system

Abstract

We provide the system. [Solution] A user interface means for receiving instructions from the user, An image generation apparatus means that generates an image based on the aforementioned instructions, A registration method for registering the generated image as a digital asset, A marketplace registration means for listing the aforementioned digital assets on the market in a state where they can be sold, A system including a sales management means for managing and returning sales obtained from the sale of the aforementioned digital assets.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Regarding the generation and sale of digital art, it is required to enable users without technical knowledge to easily create art and enter the commercial field. However, in existing systems, the generation procedure is complex, and there is a problem that the technical hurdles in the process up to the sale are high. Therefore, it is an issue to provide a simple and effective digital art generation and sales platform that can be used by general users.

Means for Solving the Problems

[0005] This invention provides a platform in which an image generation device automatically generates digital art based on user input prompts and registers it as a digital asset. The generated digital assets can be easily listed on the market and made available for sale. Furthermore, the system has a function to effectively manage the revenue generated from sales and allocate it to specific uses. This system allows users to easily create digital art and leverage its commercial value without requiring special technical skills.

[0006] A "user interface means" is an interface that allows a user to give instructions or input to a system.

[0007] "Image generation device means" refers to a device or software for generating an image based on input instructions.

[0008] "Registration means" refers to a method or device for recording generated digital content as a digital asset and registering it in a database, blockchain, or similar.

[0009] "Market registration means" refers to a function or process that enables the listing and sale of digital assets on an online or physical marketplace.

[0010] A "sales management system" is a system or process for tracking, recording, and allocating revenue generated from the sale of digital assets for specific purposes. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

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

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] As embodiments of the present invention, each element constituting the image generation and sales platform will be described.

[0033] The user first accesses the user interface using a terminal. There, they provide instructions by entering prompts regarding themes and styles for image generation.

[0034] The terminal sends the input prompt to the server, which processes it using an image generation device. Specifically, the server analyzes the received prompt and provides appropriate instructions to the image generation AI algorithm. This AI algorithm uses deep learning technology and other methods to generate digital art that meets the user's wishes.

[0035] The generated images are registered as digital assets by the server using a registration system. At this time, related metadata is also recorded, facilitating subsequent tracking and sales management.

[0036] Next, the server uses a marketplace registration mechanism to list the created digital assets on an online NFT marketplace. This gives users the opportunity to sell their work to a wide range of buyers.

[0037] Once a sale is completed, the server processes the revenue using sales management tools. A portion of the revenue is either returned to the digital asset issuer, depending on the settings, or allocated to specific projects or points programs.

[0038] As a concrete example, if a user wants to create art on the theme of "futuristic city," they input this theme through their device. The server processes the input using an image generation device and generates an image of a futuristic city. This digital art is immediately converted into an NFT and listed on an online marketplace. Subsequently, the revenue from sales is managed for a specified purpose and used to achieve the ultimate goal in line with the user's intentions.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a terminal to access the user interface and enter prompts for image generation.

[0042] Step 2:

[0043] The terminal sends the entered prompt to the server. Here, the data is transmitted using a secure communication method.

[0044] Step 3:

[0045] The server receives a prompt and starts the image generation device. It processes the received prompt appropriately using a text analysis algorithm and prepares it to be passed to the generation AI.

[0046] Step 4:

[0047] The server uses image generation AI to generate images based on prompts. This process utilizes deep learning technology to create new digital images based on the selected theme.

[0048] Step 5:

[0049] The generated images are temporarily stored on the server and then registered as digital assets using a registration mechanism. Blockchain technology is used for registration to ensure transparency and traceability.

[0050] Step 6:

[0051] The server uses a marketplace registration system to list this digital asset on an online NFT marketplace. There, the necessary metadata is configured, and it is made public.

[0052] Step 7:

[0053] Users can view the generated digital art and check its sales status on the marketplace using their devices.

[0054] Step 8:

[0055] The server uses sales management tools to properly manage the revenue generated from sold NFTs. Revenue is distributed to stakeholders or allocated to designated projects based on established rules.

[0056] Step 9:

[0057] Users can check their sales usage and point update information on their devices. This makes the entire process transparent and easy to manage.

