system

Abstract

We provide the system. [Solution] Means of obtaining match information, A means of analyzing player performance based on acquired match information, A means for generating the corresponding digital recording medium based on the analysis results, A means of exchanging information with users using the generated digital recording medium, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Sports fans enjoy collecting memorabilia related to game records and player performances, but there is a problem that it takes time and effort to store and manage them with conventional physical recording media. In addition, since there is a lack of means to quickly create memorabilia based on specific plays or highlights that fans are interested in, the charm of collecting has not been fully demonstrated.

Means for Solving the Problems

[0005] This invention provides a system that acquires match information in real time and analyzes player performance based on that information. This system uses artificial intelligence to automatically select important moments and highlights, and generates digital recording media based on the results. Furthermore, the generated digital recording media can be directly exchanged between users, and specific recording media can be efficiently acquired through a random selection function. This system makes it easy for fans to collect and manage digital recording media.

[0006] "Match information" refers to various data and event-related information concerning sports matches, including scores, player performances, and details of plays.

[0007] "Player's play" refers to the specific actions and behaviors of a player during a sports match, including plays such as scoring, defending, and assisting.

[0008] A "digital recording medium" is a digital commemorative item or information card that is generated and displayed on a computer system and includes images and text data.

[0009] "Users" refers to end users who operate the digital recording media system and collect, manage, and exchange commemorative items.

[0010] "Artificial intelligence" refers to a computer program that uses machine learning and data analysis techniques to automatically analyze match information and identify important moments.

[0011] A "crucial moment" refers to a specific moment or play in the course of a match that is considered valuable to the fans, and includes scoring opportunities and game-winning plays.

[0012] The "random selection function" refers to a function that randomly selects digital recording media based on certain criteria or algorithms within the system and provides them to the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] 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]

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

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

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] The present invention provides a system for acquiring sports match information in real time and automatically generating and managing it as a digital recording medium. This platform consists of three elements: a server, a terminal, and a user, each of which plays a specific role in its operation.

[0035] First, the server collects sports match information from various data sources. It connects to external APIs in accordance with the match start time to retrieve scoreboards, player performance statistics, and detailed event data regarding the match's progress. This information is stored in the server's database and used for subsequent data analysis.

[0036] Next, the server analyzes the acquired match information using an AI engine. The AI ​​engine uses machine learning algorithms to evaluate the importance of plays and identify "key moments" that fans are likely to be interested in. Based on the identified key moments, it automatically generates digital trading cards that reflect the movements of the relevant players and the dynamics of the match. These cards include player images, performance data, and the story of the match.

[0037] The terminal displays digital storage media to the user through its user interface. Users can log in to their account via the terminal and view previously collected cards, receive new cards, and perform management operations as needed.

[0038] Furthermore, this system includes a gacha (loot box) function that users can participate in. By spending points, users can acquire cards randomly selected by the server and add them to their collection. In this process, there is a chance of obtaining rare cards based on rarity and player popularity.

[0039] As a concrete example of the present invention, consider a scenario where a user logs into a terminal and receives a new digital recording medium based on the day's match results. Upon user login, the server automatically adds the latest generated card to the user's collection. Furthermore, through real-time match updates, users can instantly view new cards and enjoy reviewing matches and interacting with other fans.

[0040] As a result, sports fans are freed from the hassle of managing physical memorabilia, can easily expand their collections in digital format, and have more opportunities to interact with other fans.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Before the match starts, the server connects to an external API to prepare for retrieving the match schedule, player information, and live data.

[0044] Step 2:

[0045] As soon as the match starts, the server continuously retrieves match data and player performance data in real time via API and stores it in its internal database.

[0046] Step 3:

[0047] The server sends accumulated data to the AI ​​engine, which analyzes it to identify important scenes and plays during the match. The AI ​​automatically selects scoring opportunities and noteworthy plays.

[0048] Step 4:

[0049] The server generates digital trading cards based on key moments extracted by the AI. The cards include player photos, performance data, and the story of the match.

[0050] Step 5:

[0051] The terminal displays the generated digital cards to the user through a user interface. Users can log in to view and organize their collections.

[0052] Step 6:

[0053] Users utilize the gacha function, spending points to randomly acquire new cards.

[0054] Step 7:

[0055] The server accepts gacha requests, uses a random algorithm to select cards, and adds new cards to the user's collection.

[0056] Step 8:

[0057] The device instantly notifies the user of acquired cards and automatically reflects them in their collection. Users can then share or trade newly acquired cards with other fans.

[0058] (Example 1)

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

[0060] The goal is to provide a system that collects sports match information in real time, identifies important moments of interest to fans, and allows users to easily manage and collect this information as a digital recording medium. Furthermore, it aims to promote interaction among fans and eliminate the hassle of managing physical memorabilia.

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

[0062] In this invention, the server includes means for acquiring match information, means for analyzing player performance based on the acquired match information, and means for generating a digital recording medium based on the analysis results. This enables users to effectively collect and manage digital trading cards that identify important moments in matches.

[0063] "Match information" refers to all data related to a sports match, including scores, player statistics, and event data during the match.

[0064] "Player performance" refers to an indicator that shows a player's achievements and abilities, derived from their movements, results, and statistical information during a match.

[0065] A "digital recording medium" is a recording medium that contains images of players, performance data, and match stories, and is managed and displayed electronically.

[0066] A "machine learning algorithm" is an algorithm that uses data to enable computers to learn patterns and make decisions or predictions.

[0067] A "random selection function" is a function that randomly selects from a set of options based on specific conditions or probabilities.

[0068] The following are the embodiments for carrying out the invention.

[0069] This system consists of three main components: a server, terminals, and users. The server is responsible for collecting match information from external data sources and processing it into a digital recording medium. Specifically, it uses the Python programming language and related libraries (e.g., Requests, Pandas) to connect to sports data APIs and collect match scores, player performance, and in-game event data.

[0070] Next, the server analyzes the collected data using deep learning frameworks (e.g., TENSORFLOW® or PyTorch) to identify key moments. The analysis uses machine learning algorithms to evaluate player performance and the flow of the game. This identifies decisive moments that fans are likely to be interested in, and based on these results, a generative AI model is used to create digital trading cards.

[0071] The terminal provides an interface with the user, displaying new digital trading cards in real time using a browser-based or mobile application. Users can log in to their account and manage, view, and retrieve their collections on digital media.

[0072] Furthermore, users can utilize the gacha function, allowing them to obtain new cards through random selection. The server executes the random selection function, choosing which cards to offer to users. In this process, appearance probabilities are set considering factors such as rarity and popularity.

[0073] As a concrete example, an example of a prompt message would be: "Identify the most noteworthy moment from today's match and generate a digital trading card based on that moment." This would then be input into the generation AI model. As a result, the user can add the digitally generated card, based on the highlight scene of a specific match, to their collection and enjoy the details.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects match information from external data sources. Specifically, it sends requests to sports data APIs to retrieve match scores, player performance data, and in-game event data. This input data is stored in the server's database for analysis.

[0077] Step 2:

[0078] The server analyzes the collected match information using machine learning algorithms. This process takes match data as input and performs data calculations to identify important moments and player highlights. The analysis results then output the key moments.

[0079] Step 3:

[0080] The server uses a generative AI model to create digital trading cards based on the identified key moments. The prompt sent to the model is: "Identify the most noteworthy moments from today's match and generate digital trading cards based on them." A digital storage medium is then generated as the output of this process.

[0081] Step 4:

[0082] The terminal displays the generated digital trading cards to the user. The user can log in through the terminal's interface and view the newly generated cards. During this process, the terminal receives files from the server and displays them on the user interface.

[0083] Step 5:

[0084] The server handles situations where a user utilizes the gacha (loot box) function. The user spends points via their device to perform the gacha, and the server randomly selects a digital trading card. The selected card is added to the user's digital collection in real time.

[0085] (Application Example 1)

[0086] 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."

[0087] There is a need to manage sports match information in real time, select important moments based on that information, and provide an environment that enables users to view and interact with this information in digital format. Furthermore, there is a need for efficient means for users to instantly enjoy the dynamics of a match and easily share information with other users.

[0088] 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.

[0089] In this invention, the server includes means for acquiring match information, means for analyzing player activity, and means for generating corresponding digital display materials. This makes it possible to provide digital trading cards in real time based on match information.

[0090] "Match information" refers to detailed data about a sporting event, including scores, player performance, and ongoing situations.

[0091] "Athlete activity" refers to the actions and plays performed by athletes during a match, and its analysis allows us to understand the dynamics of the game.

[0092] "Digital display materials" are digital recording media created based on information about matches and players, with digital trading cards being an example.

[0093] "Means of exchanging information" refers to methods for sharing generated digital display materials among users and for exchanging opinions.

[0094] "A means for users to receive digital trading in real time" refers to a method for users to instantly receive and enjoy digital trading cards as the game progresses.

[0095] The system for implementing this invention consists of three elements: a server, a terminal, and a user.

[0096] The server retrieves match information from external data sources via the internet. Specifically, it collects scores, player performance, and match progress related to sports events from external APIs and stores this data in a database. The stored data is analyzed using machine learning models such as TensorFlow to identify important moments during the match. As a result, digital display materials reflecting the players' activities are automatically generated.

[0097] The terminal plays the role of providing users with digitally displayed materials generated through a user interface. The applications installed on the terminal are developed using languages ​​such as Swift and Kotlin, enabling real-time data delivery. As soon as the terminal receives notifications from the server, it instantly and visually conveys important match information to the user.

[0098] Users can receive the latest match information via their devices and enjoy the generated digital trading cards. Furthermore, users can share their creations with other users and communicate through the application on their devices. This interaction enhances the user experience and sustains interest in the matches.

[0099] A concrete example would be a notification arriving on a user's device during a sports match saying, "Player A has scored a decisive goal!", and a digital trading card of that moment would be generated and added to the user's collection. This card could then be sent to friends via message, stimulating real-time communication about the match.

[0100] Examples of prompts to input into the generation AI model include, "Generate digital trading cards of featured players and plays based on the data from match X."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server collects match information from external data sources via an internet connection. It uses match IDs and API authentication information as input. It calls external APIs to retrieve data such as scores, player performance, and match progress. The retrieved data is stored in a database.

[0104] Step 2:

[0105] The server applies a machine learning model to the stored match data. The input is match information stored in a database. Generative AI models such as TensorFlow are used to perform analysis to identify important moments in the match. The output of this analysis is a list of important moments and players to watch out for.

[0106] Step 3:

[0107] The server generates digital display materials based on identified key moments. The prompt is: "Generate digital trading cards of featured players and plays based on match data." The generation AI model uses this prompt to create digital trading cards containing player images and performance data. The output is digital display material for distribution to the user.

[0108] Step 4:

[0109] The terminal receives digital display materials distributed from the server. It uses digital trading card data sent from the server as input. The application installed on the terminal displays this data on the user interface and notifies the user. The output is the user's visual display of the data.

[0110] Step 5:

[0111] Users receive digital trading cards generated via their device and add them to their collection. The application operates based on the information displayed on the digital trading card from the device as input. Users can use the application's features to save cards and share them with other users. The output is the user's updated digital collection and shared information.

[0112] 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.

[0113] This invention relates to a digital platform that, in addition to processing sports-related match information and providing a digital recording medium, incorporates an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user, as well as an emotion engine that identifies user emotions and optimizes interactions.

[0114] First, the server retrieves match information from an external API. This match data includes detailed information about player performance and the progress of the match, and is stored on the server. The server then uses an AI engine to select key moments from the match and generate digital trading cards. These cards include player images, performance statistics, and a match story.

[0115] Next, the server uses an emotion engine to collect the user's emotions. Through audio data and video feeds obtained from the user's device, the emotion engine analyzes emotional states, including joy, sadness, and excitement. This analysis influences the content and card selections provided by the server.

