies are effective in the current situation. This can generate useful strategic insights for players and coaches. This information is then transmitted to a terminal and used in the next step.

[0394] Terminal-side embodiment

[0395] The terminal receives analytical data from the server and presents the information to the user in an easy-to-understand manner using visualization tools. For example, it can visualize how a particular player moves during a match, or which shooting positions are effective, using a 2D or 3D court display. It can also display player performance using a heatmap to provide a more detailed understanding of tactics. As a result, spectators can gain a deep, real-time understanding of the flow of the game and the players' movements.

[0396] User-side embodiment

[0397] Users can interactively manipulate the information provided through the interface on their devices, gaining a deeper understanding of the match. For example, users can click on a specific player to view that player's past performance data and trends. After the match, they can also view automatically generated strategy improvement reports to understand what needs to be improved for the next game. This enhances the viewing experience and deepens the strategic capabilities of the team.

[0398] As described above, this invention functions through the cooperation of the server, terminal, and user, providing a practical and effective solution for deepening understanding of the game.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The server collects real-time situational data from sensors placed on the court and devices worn by players as soon as the match starts. This includes player location and movement, ball position, and the success or failure of each shot.

[0402] Step 2:

[0403] The server instantly saves the collected match data to a database and sends it to the generated AI model. The AI ​​model uses this data to analyze the performance of each player and the strategic movements of the team. For example, it analyzes the movement patterns of players and calculates the success rate at specific positions.

[0404] Step 3:

[0405] The server converts the analysis results into a format that can be visualized. This includes heatmaps, shot charts, and player movement tracking information. This data is then immediately delivered to the terminal.

[0406] Step 4:

[0407] The terminal receives data transmitted from the server and visualizes it in real time through the user interface. The terminal renders the flow of the game using 3D or 2D models, visually displaying the players' movements and performance.

[0408] Step 5:

[0409] Users interact with visualized information through their device's interface to view specific events during a match or detailed performance data for individual players. For example, a user can tap on a specific player to see their past performance trends in matches.

[0410] Step 6:

[0411] After the match ends, the server integrates all match data and creates an automatically generated strategy improvement report. This report summarizes the team's strengths and areas for improvement, and includes specific strategic suggestions for the next match. Users can view this report on their devices and use it to improve their team strategy.

[0412] (Example 1)

[0413] 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."

[0414] In modern sports matches, the information available to spectators and teams in real time is limited, making it difficult to deeply understand the flow of the game and the movements of the players. Therefore, it is difficult to formulate effective strategies during a match or identify areas for improvement afterward. A solution to this problem is needed.

[0415] 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.

[0416] In this invention, the server includes means for collecting match data in real time using an input device and analyzing the data, means for generating strategic insights into the match based on the analyzed data, and means for providing explanatory information to spectators using a visualization device. This allows spectators and teams to gain deep insights in real time during the match and to clearly identify areas for improvement after the match.

[0417] An "input device" is a device used to collect match data in real time.

[0418] An "information processing device" is a device that analyzes data collected in real time and generates analysis results.

[0419] A "terminal device" is a device that receives analysis results provided by an information processing device and presents the information to the user.

[0420] A "visualization device" is a device that presents analyzed data to the user visually, and is capable of displaying data in 2D or 3D.

[0421] A "computational device" is a device used to automatically generate a strategy improvement report after the end of a match.

[0422] A "user" is a person who manipulates and uses the data and information presented from a device.

[0423] This invention is a system that functions through the cooperation of a server, terminal, and user, and aims to provide real-time data analysis of matches and strategic insights.

[0424] Server-side embodiment

[0425] The server uses sensors and video equipment to collect data in real time during the match. This data includes player movements, shooting success rates, and ball position information. The collected data is processed using software such as Python and Node.js, and preprocessing such as data cleaning and normalization is performed. Next, analysis is performed using generative AI models with AI frameworks such as TensorFlow and PyTorch. The analysis results are generated in the form of effective strategy suggestions and sent to the terminal in JSON format.

[0426] Terminal-side embodiment

[0427] The terminal receives analysis results from the server and visualizes the information using visualization tools such as "D3.js" and "Three.js". Specifically, it reproduces player movements and positioning in 2D or 3D and effectively visualizes them using heatmaps and court displays. This allows users to gain strategic insights and understand the flow of the game and player movements in detail.

[0428] User-side embodiment

[0429] Users can access multiple functions through the device interface. For example, clicking on a specific player allows them to view detailed performance data about that player. After a match, they can refer to an automatically generated strategy improvement report to identify areas for improvement for the next match. Furthermore, by entering a prompt such as, "What is the optimal strategy for the current basketball game?" into the AI ​​model, users can receive strategic suggestions tailored to the game situation.

[0430] Through the embodiments described above, this invention can provide spectators and teams with deep insights and strategic advantages in the game.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The server uses sensors and video equipment to collect real-time data on player movements, shooting success rates, ball position, and more during the match. This collected data is input to the server as raw data. Based on this raw data input, the server performs noise reduction and data cleaning processes to generate an analyzable dataset. This dataset is then used for analysis by an AI model in the next step.

[0434] Step 2:

[0435] The server uses the dataset obtained in Step 1 to perform analysis using a generative AI model. This analysis utilizes "TensorFlow" and "PyTorch". The input dataset is fed into the AI ​​model, which generates output such as player performance and strategic insights. This output includes optimal strategies and predictions based on the current state of the match. The generated analysis results are converted into "JSON" format, ready for use in subsequent steps.

[0436] Step 3:

[0437] The server sends parsed data in JSON format to the terminal. This transmission is done via a REST API or WebSocket, allowing the terminal to receive the parsing results in real time. The input here is the parsed data, and the output is the completion of data transmission to the terminal.

[0438] Step 4:

[0439] The terminal visualizes the analysis data received from the server using "D3.js" or "Three.js". The input for this visualization process is the analysis data obtained from the server, and the output is 2D or 3D graphics of the match situation or player movements presented to the user. This allows the user to understand the actual match movements more intuitively.

