system

Abstract

We provide the system. [Solution] A system that includes means for generating and distributing digital cards in real time for all sports using AI, means for uploading photos taken on-site to the cloud, means for image AI to create multiple card patterns, means for linking with data from various sports, and means for realizing a new card sales service with a design that reflects the game situation and real-time capabilities.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional sports cards have their designs and information determined in advance, making it difficult to reflect real-time game situations. Also, the production and distribution of physical cards require time and cost, and there is a demand for efficiency improvement through digitalization.

Means for Solving the Problems

[0005] This invention provides a system that uses AI to generate and distribute digital cards in real time. Photos taken on-site are uploaded to the cloud, and image AI creates multiple card patterns. Furthermore, by linking with data from various sports, it realizes a new card sales service with designs that reflect the game situation and real-time capabilities. This enables the provision of sports cards that reflect real-time game situations and improves efficiency through digitalization. [Brief explanation of the drawing]

[0006] [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 Embodiment 1 of Example 1. [Figure 12]This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] This is a sequence diagram showing the processing flow of the data processing system in Example 1 of the Form 1 when an emotion engine is combined. [Figure 18] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of the Form 2 when an emotion engine is combined. [Figure 20] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] This is a sequence diagram showing the processing flow of the data processing system in Example 3 of the Form 3 when an emotion engine is combined. [Figure 22] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. [Figure 23] This is a sequence diagram showing the processing flow of a data processing system in another embodiment. [Modes for carrying out the invention]

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a processor with a reference number (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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, a storage with a reference number 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.

[0012] 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).

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of this system is an AI-powered real-time digital card generation system. This system uploads photos taken on-site to the cloud. The uploaded photos are analyzed by image AI, and multiple card design patterns are generated. The generated cards are linked with data from various sports, resulting in designs that reflect the progress of the match. As a result, digital cards that reflect the match situation in real time are generated and delivered to users.

[0029] "Example of form 2"

[0030] Another embodiment of the present invention involves a system in which generated digital cards are provided in application format. Users can purchase and collect digital cards generated in real time through a dedicated application. For example, a digital card of a player who scores a goal during a soccer match is generated at that moment and sold on the application.

[0031] "Example of form 3"

[0032] Furthermore, in another embodiment of the present invention, there is a system in which the generated digital cards are provided as NFTs (Non-Fungible Tokens). Digital cards provided as NFTs have their ownership verified using blockchain technology, and their uniqueness and scarcity are guaranteed. As a result, the digital cards can have value as digital assets and can be bought and sold among users.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: Upload the photos taken on site to the cloud.

[0036] Step 2: The image AI analyzes the uploaded photo and generates multiple design pattern cards.

[0037] Step 3: The generated cards will be linked with data from various sports and will have a design that reflects the progress of the match.

[0038] Step 4: A digital card reflecting the match situation in real time is generated and delivered to the user.

[0039] "Example of form 2"

[0040] Step 1: Users purchase digital cards generated in real time through a dedicated application.

[0041] Step 2: For example, a digital card of a player who scores a goal during a soccer match is generated at that moment.

[0042] Step 3: The generated digital cards are sold on the application, and users can collect them.

[0043] "Example of form 3"

[0044] Step 1: The generated digital card is provided as an NFT (Non-Fungible Token).

[0045] Step 2: Digital cards provided as NFTs utilize blockchain technology to verify ownership and guarantee uniqueness and scarcity.

[0046] Step 3: This allows the digital cards to have value as digital assets and to be bought and sold between users.

[0047] (Example 1)

[0048] Next, we will describe Example 1 of Form 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."

[0049] In modern sports viewing, spectators demand digital content that reflects the game situation in real time. However, conventional technology has made it difficult to generate and distribute digital cards that instantly reflect the game situation. Furthermore, it has been impossible to provide designs that highlight the unique characteristics of individual matches, thus failing to enhance user satisfaction.

[0050] 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.

[0051] In this invention, the server includes means for transmitting images taken by the user to an information processing device; means for the information processing device to store the received images in a data storage device; means for the information processing device to analyze the images using image analysis technology and extract features; means for the information processing device to generate multiple designs based on the extracted features using a generative model; means for the information processing device to acquire competition information from an external database and integrate it into the generated designs; means for the information processing device to transmit the generated digital information to the user's terminal; and means for providing digital information that reflects the competition situation in real time. This makes it possible to generate and deliver digital cards that immediately reflect the match situation to the user.

[0052] A "user" is an individual or group that uses the system to take images and receive digital information.

[0053] An "information processing device" is a device that receives, stores, analyzes, generates designs for images, integrates data, and transmits digital information.

[0054] A "data storage device" is a storage medium used to store received image data.

[0055] "Image analysis technology" refers to techniques for recognizing and extracting objects and features within an image.

[0056] A "generative model" is an algorithm or program for generating a design based on extracted features.

[0057] An "external database" is a collection of data that an information processing device accesses to provide competition information.

[0058] "Competition information" refers to data related to the competition, such as match scores and player performance.

[0059] "Digital information" refers to digital content that integrates generated designs and competition information.

[0060] This invention is a system that generates and distributes digital information reflecting the competition situation in real time, based on images taken by users. Users take photos at the competition site using a device such as a smartphone or tablet. The captured images are transmitted from the device to a server via the internet.

[0061] The server saves the received images to cloud storage. Data storage devices such as Amazon S3 can be used for this purpose. The saved images are then analyzed using image analysis technologies such as Google Cloud Vision API, and objects and features within the images are extracted.

[0062] Next, the server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on the extracted features. The generated designs are then integrated with competition information obtained by the server from an external database (e.g., SportsDB API). This allows information such as match scores and player performance to be reflected in the designs.

[0063] Finally, the server sends the generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time.

[0064] As a concrete example, suppose a user takes a photo of a player during a soccer match and uploads it to this system. The server analyzes the photo and generates a design pattern based on the player's movements and facial expressions. At the same time, it acquires match score and player performance data and incorporates this into the card design. As a result, a digital card that reflects the match situation in real time is delivered to the user.

[0065] An example of a prompt message would be, "Upload photos of players taken during a soccer match and generate digital cards that reflect the match's scores and player performance."

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

[0067] Step 1:

[0068] The user takes photos at the competition site using their device. The captured images are sent to the server through the application on the device. The input is the captured image data, and the output is the transmission of the image data to the server.

[0069] Step 2:

[0070] The server saves the received image data to cloud storage. Here, data storage devices such as Amazon S3 are used. The input is image data sent from the terminal, and the output is image data stored in cloud storage.

[0071] Step 3:

[0072] The server analyzes the stored image data using image analysis technologies such as the Google Cloud Vision API. The analysis extracts objects and features from the image. The input is image data stored in cloud storage, and the output is the extracted image feature data.

[0073] Step 4:

[0074] The server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on extracted feature data. The input is image feature data, and the output is the generated design data.

[0075] Step 5:

[0076] The server retrieves competition information from external databases such as the SportsDB API. The retrieved competition information is integrated into the generated design data. The input is the generated design data and competition information from the external database, and the output is the design data reflecting the competition information.

[0077] Step 6:

[0078] The server transmits the final generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time. The input is design data that reflects the match information, and the output is a digital card displayed on the user's device.

[0079] (Application Example 1)

[0080] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0081] In modern sports viewing, it is difficult for spectators to obtain real-time information that reflects the progress of the game. Furthermore, there is a lack of digital content to enrich the viewing experience. Therefore, there is a need for a system that allows spectators to receive digital information that instantly reflects the game situation.

[0082] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0083] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading images taken on-site to the cloud, and means for image analysis technology to create multiple information patterns. This makes it possible for spectators to receive digital information that reflects the game situation in real time, based on images they took during the match.

[0084] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence and perform learning and reasoning.

[0085] "Real-time" refers to the immediate processing or response that occurs the moment an event takes place.

[0086] "Digital information" refers to information that is generated and stored electronically, and is data that is represented in digital format.

[0087] "Cloud" refers to a collection of computer resources and services provided via the internet, serving as a platform for data storage and processing.

[0088] "Image analysis technology" is a technique that uses computer vision to analyze image data and extract specific information.

[0089] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0090] "Digital assets" are valuable information and content that exist in digital format and are subject to ownership and trading.

[0091] "Spectators" refer to people who watch sports or events, gathering together to enjoy the game or performance.

[0092] The system for implementing this invention primarily utilizes a server, a user terminal, and cloud infrastructure. The server uses AI technology to analyze images captured by the user in real time and generate digital information. Specifically, images captured by the user's terminal are uploaded to cloud storage (e.g., AWS® S3). The server analyzes the images using image analysis technology (e.g., TENSORFLOW®) to determine the situation of the competition. The analysis results are linked with competition data, and a digital information generation algorithm generates digital information that reflects the match situation. The generated digital information is delivered to the user's terminal in real time.

[0093] As a concrete example, if a user captures a goal during a soccer match, the image is uploaded to the cloud. The server uses image analysis technology to recognize the moment of the goal and, in conjunction with match data, generates digital information reflecting the goal. This digital information is immediately delivered to the user's device, allowing the user to receive updated information as the match progresses.

[0094] An example of a prompt message would be, "Upload images taken during a soccer match and generate digital information reflecting the goal scenes."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The user takes photos of interesting scenes during a sporting event using their device. The captured images are uploaded to cloud storage via an application on the device. The input is the image taken by the user, and the output is the image data stored in cloud storage.

[0098] Step 2:

[0099] The server retrieves image data from cloud storage. Using the retrieved image data as input, it performs analysis using image analysis techniques (e.g., TensorFlow). The purpose of the analysis is to identify important events within the image (e.g., goal scenes). The output is event information as a result of the analysis.

[0100] Step 3:

[0101] The server, based on the analysis results, works in conjunction with the competition database to generate digital information that reflects the match situation. The inputs are the analysis results and competition data, and the output is the generated digital information. This digital information includes real-time updates as the match progresses.

[0102] Step 4:

[0103] The server delivers the generated digital information to the user's terminal. The user's terminal displays the received digital information, providing the user with real-time updates on the match status. The input is the generated digital information, and the output is the information displayed on the user's terminal.

[0104] Step 5:

[0105] Users check the digital information displayed on their devices and receive new information as the match progresses. This allows users to understand the match situation in real time. The input is the information displayed on the device, and the output is the user's understanding and experience.

[0106] (Example 2)

[0107] Next, we will describe Example 2 of Form 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".

[0108] Conventional digital card generation systems did not adequately consider real-time capabilities or user interaction, making it difficult to provide digital cards that instantly reflect the moments of sporting events. Furthermore, there was a lack of easy ways for users to search for and retrieve cards for specific players or matches.

[0109] 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.

[0110] In this invention, the server includes means for analyzing digital information in real time using a generation AI model and generating digital cards, means for distributing the digital cards through an application to provide them to a user terminal, and means for searching for and displaying a specific digital card when the user enters a prompt. This makes it possible for the user to instantly obtain and enjoy digital cards that have been generated in real time.

[0111] A "generative AI model" is an artificial intelligence technology that analyzes digital information and automatically generates digital content tailored to specific purposes.

[0112] A "digital card" is a digital content in the form of an electronic card, generated based on a sporting event or a specific theme.

[0113] A "user terminal" is an electronic device used by a user to receive and display digital content.

[0114] A "prompt message" is a set of instructions that a user enters to search for specific information.

[0115] A "database management system" is a software system for organizing, storing, and retrieving data.

[0116] "Analyzing digital information in real time" means processing and analyzing information instantly as a real-world event occurs.

[0117] The following system is constructed as an embodiment of this invention.

[0118] The server uses a generative AI model to analyze digital information in real time and generate digital cards. Specifically, the server collects data from sports events in real time and organizes and stores it using a database management system. This makes it possible to instantly grasp the movements of players and the moments of scoring during a match. Machine learning frameworks such as TensorFlow and PyTorch are commonly used for generative AI models.

[0119] The generated digital cards are provided from the server to the user terminal. The user terminal receives and displays the digital cards through a dedicated application. Users can search for and retrieve specific digital cards by entering prompts within the application. For example, if a user enters a prompt such as "Show me the card of the player who scored a goal in the most recent match," the server will search for the corresponding card and display it on the terminal.

[0120] This system allows users to instantly obtain and enjoy digital cards generated in real time, making it possible to feel closer to the moments of sporting events.

[0121] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0122] Step 1:

[0123] The server collects sports event data in real time. It takes information such as match scores, player movements, and goal moments from sports data APIs and streaming services as input. This data is stored and organized in a database management system. The output is the organized match data.

[0124] Step 2:

[0125] The server analyzes the organized match data to identify which players scored goals. It uses match data stored in a database as input. The data analysis includes extracting player IDs and goal timestamps. The output provides information about the players who scored the goals.

[0126] Step 3:

[0127] The server uses a generative AI model to generate digital cards for identified players. The input consists of information about goal-scoring players and match highlights. The generative AI model combines player images and match highlights to create visually appealing cards. The output is the generated digital card.

[0128] Step 4:

[0129] The server sends the generated digital card to a dedicated application. The terminal displays the received card to the user. The input is the generated digital card, which is then provided to the user through the application. The output is the digital card displayed for the user to view.

[0130] Step 5:

[0131] The user searches for a specific digital card by entering a prompt within the application. For example, the user might enter a prompt such as, "Show me the card of the player who scored a goal in the most recent match." The server receives this prompt, searches for the corresponding card, and displays it on the terminal. The output displays the digital card specified by the user.

[0132] (Application Example 2)

[0133] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0134] In real-time competitive events, there is a need for a system that can instantly generate digital information capturing specific moments, and allow users to purchase and collect that information. However, conventional technologies have shortcomings in terms of real-time capabilities and the ability to reflect situational design, making it difficult to improve the user experience.

[0135] 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.

[0136] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading on-site video footage to a database, and means for image processing technology to create multiple patterns of information. This enables the generation of digital information capturing specific moments in real time, and allows users to immediately purchase and collect it.

[0137] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence to learn and reason.

[0138] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0139] "Digital information" refers to information that is generated and stored electronically, including images, text, and audio.

[0140] "Distribution" refers to the act of delivering digital information to users via a network.

[0141] "Image" refers to visual information captured by a camera or other device, and includes both moving images and still images.

[0142] A "database" is a system for efficiently storing, searching, and managing information.

[0143] "Image processing technology" refers to the technology of analyzing, transforming, and processing digital images, including image recognition and editing.

[0144] A "user" refers to an individual or group that uses a system or service.

[0145] "Purchase" is the act of acquiring goods or services in exchange for money.

[0146] "Collection" is the act of gathering information or goods for a specific purpose.

[0147] The system for carrying out this invention consists of a server, user terminals, and a network. The server analyzes live video of the sporting event using AI technology and detects specific moments. Specifically, it uses an AI framework such as TensorFlow to recognize important events in the video (e.g., goal scenes and highlight scenes) in real time.

[0148] The server uses image processing software such as OpenCV to process the detected moment into digital information. This digital information is stored in a database so that users can purchase and collect it. A database service such as Firebase is used to manage users' purchase history and collection information.

[0149] User terminals are devices such as smartphones and tablets that receive digital information delivered from a server through a dedicated application. Users can operate the application to purchase real-time generated digital information and add it to their collection.

[0150] As a concrete example, when a player scores a goal during a soccer match, the server detects the scene and generates it as digital information. The user receives a notification within the app asking, "Would you like to purchase the digital information for this goal?" and can add the information to their collection by pressing the purchase button.

[0151] An example of a prompt message would be, "Detect the moment a goal is scored during a soccer match and generate that scene as digital information."

[0152] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0153] Step 1:

[0154] The server receives live video footage of the sporting event. The input is real-time video data transmitted from the camera. The server inputs this video data into an AI model and performs analysis to detect important events. The output is the timestamp and location information of the detected events.

[0155] Step 2:

[0156] The server uses an AI model to detect specific moments in the video (e.g., goal scenes). The input is the timestamp and location information obtained in step 1. Based on this information, the server uses OpenCV to extract the relevant scene and process it as digital information. The output is the processed digital information.

[0157] Step 3:

[0158] The server stores the processed digital information in a database. The input is the digital information generated in step 2. The server uses Firebase to store it in association with the user's purchase history and collection information. The output is the record of the digital information stored in the database.

[0159] Step 4:

[0160] The user terminal receives digital information distributed from the server. The input is a notification of the digital information sent from the server. The user terminal displays this information within the application, presenting the user with purchase options. The output is the purchase options displayed to the user.

[0161] Step 5:

[0162] The user purchases digital information through the application and adds it to their collection. The input is the purchase option presented in step 4. The user adds the digital information to their collection by pressing the purchase button. The output is the digital information added to the user's collection.

[0163] (Example 3)

[0164] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] There is a need to clarify the ownership of digital content and provide an environment where users can create and trade their own digital cards. However, conventional systems have faced challenges in guaranteeing the uniqueness and rarity of digital cards, and insufficient means of proving ownership of the generated cards.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0167] In this invention, the server includes means for generating digital cards based on digital information using generative AI technology, means for analyzing instruction text entered by the user and converting it into a format suitable for generative AI technology, and means for registering the generated digital cards using digital ledger technology and proving ownership. This guarantees the uniqueness and scarcity of the digital cards, enabling users to generate their own digital cards and trade them with ownership rights.

[0168] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate digital content.

[0169] "Digital information" refers to all information expressed in a format that can be processed by a computer.

[0170] A "digital card" is a card-shaped piece of content created in digital format, which can include images and text.

[0171] "User" refers to an individual or entity that uses this system to generate or trade digital cards.

[0172] A "command statement" is text that a user inputs to the AI ​​generation technology to specify the characteristics of the digital card they want to generate.

[0173] "Digital ledger technology" refers to technology that uses distributed ledger technologies such as blockchain to record the transaction history and ownership of digital assets.

[0174] "Ownership" refers to a legal or substantive right to a particular digital asset.

[0175] This invention is a system that generates digital cards using generative AI technology and proves ownership using digital ledger technology. Specific embodiments of this system are described below.

[0176] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. For example, they might enter the prompt, "Create a retro game-style character card." The terminal then sends this prompt to the server.

[0177] The server analyzes the received prompt message and converts it into a format suitable for generative AI technology. This analysis uses natural language processing techniques. Specifically, Python's natural language processing library can be used.

[0178] Next, the server generates digital cards using a generative AI model. During this process, the server leverages NVIDIA GPUs to efficiently perform calculations on the AI ​​model. The generated digital cards can include images and text.

[0179] The generated digital cards are registered by a server using digital ledger technology. Specifically, the digital cards are registered as NFTs using a blockchain platform to prove ownership. This registration guarantees the uniqueness and scarcity of the digital cards.

[0180] Finally, the server sends the information of the generated NFT digital card to the terminal. The user can then verify the generation result through the terminal and trade the digital card. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0181] Step 1:

[0182] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. The entered prompt is written in text format in the terminal's input field. For example, the user might enter the prompt, "Generate a fantasy-style dragon digital card." The terminal then prepares to send this prompt to the server.

[0183] Step 2:

[0184] The terminal sends the entered prompt message to the server. Specifically, it sends the prompt message to the server using an HTTP request. The input is the prompt message, and the output is the request sent to the server. The server receives the prompt message and prepares to parse it.

[0185] Step 3:

[0186] The server parses the received prompt message and converts it into a format suitable for generative AI technology. The input is the prompt message, and the output is the parsed data. This parsing uses natural language processing techniques, specifically Python's natural language processing library. The server understands the content of the prompt message and converts it into a data format suitable for the generative AI model.

[0187] Step 4:

[0188] The server generates digital cards using a generative AI model based on the analysis results. The input is the analyzed data, and the output is the generated digital card. The server utilizes NVIDIA GPUs to efficiently perform calculations for the AI ​​model. The generated digital cards can include images and text.

[0189] Step 5:

[0190] The server registers the generated digital cards using digital ledger technology. The input is the generated digital card, and the output is the registered NFT digital card. Specifically, a blockchain platform is used to register the digital card as an NFT and prove ownership.