[0116] The device not only displays the generated digital recording media to the user through its user interface, but also provides interaction that responds to the user's emotional state. For example, if the device determines that the user is happy, it can prompt them to experience match highlights or acquire rare cards to further excite them.

[0117] Furthermore, users can acquire cards randomly using a gacha (loot box) function. During this process, an emotion engine analyzes the user's psychological tendencies in real time, allowing it to adjust the gacha results to better match the user's interests. This intuitively presents the cards and information that the user is most interested in, thereby improving user satisfaction.

[0118] A concrete example of the present invention is a scenario where, after a match ends, the user reviews a summary of the match via their device and is provided with a pleasant experience through sentiment analysis. Based on the user's response, access to alternative highlights of related matches or premium content is proposed.

[0119] Thus, the present invention enables users to continuously acquire valuable experiences from both match data and emotional data, realizing a sports fan experience in a digital environment free from physical constraints.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server connects to an external API before the match begins to prepare to retrieve the match schedule, player information, and real-time match data.

[0123] Step 2:

[0124] Once the match begins, the server stores the acquired match data in a database and uses an AI engine to analyze key moments of the match in real time.

[0125] Step 3:

[0126] Based on key moments identified by the AI ​​engine, the server generates digital trading cards containing player images and match performance data.

[0127] Step 4:

[0128] The terminal displays the generated digital trading cards to the user. After logging in, the user can manage their collection and view card details.

[0129] Step 5:

[0130] While the user is interacting with the application, the device collects data through an interface that captures the user's reactions (e.g., audio and video).

[0131] Step 6:

[0132] Using data sent from the terminal, the emotion engine installed on the server analyzes the user's current emotional state.

[0133] Step 7:

[0134] Based on sentiment analysis results, the server dynamically adjusts content for the user and determines what content to recommend. For example, if it determines that the user is enjoying themselves, it will suggest further highlights or rare cards.

[0135] Step 8:

[0136] When a user uses the gacha function, the server adjusts the randomly selected cards based on the emotion engine, presenting the cards that are most likely to interest them.

[0137] Step 9:

[0138] The device displays the gacha results to the user and adds newly acquired cards to their collection. The user can then use this information to interact with other fans and manage their own cards.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In recent years, there has been a growing demand to enrich the competitive experience through digital media by visualizing participants' actions in real time and providing information tailored to the user's emotional state. However, conventional systems have been unable to adequately enhance user satisfaction because they provide one-way information and lack interaction that adapts to the user's emotions. In addition, they have been unable to adjust information according to the user's interests, making it difficult to improve the quality of the digital experience.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for acquiring competition information, means for analyzing participants' actions based on the acquired competition information, and means for adjusting the content of the medium based on the analyzed emotions. This enables the provision of information that matches the user's emotional state and allows for two-way interaction.

[0144] "Competition" refers to an activity or event in which participants compete for performance or results based on specific rules.

[0145] "Information" refers to facts and knowledge treated as data, which are acquired, processed, or provided for a specific purpose.

[0146] "Participants" refer to individuals or teams participating in a competition or event, and their actions and results are the subject of recording and analysis.

[0147] "Movement" refers to the physical movements and actions that participants perform during the competition, and is an element of performance evaluation.

[0148] "Digital media" refers to information content that is stored or displayed in an electronic format and can be accessed or received by a user.

[0149] "User" refers to an individual or organization that receives information or services through a system or platform.

[0150] "Emotion" refers to the psychological response that a user shows to a particular situation or stimulus, and includes states such as joy, excitement, and sadness.

[0151] "Interaction" refers to two-way communication or response that takes place between a system and a user.

[0152] "Adjustment" refers to the act of changing or optimizing the operation or content of a system in accordance with specific conditions or circumstances.

[0153] This system is designed to acquire information related to the competition, analyze participants' behavior, and provide digital media based on the user's emotions. The following shows a specific example of how the system works.

[0154] The server obtains detailed competition information from external sources via APIs. In this process, it receives participant action data and competition progress in a specific data format (e.g., JSON) and stores it in its own database. Existing sports data provision services are often used for this purpose. Machine learning frameworks such as TensorFlow and PyTorch are used as the AI ​​engine to extract competition highlights and analyze participant performance.

[0155] Based on the analyzed information, the server generates digital media containing participants' images and scores. This media is later provided to users and plays a crucial role in supporting their interactions.

[0156] Simultaneously, the device acquires voice and video input from the user and sends it to the server. This data is processed by an emotion analysis engine (e.g., Azure Cognitive Services) to recognize the user's emotional state in real time. This makes it possible to adjust digital content based on emotions such as tension, excitement, and relaxation.

[0157] Based on the generated digital media and analysis results, users receive media that has been adjusted for easy reading via their devices, and can also interact with cards and content randomly acquired through a gacha function. In this process, the user's past interests and emotional state are taken into consideration to provide an individually optimized media experience.

[0158] As a concrete example, imagine a user is watching highlights of a match on their device after it has finished. If the emotion analysis engine detects that the user is excited via voice input, the device will display more match-related content and special offers. This further enhances the user's interest and deepens their experience of the sport.

[0159] An example of a prompt message might be: "Please display the results of last night's basketball game and highlights based on the user's excitement. Please also suggest gacha cards that match the user's emotions." This aims to provide users with an optimal sports viewing experience and establish a new form of entertainment on digital devices.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server retrieves competition information via an external API. Inputs include parameters such as a specific competition ID and date / time, while output is detailed competition information (participant data in JSON format, scores, action logs, etc.). The server stores this data in a database, forming the basis for subsequent data analysis.

[0163] Step 2:

[0164] The server analyzes competition data stored in the database using an AI engine. The input is the data acquired in Step 1, and the output is analysis results including the identification of important moments and an evaluation of participant performance. Specifically, the process involves analyzing action data using a TensorFlow model and extracting highlight scenes.

[0165] Step 3:

[0166] The server generates digital trading cards based on the analysis results. The input is the analysis results from step 2, and the output generates card data including player images, competition statistics, and match stories. These cards are stored in a database and become digital content provided to users.

[0167] Step 4:

[0168] The device collects voice input and video data from the user and transmits it to the server in real time. The input is raw voice and video data obtained from the user, and the output is prepared as a dataset for processing by the emotion analysis engine. Specific operations include data collection using microphones and cameras.

[0169] Step 5:

[0170] The server analyzes the transmitted audio and video data by passing it through an emotion analysis engine. The input is the dataset from step 4, and the output is the user's emotional state (e.g., joy, sadness, excitement). This analysis utilizes Azure Cognitive Services or similar technologies.

[0171] Step 6:

[0172] The device dynamically changes the UI based on the emotional state received from the server. The input is the emotional state information from step 5, and the output is a modified digital interface presented to the user. For example, if the user is excited, a colorful UI and animation effects are used to display match highlights or special offers.

[0173] Step 7:

[0174] Users acquire digital cards using the gacha function provided on their device. In this process, the gacha draw results are optimized for the individual based on their emotional state as a result of the content provided in step 6. The inputs are the user's selections and the server's emotional analysis results, and the output is a set of cards that are most interesting to the user.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] This solution addresses the challenge of dynamically changing content to reflect user emotions and provide a more personalized experience when generating digital recordings of sports matches. Furthermore, conventional random selection functions fail to accurately capture user interests, limiting methods for increasing satisfaction.

[0178] 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.

[0179] In this invention, the server includes means for acquiring match information, means for analyzing player performance, means for generating a digital recording medium based on the analysis results, means for exchanging information with the user using the generated digital recording medium, and means for analyzing the user's emotions and dynamically adjusting the displayed content based on the analysis results. This makes it possible to provide personalized content that responds to the user's emotions. Furthermore, even in the random selection function, by optimizing the selection results while considering the user's emotional state, it becomes possible to accurately capture the user's interests and improve satisfaction.

[0180] "Match information" refers to data related to a sporting event, including player performance and the progress of the match.

[0181] "Means of analyzing player performance" refers to software or algorithms used to analyze player behavior and performance based on acquired match information.

[0182] A "digital recording medium" is data in digital format that includes images of athletes, competition results, and match stories, and that can be stored and displayed electronically.

[0183] A "means of exchanging information with users" refers to a system that displays the generated digital recording medium on the user's terminal, allowing the user to respond or provide feedback accordingly.

[0184] "Means for analyzing emotions and dynamically adjusting displayed content based on the analysis results" refers to algorithms or software that identify emotions from data such as the user's facial expressions and voice, and change related information in real time.

[0185] The "random selection function" is a function that randomly selects and presents digital recording media to the user.

[0186] "Optimizing selection results" means adjusting randomly selected results to match the user's interests, based on the user's current emotional state and past preferences.

[0187] The system implementing this invention consists of three main components: a server, a terminal, and a user. The server acquires match information and utilizes artificial intelligence to analyze player performance based on that information. Specifically, the server acquires match information from an external sports data API and uses software such as OpenCV and TensorFlow to analyze player performance in detail. Based on this analysis, digital trading cards are generated. The cards contain information such as player images, match results, and the story of the match.

[0188] The terminal displays generated digital trading cards to the user and analyzes the user's emotions in real time. It uses the Google® Cloud Speech-to-Text API to collect the user's facial expressions and voice data via the smartphone's camera and microphone, and to identify emotions. Based on the emotion analysis results, it dynamically adjusts the displayed content to match the user's interests and emotions.

[0189] Users can acquire digital recording media through their devices. Furthermore, a random selection function based on sentiment analysis allows users to acquire premium content in a gacha (loot box) format. The selection is optimized according to the user's emotional state, designed to enhance user satisfaction.

[0190] For example, when a user is watching a live broadcast of a sporting event on their smartphone, the system recognizes the user's enthusiastic reaction and immediately presents highlights of past memorable matches featuring the same team. Simultaneously, it can further pique the user's interest by randomly providing trading cards of notable players.

[0191] An example of a prompt for a generative AI model might be: "If the user's emotion is 'excited,' select highlights from relevant sporting events and suggest premium content that will maintain the excitement."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server retrieves match information from an external sports data API. This information includes data such as player performance, match progress, and scores. The input is match-related information from the sports data API, and the output is match information stored in the server's database. Once this storage is complete, the server can proceed to the next analysis step.

[0195] Step 2:

[0196] The server analyzes player performance using software such as OpenCV and TensorFlow based on the acquired match information. Specifically, it analyzes data related to player movements and performance, and quantifies and evaluates the results. The input is the match information acquired in step 1, and the output is an evaluation score for each player and identification of important scenes. This prepares the data necessary for generating the next trading cards.

[0197] Step 3:

[0198] The server generates digital trading cards based on the analysis results. These cards include player images, performance statistics, and match stories. The input is the analysis results obtained in step 2, and the output is the digital trading card for display to the user. The generated card is sent from the server to the terminal.

[0199] Step 4:

[0200] The terminal displays digital trading cards received from the server to the user. The terminal also uses a camera and microphone to collect user emotion data (facial expressions and voice). Inputs include digital trading cards from the server and user emotion data, while outputs include content presentation on the user's screen and further analysis of the collected data. The emotion data is used in the subsequent emotion analysis step.

[0201] Step 5:

[0202] The device analyzes the collected facial and voice data using the Google Cloud Speech-to-Text API to infer the user's emotional state. The input is the emotional data collected in step 4, and the output is the user's emotional state (e.g., excitement, joy, sadness). This emotional state is used to dynamically adjust the content displayed.

[0203] Step 6:

[0204] The server dynamically adjusts the content presented to the user based on the sentiment analysis results. If the server determines that the user is excited, it selects and displays highlights or premium content related to relevant sporting events. The input is the sentiment analysis results from step 5, and the output is the customized content displayed on the user's device. The user can achieve further satisfaction at this stage.