[0440] Step 5:

[0441] Users interact with visualized data through an interface on their device. By selecting specific players or situations, they can view detailed data and historical trends for those players. The input to this interaction is the user's interaction, and the output is the displayed information. Users can also directly question the AI ​​model by entering prompts.

[0442] Step 6:

[0443] After the match ends, the server automatically generates a strategy improvement report using computing power. This generation utilizes analysis data from the match and improvement algorithms. The input is all the match data, and the output is a report containing strategic improvements for the next match. Users can review this report and use it to help develop their team's strategy.

[0444] (Application Example 1)

[0445] 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."

[0446] The aim is to address the challenge faced by participants and spectators in local sports events and community leagues, where it is difficult for them to grasp competition performance in real time and gain strategic insights. Currently, real-time data collection and analysis are insufficient, resulting in a limited understanding of the competition and the viewing experience.

[0447] 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.

[0448] In this invention, the server includes means for collecting and analyzing competition data in real time, means for generating strategic insights based on the analyzed data, and means for providing commentary to participants and spectators through visualization means. This allows spectators to gain a more detailed understanding of the athletes' performance and competition highlights in real time through visualized information.

[0449] "Real-time collection of competition data" means instantly acquiring various types of information using sensors and cameras while a match or competition is in progress.

[0450] "Analysis of generated data" refers to the process of using AI and algorithms to derive meaningful information based on collected data.

[0451] "Generating strategic insights" is a method of finding effective tactics and strategies based on analyzed competition data.

[0452] "Visualization methods" refer to technologies and tools used to display analyzed data and insights in an easily understandable way.

[0453] "Providing explanatory content through visualization means" refers to the act of explaining a competition in an easily understandable way for spectators and participants using visualized information.

[0454] "Automatic generation of strategy improvement reports" refers to the process of automatically creating a report in a report format after a match, using a generation AI model or similar tool, to identify areas for strategic improvement based on the data obtained.

[0455] For this invention to be implemented, three main components—a server, a terminal, and a user—work together.

[0456] Server-side embodiment

[0457] The server collects data in real time during the competition and runs a generative AI model on the accumulated data for analysis. Specifically, high-performance servers (e.g., AWS EC2) are used. For software, PyTorch or TensorFlow are utilized for data processing and analysis. This analysis derives strategic insights into the current state of the competition. This allows the server to provide real-time insights to terminals.

[0458] Terminal-side embodiment

[0459] The terminal will visualize the analysis data received from the server and present it to the user in an easy-to-understand manner. Specific hardware such as smartphones and tablets will be used. Libraries such as D3.js and Three.js will be used for visualization, displaying player movements and competition highlights in 2D or 3D. This visualization will allow users to understand player performance in real time. Frontend frameworks such as Vue.js and React.js are also being considered as software to use.

[0460] User-side embodiment

[0461] Users can manipulate information provided through the interface on their device to gain a deeper understanding of the sport. For example, a user can select a specific player and examine their movements and past performance data. After the match, they can also view automatically generated strategy improvement reports and review their tactics. This series of operations dramatically improves the spectator's understanding and experience of the sport. An example of a specific prompt would be, "Please use the generating AI to analyze the performance of a specific player during the match in real time and display the visualization results on the device."

[0462] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0463] Step 1:

[0464] The server collects data in real time during the competition. The input is raw data from data acquisition devices such as sensors and cameras. The server receives this data, converts it to a structured data format (e.g., JSON), and stores it in the database.

[0465] Step 2:

[0466] The server collects data and analyzes it using an AI model. The input is the structured data stored in Step 1. The server passes this data to an AI model built with TensorFlow or PyTorch to generate strategic insights. The output is an analysis of strategies and player performance based on the current state of the competition.

[0467] Step 3:

[0468] The server sends the analysis results to the terminal. The input is the analysis results obtained in step 2. The server sends data to the terminal using WebSocket or REST API to send the analysis results in real time. The output is a data stream containing the analysis results.

[0469] Step 4:

[0470] The terminal visualizes the analysis results it receives. The input is the data stream sent in step 3. The terminal uses a visualization library such as D3.js or Three.js to display this data in 2D or 3D. The output is visualized competition information, which the user can view through the interface.

[0471] Step 5:

[0472] The user interacts with the visualized information. The input is the information visualized in step 4. Through the terminal interface, the user can examine detailed performance data and past trends of specific players. The output is a detailed analysis result based on the specific information selected by the user.

[0473] Step 6:

[0474] After the competition ends, the server automatically generates a strategy improvement report. The input consists of the entire competition data history and prompt messages. The server uses a generation AI model to automatically generate improvement points in report format. The output is a strategy improvement report that shows points that should be improved for the next competition.

[0475] 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.

[0476] This invention provides a more personalized match viewing experience by combining an emotion engine that recognizes user emotions with an existing real-time data analysis system. The following details the embodiments of the invention from three perspectives: server, terminal, and user.

[0477] Server-side embodiment

[0478] The server collects real-time data during the match and analyzes it using a generative AI model, as well as collecting user emotion data. This emotion data includes changes in the user's facial expressions and voice, captured through the device's camera and microphone. The server analyzes this emotion data with an emotion engine and provides the user with commentary on the match based on the results. For example, if the emotion engine determines that the user is highly excited, it adjusts the analysis results to provide more detailed commentary, especially on the most intense battle zones.

[0479] Terminal-side embodiment

[0480] The device combines match data and analysis results received from the server, utilizes feedback from the emotion engine, and presents individually optimized information to the user using visualization tools. For example, if the user shows excitement over a successful shot, the device can provide a more detailed explanation of the importance of that shot and the player's backstory.

[0481] User-side embodiment

[0482] Users can receive match data and commentary in real time through their devices and enjoy content tailored to their emotions. For example, if a user expresses stress during a match, the system will display information that highlights the positive aspects of the match, adjusting the viewing experience. Furthermore, after the match, a detailed strategy improvement report, including changes in the user's emotions, is provided to help them prepare for future matches.

