system

Abstract

We provide the system. [Solution] A means of analyzing dynamic data acquired during a match in real time, A means of visualizing the progress of the match based on the analyzed dynamic data, A means of automatically generating tactical explanations based on analyzed dynamic data, A method for automatically generating a match analysis report after the match has ended, A means of analyzing customer behavior data in real time and automatically generating recommendations for purchased items, A means of personalizing and visualizing purchasing information based on customer behavior data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Basketball game spectators and team-related personnel spend a lot of time and effort in understanding game tactics and post-game analysis, and efficient and effective tactical understanding and analysis are required. To solve this problem, a system that can understand the game situation in real time and quickly provide detailed analysis for subsequent training and strategy formulation is needed.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that analyzes dynamic data acquired during a match in real time, visualizes the progress of the match based on the analyzed dynamic data, and automatically generates tactical commentary. As a result, match spectators can deepen their understanding of tactics, and team officials can quickly and efficiently formulate their next strategy using the detailed match analysis report that is automatically generated after the match.

[0006] "Dynamic data acquired during a match" refers to information collected during the course of a match, and includes player positioning, ball movement, scoring, and foul information.

[0007] "Methods for real-time analysis" refer to technologies and algorithms that process data immediately during a match and identify and evaluate information on the spot.

[0008] "Means of visualization" refers to methods and techniques for displaying data and information in an easy-to-understand format, such as diagrams and graphs.

[0009] "Methods for automatically generating tactical explanations" refers to technologies and systems that automatically create detailed explanations of player and team tactics based on the match situation and the data obtained.

[0010] "Methods for automatically generating match analysis reports" refer to technologies and methods that automatically create detailed reports based on data accumulated after a match, including an overview of the match as a whole, individual performance, and areas for tactical improvement. [Brief explanation of the drawing]

[0011] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system for basketball games that acquires and analyzes dynamic data such as the movement of players and the ball in real time during the game, thereby providing more easily understandable game commentary for spectators and detailed analysis useful for coaches and players to review the game. An example of this system is shown below.

[0033] The system continuously collects massive amounts of data from cameras and sensors during a match and performs real-time analysis on a server. The server monitors the progress of the match and generates visualizations and text-based match commentary based on the analysis results. Machine learning algorithms are used for analysis, including tactical detection and player performance evaluation. The server sends these analysis results to a terminal, which displays them to the user in real time.

[0034] After the match ends, the server re-analyzes the accumulated data and automatically generates an analysis report of the entire match. The report includes a team tactical evaluation, an analysis of individual player abilities, and areas for future improvement. Coaches and team personnel can view this report and use it to inform their next match or training plans.

[0035] As a concrete example, during a match, the server detects player A's movements as they break through the defense and analyzes the correlation with other players at that moment, identifying that a pick-and-roll tactic was used. Based on this, the terminal provides the user with real-time commentary such as "Player A is executing a pick-and-roll." After the match ends, a detailed report is generated and provided to the user, including player A's movements and the success rate of coordinated plays.

[0036] Thus, the present invention provides information that supports understanding of matches and contributes to strategic improvement through real-time analysis, visualization, and commentary generation.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server acquires real-time dynamic data on the movements of players and the ball from cameras and sensors installed at the match venue. This includes player position coordinates, ball speed and direction, score status, and foul information.

[0040] Step 2:

[0041] The server inputs the acquired dynamic data into an AI algorithm to analyze players' movement patterns and the progress of the game. This analysis allows for the recognition of specific tactics (e.g., pick-and-roll) and the evaluation of individual player movements and the overall team performance.

[0042] Step 3:

[0043] The server converts the analysis results into visualized data and sends it to the terminal. This visualized data includes player movement paths, ball trajectories, and heatmaps of shooting success rates.

[0044] Step 4:

[0045] The terminal displays the match status in real time on the user interface based on the received visualization data. Meanwhile, the server generates text commentary according to the progress of the match, and the terminal conveys this to the user.

[0046] Step 5:

[0047] After the match ends, the server re-analyzes all the accumulated data and automatically generates a detailed match analysis report. This report includes player performance evaluations, tactical efficiency analysis, and information on areas for improvement.

[0048] Step 6:

[0049] Users (coaches and team staff) can view reports generated by the server and use them to plan strategies for the next match and develop training plans.

[0050] (Example 1)

[0051] 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."

[0052] Traditional match commentary systems have presented challenges in providing detailed, real-time information to spectators and coaches, as it is difficult to understand the movements and tactics of players during a match. Furthermore, post-match analysis is often done manually, making it difficult to obtain efficient and objective feedback. This leads to delays in tactical improvements and rapid preparation for future competitions.

[0053] 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.

[0054] In this invention, the server includes means for analyzing dynamic data acquired during a competition in real time, means for visualizing the progress of the competition based on the analyzed dynamic data, and means for transmitting the analysis results to a terminal and displaying the information to the user. This makes it possible to understand the movements and tactics of the players during the match in real time and provide easy-to-understand match commentary to spectators. Furthermore, a detailed analysis report can be automatically generated after the competition ends, allowing for quick and objective feedback to be provided to relevant parties.

[0055] "Dynamic data" is a general term for information that represents the position, movement, and changes of athletes and objects during a competition.

[0056] "Real-time analysis" is the process of collecting data instantly while a competition is in progress and performing analysis on the spot.

[0057] "Visualization" refers to the graphical display of the progress of a competition based on analyzed data.

[0058] "Tactical commentary" refers to explaining strategies and intentions in sports through text and video, based on analyzed data.

[0059] An "analysis report" is a document automatically generated after a competition, containing a detailed evaluation and improvement suggestions based on data from the entire match.

[0060] A "terminal" is a device or apparatus that receives information transmitted from a server and displays it to the user.

[0061] To implement this invention, a server first installs cameras and sensors to track the movements of athletes and objects during a competition. This allows for the collection of dynamic data in real time. The server then uses machine learning libraries such as TENSORFLOW® and PyTorch to analyze the collected data and identify the athletes' movements and the tactics used.

[0062] The analyzed data is transformed graphically to visualize the progress of the match. Visual processing libraries such as OpenCV are used for this purpose. This visualized data is sent to the terminal in a way that is easy for the user to understand. Dedicated applications and interfaces allow users to view this information in real time.

[0063] Furthermore, the server utilizes a generation AI model based on the analysis results to automatically generate tactical explanations through prompt messages. For example, it can generate an explanation of "the background of the play in which player A broke through the defense and scored a decisive shot." This generated explanation is output based on the prompt message "Explain the impressive move player A made during the match and its impact on the entire team."

[0064] After the match ends, the server re-analyzes the accumulated data and automatically generates a detailed analysis report. This report is provided through a dedicated web portal and application so that coaches and team personnel can use it for future match planning and strategy development. The report clearly presents individual player data, team tactical evaluations, and areas for future improvement.

[0065] This enables the system to provide rapid and accurate information during matches, allowing spectators and coaches to deepen their understanding of tactics and provide a foundation for strategic improvement.

[0066] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0067] Step 1:

[0068] The server collects dynamic data from the competition in real time through cameras and sensors. Input data includes the position, velocity, and direction of athletes and objects. This data is fed in a time series and converted into basic coordinate data using a visual processing library such as OpenCV. Specifically, it analyzes the video from the cameras frame by frame to identify the position information of each athlete.

[0069] Step 2:

[0070] The server analyzes the acquired coordinate data using machine learning algorithms such as TensorFlow and PyTorch. Using the coordinate data as input, it extracts characteristic features of each player's movements. This enables the detection of tactical patterns and the evaluation of player performance. Specific actions include vector analysis of movement and calculation of velocity and acceleration. This process clarifies, for example, what kind of tactical movements player A is performing.

[0071] Step 3:

[0072] The server generates visualization data and tactical text explanations based on the analysis results. The input is the analysis results obtained in the previous step, and the generation AI model is used to create prompt sentences. The specific operation includes a process of converting the analysis results into easily understandable language using natural language processing techniques. Outputs include chart displays on a GUI and explanatory text information.

[0073] Step 4:

[0074] The server transmits the generated visualization data and explanatory information to the terminal in real time. Specifically, it transfers the data to the client device via the network and formats it for display. The output is presented as detailed match commentary that the user can actually view on the terminal.

[0075] Step 5:

[0076] The terminal displays the received information to the user through an appropriate interface. Input consists of explanatory data and visualizations sent from the server. Specific operations include the ability to arrange information visually and text-based using mobile apps or web browsers. As a result, users can accurately understand the flow of the game and obtain real-time information for decision-making.

[0077] Step 6:

[0078] After the match ends, the server re-analyzes all the accumulated data and generates a detailed analysis report. It uses all the data collected during the match as input and performs multivariate analysis. Specific actions include outputting performance indicators for individual players and the team as a whole. The resulting report will include successful tactics, individual player performance evaluations, and areas for improvement, which will be used for subsequent analysis.

[0079] (Application Example 1)

[0080] 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."

[0081] In analyzing customer behavior at basketball games and in virtual stores, there is a challenge in that real-time dynamic analysis and the resulting display information are not being provided in a way that is sufficiently useful to spectators, coaches, and customers. Furthermore, providing customers with a personalized purchasing experience is also difficult.

[0082] 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.

[0083] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for automatically generating tactical commentary based on the analyzed dynamic data, means for analyzing customer dynamic data in real time and automatically generating recommendations for purchases, and means for personalizing and visualizing purchase information based on customer dynamic data. As a result, match spectators and organizers can understand the progress and tactics of the match in more detail and intuitively, and customers will be provided with product information based on their interests, enabling a high-quality purchasing experience.

[0084] "Dynamic data" is a collection of information about the movement of objects or people that changes over time.

[0085] "Real-time analysis" is a process that analyzes data immediately as soon as it is acquired.

[0086] "Visualization" is a method of displaying analyzed data in a graphical format to make the information easier to understand intuitively.

[0087] "Tactical commentary" refers to information that interprets and explains the strategies and player movements during the course of a match.

[0088] An "analysis report" is a report that summarizes the results of a detailed analysis of data from matches and actions.

[0089] "Product recommendations" is a process that suggests appropriate products based on a customer's past behavior and real-time activity.

[0090] "Personalization" refers to adjusting information and services to suit the individual characteristics and preferences of each user.

[0091] The embodiments for carrying out the invention are as follows:

[0092] This system utilizes high-performance servers and multi-functional terminals to enable real-time data analysis and information provision. The servers use software libraries such as OpenCV and TensorFlow to acquire and analyze dynamic data during matches or in virtual stores. This analysis allows for the immediate generation of visual information about the match, enabling a detailed understanding of player movements and customer walking patterns.

[0093] The terminal uses devices such as smart glasses or head-mounted displays to instantly show users visualization data and explanatory information transmitted from the server. For example, as a user moves around a virtual store, relevant product information is customized and displayed based on their gaze direction and time spent in the area.

[0094] As a concrete example, if a user visits a virtual electronics store, their eye-tracking data is analyzed to identify their interest in new smart devices. This then causes relevant, up-to-date accessories and accessories to pop up in their field of view, assisting them in making a purchase decision.

[0095] This system requires massive processing power for dynamic data, but by utilizing cloud technology, smooth data processing and real-time information provision become possible.

[0096] An example of a prompt to input into the generating AI model is as follows: "In the virtual store, analyze the user's eye-tracking data in real time and identify products of interest. Based on those products, propose personalized offers."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server acquires dynamic data within matches and virtual stores. Location and gaze data of players and customers are collected via camera sensors embedded in smart glasses or head-mounted displays. This input data is organized chronologically and sent to the server.

[0100] Step 2:

[0101] The server preprocesses the acquired dynamic data using OpenCV. Unnecessary noise is removed from the image data, and the data is processed to accurately identify the positions of people and objects. This preprocessing prepares clear data for analysis.

[0102] Step 3:

[0103] The server uses TensorFlow to analyze dynamic data in real time. Specifically, it applies machine learning algorithms to perform data calculations to identify player movements and customer interests. This generates the necessary output for tactical analysis of matches and product recommendations.