[0058] (Example 1)

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

[0060] Traditional image generation and sales systems have faced challenges in efficiency and automation in individual image generation, digital asset registration, and sales. In particular, it is currently difficult for users to efficiently list digital images they have generated individually on the market and to properly manage and distribute profits.

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

[0062] In this invention, the server includes an information input means for receiving theme and style instructions, including prompt text; an information processing means for generating images using a generation AI model based on the instructions; and an asset management means for registering the generated images as digital information and recording related information. This makes it possible for users to easily specify themes and styles, immediately register the generated images as digital information, and efficiently and automatically list them on the market.

[0063] A "prompt statement" is a set of instructions used by the user to describe the theme and style of the image they want to generate.

[0064] An "information input means" is an interface that has the function of receiving instructions from the user.

[0065] A "generative AI model" is a technology that uses artificial intelligence algorithms, such as deep learning techniques, to generate images based on input prompt text.

[0066] "Information processing means" refers to a server component that has the function of generating images using a generation AI model based on the received prompt message.

[0067] "Digital information" refers to assets that are registered and managed electronically, including generated images.

[0068] An "asset management system" is a system that has the function of registering generated digital information and managing and tracking related information.

[0069] An "information provision means" is a component that has the function of putting digital information on the market and enables online information distribution.

[0070] A "profit management system" is a system that manages the profits obtained from the sale of digital information and distributes them based on a pre-established mechanism.

[0071] This invention details an embodiment of a system that allows users to easily generate images with specified themes and styles, and register and list them as digital information. Specifically, it comprises information input means, information processing means, asset management means, information provision means, and profit management means.

[0072] The user first accesses the information input method using a terminal. Here, they input instructions regarding the theme and style of the image they want to generate as prompt text. A concrete example of a prompt text might be "a night view of a cyberpunk futuristic city."

[0073] The terminal sends the input prompt message to the server. The server utilizes information processing tools and generates an image using a generative AI model. The generative AI model incorporates deep learning technology and generates images using the latest technology based on the input prompt message.

[0074] The generated images are registered as digital information by the server using asset management tools. This process records associated metadata along with the generated images, enabling later tracking and management.

[0075] Next, the server uses information provision methods to list digital information on the marketplace. Specifically, it uses a dedicated API to publish image listing information on the marketplace.

[0076] If a sale is completed, the server processes the profits using profit management mechanisms. A portion of the profits is distributed to the user or specific project. In this way, users can sell their work on the market and take advantage of the automatically configured profit-sharing function.

[0077] As described above, in order to implement the invention, it is necessary to properly operate the information input means, the generation AI model, and the information processing means, and to manage digital information and provide it appropriately to the market. This will enable users to easily and efficiently generate and sell digital art.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user accesses the terminal's user interface and enters prompt text regarding the theme and style of the image they want to generate. Specifically, the user enters the theme "Cyberpunk futuristic city night view" into the text box on the screen and clicks the "Submit" button. The input is the prompt text, and the output is the submitted prompt text.

[0081] Step 2:

[0082] The terminal sends the entered prompt message to the server. Specifically, the terminal sends the prompt message to the server via the network. The input is the prompt message received from the user, and the output is the prompt message passed to the server.

[0083] Step 3:

[0084] The server analyzes the received prompt and issues instructions to the generative AI model. Specifically, the server uses a natural language processing engine to analyze the prompt text and provides instructions to the generative AI model that reflect the theme and style. The input is the prompt text, and the output is the instructions to the generative AI model.

[0085] Step 4:

[0086] The server generates images using a generative AI model. Specifically, the generative AI model utilizes deep learning techniques to generate images based on the analysis results. The input is instructions for the generative AI model, and the output is the generated image.

[0087] Step 5:

[0088] The server registers the generated images as digital information and records related information. Specifically, the server hashes the image files and stores them in a database along with metadata such as creation date and theme. The input is the generated image, and the output is the registered digital information.

[0089] Step 6:

[0090] The server uses an information provision method to list digital information on the marketplace. Specifically, the server connects to the marketplace's API and registers product information. The input is the registered digital information, and the output is the product information listed on the marketplace.