[0205] Step 7:

[0206] Users acquire digital trading cards using a random selection function on their device. In this case, the server optimizes the content of the cards acquired based on the user's emotional state. The input is the user's emotional data and the random selection function's request, and the output is premium content tailored to the user's interests. This makes it possible to further enhance user satisfaction.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The present invention provides a system for acquiring sports match information in real time and automatically generating and managing it as a digital recording medium. This platform consists of three elements: a server, a terminal, and a user, each of which plays a specific role in its operation.

[0224] First, the server collects sports match information from various data sources. It connects to external APIs in accordance with the match start time to retrieve scoreboards, player performance statistics, and detailed event data regarding the match's progress. This information is stored in the server's database and used for subsequent data analysis.

[0225] Next, the server analyzes the acquired match information using an AI engine. The AI ​​engine uses machine learning algorithms to evaluate the importance of plays and identify "key moments" that fans are likely to be interested in. Based on the identified key moments, it automatically generates digital trading cards that reflect the movements of the relevant players and the dynamics of the match. These cards include player images, performance data, and the story of the match.

[0226] The terminal displays digital storage media to the user through its user interface. Users can log in to their account via the terminal and view previously collected cards, receive new cards, and perform management operations as needed.

[0227] Furthermore, this system includes a gacha (loot box) function that users can participate in. By spending points, users can acquire cards randomly selected by the server and add them to their collection. In this process, there is a chance of obtaining rare cards based on rarity and player popularity.

[0228] As a concrete example of the present invention, consider a scenario where a user logs into a terminal and receives a new digital recording medium based on the day's match results. Upon user login, the server automatically adds the latest generated card to the user's collection. Furthermore, through real-time match updates, users can instantly view new cards and enjoy reviewing matches and interacting with other fans.

[0229] As a result, sports fans are freed from the hassle of managing physical memorabilia, can easily expand their collections in digital format, and have more opportunities to interact with other fans.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] Before the match starts, the server connects to an external API to prepare for retrieving the match schedule, player information, and live data.

[0233] Step 2:

[0234] As soon as the match starts, the server continuously retrieves match data and player performance data in real time via API and stores it in its internal database.

[0235] Step 3:

[0236] The server sends accumulated data to the AI ​​engine, which analyzes it to identify important scenes and plays during the match. The AI ​​automatically selects scoring opportunities and noteworthy plays.

[0237] Step 4:

[0238] The server generates digital trading cards based on key moments extracted by the AI. The cards include player photos, performance data, and the story of the match.

[0239] Step 5:

[0240] The terminal displays the generated digital cards to the user through a user interface. Users can log in to view and organize their collections.

[0241] Step 6:

[0242] Users utilize the gacha function, spending points to randomly acquire new cards.

[0243] Step 7:

[0244] The server accepts gacha requests, uses a random algorithm to select cards, and adds new cards to the user's collection.

[0245] Step 8:

[0246] The device instantly notifies the user of acquired cards and automatically reflects them in their collection. Users can then share or trade newly acquired cards with other fans.

[0247] (Example 1)

[0248] 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."

[0249] The goal is to provide a system that collects sports match information in real time, identifies important moments of interest to fans, and allows users to easily manage and collect this information as a digital recording medium. Furthermore, it aims to promote interaction among fans and eliminate the hassle of managing physical memorabilia.

[0250] 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.

[0251] In this invention, the server includes means for acquiring match information, means for analyzing player performance based on the acquired match information, and means for generating a digital recording medium based on the analysis results. This enables users to effectively collect and manage digital trading cards that identify important moments in matches.

[0252] "Match information" refers to all data related to a sports match, including scores, player statistics, and event data during the match.

[0253] "Player performance" refers to an indicator that shows a player's achievements and abilities, derived from their movements, results, and statistical information during a match.

[0254] A "digital recording medium" is a recording medium that contains images of players, performance data, and match stories, and is managed and displayed electronically.

[0255] A "machine learning algorithm" is an algorithm that uses data to enable computers to learn patterns and make decisions or predictions.

[0256] A "random selection function" is a function that randomly selects from a set of options based on specific conditions or probabilities.

[0257] The following are the embodiments for carrying out the invention.

[0258] This system consists of three main components: a server, terminals, and users. The server is responsible for collecting match information from external data sources and processing it into a digital recording medium. Specifically, it uses the Python programming language and related libraries (e.g., Requests, Pandas) to connect to sports data APIs and collect match scores, player performance, and in-game event data.

[0259] Next, the server analyzes the collected data using a deep learning framework (e.g., TensorFlow or PyTorch) to identify key moments. The analysis uses machine learning algorithms to evaluate player performance and the flow of the game. This identifies decisive moments that fans are likely to be interested in, and based on these results, a generative AI model is used to create digital trading cards.

[0260] The terminal provides an interface with the user, displaying new digital trading cards in real time using a browser-based or mobile application. Users can log in to their account and manage, view, and retrieve their collections on digital media.

[0261] Furthermore, users can utilize the gacha function, allowing them to obtain new cards through random selection. The server executes the random selection function, choosing which cards to offer to users. In this process, appearance probabilities are set considering factors such as rarity and popularity.

[0262] As a concrete example, an example of a prompt message would be: "Identify the most noteworthy moment from today's match and generate a digital trading card based on that moment." This would then be input into the generation AI model. As a result, the user can add the digitally generated card, based on the highlight scene of a specific match, to their collection and enjoy the details.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server collects match information from external data sources. Specifically, it sends requests to sports data APIs to retrieve match scores, player performance data, and in-game event data. This input data is stored in the server's database for analysis.

[0266] Step 2:

[0267] The server analyzes the collected match information using machine learning algorithms. This process takes match data as input and performs data calculations to identify important moments and player highlights. The analysis results then output the key moments.

[0268] Step 3:

[0269] The server uses a generative AI model to create digital trading cards based on the identified key moments. The prompt sent to the model is: "Identify the most noteworthy moments from today's match and generate digital trading cards based on them." A digital storage medium is then generated as the output of this process.

[0270] Step 4:

[0271] The terminal displays the generated digital trading cards to the user. The user can log in through the terminal's interface and view the newly generated cards. During this process, the terminal receives files from the server and displays them on the user interface.

[0272] Step 5:

[0273] The server handles situations where a user utilizes the gacha (loot box) function. The user spends points via their device to perform the gacha, and the server randomly selects a digital trading card. The selected card is added to the user's digital collection in real time.

[0274] (Application Example 1)

[0275] 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."

[0276] There is a need to manage sports match information in real time, select important moments based on that information, and provide an environment that enables users to view and interact with this information in digital format. Furthermore, there is a need for efficient means for users to instantly enjoy the dynamics of a match and easily share information with other users.

[0277] 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.

[0278] In this invention, the server includes means for acquiring game information, means for analyzing the activities of players, and means for generating corresponding digital display materials. Thereby, it becomes possible to provide real-time digital trading cards based on game information.

[0279] "Game information" refers to detailed data regarding sports events, including scores, player performances, ongoing situations, and the like.

[0280] "Player activities" refer to the actions and content of plays performed by sports players during a game, and the dynamics of the game can be understood through the analysis thereof.

[0281] "Digital display materials" are digital recording media created based on game and player information, and digital trading cards are cited as an example.

[0282] "Means for exchanging information" refers to methods for sharing the generated digital display materials among users and for conducting opinion exchanges.

[0283] "Means for users to receive digital trading in real time" refers to methods for users to immediately receive digital trading cards and enjoy them according to the progress of the game.

[0284] The system for implementing this invention consists of three elements: a server, a terminal, and a user.

[0285] The server acquires game information from external data sources through the Internet. Specifically, it collects scores, player performances, and the progress of the game regarding sports events from external APIs and stores the data in a database. The stored data is analyzed using a machine learning model such as TensorFlow to identify important scenes during the game. As a result, digital display materials reflecting the activities of players are automatically generated.

[0286] The terminal plays a role in providing the user with digital display materials generated through the user interface. The applications installed on the terminal are developed using Swift, Kotlin, etc., and can deliver data in real time. As soon as the terminal receives a notification from the server, it immediately visually conveys important information about the game to the user.

[0287] The user can receive the latest information about the game via the terminal and enjoy the generated digital trading cards. Furthermore, the user can share products with other users and communicate through the terminal's application. This interaction enhances the user experience and sustains the interest in the game.

[0288] As a specific example, in the middle of a sports game, a notification saying "Player A scored a decisive goal!" arrives at the user's terminal, and at that moment, a digital trading card is generated and added to the user's collection. This card can be sent as a message to friends, activating real-time exchanges regarding the game.

[0289] Examples of prompt texts input into the generative AI model include "Please generate a digital trading card of the player in focus and the play based on the data of Game X."

[0290] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The server collects game information from an external data source via an internet connection. It uses the game ID and API authentication information as input. It calls an external API to obtain data regarding the score, player performance, and game progress. The acquired data is saved in a database.

[0293] Step 2:

[0294] The server applies a machine learning model to the stored match data. The input is match information stored in a database. Generative AI models such as TensorFlow are used to perform analysis to identify important moments in the match. The output of this analysis is a list of important moments and players to watch out for.

[0295] Step 3:

[0296] The server generates digital display materials based on identified key moments. The prompt is: "Generate digital trading cards of featured players and plays based on match data." The generation AI model uses this prompt to create digital trading cards containing player images and performance data. The output is digital display material for distribution to the user.

[0297] Step 4:

[0298] The terminal receives digital display materials distributed from the server. It uses digital trading card data sent from the server as input. The application installed on the terminal displays this data on the user interface and notifies the user. The output is the user's visual display of the data.

[0299] Step 5:

[0300] Users receive digital trading cards generated via their device and add them to their collection. The application operates based on the information displayed on the digital trading card from the device as input. Users can use the application's features to save cards and share them with other users. The output is the user's updated digital collection and shared information.

[0301] 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.

[0302] This invention relates to a digital platform that, in addition to processing sports-related match information and providing a digital recording medium, incorporates an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user, as well as an emotion engine that identifies user emotions and optimizes interactions.

[0303] First, the server retrieves match information from an external API. This match data includes detailed information about player performance and the progress of the match, and is stored on the server. The server then uses an AI engine to select key moments from the match and generate digital trading cards. These cards include player images, performance statistics, and a match story.

[0304] Next, the server uses an emotion engine to collect the user's emotions. Through audio data and video feeds obtained from the user's device, the emotion engine analyzes emotional states, including joy, sadness, and excitement. This analysis influences the content and card selections provided by the server.

[0305] The device not only displays the generated digital recording media to the user through its user interface, but also provides interaction that responds to the user's emotional state. For example, if the device determines that the user is happy, it can prompt them to experience match highlights or acquire rare cards to further excite them.

[0306] Furthermore, the user can randomly obtain cards using the gacha function. At this time, the emotion engine can analyze the user's psychological tendencies in real time and adjust the gacha results to better match the user's interests. As a result, the cards and information that the user is most interested in can be intuitively presented, improving the satisfaction level.

[0307] As a specific example of the present invention, there is a scene where after the end of a game, the user checks the game summary through a terminal and is provided with a comfortable experience through emotional analysis. Based on the user's reaction, access to alternative highlights of related games and premium content is proposed.

[0308] Thus, according to the present invention, the user can continuously obtain valuable experiences from both the game data and the emotion data, and a sports fan experience in a digital environment without physical constraints is realized.

[0309] The processing flow will be described below.

[0310] Step 1:

[0311] Before the start of the game, the server connects to an external API and prepares to obtain the game schedule, player information, and real-time game data.