[0191] Step 6:

[0192] The server transmits information about the generated NFT digital card to the terminal. The input is the registered NFT digital card, and the output is the transmission of information to the terminal. The user can verify the generation result through the terminal and trade the digital card.

[0193] (Application Example 3)

[0194] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] There is a need to improve the viewing experience of digital content and to build new revenue models for content distribution services. However, conventional digital card systems lack the means to provide personalized digital cards based on users' viewing history and to prove ownership of digital cards, making it difficult to improve user engagement and create value as digital assets.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0197] This invention includes a server that utilizes AI to generate and distribute digital cards in real time for all sports, a means for uploading photos taken on-site to the cloud, a means for image AI to create multiple card patterns, a means for linking with data from various sports, a means for realizing a new card sales service with a design that reflects the game situation and real-time capabilities, a means for generating relevant digital cards based on viewing history and registering them on the blockchain, a means for providing an interface for users to manage, buy, sell, and exchange digital cards, and a means for providing limited edition digital cards related to content. This enables the provision of personalized digital cards based on the user's viewing history and proof of ownership of the digital cards.

[0198] "A method for generating and distributing digital cards in real time for all sports using AI" refers to a method that uses artificial intelligence technology to instantly create digital cards tailored to the situation of various sports events and provide them to users.

[0199] "A method for uploading photos taken on-site to the cloud" refers to a method of saving image data taken at sporting events to cloud storage via the internet.

[0200] "A method for image AI to create multiple card patterns" refers to a method of generating digital cards with different designs and layouts using artificial intelligence specialized in image processing.

[0201] "Methods for linking with data from various sports" refers to methods of acquiring statistical information and match data related to various sports and updating the content of digital cards based on that information.

[0202] "A means to realize a new card sales service with a design that reflects the battle situation and real-time functionality" refers to a method of providing and selling digital cards with designs that correspond to the progress of the match in real time.

[0203] "A means of generating related digital cards based on viewing history and registering them on the blockchain" refers to a method of creating related digital cards based on the viewing history of content by a user and registering those cards using blockchain technology.

[0204] "Means of providing an interface for users to manage, buy, sell, and exchange digital cards" refers to methods of providing user interfaces and functions for viewing, trading, and exchanging digital cards owned by users.

[0205] "Means of providing limited edition digital cards related to content" refers to a method of providing users with rare digital cards related to specific digital content.

[0206] The system for implementing this invention mainly consists of a server, a user terminal, and a blockchain network. The server utilizes artificial intelligence technology to generate digital cards based on the user's viewing history. Specifically, the server collects viewing history data and inputs it into a generation AI model to create the associated digital card. This generated digital card is registered as an NFT using blockchain technology, and ownership is proven.

[0207] The user terminal is a device such as a smartphone or smart glasses, through which the user manages digital cards. The user can view, buy, sell, and exchange digital cards using an application on the terminal. This application provides a user interface and is designed for easy operation.

[0208] Blockchain networks are used to record ownership of digital cards and ensure transparency and reliability in transactions. This allows for the secure buying, selling, and exchange of digital cards between users.

[0209] As a concrete example, after a user watches a specific movie, a limited edition digital card related to that movie is generated. This card features characters and scenes from the movie, and users can exchange it with other users or buy and sell it on a marketplace.

[0210] An example of a prompt message is, "Generate a limited edition digital card based on the movie the user has watched and register it as an NFT." By inputting this prompt message into the generation AI model, the corresponding digital card will be generated.

[0211] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0212] Step 1:

[0213] The server collects user viewing history data. It receives historical information about the content the user has viewed as input and stores this in a database. As output, the viewing history data is ready to be input into a generated AI model.

[0214] Step 2:

[0215] The server inputs viewing history data into a generating AI model and generates a related digital card. The inputs used are viewing history data and the prompt message "Generate a limited edition digital card based on the movies the user has watched and register it as an NFT." For data processing, the generating AI model analyzes the viewing history and creates a design for the related digital card. The output is the digital card design data.

[0216] Step 3:

[0217] The server registers the generated digital card on the blockchain, proving ownership as an NFT. It receives the digital card design data as input and sends it to the blockchain network. As a data calculation, it uses blockchain technology to record the uniqueness and ownership of the digital card. The output is the digital card registered as an NFT.

[0218] Step 4:

[0219] The user terminal manages digital cards through an application. It receives information about digital cards registered as NFTs as input. Specifically, the user can view, buy, sell, and exchange digital cards using the application's interface. As output, the transaction information for the digital cards is updated according to the user's actions.

[0220] 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.

[0221] "Example of form 1"

[0222] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0223] "Example of form 2"

[0224] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. If the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited with the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0225] "Example of form 3"

[0226] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0227] The following describes the processing flow for each example of the form.

[0228] "Example of form 1"

[0229] Step 1: Users express their emotions regarding the match results. These emotions can be expressed in various ways, such as through the tone of their voice, facial expressions, and social media posts.

[0230] Step 2: The emotion engine recognizes the user's emotions. This emotion engine analyzes the user's emotions using technologies such as speech recognition, image recognition, and natural language processing.

[0231] Step 3: Based on the emotions recognized by the emotion engine, a pattern for generating digital cards is selected. For example, if the user is feeling happy, a pattern for generating digital cards that amplifies that happiness is selected. If the user is feeling sad, a pattern for generating digital cards that alleviates that sadness is selected.

[0232] Step 4: Adjust the timing of digital card delivery based on the emotions recognized by the emotion engine. For example, if the user is excited about the match result, the timing of digital card delivery will be sped up to maintain that excitement. On the other hand, if the user is disappointed with the match result, the timing of digital card delivery will be delayed to alleviate that disappointment.

[0233] (Example 1)

[0234] Next, we will describe Example 1 of Form 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."

[0235] In modern sports viewing, there is a demand for digital content that reflects the game situation and the emotions of spectators in real time. However, conventional systems do not adequately generate content in real time according to the progress of the game or personalize it according to the emotions of the spectators. Therefore, new methods are needed to enrich the viewing experience.

[0236] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0237] In this invention, the server includes means for instantly generating and distributing electronic cards in various sports using artificial intelligence, means for transmitting images acquired on-site to data storage, and means for image analysis technology to create cards in multiple formats. This makes it possible to provide digital content that reflects the situation of the match and the emotions of the spectators in real time.

[0238] Artificial intelligence is a technology in which computer systems imitate human intellectual behavior and perform learning and reasoning.

[0239] An "electronic card" is a card-shaped piece of content that is generated in digital format and displayed on an electronic device.

[0240] "Data storage" refers to storage devices and cloud services used to store digital data.

[0241] "Image analysis technology" is a technique in which computers process image data and extract information from it.

[0242] "Competition data" refers to data that includes statistical information and real-time match status related to sports and games.

[0243] "Immediacy" refers to the characteristic that an action or process is performed quickly and without delay.

[0244] "Personalization" refers to adjusting content and services according to the individual user's characteristics and preferences.

[0245] "Digital assets" are digital assets whose ownership is managed using blockchain technology.

[0246] The following system is constructed as an embodiment of this invention.

[0247] The server runs a program that utilizes artificial intelligence to instantly generate and distribute electronic cards for various sports. This program also has the function of sending images acquired by users on-site to data storage. Specifically, users use devices such as smartphones and tablets to upload photos taken during matches to the cloud via a dedicated application. The device then transmits the photos to data storage via an internet connection.

[0248] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. Google Cloud's Vision AI is used for this process. Vision AI recognizes objects and scenes within the photos and generates multiple design patterns based on this recognition. The generated design patterns are then linked to sports data using the Sports Data API. This allows real-time data, such as match scores and player performance, to be reflected on the electronic cards.

[0249] Furthermore, the server uses Microsoft® Azure® Emotion API to recognize the user's emotions. Emotions are obtained either by the user entering their emotions about the match result through the application or by the server reading their facial expressions with the device's camera. The server adjusts the design pattern of the electronic card and optimizes the delivery timing according to the recognized emotions.

[0250] As a concrete example, imagine a user is watching a soccer match. During the match, they take a photo of a goal and upload it to the app. The server analyzes the goal using Vision AI and generates a design that includes the score information. The Emotion API recognizes the user's excitement and instantly delivers a card with a cheerful design. The user can view the card in the app and share it with friends on social media.

[0251] An example of a prompt to input into the generation AI model might be: "Analyze a photo taken during a soccer match and generate a digital card that reflects the score of the match. Also, adjust the card design according to the user's emotions."

[0252] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0253] Step 1:

[0254] Users upload photos taken during matches using their devices to the cloud via a dedicated application. The input is the image data captured by the user, and the output is the image file stored in cloud storage. The device transmits the photos to the data storage via an internet connection.

[0255] Step 2:

[0256] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. The input is image data retrieved from cloud storage, and the output is design patterns based on the analysis results. The server uses Google Cloud's Vision AI to recognize objects and scenes in the photos and generate multiple design patterns.

[0257] Step 3:

[0258] The server integrates the generated design patterns with competition data. The input consists of design patterns generated by Vision AI and competition data obtained from the sports data API, while the output is an electronic card reflecting the match's score and player performance. The server uses the sports data API to acquire real-time data and integrate it into the design patterns.

[0259] Step 4:

[0260] The server recognizes the user's emotions and adjusts the design pattern of the electronic card accordingly. The input is the user's emotion data, and the output is an electronic card adjusted according to those emotions. The server uses Microsoft Azure's Emotion API to analyze the emotions the user enters through the application and the facial expressions captured by the device's camera, and adjusts the design accordingly.

[0261] Step 5:

[0262] The server delivers the final generated electronic card to the user's device. The input is the adjusted electronic card, and the output is the electronic card displayed on the user's device. The server optimizes the delivery timing according to the user's emotions and delivers the card instantly. The user can view, save, and share the received electronic card within the application.

[0263] (Application Example 1)

[0264] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0265] In modern sports viewing, viewers demand real-time information relevant to the game situation, but traditional methods fail to adequately meet this need. Furthermore, information is not provided in a way that responds to viewers' emotions, making it difficult to improve individual viewing experiences.

[0266] 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.

[0267] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a server that uploads images acquired on-site to data storage, an image analysis technology that creates multiple information patterns, an emotion recognition technology that analyzes the user's emotions and selects an information generation pattern corresponding to those emotions, and a server that adjusts the timing of information distribution according to changes in the user's emotions. This makes it possible to provide information in real time that responds to the viewer's emotions.

[0268] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[0269] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0270] "Digital information" refers to information that is generated electronically and displayed visually or audibly.

[0271] "Data storage" refers to a storage device or service for storing digital data.

[0272] "Image analysis technology" is a technique that processes image data and extracts useful information from it.

[0273] "Emotion recognition technology" is a technology that analyzes and identifies emotions from a user's facial expressions, voice, and other data.

[0274] An "information generation pattern" is a design or method for generating information based on specific conditions.

[0275] "Delivery timing" refers to the timing adjustments made when sending information or content to recipients.

[0276] "Viewers" refers to people who watch sports events or content.

[0277] To implement this invention, it is necessary for the server, terminal, and user to cooperate to build a system. The server is responsible for generating and distributing real-time digital information of the competition by leveraging artificial intelligence technology. Specifically, the server uploads the images acquired on-site to the data storage and creates information in multiple patterns using image analysis technology. Furthermore, it analyzes the user's emotions using emotion recognition technology and selects an information generation pattern according to the emotions. Thus, it is possible to adjust the information distribution timing according to the changes in the user's emotions.

[0278] The terminal is a device for the user to receive information and experience it visually or auditorily. Smartphones, smart glasses, etc. fall into this category. The terminal receives the information distributed from the server and presents it to the user.

[0279] The user can receive the information of the competition through the terminal and enjoy the real-time experience. The user's emotions are transmitted to the server through the sensors and cameras of the terminal and analyzed by emotion recognition technology.

[0280] As a specific example, at the moment when a goal is scored during a soccer game, if the user is happy, the emotion can be recognized and digital information with a design that emphasizes the goal scene can be immediately distributed. At this time, for the generation AI model, a prompt sentence such as "Analyze the photo at the moment when a goal is scored in a soccer game and generate a digital card that amplifies the joy of the viewers." is used.

[0281] The flow of the specific process in Application Example 1 will be described using FIG. 18.

[0282] Step 1:

[0283] The server receives the on-site image data transmitted from the terminal. The input is the image data from the terminal, and the output is the image data stored in the data storage within the server. In this step, the operation of uploading the image data to the cloud is performed.

[0284] Step 2:

[0285] The server analyzes the received image data using image analysis technology. The input is image data stored in data storage, and the output is the analyzed image information. In this step, image analysis technology (e.g., TensorFlow) is used to identify the situation of the competition from the image and create multiple information generation patterns.

[0286] Step 3:

[0287] The server receives user emotion data sent from the terminal and analyzes it using emotion recognition technology. The input is emotion data from the terminal, and the output is the analyzed emotion information. In this step, emotion recognition technology (e.g., Affectiva) is used to identify the user's emotions.

[0288] Step 4:

[0289] The server selects an information generation pattern based on the analyzed image and emotion information. The input is the analyzed image and emotion information, and the output is the selected information generation pattern. In this step, the server selects the optimal information generation pattern according to the user's emotions.

[0290] Step 5:

[0291] The server generates digital information based on the selected information generation pattern and adjusts the delivery timing. The input is the selected information generation pattern, and the output is the generated digital information. In this step, a generation AI model is used to generate digital information, and the delivery timing is adjusted according to the user's emotions.

[0292] Step 6:

[0293] The terminal receives digital information delivered from the server and presents it to the user. The input is digital information from the server, and the output is visual or auditory information presented to the user. In this step, the terminal displays the received information to the user.

[0294] (Example 2)

[0295] Next, we will describe Example 2 of Form 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".

[0296] In modern sporting events, there is a demand for the creation and distribution of information media in real time, but traditional methods do not adequately provide immediacy or customization to meet the emotions of users. Furthermore, it is difficult to provide information media that takes users' emotions into consideration, which presents a challenge in maximizing the excitement and emotion of the competition.

[0297] 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.

[0298] In this invention, the server includes means for generating and distributing information media in real time for any sport using a generative AI model, means for analyzing the user's emotions using emotion recognition technology and adjusting the generation pattern and distribution timing of the information media according to those emotions, and means for linking with information on various sports. This makes it possible to provide timely information media that responds to the user's emotions.

[0299] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate new information media.

[0300] "Information media" refers to visual content such as cards and images provided in digital format, including information related to competitive events.

[0301] "Data storage" refers to an online or cloud-based storage device for storing and managing digital data.

[0302] "Image processing technology" refers to technology for analyzing digital images, performing processing and conversion, and is used for generating information media.

[0303] "Emotion recognition technology" refers to technology for analyzing data such as the user's expression and voice to determine their emotional state.

[0304] "Immediacy" refers to the characteristic that information and services are provided in real time, and is the ability to quickly respond to user requests.

[0305] "Digital assets" refer to digital-form assets whose ownership is proven using blockchain technology, and are provided as information media.

[0306] The embodiments for implementing this invention will be described.

[0307] The server uses a generative AI model to generate information media based on real-time data of competitive events. Specifically, the server obtains data such as the progress of the game and the movements of players from an external sports data API. Based on this data, a prompt sentence is input into the generative AI model to generate information media. The generative AI model used is an algorithm that analyzes digital images and text data to automatically create new information media.

[0308] The terminal uses emotion recognition technology to analyze the user's emotions in real time. The terminal uses a camera and a microphone to obtain the user's expression and voice, and emotion recognition software analyzes this data. The analysis result is sent to the server, and the server adjusts the generation pattern and distribution timing of the information media according to the user's emotions.

[0309] As a concrete example, the server inputs the prompt message "Generate an information medium depicting the moment player A scores a goal" into the generating AI model. The generating AI model creates an information medium containing an image of player A and detailed information about the goal. Additionally, the terminal detects the user's smile, and emotion recognition software determines that "the user is happy." Based on this information, the server inputs the prompt message "Generate an information medium that amplifies the joy" into the generating AI model, and generates an information medium with special effects added.

[0310] In this way, the server and terminal can cooperate to provide timely information that responds to the user's emotions.

[0311] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0312] Step 1:

[0313] The server retrieves real-time match data from an external sports data API. The input consists of data regarding the progress of the match and player movements. The server analyzes this data to determine if a significant event (e.g., a goal) has occurred. The output is a flag indicating that the event occurred.

[0314] Step 2:

[0315] The server inputs a prompt message into the generating AI model based on the event data acquired in Step 1. Specifically, the server generates the prompt message, "Generate an information medium at the moment player A scored a goal." The input consists of event data and the prompt message, and the generating AI model generates the information medium based on these. The output is an information medium containing an image of player A and detailed information about the goal.

[0316] Step 3:

[0317] The device uses a camera and microphone to capture the user's facial expressions and voice. The input is real-time facial expression and voice data from the user. The device uses emotion recognition software to analyze this data and determine the user's emotional state. The output is emotion data, such as whether the user is happy or sad.

[0318] Step 4:

[0319] The server receives the user's emotion data obtained in step 3 and adjusts the generation pattern and delivery timing of the information media. Specifically, the server inputs a prompt message to the generating AI model: "Generate information media that amplifies joy." The input consists of emotion data and the prompt message, and the generating AI model uses this to generate information media with special effects added. The output is the customized information media.

[0320] Step 5:

[0321] The server delivers customized information media to the user's terminal. The input is the customized information media. The server delivers the information media at an appropriate time based on the user's emotions. The output is the information media received by the user on their terminal. The user can view and collect the information media in real time through a dedicated application.

[0322] (Application Example 2)

[0323] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0324] In today's digital content market, there are challenges such as insufficient personalization of digital information generated in real time, making it difficult to provide content that responds to users' emotions. Furthermore, there is a problem in that digital information capturing crucial moments in competitions cannot be provided immediately, thus failing to maximize the user experience.

[0325] 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.

[0326] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for recognizing the user's emotions and adjusting the generation pattern and distribution timing of digital information according to those emotions, and means for providing personalized digital information based on the user's emotions. This makes it possible to provide real-time and personalized digital information that responds to the user's emotions.

[0327] "A means of generating and distributing digital information in real time using AI" refers to a method of using artificial intelligence technology to instantly create digital information in accordance with the progress of a competition and provide it to users.

[0328] "Methods for uploading images taken on-site to an information processing device" refers to methods for transferring and saving image data taken at the competition site to an information processing device such as the cloud.

[0329] "Image processing technology as a means of creating multiple patterns of information" refers to a method of generating digital information of different designs or formats from original image data using image processing algorithms.

[0330] "Means of linking with information from various competitions" refers to methods for acquiring data on the progress and results of competitions and reflecting that data in the generation of digital information.

[0331] "A means to realize a new information sales service with a design that reflects the situation and real-time capabilities" refers to a method for selling digital information that has a design tailored to the situation of the competition and is available in real time.

[0332] "Means for recognizing user emotions and adjusting the generation patterns and delivery timing of digital information accordingly" refers to methods for detecting the emotional state of users and optimizing the generation methods and delivery timing of digital information based on those emotional states.

[0333] "Means of providing personalized digital information based on user emotions" refers to methods for generating and providing digital information that is customized according to the user's emotions.

[0334] The system for carrying out this invention includes a server, a user terminal, and a cloud service. The server utilizes artificial intelligence technology to generate digital information in real time according to the progress of the competition and delivers it to the user terminal. Specifically, the server uploads image data taken at the competition site to the cloud and generates multiple design patterns using image processing technology. This creates digital information that corresponds to the situation of the competition.

[0335] The user terminal is equipped with an emotion recognition engine that detects the user's emotions in real time. For emotion recognition, for example, the Microsoft Azure Emotion API can be used. The user's emotion data is sent to the server, which adjusts the generation patterns and delivery timing of digital information based on this data. As the generation AI model, OpenAI's GPT-3 (registered trademark) is used, and personalized digital information is generated by inputting prompts that correspond to the user's emotions.

[0336] As a concrete example, when a user is watching a soccer match and their favorite team scores a goal, the emotion recognition engine on the user's device detects the user's joy. Based on this information, the server prompts the generation AI model with "Generate a digital card of the soccer goal scene that the user is celebrating." The generated digital information includes an image of the goal-scoring player and highlights from the match, and is designed to amplify the user's joy. In this way, it becomes possible to provide real-time and personalized digital information that responds to the user's emotions.