[0104] Step 4:

[0105] The server generates visualization data and text explanations based on the analysis results. It performs data processing, converting information such as tactical explanations and recommended product lists into intuitive graphics and natural language. The generated information is presented in a format that is easy for viewers and customers to understand.

[0106] Step 5:

[0107] The device instantly displays visualized data and text explanations to the user. Information is presented in real time via smart glasses or head-mounted displays. This output allows users to stay informed about the progress of a match or efficiently receive information about products they are interested in.

[0108] 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.

[0109] This invention relates to a system that combines real-time analysis of dynamic data during a basketball game with an emotion engine that recognizes the user's emotions. This enables customized game commentary and visualization that takes into account the user's emotional state, providing a more immersive viewing experience. A specific embodiment of the system is shown below.

[0110] The system analyzes dynamic data collected by cameras and sensors at the match venue in real time on a server. This includes player location information, ball movement, scoring information, and foul information. The server uses AI algorithms to identify and evaluate the tactical aspects of player movements and the progress of the match.

[0111] Furthermore, the emotion engine analyzes the user's emotions in real time using facial recognition technology and biosensors. Emotion recognition data is used to identify whether the user is excited, calm, or experiencing moments of heightened emotion.

[0112] Based on the analysis results and sentiment data, the server generates customized visualizations and tactical explanations and sends them to the terminal. The terminal displays the real-time updated visualizations to the user and provides commentary tailored to their emotions. This allows the user to receive additional information and explanations about plays that evoked emotional responses, leading to a deeper understanding.

[0113] For example, if a user's emotion is recognized as "excitement," the server will generate commentary highlighting outstanding plays and tactical points of players related to that moment of excitement. After the match ends, the server automatically generates a detailed analysis report based on all the data, and the user's emotion data is also integrated into it. Users can view this report and use it for analysis to improve their next viewing experience and match strategy.

[0114] This embodiment provides more personalized commentary and visualization than conventional match viewing systems, enabling a dramatic improvement in the user experience.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] The server acquires real-time information on player locations, ball movement, scores, and fouls from cameras and sensors installed at the match venue.

[0118] Step 2:

[0119] The server inputs the acquired dynamic data into an AI algorithm to analyze tactics and player movements during the match. This allows it to determine whether specific tactics are being executed and to assess the performance of each player.

[0120] Step 3:

[0121] The emotion engine recognizes the user's emotions in real time. This uses facial expression analysis with a camera and biometric data from wearable devices.

[0122] Step 4:

[0123] The server integrates analyzed dynamic data with user emotion data to generate visualizations and explanations tailored to the user's emotions. For example, if the server determines that the user is excited, it will highlight match highlights and notable strategies related to that excitement.

[0124] Step 5:

[0125] The device displays real-time visualization data and commentary of the match, taking into account the user's emotions, on the user interface. This allows users to watch the match in a way that best matches their own emotional state.

[0126] Step 6:

[0127] After the match ends, the server comprehensively re-analyzes all the data and automatically generates a detailed match analysis report that takes into account user sentiment data. This report can be used to help with future match viewing and analysis.

[0128] Step 7:

[0129] Users can view analysis reports provided by the server to review matches and gain insights for future viewing.

[0130] (Example 2)

[0131] 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".

[0132] In recent years, there has been a demand for spectators to experience matches with greater immersion. However, traditional systems only provide simple visualizations of player and ball movements and the overall situation of the game, making it difficult to offer a personalized experience that takes into account the emotional state of individual spectators.

[0133] 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.

[0134] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for recognizing and analyzing the user's emotional state in real time, and means for generating and providing customized commentary and visualization data according to the user's emotional state. This makes it possible to provide personalized information that takes into account the emotional state of the spectators during match viewing, thereby realizing an immersive viewing experience.

[0135] "Dynamic data" refers to information about the position and movement of players, the ball, and other elements of the game during a match.

[0136] "Real-time analysis" refers to a process that processes data and generates results almost instantly, with virtually no delay, from data acquisition to completion.

[0137] "Visualized data" refers to graphical information used to display analyzed information in a visually easy-to-understand manner.

[0138] "Tactical commentary" refers to an evaluation of tactical aspects related to the progress of a match and the movements of the players, and the explanations and analyses generated based on that evaluation.

[0139] "User emotional state" refers to information about the user's emotions, including the type and degree of emotion recognized by facial recognition technology and biosensors.

[0140] "Customized commentary" refers to commentary information about a match that has been adjusted according to the user's individual circumstances and preferences.

[0141] An "analysis report" refers to a document that aggregates all data from the match and includes a detailed and comprehensive explanation that integrates user sentiment data.

[0142] This invention is a system that provides customized information in real time, taking into account the user's emotions, in order to improve the viewing experience during a match. This system mainly consists of various components, including a server, terminals, and users.

[0143] The server collects dynamic data from various sensors and cameras installed at the match venue. This includes location information, object movement information, and information on scores and fouls. The collected data is analyzed in real time using AI algorithms to evaluate the flow of the match and tactics. The server also understands the user's emotional state through an emotion recognition engine and generates optimal commentary based on the current situation. This process includes the use of machine learning models and technology to recognize emotions from the user's facial expressions.

[0144] The terminal displays customized commentary and visualization data transmitted from the server to the user via the screen. The terminal displays real-time updated information and has the function of providing commentary tailored to the user's emotions. In this way, the user can gain a deeper understanding of the match.

[0145] Users must install a dedicated app on their device beforehand and set up an emotion recognition profile. This profile serves as the foundational data for the emotion engine, which identifies the user's emotions at various points during viewing and provides appropriate information.

[0146] For example, if a user's emotion is recognized as "excitement," the server will generate commentary that highlights outstanding plays or tactical points of players related to that moment of excitement. An example of a prompt to the generating AI model might be, "Generate customized commentary based on the user's emotion recognition data and match dynamics data. Highlight special moments when the user is excited." In this way, the entire system works together to create a more immersive viewing experience.

[0147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0148] Step 1:

[0149] The server collects dynamic data from cameras and sensors installed at the match venue. Inputs include player location information, ball movement, scores, and foul information. This data is transmitted to the server in real time and stored in a database. Specifically, the server receives location coordinates and dynamic event data, which are updated every second, and immediately prepares them for storage and analysis.

[0150] Step 2:

[0151] The server analyzes the collected dynamic data using an AI algorithm. The input is the dynamic data collected in step 1, and the output is the tactical flow of the match and the players' movement patterns. Specifically, it uses a pattern recognition algorithm to compare and analyze the players' movements and perform a process to identify the progress of the match and tactics.

[0152] Step 3:

[0153] The server performs emotion recognition using facial images and biosensor data transmitted from the user's terminal. The input is data related to the user's emotions, and the output is the user's emotional state (e.g., excitement, calmness). Specifically, the server applies a facial recognition algorithm that identifies the emotion engine and performs a process to analyze the user's emotions.

[0154] Step 4:

[0155] The server generates customized commentary and visualization data based on the analysis results and emotion recognition data. The input is the output from steps 2 and 3, and the output is the commentary content displayed on the user's terminal. Specifically, a prompt sentence (e.g., "What plays should be emphasized when excited?") is input to the generating AI model, and the AI ​​generates appropriate content.

[0156] Step 5:

[0157] The terminal displays the explanations and visualization data sent from the server on its screen and provides them to the user. The input is the explanation content generated in step 4, and the output is the presentation of information to the user. Specifically, the application on the terminal visualizes the explanation data and presents the real-time updated information to the user in an easy-to-understand manner.

[0158] Step 6:

[0159] After the match ends, the server integrates all the data and automatically generates a detailed analysis report. The input consists of all match data and sentiment data, and the output is an analysis report that users can refer to later. Specifically, the server-side analysis tool summarizes the entire match and creates a detailed summary of the match, including the user's emotional reactions.

[0160] (Application Example 2)

[0161] 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 device 14 will be referred to as the "terminal."

[0162] This invention aims to solve the problem of not being able to provide more personalized content based on the user's emotional state when watching sports. In conventional game viewing, only general commentary is provided, making it difficult for users to gain a deeper understanding or sense of immersion in moments that particularly interest them.

[0163] 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.

[0164] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for recognizing the user's emotional state and generating customized commentary accordingly, and means for automatically generating an analysis report of the match after the match and integrating the user's emotional data. This enables the provision of personalized sports commentary and visualizations that respond to the user's emotions in real time, resulting in a more immersive viewing experience.

[0165] "Dynamic data" refers to information collected in real time during a match, including the location of participants, information on the movement of objects, score-related information, and information on rule violations.

[0166] "Real-time analysis methods" refer to technical techniques that process and analyze dynamic data acquired during a match immediately.

[0167] "Methods for visualizing the progress of a match" refer to technologies that use analyzed dynamic data to visually display the development and flow of a match to the user.

[0168] "Methods for automatically generating tactical commentary" refers to technology that mechanically generates tactical points and play commentary in a match based on analyzed dynamic data.

[0169] "Means for recognizing a user's emotional state" refers to technologies that analyze a user's facial expressions and biometric information to identify changes in their emotions.

[0170] "Means for generating customized explanations" refers to technologies that individually create information and explanations that are highly relevant to the user's emotional state at that time.

[0171] "Methods for automatically generating analysis reports" refer to technologies that mechanically create an overall analysis of a match based on data collected after the match has ended.

[0172] "Methods for integrating emotional data" refers to technologies that incorporate and record the user's emotional state while watching a game into the analysis data of the match.

[0173] This system enhances the sports viewing experience by analyzing dynamic data acquired during a match and the user's emotional state in real time, and providing customized commentary based on that analysis. The server collects data from the match venue and analyzes information such as the location of players and objects, information related to scores, and information on rule violations. This analysis can be performed using the Python-based image processing library OpenCV and the data analysis library NumPy. The server also uses facial recognition technology and biosensors to understand the user's emotional state. Emotional data is processed using TensorFlow and PyTorch to determine whether the user is excited, calm, or in other states.

[0174] Based on these analysis results, the server generates customized visualizations and explanations tailored to the user's emotions. For example, if a user is excited about a particular player's performance, information about that player's past performance and tactical aspects will be highlighted. This allows the user to gain a deeper understanding of the match.

[0175] After the match ends, the server integrates all the collected data and automatically generates an analysis report, which also incorporates sentiment data. This allows users to gain strategic insights for their next match viewing.

[0176] For example, if a user becomes very excited about a particular player's crucial scoring play during a match, they will be provided with tactical commentary related to that play and video clips of similar plays from the past. This functionality is achieved by utilizing generative AI models. An example of a prompt would be: "Analyze the user's emotions in real time and identify moments of excitement during the match. At those moments, highlight and provide background information on the players and tactical points the user wants to know about."

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The server acquires real-time dynamic data from cameras and sensors installed at the match venue. It receives player location information, object movement information, and score and foul information as input, and analyzes this data. As output, it generates basic data for understanding player movements and the progress of the match. In this process, it efficiently processes large amounts of data using data processing libraries such as Python's NumPy and Pandas.

[0180] Step 2:

[0181] The server visualizes the progress of the match based on the acquired dynamic data. It converts the input data into a format that is easy to plot and generates visualization data to be displayed on the user's terminal in real time using Matplotlib or Plotly. The output is a graphical representation that allows the user to intuitively understand the flow of the match.

[0182] Step 3:

[0183] The server recognizes the user's emotions using facial recognition technology and data from biosensors. It receives camera footage and biometric data as input and performs facial expression analysis using OpenCV and Dlib. The output is data that identifies the user's emotional state (e.g., excited, calm, interested, etc.).

[0184] Step 4:

[0185] The server automatically generates customized explanations tailored to the user's emotions, based on emotion recognition data and dynamic data. Using a generation AI model, it converts the input emotion data into prompts and generates additional information and tactical explanations. As output, explanations and visualizations tailored to the user's level of excitement are prepared and delivered to the terminal in real time.