[0312] Step 2:

[0313] When the game starts, the server saves the obtained game data in a database and uses an AI engine to analyze important scenes of the game in real time.

[0314] Step 3:

[0315] Based on the important scenes identified by the AI engine, the server generates digital trading cards including player images and game performance data.

[0316] Step 4:

[0317] The terminal displays the generated digital trading cards to the user. After logging in, the user can manage their collection and view card details.

[0318] Step 5:

[0319] While the user is interacting with the application, the device collects data through an interface that captures the user's reactions (e.g., audio and video).

[0320] Step 6:

[0321] Using data sent from the terminal, the emotion engine installed on the server analyzes the user's current emotional state.

[0322] Step 7:

[0323] Based on sentiment analysis results, the server dynamically adjusts content for the user and determines what content to recommend. For example, if it determines that the user is enjoying themselves, it will suggest further highlights or rare cards.

[0324] Step 8:

[0325] When a user uses the gacha function, the server adjusts the randomly selected cards based on the emotion engine, presenting the cards that are most likely to interest them.

[0326] Step 9:

[0327] The device displays the gacha results to the user and adds newly acquired cards to their collection. The user can then use this information to interact with other fans and manage their own cards.

[0328] (Example 2)

[0329] 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".

[0330] In recent years, there has been a growing demand to enrich the competitive experience through digital media by visualizing participants' actions in real time and providing information tailored to the user's emotional state. However, conventional systems have been unable to adequately enhance user satisfaction because they provide one-way information and lack interaction that adapts to the user's emotions. In addition, they have been unable to adjust information according to the user's interests, making it difficult to improve the quality of the digital experience.

[0331] 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.

[0332] In this invention, the server includes means for acquiring competition information, means for analyzing participants' actions based on the acquired competition information, and means for adjusting the content of the medium based on the analyzed emotions. This enables the provision of information that matches the user's emotional state and allows for two-way interaction.

[0333] "Competition" refers to an activity or event in which participants compete for performance or results based on specific rules.

[0334] "Information" refers to facts and knowledge treated as data, which are acquired, processed, or provided for a specific purpose.

[0335] "Participants" refer to individuals or teams participating in a competition or event, and their actions and results are the subject of recording and analysis.

[0336] "Movement" refers to the physical movements and actions that participants perform during the competition, and is an element of performance evaluation.

[0337] "Digital media" refers to information content that is stored or displayed in an electronic format and can be accessed or received by a user.

[0338] "User" refers to an individual or organization that receives information or services through a system or platform.

[0339] "Emotion" refers to the psychological response that a user shows to a particular situation or stimulus, and includes states such as joy, excitement, and sadness.

[0340] "Interaction" refers to two-way communication or response that takes place between a system and a user.

[0341] "Adjustment" refers to the act of changing or optimizing the operation or content of a system in accordance with specific conditions or circumstances.

[0342] This system is designed to acquire information related to the competition, analyze participants' behavior, and provide digital media based on the user's emotions. The following shows a specific example of how the system works.

[0343] The server obtains detailed competition information from external sources via APIs. In this process, it receives participant action data and competition progress in a specific data format (e.g., JSON) and stores it in its own database. Existing sports data provision services are often used for this purpose. Machine learning frameworks such as TensorFlow and PyTorch are used as the AI ​​engine to extract competition highlights and analyze participant performance.

[0344] Based on the analyzed information, the server generates digital media containing participants' images and scores. This media is later provided to users and plays a crucial role in supporting their interactions.

[0345] Simultaneously, the device acquires voice and video input from the user and sends it to the server. This data is processed by an emotion analysis engine (e.g., Azure Cognitive Services) to recognize the user's emotional state in real time. This allows for the adjustment of digital content based on emotions such as tension, excitement, and relaxation.

[0346] Based on the generated digital media and analysis results, users receive media that has been adjusted for easy reading via their devices, and can also interact with cards and content randomly acquired through a gacha function. In this process, the user's past interests and emotional state are taken into consideration to provide an individually optimized media experience.

[0347] As a concrete example, imagine a user is watching highlights of a match on their device after it has finished. If the emotion analysis engine detects that the user is excited via voice input, the device will display more match-related content and special offers. This further enhances the user's interest and deepens their experience of the sport.

[0348] An example of a prompt message might be: "Please display the results of last night's basketball game and highlights based on the user's excitement. Please also suggest gacha cards that match the user's emotions." This aims to provide users with an optimal sports viewing experience and establish a new form of entertainment on digital devices.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The server retrieves competition information via an external API. Inputs include parameters such as a specific competition ID and date / time, while output is detailed competition information (participant data in JSON format, scores, action logs, etc.). The server stores this data in a database, forming the basis for subsequent data analysis.

[0352] Step 2:

[0353] The server analyzes competition data stored in the database using an AI engine. The input is the data acquired in Step 1, and the output is analysis results including the identification of important moments and an evaluation of participant performance. Specifically, the process involves analyzing action data using a TensorFlow model and extracting highlight scenes.

[0354] Step 3:

[0355] The server generates digital trading cards based on the analysis results. The input is the analysis results from step 2, and the output generates card data including player images, competition statistics, and match stories. These cards are stored in a database and become digital content provided to users.

[0356] Step 4:

[0357] The device collects voice input and video data from the user and transmits it to the server in real time. The input is raw voice and video data obtained from the user, and the output is prepared as a dataset for processing by the emotion analysis engine. Specific operations include data collection using microphones and cameras.

[0358] Step 5:

[0359] The server analyzes the transmitted audio and video data by passing it through an emotion analysis engine. The input is the dataset from step 4, and the output is the user's emotional state (e.g., joy, sadness, excitement). This analysis utilizes Azure Cognitive Services or similar technologies.

[0360] Step 6:

[0361] The device dynamically changes the UI based on the emotional state received from the server. The input is the emotional state information from step 5, and the output is a modified digital interface presented to the user. For example, if the user is excited, a colorful UI and animation effects are used to display match highlights or special offers.

[0362] Step 7:

[0363] Users acquire digital cards using the gacha function provided on their device. In this process, the gacha draw results are optimized for the individual based on their emotional state as a result of the content provided in step 6. The inputs are the user's selections and the server's emotional analysis results, and the output is a set of cards that are most interesting to the user.

[0364] (Application Example 2)

[0365] 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."

[0366] This solution addresses the challenge of dynamically changing content to reflect user emotions and provide a more personalized experience when generating digital recordings of sports matches. Furthermore, conventional random selection functions fail to accurately capture user interests, limiting methods for increasing satisfaction.

[0367] 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.

[0368] In this invention, the server includes means for acquiring match information, means for analyzing player performance, means for generating a digital recording medium based on the analysis results, means for exchanging information with the user using the generated digital recording medium, and means for analyzing the user's emotions and dynamically adjusting the displayed content based on the analysis results. This makes it possible to provide personalized content that responds to the user's emotions. Furthermore, even in the random selection function, by optimizing the selection results while considering the user's emotional state, it becomes possible to accurately capture the user's interests and improve satisfaction.

[0369] "Match information" refers to data related to a sporting event, including player performance and the progress of the match.

[0370] "Means of analyzing player performance" refers to software or algorithms used to analyze player behavior and performance based on acquired match information.

[0371] A "digital recording medium" is data in digital format that includes images of athletes, competition results, and match stories, and that can be stored and displayed electronically.

[0372] A "means of exchanging information with users" refers to a system that displays the generated digital recording medium on the user's terminal, allowing the user to respond or provide feedback accordingly.

[0373] "Means for analyzing emotions and dynamically adjusting displayed content based on the analysis results" refers to algorithms or software that identify emotions from data such as the user's facial expressions and voice, and change related information in real time.

[0374] The "random selection function" is a function that randomly selects and presents digital recording media to the user.

[0375] "Optimizing selection results" means adjusting randomly selected results to match the user's interests, based on the user's current emotional state and past preferences.

[0376] The system implementing this invention consists of three main components: a server, a terminal, and a user. The server acquires match information and utilizes artificial intelligence to analyze player performance based on that information. Specifically, the server acquires match information from an external sports data API and uses software such as OpenCV and TensorFlow to analyze player performance in detail. Based on this analysis, digital trading cards are generated. The cards contain information such as player images, match results, and the story of the match.

[0377] The terminal displays generated digital trading cards to the user and analyzes the user's emotions in real time. It uses the Google Cloud Speech-to-Text API to collect the user's facial expressions and voice data via the smartphone's camera and microphone, and to identify emotions. Based on the emotion analysis results, it dynamically adjusts the displayed content to match the user's interests and emotions.

[0378] Users can acquire digital recording media through their devices. Furthermore, a random selection function based on sentiment analysis allows users to acquire premium content in a gacha (loot box) format. The selection is optimized according to the user's emotional state, designed to enhance user satisfaction.

[0379] For example, when a user is watching a live broadcast of a sporting event on their smartphone, the system recognizes the user's enthusiastic reaction and immediately presents highlights of past memorable matches featuring the same team. Simultaneously, it can further pique the user's interest by randomly providing trading cards of notable players.

[0380] An example of a prompt for a generative AI model might be: "If the user's emotion is 'excited,' select highlights from relevant sporting events and suggest premium content that will maintain the excitement."

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The server retrieves match information from an external sports data API. This information includes data such as player performance, match progress, and scores. The input is match-related information from the sports data API, and the output is match information stored in the server's database. Once this storage is complete, the server can proceed to the next analysis step.

[0384] Step 2:

[0385] The server analyzes player performance using software such as OpenCV and TensorFlow based on the acquired match information. Specifically, it analyzes data related to player movements and performance, and quantifies and evaluates the results. The input is the match information acquired in step 1, and the output is an evaluation score for each player and identification of important scenes. This prepares the data necessary for generating the next trading cards.

[0386] Step 3:

[0387] The server generates digital trading cards based on the analysis results. These cards include player images, performance statistics, and match stories. The input is the analysis results obtained in step 2, and the output is the digital trading card for display to the user. The generated card is sent from the server to the terminal.

[0388] Step 4:

[0389] The terminal displays digital trading cards received from the server to the user. The terminal also uses a camera and microphone to collect user emotion data (facial expressions and voice). Inputs include digital trading cards from the server and user emotion data, while outputs include content presentation on the user's screen and further analysis of the collected data. The emotion data is used in the subsequent emotion analysis step.

[0390] Step 5:

[0391] The device analyzes the collected facial and voice data using the Google Cloud Speech-to-Text API to infer the user's emotional state. The input is the emotional data collected in step 4, and the output is the user's emotional state (e.g., excitement, joy, sadness). This emotional state is used to dynamically adjust the content displayed.

[0392] Step 6:

[0393] The server dynamically adjusts the content presented to the user based on the sentiment analysis results. If the server determines that the user is excited, it selects and displays highlights or premium content related to relevant sporting events. The input is the sentiment analysis results from step 5, and the output is the customized content displayed on the user's device. The user can achieve further satisfaction at this stage.

[0394] Step 7:

[0395] Users acquire digital trading cards using a random selection function on their device. In this case, the server optimizes the content of the cards acquired based on the user's emotional state. The input is the user's emotional data and the random selection function's request, and the output is premium content tailored to the user's interests. This makes it possible to further enhance user satisfaction.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] The present invention provides a system for acquiring sports match information in real time and automatically generating and managing it as a digital recording medium. This platform consists of three elements: a server, a terminal, and a user, each of which plays a specific role in its operation.

[0413] First, the server collects sports match information from various data sources. It connects to external APIs in accordance with the match start time to retrieve scoreboards, player performance statistics, and detailed event data regarding the match's progress. This information is stored in the server's database and used for subsequent data analysis.