[0337] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0338] Step 1:

[0339] The server uploads image data taken at the competition site to the cloud. The input is image data taken at the site, and the output is image data stored in the cloud. In this step, the image data is transferred to and saved to a cloud storage service.

[0340] Step 2:

[0341] The server retrieves image data stored in the cloud and generates multiple design patterns using image processing technology. The input is image data in the cloud, and the output is digital information containing multiple design patterns. In this step, an image processing algorithm is applied to generate digital information with different designs from the original image.

[0342] Step 3:

[0343] The user terminal uses an emotion recognition engine to detect the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the user's emotion data. In this step, emotion recognition technology is used to analyze the user's emotional state.

[0344] Step 4:

[0345] The user terminal sends the detected emotion data to the server. The input is the user's emotion data, and the output is the emotion data sent to the server. This step involves transferring the emotion data to the server over the network.

[0346] Step 5:

[0347] The server prompts a generative AI model based on the received emotion data to generate personalized digital information. The input is the user's emotion data and a prompt sentence, and the output is personalized digital information. In this step, the generative AI model is prompted with the message, "Generate a digital card of a soccer goal scene that the user is happy about," and performs the operation of generating digital information that corresponds to the user's emotion.

[0348] Step 6:

[0349] The server delivers the generated personalized digital information to the user terminal. The input is the personalized digital information, and the output is the digital information delivered to the user terminal. In this step, the generated digital information is transmitted to the user terminal via the network.

[0350] Step 7:

[0351] The user terminal displays and provides the received digital information to the user. The input is digital information delivered from the server, and the output is the digital information displayed to the user. In this step, the digital information is displayed on the user interface, making it viewable by the user.

[0352] (Example 3)

[0353] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0354] In generating digital information, there is a challenge in providing personalized content that reflects user emotions in real time. Furthermore, there is a lack of means to clearly define ownership of the generated digital information and guarantee its value. Additionally, there is a need for content delivery at the appropriate time in response to changes in user emotions.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0356] In this invention, the server includes means for analyzing the user's emotions, means for generating prompt sentences based on the analyzed emotions, means for generating digital information using the generated prompt sentences, means for registering the generated digital information as a non-fungible token, and means for adjusting the timing of digital information delivery according to changes in the user's emotions. This makes it possible to generate and deliver personalized digital information in accordance with the user's emotions, and furthermore, to guarantee its ownership and value.

[0357] "Methods for analyzing user emotions" refer to technologies that identify a user's emotional state based on data obtained from them.

[0358] "Methods for generating prompt sentences" refers to techniques that create instruction sentences to be input into a generative AI model based on analyzed emotional information.

[0359] "Means for generating digital information" refers to a technology that uses generated prompt messages to create digital content that responds to the user's emotions.

[0360] "Methods for registering as non-fungible tokens" refer to technologies that use blockchain technology to register generated digital information and guarantee its ownership and uniqueness.

[0361] "Methods for adjusting delivery timing" refer to technologies that optimize the timing of digital information delivery in response to changes in users' emotions.

[0362] This invention is a system that generates digital information in response to a user's emotions and provides it as a non-fungible token. Specific embodiments are shown below.

[0363] The server uses an emotion engine to analyze the user's emotions. The user inputs voice or text data through the terminal, which then sends it to the server. The server analyzes the received data with the emotion engine to identify the user's emotions. This emotion engine utilizes natural language processing technology to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment."

[0364] Next, the server generates a prompt based on the analyzed emotion. For example, if the user is feeling "joy," the server will create a prompt that says, "Generate a digital card that amplifies joy." This prompt is then input into the generative AI model.

[0365] The generative AI model generates digital information based on prompt text. This digital information includes designs and messages that respond to the user's emotions. For example, a digital card with bright colors and a positive message might be generated.

[0366] The generated digital information is registered as a non-fungible token by the server using blockchain technology. This proves ownership of the digital information and guarantees its uniqueness and scarcity.

[0367] Furthermore, the server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions.

[0368] In this way, it becomes possible to generate and deliver personalized digital information that responds to the user's emotions, and furthermore, its ownership and value can be guaranteed. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[0369] Step 1:

[0370] The user inputs their emotions regarding the match outcome into a device. The device collects the user's voice and text data and sends it to a server. This input data serves as the basis for analyzing the user's emotions.

[0371] Step 2:

[0372] The server analyzes the data received from the terminal using an emotion engine. The emotion engine uses natural language processing techniques to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment." This analysis identifies the user's emotional state. The output is the identified emotion information.

[0373] Step 3:

[0374] The server generates prompts based on the analyzed emotional information. For example, if the user is feeling "joy," it will generate a prompt that says, "Generate a digital card that amplifies joy." This prompt is an instruction that is input into the generative AI model. The output is the generated prompt.

[0375] Step 4:

[0376] The server inputs the generated prompt text into the generation AI model. The generation AI model generates digital information based on the prompt text. Specifically, it generates digital cards containing bright colors and positive messages. The output is the generated digital information.

[0377] Step 5:

[0378] The server registers the generated digital information as a non-fungible token using blockchain technology. This registration proves ownership of the digital information and guarantees its uniqueness and scarcity. The output is the registered non-fungible token.

[0379] Step 6:

[0380] The server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions. The output is the adjusted delivery timing.

[0381] (Application Example 3)

[0382] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0383] In today's digital content market, there is a demand for personalized digital information that responds to users' emotions. However, conventional systems have struggled to recognize users' emotions in real time and generate and deliver digital information based on them. Furthermore, there has been a lack of clear means to prove ownership of digital information. As a result, it has been difficult to improve the user experience and maximize the value of digital information.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0385] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a means for recognizing the user's emotions using emotion analysis technology and adjusting the generation pattern and distribution timing of digital information according to those emotions, and a means for generating digital information based on the user's emotions and using distributed ledger technology to prove ownership. This makes it possible to provide personalized digital information that responds to the user's emotions, thereby maximizing the value of the digital information.

[0386] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[0387] "Digital information" refers to electronically generated data and content, including images, audio, and text.

[0388] "Emotional analysis technology" is a technology that recognizes and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[0389] "Distributed ledger technology" refers to technologies, such as blockchain technology, that distribute and record data in a distributed manner to prevent tampering.

[0390] "Real-time" refers to processing or responding to an event at the very moment it occurs.

[0391] Personalization refers to adjusting services and content according to the individual characteristics and preferences of each user.

[0392] "Ownership" refers to the legal right to a specific object or data, including the right to use or dispose of it.

[0393] The system for implementing this invention mainly consists of a server and terminals. The server uses AI to generate digital information in real time for all kinds of competitions and delivers it to the terminals. The terminals are information processing devices such as smartphones and tablets, and use cameras and microphones to recognize the user's emotions in real time.

[0394] The server uses emotion analysis technology to analyze the user's facial expressions and voice data transmitted from the terminal and recognize the user's emotions. For this, image processing libraries such as OpenCV are used to analyze the user's facial expressions. Speech recognition technology is used to analyze voice data. Based on the recognized emotions, the server adjusts the generation patterns and delivery timing of digital information.

[0395] Furthermore, the server registers the generated digital information using distributed ledger technology to prove ownership. Blockchain technology is used in this process, guaranteeing the uniqueness and scarcity of the digital information.

[0396] As a concrete example, consider a scenario where a user is watching a soccer match. The device recognizes the user's excitement and sends that data to the server. The server generates digital information that amplifies the excitement and immediately delivers it to the device. In this process, a generative AI model is used to select a design that corresponds to the user's emotions.

[0397] An example of a prompt for a generative AI model is: "Analyze the user's emotions and generate digital information that reflects those emotions. If the user is happy, select a design that amplifies that happiness and deliver it immediately."

[0398] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0399] Step 1:

[0400] The device captures the user's facial expressions and voice using a camera and microphone. It acquires real-time video and audio data as input. This data serves as foundational data for analyzing the user's emotions.

[0401] Step 2:

[0402] The device analyzes acquired video data using image processing libraries such as OpenCV to recognize emotions from the user's facial expressions. Audio data is analyzed using speech recognition technology to recognize emotions from the tone of voice. The output generates data indicating the user's emotions.

[0403] Step 3:

[0404] The device sends recognized emotion data to the server. The server receives this emotion data as input and uses a generative AI model to determine a pattern for generating digital information that corresponds to the emotion. The prompt text is input to the generative AI model, and a design appropriate to the user's emotion is selected.

[0405] Step 4:

[0406] The server generates digital information based on the selected design, adjusting patterns and delivery timing according to emotions. The generated digital information is obtained as output.

[0407] Step 5:

[0408] The server registers the generated digital information using distributed ledger technology and proves ownership. Blockchain technology is used to guarantee the uniqueness and scarcity of the digital information.

[0409] Step 6:

[0410] The server delivers registered digital information to the terminal. The terminal displays the received digital information to the user, improving the user experience. As output, personalized digital information is provided to the user.

[0411] (Other examples)

[0412] Next, other embodiments will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0413] In sporting events, there is a need to generate and provide users with digital cards that reflect the excitement of the match in real time. However, conventional technologies do not adequately perform image analysis, design generation, integration of competition data, or adjustments based on user emotions, making it challenging to improve the user experience.

[0414] The identification process performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0415] This invention includes a server that generates a prompt to instruct the uploading of images taken on-site to the cloud and inputs the images into an AI model based on the prompt; a server that analyzes the uploaded images using image analysis technology and generates multiple design patterns based on the analysis results; and an emotion engine that recognizes the user's emotions and generates a prompt to instruct the adjustment of the digital card generation pattern and delivery timing according to the recognized emotions. This makes it possible to provide customized digital cards that reflect the match situation in real time and respond to the user's emotions.

[0416] A "generative AI model" is an artificial intelligence algorithm used to generate new designs and information based on input data, and is particularly used for generating images and text.

[0417] A "prompt" is a statement of instruction generated to direct a specific action or process, and is used to prompt a user or system to take concrete action.

[0418] A "digital card" is electronic card-format data that reflects the details and design of a sporting event, and is intended to provide users with visual information.

[0419] "Image analysis technology" refers to techniques for analyzing digital images and extracting features and patterns from them, and is implemented using image processing libraries and algorithms.

[0420] An "emotion engine" is a technology that recognizes the user's emotional state and adjusts the system's operation based on that information, and is implemented using an emotion analysis algorithm.

[0421] This invention is a system for generating and providing digital cards to users in real time during sporting events. Specific embodiments of this system are described below.

[0422] The server generates a prompt message instructing the user to upload images taken on-site to the cloud. This prompt message includes instructions for properly uploading the images to cloud storage. An example of a prompt message is, "Please upload the images you have taken to cloud storage." Following this prompt, the user uploads images taken with a smartphone or digital camera to a cloud service such as Amazon S3 or Google Cloud Storage.

[0423] The server retrieves images uploaded to cloud storage and analyzes them using image processing libraries such as OpenCV. Through this analysis, it extracts image features and generates multiple design patterns by inputting prompts to a generative AI model (e.g., Stable Diffusion). An example of a prompt is, "Generate digital cards based on goal scenes from a soccer match."

[0424] Furthermore, the server retrieves real-time competition data through a sports data API. This retrieved data includes match scores and player performance data. This data is then integrated into the generated design to create digital cards that reflect the match situation.

[0425] The server uses Microsoft Azure's Sentiment Analysis API to recognize the user's emotions. It analyzes reaction data (e.g., facial expressions and voice) sent from the user's device to understand the user's emotional state. Based on the recognized emotion, it generates prompts to adjust the digital card generation pattern and delivery timing. An example of a prompt might be, "If the user is excited, please select a more dynamic design."

[0426] Ultimately, the server delivers the generated digital cards to the user's device through an application. Users can view the generated digital cards in real time using a mobile or web app. This system allows users to experience the excitement of sporting events in real time while receiving individually customized digital cards.

[0427] The flow of specific processing in other embodiments will be explained using Figure 23.

[0428] Step 1:

[0429] Users take pictures at sporting events using their smartphones or digital cameras. The captured images are sent to a server via a dedicated application on their devices. The server generates a prompt message for the user, "Please upload the captured images to cloud storage," and sends it to the user's device. Based on this prompt, the user uploads the images to a cloud service such as Amazon S3 or Google Cloud Storage.

[0430] Step 2:

[0431] The server retrieves images uploaded to cloud storage. Using these images as input, it performs image analysis using image processing libraries such as OpenCV. Specifically, it extracts image features and uses them to input prompts to a generative AI model (e.g., Stable Diffusion). An example of a prompt is, "Generate digital cards based on goal scenes from a soccer match." Based on these prompts, the generative AI model outputs multiple design patterns.

[0432] Step 3:

[0433] The server retrieves real-time competition data via a sports data API. This data includes match scores and player performance data. This data is then used as input and integrated into the generated design. Specifically, the data is incorporated into the design to reflect the match situation in the design pattern. As a result, a digital card reflecting the match situation is output.

[0434] Step 4:

[0435] The server uses Microsoft Azure's Sentiment Analysis API to recognize the user's emotions. It analyzes reaction data (e.g., facial expressions and voice) sent from the user's device as input and outputs the user's emotional state. Based on the recognized emotion, it generates prompts to adjust the generation pattern and delivery timing of digital cards. An example of a prompt message is, "If the user is excited, please select a more dynamic design."

[0436] Step 5:

[0437] The server delivers the final generated digital card to the user's device via an application. Users can view the generated digital card in real time using a mobile or web app. This process allows users to experience the excitement of a sporting event in real time while receiving a personalized digital card.

[0438] 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.

[0439] Data generation model 58 is a form of 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> 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.

[0440] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0441] 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.

[0442] [Second Embodiment]

[0443] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0455] "Example of form 1"

[0456] One embodiment of this system is an AI-powered real-time digital card generation system. This system uploads photos taken on-site to the cloud. The uploaded photos are analyzed by image AI, and multiple card design patterns are generated. The generated cards are linked with data from various sports, resulting in designs that reflect the progress of the match. As a result, digital cards that reflect the match situation in real time are generated and delivered to users.

[0457] "Example of form 2"

[0458] Another embodiment of the present invention involves a system in which generated digital cards are provided in application format. Users can purchase and collect digital cards generated in real time through a dedicated application. For example, a digital card of a player who scores a goal during a soccer match is generated at that moment and sold on the application.

[0459] "Example of form 3"

[0460] Furthermore, in another embodiment of the present invention, there is a system in which the generated digital cards are provided as NFTs (Non-Fungible Tokens). Digital cards provided as NFTs have their ownership verified using blockchain technology, and their uniqueness and scarcity are guaranteed. As a result, the digital cards can have value as digital assets and can be bought and sold among users.

[0461] The following describes the processing flow for each example of the form.

[0462] "Example of form 1"

[0463] Step 1: Upload the photos taken on site to the cloud.

[0464] Step 2: The image AI analyzes the uploaded photo and generates multiple design pattern cards.

[0465] Step 3: The generated cards will be linked with data from various sports and will have a design that reflects the progress of the match.

[0466] Step 4: A digital card reflecting the match situation in real time is generated and delivered to the user.

[0467] "Example of form 2"

[0468] Step 1: Users purchase digital cards generated in real time through a dedicated application.

[0469] Step 2: For example, a digital card of a player who scores a goal during a soccer match is generated at that moment.

[0470] Step 3: The generated digital cards are sold on the application, and users can collect them.

[0471] "Example of form 3"

[0472] Step 1: The generated digital card is provided as an NFT (Non-Fungible Token).

[0473] Step 2: Digital cards provided as NFTs utilize blockchain technology to verify ownership and guarantee uniqueness and scarcity.

[0474] Step 3: This allows the digital cards to have value as digital assets and to be bought and sold between users.

[0475] (Example 1)

[0476] Next, we will describe Example 1 of Form 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".

[0477] In modern sports viewing, spectators demand digital content that reflects the game situation in real time. However, conventional technology has made it difficult to generate and distribute digital cards that instantly reflect the game situation. Furthermore, it has been impossible to provide designs that highlight the unique characteristics of individual matches, thus failing to enhance user satisfaction.

[0478] 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.

[0479] In this invention, the server includes means for transmitting images taken by the user to an information processing device; means for the information processing device to store the received images in a data storage device; means for the information processing device to analyze the images using image analysis technology and extract features; means for the information processing device to generate multiple designs based on the extracted features using a generative model; means for the information processing device to acquire competition information from an external database and integrate it into the generated designs; means for the information processing device to transmit the generated digital information to the user's terminal; and means for providing digital information that reflects the competition situation in real time. This makes it possible to generate and deliver digital cards that immediately reflect the match situation to the user.

[0480] A "user" is an individual or group that uses the system to take images and receive digital information.

[0481] An "information processing device" is a device that receives, stores, analyzes, generates designs for images, integrates data, and transmits digital information.

[0482] A "data storage device" is a storage medium used to store received image data.

[0483] "Image analysis technology" refers to techniques for recognizing and extracting objects and features within an image.

[0484] A "generative model" is an algorithm or program for generating a design based on extracted features.

[0485] An "external database" is a collection of data that an information processing device accesses to provide competition information.

[0486] "Competition information" refers to data related to the competition, such as match scores and player performance.

[0487] "Digital information" refers to digital content that integrates generated designs and competition information.

[0488] This invention is a system that generates and distributes digital information reflecting the competition situation in real time, based on images taken by users. Users take photos at the competition site using a device such as a smartphone or tablet. The captured images are transmitted from the device to a server via the internet.

[0489] The server saves the received images to cloud storage. Data storage services such as Amazon S3 can be used for this purpose. The saved images are then analyzed using image analysis technologies such as the Google Cloud Vision API, and objects and features within the images are extracted.

[0490] Next, the server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on the extracted features. The generated designs are then integrated with competition information obtained by the server from an external database (e.g., SportsDB API). This allows information such as match scores and player performance to be reflected in the designs.

[0491] Finally, the server sends the generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time.

[0492] As a concrete example, suppose a user takes a photo of a player during a soccer match and uploads it to this system. The server analyzes the photo and generates a design pattern based on the player's movements and facial expressions. At the same time, it acquires match score and player performance data and incorporates this into the card design. As a result, a digital card that reflects the match situation in real time is delivered to the user.

[0493] An example of a prompt message would be, "Upload photos of players taken during a soccer match and generate digital cards that reflect the match's scores and player performance."

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The user takes photos at the competition site using their device. The captured images are sent to the server through the application on the device. The input is the captured image data, and the output is the transmission of the image data to the server.

[0497] Step 2:

[0498] The server saves the received image data to cloud storage. Here, data storage devices such as Amazon S3 are used. The input is image data sent from the terminal, and the output is image data stored in cloud storage.

[0499] Step 3:

[0500] The server analyzes the stored image data using image analysis technologies such as the Google Cloud Vision API. The analysis extracts objects and features from the image. The input is image data stored in cloud storage, and the output is the extracted image feature data.

[0501] Step 4:

[0502] The server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on extracted feature data. The input is image feature data, and the output is the generated design data.

[0503] Step 5:

[0504] The server retrieves competition information from external databases such as the SportsDB API. The retrieved competition information is integrated into the generated design data. The input is the generated design data and competition information from the external database, and the output is the design data reflecting the competition information.

[0505] Step 6:

[0506] The server transmits the final generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time. The input is design data that reflects the match information, and the output is a digital card displayed on the user's device.

[0507] (Application Example 1)

[0508] Next, we will describe Application Example 1 of Form 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."

[0509] In modern sports viewing, it is difficult for spectators to obtain real-time information that reflects the progress of the game. Furthermore, there is a lack of digital content to enrich the viewing experience. Therefore, there is a need for a system that allows spectators to receive digital information that instantly reflects the game situation.

[0510] 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.

[0511] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading images taken on-site to the cloud, and means for image analysis technology to create multiple information patterns. This makes it possible for spectators to receive digital information that reflects the game situation in real time, based on images they took during the match.

[0512] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence and perform learning and reasoning.