[0186] Step 5:

[0187] After the match ends, the server integrates all dynamic and sentiment data and automatically generates a detailed analysis report. It uses data accumulated throughout all matches as input and performs analysis using Python and R. The output is a comprehensive report that provides users with strategic insights for the next match. This report is sent to the user's device for viewing.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] This invention provides a system for basketball games that acquires and analyzes dynamic data such as the movement of players and the ball in real time during the game, thereby providing more easily understandable game commentary for spectators and detailed analysis useful for coaches and players to review the game. An example of this system is shown below.

[0205] The system continuously collects massive amounts of data from cameras and sensors during a match and performs real-time analysis on a server. The server monitors the progress of the match and generates visualizations and text-based match commentary based on the analysis results. Machine learning algorithms are used for analysis, including tactical detection and player performance evaluation. The server sends these analysis results to a terminal, which displays them to the user in real time.

[0206] After the match ends, the server re-analyzes the accumulated data and automatically generates an analysis report of the entire match. The report includes a team tactical evaluation, an analysis of individual player abilities, and areas for future improvement. Coaches and team personnel can view this report and use it to inform their next match or training plans.

[0207] As a concrete example, during a match, the server detects player A's movements as they break through the defense and analyzes the correlation with other players at that moment, identifying that a pick-and-roll tactic was used. Based on this, the terminal provides the user with real-time commentary such as "Player A is executing a pick-and-roll." After the match ends, a detailed report is generated and provided to the user, including player A's movements and the success rate of coordinated plays.

[0208] Thus, the present invention provides information that supports understanding of matches and contributes to strategic improvement through real-time analysis, visualization, and commentary generation.

[0209] The following describes the processing flow.

[0210] Step 1:

[0211] The server acquires real-time dynamic data on the movements of players and the ball from cameras and sensors installed at the match venue. This includes player position coordinates, ball speed and direction, score status, and foul information.

[0212] Step 2:

[0213] The server inputs the acquired dynamic data into an AI algorithm to analyze players' movement patterns and the progress of the game. This analysis allows for the recognition of specific tactics (e.g., pick-and-roll) and the evaluation of individual player movements and the overall team performance.

[0214] Step 3:

[0215] The server converts the analysis results into visualized data and sends it to the terminal. This visualized data includes player movement paths, ball trajectories, and heatmaps of shooting success rates.

[0216] Step 4:

[0217] The terminal displays the match status in real time on the user interface based on the received visualization data. Meanwhile, the server generates text commentary according to the progress of the match, and the terminal conveys this to the user.

[0218] Step 5:

[0219] After the match ends, the server re-analyzes all the accumulated data and automatically generates a detailed match analysis report. This report includes player performance evaluations, tactical efficiency analysis, and information on areas for improvement.

[0220] Step 6:

[0221] Users (coaches and team staff) can view reports generated by the server and use them to plan strategies for the next match and develop training plans.

[0222] (Example 1)

[0223] 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."

[0224] Traditional match commentary systems have presented challenges in providing detailed, real-time information to spectators and coaches, as it is difficult to understand the movements and tactics of players during a match. Furthermore, post-match analysis is often done manually, making it difficult to obtain efficient and objective feedback. This leads to delays in tactical improvements and rapid preparation for future competitions.

[0225] 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.

[0226] In this invention, the server includes means for analyzing dynamic data acquired during a competition in real time, means for visualizing the progress of the competition based on the analyzed dynamic data, and means for transmitting the analysis results to a terminal and displaying the information to the user. This makes it possible to understand the movements and tactics of the players during the match in real time and provide easy-to-understand match commentary to spectators. Furthermore, a detailed analysis report can be automatically generated after the competition ends, allowing for quick and objective feedback to be provided to relevant parties.

[0227] "Dynamic data" is a general term for information that represents the position, movement, and changes of athletes and objects during a competition.

[0228] "Real-time analysis" is the process of collecting data instantly while a competition is in progress and performing analysis on the spot.

[0229] "Visualization" refers to the graphical display of the progress of a competition based on analyzed data.

[0230] "Tactical commentary" refers to explaining strategies and intentions in sports through text and video, based on analyzed data.

[0231] An "analysis report" is a document automatically generated after a competition, containing a detailed evaluation and improvement suggestions based on data from the entire match.

[0232] A "terminal" is a device or apparatus that receives information transmitted from a server and displays it to the user.

[0233] To implement this invention, a server first installs cameras and sensors to track the movements of players and objects during the competition. This allows for the collection of dynamic data in real time. The server then uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and identify the players' movements and the tactics used.

[0234] The analyzed data is transformed graphically to visualize the progress of the match. Visual processing libraries such as OpenCV are used for this purpose. This visualized data is sent to the terminal in a way that is easy for the user to understand. Dedicated applications and interfaces allow users to view this information in real time.

[0235] Furthermore, the server utilizes a generation AI model based on the analysis results to automatically generate tactical explanations through prompt messages. For example, it can generate an explanation of "the background of the play in which player A broke through the defense and scored a decisive shot." This generated explanation is output based on the prompt message "Explain the impressive move player A made during the match and its impact on the entire team."

[0236] After the match ends, the server re-analyzes the accumulated data and automatically generates a detailed analysis report. This report is provided through a dedicated web portal and application so that coaches and team personnel can use it for future match planning and strategy development. The report clearly presents individual player data, team tactical evaluations, and areas for future improvement.

[0237] This enables the system to provide rapid and accurate information during matches, allowing spectators and coaches to deepen their understanding of tactics and provide a foundation for strategic improvement.

[0238] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0239] Step 1:

[0240] The server collects dynamic data from the competition in real time through cameras and sensors. Input data includes the position, velocity, and direction of athletes and objects. This data is fed in a time series and converted into basic coordinate data using a visual processing library such as OpenCV. Specifically, it analyzes the video from the cameras frame by frame to identify the position information of each athlete.

[0241] Step 2:

[0242] The server analyzes the acquired coordinate data using machine learning algorithms such as TensorFlow and PyTorch. Using the coordinate data as input, it extracts characteristic features of each player's movements. This enables the detection of tactical patterns and the evaluation of player performance. Specific actions include vector analysis of movement and calculation of velocity and acceleration. This process clarifies, for example, what kind of tactical movements player A is performing.

[0243] Step 3:

[0244] The server generates visualization data and tactical text explanations based on the analysis results. The input is the analysis results obtained in the previous step, and the generation AI model is used to create prompt sentences. The specific operation includes a process of converting the analysis results into easily understandable language using natural language processing techniques. Outputs include chart displays on a GUI and explanatory text information.

[0245] Step 4:

[0246] The server transmits the generated visualization data and explanatory information to the terminal in real time. Specifically, it transfers the data to the client device via the network and formats it for display. The output is presented as detailed match commentary that the user can actually view on the terminal.

[0247] Step 5:

[0248] The terminal displays the received information to the user through an appropriate interface. Input consists of explanatory data and visualizations sent from the server. Specific operations include the ability to arrange information visually and text-based using mobile apps or web browsers. As a result, users can accurately understand the flow of the game and obtain real-time information for decision-making.

[0249] Step 6:

[0250] After the match ends, the server re-analyzes all the accumulated data and generates a detailed analysis report. It uses all the data collected during the match as input and performs multivariate analysis. Specific actions include outputting performance indicators for individual players and the team as a whole. The resulting report will include successful tactics, individual player performance evaluations, and areas for improvement, which will be used for subsequent analysis.

[0251] (Application Example 1)

[0252] 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."

[0253] In analyzing customer behavior at basketball games and in virtual stores, there is a challenge in that real-time dynamic analysis and the resulting display information are not being provided in a way that is sufficiently useful to spectators, coaches, and customers. Furthermore, providing customers with a personalized purchasing experience is also difficult.

[0254] 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.

[0255] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for automatically generating tactical commentary based on the analyzed dynamic data, means for analyzing customer dynamic data in real time and automatically generating recommendations for purchases, and means for personalizing and visualizing purchase information based on customer dynamic data. As a result, match spectators and organizers can understand the progress and tactics of the match in more detail and intuitively, and customers will be provided with product information based on their interests, enabling a high-quality purchasing experience.

[0256] "Dynamic data" is a collection of information about the movement of objects or people that changes over time.

[0257] "Real-time analysis" is a process that analyzes data immediately as soon as it is acquired.

[0258] "Visualization" is a method of displaying analyzed data in a graphical format to make the information easier to understand intuitively.

[0259] "Tactical commentary" refers to information that interprets and explains the strategies and player movements during the course of a match.

[0260] An "analysis report" is a report that summarizes the results of a detailed analysis of data from matches and actions.

[0261] "Product recommendations" is a process that suggests appropriate products based on a customer's past behavior and real-time activity.

[0262] "Personalization" refers to adjusting information and services to suit the individual characteristics and preferences of each user.

[0263] The embodiments for carrying out the invention are as follows:

[0264] This system utilizes high-performance servers and multi-functional terminals to enable real-time data analysis and information provision. The servers use software libraries such as OpenCV and TensorFlow to acquire and analyze dynamic data during matches or in virtual stores. This analysis allows for the immediate generation of visual information about the match, enabling a detailed understanding of player movements and customer walking patterns.

[0265] The terminal uses devices such as smart glasses or head-mounted displays to instantly show users visualization data and explanatory information transmitted from the server. For example, as a user moves around a virtual store, relevant product information is customized and displayed based on their gaze direction and time spent in the area.

[0266] As a concrete example, if a user visits a virtual electronics store, their eye-tracking data is analyzed to identify their interest in new smart devices. This then causes relevant, up-to-date accessories and accessories to pop up in their field of view, assisting them in making a purchase decision.

[0267] This system requires massive processing power for dynamic data, but by utilizing cloud technology, smooth data processing and real-time information provision become possible.

[0268] An example of a prompt to input into the generating AI model is as follows: "In the virtual store, analyze the user's eye-tracking data in real time and identify products of interest. Based on those products, propose personalized offers."

[0269] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0270] Step 1:

[0271] The server acquires dynamic data within matches and virtual stores. Location and gaze data of players and customers are collected via camera sensors embedded in smart glasses or head-mounted displays. This input data is organized chronologically and sent to the server.

[0272] Step 2:

[0273] The server preprocesses the acquired dynamic data using OpenCV. Unnecessary noise is removed from the image data, and the data is processed to accurately identify the positions of people and objects. This preprocessing prepares clear data for analysis.

[0274] Step 3:

[0275] The server uses TensorFlow to analyze dynamic data in real time. Specifically, it applies machine learning algorithms to perform data calculations to identify player movements and customer interests. This generates the necessary output for tactical analysis of matches and product recommendations.

[0276] Step 4:

[0277] The server generates visualization data and text explanations based on the analysis results. It performs data processing, converting information such as tactical explanations and recommended product lists into intuitive graphics and natural language. The generated information is presented in a format that is easy for viewers and customers to understand.

[0278] Step 5:

[0279] The device instantly displays visualized data and text explanations to the user. Information is presented in real time via smart glasses or head-mounted displays. This output allows users to stay informed about the progress of a match or efficiently receive information about products they are interested in.

[0280] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0281] The present invention relates to a system that combines an emotion engine that analyzes dynamic data during a basketball game in real time and recognizes the user's emotion. Thereby, customized game commentary and visualization considering the user's emotional state are realized, and a more immersive viewing experience is provided. Specific embodiments of the system are shown below.

[0282] The system analyzes the dynamic data collected by cameras and sensors at the game venue in real time on the server. This includes the position information of players, the movement of the ball, score information, foul information, and the like. The server identifies and evaluates the movement of players and the tactical aspects of the game progress using AI algorithms.

[0283] Furthermore, the emotion engine analyzes the user's emotion in real time using face recognition technology and biosensors. The emotion recognition data is used to identify whether the user is excited, calm, or in a moment of heightened emotion.

[0284] Based on the analysis results and emotion data, the server generates customized visualization data and tactical commentary and transmits them to the terminal. The terminal displays the visualization data updated in real time to the user and provides commentary according to the emotion. Thereby, the user can receive additional information and commentary on emotionally responsive plays and gain a deeper understanding.