[0414] Next, the server analyzes the acquired match information using an AI engine. The AI ​​engine uses machine learning algorithms to evaluate the importance of plays and identify "key moments" that fans are likely to be interested in. Based on the identified key moments, it automatically generates digital trading cards that reflect the movements of the relevant players and the dynamics of the match. These cards include player images, performance data, and the story of the match.

[0415] The terminal displays digital storage media to the user through its user interface. Users can log in to their account via the terminal and view previously collected cards, receive new cards, and perform management operations as needed.

[0416] Furthermore, this system includes a gacha (loot box) function that users can participate in. By spending points, users can acquire cards randomly selected by the server and add them to their collection. In this process, there is a chance of obtaining rare cards based on rarity and player popularity.

[0417] As a concrete example of the present invention, consider a scenario where a user logs into a terminal and receives a new digital recording medium based on the day's match results. Upon user login, the server automatically adds the latest generated card to the user's collection. Furthermore, through real-time match updates, users can instantly view new cards and enjoy reviewing matches and interacting with other fans.

[0418] As a result, sports fans are freed from the hassle of managing physical memorabilia, can easily expand their collections in digital format, and have more opportunities to interact with other fans.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] Before the match starts, the server connects to an external API to prepare for retrieving the match schedule, player information, and live data.

[0422] Step 2:

[0423] As soon as the match starts, the server continuously retrieves match data and player performance data in real time via API and stores it in its internal database.

[0424] Step 3:

[0425] The server sends accumulated data to the AI ​​engine, which analyzes it to identify important scenes and plays during the match. The AI ​​automatically selects scoring opportunities and noteworthy plays.

[0426] Step 4:

[0427] The server generates digital trading cards based on key moments extracted by the AI. The cards include player photos, performance data, and the story of the match.

[0428] Step 5:

[0429] The terminal displays the generated digital cards to the user through a user interface. Users can log in to view and organize their collections.

[0430] Step 6:

[0431] Users utilize the gacha function, spending points to randomly acquire new cards.

[0432] Step 7:

[0433] The server accepts gacha requests, uses a random algorithm to select cards, and adds new cards to the user's collection.

[0434] Step 8:

[0435] The device instantly notifies the user of acquired cards and automatically reflects them in their collection. Users can then share or trade newly acquired cards with other fans.

[0436] (Example 1)

[0437] 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."

[0438] The goal is to provide a system that collects sports match information in real time, identifies important moments of interest to fans, and allows users to easily manage and collect this information as a digital recording medium. Furthermore, it aims to promote interaction among fans and eliminate the hassle of managing physical memorabilia.

[0439] 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.

[0440] In this invention, the server includes means for acquiring match information, means for analyzing player performance based on the acquired match information, and means for generating a digital recording medium based on the analysis results. This enables users to effectively collect and manage digital trading cards that identify important moments in matches.

[0441] "Match information" refers to all data related to a sports match, including scores, player statistics, and event data during the match.

[0442] "Player performance" refers to an indicator that shows a player's achievements and abilities, derived from their movements, results, and statistical information during a match.

[0443] A "digital recording medium" is a recording medium that contains images of players, performance data, and match stories, and is managed and displayed electronically.

[0444] A "machine learning algorithm" is an algorithm that uses data to enable computers to learn patterns and make decisions or predictions.

[0445] A "random selection function" is a function that randomly selects from a set of options based on specific conditions or probabilities.

[0446] The following are the embodiments for carrying out the invention.

[0447] This system consists of three main components: a server, terminals, and users. The server is responsible for collecting match information from external data sources and processing it into a digital recording medium. Specifically, it uses the Python programming language and related libraries (e.g., Requests, Pandas) to connect to sports data APIs and collect match scores, player performance, and in-game event data.

[0448] Next, the server analyzes the collected data using a deep learning framework (e.g., TensorFlow or PyTorch) to identify key moments. The analysis uses machine learning algorithms to evaluate player performance and the flow of the game. This identifies decisive moments that fans are likely to be interested in, and based on these results, a generative AI model is used to create digital trading cards.

[0449] The terminal provides an interface with the user, displaying new digital trading cards in real time using a browser-based or mobile application. Users can log in to their account and manage, view, and retrieve their collections on digital media.

[0450] Furthermore, users can utilize the gacha function, allowing them to obtain new cards through random selection. The server executes the random selection function, choosing which cards to offer to users. In this process, appearance probabilities are set considering rarity and popularity.

[0451] As a concrete example, an example of a prompt message would be: "Identify the most noteworthy moment from today's match and generate a digital trading card based on that moment." This would then be input into the generation AI model. As a result, the user can add the digitally generated card, based on the highlight scene of a specific match, to their collection and enjoy the details.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The server collects match information from external data sources. Specifically, it sends requests to sports data APIs to retrieve match scores, player performance data, and in-game event data. This input data is stored in the server's database for analysis.

[0455] Step 2:

[0456] The server analyzes the collected match information using machine learning algorithms. This process takes match data as input and performs data calculations to identify important moments and player highlights. The analysis results then output the key moments.

[0457] Step 3:

[0458] The server uses a generative AI model to create digital trading cards based on the identified key moments. The prompt sent to the model is: "Identify the most noteworthy moments from today's match and generate digital trading cards based on them." A digital storage medium is then generated as the output of this process.

[0459] Step 4:

[0460] The terminal displays the generated digital trading cards to the user. The user can log in through the terminal's interface and view the newly generated cards. During this process, the terminal receives files from the server and displays them on the user interface.

[0461] Step 5:

[0462] The server handles situations where a user utilizes the gacha (loot box) function. The user spends points via their device to perform the gacha, and the server randomly selects a digital trading card. The selected card is added to the user's digital collection in real time.

[0463] (Application Example 1)

[0464] 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."

[0465] There is a need to manage sports match information in real time, select important moments based on that information, and provide an environment that enables users to view and interact with this information in digital format. Furthermore, there is a need for efficient means for users to instantly enjoy the dynamics of a match and easily share information with other users.

[0466] 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.

[0467] In this invention, the server includes means for acquiring match information, means for analyzing player activity, and means for generating corresponding digital display materials. This makes it possible to provide digital trading cards in real time based on match information.

[0468] "Match information" refers to detailed data about a sporting event, including scores, player performance, and ongoing situations.

[0469] "Athlete activity" refers to the actions and plays performed by athletes during a match, and its analysis allows us to understand the dynamics of the game.

[0470] "Digital display materials" are digital recording media created based on information about matches and players, with digital trading cards being an example.

[0471] "Means of exchanging information" refers to methods for sharing generated digital display materials among users and for exchanging opinions.

[0472] "A means for users to receive digital trading in real time" refers to a method that allows users to instantly receive and enjoy digital trading cards as the game progresses.

[0473] The system for implementing this invention consists of three elements: a server, a terminal, and a user.

[0474] The server retrieves match information from external data sources via the internet. Specifically, it collects scores, player performance, and match progress related to sports events from external APIs and stores this data in a database. The stored data is analyzed using machine learning models such as TensorFlow to identify important moments during the match. As a result, digital display materials reflecting the players' activities are automatically generated.

[0475] The terminal plays the role of providing users with digitally displayed materials generated through a user interface. The applications installed on the terminal are developed using languages ​​such as Swift and Kotlin, enabling real-time data delivery. As soon as the terminal receives notifications from the server, it instantly and visually conveys important match information to the user.

[0476] Users can receive the latest match information via their devices and enjoy the generated digital trading cards. Furthermore, users can share their creations with other users and communicate through the application on their devices. This interaction enhances the user experience and sustains interest in the matches.

[0477] A concrete example would be a notification arriving on a user's device during a sports match saying, "Player A has scored a decisive goal!", and a digital trading card of that moment would be generated and added to the user's collection. This card could then be sent to friends via message, stimulating real-time communication about the match.

[0478] Examples of prompts to input into the generation AI model include, "Generate digital trading cards of featured players and plays based on the data from match X."

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The server collects match information from external data sources via an internet connection. It uses match IDs and API authentication information as input. It calls external APIs to retrieve data such as scores, player performance, and match progress. The retrieved data is stored in a database.

[0482] Step 2:

[0483] The server applies a machine learning model to the stored match data. The input is match information stored in a database. Generative AI models such as TensorFlow are used to perform analysis to identify important moments in the match. The output of this analysis is a list of important moments and players to watch out for.

[0484] Step 3:

[0485] The server generates digital display materials based on identified key moments. The prompt is: "Generate digital trading cards of featured players and plays based on match data." The generation AI model uses this prompt to create digital trading cards containing player images and performance data. The output is digital display material for distribution to the user.

[0486] Step 4:

[0487] The terminal receives digital display materials distributed from the server. It uses digital trading card data sent from the server as input. The application installed on the terminal displays this data on the user interface and notifies the user. The output is the user's visual display of the data.

[0488] Step 5:

[0489] Users receive digital trading cards generated via their device and add them to their collection. The application operates based on the information displayed on the digital trading card from the device as input. Users can use the application's features to save cards and share them with other users. The output is the user's updated digital collection and shared information.

[0490] 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.

[0491] This invention relates to a digital platform that, in addition to processing sports-related match information and providing digital recording media, incorporates an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user, as well as an emotion engine that identifies user emotions and optimizes interactions.

[0492] First, the server retrieves match information from an external API. This match data includes detailed information about player performance and the progress of the match, and is stored on the server. The server then uses an AI engine to select key moments from the match and generate digital trading cards. These cards include player images, performance statistics, and a match story.

[0493] Next, the server uses an emotion engine to collect the user's emotions. Through audio data and video feeds obtained from the user's device, the emotion engine analyzes emotional states, including joy, sadness, and excitement. This analysis influences the content and card selections provided by the server.

[0494] The device not only displays the generated digital recording medium to the user through its user interface, but also provides interaction that responds to the user's emotional state. For example, if the device determines that the user is happy, it can prompt them to experience match highlights or acquire rare cards to further excite them.

[0495] Furthermore, users can acquire cards randomly using a gacha (loot box) function. During this process, an emotion engine analyzes the user's psychological tendencies in real time, allowing it to adjust the gacha results to better match the user's interests. This intuitively presents the cards and information that the user is most interested in, thereby improving user satisfaction.

[0496] A concrete example of the present invention is a scenario where, after a match ends, the user reviews a summary of the match via their device and is provided with a pleasant experience through sentiment analysis. Based on the user's response, access to alternative highlights of related matches or premium content is proposed.

[0497] Thus, the present invention enables users to continuously acquire valuable experiences from both match data and emotional data, realizing a sports fan experience in a digital environment free from physical constraints.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The server connects to an external API before the match begins to prepare to retrieve the match schedule, player information, and real-time match data.

[0501] Step 2:

[0502] Once the match begins, the server stores the acquired match data in a database and uses an AI engine to analyze key moments of the match in real time.

[0503] Step 3:

[0504] Based on key moments identified by the AI ​​engine, the server generates digital trading cards containing player images and match performance data.

[0505] Step 4:

[0506] The terminal displays the generated digital trading cards to the user. After logging in, the user can manage their collection and view card details.

[0507] Step 5:

[0508] While the user is interacting with the application, the device collects data through an interface that captures the user's reactions (e.g., audio and video).

[0509] Step 6:

[0510] Using data sent from the terminal, the emotion engine installed on the server analyzes the user's current emotional state.

[0511] Step 7:

[0512] Based on sentiment analysis results, the server dynamically adjusts content for the user and determines what content to recommend. For example, if it determines that the user is enjoying themselves, it will suggest further highlights or rare cards.

[0513] Step 8:

[0514] When a user uses the gacha function, the server adjusts the randomly selected cards based on the emotion engine, presenting the cards that are most likely to interest them.

[0515] Step 9:

[0516] The device displays the gacha results to the user and adds newly acquired cards to their collection. The user can then use this information to interact with other fans and manage their own cards.