[0513] "Real-time" refers to the immediate processing or response that occurs the moment an event takes place.

[0514] "Digital information" refers to information that is generated and stored electronically, and is data that is represented in digital format.

[0515] "Cloud" refers to a collection of computer resources and services provided via the internet, serving as a platform for data storage and processing.

[0516] "Image analysis technology" is a technique that uses computer vision to analyze image data and extract specific information.

[0517] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0518] "Digital assets" are valuable information and content that exist in digital format and are subject to ownership and trading.

[0519] "Spectators" refer to people who watch sports or events, gathering together to enjoy the game or performance.

[0520] The system for implementing this invention primarily utilizes a server, a user terminal, and cloud infrastructure. The server uses AI technology to analyze images captured by the user in real time and generate digital information. Specifically, images captured by the user's terminal are uploaded to cloud storage (e.g., AWS S3). The server analyzes the images using image analysis technology (e.g., TensorFlow) to determine the situation of the competition. The analysis results are linked with competition data, and a digital information generation algorithm generates digital information that reflects the match situation. The generated digital information is delivered to the user's terminal in real time.

[0521] As a concrete example, if a user captures a goal during a soccer match, the image is uploaded to the cloud. The server uses image analysis technology to recognize the moment of the goal and, in conjunction with match data, generates digital information reflecting the goal. This digital information is immediately delivered to the user's device, allowing the user to receive updated information as the match progresses.

[0522] An example of a prompt message would be, "Upload images taken during a soccer match and generate digital information reflecting the goal scenes."

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The user takes photos of interesting scenes during a sporting event using their device. The captured images are uploaded to cloud storage via an application on the device. The input is the image taken by the user, and the output is the image data stored in cloud storage.

[0526] Step 2:

[0527] The server retrieves image data from cloud storage. Using the retrieved image data as input, it performs analysis using image analysis techniques (e.g., TensorFlow). The purpose of the analysis is to identify important events within the image (e.g., goal scenes). The output is event information as a result of the analysis.

[0528] Step 3:

[0529] The server, based on the analysis results, works in conjunction with the competition database to generate digital information that reflects the match situation. The inputs are the analysis results and competition data, and the output is the generated digital information. This digital information includes real-time updates as the match progresses.

[0530] Step 4:

[0531] The server delivers the generated digital information to the user's terminal. The user's terminal displays the received digital information, providing the user with real-time updates on the match status. The input is the generated digital information, and the output is the information displayed on the user's terminal.

[0532] Step 5:

[0533] Users check the digital information displayed on their devices and receive new information as the match progresses. This allows users to understand the match situation in real time. The input is the information displayed on the device, and the output is the user's understanding and experience.

[0534] (Example 2)

[0535] Next, we will describe Example 2 of Form 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".

[0536] Conventional digital card generation systems did not adequately consider real-time capabilities or user interaction, making it difficult to provide digital cards that instantly reflect the moments of sporting events. Furthermore, there was a lack of easy ways for users to search for and retrieve cards for specific players or matches.

[0537] 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.

[0538] In this invention, the server includes means for analyzing digital information in real time using a generation AI model and generating digital cards, means for distributing the digital cards through an application to provide them to a user terminal, and means for searching for and displaying a specific digital card when the user enters a prompt. This makes it possible for the user to instantly obtain and enjoy digital cards that have been generated in real time.

[0539] A "generative AI model" is an artificial intelligence technology that analyzes digital information and automatically generates digital content tailored to specific purposes.

[0540] A "digital card" is a digital content in the form of an electronic card, generated based on a sporting event or a specific theme.

[0541] A "user terminal" is an electronic device used by a user to receive and display digital content.

[0542] A "prompt message" is a set of instructions that a user enters to search for specific information.

[0543] A "database management system" is a software system for organizing, storing, and retrieving data.

[0544] "Analyzing digital information in real time" means processing and analyzing information instantly as a real-world event occurs.

[0545] The following system is constructed as an embodiment of this invention.

[0546] The server uses a generative AI model to analyze digital information in real time and generate digital cards. Specifically, the server collects data from sports events in real time and organizes and stores it using a database management system. This makes it possible to instantly grasp the movements of players and the moments of scoring during a match. Machine learning frameworks such as TensorFlow and PyTorch are commonly used for generative AI models.

[0547] The generated digital cards are provided from the server to the user terminal. The user terminal receives and displays the digital cards through a dedicated application. Users can search for and retrieve specific digital cards by entering prompts within the application. For example, if a user enters a prompt such as "Show me the card of the player who scored a goal in the most recent match," the server will search for the corresponding card and display it on the terminal.

[0548] This system allows users to instantly obtain and enjoy digital cards generated in real time, making it possible to feel closer to the moments of sporting events.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server collects sports event data in real time. It takes information such as match scores, player movements, and goal moments from sports data APIs and streaming services as input. This data is stored and organized in a database management system. The output is the organized match data.

[0552] Step 2:

[0553] The server analyzes the organized match data to identify which players scored goals. It uses match data stored in a database as input. The data analysis includes extracting player IDs and goal timestamps. The output provides information about the players who scored the goals.

[0554] Step 3:

[0555] The server uses a generative AI model to generate digital cards for identified players. The input consists of information about goal-scoring players and match highlights. The generative AI model combines player images and match highlights to create visually appealing cards. The output is the generated digital card.

[0556] Step 4:

[0557] The server sends the generated digital card to a dedicated application. The terminal displays the received card to the user. The input is the generated digital card, which is then provided to the user through the application. The output is the digital card displayed for the user to view.

[0558] Step 5:

[0559] The user searches for a specific digital card by entering a prompt within the application. For example, the user might enter a prompt such as, "Show me the card of the player who scored a goal in the most recent match." The server receives this prompt, searches for the corresponding card, and displays it on the terminal. The output displays the digital card specified by the user.

[0560] (Application Example 2)

[0561] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0562] In real-time competitive events, there is a need for a system that can instantly generate digital information capturing specific moments, and allow users to purchase and collect that information. However, conventional technologies have shortcomings in terms of real-time capabilities and the ability to reflect situational design, making it difficult to improve the user experience.

[0563] 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.

[0564] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading on-site video footage to a database, and means for image processing technology to create multiple patterns of information. This enables the generation of digital information capturing specific moments in real time, and allows users to immediately purchase and collect it.

[0565] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence to learn and reason.

[0566] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0567] "Digital information" refers to information that is generated and stored electronically, including images, text, and audio.

[0568] "Distribution" refers to the act of delivering digital information to users via a network.

[0569] "Image" refers to visual information captured by a camera or other device, and includes both moving images and still images.

[0570] A "database" is a system for efficiently storing, searching, and managing information.

[0571] "Image processing technology" refers to the technology of analyzing, transforming, and processing digital images, including image recognition and editing.

[0572] A "user" refers to an individual or group that uses a system or service.

[0573] "Purchase" is the act of acquiring goods or services in exchange for money.

[0574] "Collection" is the act of gathering information or goods for a specific purpose.

[0575] The system for carrying out this invention consists of a server, user terminals, and a network. The server analyzes live video of the sporting event using AI technology and detects specific moments. Specifically, it uses an AI framework such as TensorFlow to recognize important events in the video (e.g., goal scenes and highlight scenes) in real time.

[0576] The server uses image processing software such as OpenCV to process the detected moment into digital information. This digital information is stored in a database so that users can purchase and collect it. A database service such as Firebase is used to manage users' purchase history and collection information.

[0577] User terminals are devices such as smartphones and tablets that receive digital information delivered from a server through a dedicated application. Users can operate the application to purchase real-time generated digital information and add it to their collection.

[0578] As a concrete example, when a player scores a goal during a soccer match, the server detects the scene and generates it as digital information. The user receives a notification within the app asking, "Would you like to purchase the digital information for this goal?" and can add the information to their collection by pressing the purchase button.

[0579] An example of a prompt message would be, "Detect the moment a goal is scored during a soccer match and generate that scene as digital information."

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The server receives live video footage of the sporting event. The input is real-time video data transmitted from the camera. The server inputs this video data into an AI model and performs analysis to detect important events. The output is the timestamp and location information of the detected events.

[0583] Step 2:

[0584] The server uses an AI model to detect specific moments in the video (e.g., goal scenes). The input is the timestamp and location information obtained in step 1. Based on this information, the server uses OpenCV to extract the relevant scene and process it as digital information. The output is the processed digital information.

[0585] Step 3:

[0586] The server stores the processed digital information in a database. The input is the digital information generated in step 2. The server uses Firebase to store it in association with the user's purchase history and collection information. The output is the record of the digital information stored in the database.

[0587] Step 4:

[0588] The user terminal receives digital information distributed from the server. The input is a notification of the digital information sent from the server. The user terminal displays this information within the application, presenting the user with purchase options. The output is the purchase options displayed to the user.

[0589] Step 5:

[0590] The user purchases digital information through the application and adds it to their collection. The input is the purchase option presented in step 4. The user adds the digital information to their collection by pressing the purchase button. The output is the digital information added to the user's collection.

[0591] (Example 3)

[0592] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0593] There is a need to clarify the ownership of digital content and provide an environment where users can create and trade their own digital cards. However, conventional systems have faced challenges in guaranteeing the uniqueness and rarity of digital cards, and insufficient means of proving ownership of the generated cards.

[0594] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0595] In this invention, the server includes means for generating digital cards based on digital information using generative AI technology, means for analyzing instruction text entered by the user and converting it into a format suitable for generative AI technology, and means for registering the generated digital cards using digital ledger technology and proving ownership. This guarantees the uniqueness and scarcity of the digital cards, enabling users to generate their own digital cards and trade them with ownership rights.

[0596] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate digital content.

[0597] "Digital information" refers to all information expressed in a format that can be processed by a computer.

[0598] A "digital card" is a card-shaped piece of content created in digital format, which can include images and text.

[0599] "User" refers to an individual or entity that uses this system to generate or trade digital cards.

[0600] A "command statement" is text that a user inputs to the AI ​​generation technology to specify the characteristics of the digital card they want to generate.

[0601] "Digital ledger technology" refers to technology that uses distributed ledger technologies such as blockchain to record the transaction history and ownership of digital assets.

[0602] "Ownership" refers to a legal or substantive right to a particular digital asset.

[0603] This invention is a system that generates digital cards using generative AI technology and proves ownership using digital ledger technology. Specific embodiments of this system are described below.

[0604] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. For example, they might enter the prompt, "Create a retro game-style character card." The terminal then sends this prompt to the server.

[0605] The server analyzes the received prompt message and converts it into a format suitable for generative AI technology. This analysis uses natural language processing techniques. Specifically, Python's natural language processing library can be used.

[0606] Next, the server generates digital cards using a generative AI model. During this process, the server leverages NVIDIA GPUs to efficiently perform calculations on the AI ​​model. The generated digital cards can include images and text.

[0607] The generated digital cards are registered by a server using digital ledger technology. Specifically, the digital cards are registered as NFTs using a blockchain platform to prove ownership. This registration guarantees the uniqueness and scarcity of the digital cards.

[0608] Finally, the server sends the information of the generated NFT digital card to the terminal. The user can then verify the generation result through the terminal and trade the digital card. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0609] Step 1:

[0610] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. The entered prompt is written in text format in the terminal's input field. For example, the user might enter the prompt, "Generate a fantasy-style dragon digital card." The terminal then prepares to send this prompt to the server.

[0611] Step 2:

[0612] The terminal sends the entered prompt message to the server. Specifically, it sends the prompt message to the server using an HTTP request. The input is the prompt message, and the output is the request sent to the server. The server receives the prompt message and prepares to parse it.

[0613] Step 3:

[0614] The server parses the received prompt message and converts it into a format suitable for generative AI technology. The input is the prompt message, and the output is the parsed data. This parsing uses natural language processing techniques, specifically Python's natural language processing library. The server understands the content of the prompt message and converts it into a data format suitable for the generative AI model.

[0615] Step 4:

[0616] The server generates digital cards using a generative AI model based on the analysis results. The input is the analyzed data, and the output is the generated digital card. The server utilizes NVIDIA GPUs to efficiently perform calculations for the AI ​​model. The generated digital cards can include images and text.

[0617] Step 5:

[0618] The server registers the generated digital cards using digital ledger technology. The input is the generated digital card, and the output is the registered NFT digital card. Specifically, a blockchain platform is used to register the digital card as an NFT and prove ownership.

[0619] Step 6:

[0620] The server transmits information about the generated NFT digital card to the terminal. The input is the registered NFT digital card, and the output is the transmission of information to the terminal. The user can verify the generation result through the terminal and trade the digital card.

[0621] (Application Example 3)

[0622] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0623] There is a need to improve the viewing experience of digital content and to build new revenue models for content distribution services. However, conventional digital card systems lack the means to provide personalized digital cards based on users' viewing history and to prove ownership of digital cards, making it difficult to improve user engagement and create value as digital assets.

[0624] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0625] This invention includes a server that utilizes AI to generate and distribute digital cards in real time for all sports, a means for uploading photos taken on-site to the cloud, a means for image AI to create multiple card patterns, a means for linking with data from various sports, a means for realizing a new card sales service with a design that reflects the game situation and real-time capabilities, a means for generating relevant digital cards based on viewing history and registering them on the blockchain, a means for providing an interface for users to manage, buy, sell, and exchange digital cards, and a means for providing limited edition digital cards related to content. This enables the provision of personalized digital cards based on the user's viewing history and proof of ownership of the digital cards.

[0626] "A method for generating and distributing digital cards in real time for all sports using AI" refers to a method that uses artificial intelligence technology to instantly create digital cards tailored to the situation of various sports events and provide them to users.

[0627] "A method for uploading photos taken on-site to the cloud" refers to a method of saving image data taken at sporting events to cloud storage via the internet.

[0628] "A method for image AI to create multiple card patterns" refers to a method of generating digital cards with different designs and layouts using artificial intelligence specialized in image processing.

[0629] "Methods for linking with data from various sports" refers to methods of acquiring statistical information and match data related to various sports and updating the content of digital cards based on that information.

[0630] "A means to realize a new card sales service with a design that reflects the battle situation and real-time functionality" refers to a method of providing and selling digital cards with designs that correspond to the progress of the match in real time.

[0631] "A means of generating related digital cards based on viewing history and registering them on the blockchain" refers to a method of creating related digital cards based on the viewing history of content by a user and registering those cards using blockchain technology.

[0632] "Means of providing an interface for users to manage, buy, sell, and exchange digital cards" refers to methods of providing user interfaces and functions for viewing, trading, and exchanging digital cards owned by users.

[0633] "Means of providing limited edition digital cards related to content" refers to a method of providing users with rare digital cards related to specific digital content.

[0634] The system for implementing this invention mainly consists of a server, a user terminal, and a blockchain network. The server utilizes artificial intelligence technology to generate digital cards based on the user's viewing history. Specifically, the server collects viewing history data and inputs it into a generation AI model to create the associated digital card. This generated digital card is registered as an NFT using blockchain technology, and ownership is proven.

[0635] The user terminal is a device such as a smartphone or smart glasses, through which the user manages digital cards. The user can view, buy, sell, and exchange digital cards using an application on the terminal. This application provides a user interface and is designed for easy operation.

[0636] Blockchain networks are used to record ownership of digital cards and ensure transparency and reliability in transactions. This allows for the secure buying, selling, and exchange of digital cards between users.

[0637] As a concrete example, after a user watches a specific movie, a limited edition digital card related to that movie is generated. This card features characters and scenes from the movie, and users can exchange it with other users or buy and sell it on a marketplace.

[0638] An example of a prompt message is, "Generate a limited edition digital card based on the movie the user has watched and register it as an NFT." By inputting this prompt message into the generation AI model, the corresponding digital card will be generated.

[0639] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0640] Step 1:

[0641] The server collects user viewing history data. It receives historical information about the content the user has viewed as input and stores this in a database. As output, the viewing history data is ready to be input into a generated AI model.

[0642] Step 2:

[0643] The server inputs viewing history data into a generating AI model and generates a related digital card. The inputs used are viewing history data and the prompt message "Generate a limited edition digital card based on the movies the user has watched and register it as an NFT." For data processing, the generating AI model analyzes the viewing history and creates a design for the related digital card. The output is the digital card design data.

[0644] Step 3:

[0645] The server registers the generated digital card on the blockchain, proving ownership as an NFT. It receives the digital card design data as input and sends it to the blockchain network. As a data calculation, it uses blockchain technology to record the uniqueness and ownership of the digital card. The output is the digital card registered as an NFT.

[0646] Step 4:

[0647] The user terminal manages digital cards through an application. It receives information about digital cards registered as NFTs as input. Specifically, the user can view, buy, sell, and exchange digital cards using the application's interface. As output, the transaction information for the digital cards is updated according to the user's actions.

[0648] 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.

[0649] "Example of form 1"

[0650] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0651] "Example of form 2"

[0652] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0653] "Example of form 3"

[0654] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[0655] The following describes the processing flow for each example of the form.

[0656] "Example of form 1"

[0657] Step 1: Users express their emotions regarding the match results. These emotions can be expressed in various ways, such as through the tone of their voice, facial expressions, and social media posts.

[0658] Step 2: The emotion engine recognizes the user's emotions. This emotion engine analyzes the user's emotions using technologies such as speech recognition, image recognition, and natural language processing.

[0659] Step 3: Based on the emotions recognized by the emotion engine, a pattern for generating digital cards is selected. For example, if the user is feeling happy, a pattern for generating digital cards that amplifies that happiness is selected. If the user is feeling sad, a pattern for generating digital cards that alleviates that sadness is selected.

[0660] Step 4: Adjust the timing of digital card delivery based on the emotions recognized by the emotion engine. For example, if the user is excited about the match result, the timing of digital card delivery will be sped up to maintain that excitement. On the other hand, if the user is disappointed with the match result, the timing of digital card delivery will be delayed to alleviate that disappointment.

[0661] (Example 1)

[0662] Next, we will describe Example 1 of Form 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".

[0663] In modern sports viewing, there is a demand for digital content that reflects the game situation and the emotions of spectators in real time. However, conventional systems do not adequately generate content in real time according to the progress of the game or personalize it according to the emotions of the spectators. Therefore, new methods are needed to enrich the viewing experience.

[0664] 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.

[0665] In this invention, the server includes means for instantly generating and distributing electronic cards in various sports using artificial intelligence, means for transmitting images acquired on-site to data storage, and means for image analysis technology to create cards in multiple formats. This makes it possible to provide digital content that reflects the situation of the match and the emotions of the spectators in real time.

[0666] Artificial intelligence is a technology in which computer systems imitate human intellectual behavior and perform learning and reasoning.

[0667] An "electronic card" is a card-shaped piece of content that is generated in digital format and displayed on an electronic device.

[0668] "Data storage" refers to storage devices and cloud services used to store digital data.

[0669] "Image analysis technology" is a technique in which computers process image data and extract information from it.

[0670] "Competition data" refers to data that includes statistical information and real-time match status related to sports and games.

[0671] "Immediacy" refers to the characteristic that an action or process is performed quickly and without delay.

[0672] "Personalization" refers to adjusting content and services according to the individual user's characteristics and preferences.

[0673] "Digital assets" are digital assets whose ownership is managed using blockchain technology.

[0674] The following system is constructed as an embodiment of this invention.

[0675] The server runs a program that utilizes artificial intelligence to instantly generate and distribute electronic cards for various sports. This program also has the function of sending images acquired by users on-site to data storage. Specifically, users use devices such as smartphones and tablets to upload photos taken during matches to the cloud via a dedicated application. The device then transmits the photos to data storage via an internet connection.

[0676] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. Google Cloud's Vision AI is used for this process. Vision AI recognizes objects and scenes within the photos and generates multiple design patterns based on this recognition. The generated design patterns are then linked to sports data using the Sports Data API. This allows real-time data, such as match scores and player performance, to be reflected on the electronic cards.

[0677] Furthermore, the server uses Microsoft Azure's Emotion API to recognize the user's emotions. Emotions are obtained either by the user entering their emotions about the match result through the application or by the server reading their facial expressions with the device's camera. The server adjusts the design pattern of the electronic card and optimizes the delivery timing based on the recognized emotions.