[0285] For example, when the user's emotion is recognized as "excitement", the server generates an explanation that emphasizes the excellent plays and tactical points of the players related to that exciting moment. After the game ends, the server automatically generates a detailed analysis report based on all the data, and the user's emotion data is also integrated into it. The user can view this report and utilize it for analysis for the next viewing or game strategy.

[0286] This embodiment provides more personalized explanations and visualizations than the conventional game viewing system, making it possible to significantly improve the user experience.

[0287] The following explains the processing flow.

[0288] Step 1:

[0289] The server acquires in real time the position information of the players, the movement of the ball, the scores, and the foul information from the cameras and sensors installed at the game venue.

[0290] Step 2:

[0291] The server inputs the acquired dynamic data into an AI algorithm to analyze the tactics and the movements of the players during the game. Thereby, it discriminates whether a specific tactic is being executed and what the performance of each player is like.

[0292] Step 3:

[0293] The emotion engine recognizes the user's emotion in real time. For this, face expression analysis using a camera and biometric data from wearable devices are used.

[0294] Step 4:

[0295] The server integrates analyzed dynamic data with user emotion data to generate visualizations and explanations tailored to the user's emotions. For example, if the server determines that the user is excited, it will highlight match highlights and notable strategies related to that excitement.

[0296] Step 5:

[0297] The device displays real-time visualization data and commentary of the match, taking into account the user's emotions, on the user interface. This allows users to watch the match in a way that best matches their own emotional state.

[0298] Step 6:

[0299] After the match ends, the server comprehensively re-analyzes all the data and automatically generates a detailed match analysis report that takes into account user sentiment data. This report can be used to help with future match viewing and analysis.

[0300] Step 7:

[0301] Users can view analysis reports provided by the server to review matches and gain insights for future viewing.

[0302] (Example 2)

[0303] 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".

[0304] In recent years, there has been a demand for spectators to experience matches with greater immersion. However, traditional systems only provide simple visualizations of player and ball movements and the overall situation of the game, making it difficult to offer a personalized experience that takes into account the emotional state of individual spectators.

[0305] 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.

[0306] In this invention, the server includes means for analyzing in real time the dynamic data acquired during the game, means for recognizing and analyzing in real time the emotional state of the user, and means for generating and providing customized explanations and visualization data according to the emotional state of the user. Thereby, it becomes possible to provide personalized information considering the emotional state of the audience in watching the game, and it becomes possible to realize an immersive viewing experience.

[0307] "Dynamic data" refers to information regarding the positions and movements of players, balls, and other game elements during the game.

[0308] "Real-time analysis" refers to a process of immediately processing data with almost no delay from data acquisition and generating results.

[0309] "Visualization data" refers to graphical information for visually displaying the analyzed information in an easy-to-understand manner.

[0310] "Tactical explanation" refers to an explanation and analysis generated based on an evaluation of the tactical aspects related to the progress of the game and the movements of the players.

[0311] "The emotional state of the user" refers to information regarding the emotions of the user, and refers to the types and degrees of emotions recognized by face recognition technology and biosensors.

[0312] "Customized explanation" refers to explanation information regarding the game adjusted according to the individual state and preferences of the user.

[0313] "Analysis report" refers to a document that aggregates all the data during the game and includes a detailed and comprehensive explanation integrating the emotional data of the user.

[0314] This invention is a system that provides customized information in real time, taking into account the user's emotions, in order to improve the viewing experience during a match. This system mainly consists of various components, including a server, terminals, and users.

[0315] The server collects dynamic data from various sensors and cameras installed at the match venue. This includes location information, object movement information, and information on scores and fouls. The collected data is analyzed in real time using AI algorithms to evaluate the flow of the match and tactics. The server also understands the user's emotional state through an emotion recognition engine and generates optimal commentary based on the current situation. This process includes the use of machine learning models and technology to recognize emotions from the user's facial expressions.

[0316] The terminal displays customized commentary and visualization data transmitted from the server to the user via the screen. The terminal displays real-time updated information and has the function of providing commentary tailored to the user's emotions. In this way, the user can gain a deeper understanding of the match.

[0317] Users must install a dedicated app on their device beforehand and set up an emotion recognition profile. This profile serves as the foundational data for the emotion engine, which identifies the user's emotions at various points during viewing and provides appropriate information.

[0318] For example, if a user's emotion is recognized as "excitement," the server will generate commentary that highlights outstanding plays or tactical points of players related to that moment of excitement. An example of a prompt to the generating AI model might be, "Generate customized commentary based on the user's emotion recognition data and match dynamics data. Highlight special moments when the user is excited." In this way, the entire system works together to create a more immersive viewing experience.

[0319] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0320] Step 1:

[0321] The server collects dynamic data from cameras and sensors installed at the match venue. Inputs include player location information, ball movement, scores, and foul information. This data is transmitted to the server in real time and stored in a database. Specifically, the server receives location coordinates and dynamic event data, which are updated every second, and immediately prepares them for storage and analysis.

[0322] Step 2:

[0323] The server analyzes the collected dynamic data using an AI algorithm. The input is the dynamic data collected in step 1, and the output is the tactical flow of the match and the players' movement patterns. Specifically, it uses a pattern recognition algorithm to compare and analyze the players' movements and perform a process to identify the progress of the match and tactics.

[0324] Step 3:

[0325] The server performs emotion recognition using facial images and biosensor data transmitted from the user's terminal. The input is data related to the user's emotions, and the output is the user's emotional state (e.g., excitement, calmness). Specifically, the server applies a facial recognition algorithm that identifies the emotion engine and performs a process to analyze the user's emotions.

[0326] Step 4:

[0327] The server generates customized commentary and visualization data based on the analysis results and emotion recognition data. The input is the output from steps 2 and 3, and the output is the commentary content displayed on the user's terminal. Specifically, a prompt sentence (e.g., "What plays should be emphasized when excited?") is input to the generating AI model, and the AI ​​generates appropriate content.

[0328] Step 5:

[0329] The terminal displays the explanations and visualization data sent from the server on its screen and provides them to the user. The input is the explanation content generated in step 4, and the output is the presentation of information to the user. Specifically, the application on the terminal visualizes the explanation data and presents the real-time updated information to the user in an easy-to-understand manner.

[0330] Step 6:

[0331] After the match ends, the server integrates all the data and automatically generates a detailed analysis report. The input consists of all match data and sentiment data, and the output is an analysis report that users can refer to later. Specifically, the server-side analysis tool summarizes the entire match and creates a detailed summary of the match, including the user's emotional reactions.

[0332] (Application Example 2)

[0333] 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."

[0334] This invention aims to solve the problem of not being able to provide more personalized content based on the user's emotional state when watching sports. In conventional game viewing, only general commentary is provided, making it difficult for users to gain a deeper understanding or sense of immersion in moments that particularly interest them.

[0335] 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.

[0336] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for recognizing the user's emotional state and generating customized commentary accordingly, and means for automatically generating an analysis report of the match after the match and integrating the user's emotional data. This enables the provision of personalized sports commentary and visualizations that respond to the user's emotions in real time, resulting in a more immersive viewing experience.

[0337] "Dynamic data" refers to information collected in real time during a match, including the location of participants, information on the movement of objects, score-related information, and information on rule violations.

[0338] "Real-time analysis methods" refer to technical techniques that process and analyze dynamic data acquired during a match immediately.

[0339] "Methods for visualizing the progress of a match" refer to technologies that use analyzed dynamic data to visually display the development and flow of a match to the user.

[0340] "Methods for automatically generating tactical commentary" refers to technology that mechanically generates tactical points and play commentary in a match based on analyzed dynamic data.

[0341] "Means for recognizing a user's emotional state" refers to technologies that analyze a user's facial expressions and biometric information to identify changes in their emotions.

[0342] "Means for generating customized explanations" refers to technologies that individually create information and explanations that are highly relevant to the user's emotional state at that time.

[0343] "Methods for automatically generating analysis reports" refer to technologies that mechanically create an overall analysis of a match based on data collected after the match has ended.

[0344] "Methods for integrating emotional data" refers to technologies that incorporate and record the user's emotional state while watching a game into the analysis data of the match.

[0345] This system enhances the sports viewing experience by analyzing dynamic data acquired during a match and the user's emotional state in real time, and providing customized commentary based on that analysis. The server collects data from the match venue and analyzes information such as the location of players and objects, information related to scores, and information on rule violations. This analysis can be performed using the Python-based image processing library OpenCV and the data analysis library NumPy. The server also uses facial recognition technology and biosensors to understand the user's emotional state. Emotional data is processed using TensorFlow and PyTorch to determine whether the user is excited, calm, or in other states.

[0346] Based on these analysis results, the server generates customized visualizations and explanations tailored to the user's emotions. For example, if a user is excited about a particular player's performance, information about that player's past performance and tactical aspects will be highlighted. This allows the user to gain a deeper understanding of the match.

[0347] After the match ends, the server integrates all the collected data and automatically generates an analysis report, which also incorporates sentiment data. This allows users to gain strategic insights for their next match viewing.

[0348] For example, if a user becomes very excited about a particular player's crucial scoring play during a match, they will be provided with tactical commentary related to that play and video clips of similar plays from the past. This functionality is achieved by utilizing generative AI models. An example of a prompt would be: "Analyze the user's emotions in real time and identify moments of excitement during the match. At those moments, highlight and provide background information on the players and tactical points the user wants to know about."

[0349] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0350] Step 1:

[0351] The server acquires real-time dynamic data from cameras and sensors installed at the match venue. It receives player location information, object movement information, and score and foul information as input, and analyzes this data. As output, it generates basic data for understanding player movements and the progress of the match. In this process, it efficiently processes large amounts of data using data processing libraries such as Python's NumPy and Pandas.

[0352] Step 2:

[0353] The server visualizes the progress of the match based on the acquired dynamic data. It converts the input data into a format that is easy to plot and generates visualization data to be displayed on the user's terminal in real time using Matplotlib or Plotly. The output is a graphical representation that allows the user to intuitively understand the flow of the match.

[0354] Step 3:

[0355] The server recognizes the user's emotions using facial recognition technology and data from biosensors. It receives camera footage and biometric data as input and performs facial expression analysis using OpenCV and Dlib. The output is data that identifies the user's emotional state (e.g., excited, calm, interested, etc.).

[0356] Step 4:

[0357] The server automatically generates customized explanations tailored to the user's emotions, based on emotion recognition data and dynamic data. Using a generation AI model, it converts the input emotion data into prompts and generates additional information and tactical explanations. As output, explanations and visualizations tailored to the user's level of excitement are prepared and delivered to the terminal in real time.

[0358] Step 5:

[0359] After the match ends, the server integrates all dynamic and sentiment data and automatically generates a detailed analysis report. It uses data accumulated throughout all matches as input and performs analysis using Python and R. The output is a comprehensive report that provides users with strategic insights for the next match. This report is sent to the user's device for viewing.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] [Third Embodiment]

[0364] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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".

[0376] This invention provides a system for basketball games that acquires and analyzes dynamic data such as the movement of players and the ball in real time during the game, thereby providing more easily understandable game commentary for spectators and detailed analysis useful for coaches and players to review the game. An example of this system is shown below.

[0377] The system continuously collects massive amounts of data from cameras and sensors during a match and performs real-time analysis on a server. The server monitors the progress of the match and generates visualizations and text-based match commentary based on the analysis results. Machine learning algorithms are used for analysis, including tactical detection and player performance evaluation. The server sends these analysis results to a terminal, which displays them to the user in real time.

[0378] After the match ends, the server re-analyzes the accumulated data and automatically generates an analysis report of the entire match. The report includes a team tactical evaluation, an analysis of individual player abilities, and areas for future improvement. Coaches and team personnel can view this report and use it to inform their next match or training plans.

[0379] As a concrete example, during a match, the server detects player A's movements as they break through the defense and analyzes the correlation with other players at that moment, identifying that a pick-and-roll tactic was used. Based on this, the terminal provides the user with real-time commentary such as "Player A is executing a pick-and-roll." After the match ends, a detailed report is generated and provided to the user, including player A's movements and the success rate of coordinated plays.