[0517] (Example 2)

[0518] 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."

[0519] In recent years, there has been a growing demand to enrich the competitive experience through digital media by visualizing participants' actions in real time and providing information tailored to the user's emotional state. However, conventional systems have been unable to adequately enhance user satisfaction because they provide one-way information and lack interaction that adapts to the user's emotions. In addition, they have been unable to adjust information according to the user's interests, making it difficult to improve the quality of the digital experience.

[0520] 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.

[0521] In this invention, the server includes means for acquiring competition information, means for analyzing participants' actions based on the acquired competition information, and means for adjusting the content of the medium based on the analyzed emotions. This enables the provision of information that matches the user's emotional state and allows for two-way interaction.

[0522] "Competition" refers to an activity or event in which participants compete for performance or results based on specific rules.

[0523] "Information" refers to facts and knowledge treated as data, which are acquired, processed, or provided for a specific purpose.

[0524] "Participants" refer to individuals or teams participating in a competition or event, and their actions and results are the subject of recording and analysis.

[0525] "Movement" refers to the physical movements and actions that participants perform during the competition, and is an element of performance evaluation.

[0526] "Digital media" refers to information content that is stored or displayed in an electronic format and can be accessed or received by a user.

[0527] "User" refers to an individual or organization that receives information or services through a system or platform.

[0528] "Emotion" refers to the psychological response that a user shows to a particular situation or stimulus, and includes states such as joy, excitement, and sadness.

[0529] "Interaction" refers to two-way communication or response that takes place between a system and a user.

[0530] "Adjustment" refers to the act of changing or optimizing the operation or content of a system in accordance with specific conditions or circumstances.

[0531] This system is designed to acquire information related to the competition, analyze participants' behavior, and provide digital media based on the user's emotions. The following shows a specific example of how the system works.

[0532] The server obtains detailed competition information from external sources via APIs. In this process, it receives participant action data and competition progress in a specific data format (e.g., JSON) and stores it in its own database. Existing sports data provision services are often used for this purpose. Machine learning frameworks such as TensorFlow and PyTorch are used as the AI ​​engine to extract competition highlights and analyze participant performance.

[0533] Based on the analyzed information, the server generates digital media containing participants' images and scores. This media is later provided to users and plays a crucial role in supporting their interactions.

[0534] Simultaneously, the device acquires voice and video input from the user and sends it to the server. This data is processed by an emotion analysis engine (e.g., Azure Cognitive Services) to recognize the user's emotional state in real time. This allows for the adjustment of digital content based on emotions such as tension, excitement, and relaxation.

[0535] Based on the generated digital media and analysis results, users receive media that has been adjusted for easy reading via their devices, and can also interact with cards and content randomly acquired through a gacha function. In this process, the user's past interests and emotional state are taken into consideration to provide an individually optimized media experience.

[0536] As a concrete example, imagine a user is watching highlights of a match on their device after it has finished. If the emotion analysis engine detects that the user is excited via voice input, the device will display more match-related content and special offers. This further enhances the user's interest and deepens their experience of the sport.

[0537] An example of a prompt message might be: "Please display the results of last night's basketball game and highlights based on the user's excitement. Please also suggest gacha cards that match the user's emotions." This aims to provide users with an optimal sports viewing experience and establish a new form of entertainment on digital devices.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The server retrieves competition information via an external API. Inputs include parameters such as a specific competition ID and date / time, while output is detailed competition information (participant data in JSON format, scores, action logs, etc.). The server stores this data in a database, forming the basis for subsequent data analysis.

[0541] Step 2:

[0542] The server analyzes competition data stored in the database using an AI engine. The input is the data acquired in Step 1, and the output is analysis results including the identification of important moments and an evaluation of participant performance. Specifically, the process involves analyzing action data using a TensorFlow model and extracting highlight scenes.

[0543] Step 3:

[0544] The server generates digital trading cards based on the analysis results. The input is the analysis results from step 2, and the output generates card data including player images, competition statistics, and match stories. These cards are stored in a database and become digital content provided to users.

[0545] Step 4:

[0546] The device collects voice input and video data from the user and transmits it to the server in real time. The input is raw voice and video data obtained from the user, and the output is prepared as a dataset for processing by the emotion analysis engine. Specific operations include data collection using microphones and cameras.

[0547] Step 5:

[0548] The server analyzes the transmitted audio and video data by passing it through an emotion analysis engine. The input is the dataset from step 4, and the output is the user's emotional state (e.g., joy, sadness, excitement). This analysis utilizes Azure Cognitive Services or similar technologies.

[0549] Step 6:

[0550] The device dynamically changes the UI based on the emotional state received from the server. The input is the emotional state information from step 5, and the output is a modified digital interface presented to the user. For example, if the user is excited, a colorful UI and animation effects are used to display match highlights or special offers.

[0551] Step 7:

[0552] Users acquire digital cards using the gacha function provided on their device. In this process, the gacha draw results are optimized for the individual based on their emotional state as a result of the content provided in step 6. The inputs are the user's selections and the server's emotional analysis results, and the output is a set of cards that are most interesting to the user.

[0553] (Application Example 2)

[0554] 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."

[0555] This solution addresses the challenge of dynamically changing content to reflect user emotions and provide a more personalized experience when generating digital recordings of sports matches. Furthermore, conventional random selection functions fail to accurately capture user interests, limiting methods for increasing satisfaction.

[0556] 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.

[0557] In this invention, the server includes means for acquiring match information, means for analyzing player performance, means for generating a digital recording medium based on the analysis results, means for exchanging information with the user using the generated digital recording medium, and means for analyzing the user's emotions and dynamically adjusting the displayed content based on the analysis results. This makes it possible to provide personalized content that responds to the user's emotions. Furthermore, even in the random selection function, by optimizing the selection results while considering the user's emotional state, it becomes possible to accurately capture the user's interests and improve satisfaction.

[0558] "Match information" refers to data related to a sporting event, including player performance and the progress of the match.

[0559] "Means of analyzing player performance" refers to software or algorithms used to analyze player behavior and performance based on acquired match information.

[0560] A "digital recording medium" is data in digital format that includes images of athletes, competition results, and match stories, and that can be stored and displayed electronically.

[0561] A "means of exchanging information with users" refers to a system that displays the generated digital recording medium on the user's terminal, allowing the user to respond or provide feedback accordingly.

[0562] "Means for analyzing emotions and dynamically adjusting displayed content based on the analysis results" refers to algorithms or software that identify emotions from data such as the user's facial expressions and voice, and change related information in real time.

[0563] The "random selection function" is a function that randomly selects and presents digital recording media to the user.

[0564] "Optimizing selection results" means adjusting randomly selected results to match the user's interests, based on the user's current emotional state and past preferences.

[0565] The system implementing this invention consists of three main components: a server, a terminal, and a user. The server acquires match information and utilizes artificial intelligence to analyze player performance based on that information. Specifically, the server acquires match information from an external sports data API and uses software such as OpenCV and TensorFlow to analyze player performance in detail. Based on this analysis, digital trading cards are generated. The cards contain information such as player images, match results, and the story of the match.

[0566] The terminal displays generated digital trading cards to the user and analyzes the user's emotions in real time. It uses the Google Cloud Speech-to-Text API to collect the user's facial expressions and voice data via the smartphone's camera and microphone, and to identify emotions. Based on the emotion analysis results, it dynamically adjusts the displayed content to match the user's interests and emotions.

[0567] Users can acquire digital recording media through their devices. Furthermore, a random selection function based on sentiment analysis allows users to acquire premium content in a gacha (loot box) format. The selection is optimized according to the user's emotional state, designed to enhance user satisfaction.

[0568] For example, when a user is watching a live broadcast of a sporting event on their smartphone, the system recognizes the user's enthusiastic reaction and immediately presents highlights of past memorable matches featuring the same team. Simultaneously, it can further pique the user's interest by randomly providing trading cards of notable players.

[0569] An example of a prompt for a generative AI model might be: "If the user's emotion is 'excited,' select highlights from relevant sporting events and suggest premium content that will maintain the excitement."

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The server retrieves match information from an external sports data API. This information includes data such as player performance, match progress, and scores. The input is match-related information from the sports data API, and the output is match information stored in the server's database. Once this storage is complete, the server can proceed to the next analysis step.

[0573] Step 2:

[0574] The server analyzes player performance using software such as OpenCV and TensorFlow based on the acquired match information. Specifically, it analyzes data related to player movements and performance, and quantifies and evaluates the results. The input is the match information acquired in step 1, and the output is an evaluation score for each player and identification of important scenes. This prepares the data necessary for generating the next trading cards.

[0575] Step 3:

[0576] The server generates digital trading cards based on the analysis results. These cards include player images, performance statistics, and match stories. The input is the analysis results obtained in step 2, and the output is the digital trading card for display to the user. The generated card is sent from the server to the terminal.

[0577] Step 4:

[0578] The terminal displays digital trading cards received from the server to the user. The terminal also uses a camera and microphone to collect user emotion data (facial expressions and voice). Inputs include digital trading cards from the server and user emotion data, while outputs include content presentation on the user's screen and further analysis of the collected data. The emotion data is used in the subsequent emotion analysis step.

[0579] Step 5:

[0580] The device analyzes the collected facial and voice data using the Google Cloud Speech-to-Text API to infer the user's emotional state. The input is the emotional data collected in step 4, and the output is the user's emotional state (e.g., excitement, joy, sadness). This emotional state is used to dynamically adjust the content displayed.

[0581] Step 6:

[0582] The server dynamically adjusts the content presented to the user based on the sentiment analysis results. If the server determines that the user is excited, it selects and displays highlights or premium content related to relevant sporting events. The input is the sentiment analysis results from step 5, and the output is the customized content displayed on the user's device. The user can achieve further satisfaction at this stage.

[0583] Step 7:

[0584] Users acquire digital trading cards using a random selection function on their device. In this case, the server optimizes the content of the cards acquired based on the user's emotional state. The input is the user's emotional data and the random selection function's request, and the output is premium content tailored to the user's interests. This makes it possible to further enhance user satisfaction.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] The present invention provides a system for acquiring sports match information in real time and automatically generating and managing it as a digital recording medium. This platform consists of three elements: a server, a terminal, and a user, each of which plays a specific role in its operation.

[0603] First, the server collects sports match information from various data sources. It connects to external APIs in accordance with the match start time to retrieve scoreboards, player performance statistics, and detailed event data regarding the match's progress. This information is stored in the server's database and used for subsequent data analysis.

[0604] Next, the server analyzes the acquired match information using an AI engine. The AI ​​engine uses machine learning algorithms to evaluate the importance of plays and identify "key moments" that fans are likely to be interested in. Based on the identified key moments, it automatically generates digital trading cards that reflect the movements of the relevant players and the dynamics of the match. These cards include player images, performance data, and the story of the match.

[0605] The terminal displays digital storage media to the user through its user interface. Users can log in to their account via the terminal and view previously collected cards, receive new cards, and perform management operations as needed.

[0606] Furthermore, this system includes a gacha (loot box) function that users can participate in. By spending points, users can acquire cards randomly selected by the server and add them to their collection. In this process, there is a chance of obtaining rare cards based on rarity and player popularity.

[0607] As a concrete example of the present invention, consider a scenario where a user logs into a terminal and receives a new digital recording medium based on the day's match results. Upon user login, the server automatically adds the latest generated card to the user's collection. Furthermore, through real-time match updates, users can instantly view new cards and enjoy reviewing matches and interacting with other fans.

[0608] As a result, sports fans are freed from the hassle of managing physical memorabilia, can easily expand their collections in digital format, and have more opportunities to interact with other fans.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] Before the match starts, the server connects to an external API to prepare for retrieving the match schedule, player information, and live data.