[0091] Step 7:

[0092] If a sale is completed, the server processes the profits using profit management tools. Specifically, the server distributes revenue through a payment system based on predefined settings. The input is sales information from the market, and the output is the profits already distributed to users, etc.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] Existing image generation platforms have faced challenges such as the difficulty of immediately listing generated images on the market, preventing users from experiencing real-time interaction with the visual information. Furthermore, the procedures for selling generated digital art are cumbersome, and revenue redistribution lacks flexibility.

[0096] 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.

[0097] In this invention, the server includes information acquisition means for inputting instructions from a user, visual generation device means for generating visual information based on the instructions, asset registration means for registering the generated visual information as a digital asset, market listing means for listing the digital asset in an exchangeable state on an information market, revenue management means for managing and redistributing the revenue obtained from the exchange of the digital asset, and visual output means for displaying the visual information generated in real time through the user's eye-tracking device and immediately listing it on the information market. This enables the user to immediately list the generated visual information on the market and obtain a real-time experience.

[0098] "Information acquisition means" refers to devices such as input devices or interfaces for receiving instructions from the user.

[0099] A "visual generation device means" is a processing device for generating visual information or images based on user instructions.

[0100] An "asset registration system" is a system for registering generated visual information as a digital asset in a database.

[0101] "Market listing method" refers to a method for displaying generated digital assets on an online marketplace in an exchangeable state.

[0102] A "revenue management mechanism" is a management system for managing and redistributing revenue obtained from the sale or exchange of digital assets.

[0103] A "visual output means" is a device that displays visual information generated in real time through the user's eye-tracking device, enabling immediate listing on the information market.

[0104] A system for carrying out this invention includes a process in which a user intuitively inputs instructions through an eye-tracking device, and generates and manages visual information using a generative AI model.

[0105] First, the user uses smart glasses or other eye-tracking devices to input prompts as instructions. This input is received by an information acquisition mechanism. Specifically, instructions can be easily entered using methods such as voice recognition or touch gestures.

[0106] Next, the terminal sends the received prompt message to the server. The server uses a visual generation device to generate visual information based on a generation AI model. This model incorporates deep learning technology and generates high-quality images based on the style and theme desired by the user.

[0107] The generated visual information is registered as a digital asset in a database using an asset registration system. Subsequently, the digital asset is listed on an online marketplace in an exchangeable state using a marketplace listing system. This allows users to immediately list their generated images and access them from a large number of buyers.

[0108] Furthermore, the revenue management system manages the revenue generated from sales and exchanges and redistributes it to users and stakeholders. This system supports flexible settings for redistributing revenue according to specific purposes.

[0109] As a concrete example, when a user inputs a prompt such as "a futuristic city night view" using an eye-tracking device, the server generates an image of a futuristic city night view based on that instruction and registers it as an NFT. It is then immediately put up for sale on an online marketplace, and when it is sold, the revenue is appropriately managed.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user uses an eye-tracking device to input a prompt such as "futuristic city night view." The eye-tracking device receives the user's instructions via voice recognition or gestures and sends the input to the terminal.

[0113] Step 2:

[0114] The terminal sends the received prompt message to the server. Here, the terminal converts the prompt message into the appropriate format and prepares a data packet to be sent to the server via the communication module.

[0115] Step 3:

[0116] The server causes the visual generation device to process the received prompt message. In this process, a generative AI model is used to generate visual information based on the input prompt. Specifically, the generative AI model uses a deep learning algorithm to predict visual information and outputs it as a final image.

[0117] Step 4:

[0118] The generated visual information is registered as a digital asset in the database by the asset registration mechanism on the server. The server adds relevant metadata and stores the digital asset in an identifiable format.

[0119] Step 5:

[0120] The server uses marketplace listing methods to list registered digital assets on the online marketplace. Here, an API is used to send detailed asset information to the marketplace platform, making it immediately available for listing.

[0121] Step 6:

[0122] After a sale is completed, the server manages the revenue generated through revenue management mechanisms. It aggregates sales information, distributes the revenue to users and other stakeholders according to predetermined ratios, and allocates it to specific purposes as needed.

[0123] Through the steps described above, a system is realized that allows users to intuitively generate and sell images through eye-tracking devices.

[0124] 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.