[0483] Thus, the present invention utilizes an emotion engine to provide flexible match commentary and strategic information tailored to the user's emotional state, thereby creating a groundbreaking system that can offer individual spectators a deeper experience and greater satisfaction.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The server collects real-time information on movement, shot success / failure, and ball position from sensors on the court and from players at the start of the match. This data is sent to a generative AI model and used to analyze in-game strategies and player performance.

[0487] Step 2:

[0488] The server collects emotional data from the user's facial expressions and changes in voice through the device's camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state in real time. For example, if there are signs of excitement in the user's voice, the engine recognizes that emotion as "excitement."

[0489] Step 3:

[0490] Based on the user's emotional state, the server customizes the analyzed match data. For example, if the user is excited, the system will emphasize match highlights and commentary on important plays.

[0491] Step 4:

[0492] The terminal receives customized match data and sentiment analysis results sent from the server, and presents the information in an interactive user interface using visualization tools. This makes it easier for users to visually understand the flow of the match and the movements of the players.

[0493] Step 5:

[0494] Users can watch match commentary provided through their devices in real time and obtain information that matches their interests and feelings. If a user shows particular interest in a specific play, the device will display detailed commentary and player profiles related to that play.

[0495] Step 6:

[0496] After the match ends, the server automatically generates a detailed strategy improvement report based on the data collected during the match and the history of emotional changes. This report includes key moments from the match and insights based on the user's emotional shifts, which users can use to inform their next match viewing and team strategy planning.

[0497] (Example 2)

[0498] 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."

[0499] Traditional match viewing systems have struggled to provide viewers with information tailored to their individual emotions and the dynamic flow of the game, failing to deliver a highly satisfying and personalized experience. Furthermore, the lack of strategic information based on real-time emotional states made it difficult to maintain viewers' interest.

[0500] 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.

[0501] This invention includes a server that collects information related to the match in real time and analyzes the data using a generative AI model; a server that combines the analyzed information with collected sentiment-based insights to generate strategic information about the match; and a server that provides individually optimized commentary information to spectators using visualization technology. This enables spectators to receive personalized commentary information based on their own sentiments in real time.

[0502] "Real-time" refers to processing or using data almost simultaneously with the event occurring.

[0503] "Information related to the match" includes various data related to the detailed content of the match, such as the progress of the match, the score, player movements, and statistical data.

[0504] A "generative AI model" refers to an artificial intelligence algorithm that uses previously trained data to understand new data and perform predictions and analyses.

[0505] "Emotion-based insights" refer to knowledge gained by analyzing a user's emotional state and generating analysis and information based on that analysis.

[0506] "Visualization technology" refers to techniques that visually represent data and information to make them easier for users to understand.

[0507] "Personalized explanatory information" refers to explanatory content that is customized to the individual user's preferences and circumstances.

[0508] Modes for carrying out the invention

[0509] Server Embodiment

[0510] The server is connected to an external database that supplies match data, serving as a means of collecting match-related information in real time. This allows for the acquisition of details such as scores and player performance. The server utilizes a "generative AI model" to analyze match data and user emotion data transmitted from the terminal. Emotion data is acquired through facial recognition technology using the terminal's camera and voice analysis technology using the microphone. An "artificial intelligence algorithm" is used for this analysis to identify the user's emotional state in real time. For example, if an increase in the user's heart rate is detected during a tense match, the emotion analysis engine analyzes this state as excitement.

[0511] Terminal embodiment

[0512] The terminal displays information using "visualization technology" based on analyzed data received from the server. This utilizes "data visualization software" that dynamically updates information in accordance with the progress of the match. For example, visualization technology can be used to highlight important moments during the match or to present detailed information about specific players. The terminal customizes the interface according to the user's emotional state and adjusts the priority of the information displayed.

[0513] User Embodiment

[0514] Users can enjoy watching the game based on match data and personalized commentary presented through their device. After the game ends, the system provides users with "strategy improvement information" based on their emotional changes. This includes advice to further enhance the viewing experience and can be applied to future viewings. A specific example of a prompt is, "Generate detailed commentary on highlight scenes for users who are in an excited state."

[0515] This invention allows users to take advantage of richly personalized information when watching matches in real time, resulting in a deeper level of satisfaction.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] The server collects match-related information in real time from external data providers. The input is formatted match data, including scores, player statistics, and match progress. The server stores this data in a database, preparing it for later analysis. During this process, data processing is performed to supplement and standardize any incomplete match data.

[0519] Step 2:

[0520] The device collects user emotion data using its built-in camera and microphone. The input consists of user facial expression data and voice data. This data is preprocessed using image processing algorithms and voice analysis techniques before being sent to the server. For example, face detection and landmark detection are performed to extract facial features, and voice recognition and feature extraction are performed on voice data.

[0521] Step 3:

[0522] The server uses a generative AI model to analyze collected match data and user emotion data. The inputs are match data and emotion data. The AI ​​model estimates the user's emotional state in real time and generates necessary insights according to the flow of the match. In this process, emotional features are extracted, analyzed, and emotion determination is made using a classification model. The output is the analysis result regarding the user's emotional state.

[0523] Step 4:

[0524] The server generates prompts based on the analysis results and uses them to generate personalized commentary information tailored to the user's emotional state. The input consists of AI analysis results and predefined prompts. The AI ​​generates commentary that matches the user's specific emotions, creating match commentary content. The output is custom commentary information based on those emotions.

[0525] Step 5:

[0526] The terminal presents custom explanatory information received from the server to the user using visualization technology. The input is the custom explanatory information. Based on this information, the terminal uses visualization tools to display the information on the screen in the most optimal format. For example, for highly excited users, important scenes can be displayed as large video clips. The output is an interactive explanatory interface presented to the user.

[0527] Step 6:

[0528] Users watch the match based on information provided through their device, and after the match ends, the system generates and provides an improvement report based on the user's emotional changes. The input is all emotional data collected during the match. The system analyzes this data and generates strategic improvement information to enhance the viewing experience. The output is an emotional strategic improvement report for the next match.