[0380] Thus, the present invention provides information that supports understanding of matches and contributes to strategic improvement through real-time analysis, visualization, and commentary generation.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The server acquires real-time dynamic data on the movements of players and the ball from cameras and sensors installed at the match venue. This includes player position coordinates, ball speed and direction, score status, and foul information.

[0384] Step 2:

[0385] The server inputs the acquired dynamic data into an AI algorithm to analyze players' movement patterns and the progress of the game. This analysis allows for the recognition of specific tactics (e.g., pick-and-roll) and the evaluation of individual player movements and the overall team performance.

[0386] Step 3:

[0387] The server converts the analysis results into visualized data and sends it to the terminal. This visualized data includes player movement paths, ball trajectories, and heatmaps of shooting success rates.

[0388] Step 4:

[0389] The terminal displays the match status in real time on the user interface based on the received visualization data. Meanwhile, the server generates text commentary according to the progress of the match, and the terminal conveys this to the user.

[0390] Step 5:

[0391] After the match ends, the server re-analyzes all the accumulated data and automatically generates a detailed match analysis report. This report includes player performance evaluations, tactical efficiency analysis, and information on areas for improvement.

[0392] Step 6:

[0393] Users (coaches and team staff) can view reports generated by the server and use them to plan strategies for the next match and develop training plans.

[0394] (Example 1)

[0395] 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."

[0396] Traditional match commentary systems have presented challenges in providing detailed, real-time information to spectators and coaches, as it is difficult to understand the movements and tactics of players during a match. Furthermore, post-match analysis is often done manually, making it difficult to obtain efficient and objective feedback. This leads to delays in tactical improvements and rapid preparation for future competitions.

[0397] 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.

[0398] In this invention, the server includes means for analyzing dynamic data acquired during a competition in real time, means for visualizing the progress of the competition based on the analyzed dynamic data, and means for transmitting the analysis results to a terminal and displaying the information to the user. This makes it possible to understand the movements and tactics of the players during the match in real time and provide easy-to-understand match commentary to spectators. Furthermore, a detailed analysis report can be automatically generated after the competition ends, allowing for quick and objective feedback to be provided to relevant parties.

[0399] "Dynamic data" is a general term for information that represents the position, movement, and changes of athletes and objects during a competition.

[0400] "Real-time analysis" is the process of collecting data instantly while a competition is in progress and performing analysis on the spot.

[0401] "Visualization" refers to the graphical display of the progress of a competition based on analyzed data.

[0402] "Tactical commentary" refers to explaining strategies and intentions in sports through text and video, based on analyzed data.

[0403] An "analysis report" is a document automatically generated after a competition, containing a detailed evaluation and improvement suggestions based on data from the entire match.

[0404] A "terminal" is a device or apparatus that receives information transmitted from a server and displays it to the user.

[0405] To implement this invention, a server first installs cameras and sensors to track the movements of players and objects during the competition. This allows for the collection of dynamic data in real time. The server then uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and identify the players' movements and the tactics used.

[0406] The analyzed data is transformed graphically to visualize the progress of the match. Visual processing libraries such as OpenCV are used for this purpose. This visualized data is sent to the terminal in a way that is easy for the user to understand. Dedicated applications and interfaces allow users to view this information in real time.

[0407] Furthermore, the server utilizes a generation AI model based on the analysis results to automatically generate tactical explanations through prompt messages. For example, it can generate an explanation of "the background of the play in which player A broke through the defense and scored a decisive shot." This generated explanation is output based on the prompt message "Explain the impressive move player A made during the match and its impact on the entire team."

[0408] After the match ends, the server re-analyzes the accumulated data and automatically generates a detailed analysis report. This report is provided through a dedicated web portal and application so that coaches and team personnel can use it for future match planning and strategy development. The report clearly presents individual player data, team tactical evaluations, and areas for future improvement.

[0409] This enables the system to provide rapid and accurate information during matches, allowing spectators and coaches to deepen their understanding of tactics and provide a foundation for strategic improvement.

[0410] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0411] Step 1:

[0412] The server collects dynamic data from the competition in real time through cameras and sensors. Input data includes the position, velocity, and direction of athletes and objects. This data is fed in a time series and converted into basic coordinate data using a visual processing library such as OpenCV. Specifically, it analyzes the video from the cameras frame by frame to identify the position information of each athlete.

[0413] Step 2:

[0414] The server analyzes the acquired coordinate data using machine learning algorithms such as TensorFlow and PyTorch. Using the coordinate data as input, it extracts characteristic features of each player's movements. This enables the detection of tactical patterns and the evaluation of player performance. Specific actions include vector analysis of movement and calculation of velocity and acceleration. This process clarifies, for example, what kind of tactical movements player A is performing.

[0415] Step 3:

[0416] The server generates visualization data and tactical text explanations based on the analysis results. The input is the analysis results obtained in the previous step, and the generation AI model is used to create prompt sentences. The specific operation includes a process of converting the analysis results into easily understandable language using natural language processing techniques. Outputs include chart displays on a GUI and explanatory text information.

[0417] Step 4:

[0418] The server transmits the generated visualization data and explanatory information to the terminal in real time. Specifically, it transfers the data to the client device via the network and formats it for display. The output is presented as detailed match commentary that the user can actually view on the terminal.

[0419] Step 5:

[0420] The terminal displays the received information to the user through an appropriate interface. Input consists of explanatory data and visualizations sent from the server. Specific operations include the ability to arrange information visually and text-based using mobile apps or web browsers. As a result, users can accurately understand the flow of the game and obtain real-time information for decision-making.

[0421] Step 6:

[0422] After the match ends, the server re-analyzes all the accumulated data and generates a detailed analysis report. It uses all the data collected during the match as input and performs multivariate analysis. Specific actions include outputting performance indicators for individual players and the team as a whole. The resulting report will include successful tactics, individual player performance evaluations, and areas for improvement, which will be used for subsequent analysis.

[0423] (Application Example 1)

[0424] 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."

[0425] In analyzing customer behavior at basketball games and in virtual stores, there is a challenge in that real-time dynamic analysis and the resulting display information are not being provided in a way that is sufficiently useful to spectators, coaches, and customers. Furthermore, providing customers with a personalized purchasing experience is also difficult.

[0426] 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.

[0427] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for automatically generating tactical commentary based on the analyzed dynamic data, means for analyzing customer dynamic data in real time and automatically generating recommendations for purchases, and means for personalizing and visualizing purchase information based on customer dynamic data. As a result, match spectators and organizers can understand the progress and tactics of the match in more detail and intuitively, and customers will be provided with product information based on their interests, enabling a high-quality purchasing experience.

[0428] "Dynamic data" is a collection of information about the movement of objects or people that changes over time.

[0429] "Real-time analysis" is a process that analyzes data immediately as soon as it is acquired.

[0430] "Visualization" is a method of displaying analyzed data in a graphical format to make the information easier to understand intuitively.

[0431] "Tactical commentary" refers to information that interprets and explains the strategies and player movements during the course of a match.

[0432] An "analysis report" is a report that summarizes the results of a detailed analysis of data from matches and actions.

[0433] "Product recommendations" is a process that suggests appropriate products based on a customer's past behavior and real-time activity.

[0434] "Personalization" refers to adjusting information and services to suit the individual characteristics and preferences of each user.

[0435] The embodiments for carrying out the invention are as follows:

[0436] This system utilizes high-performance servers and multi-functional terminals to enable real-time data analysis and information provision. The servers use software libraries such as OpenCV and TensorFlow to acquire and analyze dynamic data during matches or in virtual stores. This analysis allows for the immediate generation of visual information about the match, enabling a detailed understanding of player movements and customer walking patterns.

[0437] The terminal uses devices such as smart glasses or head-mounted displays to instantly show users visualization data and explanatory information transmitted from the server. For example, as a user moves around a virtual store, relevant product information is customized and displayed based on their gaze direction and time spent in the area.

[0438] As a concrete example, if a user visits a virtual electronics store, their eye-tracking data is analyzed to identify their interest in new smart devices. This then causes relevant, up-to-date accessories and accessories to pop up in their field of view, assisting them in making a purchase decision.

[0439] This system requires massive processing power for dynamic data, but by utilizing cloud technology, smooth data processing and real-time information provision become possible.

[0440] An example of a prompt to input into the generating AI model is as follows: "In the virtual store, analyze the user's eye-tracking data in real time and identify products of interest. Based on those products, propose personalized offers."

[0441] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0442] Step 1:

[0443] The server acquires dynamic data within matches and virtual stores. Location and gaze data of players and customers are collected via camera sensors embedded in smart glasses or head-mounted displays. This input data is organized chronologically and sent to the server.

[0444] Step 2:

[0445] The server preprocesses the acquired dynamic data using OpenCV. Unnecessary noise is removed from the image data, and the data is processed to accurately identify the positions of people and objects. This preprocessing prepares clear data for analysis.

[0446] Step 3:

[0447] The server uses TensorFlow to analyze dynamic data in real time. Specifically, it applies machine learning algorithms to perform data calculations to identify player movements and customer interests. This generates the necessary output for tactical analysis of matches and product recommendations.

[0448] Step 4:

[0449] The server generates visualization data and text explanations based on the analysis results. It performs data processing, converting information such as tactical explanations and recommended product lists into intuitive graphics and natural language. The generated information is presented in a format that is easy for viewers and customers to understand.

[0450] Step 5:

[0451] The device instantly displays visualized data and text explanations to the user. Information is presented in real time via smart glasses or head-mounted displays. This output allows users to stay informed about the progress of a match or efficiently receive information about products they are interested in.

[0452] 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.

[0453] This invention relates to a system that combines real-time analysis of dynamic data during a basketball game with an emotion engine that recognizes the user's emotions. This enables customized game commentary and visualization that takes into account the user's emotional state, providing a more immersive viewing experience. A specific embodiment of the system is shown below.

[0454] The system analyzes dynamic data collected by cameras and sensors at the match venue in real time on a server. This includes player location information, ball movement, scoring information, and foul information. The server uses AI algorithms to identify and evaluate the tactical aspects of player movements and the progress of the match.

[0455] Furthermore, the emotion engine analyzes the user's emotions in real time using facial recognition technology and biosensors. Emotion recognition data is used to identify whether the user is excited, calm, or experiencing moments of heightened emotion.

[0456] Based on the analysis results and sentiment data, the server generates customized visualizations and tactical explanations and sends them to the terminal. The terminal displays the real-time updated visualizations to the user and provides commentary tailored to their emotions. This allows the user to receive additional information and explanations about plays that evoked emotional responses, leading to a deeper understanding.

[0457] For example, if a user's emotion is recognized as "excitement," the server will generate commentary highlighting outstanding plays and tactical points of players related to that moment of excitement. After the match ends, the server automatically generates a detailed analysis report based on all the data, and the user's emotion data is also integrated into it. Users can view this report and use it for analysis to improve their next viewing experience and match strategy.

[0458] This embodiment provides more personalized commentary and visualization than conventional match viewing systems, enabling a dramatic improvement in the user experience.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The server acquires real-time information on player locations, ball movement, scores, and fouls from cameras and sensors installed at the match venue.

[0462] Step 2:

[0463] The server inputs the acquired dynamic data into an AI algorithm to analyze tactics and player movements during the match. This allows it to determine whether specific tactics are being executed and to assess the performance of each player.

[0464] Step 3:

[0465] The emotion engine recognizes the user's emotions in real time. This uses facial expression analysis with a camera and biometric data from wearable devices.

[0466] Step 4:

[0467] The server integrates analyzed dynamic data with user emotion data to generate visualizations and explanations tailored to the user's emotions. For example, if the server determines that the user is excited, it will highlight match highlights and notable strategies related to that excitement.

[0468] Step 5:

[0469] The device displays real-time visualization data and commentary of the match, taking into account the user's emotions, on the user interface. This allows users to watch the match in a way that best matches their own emotional state.

[0470] Step 6:

[0471] After the match ends, the server comprehensively re-analyzes all the data and automatically generates a detailed match analysis report that takes into account user sentiment data. This report can be used to help with future match viewing and analysis.