[0612] Step 2:

[0613] As soon as the match starts, the server continuously retrieves match data and player performance data in real time via API and stores it in its internal database.

[0614] Step 3:

[0615] The server sends accumulated data to the AI ​​engine, which analyzes it to identify important scenes and plays during the match. The AI ​​automatically selects scoring opportunities and noteworthy plays.

[0616] Step 4:

[0617] The server generates digital trading cards based on key moments extracted by the AI. The cards include player photos, performance data, and the story of the match.

[0618] Step 5:

[0619] The terminal displays the generated digital cards to the user through a user interface. Users can log in to view and organize their collections.

[0620] Step 6:

[0621] Users utilize the gacha function, spending points to randomly acquire new cards.

[0622] Step 7:

[0623] The server accepts gacha requests, uses a random algorithm to select cards, and adds new cards to the user's collection.

[0624] Step 8:

[0625] The device instantly notifies the user of acquired cards and automatically reflects them in their collection. Users can then share or trade newly acquired cards with other fans.

[0626] (Example 1)

[0627] 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".

[0628] The goal is to provide a system that collects sports match information in real time, identifies important moments of interest to fans, and allows users to easily manage and collect this information as a digital recording medium. Furthermore, it aims to promote interaction among fans and eliminate the hassle of managing physical memorabilia.

[0629] 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.

[0630] In this invention, the server includes means for acquiring match information, means for analyzing player performance based on the acquired match information, and means for generating a digital recording medium based on the analysis results. This enables users to effectively collect and manage digital trading cards that identify important moments in matches.

[0631] "Match information" refers to all data related to a sports match, including scores, player statistics, and event data during the match.

[0632] "Player performance" refers to an indicator that shows a player's achievements and abilities, derived from their movements, results, and statistical information during a match.

[0633] A "digital recording medium" is a recording medium that contains images of players, performance data, and match stories, and is managed and displayed electronically.

[0634] A "machine learning algorithm" is an algorithm that uses data to enable computers to learn patterns and make decisions or predictions.

[0635] A "random selection function" is a function that randomly selects from a set of options based on specific conditions or probabilities.

[0636] The following are the embodiments for carrying out the invention.

[0637] This system consists of three main components: a server, terminals, and users. The server is responsible for collecting match information from external data sources and processing it into a digital recording medium. Specifically, it uses the Python programming language and related libraries (e.g., Requests, Pandas) to connect to sports data APIs and collect match scores, player performance, and in-game event data.

[0638] Next, the server analyzes the collected data using a deep learning framework (e.g., TensorFlow or PyTorch) to identify key moments. The analysis uses machine learning algorithms to evaluate player performance and the flow of the game. This identifies decisive moments that fans are likely to be interested in, and based on these results, a generative AI model is used to create digital trading cards.

[0639] The terminal provides an interface with the user, displaying new digital trading cards in real time using a browser-based or mobile application. Users can log in to their account and manage, view, and retrieve their collections on digital media.

[0640] Furthermore, users can utilize the gacha function, allowing them to obtain new cards through random selection. The server executes the random selection function, choosing which cards to offer to users. In this process, appearance probabilities are set considering factors such as rarity and popularity.

[0641] As a concrete example, an example of a prompt message would be: "Identify the most noteworthy moment from today's match and generate a digital trading card based on that moment." This would then be input into the generation AI model. As a result, the user can add the digitally generated card, based on the highlight scene of a specific match, to their collection and enjoy the details.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The server collects match information from external data sources. Specifically, it sends requests to sports data APIs to retrieve match scores, player performance data, and in-game event data. This input data is stored in the server's database for analysis.

[0645] Step 2:

[0646] The server analyzes the collected match information using machine learning algorithms. This process takes match data as input and performs data calculations to identify important moments and player highlights. The analysis results then output the key moments.

[0647] Step 3:

[0648] The server uses a generative AI model to create digital trading cards based on the identified key moments. The prompt sent to the model is: "Identify the most noteworthy moments from today's match and generate digital trading cards based on them." A digital storage medium is then generated as the output of this process.

[0649] Step 4:

[0650] The terminal displays the generated digital trading cards to the user. The user can log in through the terminal's interface and view the newly generated cards. During this process, the terminal receives files from the server and displays them on the user interface.

[0651] Step 5:

[0652] The server handles situations where a user utilizes the gacha (loot box) function. The user spends points via their device to perform the gacha, and the server randomly selects a digital trading card. The selected card is added to the user's digital collection in real time.

[0653] (Application Example 1)

[0654] 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".

[0655] There is a need to manage sports match information in real time, select important moments based on that information, and provide an environment that enables users to view and interact with this information in digital format. Furthermore, there is a need for efficient means for users to instantly enjoy the dynamics of a match and easily share information with other users.

[0656] 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.

[0657] In this invention, the server includes means for acquiring match information, means for analyzing player activity, and means for generating corresponding digital display materials. This makes it possible to provide digital trading cards in real time based on match information.

[0658] "Match information" refers to detailed data about a sporting event, including scores, player performance, and ongoing situations.

[0659] "Athlete activity" refers to the actions and plays performed by athletes during a match, and its analysis allows us to understand the dynamics of the game.

[0660] "Digital display materials" are digital recording media created based on information about matches and players, with digital trading cards being an example.

[0661] "Means of exchanging information" refers to methods for sharing generated digital display materials among users and for exchanging opinions.

[0662] "A means for users to receive digital trading in real time" refers to a method for users to instantly receive and enjoy digital trading cards as the game progresses.

[0663] The system for implementing this invention consists of three elements: a server, a terminal, and a user.

[0664] The server retrieves match information from external data sources via the internet. Specifically, it collects scores, player performance, and match progress related to sports events from external APIs and stores this data in a database. The stored data is analyzed using machine learning models such as TensorFlow to identify important moments during the match. As a result, digital display materials reflecting the players' activities are automatically generated.

[0665] The terminal plays the role of providing users with digitally displayed materials generated through a user interface. The applications installed on the terminal are developed using languages ​​such as Swift and Kotlin, enabling real-time data delivery. As soon as the terminal receives notifications from the server, it instantly and visually conveys important match information to the user.

[0666] Users can receive the latest match information via their devices and enjoy the generated digital trading cards. Furthermore, users can share their creations with other users and communicate through the application on their devices. This interaction enhances the user experience and sustains interest in the matches.

[0667] A concrete example would be a notification arriving on a user's device during a sports match saying, "Player A has scored a decisive goal!", and a digital trading card of that moment would be generated and added to the user's collection. This card could then be sent to friends via message, stimulating real-time communication about the match.

[0668] Examples of prompts to input into the generation AI model include, "Generate digital trading cards of featured players and plays based on the data from match X."

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The server collects match information from external data sources via an internet connection. It uses match IDs and API authentication information as input. It calls external APIs to retrieve data such as scores, player performance, and match progress. The retrieved data is stored in a database.

[0672] Step 2:

[0673] The server applies a machine learning model to the stored match data. The input is match information stored in a database. Generative AI models such as TensorFlow are used to perform analysis to identify important moments in the match. The output of this analysis is a list of important moments and players to watch out for.

[0674] Step 3:

[0675] The server generates digital display materials based on identified key moments. The prompt is: "Generate digital trading cards of featured players and plays based on match data." The generation AI model uses this prompt to create digital trading cards containing player images and performance data. The output is digital display material for distribution to the user.

[0676] Step 4:

[0677] The terminal receives digital display materials distributed from the server. It uses digital trading card data sent from the server as input. The application installed on the terminal displays this data on the user interface and notifies the user. The output is the user's visual display of the data.

[0678] Step 5:

[0679] Users receive digital trading cards generated via their device and add them to their collection. The application operates based on the information displayed on the digital trading card from the device as input. Users can use the application's features to save cards and share them with other users. The output is the user's updated digital collection and shared information.

[0680] 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.

[0681] This invention relates to a digital platform that, in addition to processing sports-related match information and providing a digital recording medium, incorporates an emotion engine that recognizes user emotions. This system consists of three elements: a server, a terminal, and a user, as well as an emotion engine that identifies user emotions and optimizes interactions.

[0682] First, the server retrieves match information from an external API. This match data includes detailed information about player performance and the progress of the match, and is stored on the server. The server then uses an AI engine to select key moments from the match and generate digital trading cards. These cards include player images, performance statistics, and a match story.

[0683] Next, the server uses an emotion engine to collect the user's emotions. Through audio data and video feeds obtained from the user's device, the emotion engine analyzes emotional states, including joy, sadness, and excitement. This analysis influences the content and card selections provided by the server.

[0684] The device not only displays the generated digital recording media to the user through its user interface, but also provides interaction that responds to the user's emotional state. For example, if the device determines that the user is happy, it can prompt them to experience match highlights or acquire rare cards to further excite them.

[0685] Furthermore, users can acquire cards randomly using a gacha (loot box) function. During this process, an emotion engine analyzes the user's psychological tendencies in real time, allowing it to adjust the gacha results to better match the user's interests. This intuitively presents the cards and information that the user is most interested in, thereby improving user satisfaction.

[0686] A concrete example of the present invention is a scenario where, after a match ends, the user reviews a summary of the match via their device and is provided with a pleasant experience through sentiment analysis. Based on the user's response, access to alternative highlights of related matches or premium content is proposed.

[0687] Thus, the present invention enables users to continuously acquire valuable experiences from both match data and emotional data, realizing a sports fan experience in a digital environment free from physical constraints.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The server connects to an external API before the match begins to prepare to retrieve the match schedule, player information, and real-time match data.

[0691] Step 2:

[0692] Once the match begins, the server stores the acquired match data in a database and uses an AI engine to analyze key moments of the match in real time.

[0693] Step 3:

[0694] Based on key moments identified by the AI ​​engine, the server generates digital trading cards containing player images and match performance data.

[0695] Step 4:

[0696] The terminal displays the generated digital trading cards to the user. After logging in, the user can manage their collection and view card details.

[0697] Step 5:

[0698] While the user is interacting with the application, the device collects data through an interface that captures the user's reactions (e.g., audio and video).

[0699] Step 6:

[0700] Using data sent from the terminal, the emotion engine installed on the server analyzes the user's current emotional state.

[0701] Step 7:

[0702] Based on sentiment analysis results, the server dynamically adjusts content for the user and determines what content to recommend. For example, if it determines that the user is enjoying themselves, it will suggest further highlights or rare cards.

[0703] Step 8:

[0704] When a user uses the gacha function, the server adjusts the randomly selected cards based on the emotion engine, presenting the cards that are most likely to interest them.

[0705] Step 9:

[0706] The device displays the gacha results to the user and adds newly acquired cards to their collection. The user can then use this information to interact with other fans and manage their own cards.

[0707] (Example 2)

[0708] 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".

[0709] In recent years, there has been a growing demand to enrich the competitive experience through digital media by visualizing participants' actions in real time and providing information tailored to the user's emotional state. However, conventional systems have been unable to adequately enhance user satisfaction because they provide one-way information and lack interaction that adapts to the user's emotions. In addition, they have been unable to adjust information according to the user's interests, making it difficult to improve the quality of the digital experience.

[0710] 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.

[0711] In this invention, the server includes means for acquiring competition information, means for analyzing participants' actions based on the acquired competition information, and means for adjusting the content of the medium based on the analyzed emotions. This enables the provision of information that matches the user's emotional state and allows for two-way interaction.

[0712] "Competition" refers to an activity or event in which participants compete for performance or results based on specific rules.

[0713] "Information" refers to facts and knowledge treated as data, which are acquired, processed, or provided for a specific purpose.

[0714] "Participants" refer to individuals or teams participating in a competition or event, and their actions and results are the subject of recording and analysis.