[0529] (Application Example 2)

[0530] 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."

[0531] Existing event viewing systems lack the ability to provide information that takes into account the individual emotional states of spectators, limiting personalized viewing experiences. Therefore, there is a need for a system that can flexibly deliver content tailored to users' interests and emotions.

[0532] 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.

[0533] In this invention, the server includes means for acquiring event data in real time and analyzing the generated information, means for generating strategic knowledge based on the analyzed information, and means for providing personalized content according to the emotions, equipped with an emotion engine that recognizes and analyzes the emotions of the user. This enables a more engaging and personalized viewing experience for the user.

[0534] "Acquiring event data in real time" is a technology that processes information without delay by promptly obtaining event information at the current moment.

[0535] "Analyzing generated information" refers to the process of conducting a detailed analysis of acquired data in order to derive useful knowledge and insights from it.

[0536] "Generating strategic knowledge" means creating planned information to support decision-making based on analytical results.

[0537] "Providing explanatory information using visualization devices" refers to the technique of using visual tools to deliver event-related information in a way that is easy for the audience to understand.

[0538] "Providing automatically generated strategy improvement reports after the event ends" means that after an event has concluded, it analyzes its content and automatically creates and provides a report document that includes improvement measures.

[0539] An "emotion engine that recognizes and analyzes user emotions" is a technology that uses camera and audio data to analyze changes in the user's facial expressions and voice, and to determine their emotional state.

[0540] "Providing personalized content based on emotions" means selecting and delivering the most suitable information and entertainment based on analyzed emotional data of the user.

[0541] This invention aims to provide personalized content that responds to the user's emotional state and is based on a system that acquires and analyzes event data and user emotional data in real time.

[0542] The server collects data in real time as the event begins. This data includes information about the event's progress and important events. The server also integrates emotion data sent from client terminals. Emotion data is acquired using the terminal's camera and microphone, facial expressions are analyzed using OpenCV, and emotions are determined from the audio using TensorFlow. The information generated through this process reflects the user's psychological state.

[0543] The terminal provides users with customized explanatory information through a visualization device, based on analysis results received from the server. This information is accompanied by data delivered via Flask, and the terminal delivers it to the user using visual and auditory means. This allows users to receive detailed explanations and related stories about specific moments during the event, enriching their viewing experience.

[0544] To give a concrete example, if a user shows a high level of excitement while watching a sporting event, the system will provide content that includes a video detailing the highlights of that moment, as well as backstories of the players. A generative AI model assists with this task, supplementing the information with highly relevant prompts. By utilizing prompts such as, "The user's current emotion is excitement. Please suggest specific commentary and stories to provide during the match," the system can deliver content that is more accurately optimized for the user.

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The server acquires event data in real time as the event begins. The input consists of various event data feeds, stored in a database. The server analyzes the acquired data and extracts important information. This analysis uses algorithms to identify the progress of the match and key moments. The output is immediately usable strategic insights.

[0548] Step 2:

[0549] The terminal receives the results analyzed from the server. The data the terminal receives as input contains important information about the event. In addition, the terminal acquires user emotion data through the camera and microphone. This emotion data is used as input to analyze facial expressions using OpenCV and to estimate emotions from speech using a TensorFlow model. The output is the user's real-time emotional state.

[0550] Step 3:

[0551] The server re-analyzes the user's sentiment data sent from the terminal. This input sentiment data is then used by a generating AI model to create prompts that determine what information is appropriate. The output is instructions regarding the most relevant and customized content for the user.

[0552] Step 4:

[0553] The terminal, based on instructions from the server, presents personalized information to the user through a visualization device. Input consists of strategic knowledge and content tailored to the user's emotional state, which the terminal integrates to form the displayed data. Output is personalized video and audio content for the viewer. Specific actions include playing match highlights or displaying player backstories.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] [Fourth Embodiment]

[0558] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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".

[0571] This invention will be described from three perspectives: server, terminal, and user.

[0572] Server-side embodiment

[0573] The server collects real-time data during a match and runs a generative AI model to analyze the data. As a specific example, during a basketball game, it collects data on player movement, shot success / failure, and ball position. Using this collected data, the AI ​​analyzes which strategies are effective in the current situation. This can generate useful strategic insights for players and coaches. This information is then transmitted to a terminal and used in the next step.

[0574] Terminal-side embodiment

[0575] The terminal receives analytical data from the server and presents the information to the user in an easy-to-understand manner using visualization tools. For example, it can visualize how a particular player moves during a match, or which shooting positions are effective, using a 2D or 3D court display. It can also display player performance using a heatmap to provide a more detailed understanding of tactics. As a result, spectators can gain a deep, real-time understanding of the flow of the game and the players' movements.

[0576] User-side embodiment

[0577] Users can interactively manipulate the information provided through the interface on their devices, gaining a deeper understanding of the match. For example, users can click on a specific player to view that player's past performance data and trends. After the match, they can also view automatically generated strategy improvement reports to understand what needs to be improved for the next game. This enhances the viewing experience and deepens the strategic capabilities of the team.

[0578] As described above, this invention functions through the cooperation of the server, terminal, and user, providing a practical and effective solution for deepening understanding of the game.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server collects real-time situational data from sensors placed on the court and devices worn by players as soon as the match starts. This includes player location and movement, ball position, and the success or failure of each shot.

[0582] Step 2:

[0583] The server instantly saves the collected match data to a database and sends it to the generated AI model. The AI ​​model uses this data to analyze the performance of each player and the strategic movements of the team. For example, it analyzes the movement patterns of players and calculates the success rate at specific positions.

[0584] Step 3:

[0585] The server converts the analysis results into a format that can be visualized. This includes heatmaps, shot charts, and player movement tracking information. This data is then immediately delivered to the terminal.