[0678] As a concrete example, imagine a user is watching a soccer match. During the match, they take a photo of a goal and upload it to the app. The server analyzes the goal using Vision AI and generates a design that includes the score information. The Emotion API recognizes the user's excitement and instantly delivers a card with a cheerful design. The user can view the card in the app and share it with friends on social media.

[0679] An example of a prompt to input into the generation AI model might be: "Analyze a photo taken during a soccer match and generate a digital card that reflects the score of the match. Also, adjust the card design according to the user's emotions."

[0680] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0681] Step 1:

[0682] Users upload photos taken during matches using their devices to the cloud via a dedicated application. The input is the image data captured by the user, and the output is the image file stored in cloud storage. The device transmits the photos to the data storage via an internet connection.

[0683] Step 2:

[0684] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. The input is image data retrieved from cloud storage, and the output is design patterns based on the analysis results. The server uses Google Cloud's Vision AI to recognize objects and scenes in the photos and generate multiple design patterns.

[0685] Step 3:

[0686] The server integrates the generated design patterns with competition data. The input consists of design patterns generated by Vision AI and competition data obtained from the sports data API, while the output is an electronic card reflecting the match's score and player performance. The server uses the sports data API to acquire real-time data and integrate it into the design patterns.

[0687] Step 4:

[0688] The server recognizes the user's emotions and adjusts the design pattern of the electronic card accordingly. The input is the user's emotion data, and the output is an electronic card adjusted according to those emotions. The server uses Microsoft Azure's Emotion API to analyze the emotions the user enters through the application and the facial expressions captured by the device's camera, and adjusts the design accordingly.

[0689] Step 5:

[0690] The server delivers the final generated electronic card to the user's device. The input is the adjusted electronic card, and the output is the electronic card displayed on the user's device. The server optimizes the delivery timing according to the user's emotions and delivers the card instantly. The user can view, save, and share the received electronic card within the application.

[0691] (Application Example 1)

[0692] Next, we will describe Application Example 1 of Form 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."

[0693] In modern sports viewing, viewers demand real-time information relevant to the game situation, but traditional methods fail to adequately meet this need. Furthermore, information is not provided in a way that responds to viewers' emotions, making it difficult to improve individual viewing experiences.

[0694] 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.

[0695] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a server that uploads images acquired on-site to data storage, an image analysis technology that creates multiple information patterns, an emotion recognition technology that analyzes the user's emotions and selects an information generation pattern corresponding to those emotions, and a server that adjusts the timing of information distribution according to changes in the user's emotions. This makes it possible to provide information in real time that responds to the viewer's emotions.

[0696] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[0697] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0698] "Digital information" refers to information that is generated electronically and displayed visually or audibly.

[0699] "Data storage" refers to a storage device or service for storing digital data.

[0700] "Image analysis technology" is a technique that processes image data and extracts useful information from it.

[0701] "Emotion recognition technology" is a technology that analyzes and identifies emotions from a user's facial expressions, voice, and other data.

[0702] An "information generation pattern" is a design or method for generating information based on specific conditions.

[0703] "Delivery timing" refers to the timing adjustments made when sending information or content to recipients.

[0704] "Viewers" refers to people who watch sports events or content.

[0705] To implement this invention, a server, terminal, and user must cooperate to build a system. The server is responsible for generating and distributing real-time digital information about the competition using artificial intelligence technology. Specifically, the server uploads images acquired on-site to data storage and uses image analysis technology to create information in multiple patterns. Furthermore, it analyzes the user's emotions using emotion recognition technology and selects an information generation pattern corresponding to those emotions. This makes it possible to adjust the timing of information distribution according to changes in the user's emotions.

[0706] A terminal is a device that allows users to receive information and experience it visually or audibly. Examples include smartphones and smart glasses. The terminal receives information delivered from a server and presents it to the user.

[0707] Users can receive information about the competition through their devices and enjoy a real-time experience. The user's emotions are transmitted to a server via the device's sensors and camera and analyzed using emotion recognition technology.

[0708] As a concrete example, if a user is excited at the moment a goal is scored during a soccer match, the system can recognize that emotion and instantly deliver digital information with a design that emphasizes the goal scene. In this case, the prompt given to the generative AI model would be, "Analyze a photo of the moment a goal is scored in a soccer match and generate a digital card that amplifies the viewer's excitement."

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0710] Step 1:

[0711] The server receives image data from the terminal. The input is image data from the terminal, and the output is image data stored in the server's data storage. In this step, the image data is uploaded to the cloud.

[0712] Step 2:

[0713] The server analyzes the received image data using image analysis technology. The input is image data stored in data storage, and the output is the analyzed image information. In this step, image analysis technology (e.g., TensorFlow) is used to identify the situation of the competition from the image and create multiple information generation patterns.

[0714] Step 3:

[0715] The server receives user emotion data sent from the terminal and analyzes it using emotion recognition technology. The input is emotion data from the terminal, and the output is the analyzed emotion information. In this step, emotion recognition technology (e.g., Affectiva) is used to identify the user's emotions.

[0716] Step 4:

[0717] The server selects an information generation pattern based on the analyzed image and emotion information. The input is the analyzed image and emotion information, and the output is the selected information generation pattern. In this step, the server selects the optimal information generation pattern according to the user's emotions.

[0718] Step 5:

[0719] The server generates digital information based on the selected information generation pattern and adjusts the delivery timing. The input is the selected information generation pattern, and the output is the generated digital information. In this step, a generation AI model is used to generate digital information, and the delivery timing is adjusted according to the user's emotions.

[0720] Step 6:

[0721] The terminal receives digital information delivered from the server and presents it to the user. The input is digital information from the server, and the output is visual or auditory information presented to the user. In this step, the terminal displays the received information to the user.

[0722] (Example 2)

[0723] Next, we will describe Example 2 of Form 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".

[0724] In modern sporting events, there is a demand for the creation and distribution of information media in real time, but traditional methods do not adequately provide immediacy or customization to meet the emotions of users. Furthermore, it is difficult to provide information media that takes users' emotions into consideration, which presents a challenge in maximizing the excitement and emotion of the competition.

[0725] 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.

[0726] In this invention, the server includes means for generating and distributing information media in real time for any sport using a generative AI model, means for analyzing the user's emotions using emotion recognition technology and adjusting the generation pattern and distribution timing of the information media according to those emotions, and means for linking with information on various sports. This makes it possible to provide timely information media that responds to the user's emotions.

[0727] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate new information media.

[0728] "Information media" refers to visual content such as cards and images provided in digital format, including information related to competitive events.

[0729] "Data storage" refers to online or cloud-based storage devices for storing and managing digital data.

[0730] "Image processing technology" refers to the technology used to analyze, process, and transform digital images, and is utilized in the creation of information media.

[0731] "Emotion recognition technology" is a technology that analyzes data such as a user's facial expressions and voice to determine their emotional state.

[0732] "Immediacy" refers to the characteristic of providing information and services in real time, and the ability to respond quickly to user requests.

[0733] "Digital assets" are digital assets whose ownership is verified using blockchain technology, and which are provided as information media.

[0734] A description of embodiments for carrying out this invention will be given.

[0735] The server uses a generative AI model to generate information media based on real-time data from sports events. Specifically, the server obtains data such as the progress of the match and the movements of the players from an external sports data API. Based on this data, it inputs prompts into the generative AI model to generate information media. The generative AI model used is an algorithm that analyzes digital images and text data to automatically create new information media.

[0736] The device analyzes the user's emotions in real time using emotion recognition technology. The device uses a camera and microphone to capture the user's facial expressions and voice, and emotion recognition software analyzes this data. The results of this analysis are sent to a server, which adjusts the generation patterns and delivery timing of information media according to the user's emotions.

[0737] As a concrete example, the server inputs the prompt message "Generate an information medium depicting the moment player A scores a goal" into the generating AI model. The generating AI model creates an information medium containing an image of player A and detailed information about the goal. Additionally, the terminal detects the user's smile, and emotion recognition software determines that "the user is happy." Based on this information, the server inputs the prompt message "Generate an information medium that amplifies the joy" into the generating AI model, and generates an information medium with special effects added.

[0738] In this way, the server and terminal can cooperate to provide timely information that responds to the user's emotions.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0740] Step 1:

[0741] The server retrieves real-time match data from an external sports data API. The input consists of data regarding the progress of the match and player movements. The server analyzes this data to determine if a significant event (e.g., a goal) has occurred. The output is a flag indicating that the event occurred.

[0742] Step 2:

[0743] The server inputs a prompt message into the generating AI model based on the event data acquired in Step 1. Specifically, the server generates the prompt message, "Generate an information medium at the moment player A scored a goal." The input consists of event data and the prompt message, and the generating AI model generates the information medium based on these. The output is an information medium containing an image of player A and detailed information about the goal.

[0744] Step 3:

[0745] The device uses a camera and microphone to capture the user's facial expressions and voice. The input is real-time facial expression and voice data from the user. The device uses emotion recognition software to analyze this data and determine the user's emotional state. The output is emotion data, such as whether the user is happy or sad.

[0746] Step 4:

[0747] The server receives the user's emotion data obtained in step 3 and adjusts the generation pattern and delivery timing of the information media. Specifically, the server inputs a prompt message to the generating AI model: "Generate information media that amplifies joy." The input consists of emotion data and the prompt message, and the generating AI model uses this to generate information media with special effects added. The output is the customized information media.

[0748] Step 5:

[0749] The server delivers customized information media to the user's terminal. The input is the customized information media. The server delivers the information media at an appropriate time based on the user's emotions. The output is the information media received by the user on their terminal. The user can view and collect the information media in real time through a dedicated application.

[0750] (Application Example 2)

[0751] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0752] In today's digital content market, there are challenges such as insufficient personalization of digital information generated in real time, making it difficult to provide content that responds to users' emotions. Furthermore, there is a problem in that digital information capturing crucial moments in competitions cannot be provided immediately, thus failing to maximize the user experience.

[0753] 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.

[0754] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for recognizing the user's emotions and adjusting the generation pattern and distribution timing of digital information according to those emotions, and means for providing personalized digital information based on the user's emotions. This makes it possible to provide real-time and personalized digital information that responds to the user's emotions.

[0755] "A means of generating and distributing digital information in real time using AI" refers to a method of using artificial intelligence technology to instantly create digital information in accordance with the progress of a competition and provide it to users.

[0756] "Methods for uploading images taken on-site to an information processing device" refers to methods for transferring and saving image data taken at the competition site to an information processing device such as the cloud.

[0757] "Image processing technology as a means of creating multiple patterns of information" refers to a method of generating digital information of different designs or formats from original image data using image processing algorithms.

[0758] "Means of linking with information from various competitions" refers to methods for acquiring data on the progress and results of competitions and reflecting that data in the generation of digital information.

[0759] "A means to realize a new information sales service with a design that reflects the situation and real-time capabilities" refers to a method for selling digital information that has a design tailored to the situation of the competition and is available in real time.

[0760] "Means for recognizing user emotions and adjusting the generation patterns and delivery timing of digital information accordingly" refers to methods for detecting the emotional state of users and optimizing the generation methods and delivery timing of digital information based on those emotional states.

[0761] "Means of providing personalized digital information based on user emotions" refers to methods for generating and providing digital information that is customized according to the user's emotions.

[0762] The system for carrying out this invention includes a server, a user terminal, and a cloud service. The server utilizes artificial intelligence technology to generate digital information in real time according to the progress of the competition and delivers it to the user terminal. Specifically, the server uploads image data taken at the competition site to the cloud and generates multiple design patterns using image processing technology. This creates digital information that corresponds to the situation of the competition.

[0763] The user terminal is equipped with an emotion recognition engine that detects the user's emotions in real time. For emotion recognition, for example, the Microsoft Azure Emotion API can be used. The user's emotion data is sent to the server, which adjusts the generation patterns and delivery timing of digital information based on this data. As the generation AI model, OpenAI's GPT-3 is used, and personalized digital information is generated by inputting prompts that correspond to the user's emotions.

[0764] As a concrete example, when a user is watching a soccer match and their favorite team scores a goal, the emotion recognition engine on the user's device detects the user's joy. Based on this information, the server prompts the generation AI model with "Generate a digital card of the soccer goal scene that the user is celebrating." The generated digital information includes an image of the goal-scoring player and highlights from the match, and is designed to amplify the user's joy. In this way, it becomes possible to provide real-time and personalized digital information that responds to the user's emotions.

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0766] Step 1:

[0767] The server uploads image data taken at the competition site to the cloud. The input is image data taken at the site, and the output is image data stored in the cloud. In this step, the image data is transferred to and saved to a cloud storage service.

[0768] Step 2:

[0769] The server retrieves image data stored in the cloud and generates multiple design patterns using image processing technology. The input is image data in the cloud, and the output is digital information containing multiple design patterns. In this step, an image processing algorithm is applied to generate digital information with different designs from the original image.

[0770] Step 3:

[0771] The user terminal uses an emotion recognition engine to detect the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the user's emotion data. In this step, emotion recognition technology is used to analyze the user's emotional state.

[0772] Step 4:

[0773] The user terminal sends the detected emotion data to the server. The input is the user's emotion data, and the output is the emotion data sent to the server. This step involves transferring the emotion data to the server over the network.

[0774] Step 5:

[0775] The server prompts a generative AI model based on the received emotion data to generate personalized digital information. The input is the user's emotion data and a prompt sentence, and the output is personalized digital information. In this step, the generative AI model is prompted with the message, "Generate a digital card of a soccer goal scene that the user is happy about," and performs the operation of generating digital information that corresponds to the user's emotion.

[0776] Step 6:

[0777] The server delivers the generated personalized digital information to the user terminal. The input is the personalized digital information, and the output is the digital information delivered to the user terminal. In this step, the generated digital information is transmitted to the user terminal via the network.

[0778] Step 7:

[0779] The user terminal displays and provides the received digital information to the user. The input is digital information delivered from the server, and the output is the digital information displayed to the user. In this step, the digital information is displayed on the user interface, making it viewable by the user.

[0780] (Example 3)

[0781] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0782] In generating digital information, there is a challenge in providing personalized content that reflects user emotions in real time. Furthermore, there is a lack of means to clearly define ownership of the generated digital information and guarantee its value. Additionally, there is a need for content delivery at the appropriate time in response to changes in user emotions.

[0783] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0784] In this invention, the server includes means for analyzing the user's emotions, means for generating prompt sentences based on the analyzed emotions, means for generating digital information using the generated prompt sentences, means for registering the generated digital information as a non-fungible token, and means for adjusting the timing of digital information delivery according to changes in the user's emotions. This makes it possible to generate and deliver personalized digital information in accordance with the user's emotions, and furthermore, to guarantee its ownership and value.

[0785] "Methods for analyzing user emotions" refer to technologies that identify a user's emotional state based on data obtained from them.

[0786] "Methods for generating prompt sentences" refers to techniques that create instruction sentences to be input into a generative AI model based on analyzed emotional information.

[0787] "Means for generating digital information" refers to a technology that uses generated prompt messages to create digital content that responds to the user's emotions.

[0788] "Methods for registering as non-fungible tokens" refer to technologies that use blockchain technology to register generated digital information and guarantee its ownership and uniqueness.

[0789] "Methods for adjusting delivery timing" refer to technologies that optimize the timing of digital information delivery in response to changes in users' emotions.

[0790] This invention is a system that generates digital information in response to a user's emotions and provides it as a non-fungible token. Specific embodiments are shown below.

[0791] The server uses an emotion engine to analyze the user's emotions. The user inputs voice or text data through the terminal, which then sends it to the server. The server analyzes the received data with the emotion engine to identify the user's emotions. This emotion engine utilizes natural language processing technology to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment."

[0792] Next, the server generates a prompt based on the analyzed emotion. For example, if the user is feeling "joy," the server will create a prompt that says, "Generate a digital card that amplifies joy." This prompt is then input into the generative AI model.

[0793] The generative AI model generates digital information based on prompt text. This digital information includes designs and messages that respond to the user's emotions. For example, a digital card with bright colors and a positive message might be generated.

[0794] The generated digital information is registered as a non-fungible token by the server using blockchain technology. This proves ownership of the digital information and guarantees its uniqueness and scarcity.

[0795] Furthermore, the server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions.

[0796] In this way, it becomes possible to generate and deliver personalized digital information that responds to the user's emotions, and furthermore, its ownership and value can be guaranteed. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[0797] Step 1:

[0798] The user inputs their emotions regarding the match outcome into a device. The device collects the user's voice and text data and sends it to a server. This input data serves as the basis for analyzing the user's emotions.

[0799] Step 2:

[0800] The server analyzes the data received from the terminal using an emotion engine. The emotion engine uses natural language processing techniques to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment." This analysis identifies the user's emotional state. The output is the identified emotion information.

[0801] Step 3:

[0802] The server generates prompts based on the analyzed emotional information. For example, if the user is feeling "joy," it will generate a prompt that says, "Generate a digital card that amplifies joy." This prompt is an instruction that is input into the generative AI model. The output is the generated prompt.

[0803] Step 4:

[0804] The server inputs the generated prompt text into the generation AI model. The generation AI model generates digital information based on the prompt text. Specifically, it generates digital cards containing bright colors and positive messages. The output is the generated digital information.

[0805] Step 5:

[0806] The server registers the generated digital information as a non-fungible token using blockchain technology. This registration proves ownership of the digital information and guarantees its uniqueness and scarcity. The output is the registered non-fungible token.

[0807] Step 6:

[0808] The server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions. The output is the adjusted delivery timing.

[0809] (Application Example 3)

[0810] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0811] In today's digital content market, there is a demand for personalized digital information that responds to users' emotions. However, conventional systems have struggled to recognize users' emotions in real time and generate and deliver digital information based on them. Furthermore, there has been a lack of clear means to prove ownership of digital information. As a result, it has been difficult to improve the user experience and maximize the value of digital information.

[0812] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0813] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a means for recognizing the user's emotions using emotion analysis technology and adjusting the generation pattern and distribution timing of digital information according to those emotions, and a means for generating digital information based on the user's emotions and using distributed ledger technology to prove ownership. This makes it possible to provide personalized digital information that responds to the user's emotions, thereby maximizing the value of the digital information.

[0814] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[0815] "Digital information" refers to electronically generated data and content, including images, audio, and text.

[0816] "Emotional analysis technology" is a technology that recognizes and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[0817] "Distributed ledger technology" refers to technologies, such as blockchain technology, that distribute and record data in a distributed manner to prevent tampering.

[0818] "Real-time" refers to processing or responding to an event at the very moment it occurs.

[0819] Personalization refers to adjusting services and content according to the individual characteristics and preferences of each user.

[0820] "Ownership" refers to the legal right to a specific object or data, including the right to use or dispose of it.

[0821] The system for implementing this invention mainly consists of a server and terminals. The server uses AI to generate digital information in real time for all kinds of competitions and delivers it to the terminals. The terminals are information processing devices such as smartphones and tablets, and use cameras and microphones to recognize the user's emotions in real time.

[0822] The server uses emotion analysis technology to analyze the user's facial expressions and voice data transmitted from the terminal and recognize the user's emotions. For this, image processing libraries such as OpenCV are used to analyze the user's facial expressions. Speech recognition technology is used to analyze voice data. Based on the recognized emotions, the server adjusts the generation patterns and delivery timing of digital information.

[0823] Furthermore, the server registers the generated digital information using distributed ledger technology to prove ownership. Blockchain technology is used in this process, guaranteeing the uniqueness and scarcity of the digital information.

[0824] As a concrete example, consider a scenario where a user is watching a soccer match. The device recognizes the user's excitement and sends that data to the server. The server generates digital information that amplifies the excitement and immediately delivers it to the device. In this process, a generative AI model is used to select a design that corresponds to the user's emotions.

[0825] An example of a prompt for a generative AI model is: "Analyze the user's emotions and generate digital information that reflects those emotions. If the user is happy, select a design that amplifies that happiness and deliver it immediately."