[0125] In embodiments of the present invention, a system specialized in image generation and sales is combined with an emotion engine that recognizes and utilizes user emotions. As a result, this system can provide a more personalized digital art generation experience.

[0126] When a user accesses the user interface via a device, a prompt for sentiment analysis is displayed. When the user enters text in response to the prompt, the device sends this information to the server. The sentiment engine on the server analyzes this text to recognize the user's emotional state.

[0127] The recognized emotions are reflected in the image generation process of the image generation device. Specifically, the selection of themes, colors, and styles based on the emotional state is automatically adjusted, resulting in more personalized digital art. This process produces artwork that matches the user's current emotions and preferences, leading to greater satisfaction.

[0128] The generated digital art is registered as a digital asset by the server. Once registration is complete, it is published on an online marketplace using a marketplace registration method, and sales begin.

[0129] Furthermore, the server also has the ability to provide feedback to the user through an emotion engine. This feedback includes information about how the generated art reflects the user's emotional state, offering the user a new experience of enjoying a visual representation of their own emotional state.

[0130] As a concrete example, if a user inputs text indicating they are feeling "happy today," the emotion engine recognizes this state as "joy." Based on this information, the server instructs the image generator to use a bright and colorful theme and style. As a result, the generated digital art visually expresses the user's feeling of "joy." This art is immediately put on the market as an NFT, allowing the user's emotional value to be evaluated in a new way.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses a terminal to access the user interface and enter text in response to prompts for sentiment analysis.

[0134] Step 2:

[0135] The terminal sends the entered text data to the server. The transmission is performed via a secure protocol.

[0136] Step 3:

[0137] The emotion engine on the server analyzes the received text data to recognize the user's emotional state. Natural language processing techniques are used for this analysis.

[0138] Step 4:

[0139] Based on the emotional state recognized by the server, it provides instructions to the image generator regarding theme, color, and style. These instructions are provided as settings optimized for the emotional state.

[0140] Step 5:

[0141] An image generation device generates digital art based on instructions from a server. The generation process utilizes machine learning models to create designs that reflect the user's emotions.

[0142] Step 6:

[0143] The generated digital art is temporarily stored on the server. It is then authenticated and registered as a digital asset using a registration system.

[0144] Step 7:

[0145] The server utilizes marketplace registration methods to list authenticated digital assets on online NFT marketplaces. Sales begin once the necessary metadata settings are complete.

[0146] Step 8:

[0147] Users can view the content and sales status of the digitally generated art on their devices. This allows users to understand how the art reflects their own emotions.

[0148] Step 9:

[0149] The server uses a sales management system to track revenue from sold digital art. Revenue is distributed according to settings, and is distributed to specific uses or users.

[0150] Step 10:

[0151] Users receive sales data and feedback on their devices, confirming that the art is a visual representation based on emotional states.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] Conventional digital art generation systems have struggled to create works that reflect individual user emotions and preferences, resulting in insufficient personalization of the user experience. Furthermore, the manual nature of sales and revenue management for generated works leads to inefficiencies and cumbersome processes.

[0155] 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.

[0156] In this invention, the server includes an information acquisition means for receiving emotion-based instructions from a user, an image generation means for generating images based on the emotion information, and an information management means for registering the generated images as digital information. This enables the generation of personalized digital art that responds to the user's emotions, as well as efficient sales and revenue management.

[0157] "Information acquisition means" refers to a configuration equipped with functions for collecting and analyzing emotionally-based instructions from users.

[0158] The "image generation means" is a configuration for executing the process of creating a digital image based on acquired emotional information.

[0159] "Information management means" refers to a configuration for securely storing and registering generated digital images as digital information.

[0160] A "marketplace listing method" is a configuration that presents registered digital information to an online marketplace, making it available for sale.

[0161] An "income management system" is a configuration equipped with functions for tracking, managing, and appropriately redistributing income obtained from the sale of digital information.

[0162] To implement this invention, a user accesses the system via a terminal and provides emotion-based input. When the user inputs their emotional state in text format, the terminal sends this information to the server. The server first uses an emotion analysis engine to analyze this text information. Natural language processing libraries (e.g., Python's NLTK or Hugging Face's Transformers) can be used for emotion analysis.