[0586] Step 4:

[0587] The terminal receives data transmitted from the server and visualizes it in real time through the user interface. The terminal renders the flow of the game using 3D or 2D models, visually displaying the players' movements and performance.

[0588] Step 5:

[0589] Users interact with visualized information through their device's interface to view specific events during a match or detailed performance data for individual players. For example, a user can tap on a specific player to see their past performance trends in matches.

[0590] Step 6:

[0591] After the match ends, the server integrates all match data and creates an automatically generated strategy improvement report. This report summarizes the team's strengths and areas for improvement, and includes specific strategic suggestions for the next match. Users can view this report on their devices and use it to improve their team strategy.

[0592] (Example 1)

[0593] 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".

[0594] In modern sports matches, the information available to spectators and teams in real time is limited, making it difficult to deeply understand the flow of the game and the movements of the players. Therefore, it is difficult to formulate effective strategies during a match or identify areas for improvement afterward. A solution to this problem is needed.

[0595] 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.

[0596] In this invention, the server includes means for collecting match data in real time using an input device and analyzing the data, means for generating strategic insights into the match based on the analyzed data, and means for providing explanatory information to spectators using a visualization device. This allows spectators and teams to gain deep insights in real time during the match and to clearly identify areas for improvement after the match.

[0597] An "input device" is a device used to collect match data in real time.

[0598] An "information processing device" is a device that analyzes data collected in real time and generates analysis results.

[0599] A "terminal device" is a device that receives analysis results provided by an information processing device and presents the information to the user.

[0600] A "visualization device" is a device that presents analyzed data to the user visually, and is capable of displaying data in 2D or 3D.

[0601] A "computational device" is a device used to automatically generate a strategy improvement report after the end of a match.

[0602] A "user" is a person who manipulates and uses the data and information presented from a device.

[0603] This invention is a system that functions through the cooperation of a server, terminal, and user, and aims to provide real-time data analysis of matches and strategic insights.

[0604] Server-side embodiment

[0605] The server uses sensors and video equipment to collect data in real time during the match. This data includes player movements, shooting success rates, and ball position information. The collected data is processed using software such as Python and Node.js, and preprocessing such as data cleaning and normalization is performed. Next, analysis is performed using generative AI models with AI frameworks such as TensorFlow and PyTorch. The analysis results are generated in the form of effective strategy suggestions and sent to the terminal in JSON format.

[0606] Terminal-side embodiment

[0607] The terminal receives analysis results from the server and visualizes the information using visualization tools such as "D3.js" and "Three.js". Specifically, it reproduces player movements and positioning in 2D or 3D and effectively visualizes them using heatmaps and court displays. This allows users to gain strategic insights and understand the flow of the game and player movements in detail.

[0608] User-side embodiment

[0609] Users can access multiple functions through the device interface. For example, clicking on a specific player allows them to view detailed performance data about that player. After a match, they can refer to an automatically generated strategy improvement report to identify areas for improvement for the next match. Furthermore, by entering a prompt such as, "What is the optimal strategy for the current basketball game?" into the AI ​​model, users can receive strategic suggestions tailored to the game situation.

[0610] Through the embodiments described above, this invention can provide spectators and teams with deep insights and strategic advantages in the game.

[0611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0612] Step 1:

[0613] The server uses sensors and video equipment to collect real-time data on player movements, shooting success rates, ball position, and more during the match. This collected data is input to the server as raw data. Based on this raw data input, the server performs noise reduction and data cleaning processes to generate an analyzable dataset. This dataset is then used for analysis by an AI model in the next step.

[0614] Step 2:

[0615] The server uses the dataset obtained in Step 1 to perform analysis using a generative AI model. This analysis utilizes "TensorFlow" and "PyTorch". The input dataset is fed into the AI ​​model, which generates output such as player performance and strategic insights. This output includes optimal strategies and predictions based on the current state of the match. The generated analysis results are converted into "JSON" format, ready for use in subsequent steps.

[0616] Step 3:

[0617] The server sends parsed data in JSON format to the terminal. This transmission is done via a REST API or WebSocket, allowing the terminal to receive the parsing results in real time. The input here is the parsed data, and the output is the completion of data transmission to the terminal.

[0618] Step 4:

[0619] The terminal visualizes the analysis data received from the server using "D3.js" or "Three.js". The input for this visualization process is the analysis data obtained from the server, and the output is 2D or 3D graphics of the match situation or player movements presented to the user. This allows the user to understand the actual match movements more intuitively.

[0620] Step 5:

[0621] Users interact with visualized data through an interface on their device. By selecting specific players or situations, they can view detailed data and historical trends for those players. The input to this interaction is the user's interaction, and the output is the displayed information. Users can also directly question the AI ​​model by entering prompts.

[0622] Step 6:

[0623] After the match ends, the server automatically generates a strategy improvement report using computing power. This generation utilizes analysis data from the match and improvement algorithms. The input is all the match data, and the output is a report containing strategic improvements for the next match. Users can review this report and use it to help develop their team's strategy.

[0624] (Application Example 1)

[0625] 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".

[0626] The aim is to address the challenge faced by participants and spectators in local sports events and community leagues, where it is difficult for them to grasp competition performance in real time and gain strategic insights. Currently, real-time data collection and analysis are insufficient, resulting in a limited understanding of the competition and the viewing experience.

[0627] 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.

[0628] In this invention, the server includes means for collecting and analyzing competition data in real time, means for generating strategic insights based on the analyzed data, and means for providing commentary to participants and spectators through visualization means. This allows spectators to gain a more detailed understanding of the athletes' performance and competition highlights in real time through visualized information.

[0629] "Real-time collection of competition data" means instantly acquiring various types of information using sensors and cameras while a match or competition is in progress.

[0630] "Analysis of generated data" refers to the process of using AI and algorithms to derive meaningful information based on collected data.

[0631] "Generating strategic insights" is a method of finding effective tactics and strategies based on analyzed competition data.

[0632] "Visualization methods" refer to technologies and tools used to display analyzed data and insights in an easily understandable way.