[0826] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0827] Step 1:

[0828] The device captures the user's facial expressions and voice using a camera and microphone. It acquires real-time video and audio data as input. This data serves as foundational data for analyzing the user's emotions.

[0829] Step 2:

[0830] The device analyzes acquired video data using image processing libraries such as OpenCV to recognize emotions from the user's facial expressions. Audio data is analyzed using speech recognition technology to recognize emotions from the tone of voice. The output generates data indicating the user's emotions.

[0831] Step 3:

[0832] The device sends recognized emotion data to the server. The server receives this emotion data as input and uses a generative AI model to determine a pattern for generating digital information that corresponds to the emotion. The prompt text is input to the generative AI model, and a design appropriate to the user's emotion is selected.

[0833] Step 4:

[0834] The server generates digital information based on the selected design, adjusting patterns and delivery timing according to emotions. The generated digital information is obtained as output.

[0835] Step 5:

[0836] The server registers the generated digital information using distributed ledger technology and proves ownership. Blockchain technology is used to guarantee the uniqueness and scarcity of the digital information.

[0837] Step 6:

[0838] The server delivers registered digital information to the terminal. The terminal displays the received digital information to the user, improving the user experience. As output, personalized digital information is provided to the user.

[0839] (Other examples)

[0840] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0841] 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.

[0842] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[0843] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0844] 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.

[0845] [Third Embodiment]

[0846] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0847] 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.

[0848] 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).

[0849] 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.

[0850] 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.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0858] "Example of form 1"

[0859] One embodiment of this system is an AI-powered real-time digital card generation system. This system uploads photos taken on-site to the cloud. The uploaded photos are analyzed by image AI, and multiple card design patterns are generated. The generated cards are linked with data from various sports, resulting in designs that reflect the progress of the match. As a result, digital cards that reflect the match situation in real time are generated and delivered to users.

[0860] "Example of form 2"

[0861] Another embodiment of the present invention involves a system in which generated digital cards are provided in application format. Users can purchase and collect digital cards generated in real time through a dedicated application. For example, a digital card of a player who scores a goal during a soccer match is generated at that moment and sold on the application.

[0862] "Example of form 3"

[0863] Furthermore, in another embodiment of the present invention, there is a system in which the generated digital card is provided as an NFT (Non-Fungible Token). Digital cards provided as NFTs have their ownership verified using blockchain technology, guaranteeing uniqueness and scarcity. This allows the digital card to have value as a digital asset, enabling trading between users.

[0864] The following describes the processing flow for each example of the form.

[0865] "Example of form 1"

[0866] Step 1: Upload the photos taken on site to the cloud.

[0867] Step 2: The image AI analyzes the uploaded photo and generates multiple design pattern cards.

[0868] Step 3: The generated cards will be linked with data from various sports and will have a design that reflects the progress of the match.

[0869] Step 4: A digital card reflecting the match situation in real time is generated and delivered to the user.

[0870] "Example of form 2"

[0871] Step 1: Users purchase digital cards generated in real time through a dedicated application.

[0872] Step 2: For example, a digital card of a player who scores a goal during a soccer match is generated at that moment.

[0873] Step 3: The generated digital cards are sold on the application, and users can collect them.

[0874] "Example of form 3"

[0875] Step 1: The generated digital card is provided as an NFT (Non-Fungible Token).

[0876] Step 2: Digital cards provided as NFTs utilize blockchain technology to verify ownership and guarantee uniqueness and scarcity.

[0877] Step 3: This allows the digital cards to have value as digital assets and to be bought and sold between users.

[0878] (Example 1)

[0879] Next, we will describe Embodiment 1 of 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."

[0880] In modern sports viewing, spectators demand digital content that reflects the game situation in real time. However, conventional technology has made it difficult to generate and distribute digital cards that instantly reflect the game situation. Furthermore, it has been impossible to provide designs that highlight the unique characteristics of individual matches, thus failing to enhance user satisfaction.

[0881] 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.

[0882] In this invention, the server includes means for transmitting images taken by the user to an information processing device; means for the information processing device to store the received images in a data storage device; means for the information processing device to analyze the images using image analysis technology and extract features; means for the information processing device to generate multiple designs based on the extracted features using a generative model; means for the information processing device to acquire competition information from an external database and integrate it into the generated designs; means for the information processing device to transmit the generated digital information to the user's terminal; and means for providing digital information that reflects the competition situation in real time. This makes it possible to generate and deliver digital cards that immediately reflect the match situation to the user.

[0883] A "user" is an individual or group that uses the system to take images and receive digital information.

[0884] An "information processing device" is a device that receives, stores, analyzes, generates designs for images, integrates data, and transmits digital information.

[0885] A "data storage device" is a storage medium used to store received image data.

[0886] "Image analysis technology" refers to techniques for recognizing and extracting objects and features within an image.

[0887] A "generative model" is an algorithm or program for generating a design based on extracted features.

[0888] An "external database" is a collection of data that an information processing device accesses to provide competition information.

[0889] "Competition information" refers to data related to the competition, such as match scores and player performance.

[0890] "Digital information" refers to digital content that integrates generated designs and competition information.

[0891] This invention is a system that generates and distributes digital information reflecting the competition situation in real time, based on images taken by users. Users take photos at the competition site using a device such as a smartphone or tablet. The captured images are transmitted from the device to a server via the internet.

[0892] The server saves the received images to cloud storage. Data storage services such as Amazon S3 can be used for this purpose. The saved images are then analyzed using image analysis technologies such as the Google Cloud Vision API, and objects and features within the images are extracted.

[0893] Next, the server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on the extracted features. The generated designs are then integrated with competition information obtained by the server from an external database (e.g., SportsDB API). This allows information such as match scores and player performance to be reflected in the designs.

[0894] Finally, the server sends the generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time.

[0895] As a concrete example, suppose a user takes a photo of a player during a soccer match and uploads it to this system. The server analyzes the photo and generates a design pattern based on the player's movements and facial expressions. At the same time, it acquires match score and player performance data and incorporates this into the card design. As a result, a digital card that reflects the match situation in real time is delivered to the user.

[0896] An example of a prompt message would be, "Upload photos of players taken during a soccer match and generate digital cards that reflect the match's scores and player performance."

[0897] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0898] Step 1:

[0899] The user takes photos at the competition site using their device. The captured images are sent to the server through the application on the device. The input is the captured image data, and the output is the transmission of the image data to the server.

[0900] Step 2:

[0901] The server saves the received image data to cloud storage. Here, data storage devices such as Amazon S3 are used. The input is image data sent from the terminal, and the output is image data stored in cloud storage.

[0902] Step 3:

[0903] The server analyzes the stored image data using image analysis technologies such as the Google Cloud Vision API. The analysis extracts objects and features from the image. The input is image data stored in cloud storage, and the output is the extracted image feature data.

[0904] Step 4:

[0905] The server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on extracted feature data. The input is image feature data, and the output is the generated design data.

[0906] Step 5:

[0907] The server retrieves competition information from external databases such as the SportsDB API. The retrieved competition information is integrated into the generated design data. The input is the generated design data and competition information from the external database, and the output is the design data reflecting the competition information.

[0908] Step 6:

[0909] The server transmits the final generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time. The input is design data that reflects the match information, and the output is a digital card displayed on the user's device.

[0910] (Application Example 1)

[0911] Next, we will describe Application Example 1 of Form 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."

[0912] In modern sports viewing, it is difficult for spectators to obtain real-time information that reflects the progress of the game. Furthermore, there is a lack of digital content to enrich the viewing experience. Therefore, there is a need for a system that allows spectators to receive digital information that instantly reflects the game situation.

[0913] 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.

[0914] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading images taken on-site to the cloud, and means for image analysis technology to create multiple information patterns. This makes it possible for spectators to receive digital information that reflects the game situation in real time, based on images they took during the match.

[0915] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence and perform learning and reasoning.

[0916] "Real-time" refers to the immediate processing or response that occurs the moment an event takes place.

[0917] "Digital information" refers to information that is generated and stored electronically, and is data that is represented in digital format.

[0918] "Cloud" refers to a collection of computer resources and services provided via the internet, serving as a platform for data storage and processing.

[0919] "Image analysis technology" is a technique that uses computer vision to analyze image data and extract specific information.

[0920] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0921] "Digital assets" are valuable information and content that exist in digital format and are subject to ownership and trading.

[0922] "Spectators" refer to people who watch sports or events, gathering together to enjoy the game or performance.

[0923] The system for implementing this invention primarily utilizes a server, a user terminal, and cloud infrastructure. The server uses AI technology to analyze images captured by the user in real time and generate digital information. Specifically, images captured by the user's terminal are uploaded to cloud storage (e.g., AWS S3). The server analyzes the images using image analysis technology (e.g., TensorFlow) to determine the situation of the competition. The analysis results are linked with competition data, and a digital information generation algorithm generates digital information that reflects the match situation. The generated digital information is delivered to the user's terminal in real time.

[0924] As a concrete example, if a user captures a goal during a soccer match, the image is uploaded to the cloud. The server uses image analysis technology to recognize the moment of the goal and, in conjunction with match data, generates digital information reflecting the goal. This digital information is immediately delivered to the user's device, allowing the user to receive updated information as the match progresses.

[0925] An example of a prompt message would be, "Upload images taken during a soccer match and generate digital information reflecting the goal scenes."

[0926] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0927] Step 1:

[0928] The user takes photos of interesting scenes during a sporting event using their device. The captured images are uploaded to cloud storage via an application on the device. The input is the image taken by the user, and the output is the image data stored in cloud storage.

[0929] Step 2:

[0930] The server retrieves image data from cloud storage. Using the retrieved image data as input, it performs analysis using image analysis techniques (e.g., TensorFlow). The purpose of the analysis is to identify important events within the image (e.g., goal scenes). The output is event information as a result of the analysis.

[0931] Step 3:

[0932] The server, based on the analysis results, works in conjunction with the competition database to generate digital information that reflects the match situation. The inputs are the analysis results and competition data, and the output is the generated digital information. This digital information includes real-time updates as the match progresses.

[0933] Step 4:

[0934] The server delivers the generated digital information to the user's terminal. The user's terminal displays the received digital information, providing the user with real-time updates on the match status. The input is the generated digital information, and the output is the information displayed on the user's terminal.

[0935] Step 5:

[0936] Users check the digital information displayed on their devices and receive new information as the match progresses. This allows users to understand the match situation in real time. The input is the information displayed on the device, and the output is the user's understanding and experience.

[0937] (Example 2)

[0938] Next, we will describe Example 2 of the morphological example. 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."

[0939] Conventional digital card generation systems did not adequately consider real-time capabilities or user interaction, making it difficult to provide digital cards that instantly reflect the moments of sporting events. Furthermore, there was a lack of easy ways for users to search for and retrieve cards for specific players or matches.

[0940] 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.

[0941] In this invention, the server includes means for analyzing digital information in real time using a generation AI model and generating digital cards, means for distributing the digital cards through an application to provide them to a user terminal, and means for searching for and displaying a specific digital card when the user enters a prompt. This makes it possible for the user to instantly obtain and enjoy digital cards that have been generated in real time.

[0942] A "generative AI model" is an artificial intelligence technology that analyzes digital information and automatically generates digital content tailored to specific purposes.

[0943] A "digital card" is a digital content in the form of an electronic card, generated based on a sporting event or a specific theme.

[0944] A "user terminal" is an electronic device used by a user to receive and display digital content.

[0945] A "prompt message" is a set of instructions that a user enters to search for specific information.

[0946] A "database management system" is a software system for organizing, storing, and retrieving data.

[0947] "Analyzing digital information in real time" means processing and analyzing information instantly as a real-world event occurs.

[0948] The following system is constructed as an embodiment of this invention.

[0949] The server uses a generative AI model to analyze digital information in real time and generate digital cards. Specifically, the server collects data from sports events in real time and organizes and stores it using a database management system. This makes it possible to instantly grasp the movements of players and the moments of scoring during a match. Machine learning frameworks such as TensorFlow and PyTorch are commonly used for generative AI models.

[0950] The generated digital cards are provided from the server to the user terminal. The user terminal receives and displays the digital cards through a dedicated application. Users can search for and retrieve specific digital cards by entering prompts within the application. For example, if a user enters a prompt such as "Show me the card of the player who scored a goal in the most recent match," the server will search for the corresponding card and display it on the terminal.

[0951] This system allows users to instantly obtain and enjoy digital cards generated in real time, making it possible to feel closer to the moments of sporting events.

[0952] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0953] Step 1:

[0954] The server collects sports event data in real time. It takes information such as match scores, player movements, and goal moments from sports data APIs and streaming services as input. This data is stored and organized in a database management system. The output is the organized match data.

[0955] Step 2:

[0956] The server analyzes the organized match data to identify which players scored goals. It uses match data stored in a database as input. The data analysis includes extracting player IDs and goal timestamps. The output provides information about the players who scored the goals.

[0957] Step 3:

[0958] The server uses a generative AI model to generate digital cards for identified players. The input consists of information about goal-scoring players and match highlights. The generative AI model combines player images and match highlights to create visually appealing cards. The output is the generated digital card.

[0959] Step 4:

[0960] The server sends the generated digital card to a dedicated application. The terminal displays the received card to the user. The input is the generated digital card, which is then provided to the user through the application. The output is the digital card displayed for the user to view.

[0961] Step 5:

[0962] The user searches for a specific digital card by entering a prompt within the application. For example, the user might enter a prompt such as, "Show me the card of the player who scored a goal in the most recent match." The server receives this prompt, searches for the corresponding card, and displays it on the terminal. The output displays the digital card specified by the user.

[0963] (Application Example 2)

[0964] Next, we will describe application example 2 of form 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."

[0965] In real-time competitive events, there is a need for a system that can instantly generate digital information capturing specific moments, and allow users to purchase and collect that information. However, conventional technologies have shortcomings in terms of real-time capabilities and the ability to reflect situational design, making it difficult to improve the user experience.

[0966] 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.

[0967] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading on-site video footage to a database, and means for image processing technology to create multiple patterns of information. This enables the generation of digital information capturing specific moments in real time, and allows users to immediately purchase and collect it.

[0968] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence to learn and reason.

[0969] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[0970] "Digital information" refers to information that is generated and stored electronically, including images, text, and audio.

[0971] "Distribution" refers to the act of delivering digital information to users via a network.

[0972] "Image" refers to visual information captured by a camera or other device, and includes both moving images and still images.

[0973] A "database" is a system for efficiently storing, searching, and managing information.

[0974] "Image processing technology" refers to the technology of analyzing, transforming, and processing digital images, including image recognition and editing.

[0975] A "user" refers to an individual or group that uses a system or service.

[0976] "Purchase" is the act of acquiring goods or services in exchange for money.

[0977] "Collection" is the act of gathering information or goods for a specific purpose.

[0978] The system for carrying out this invention consists of a server, user terminals, and a network. The server analyzes live video of the sporting event using AI technology and detects specific moments. Specifically, it uses an AI framework such as TensorFlow to recognize important events in the video (e.g., goal scenes and highlight scenes) in real time.

[0979] The server uses image processing software such as OpenCV to process the detected moment into digital information. This digital information is stored in a database so that users can purchase and collect it. A database service such as Firebase is used to manage users' purchase history and collection information.

[0980] User terminals are devices such as smartphones and tablets that receive digital information delivered from a server through a dedicated application. Users can operate the application to purchase real-time generated digital information and add it to their collection.

[0981] As a concrete example, when a player scores a goal during a soccer match, the server detects the scene and generates it as digital information. The user receives a notification within the app asking, "Would you like to purchase the digital information for this goal?" and can add the information to their collection by pressing the purchase button.

[0982] An example of a prompt message would be, "Detect the moment a goal is scored during a soccer match and generate that scene as digital information."

[0983] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0984] Step 1:

[0985] The server receives live video footage of the sporting event. The input is real-time video data transmitted from the camera. The server inputs this video data into an AI model and performs analysis to detect important events. The output is the timestamp and location information of the detected events.

[0986] Step 2:

[0987] The server uses an AI model to detect specific moments in the video (e.g., goal scenes). The input is the timestamp and location information obtained in step 1. Based on this information, the server uses OpenCV to extract the relevant scene and process it as digital information. The output is the processed digital information.

[0988] Step 3:

[0989] The server stores the processed digital information in a database. The input is the digital information generated in step 2. The server uses Firebase to store it in association with the user's purchase history and collection information. The output is the record of the digital information stored in the database.

[0990] Step 4:

[0991] The user terminal receives digital information distributed from the server. The input is a notification of the digital information sent from the server. The user terminal displays this information within the application, presenting the user with purchase options. The output is the purchase options displayed to the user.

[0992] Step 5:

[0993] The user purchases digital information through the application and adds it to their collection. The input is the purchase option presented in step 4. The user adds the digital information to their collection by pressing the purchase button. The output is the digital information added to the user's collection.

[0994] (Example 3)

[0995] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[0996] There is a need to clarify the ownership of digital content and provide an environment where users can create and trade their own digital cards. However, conventional systems have faced challenges in guaranteeing the uniqueness and rarity of digital cards, and insufficient means of proving ownership of the generated cards.

[0997] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0998] In this invention, the server includes means for generating digital cards based on digital information using generative AI technology, means for analyzing instruction text entered by the user and converting it into a format suitable for generative AI technology, and means for registering the generated digital cards using digital ledger technology and proving ownership. This guarantees the uniqueness and scarcity of the digital cards, enabling users to generate their own digital cards and trade them with ownership rights.

[0999] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate digital content.

[1000] "Digital information" refers to all information expressed in a format that can be processed by a computer.

[1001] A "digital card" is a card-shaped piece of content created in digital format, which can include images and text.

[1002] "User" refers to an individual or entity that uses this system to generate or trade digital cards.

[1003] A "command statement" is text that a user inputs to the AI ​​generation technology to specify the characteristics of the digital card they want to generate.

[1004] "Digital ledger technology" refers to technology that uses distributed ledger technologies such as blockchain to record the transaction history and ownership of digital assets.

[1005] "Ownership" refers to a legal or substantive right to a particular digital asset.

[1006] This invention is a system that generates digital cards using generative AI technology and proves ownership using digital ledger technology. Specific embodiments of this system are described below.

[1007] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. For example, they might enter the prompt, "Create a retro game-style character card." The terminal then sends this prompt to the server.

[1008] The server analyzes the received prompt message and converts it into a format suitable for generative AI technology. This analysis uses natural language processing techniques. Specifically, Python's natural language processing library can be used.

[1009] Next, the server generates digital cards using a generative AI model. During this process, the server leverages NVIDIA GPUs to efficiently perform calculations on the AI ​​model. The generated digital cards can include images and text.

[1010] The generated digital cards are registered by a server using digital ledger technology. Specifically, the digital cards are registered as NFTs using a blockchain platform to prove ownership. This registration guarantees the uniqueness and scarcity of the digital cards.

[1011] Finally, the server sends the information of the generated NFT digital card to the terminal. The user can then verify the generation result through the terminal and trade the digital card. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1012] Step 1:

[1013] The user enters a prompt using the terminal that describes the characteristics of the digital card they want to generate. The entered prompt is written in text format in the terminal's input field. For example, the user might enter the prompt, "Generate a fantasy-style dragon digital card." The terminal then prepares to send this prompt to the server.

[1014] Step 2:

[1015] The terminal sends the entered prompt message to the server. Specifically, it sends the prompt message to the server using an HTTP request. The input is the prompt message, and the output is the request sent to the server. The server receives the prompt message and prepares to parse it.

[1016] Step 3:

[1017] The server parses the received prompt message and converts it into a format suitable for generative AI technology. The input is the prompt message, and the output is the parsed data. This parsing uses natural language processing techniques, specifically Python's natural language processing library. The server understands the content of the prompt message and converts it into a data format suitable for the generative AI model.

[1018] Step 4:

[1019] The server generates digital cards using a generative AI model based on the analysis results. The input is the analyzed data, and the output is the generated digital card. The server utilizes NVIDIA GPUs to efficiently perform calculations for the AI ​​model. The generated digital cards can include images and text.