[0472] Step 7:

[0473] Users can view analysis reports provided by the server to review matches and gain insights for future viewing.

[0474] (Example 2)

[0475] 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."

[0476] In recent years, there has been a demand for spectators to experience matches with greater immersion. However, traditional systems only provide simple visualizations of player and ball movements and the overall situation of the game, making it difficult to offer a personalized experience that takes into account the emotional state of individual spectators.

[0477] 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.

[0478] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for recognizing and analyzing the user's emotional state in real time, and means for generating and providing customized commentary and visualization data according to the user's emotional state. This makes it possible to provide personalized information that takes into account the emotional state of the spectators during match viewing, thereby realizing an immersive viewing experience.

[0479] "Dynamic data" refers to information about the position and movement of players, the ball, and other elements of the game during a match.

[0480] "Real-time analysis" refers to a process that processes data and generates results almost instantly, with virtually no delay, from data acquisition to completion.

[0481] "Visualized data" refers to graphical information used to display analyzed information in a visually easy-to-understand manner.

[0482] "Tactical commentary" refers to an evaluation of tactical aspects related to the progress of a match and the movements of the players, and the explanations and analyses generated based on that evaluation.

[0483] "User emotional state" refers to information about the user's emotions, including the type and degree of emotion recognized by facial recognition technology and biosensors.

[0484] "Customized commentary" refers to commentary information about a match that has been adjusted according to the user's individual circumstances and preferences.

[0485] An "analysis report" refers to a document that aggregates all data from the match and includes a detailed and comprehensive explanation that integrates user sentiment data.

[0486] This invention is a system that provides customized information in real time, taking into account the user's emotions, in order to improve the viewing experience during a match. This system mainly consists of various components, including a server, terminals, and users.

[0487] The server collects dynamic data from various sensors and cameras installed at the match venue. This includes location information, object movement information, and information on scores and fouls. The collected data is analyzed in real time using AI algorithms to evaluate the flow of the match and tactics. The server also understands the user's emotional state through an emotion recognition engine and generates optimal commentary based on the current situation. This process includes the use of machine learning models and technology to recognize emotions from the user's facial expressions.

[0488] The terminal displays customized commentary and visualization data transmitted from the server to the user via the screen. The terminal displays real-time updated information and has the function of providing commentary tailored to the user's emotions. In this way, the user can gain a deeper understanding of the match.

[0489] Users must install a dedicated app on their device beforehand and set up an emotion recognition profile. This profile serves as the foundational data for the emotion engine, which identifies the user's emotions at various points during viewing and provides appropriate information.

[0490] For example, if a user's emotion is recognized as "excitement," the server will generate commentary that highlights outstanding plays or tactical points of players related to that moment of excitement. An example of a prompt to the generating AI model might be, "Generate customized commentary based on the user's emotion recognition data and match dynamics data. Highlight special moments when the user is excited." In this way, the entire system works together to create a more immersive viewing experience.

[0491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0492] Step 1:

[0493] The server collects dynamic data from cameras and sensors installed at the match venue. Inputs include player location information, ball movement, scores, and foul information. This data is transmitted to the server in real time and stored in a database. Specifically, the server receives location coordinates and dynamic event data, which are updated every second, and immediately prepares them for storage and analysis.

[0494] Step 2:

[0495] The server analyzes the collected dynamic data using an AI algorithm. The input is the dynamic data collected in step 1, and the output is the tactical flow of the match and the players' movement patterns. Specifically, it uses a pattern recognition algorithm to compare and analyze the players' movements and perform a process to identify the progress of the match and tactics.

[0496] Step 3:

[0497] The server performs emotion recognition using facial images and biosensor data transmitted from the user's terminal. The input is data related to the user's emotions, and the output is the user's emotional state (e.g., excitement, calmness). Specifically, the server applies a facial recognition algorithm that identifies the emotion engine and performs a process to analyze the user's emotions.

[0498] Step 4:

[0499] The server generates customized commentary and visualization data based on the analysis results and emotion recognition data. The input is the output from steps 2 and 3, and the output is the commentary content displayed on the user's terminal. Specifically, a prompt sentence (e.g., "What plays should be emphasized when excited?") is input to the generating AI model, and the AI ​​generates appropriate content.

[0500] Step 5:

[0501] The terminal displays the explanations and visualization data sent from the server on its screen and provides them to the user. The input is the explanation content generated in step 4, and the output is the presentation of information to the user. Specifically, the application on the terminal visualizes the explanation data and presents the real-time updated information to the user in an easy-to-understand manner.

[0502] Step 6:

[0503] After the match ends, the server integrates all the data and automatically generates a detailed analysis report. The input consists of all match data and sentiment data, and the output is an analysis report that users can refer to later. Specifically, the server-side analysis tool summarizes the entire match and creates a detailed summary of the match, including the user's emotional reactions.

[0504] (Application Example 2)

[0505] 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."

[0506] This invention aims to solve the problem of not being able to provide more personalized content based on the user's emotional state when watching sports. In conventional game viewing, only general commentary is provided, making it difficult for users to gain a deeper understanding or sense of immersion in moments that particularly interest them.

[0507] 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.

[0508] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for recognizing the user's emotional state and generating customized commentary accordingly, and means for automatically generating an analysis report of the match after the match and integrating the user's emotional data. This enables the provision of personalized sports commentary and visualizations that respond to the user's emotions in real time, resulting in a more immersive viewing experience.

[0509] "Dynamic data" refers to information collected in real time during a match, including the location of participants, information on the movement of objects, score-related information, and information on rule violations.

[0510] "Real-time analysis methods" refer to technical techniques that process and analyze dynamic data acquired during a match immediately.

[0511] "Methods for visualizing the progress of a match" refer to technologies that use analyzed dynamic data to visually display the development and flow of a match to the user.

[0512] "Methods for automatically generating tactical commentary" refers to technology that mechanically generates tactical points and play commentary in a match based on analyzed dynamic data.

[0513] "Means for recognizing a user's emotional state" refers to technologies that analyze a user's facial expressions and biometric information to identify changes in their emotions.

[0514] "Means for generating customized explanations" refers to technologies that individually create information and explanations that are highly relevant to the user's emotional state at that time.

[0515] "Methods for automatically generating analysis reports" refer to technologies that mechanically create an overall analysis of a match based on data collected after the match has ended.

[0516] "Methods for integrating emotional data" refers to technologies that incorporate and record the user's emotional state while watching a game into the analysis data of the match.

[0517] This system enhances the sports viewing experience by analyzing dynamic data acquired during a match and the user's emotional state in real time, and providing customized commentary based on that analysis. The server collects data from the match venue and analyzes information such as the location of players and objects, information related to scores, and information on rule violations. This analysis can be performed using the Python-based image processing library OpenCV and the data analysis library NumPy. The server also uses facial recognition technology and biosensors to understand the user's emotional state. Emotional data is processed using TensorFlow and PyTorch to determine whether the user is excited, calm, or in other states.

[0518] Based on these analysis results, the server generates customized visualizations and explanations tailored to the user's emotions. For example, if a user is excited about a particular player's performance, information about that player's past performance and tactical aspects will be highlighted. This allows the user to gain a deeper understanding of the match.

[0519] After the match ends, the server integrates all the collected data and automatically generates an analysis report, which also incorporates sentiment data. This allows users to gain strategic insights for their next match viewing.

[0520] For example, if a user becomes very excited about a particular player's crucial scoring play during a match, they will be provided with tactical commentary related to that play and video clips of similar plays from the past. This functionality is achieved by utilizing generative AI models. An example of a prompt would be: "Analyze the user's emotions in real time and identify moments of excitement during the match. At those moments, highlight and provide background information on the players and tactical points the user wants to know about."

[0521] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0522] Step 1:

[0523] The server acquires real-time dynamic data from cameras and sensors installed at the match venue. It receives player location information, object movement information, and score and foul information as input, and analyzes this data. As output, it generates basic data for understanding player movements and the progress of the match. In this process, it efficiently processes large amounts of data using data processing libraries such as Python's NumPy and Pandas.

[0524] Step 2:

[0525] The server visualizes the progress of the match based on the acquired dynamic data. It converts the input data into a format that is easy to plot and generates visualization data to be displayed on the user's terminal in real time using Matplotlib or Plotly. The output is a graphical representation that allows the user to intuitively understand the flow of the match.

[0526] Step 3:

[0527] The server recognizes the user's emotions using facial recognition technology and data from biosensors. It receives camera footage and biometric data as input and performs facial expression analysis using OpenCV and Dlib. The output is data that identifies the user's emotional state (e.g., excited, calm, interested, etc.).

[0528] Step 4:

[0529] The server automatically generates customized explanations tailored to the user's emotions, based on emotion recognition data and dynamic data. Using a generation AI model, it converts the input emotion data into prompts and generates additional information and tactical explanations. As output, explanations and visualizations tailored to the user's level of excitement are prepared and delivered to the terminal in real time.

[0530] Step 5:

[0531] After the match ends, the server integrates all dynamic and sentiment data and automatically generates a detailed analysis report. It uses data accumulated throughout all matches as input and performs analysis using Python and R. The output is a comprehensive report that provides users with strategic insights for the next match. This report is sent to the user's device for viewing.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] [Fourth Embodiment]

[0536] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0537] 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.

[0538] 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).

[0539] 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.

[0540] 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.

[0541] 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).

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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".

[0549] This invention provides a system for basketball games that acquires and analyzes dynamic data such as the movement of players and the ball in real time during the game, thereby providing more easily understandable game commentary for spectators and detailed analysis useful for coaches and players to review the game. An example of this system is shown below.

[0550] The system continuously collects massive amounts of data from cameras and sensors during a match and performs real-time analysis on a server. The server monitors the progress of the match and generates visualizations and text-based match commentary based on the analysis results. Machine learning algorithms are used for analysis, including tactical detection and player performance evaluation. The server sends these analysis results to a terminal, which displays them to the user in real time.

[0551] After the match ends, the server re-analyzes the accumulated data and automatically generates an analysis report of the entire match. The report includes a team tactical evaluation, an analysis of individual player abilities, and areas for future improvement. Coaches and team personnel can view this report and use it to inform their next match or training plans.

[0552] As a concrete example, during a match, the server detects player A's movements as they break through the defense and analyzes the correlation with other players at that moment, identifying that a pick-and-roll tactic was used. Based on this, the terminal provides the user with real-time commentary such as "Player A is executing a pick-and-roll." After the match ends, a detailed report is generated and provided to the user, including player A's movements and the success rate of coordinated plays.

[0553] Thus, the present invention provides information that supports understanding of matches and contributes to strategic improvement through real-time analysis, visualization, and commentary generation.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The server acquires real-time dynamic data on the movements of players and the ball from cameras and sensors installed at the match venue. This includes player position coordinates, ball speed and direction, score status, and foul information.

[0557] Step 2:

[0558] The server inputs the acquired dynamic data into an AI algorithm to analyze players' movement patterns and the progress of the game. This analysis allows for the recognition of specific tactics (e.g., pick-and-roll) and the evaluation of individual player movements and the overall team performance.

[0559] Step 3:

[0560] The server converts the analysis results into visualized data and sends it to the terminal. This visualized data includes player movement paths, ball trajectories, and heatmaps of shooting success rates.

[0561] Step 4:

[0562] The terminal displays the match status in real time on the user interface based on the received visualization data. Meanwhile, the server generates text commentary according to the progress of the match, and the terminal conveys this to the user.

[0563] Step 5:

[0564] After the match ends, the server re-analyzes all the accumulated data and automatically generates a detailed match analysis report. This report includes player performance evaluations, tactical efficiency analysis, and information on areas for improvement.

[0565] Step 6:

[0566] Users (coaches and team staff) can view reports generated by the server and use them to plan strategies for the next match and develop training plans.

[0567] (Example 1)

[0568] 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".

[0569] Traditional match commentary systems have presented challenges in providing detailed, real-time information to spectators and coaches, as it is difficult to understand the movements and tactics of players during a match. Furthermore, post-match analysis is often done manually, making it difficult to obtain efficient and objective feedback. This leads to delays in tactical improvements and rapid preparation for future competitions.