[0633] "Providing explanatory content through visualization means" refers to the act of explaining a competition in an easily understandable way for spectators and participants using visualized information.

[0634] "Automatic generation of strategy improvement reports" refers to the process of automatically creating a report in a report format after a match, using a generation AI model or similar tool, to identify areas for strategic improvement based on the data obtained.

[0635] For this invention to be implemented, three main components—a server, a terminal, and a user—work together.

[0636] Server-side embodiment

[0637] The server collects data in real time during the competition and runs a generative AI model on the accumulated data for analysis. Specifically, high-performance servers (e.g., AWS EC2) are used. For software, PyTorch or TensorFlow are utilized for data processing and analysis. This analysis derives strategic insights into the current state of the competition. This allows the server to provide real-time insights to terminals.

[0638] Terminal-side embodiment

[0639] The terminal will visualize the analysis data received from the server and present it to the user in an easy-to-understand manner. Specific hardware such as smartphones and tablets will be used. Libraries such as D3.js and Three.js will be used for visualization, displaying player movements and competition highlights in 2D or 3D. This visualization will allow users to understand player performance in real time. Frontend frameworks such as Vue.js and React.js are also being considered as software to use.

[0640] User-side embodiment

[0641] Users can manipulate information provided through the interface on their device to gain a deeper understanding of the sport. For example, a user can select a specific player and examine their movements and past performance data. After the match, they can also view automatically generated strategy improvement reports and review their tactics. This series of operations dramatically improves the spectator's understanding and experience of the sport. An example of a specific prompt would be, "Please use the generating AI to analyze the performance of a specific player during the match in real time and display the visualization results on the device."

[0642] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0643] Step 1:

[0644] The server collects data in real time during the competition. The input is raw data from data acquisition devices such as sensors and cameras. The server receives this data, converts it to a structured data format (e.g., JSON), and stores it in the database.

[0645] Step 2:

[0646] The server collects data and analyzes it using an AI model. The input is the structured data stored in Step 1. The server passes this data to an AI model built with TensorFlow or PyTorch to generate strategic insights. The output is an analysis of strategies and player performance based on the current state of the competition.

[0647] Step 3:

[0648] The server sends the analysis results to the terminal. The input is the analysis results obtained in step 2. The server sends data to the terminal using WebSocket or REST API to send the analysis results in real time. The output is a data stream containing the analysis results.

[0649] Step 4:

[0650] The terminal visualizes the analysis results it receives. The input is the data stream sent in step 3. The terminal uses a visualization library such as D3.js or Three.js to display this data in 2D or 3D. The output is visualized competition information, which the user can view through the interface.

[0651] Step 5:

[0652] The user interacts with the visualized information. The input is the information visualized in step 4. Through the terminal interface, the user can examine detailed performance data and past trends of specific players. The output is a detailed analysis result based on the specific information selected by the user.

[0653] Step 6:

[0654] After the competition ends, the server automatically generates a strategy improvement report. The input consists of the entire competition data history and prompt messages. The server uses a generation AI model to automatically generate improvement points in report format. The output is a strategy improvement report that shows points that should be improved for the next competition.

[0655] 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.

[0656] This invention provides a more personalized match viewing experience by combining an emotion engine that recognizes user emotions with an existing real-time data analysis system. The following details the embodiments of the invention from three perspectives: server, terminal, and user.

[0657] Server-side embodiment

[0658] The server collects real-time data during the match and analyzes it using a generative AI model, as well as collecting user emotion data. This emotion data includes changes in the user's facial expressions and voice, captured through the device's camera and microphone. The server analyzes this emotion data with an emotion engine and provides the user with commentary on the match based on the results. For example, if the emotion engine determines that the user is highly excited, it adjusts the analysis results to provide more detailed commentary, especially on the most intense battle zones.

[0659] Terminal-side embodiment

[0660] The device combines match data and analysis results received from the server, utilizes feedback from the emotion engine, and presents individually optimized information to the user using visualization tools. For example, if the user shows excitement over a successful shot, the device can provide a more detailed explanation of the importance of that shot and the player's backstory.

[0661] User-side embodiment

[0662] Users can receive match data and commentary in real time through their devices and enjoy content tailored to their emotions. For example, if a user expresses stress during a match, the system will display information that highlights the positive aspects of the match, adjusting the viewing experience. Furthermore, after the match, a detailed strategy improvement report, including changes in the user's emotions, is provided to help them prepare for future matches.

[0663] Thus, the present invention utilizes an emotion engine to provide flexible match commentary and strategic information tailored to the user's emotional state, thereby creating a groundbreaking system that can offer individual spectators a deeper experience and greater satisfaction.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The server collects real-time information on movement, shot success / failure, and ball position from sensors on the court and from players at the start of the match. This data is sent to a generative AI model and used to analyze in-game strategies and player performance.

[0667] Step 2:

[0668] The server collects emotional data from the user's facial expressions and changes in voice through the device's camera and microphone. The emotion engine analyzes this data to determine the user's current emotional state in real time. For example, if there are signs of excitement in the user's voice, the engine recognizes that emotion as "excitement."

[0669] Step 3:

[0670] Based on the user's emotional state, the server customizes the analyzed match data. For example, if the user is excited, the system will emphasize match highlights and commentary on important plays.

[0671] Step 4:

[0672] The terminal receives customized match data and sentiment analysis results sent from the server, and presents the information in an interactive user interface using visualization tools. This makes it easier for users to visually understand the flow of the match and the movements of the players.

[0673] Step 5:

[0674] Users can watch match commentary provided through their devices in real time and obtain information that matches their interests and feelings. If a user shows particular interest in a specific play, the device will display detailed commentary and player profiles related to that play.

[0675] Step 6:

[0676] After the match ends, the server automatically generates a detailed strategy improvement report based on the data collected during the match and the history of emotional changes. This report includes key moments from the match and insights based on the user's emotional shifts, which users can use to inform their next match viewing and team strategy planning.