[1020] Step 5:

[1021] The server registers the generated digital cards using digital ledger technology. The input is the generated digital card, and the output is the registered NFT digital card. Specifically, a blockchain platform is used to register the digital card as an NFT and prove ownership.

[1022] Step 6:

[1023] The server transmits information about the generated NFT digital card to the terminal. The input is the registered NFT digital card, and the output is the transmission of information to the terminal. The user can verify the generation result through the terminal and trade the digital card.

[1024] (Application Example 3)

[1025] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1026] There is a need to improve the viewing experience of digital content and to build new revenue models for content distribution services. However, conventional digital card systems lack the means to provide personalized digital cards based on users' viewing history and to prove ownership of digital cards, making it difficult to improve user engagement and create value as digital assets.

[1027] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1028] This invention includes a server that utilizes AI to generate and distribute digital cards in real time for all sports, a means for uploading photos taken on-site to the cloud, a means for image AI to create multiple card patterns, a means for linking with data from various sports, a means for realizing a new card sales service with a design that reflects the game situation and real-time capabilities, a means for generating relevant digital cards based on viewing history and registering them on the blockchain, a means for providing an interface for users to manage, buy, sell, and exchange digital cards, and a means for providing limited edition digital cards related to content. This enables the provision of personalized digital cards based on the user's viewing history and proof of ownership of the digital cards.

[1029] "A method for generating and distributing digital cards in real time for all sports using AI" refers to a method that uses artificial intelligence technology to instantly create digital cards tailored to the situation of various sports events and provide them to users.

[1030] "A method for uploading photos taken on-site to the cloud" refers to a method of saving image data taken at sporting events to cloud storage via the internet.

[1031] "A method for image AI to create multiple card patterns" refers to a method of generating digital cards with different designs and layouts using artificial intelligence specialized in image processing.

[1032] "Methods for linking with data from various sports" refers to methods of acquiring statistical information and match data related to various sports and updating the content of digital cards based on that information.

[1033] "A means to realize a new card sales service with a design that reflects the battle situation and real-time functionality" refers to a method of providing and selling digital cards with designs that correspond to the progress of the match in real time.

[1034] "A means of generating related digital cards based on viewing history and registering them on the blockchain" refers to a method of creating related digital cards based on the viewing history of content by a user and registering those cards using blockchain technology.

[1035] "Means of providing an interface for users to manage, buy, sell, and exchange digital cards" refers to methods of providing user interfaces and functions for viewing, trading, and exchanging digital cards owned by users.

[1036] "Means of providing limited edition digital cards related to content" refers to a method of providing users with rare digital cards related to specific digital content.

[1037] The system for implementing this invention mainly consists of a server, a user terminal, and a blockchain network. The server utilizes artificial intelligence technology to generate digital cards based on the user's viewing history. Specifically, the server collects viewing history data and inputs it into a generation AI model to create the associated digital card. This generated digital card is registered as an NFT using blockchain technology, and ownership is proven.

[1038] The user terminal is a device such as a smartphone or smart glasses, through which the user manages digital cards. The user can view, buy, sell, and exchange digital cards using an application on the terminal. This application provides a user interface and is designed for easy operation.

[1039] Blockchain networks are used to record ownership of digital cards and ensure transparency and reliability in transactions. This allows for the secure buying, selling, and exchange of digital cards between users.

[1040] As a concrete example, after a user watches a specific movie, a limited edition digital card related to that movie is generated. This card features characters and scenes from the movie, and users can exchange it with other users or buy and sell it on a marketplace.

[1041] An example of a prompt message is, "Generate a limited edition digital card based on the movie the user has watched and register it as an NFT." By inputting this prompt message into the generation AI model, the corresponding digital card will be generated.

[1042] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1043] Step 1:

[1044] The server collects user viewing history data. It receives historical information about the content the user has viewed as input and stores this in a database. As output, the viewing history data is ready to be input into a generated AI model.

[1045] Step 2:

[1046] The server inputs viewing history data into a generating AI model and generates a related digital card. The inputs used are viewing history data and the prompt message "Generate a limited edition digital card based on the movies the user has watched and register it as an NFT." For data processing, the generating AI model analyzes the viewing history and creates a design for the related digital card. The output is the digital card design data.

[1047] Step 3:

[1048] The server registers the generated digital card on the blockchain, proving ownership as an NFT. It receives the digital card design data as input and sends it to the blockchain network. As a data calculation, it uses blockchain technology to record the uniqueness and ownership of the digital card. The output is the digital card registered as an NFT.

[1049] Step 4:

[1050] The user terminal manages digital cards through an application. It receives information about digital cards registered as NFTs as input. Specifically, the user can view, buy, sell, and exchange digital cards using the application's interface. As output, the transaction information for the digital cards is updated according to the user's actions.

[1051] 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.

[1052] "Example of form 1"

[1053] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[1054] "Example of form 2"

[1055] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. If the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited with the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[1056] "Example of form 3"

[1057] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts the generation pattern and delivery timing of digital cards accordingly. Specifically, if the user is happy with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that amplifies the happiness. Similarly, if the user is sad with the outcome of the match, the system recognizes this emotion and selects a digital card generation pattern that alleviates the sadness. Furthermore, the delivery timing of the digital cards is also adjusted according to the user's emotional changes. For example, if the user is excited about the outcome of the match, the delivery timing of the digital cards is sped up to maintain that excitement. On the other hand, if the user is disappointed with the outcome of the match, the delivery timing of the digital cards is delayed to alleviate that disappointment.

[1058] The following describes the processing flow for each example of the form.

[1059] "Example of form 1"

[1060] Step 1: Users express their emotions regarding the match results. These emotions can be expressed in various ways, such as through the tone of their voice, facial expressions, and social media posts.

[1061] Step 2: The emotion engine recognizes the user's emotions. This emotion engine analyzes the user's emotions using technologies such as speech recognition, image recognition, and natural language processing.

[1062] Step 3: Based on the emotions recognized by the emotion engine, a pattern for generating digital cards is selected. For example, if the user is feeling happy, a pattern for generating digital cards that amplifies that happiness is selected. If the user is feeling sad, a pattern for generating digital cards that alleviates that sadness is selected.

[1063] Step 4: Adjust the timing of digital card delivery based on the emotions recognized by the emotion engine. For example, if the user is excited about the match result, the timing of digital card delivery will be sped up to maintain that excitement. On the other hand, if the user is disappointed with the match result, the timing of digital card delivery will be delayed to alleviate that disappointment.

[1064] (Example 1)

[1065] Next, we will describe Embodiment 1 of 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."

[1066] In modern sports viewing, there is a demand for digital content that reflects the game situation and the emotions of spectators in real time. However, conventional systems do not adequately generate content in real time according to the progress of the game or personalize it according to the emotions of the spectators. Therefore, new methods are needed to enrich the viewing experience.

[1067] 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.

[1068] In this invention, the server includes means for instantly generating and distributing electronic cards in various sports using artificial intelligence, means for transmitting images acquired on-site to data storage, and means for image analysis technology to create cards in multiple formats. This makes it possible to provide digital content that reflects the situation of the match and the emotions of the spectators in real time.

[1069] Artificial intelligence is a technology in which computer systems imitate human intellectual behavior and perform learning and reasoning.

[1070] An "electronic card" is a card-shaped piece of content that is generated in digital format and displayed on an electronic device.

[1071] "Data storage" refers to storage devices and cloud services used to store digital data.

[1072] "Image analysis technology" is a technique in which computers process image data and extract information from it.

[1073] "Competition data" refers to data that includes statistical information and real-time match status related to sports and games.

[1074] "Immediacy" refers to the characteristic that an action or process is performed quickly and without delay.

[1075] "Personalization" refers to adjusting content and services according to the individual user's characteristics and preferences.

[1076] "Digital assets" are digital assets whose ownership is managed using blockchain technology.

[1077] The following system is constructed as an embodiment of this invention.

[1078] The server runs a program that utilizes artificial intelligence to instantly generate and distribute electronic cards for various sports. This program also has the function of sending images acquired by users on-site to data storage. Specifically, users use devices such as smartphones and tablets to upload photos taken during matches to the cloud via a dedicated application. The device then transmits the photos to data storage via an internet connection.

[1079] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. Google Cloud's Vision AI is used for this process. Vision AI recognizes objects and scenes within the photos and generates multiple design patterns based on this recognition. The generated design patterns are then linked to sports data using the Sports Data API. This allows real-time data, such as match scores and player performance, to be reflected on the electronic cards.

[1080] Furthermore, the server uses Microsoft Azure's Emotion API to recognize the user's emotions. Emotions are obtained either by the user entering their emotions about the match result through the application or by the server reading their facial expressions with the device's camera. The server adjusts the design pattern of the electronic card and optimizes the delivery timing based on the recognized emotions.

[1081] As a concrete example, imagine a user is watching a soccer match. During the match, they take a photo of a goal and upload it to the app. The server analyzes the goal using Vision AI and generates a design that includes the score information. The Emotion API recognizes the user's excitement and instantly delivers a card with a cheerful design. The user can view the card in the app and share it with friends on social media.

[1082] An example of a prompt to input into the generation AI model might be: "Analyze a photo taken during a soccer match and generate a digital card that reflects the score of the match. Also, adjust the card design according to the user's emotions."

[1083] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1084] Step 1:

[1085] Users upload photos taken during matches using their devices to the cloud via a dedicated application. The input is the image data captured by the user, and the output is the image file stored in cloud storage. The device transmits the photos to the data storage via an internet connection.

[1086] Step 2:

[1087] The server receives photos uploaded to the cloud and analyzes them using image analysis technology. The input is image data retrieved from cloud storage, and the output is design patterns based on the analysis results. The server uses Google Cloud's Vision AI to recognize objects and scenes in the photos and generate multiple design patterns.

[1088] Step 3:

[1089] The server integrates the generated design patterns with competition data. The input consists of design patterns generated by Vision AI and competition data obtained from the sports data API, while the output is an electronic card reflecting the match's score and player performance. The server uses the sports data API to acquire real-time data and integrate it into the design patterns.

[1090] Step 4:

[1091] The server recognizes the user's emotions and adjusts the design pattern of the electronic card accordingly. The input is the user's emotion data, and the output is an electronic card adjusted according to those emotions. The server uses Microsoft Azure's Emotion API to analyze the emotions the user enters through the application and the facial expressions captured by the device's camera, and adjusts the design accordingly.

[1092] Step 5:

[1093] The server delivers the final generated electronic card to the user's device. The input is the adjusted electronic card, and the output is the electronic card displayed on the user's device. The server optimizes the delivery timing according to the user's emotions and delivers the card instantly. The user can view, save, and share the received electronic card within the application.

[1094] (Application Example 1)

[1095] Next, we will describe Application Example 1 of Form 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."

[1096] In modern sports viewing, viewers demand real-time information relevant to the game situation, but traditional methods fail to adequately meet this need. Furthermore, information is not provided in a way that responds to viewers' emotions, making it difficult to improve individual viewing experiences.

[1097] 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.

[1098] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a server that uploads images acquired on-site to data storage, an image analysis technology that creates multiple information patterns, an emotion recognition technology that analyzes the user's emotions and selects an information generation pattern corresponding to those emotions, and a server that adjusts the timing of information distribution according to changes in the user's emotions. This makes it possible to provide information in real time that responds to the viewer's emotions.

[1099] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[1100] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[1101] "Digital information" refers to information that is generated electronically and displayed visually or audibly.

[1102] "Data storage" refers to a storage device or service for storing digital data.

[1103] "Image analysis technology" is a technique that processes image data and extracts useful information from it.

[1104] "Emotion recognition technology" is a technology that analyzes and identifies emotions from a user's facial expressions, voice, and other data.

[1105] An "information generation pattern" is a design or method for generating information based on specific conditions.

[1106] "Delivery timing" refers to the timing adjustments made when sending information or content to recipients.

[1107] "Viewers" refers to people who watch sports events or content.

[1108] To implement this invention, a server, terminal, and user must cooperate to build a system. The server is responsible for generating and distributing real-time digital information about the competition using artificial intelligence technology. Specifically, the server uploads images acquired on-site to data storage and uses image analysis technology to create information in multiple patterns. Furthermore, it analyzes the user's emotions using emotion recognition technology and selects an information generation pattern corresponding to those emotions. This makes it possible to adjust the timing of information distribution according to changes in the user's emotions.

[1109] A terminal is a device that allows users to receive information and experience it visually or audibly. Examples include smartphones and smart glasses. The terminal receives information delivered from a server and presents it to the user.

[1110] Users can receive information about the competition through their devices and enjoy a real-time experience. The user's emotions are transmitted to a server via the device's sensors and camera and analyzed using emotion recognition technology.

[1111] As a concrete example, if a user is excited at the moment a goal is scored during a soccer match, the system can recognize that emotion and instantly deliver digital information with a design that emphasizes the goal scene. In this case, the prompt given to the generative AI model would be, "Analyze a photo of the moment a goal is scored in a soccer match and generate a digital card that amplifies the viewer's excitement."

[1112] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1113] Step 1:

[1114] The server receives image data from the terminal. The input is image data from the terminal, and the output is image data stored in the server's data storage. In this step, the image data is uploaded to the cloud.

[1115] Step 2:

[1116] The server analyzes the received image data using image analysis technology. The input is image data stored in data storage, and the output is the analyzed image information. In this step, image analysis technology (e.g., TensorFlow) is used to identify the situation of the competition from the image and create multiple information generation patterns.

[1117] Step 3:

[1118] The server receives user emotion data sent from the terminal and analyzes it using emotion recognition technology. The input is emotion data from the terminal, and the output is the analyzed emotion information. In this step, emotion recognition technology (e.g., Affectiva) is used to identify the user's emotions.

[1119] Step 4:

[1120] The server selects an information generation pattern based on the analyzed image and emotion information. The input is the analyzed image and emotion information, and the output is the selected information generation pattern. In this step, the server selects the optimal information generation pattern according to the user's emotions.

[1121] Step 5:

[1122] The server generates digital information based on the selected information generation pattern and adjusts the delivery timing. The input is the selected information generation pattern, and the output is the generated digital information. In this step, a generation AI model is used to generate digital information, and the delivery timing is adjusted according to the user's emotions.

[1123] Step 6:

[1124] The terminal receives digital information delivered from the server and presents it to the user. The input is digital information from the server, and the output is visual or auditory information presented to the user. In this step, the terminal displays the received information to the user.

[1125] (Example 2)

[1126] Next, we will describe Example 2 of the morphological example. 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."

[1127] In modern sporting events, there is a demand for the creation and distribution of information media in real time, but traditional methods do not adequately provide immediacy or customization to meet the emotions of users. Furthermore, it is difficult to provide information media that takes users' emotions into consideration, which presents a challenge in maximizing the excitement and emotion of the competition.

[1128] 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.

[1129] In this invention, the server includes means for generating and distributing information media in real time for any sport using a generative AI model, means for analyzing the user's emotions using emotion recognition technology and adjusting the generation pattern and distribution timing of the information media according to those emotions, and means for linking with information on various sports. This makes it possible to provide timely information media that responds to the user's emotions.

[1130] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate new information media.

[1131] "Information media" refers to visual content such as cards and images provided in digital format, including information related to competitive events.

[1132] "Data storage" refers to online or cloud-based storage devices for storing and managing digital data.

[1133] "Image processing technology" refers to the technology used to analyze, process, and transform digital images, and is utilized in the creation of information media.

[1134] "Emotion recognition technology" is a technology that analyzes data such as a user's facial expressions and voice to determine their emotional state.

[1135] "Immediacy" refers to the characteristic of providing information and services in real time, and the ability to respond quickly to user requests.

[1136] "Digital assets" are digital assets whose ownership is verified using blockchain technology, and which are provided as information media.

[1137] A description of embodiments for carrying out this invention will be given.

[1138] The server uses a generative AI model to generate information media based on real-time data from sports events. Specifically, the server obtains data such as the progress of the match and the movements of the players from an external sports data API. Based on this data, it inputs prompts into the generative AI model to generate information media. The generative AI model used is an algorithm that analyzes digital images and text data to automatically create new information media.

[1139] The device analyzes the user's emotions in real time using emotion recognition technology. The device uses a camera and microphone to capture the user's facial expressions and voice, and emotion recognition software analyzes this data. The results of this analysis are sent to a server, which adjusts the generation patterns and delivery timing of information media according to the user's emotions.

[1140] As a concrete example, the server inputs the prompt message "Generate an information medium depicting the moment player A scores a goal" into the generating AI model. The generating AI model creates an information medium containing an image of player A and detailed information about the goal. Additionally, the terminal detects the user's smile, and emotion recognition software determines that "the user is happy." Based on this information, the server inputs the prompt message "Generate an information medium that amplifies the joy" into the generating AI model, and generates an information medium with special effects added.

[1141] In this way, the server and terminal can cooperate to provide timely information that responds to the user's emotions.

[1142] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1143] Step 1:

[1144] The server retrieves real-time match data from an external sports data API. The input consists of data regarding the progress of the match and player movements. The server analyzes this data to determine if a significant event (e.g., a goal) has occurred. The output is a flag indicating that the event occurred.

[1145] Step 2:

[1146] The server inputs a prompt message into the generating AI model based on the event data acquired in Step 1. Specifically, the server generates the prompt message, "Generate an information medium at the moment player A scored a goal." The input consists of event data and the prompt message, and the generating AI model generates the information medium based on these. The output is an information medium containing an image of player A and detailed information about the goal.

[1147] Step 3:

[1148] The device uses a camera and microphone to capture the user's facial expressions and voice. The input is real-time facial expression and voice data from the user. The device uses emotion recognition software to analyze this data and determine the user's emotional state. The output is emotion data, such as whether the user is happy or sad.

[1149] Step 4:

[1150] The server receives the user's emotion data obtained in step 3 and adjusts the generation pattern and delivery timing of the information media. Specifically, the server inputs a prompt message to the generating AI model: "Generate information media that amplifies joy." The input consists of emotion data and the prompt message, and the generating AI model uses this to generate information media with special effects added. The output is the customized information media.

[1151] Step 5:

[1152] The server delivers customized information media to the user's terminal. The input is the customized information media. The server delivers the information media at an appropriate time based on the user's emotions. The output is the information media received by the user on their terminal. The user can view and collect the information media in real time through a dedicated application.

[1153] (Application Example 2)

[1154] Next, we will describe application example 2 of form 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."

[1155] In today's digital content market, there are challenges such as insufficient personalization of digital information generated in real time, making it difficult to provide content that responds to users' emotions. Furthermore, there is a problem in that digital information capturing crucial moments in competitions cannot be provided immediately, thus failing to maximize the user experience.

[1156] 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.

[1157] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for recognizing the user's emotions and adjusting the generation pattern and distribution timing of digital information according to those emotions, and means for providing personalized digital information based on the user's emotions. This makes it possible to provide real-time and personalized digital information that responds to the user's emotions.

[1158] "A means of generating and distributing digital information in real time using AI" refers to a method of using artificial intelligence technology to instantly create digital information in accordance with the progress of a competition and provide it to users.

[1159] "Methods for uploading images taken on-site to an information processing device" refers to methods for transferring and saving image data taken at the competition site to an information processing device such as the cloud.

[1160] "Image processing technology as a means of creating multiple patterns of information" refers to a method of generating digital information of different designs or formats from original image data using image processing algorithms.

[1161] "Means of linking with information from various competitions" refers to methods for acquiring data on the progress and results of competitions and reflecting that data in the generation of digital information.

[1162] "A means to realize a new information sales service with a design that reflects the situation and real-time capabilities" refers to a method for selling digital information that has a design tailored to the situation of the competition and is available in real time.

[1163] "Means for recognizing user emotions and adjusting the generation patterns and delivery timing of digital information accordingly" refers to methods for detecting the emotional state of users and optimizing the generation methods and delivery timing of digital information based on those emotional states.

[1164] "Means of providing personalized digital information based on user emotions" refers to methods for generating and providing digital information that is customized according to the user's emotions.

[1165] The system for carrying out this invention includes a server, a user terminal, and a cloud service. The server utilizes artificial intelligence technology to generate digital information in real time according to the progress of the competition and delivers it to the user terminal. Specifically, the server uploads image data taken at the competition site to the cloud and generates multiple design patterns using image processing technology. This creates digital information that corresponds to the situation of the competition.