[0715] "Movement" refers to the physical movements and actions that participants perform during the competition, and is an element of performance evaluation.

[0716] "Digital media" refers to information content that is stored or displayed in an electronic format and can be accessed or received by a user.

[0717] "User" refers to an individual or organization that receives information or services through a system or platform.

[0718] "Emotion" refers to the psychological response that a user shows to a particular situation or stimulus, and includes states such as joy, excitement, and sadness.

[0719] "Interaction" refers to two-way communication or response that takes place between a system and a user.

[0720] "Adjustment" refers to the act of changing or optimizing the operation or content of a system in accordance with specific conditions or circumstances.

[0721] This system is designed to acquire information related to the competition, analyze participants' behavior, and provide digital media based on the user's emotions. The following shows a specific example of how the system works.

[0722] The server obtains detailed competition information from external sources via APIs. In this process, it receives participant action data and competition progress in a specific data format (e.g., JSON) and stores it in its own database. Existing sports data provision services are often used for this purpose. Machine learning frameworks such as TensorFlow and PyTorch are used as the AI ​​engine to extract competition highlights and analyze participant performance.

[0723] Based on the analyzed information, the server generates digital media containing participants' images and scores. This media is later provided to users and plays a crucial role in supporting their interactions.

[0724] Simultaneously, the device acquires voice and video input from the user and sends it to the server. This data is processed by an emotion analysis engine (e.g., Azure Cognitive Services) to recognize the user's emotional state in real time. This allows for the adjustment of digital content based on emotions such as tension, excitement, and relaxation.

[0725] Based on the generated digital media and analysis results, users receive media that has been adjusted for easy reading via their devices, and can also interact with cards and content randomly acquired through a gacha function. In this process, the user's past interests and emotional state are taken into consideration to provide an individually optimized media experience.

[0726] As a concrete example, imagine a user is watching highlights of a match on their device after it has finished. If the emotion analysis engine detects that the user is excited via voice input, the device will display more match-related content and special offers. This further enhances the user's interest and deepens their experience of the sport.

[0727] An example of a prompt message might be: "Please display the results of last night's basketball game and highlights based on the user's excitement. Please also suggest gacha cards that match the user's emotions." This aims to provide users with an optimal sports viewing experience and establish a new form of entertainment on digital devices.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The server retrieves competition information via an external API. Inputs include parameters such as a specific competition ID and date / time, while output is detailed competition information (participant data in JSON format, scores, action logs, etc.). The server stores this data in a database, forming the basis for subsequent data analysis.

[0731] Step 2:

[0732] The server analyzes competition data stored in the database using an AI engine. The input is the data acquired in Step 1, and the output is analysis results including the identification of important moments and an evaluation of participant performance. Specifically, the process involves analyzing action data using a TensorFlow model and extracting highlight scenes.

[0733] Step 3:

[0734] The server generates digital trading cards based on the analysis results. The input is the analysis results from step 2, and the output generates card data including player images, competition statistics, and match stories. These cards are stored in a database and become digital content provided to users.

[0735] Step 4:

[0736] The device collects voice input and video data from the user and transmits it to the server in real time. The input is raw voice and video data obtained from the user, and the output is prepared as a dataset for processing by the emotion analysis engine. Specific operations include data collection using microphones and cameras.

[0737] Step 5:

[0738] The server analyzes the transmitted audio and video data by passing it through an emotion analysis engine. The input is the dataset from step 4, and the output is the user's emotional state (e.g., joy, sadness, excitement). This analysis utilizes Azure Cognitive Services or similar technologies.

[0739] Step 6:

[0740] The device dynamically changes the UI based on the emotional state received from the server. The input is the emotional state information from step 5, and the output is a modified digital interface presented to the user. For example, if the user is excited, a colorful UI and animation effects are used to display match highlights or special offers.

[0741] Step 7:

[0742] Users acquire digital cards using the gacha function provided on their device. In this process, the gacha draw results are optimized for the individual based on their emotional state as a result of the content provided in step 6. The inputs are the user's selections and the server's emotional analysis results, and the output is a set of cards that are most interesting to the user.

[0743] (Application Example 2)

[0744] 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".

[0745] This solution addresses the challenge of dynamically changing content to reflect user emotions and provide a more personalized experience when generating digital recordings of sports matches. Furthermore, conventional random selection functions fail to accurately capture user interests, limiting methods for increasing satisfaction.

[0746] 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.

[0747] In this invention, the server includes means for acquiring match information, means for analyzing player performance, means for generating a digital recording medium based on the analysis results, means for exchanging information with the user using the generated digital recording medium, and means for analyzing the user's emotions and dynamically adjusting the displayed content based on the analysis results. This makes it possible to provide personalized content that responds to the user's emotions. Furthermore, even in the random selection function, by optimizing the selection results while considering the user's emotional state, it becomes possible to accurately capture the user's interests and improve satisfaction.

[0748] "Match information" refers to data related to a sporting event, including player performance and the progress of the match.

[0749] "Means of analyzing player performance" refers to software or algorithms used to analyze player behavior and performance based on acquired match information.

[0750] A "digital recording medium" is data in digital format that includes images of athletes, competition results, and match stories, and that can be stored and displayed electronically.

[0751] A "means of exchanging information with users" refers to a system that displays the generated digital recording medium on the user's terminal, allowing the user to respond or provide feedback accordingly.

[0752] "Means for analyzing emotions and dynamically adjusting displayed content based on the analysis results" refers to algorithms or software that identify emotions from data such as the user's facial expressions and voice, and change related information in real time.

[0753] The "random selection function" is a function that randomly selects and presents digital recording media to the user.

[0754] "Optimizing selection results" means adjusting randomly selected results to match the user's interests, based on the user's current emotional state and past preferences.

[0755] The system implementing this invention consists of three main components: a server, a terminal, and a user. The server acquires match information and utilizes artificial intelligence to analyze player performance based on that information. Specifically, the server acquires match information from an external sports data API and uses software such as OpenCV and TensorFlow to analyze player performance in detail. Based on this analysis, digital trading cards are generated. The cards contain information such as player images, match results, and the story of the match.

[0756] The terminal displays generated digital trading cards to the user and analyzes the user's emotions in real time. It uses the Google Cloud Speech-to-Text API to collect the user's facial expressions and voice data via the smartphone's camera and microphone, and to identify emotions. Based on the emotion analysis results, it dynamically adjusts the displayed content to match the user's interests and emotions.

[0757] Users can acquire digital recording media through their devices. Furthermore, a random selection function based on sentiment analysis allows users to acquire premium content in a gacha (loot box) format. The selection is optimized according to the user's emotional state, designed to enhance user satisfaction.

[0758] For example, when a user is watching a live broadcast of a sporting event on their smartphone, the system recognizes the user's enthusiastic reaction and immediately presents highlights of past memorable matches featuring the same team. Simultaneously, it can further pique the user's interest by randomly providing trading cards of notable players.

[0759] An example of a prompt for a generative AI model might be: "If the user's emotion is 'excited,' select highlights from relevant sporting events and suggest premium content that will maintain the excitement."

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] The server retrieves match information from an external sports data API. This information includes data such as player performance, match progress, and scores. The input is match-related information from the sports data API, and the output is match information stored in the server's database. Once this storage is complete, the server can proceed to the next analysis step.

[0763] Step 2:

[0764] The server analyzes player performance using software such as OpenCV and TensorFlow based on the acquired match information. Specifically, it analyzes data related to player movements and performance, and quantifies and evaluates the results. The input is the match information acquired in step 1, and the output is an evaluation score for each player and identification of important scenes. This prepares the data necessary for generating the next trading cards.

[0765] Step 3:

[0766] The server generates digital trading cards based on the analysis results. These cards include player images, performance statistics, and match stories. The input is the analysis results obtained in step 2, and the output is the digital trading card for display to the user. The generated card is sent from the server to the terminal.

[0767] Step 4:

[0768] The terminal displays digital trading cards received from the server to the user. The terminal also uses a camera and microphone to collect user emotion data (facial expressions and voice). Inputs include digital trading cards from the server and user emotion data, while outputs include content presentation on the user's screen and further analysis of the collected data. The emotion data is used in the subsequent emotion analysis step.

[0769] Step 5:

[0770] The device analyzes the collected facial and voice data using the Google Cloud Speech-to-Text API to infer the user's emotional state. The input is the emotional data collected in step 4, and the output is the user's emotional state (e.g., excitement, joy, sadness). This emotional state is used to dynamically adjust the content displayed.

[0771] Step 6:

[0772] The server dynamically adjusts the content presented to the user based on the sentiment analysis results. If the server determines that the user is excited, it selects and displays highlights or premium content related to relevant sporting events. The input is the sentiment analysis results from step 5, and the output is the customized content displayed on the user's device. The user can achieve further satisfaction at this stage.

[0773] Step 7:

[0774] Users acquire digital trading cards using a random selection function on their device. In this case, the server optimizes the content of the cards acquired based on the user's emotional state. The input is the user's emotional data and the random selection function's request, and the output is premium content tailored to the user's interests. This makes it possible to further enhance user satisfaction.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] Means of obtaining match information,

[0799] A means of analyzing player performance based on acquired match information,

[0800] A means for generating the corresponding digital recording medium based on the analysis results,

[0801] A means of exchanging information with users using the generated digital recording medium,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, which uses artificial intelligence to select important moments from match information.

[0805] (Claim 3)

[0806] The system according to claim 1, comprising a random selection function that allows a user to acquire a digital recording medium.

[0807] "Example 1"

[0808] (Claim 1)

[0809] Means of obtaining match information,

[0810] A means of analyzing player performance based on acquired match information,

[0811] A means for generating a digital recording medium based on the analysis results,

[0812] A means of exchanging information with users using the generated digital recording medium,

[0813] Means by which users manage digital recording media,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, which uses a machine learning algorithm to select important moments from match information.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising a random selection function that allows a user to acquire a digital recording medium.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] Means of obtaining match information,

[0822] A means of analyzing player activity based on acquired match information,

[0823] A means for generating the corresponding digital display material based on the analysis results,

[0824] A means of exchanging information with users using generated digital display materials,

[0825] A means for users to receive digital trading results in real time,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] A system according to claim 1 that uses machine learning to select and provide important moments from match information.

[0829] (Claim 3)

[0830] The system according to claim 1, comprising a function that allows users to acquire digital display materials and share them with other users.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] Means of obtaining information about the competition,

[0834] A means of analyzing the participants' movements based on the acquired competition information,

[0835] A means for generating the corresponding digital media based on the analysis results,

[0836] A means of exchanging information with users using generated digital media,

[0837] A means of sensing and analyzing the emotions of users,

[0838] Means for adjusting the content of the medium based on analyzed emotions,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which uses intelligence to select noteworthy moments from information about a competition.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising an optional function that allows the user to acquire a digital medium.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] Means of obtaining match information,

[0847] A means of analyzing player performance based on acquired match information,

[0848] A means for generating the corresponding digital recording medium based on the analysis results,

[0849] A means of exchanging information with users using the generated digital recording medium,

[0850] A means for analyzing user emotions and dynamically adjusting displayed content based on the analysis results,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which uses artificial intelligence to select important moments from match information.

[0854] (Claim 3)

[0855] The system according to claim 1, which includes a random selection function that allows a user to acquire a digital recording medium, and which optimizes the selection result based on the user's emotional state. [Explanation of Symbols]

[0856] 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. Means of obtaining match information, A means of analyzing player performance based on acquired match information, A means for generating the corresponding digital recording medium based on the analysis results, A means of exchanging information with users using the generated digital recording medium, A system that includes this.

2. The system according to claim 1, which uses artificial intelligence to select important moments from match information.

3. The system according to claim 1, comprising a random selection function that allows a user to acquire a digital recording medium.

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