[0163] The server issues commands to the image generation device based on the emotion data obtained through emotion analysis. This device is equipped with a function to automatically generate specific images corresponding to emotions using a generative AI model (e.g., DALL-E or StyleGAN). For example, if "joy" is detected, the generative AI model is programmed to create an image with a bright, positive tone. As a concrete example, if the user enters "I'm having fun today" as text, the emotion engine recognizes this as "joy" and instructs the image generation device to create a digital image with colors and style appropriate to that emotional state.

[0164] The generated digital images are registered as digital information by the server. This registration is performed using information management means, and encryption is applied as needed, utilizing blockchain technology to prevent tampering. Through marketplace means, the registered images are put up for sale on the online marketplace and become immediately available for purchase. Sales management and revenue tracking are handled by revenue management means, and the revenue obtained can be reinvested for specified purposes.

[0165] An example of a prompt in this system is the text, "Tell me how you're feeling today. Based on your feelings, I will generate a special piece of art just for you." This allows users to easily communicate their emotions to the system and experience personalized digital art.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user accesses the system using a terminal. The user uses the prompts displayed on the screen to input text about their emotions. The input is given as a sentence expressing a specific emotion, such as "I'm having a good day." The terminal prepares this input data and sends it to the server.

[0169] Step 2:

[0170] The server passes the received text data to the sentiment analysis engine. The text data, as input, is analyzed using a natural language processing library. The sentiment analysis engine extracts keywords and context related to emotions from the text and outputs emotion labels such as "joy" and "sadness." This process automatically categorizes emotions.

[0171] Step 3:

[0172] The server issues generation instructions to the image generator based on the analyzed emotion data. Based on the emotion labels as input, the generation AI model sets color and style parameters and generates an appropriate image. In this step, the generated image is concretely visualized and output as digital art.

[0173] Step 4:

[0174] The generated digital art is registered as digital information by the server using information management tools. The generated images used as input are encrypted and securely stored on the server. Blockchain technology is utilized at this stage to prevent tampering with the digital information.

[0175] Step 5:

[0176] The server lists registered digital art on an online marketplace through a marketplace mechanism. The digital information used as input is transferred to the digital marketplace and becomes available for purchase. This makes the generated art accessible to a wide range of users.

[0177] Step 6:

[0178] The server manages sales revenue using revenue management systems and allocates it to designated uses. Revenue data obtained from market sales is tracked and recorded. Revenue is redistributed and returned to digital art creators based on automatically set rules.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] Currently, in the creation and sale of personalized digital art, it is difficult to analyze users' emotions in real time and provide digital representations that reflect them. This is because there is no system that automatically reflects specific art styles or themes based on the user's emotional state. As a result, there is a challenge in being able to quickly and efficiently provide digital content that matches the user's emotions.

[0182] 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.

[0183] In this invention, the server includes an information input device means for receiving information input from a user, an emotion analysis device means for analyzing the user's emotional state, and an image generation device means for generating an image based on the emotional state. This makes it possible to generate and provide customized digital art that corresponds to the emotions input by the user.

[0184] An "information input device means" is a device that receives information regarding the user's emotions and instructions, and serves as an interface connecting the user and the system.

[0185] An "emotion analysis device" is a device that analyzes the user's emotional state from the information received, and provides information necessary for the next processing based on the emotional data.

[0186] An "image generation device means" is a device that generates images using appropriate themes and styles based on an analyzed emotional state.

[0187] A "recording device" is a device that saves generated images in a digital format in preparation for later use and distribution.

[0188] A "distribution registration device" is a device that registers recorded digital formats for distribution in the market and enables their distribution.

[0189] A "revenue management device" is a device for tracking revenue obtained from transactions of distributed digital formats and for distributing it appropriately.

[0190] To implement this invention, the user first uses an information terminal such as a smartphone or smart glasses to input information about their emotions via an information input device. The text information entered by the user is sent to a server. This server is equipped with an emotion analysis device that analyzes the entered text and identifies the user's emotional state.

[0191] The analyzed emotional information is then sent to an image generation device. This device selects the optimal theme, colors, and style based on the aforementioned emotional state and generates personalized digital art using a generative AI model. This generation process can utilize, for example, the OpenAI® API.