[0570] 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.

[0571] In this invention, the server includes means for analyzing dynamic data acquired during a competition in real time, means for visualizing the progress of the competition based on the analyzed dynamic data, and means for transmitting the analysis results to a terminal and displaying the information to the user. This makes it possible to understand the movements and tactics of the players during the match in real time and provide easy-to-understand match commentary to spectators. Furthermore, a detailed analysis report can be automatically generated after the competition ends, allowing for quick and objective feedback to be provided to relevant parties.

[0572] "Dynamic data" is a general term for information that represents the position, movement, and changes of athletes and objects during a competition.

[0573] "Real-time analysis" is the process of collecting data instantly while a competition is in progress and performing analysis on the spot.

[0574] "Visualization" refers to the graphical display of the progress of a competition based on analyzed data.

[0575] "Tactical commentary" refers to explaining strategies and intentions in sports through text and video, based on analyzed data.

[0576] An "analysis report" is a document automatically generated after a competition, containing a detailed evaluation and improvement suggestions based on data from the entire match.

[0577] A "terminal" is a device or apparatus that receives information transmitted from a server and displays it to the user.

[0578] To implement this invention, a server first installs cameras and sensors to track the movements of players and objects during the competition. This allows for the collection of dynamic data in real time. The server then uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and identify the players' movements and the tactics used.

[0579] The analyzed data is transformed graphically to visualize the progress of the match. Visual processing libraries such as OpenCV are used for this purpose. This visualized data is sent to the terminal in a way that is easy for the user to understand. Dedicated applications and interfaces allow users to view this information in real time.

[0580] Furthermore, the server utilizes a generation AI model based on the analysis results to automatically generate tactical explanations through prompt messages. For example, it can generate an explanation of "the background of the play in which player A broke through the defense and scored a decisive shot." This generated explanation is output based on the prompt message "Explain the impressive move player A made during the match and its impact on the entire team."

[0581] After the match ends, the server re-analyzes the accumulated data and automatically generates a detailed analysis report. This report is provided through a dedicated web portal and application so that coaches and team personnel can use it for future match planning and strategy development. The report clearly presents individual player data, team tactical evaluations, and areas for future improvement.

[0582] This enables the system to provide rapid and accurate information during matches, allowing spectators and coaches to deepen their understanding of tactics and provide a foundation for strategic improvement.

[0583] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0584] Step 1:

[0585] The server collects dynamic data from the competition in real time through cameras and sensors. Input data includes the position, velocity, and direction of athletes and objects. This data is fed in a time series and converted into basic coordinate data using a visual processing library such as OpenCV. Specifically, it analyzes the video from the cameras frame by frame to identify the position information of each athlete.

[0586] Step 2:

[0587] The server analyzes the acquired coordinate data using machine learning algorithms such as TensorFlow and PyTorch. Using the coordinate data as input, it extracts characteristic features of each player's movements. This enables the detection of tactical patterns and the evaluation of player performance. Specific actions include vector analysis of movement and calculation of velocity and acceleration. This process clarifies, for example, what kind of tactical movements player A is performing.

[0588] Step 3:

[0589] The server generates visualization data and tactical text explanations based on the analysis results. The input is the analysis results obtained in the previous step, and the generation AI model is used to create prompt sentences. The specific operation includes a process of converting the analysis results into easily understandable language using natural language processing techniques. Outputs include chart displays on a GUI and explanatory text information.

[0590] Step 4:

[0591] The server transmits the generated visualization data and explanatory information to the terminal in real time. Specifically, it transfers the data to the client device via the network and formats it for display. The output is presented as detailed match commentary that the user can actually view on the terminal.

[0592] Step 5:

[0593] The terminal displays the received information to the user through an appropriate interface. Input consists of explanatory data and visualizations sent from the server. Specific operations include the ability to arrange information visually and text-based using mobile apps or web browsers. As a result, users can accurately understand the flow of the game and obtain real-time information for decision-making.

[0594] Step 6:

[0595] After the match ends, the server re-analyzes all the accumulated data and generates a detailed analysis report. It uses all the data collected during the match as input and performs multivariate analysis. Specific actions include outputting performance indicators for individual players and the team as a whole. The resulting report will include successful tactics, individual player performance evaluations, and areas for improvement, which will be used for subsequent analysis.

[0596] (Application Example 1)

[0597] 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".

[0598] In analyzing customer behavior at basketball games and in virtual stores, there is a challenge in that real-time dynamic analysis and the resulting display information are not being provided in a way that is sufficiently useful to spectators, coaches, and customers. Furthermore, providing customers with a personalized purchasing experience is also difficult.

[0599] 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.

[0600] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for automatically generating tactical commentary based on the analyzed dynamic data, means for analyzing customer dynamic data in real time and automatically generating recommendations for purchases, and means for personalizing and visualizing purchase information based on customer dynamic data. As a result, match spectators and organizers can understand the progress and tactics of the match in more detail and intuitively, and customers will be provided with product information based on their interests, enabling a high-quality purchasing experience.

[0601] "Dynamic data" is a collection of information about the movement of objects or people that changes over time.

[0602] "Real-time analysis" is a process that analyzes data immediately as soon as it is acquired.

[0603] "Visualization" is a method of displaying analyzed data in a graphical format to make the information easier to understand intuitively.

[0604] "Tactical commentary" refers to information that interprets and explains the strategies and player movements during the course of a match.

[0605] An "analysis report" is a report that summarizes the results of a detailed analysis of data from matches and actions.

[0606] "Product recommendations" is a process that suggests appropriate products based on a customer's past behavior and real-time activity.

[0607] "Personalization" refers to adjusting information and services to suit the individual characteristics and preferences of each user.

[0608] The embodiments for carrying out the invention are as follows:

[0609] This system utilizes high-performance servers and multi-functional terminals to enable real-time data analysis and information provision. The servers use software libraries such as OpenCV and TensorFlow to acquire and analyze dynamic data during matches or in virtual stores. This analysis allows for the immediate generation of visual information about the match, enabling a detailed understanding of player movements and customer walking patterns.

[0610] The terminal uses devices such as smart glasses or head-mounted displays to instantly show users visualization data and explanatory information transmitted from the server. For example, as a user moves around a virtual store, relevant product information is customized and displayed based on their gaze direction and time spent in the area.

[0611] As a concrete example, if a user visits a virtual electronics store, their eye-tracking data is analyzed to identify their interest in new smart devices. This then causes relevant, up-to-date accessories and accessories to pop up in their field of view, assisting them in making a purchase decision.

[0612] This system requires massive processing power for dynamic data, but by utilizing cloud technology, smooth data processing and real-time information provision become possible.

[0613] An example of a prompt to input into the generating AI model is as follows: "In the virtual store, analyze the user's eye-tracking data in real time and identify products of interest. Based on those products, propose personalized offers."

[0614] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0615] Step 1:

[0616] The server acquires dynamic data within matches and virtual stores. Location and gaze data of players and customers are collected via camera sensors embedded in smart glasses or head-mounted displays. This input data is organized chronologically and sent to the server.

[0617] Step 2:

[0618] The server preprocesses the acquired dynamic data using OpenCV. Unnecessary noise is removed from the image data, and the data is processed to accurately identify the positions of people and objects. This preprocessing prepares clear data for analysis.

[0619] Step 3:

[0620] The server uses TensorFlow to analyze dynamic data in real time. Specifically, it applies machine learning algorithms to perform data calculations to identify player movements and customer interests. This generates the necessary output for tactical analysis of matches and product recommendations.

[0621] Step 4:

[0622] The server generates visualization data and text explanations based on the analysis results. It performs data processing, converting information such as tactical explanations and recommended product lists into intuitive graphics and natural language. The generated information is presented in a format that is easy for viewers and customers to understand.

[0623] Step 5:

[0624] The device instantly displays visualized data and text explanations to the user. Information is presented in real time via smart glasses or head-mounted displays. This output allows users to stay informed about the progress of a match or efficiently receive information about products they are interested in.

[0625] 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.

[0626] This invention relates to a system that combines real-time analysis of dynamic data during a basketball game with an emotion engine that recognizes the user's emotions. This enables customized game commentary and visualization that takes into account the user's emotional state, providing a more immersive viewing experience. A specific embodiment of the system is shown below.

[0627] The system analyzes dynamic data collected by cameras and sensors at the match venue in real time on a server. This includes player location information, ball movement, scoring information, and foul information. The server uses AI algorithms to identify and evaluate the tactical aspects of player movements and the progress of the match.

[0628] Furthermore, the emotion engine analyzes the user's emotions in real time using facial recognition technology and biosensors. Emotion recognition data is used to identify whether the user is excited, calm, or experiencing moments of heightened emotion.

[0629] Based on the analysis results and sentiment data, the server generates customized visualizations and tactical explanations and sends them to the terminal. The terminal displays the real-time updated visualizations to the user and provides commentary tailored to their emotions. This allows the user to receive additional information and explanations about plays that evoked emotional responses, leading to a deeper understanding.

[0630] For example, if a user's emotion is recognized as "excitement," the server will generate commentary highlighting outstanding plays and tactical points of players related to that moment of excitement. After the match ends, the server automatically generates a detailed analysis report based on all the data, and the user's emotion data is also integrated into it. Users can view this report and use it for analysis to improve their next viewing experience and match strategy.

[0631] This embodiment provides more personalized commentary and visualization than conventional match viewing systems, enabling a dramatic improvement in the user experience.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The server acquires real-time information on player locations, ball movement, scores, and fouls from cameras and sensors installed at the match venue.

[0635] Step 2:

[0636] The server inputs the acquired dynamic data into an AI algorithm to analyze tactics and player movements during the match. This allows it to determine whether specific tactics are being executed and to assess the performance of each player.

[0637] Step 3:

[0638] The emotion engine recognizes the user's emotions in real time. This uses facial expression analysis with a camera and biometric data from wearable devices.

[0639] Step 4:

[0640] The server integrates analyzed dynamic data with user emotion data to generate visualizations and explanations tailored to the user's emotions. For example, if the server determines that the user is excited, it will highlight match highlights and notable strategies related to that excitement.

[0641] Step 5:

[0642] The device displays real-time visualization data and commentary of the match, taking into account the user's emotions, on the user interface. This allows users to watch the match in a way that best matches their own emotional state.

[0643] Step 6:

[0644] After the match ends, the server comprehensively re-analyzes all the data and automatically generates a detailed match analysis report that takes into account user sentiment data. This report can be used to help with future match viewing and analysis.

[0645] Step 7:

[0646] Users can view analysis reports provided by the server to review matches and gain insights for future viewing.

[0647] (Example 2)

[0648] 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".

[0649] In recent years, there has been a demand for spectators to experience matches with greater immersion. However, traditional systems only provide simple visualizations of player and ball movements and the overall situation of the game, making it difficult to offer a personalized experience that takes into account the emotional state of individual spectators.

[0650] 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.

[0651] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for recognizing and analyzing the user's emotional state in real time, and means for generating and providing customized commentary and visualization data according to the user's emotional state. This makes it possible to provide personalized information that takes into account the emotional state of the spectators during match viewing, thereby realizing an immersive viewing experience.

[0652] "Dynamic data" refers to information about the position and movement of players, the ball, and other elements of the game during a match.

[0653] "Real-time analysis" refers to a process that processes data and generates results almost instantly, with virtually no delay, from data acquisition to completion.

[0654] "Visualized data" refers to graphical information used to display analyzed information in a visually easy-to-understand manner.

[0655] "Tactical commentary" refers to an evaluation of tactical aspects related to the progress of a match and the movements of the players, and the explanations and analyses generated based on that evaluation.

[0656] "User emotional state" refers to information about the user's emotions, including the type and degree of emotion recognized by facial recognition technology and biosensors.

[0657] "Customized commentary" refers to commentary information about a match that has been adjusted according to the user's individual circumstances and preferences.