[0677] (Example 2)

[0678] 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".

[0679] Traditional match viewing systems have struggled to provide viewers with information tailored to their individual emotions and the dynamic flow of the game, failing to deliver a highly satisfying and personalized experience. Furthermore, the lack of strategic information based on real-time emotional states made it difficult to maintain viewers' interest.

[0680] 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.

[0681] This invention includes a server that collects information related to the match in real time and analyzes the data using a generative AI model; a server that combines the analyzed information with collected sentiment-based insights to generate strategic information about the match; and a server that provides individually optimized commentary information to spectators using visualization technology. This enables spectators to receive personalized commentary information based on their own sentiments in real time.

[0682] "Real-time" refers to processing or using data almost simultaneously with the event occurring.

[0683] "Information related to the match" includes various data related to the detailed content of the match, such as the progress of the match, the score, player movements, and statistical data.

[0684] A "generative AI model" refers to an artificial intelligence algorithm that uses previously trained data to understand new data and perform predictions and analyses.

[0685] "Emotion-based insights" refer to knowledge gained by analyzing a user's emotional state and generating analysis and information based on that analysis.

[0686] "Visualization technology" refers to techniques that visually represent data and information to make them easier for users to understand.

[0687] "Personalized explanatory information" refers to explanatory content that is customized to the individual user's preferences and circumstances.

[0688] Modes for carrying out the invention

[0689] Server Embodiment

[0690] The server is connected to an external database that supplies match data, serving as a means of collecting match-related information in real time. This allows for the acquisition of details such as scores and player performance. The server utilizes a "generative AI model" to analyze match data and user emotion data transmitted from the terminal. Emotion data is acquired through facial recognition technology using the terminal's camera and voice analysis technology using the microphone. An "artificial intelligence algorithm" is used for this analysis to identify the user's emotional state in real time. For example, if an increase in the user's heart rate is detected during a tense match, the emotion analysis engine analyzes this state as excitement.

[0691] Terminal embodiment

[0692] The terminal displays information using "visualization technology" based on analyzed data received from the server. This utilizes "data visualization software" that dynamically updates information in accordance with the progress of the match. For example, visualization technology can be used to highlight important moments during the match or to present detailed information about specific players. The terminal customizes the interface according to the user's emotional state and adjusts the priority of the information displayed.

[0693] User Embodiment

[0694] Users can enjoy watching the game based on match data and personalized commentary presented through their device. After the game ends, the system provides users with "strategy improvement information" based on their emotional changes. This includes advice to further enhance the viewing experience and can be applied to future viewings. A specific example of a prompt is, "Generate detailed commentary on highlight scenes for users who are in an excited state."

[0695] This invention allows users to take advantage of richly personalized information when watching matches in real time, resulting in a deeper level of satisfaction.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The server collects match-related information in real time from external data providers. The input is formatted match data, including scores, player statistics, and match progress. The server stores this data in a database, preparing it for later analysis. During this process, data processing is performed to supplement and standardize any incomplete match data.

[0699] Step 2:

[0700] The device collects user emotion data using its built-in camera and microphone. The input consists of user facial expression data and voice data. This data is preprocessed using image processing algorithms and voice analysis techniques before being sent to the server. For example, face detection and landmark detection are performed to extract facial features, and voice recognition and feature extraction are performed on voice data.

[0701] Step 3:

[0702] The server uses a generative AI model to analyze collected match data and user emotion data. The inputs are match data and emotion data. The AI ​​model estimates the user's emotional state in real time and generates necessary insights according to the flow of the match. In this process, emotional features are extracted, analyzed, and emotion determination is made using a classification model. The output is the analysis result regarding the user's emotional state.

[0703] Step 4:

[0704] The server generates prompts based on the analysis results and uses them to generate personalized commentary information tailored to the user's emotional state. The input consists of AI analysis results and predefined prompts. The AI ​​generates commentary that matches the user's specific emotions, creating match commentary content. The output is custom commentary information based on those emotions.

[0705] Step 5:

[0706] The terminal presents custom explanatory information received from the server to the user using visualization technology. The input is the custom explanatory information. Based on this information, the terminal uses visualization tools to display the information on the screen in the most optimal format. For example, for highly excited users, important scenes can be displayed as large video clips. The output is an interactive explanatory interface presented to the user.

[0707] Step 6:

[0708] Users watch the match based on information provided through their device, and after the match ends, the system generates and provides an improvement report based on the user's emotional changes. The input is all emotional data collected during the match. The system analyzes this data and generates strategic improvement information to enhance the viewing experience. The output is an emotional strategic improvement report for the next match.

[0709] (Application Example 2)

[0710] 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".

[0711] Existing event viewing systems lack the ability to provide information that takes into account the individual emotional states of spectators, limiting personalized viewing experiences. Therefore, there is a need for a system that can flexibly deliver content tailored to users' interests and emotions.

[0712] 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.

[0713] In this invention, the server includes means for acquiring event data in real time and analyzing the generated information, means for generating strategic knowledge based on the analyzed information, and means for providing personalized content according to the emotions, equipped with an emotion engine that recognizes and analyzes the emotions of the user. This enables a more engaging and personalized viewing experience for the user.

[0714] "Acquiring event data in real time" is a technology that processes information without delay by promptly obtaining event information at the current moment.

[0715] "Analyzing generated information" refers to the process of conducting a detailed analysis of acquired data in order to derive useful knowledge and insights from it.

[0716] "Generating strategic knowledge" means creating planned information to support decision-making based on analytical results.

[0717] "Providing explanatory information using visualization devices" refers to the technique of using visual tools to deliver event-related information in a way that is easy for the audience to understand.

[0718] "Providing automatically generated strategy improvement reports after the event ends" means that after an event has concluded, it analyzes its content and automatically creates and provides a report document that includes improvement measures.

[0719] An "emotion engine that recognizes and analyzes user emotions" is a technology that uses camera and audio data to analyze changes in the user's facial expressions and voice, and to determine their emotional state.