[1166] The user terminal is equipped with an emotion recognition engine that detects the user's emotions in real time. For emotion recognition, for example, the Microsoft Azure Emotion API can be used. The user's emotion data is sent to the server, which adjusts the generation patterns and delivery timing of digital information based on this data. As the generation AI model, OpenAI's GPT-3 is used, and personalized digital information is generated by inputting prompts that correspond to the user's emotions.

[1167] As a concrete example, when a user is watching a soccer match and their favorite team scores a goal, the emotion recognition engine on the user's device detects the user's joy. Based on this information, the server prompts the generation AI model with "Generate a digital card of the soccer goal scene that the user is celebrating." The generated digital information includes an image of the goal-scoring player and highlights from the match, and is designed to amplify the user's joy. In this way, it becomes possible to provide real-time and personalized digital information that responds to the user's emotions.

[1168] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1169] Step 1:

[1170] The server uploads image data taken at the competition site to the cloud. The input is image data taken at the site, and the output is image data stored in the cloud. In this step, the image data is transferred to and saved to a cloud storage service.

[1171] Step 2:

[1172] The server retrieves image data stored in the cloud and generates multiple design patterns using image processing technology. The input is image data in the cloud, and the output is digital information containing multiple design patterns. In this step, an image processing algorithm is applied to generate digital information with different designs from the original image.

[1173] Step 3:

[1174] The user terminal uses an emotion recognition engine to detect the user's emotions in real time. The input is the user's facial expressions and voice data, and the output is the user's emotion data. In this step, emotion recognition technology is used to analyze the user's emotional state.

[1175] Step 4:

[1176] The user terminal sends the detected emotion data to the server. The input is the user's emotion data, and the output is the emotion data sent to the server. This step involves transferring the emotion data to the server over the network.

[1177] Step 5:

[1178] The server prompts a generative AI model based on the received emotion data to generate personalized digital information. The input is the user's emotion data and a prompt sentence, and the output is personalized digital information. In this step, the generative AI model is prompted with the message, "Generate a digital card of a soccer goal scene that the user is happy about," and performs the operation of generating digital information that corresponds to the user's emotion.

[1179] Step 6:

[1180] The server delivers the generated personalized digital information to the user terminal. The input is the personalized digital information, and the output is the digital information delivered to the user terminal. In this step, the generated digital information is transmitted to the user terminal via the network.

[1181] Step 7:

[1182] The user terminal displays and provides the received digital information to the user. The input is digital information delivered from the server, and the output is the digital information displayed to the user. In this step, the digital information is displayed on the user interface, making it viewable by the user.

[1183] (Example 3)

[1184] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1185] In generating digital information, there is a challenge in providing personalized content that reflects user emotions in real time. Furthermore, there is a lack of means to clearly define ownership of the generated digital information and guarantee its value. Additionally, there is a need for content delivery at the appropriate time in response to changes in user emotions.

[1186] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1187] In this invention, the server includes means for analyzing the user's emotions, means for generating prompt sentences based on the analyzed emotions, means for generating digital information using the generated prompt sentences, means for registering the generated digital information as a non-fungible token, and means for adjusting the timing of digital information delivery according to changes in the user's emotions. This makes it possible to generate and deliver personalized digital information in accordance with the user's emotions, and furthermore, to guarantee its ownership and value.

[1188] "Methods for analyzing user emotions" refer to technologies that identify a user's emotional state based on data obtained from them.

[1189] "Methods for generating prompt sentences" refers to techniques that create instruction sentences to be input into a generative AI model based on analyzed emotional information.

[1190] "Means for generating digital information" refers to a technology that uses generated prompt messages to create digital content that responds to the user's emotions.

[1191] "Methods for registering as non-fungible tokens" refer to technologies that use blockchain technology to register generated digital information and guarantee its ownership and uniqueness.

[1192] "Methods for adjusting delivery timing" refer to technologies that optimize the timing of digital information delivery in response to changes in users' emotions.

[1193] This invention is a system that generates digital information in response to a user's emotions and provides it as a non-fungible token. Specific embodiments are shown below.

[1194] The server uses an emotion engine to analyze the user's emotions. The user inputs voice or text data through the terminal, which then sends it to the server. The server analyzes the received data with the emotion engine to identify the user's emotions. This emotion engine utilizes natural language processing technology to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment."

[1195] Next, the server generates a prompt based on the analyzed emotion. For example, if the user is feeling "joy," the server will create a prompt that says, "Generate a digital card that amplifies joy." This prompt is then input into the generative AI model.

[1196] The generative AI model generates digital information based on prompt text. This digital information includes designs and messages that respond to the user's emotions. For example, a digital card with bright colors and a positive message might be generated.

[1197] The generated digital information is registered as a non-fungible token by the server using blockchain technology. This proves ownership of the digital information and guarantees its uniqueness and scarcity.

[1198] Furthermore, the server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions.

[1199] In this way, it becomes possible to generate and deliver personalized digital information that responds to the user's emotions, and furthermore, its ownership and value can be guaranteed. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[1200] Step 1:

[1201] The user inputs their emotions regarding the match outcome into a device. The device collects the user's voice and text data and sends it to a server. This input data serves as the basis for analyzing the user's emotions.

[1202] Step 2:

[1203] The server analyzes the data received from the terminal using an emotion engine. The emotion engine uses natural language processing techniques to classify the user's emotions into categories such as "joy," "sadness," "excitement," and "disappointment." This analysis identifies the user's emotional state. The output is the identified emotion information.

[1204] Step 3:

[1205] The server generates prompts based on the analyzed emotional information. For example, if the user is feeling "joy," it will generate a prompt that says, "Generate a digital card that amplifies joy." This prompt is an instruction that is input into the generative AI model. The output is the generated prompt.

[1206] Step 4:

[1207] The server inputs the generated prompt text into the generation AI model. The generation AI model generates digital information based on the prompt text. Specifically, it generates digital cards containing bright colors and positive messages. The output is the generated digital information.

[1208] Step 5:

[1209] The server registers the generated digital information as a non-fungible token using blockchain technology. This registration proves ownership of the digital information and guarantees its uniqueness and scarcity. The output is the registered non-fungible token.

[1210] Step 6:

[1211] The server adjusts the timing of digital information delivery in response to changes in the user's emotions. For example, if the user is "excited," the server delivers digital information quickly. On the other hand, if the user is "disappointed," the delivery is delayed to soothe the user's emotions. The output is the adjusted delivery timing.

[1212] (Application Example 3)

[1213] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1214] In today's digital content market, there is a demand for personalized digital information that responds to users' emotions. However, conventional systems have struggled to recognize users' emotions in real time and generate and deliver digital information based on them. Furthermore, there has been a lack of clear means to prove ownership of digital information. As a result, it has been difficult to improve the user experience and maximize the value of digital information.

[1215] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1216] This invention includes a server that utilizes AI to generate and distribute digital information in real time for any sport, a means for recognizing the user's emotions using emotion analysis technology and adjusting the generation pattern and distribution timing of digital information according to those emotions, and a means for generating digital information based on the user's emotions and using distributed ledger technology to prove ownership. This makes it possible to provide personalized digital information that responds to the user's emotions, thereby maximizing the value of the digital information.

[1217] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intellectual activity.

[1218] "Digital information" refers to electronically generated data and content, including images, audio, and text.

[1219] "Emotional analysis technology" is a technology that recognizes and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[1220] "Distributed ledger technology" refers to technologies, such as blockchain technology, that distribute and record data in a distributed manner to prevent tampering.

[1221] "Real-time" refers to processing or responding to an event at the very moment it occurs.

[1222] Personalization refers to adjusting services and content according to the individual characteristics and preferences of each user.

[1223] "Ownership" refers to the legal right to a specific object or data, including the right to use or dispose of it.

[1224] The system for implementing this invention mainly consists of a server and terminals. The server uses AI to generate digital information in real time for all kinds of competitions and delivers it to the terminals. The terminals are information processing devices such as smartphones and tablets, and use cameras and microphones to recognize the user's emotions in real time.

[1225] The server uses emotion analysis technology to analyze the user's facial expressions and voice data transmitted from the terminal and recognize the user's emotions. For this, image processing libraries such as OpenCV are used to analyze the user's facial expressions. Speech recognition technology is used to analyze voice data. Based on the recognized emotions, the server adjusts the generation patterns and delivery timing of digital information.

[1226] Furthermore, the server registers the generated digital information using distributed ledger technology to prove ownership. Blockchain technology is used in this process, guaranteeing the uniqueness and scarcity of the digital information.

[1227] As a concrete example, consider a scenario where a user is watching a soccer match. The device recognizes the user's excitement and sends that data to the server. The server generates digital information that amplifies the excitement and immediately delivers it to the device. In this process, a generative AI model is used to select a design that corresponds to the user's emotions.

[1228] An example of a prompt for a generative AI model is: "Analyze the user's emotions and generate digital information that reflects those emotions. If the user is happy, select a design that amplifies that happiness and deliver it immediately."

[1229] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1230] Step 1:

[1231] The device captures the user's facial expressions and voice using a camera and microphone. It acquires real-time video and audio data as input. This data serves as foundational data for analyzing the user's emotions.

[1232] Step 2:

[1233] The device analyzes acquired video data using image processing libraries such as OpenCV to recognize emotions from the user's facial expressions. Audio data is analyzed using speech recognition technology to recognize emotions from the tone of voice. The output generates data indicating the user's emotions.

[1234] Step 3:

[1235] The device sends recognized emotion data to the server. The server receives this emotion data as input and uses a generative AI model to determine a pattern for generating digital information that corresponds to the emotion. The prompt text is input to the generative AI model, and a design appropriate to the user's emotion is selected.

[1236] Step 4:

[1237] The server generates digital information based on the selected design, adjusting patterns and delivery timing according to emotions. The generated digital information is obtained as output.

[1238] Step 5:

[1239] The server registers the generated digital information using distributed ledger technology and proves ownership. Blockchain technology is used to guarantee the uniqueness and scarcity of the digital information.

[1240] Step 6:

[1241] The server delivers registered digital information to the terminal. The terminal displays the received digital information to the user, improving the user experience. As output, personalized digital information is provided to the user.

[1242] (Other examples)

[1243] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1244] 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.

[1245] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[1246] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1247] 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.

[1248] [Fourth Embodiment]

[1249] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1250] 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.

[1251] 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).

[1252] 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.

[1253] 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.

[1254] 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).

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1262] "Example of form 1"

[1263] One embodiment of this system is an AI-powered real-time digital card generation system. This system uploads photos taken on-site to the cloud. The uploaded photos are analyzed by image AI, and multiple card design patterns are generated. The generated cards are linked with data from various sports, resulting in designs that reflect the progress of the match. As a result, digital cards that reflect the match situation in real time are generated and delivered to users.

[1264] "Example of form 2"

[1265] Another embodiment of the present invention involves a system in which generated digital cards are provided in application format. Users can purchase and collect digital cards generated in real time through a dedicated application. For example, a digital card of a player who scores a goal during a soccer match is generated at that moment and sold on the application.

[1266] "Example of form 3"

[1267] Furthermore, in another embodiment of the present invention, there is a system in which the generated digital cards are provided as NFTs (Non-Fungible Tokens). Digital cards provided as NFTs have their ownership verified using blockchain technology, and their uniqueness and scarcity are guaranteed. As a result, the digital cards can have value as digital assets and can be bought and sold among users.

[1268] The following describes the processing flow for each example of the form.

[1269] "Example of form 1"

[1270] Step 1: Upload the photos taken on site to the cloud.

[1271] Step 2: The image AI analyzes the uploaded photo and generates multiple design pattern cards.

[1272] Step 3: The generated cards will be linked with data from various sports and will have a design that reflects the progress of the match.

[1273] Step 4: A digital card reflecting the match situation in real time is generated and delivered to the user.

[1274] "Example of form 2"

[1275] Step 1: Users purchase digital cards generated in real time through a dedicated application.

[1276] Step 2: For example, a digital card of a player who scores a goal during a soccer match is generated at that moment.

[1277] Step 3: The generated digital cards are sold on the application, and users can collect them.

[1278] "Example of form 3"

[1279] Step 1: The generated digital card is provided as an NFT (Non-Fungible Token).

[1280] Step 2: Digital cards provided as NFTs utilize blockchain technology to verify ownership and guarantee uniqueness and scarcity.

[1281] Step 3: This allows the digital cards to have value as digital assets and to be bought and sold between users.

[1282] (Example 1)

[1283] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1284] In modern sports viewing, spectators demand digital content that reflects the game situation in real time. However, conventional technology has made it difficult to generate and distribute digital cards that instantly reflect the game situation. Furthermore, it has been impossible to provide designs that highlight the unique characteristics of individual matches, thus failing to enhance user satisfaction.

[1285] 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.

[1286] In this invention, the server includes means for transmitting images taken by the user to an information processing device; means for the information processing device to store the received images in a data storage device; means for the information processing device to analyze the images using image analysis technology and extract features; means for the information processing device to generate multiple designs based on the extracted features using a generative model; means for the information processing device to acquire competition information from an external database and integrate it into the generated designs; means for the information processing device to transmit the generated digital information to the user's terminal; and means for providing digital information that reflects the competition situation in real time. This makes it possible to generate and deliver digital cards that immediately reflect the match situation to the user.

[1287] A "user" is an individual or group that uses the system to take images and receive digital information.

[1288] An "information processing device" is a device that receives, stores, analyzes, generates designs for images, integrates data, and transmits digital information.

[1289] A "data storage device" is a storage medium used to store received image data.

[1290] "Image analysis technology" refers to techniques for recognizing and extracting objects and features within an image.

[1291] A "generative model" is an algorithm or program for generating a design based on extracted features.

[1292] An "external database" is a collection of data that an information processing device accesses to provide competition information.

[1293] "Competition information" refers to data related to the competition, such as match scores and player performance.

[1294] "Digital information" refers to digital content that integrates generated designs and competition information.

[1295] This invention is a system that generates and distributes digital information reflecting the competition situation in real time, based on images taken by users. Users take photos at the competition site using a device such as a smartphone or tablet. The captured images are transmitted from the device to a server via the internet.

[1296] The server saves the received images to cloud storage. Data storage services such as Amazon S3 can be used for this purpose. The saved images are then analyzed using image analysis technologies such as the Google Cloud Vision API, and objects and features within the images are extracted.

[1297] Next, the server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on the extracted features. The generated designs are then integrated with competition information obtained by the server from an external database (e.g., SportsDB API). This allows information such as match scores and player performance to be reflected in the designs.

[1298] Finally, the server sends the generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time.

[1299] As a concrete example, suppose a user takes a photo of a player during a soccer match and uploads it to this system. The server analyzes the photo and generates a design pattern based on the player's movements and facial expressions. At the same time, it acquires match score and player performance data and incorporates this into the card design. As a result, a digital card that reflects the match situation in real time is delivered to the user.

[1300] An example of a prompt message would be, "Upload photos of players taken during a soccer match and generate digital cards that reflect the match's scores and player performance."

[1301] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1302] Step 1:

[1303] The user takes photos at the competition site using their device. The captured images are sent to the server through the application on the device. The input is the captured image data, and the output is the transmission of the image data to the server.

[1304] Step 2:

[1305] The server saves the received image data to cloud storage. Here, data storage devices such as Amazon S3 are used. The input is image data sent from the terminal, and the output is image data stored in cloud storage.

[1306] Step 3:

[1307] The server analyzes the stored image data using image analysis technologies such as the Google Cloud Vision API. The analysis extracts objects and features from the image. The input is image data stored in cloud storage, and the output is the extracted image feature data.

[1308] Step 4:

[1309] The server uses generative AI models such as DALL-E and Stable Diffusion to generate multiple designs based on extracted feature data. The input is image feature data, and the output is the generated design data.

[1310] Step 5:

[1311] The server retrieves competition information from external databases such as the SportsDB API. The retrieved competition information is integrated into the generated design data. The input is the generated design data and competition information from the external database, and the output is the design data reflecting the competition information.

[1312] Step 6:

[1313] The server transmits the final generated digital information to the user's device. The user can then view a digital card on their device that reflects the match situation in real time. The input is design data that reflects the match information, and the output is a digital card displayed on the user's device.

[1314] (Application Example 1)

[1315] Next, we will describe Application Example 1 of Form 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".

[1316] In modern sports viewing, it is difficult for spectators to obtain real-time information that reflects the progress of the game. Furthermore, there is a lack of digital content to enrich the viewing experience. Therefore, there is a need for a system that allows spectators to receive digital information that instantly reflects the game situation.

[1317] 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.

[1318] In this invention, the server includes means for generating and distributing digital information in real time for any sport using AI, means for uploading images taken on-site to the cloud, and means for image analysis technology to create multiple information patterns. This makes it possible for spectators to receive digital information that reflects the game situation in real time, based on images they took during the match.

[1319] "AI" is an abbreviation for artificial intelligence, which is a technology in which computers imitate human intelligence and perform learning and reasoning.

[1320] "Real-time" refers to the immediate processing or response that occurs the moment an event takes place.

[1321] "Digital information" refers to information that is generated and stored electronically, and is data that is represented in digital format.

[1322] "Cloud" refers to a collection of computer resources and services provided via the internet, serving as a platform for data storage and processing.

[1323] "Image analysis technology" is a technique that uses computer vision to analyze image data and extract specific information.

[1324] "Competition" refers to competitive activities such as sports and games that are conducted according to specific rules.

[1325] "Digital assets" are valuable information and content that exist in digital format and are subject to ownership and trading.

[1326] "Spectators" refer to people who watch sports or events, gathering together to enjoy the game or performance.

[1327] The system for implementing this invention primarily utilizes a server, a user terminal, and cloud infrastructure. The server uses AI technology to analyze images captured by the user in real time and generate digital information. Specifically, images captured by the user's terminal are uploaded to cloud storage (e.g., AWS S3). The server analyzes the images using image analysis technology (e.g., TensorFlow) to determine the situation of the competition. The analysis results are linked with competition data, and a digital information generation algorithm generates digital information that reflects the match situation. The generated digital information is delivered to the user's terminal in real time.

[1328] As a concrete example, if a user captures a goal during a soccer match, the image is uploaded to the cloud. The server uses image analysis technology to recognize the moment of the goal and, in conjunction with match data, generates digital information reflecting the goal. This digital information is immediately delivered to the user's device, allowing the user to receive updated information as the match progresses.

[1329] An example of a prompt message would be, "Upload images taken during a soccer match and generate digital information reflecting the goal scenes."

[1330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1331] Step 1:

[1332] The user takes photos of interesting scenes during a sporting event using their device. The captured images are uploaded to cloud storage via an application on the device. The input is the image taken by the user, and the output is the image data stored in cloud storage.

[1333] Step 2:

[1334] The server retrieves image data from cloud storage. Using the retrieved image data as input, it performs analysis using image analysis techniques (e.g., TensorFlow). The purpose of the analysis is to identify important events within the image (e.g., goal scenes). The output is event information as a result of the analysis.

[1335] Step 3:

[1336] The server, based on the analysis results, ...

Claims

1. Equipped with a processor, The aforementioned processor, A first prompt is generated to instruct the uploading of images taken at the competition site to the cloud, the uploaded images based on the first prompt are analyzed using image analysis technology, and a second prompt is generated based on the analysis results. Real-time data regarding the progress of the competition is obtained from an external database or streaming service, and it is determined whether or not a predetermined event related to the competition has occurred. If it is determined that the event has occurred, the second prompt is input to the generating AI model to generate multiple design patterns, and the acquired real-time data is integrated into the design patterns to generate a digital card that reflects the progress of the match. Using an emotion engine that recognizes the user's emotions, a third prompt is generated to instruct the system to adjust the design pattern or delivery timing of the digital card according to the recognized emotion. system.

2. The system according to claim 1, wherein the processor distributes the generated digital card through an application for providing it to a user's terminal.

3. The system according to claim 1, wherein the processor provides the generated digital card as a non-fungible token.

Images