[0658] An "analysis report" refers to a document that aggregates all data from the match and includes a detailed and comprehensive explanation that integrates user sentiment data.

[0659] This invention is a system that provides customized information in real time, taking into account the user's emotions, in order to improve the viewing experience during a match. This system mainly consists of various components, including a server, terminals, and users.

[0660] The server collects dynamic data from various sensors and cameras installed at the match venue. This includes location information, object movement information, and information on scores and fouls. The collected data is analyzed in real time using AI algorithms to evaluate the flow of the match and tactics. The server also understands the user's emotional state through an emotion recognition engine and generates optimal commentary based on the current situation. This process includes the use of machine learning models and technology to recognize emotions from the user's facial expressions.

[0661] The terminal displays customized commentary and visualization data transmitted from the server to the user via the screen. The terminal displays real-time updated information and has the function of providing commentary tailored to the user's emotions. In this way, the user can gain a deeper understanding of the match.

[0662] Users must install a dedicated app on their device beforehand and set up an emotion recognition profile. This profile serves as the foundational data for the emotion engine, which identifies the user's emotions at various points during viewing and provides appropriate information.

[0663] For example, if a user's emotion is recognized as "excitement," the server will generate commentary that highlights outstanding plays or tactical points of players related to that moment of excitement. An example of a prompt to the generating AI model might be, "Generate customized commentary based on the user's emotion recognition data and match dynamics data. Highlight special moments when the user is excited." In this way, the entire system works together to create a more immersive viewing experience.

[0664] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0665] Step 1:

[0666] The server collects dynamic data from cameras and sensors installed at the match venue. Inputs include player location information, ball movement, scores, and foul information. This data is transmitted to the server in real time and stored in a database. Specifically, the server receives location coordinates and dynamic event data, which are updated every second, and immediately prepares them for storage and analysis.

[0667] Step 2:

[0668] The server analyzes the collected dynamic data using an AI algorithm. The input is the dynamic data collected in step 1, and the output is the tactical flow of the match and the players' movement patterns. Specifically, it uses a pattern recognition algorithm to compare and analyze the players' movements and perform a process to identify the progress of the match and tactics.

[0669] Step 3:

[0670] The server performs emotion recognition using facial images and biosensor data transmitted from the user's terminal. The input is data related to the user's emotions, and the output is the user's emotional state (e.g., excitement, calmness). Specifically, the server applies a facial recognition algorithm that identifies the emotion engine and performs a process to analyze the user's emotions.

[0671] Step 4:

[0672] The server generates customized commentary and visualization data based on the analysis results and emotion recognition data. The input is the output from steps 2 and 3, and the output is the commentary content displayed on the user's terminal. Specifically, a prompt sentence (e.g., "What plays should be emphasized when excited?") is input to the generating AI model, and the AI ​​generates appropriate content.

[0673] Step 5:

[0674] The terminal displays the explanations and visualization data sent from the server on its screen and provides them to the user. The input is the explanation content generated in step 4, and the output is the presentation of information to the user. Specifically, the application on the terminal visualizes the explanation data and presents the real-time updated information to the user in an easy-to-understand manner.

[0675] Step 6:

[0676] After the match ends, the server integrates all the data and automatically generates a detailed analysis report. The input consists of all match data and sentiment data, and the output is an analysis report that users can refer to later. Specifically, the server-side analysis tool summarizes the entire match and creates a detailed summary of the match, including the user's emotional reactions.

[0677] (Application Example 2)

[0678] 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".

[0679] This invention aims to solve the problem of not being able to provide more personalized content based on the user's emotional state when watching sports. In conventional game viewing, only general commentary is provided, making it difficult for users to gain a deeper understanding or sense of immersion in moments that particularly interest them.

[0680] 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.

[0681] In this invention, the server includes means for analyzing dynamic data acquired during a match in real time, means for visualizing the progress of the match based on the analyzed dynamic data, means for recognizing the user's emotional state and generating customized commentary accordingly, and means for automatically generating an analysis report of the match after the match and integrating the user's emotional data. This enables the provision of personalized sports commentary and visualizations that respond to the user's emotions in real time, resulting in a more immersive viewing experience.

[0682] "Dynamic data" refers to information collected in real time during a match, including the location of participants, information on the movement of objects, score-related information, and information on rule violations.

[0683] "Real-time analysis methods" refer to technical techniques that process and analyze dynamic data acquired during a match immediately.

[0684] "Methods for visualizing the progress of a match" refer to technologies that use analyzed dynamic data to visually display the development and flow of a match to the user.

[0685] "Methods for automatically generating tactical commentary" refers to technology that mechanically generates tactical points and play commentary in a match based on analyzed dynamic data.

[0686] "Means for recognizing a user's emotional state" refers to technologies that analyze a user's facial expressions and biometric information to identify changes in their emotions.

[0687] "Means for generating customized explanations" refers to technologies that individually create information and explanations that are highly relevant to the user's emotional state at that time.

[0688] "Methods for automatically generating analysis reports" refer to technologies that mechanically create an overall analysis of a match based on data collected after the match has ended.

[0689] "Methods for integrating emotional data" refers to technologies that incorporate and record the user's emotional state while watching a game into the analysis data of the match.

[0690] This system enhances the sports viewing experience by analyzing dynamic data acquired during a match and the user's emotional state in real time, and providing customized commentary based on that analysis. The server collects data from the match venue and analyzes information such as the location of players and objects, information related to scores, and information on rule violations. This analysis can be performed using the Python-based image processing library OpenCV and the data analysis library NumPy. The server also uses facial recognition technology and biosensors to understand the user's emotional state. Emotional data is processed using TensorFlow and PyTorch to determine whether the user is excited, calm, or in other states.

[0691] Based on these analysis results, the server generates customized visualizations and explanations tailored to the user's emotions. For example, if a user is excited about a particular player's performance, information about that player's past performance and tactical aspects will be highlighted. This allows the user to gain a deeper understanding of the match.

[0692] After the match ends, the server integrates all the collected data and automatically generates an analysis report, which also incorporates sentiment data. This allows users to gain strategic insights for their next match viewing.

[0693] For example, if a user becomes very excited about a particular player's crucial scoring play during a match, they will be provided with tactical commentary related to that play and video clips of similar plays from the past. This functionality is achieved by utilizing generative AI models. An example of a prompt would be: "Analyze the user's emotions in real time and identify moments of excitement during the match. At those moments, highlight and provide background information on the players and tactical points the user wants to know about."

[0694] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0695] Step 1:

[0696] The server acquires real-time dynamic data from cameras and sensors installed at the match venue. It receives player location information, object movement information, and score and foul information as input, and analyzes this data. As output, it generates basic data for understanding player movements and the progress of the match. In this process, it efficiently processes large amounts of data using data processing libraries such as Python's NumPy and Pandas.

[0697] Step 2:

[0698] The server visualizes the progress of the match based on the acquired dynamic data. It converts the input data into a format that is easy to plot and generates visualization data to be displayed on the user's terminal in real time using Matplotlib or Plotly. The output is a graphical representation that allows the user to intuitively understand the flow of the match.

[0699] Step 3:

[0700] The server recognizes the user's emotions using facial recognition technology and data from biosensors. It receives camera footage and biometric data as input and performs facial expression analysis using OpenCV and Dlib. The output is data that identifies the user's emotional state (e.g., excited, calm, interested, etc.).

[0701] Step 4:

[0702] The server automatically generates customized explanations tailored to the user's emotions, based on emotion recognition data and dynamic data. Using a generation AI model, it converts the input emotion data into prompts and generates additional information and tactical explanations. As output, explanations and visualizations tailored to the user's level of excitement are prepared and delivered to the terminal in real time.

[0703] Step 5:

[0704] After the match ends, the server integrates all dynamic and sentiment data and automatically generates a detailed analysis report. It uses data accumulated throughout all matches as input and performs analysis using Python and R. The output is a comprehensive report that provides users with strategic insights for the next match. This report is sent to the user's device for viewing.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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."

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0726] The following is further disclosed regarding the embodiments described above.

[0727] (Claim 1)

[0728] A means of analyzing dynamic data acquired during a match in real time,

[0729] A means of visualizing the progress of the match based on the analyzed dynamic data,

[0730] A means of automatically generating tactical explanations based on analyzed dynamic data,

[0731] A method for automatically generating a match analysis report after the match has ended,

[0732] A system that includes this.

[0733] (Claim 2)

[0734] The system according to claim 1, wherein the dynamic data during a match includes player position information, ball movement information, scoring information, and foul information.

[0735] (Claim 3)

[0736] The system according to claim 1, wherein tactical commentary based on analyzed dynamic data is provided to the user in real time.

[0737] "Example 1"

[0738] (Claim 1)

[0739] A device that analyzes dynamic data acquired during a competition in real time,

[0740] A device that visualizes the progress of the competition based on the analyzed dynamic data,

[0741] A device that automatically generates tactical commentary based on analyzed dynamic data,

[0742] A device that automatically generates an analysis report of the competition after it has finished,

[0743] A device that transmits analysis results to a terminal and displays the information to the user,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, wherein the dynamic data during the game includes human location information, object movement information, scoring information, and foul information.

[0747] (Claim 3)

[0748] The system according to claim 1, wherein tactical commentary based on analyzed dynamic data is provided to the user in real time.

[0749] "Application Example 1"

[0750] (Claim 1)

[0751] A means of analyzing dynamic data acquired during a match in real time,

[0752] A means of visualizing the progress of the match based on the analyzed dynamic data,

[0753] A means of automatically generating tactical explanations based on analyzed dynamic data,

[0754] A method for automatically generating a match analysis report after the match has ended,

[0755] A means of analyzing customer behavior data in real time and automatically generating recommendations for purchased items,

[0756] A means of personalizing and visualizing purchasing information based on customer behavior data,

[0757] A system that includes this.

[0758] (Claim 2)

[0759] The system according to claim 1, wherein the dynamic data during a match includes player position information, ball movement information, scoring information, and foul information.

[0760] (Claim 3)

[0761] The system according to claim 1, wherein tactical commentary based on analyzed dynamic data is provided to the user in real time.

[0762] "Example 2 of combining an emotion engine"

[0763] (Claim 1)

[0764] A means of analyzing dynamic data acquired during a match in real time,

[0765] A means of visualizing the progress of the match based on the analyzed dynamic data,

[0766] A means of automatically generating tactical explanations based on analyzed dynamic data,

[0767] A means of recognizing and analyzing the user's emotional state in real time,

[0768] A means for generating and providing customized explanations and visualization data that correspond to the user's emotional state,

[0769] A method for automatically generating a match analysis report after the match has ended,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein the dynamic data during a match includes location information of the target, movement information of the object, event information and violation information.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein tactical commentary based on analyzed dynamic data and the user's emotional state is provided to the user in real time.

[0775] "Application example 2 when combining with an emotional engine"

[0776] (Claim 1)

[0777] A means of analyzing dynamic data acquired during a match in real time,

[0778] A means of visualizing the progress of the match based on the analyzed dynamic data,

[0779] A means of automatically generating tactical explanations based on analyzed dynamic data,

[0780] A means of recognizing the user's emotional state and generating a customized explanation accordingly,

[0781] A method for automatically generating a match analysis report after the match ends and integrating user sentiment data,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the dynamic data during a match includes participant location information, object movement information, score-related information, and rule violation information.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein tactical commentary based on analyzed dynamic data and user sentiment data is provided to the user in real time. [Explanation of Symbols]

[0787] 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 analyzing dynamic data acquired during a match in real time, A means of visualizing the progress of the match based on the analyzed dynamic data, A means of automatically generating tactical explanations based on analyzed dynamic data, A method for automatically generating a match analysis report after the match has ended, A means of analyzing customer behavior data in real time and automatically generating recommendations for purchased items, A means of personalizing and visualizing purchasing information based on customer behavior data, A system that includes this.

2. The system according to claim 1, wherein the dynamic data during a match includes player position information, ball movement information, scoring information, and foul information.

3. The system according to claim 1, wherein tactical commentary based on analyzed dynamic data is provided to the user in real time.

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