[0720] "Providing personalized content based on emotions" means selecting and delivering the most suitable information and entertainment based on analyzed emotional data of the user.

[0721] This invention aims to provide personalized content that responds to the user's emotional state and is based on a system that acquires and analyzes event data and user emotional data in real time.

[0722] The server collects data in real time as the event begins. This data includes information about the event's progress and important events. The server also integrates emotion data sent from client terminals. Emotion data is acquired using the terminal's camera and microphone, facial expressions are analyzed using OpenCV, and emotions are determined from the audio using TensorFlow. The information generated through this process reflects the user's psychological state.

[0723] The terminal provides users with customized explanatory information through a visualization device, based on analysis results received from the server. This information is accompanied by data delivered via Flask, and the terminal delivers it to the user using visual and auditory means. This allows users to receive detailed explanations and related stories about specific moments during the event, enriching their viewing experience.

[0724] To give a concrete example, if a user shows a high level of excitement while watching a sporting event, the system will provide content that includes a video detailing the highlights of that moment, as well as backstories of the players. A generative AI model assists with this task, supplementing the information with highly relevant prompts. By utilizing prompts such as, "The user's current emotion is excitement. Please suggest specific commentary and stories to provide during the match," the system can deliver content that is more accurately optimized for the user.

[0725] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0726] Step 1:

[0727] The server acquires event data in real time as the event begins. The input consists of various event data feeds, stored in a database. The server analyzes the acquired data and extracts important information. This analysis uses algorithms to identify the progress of the match and key moments. The output is immediately usable strategic insights.

[0728] Step 2:

[0729] The terminal receives the results analyzed from the server. The data the terminal receives as input contains important information about the event. In addition, the terminal acquires user emotion data through the camera and microphone. This emotion data is used as input to analyze facial expressions using OpenCV and to estimate emotions from speech using a TensorFlow model. The output is the user's real-time emotional state.

[0730] Step 3:

[0731] The server re-analyzes the user's sentiment data sent from the terminal. This input sentiment data is then used by a generating AI model to create prompts that determine what information is appropriate. The output is instructions regarding the most relevant and customized content for the user.

[0732] Step 4:

[0733] The terminal, based on instructions from the server, presents personalized information to the user through a visualization device. Input consists of strategic knowledge and content tailored to the user's emotional state, which the terminal integrates to form the displayed data. Output is personalized video and audio content for the viewer. Specific actions include playing match highlights or displaying player backstories.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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."

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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 to be incorporated by reference.

[0755] The following is further disclosed regarding the embodiments described above.

[0756] (Claim 1)

[0757] A means of collecting match data in real time and analyzing the generated data,

[0758] A means of generating strategic insights into the game based on the analyzed data,

[0759] A means of providing explanatory information to spectators using visualization tools,

[0760] A means of providing an automatically generated strategy improvement report after the end of the match,

[0761] A system that includes this.

[0762] (Claim 2)

[0763] The system according to claim 1, wherein the server collects match data in real time and provides the results of the analysis to the terminal.

[0764] (Claim 3)

[0765] The system according to claim 1, wherein the terminal uses a visualization tool to present analyzed strategic information to the user.

[0766] "Example 1"

[0767] (Claim 1)

[0768] A means of collecting match data in real time using an input device and analyzing that data,

[0769] A means of generating strategic insights into the game based on analyzed data,

[0770] A means of providing explanatory information to spectators using a visualization device,

[0771] A means of providing a strategy improvement report automatically generated after the end of a match via a computing device,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, wherein the information processing device collects match data in real time and provides the results of the analysis to the terminal device.

[0775] (Claim 3)

[0776] The system according to claim 1, wherein the terminal device uses a visualization device to present analyzed strategic information to the user.

[0777] "Application Example 1"

[0778] (Claim 1)

[0779] A means of collecting competition data in real time and analyzing the generated data,

[0780] A means of generating strategic insights into the sport based on analyzed data,

[0781] A means of providing commentary to spectators using visualization tools,

[0782] A means of presenting live data and competition highlights related to local events,

[0783] A means of providing an automatically generated strategy improvement report after the end of the competition,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein a computer collects competition data in real time and provides the results of the analysis to an information terminal.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein an information terminal uses visualization means to present analyzed strategic insights to the user.

[0789] "Example 2 of combining an emotion engine"

[0790] (Claim 1)

[0791] A method for collecting match-related information in real time and analyzing the data using a generative AI model,

[0792] A means of generating strategic information for a match by combining analyzed information with insights based on collected emotions,

[0793] A means of providing individually optimized commentary information to spectators using visualization technology,

[0794] A means of providing information for strategic improvement based on changes in user emotions after the end of a match,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, wherein the information processing device collects match-related information and emotional information in real time and provides the results of the analysis to the display device.

[0798] (Claim 3)

[0799] The system according to claim 1, wherein the display device uses visualization technology to present to the user strategic information based on analyzed emotions.

[0800] "Application example 2 when combining with an emotional engine"

[0801] (Claim 1)

[0802] A means of acquiring event data in real time and analyzing the generated information,

[0803] A means of generating strategic knowledge based on analyzed information,

[0804] Means for providing explanatory information to users using a visualization device,

[0805] A means of providing automatically generated strategy improvement reports after the end,

[0806] A means of providing personalized content based on emotions, equipped with an emotion engine that recognizes and analyzes the user's emotions,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the server acquires event data in real time and provides the results of the analysis to the terminal.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein the terminal uses a visualization device to present analyzed strategic information to the user. [Explanation of Symbols]

[0812] 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 of collecting match data in real time and analyzing the generated data, A means of generating strategic insights into the game based on the analyzed data, A means of providing explanatory information to spectators using visualization tools, A means of providing an automatically generated strategy improvement report after the end of the match, A system that includes this.

2. The system according to claim 1, wherein the server collects match data in real time and provides the results of the analysis to the terminal.

3. The system according to claim 1, wherein the terminal uses a visualization tool to present analyzed strategic information to the user.

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