[0192] The generated digital art is recorded in digital format by a recording device and then listed on an online marketplace using a distribution registration device. This distribution registration device may utilize blockchain technology, allowing for the secure distribution of artwork.

[0193] Furthermore, the revenue management system tracks revenues earned based on market transactions and distributes them as needed. The revenue management system utilizes cloud-based accounting software to efficiently manage revenues.

[0194] For example, if a user enters the text "Today was a good day," the emotion analysis device identifies this as "happiness," and as a result, the image generation device generates bright, happy-looking artwork. This generated artwork is immediately put up for sale on the online marketplace by the distribution registration device.

[0195] An example of a prompt might be, "Generate a digital artwork with bright colors based on the following emotion: 'happiness'." In this way, a digital artwork tailored to the user's emotions is provided in real time.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs information about their emotions in text format via the terminal's information input device. This input is intended to accurately reflect the user's current emotional state. The entered text data is sent to the server when it is ready for subsequent processing.

[0199] Step 2:

[0200] The server processes the received text data using an emotion analysis device to identify the user's emotional state. This device uses natural language processing technology to extract emotions from the text and classify them into emotional categories such as "happiness" or "sadness." The input is text data, and the output is the analyzed emotional state.

[0201] Step 3:

[0202] The identified emotional state is then sent to an image generation device. Based on the analysis results, the server uses a generative AI model to generate an appropriate image. The generative AI model selects a theme, colors, and style that match the emotional state, creating customized digital art. The input is emotional state data, and the output is the generated digital art.

[0203] Step 4:

[0204] The generated digital art is stored by a recording device on the server. This digital format will later serve as the basis for its distribution in the market. Here, the digital art data as output is stored.

[0205] Step 5:

[0206] The saved digital art is registered on the market via a distribution and registration device. The server uses blockchain technology to securely register the generated digital art and make it available for distribution. The input is the saved digital art data, and the output is the digital art that has been registered on the market.

[0207] Step 6:

[0208] When a trade takes place in the market, the revenue management device collects the transaction data and manages the resulting profits. This device tracks profits in real time and distributes them as needed. The input is transaction data, and the output is a profit distribution instruction.

[0209] 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.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] As embodiments of the present invention, each element constituting the image generation and sales platform will be described.

[0226] The user first accesses the user interface using a terminal. There, they provide instructions by entering prompts regarding themes and styles for image generation.

[0227] The terminal sends the input prompt to the server, which processes it using an image generation device. Specifically, the server analyzes the received prompt and provides appropriate instructions to the image generation AI algorithm. This AI algorithm uses deep learning technology and other methods to generate digital art that meets the user's wishes.

[0228] The generated images are registered as digital assets by the server using a registration system. At this time, related metadata is also recorded, facilitating subsequent tracking and sales management.

[0229] Next, the server uses a marketplace registration mechanism to list the created digital assets on an online NFT marketplace. This gives users the opportunity to sell their work to a wide range of buyers.

[0230] Once a sale is completed, the server processes the revenue using sales management tools. A portion of the revenue is either returned to the digital asset issuer, depending on the settings, or allocated to specific projects or points programs.

[0231] As a concrete example, if a user wants to create art on the theme of "futuristic city," they input this theme through their device. The server processes the input using an image generation device and generates an image of a futuristic city. This digital art is immediately converted into an NFT and listed on an online marketplace. Subsequently, the revenue from sales is managed for a specified purpose and used to achieve the ultimate goal in line with the user's intentions.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user uses a terminal to access the user interface and enter prompts for image generation.

[0235] Step 2:

[0236] The terminal sends the entered prompt to the server. Here, the data is transmitted using a secure communication method.

[0237] Step 3:

[0238] The server receives a prompt and starts the image generation device. It processes the received prompt appropriately using a text analysis algorithm and prepares it to be passed to the generation AI.

[0239] Step 4:

[0240] The server uses image generation AI to generate images based on prompts. This process utilizes deep learning technology to create new digital images based on the selected theme.

[0241] Step 5:

[0242] The generated images are temporarily stored on the server and then registered as digital assets using a registration mechanism. Blockchain technology is used for registration to ensure transparency and traceability.

[0243] Step 6:

[0244] The server uses a marketplace registration system to list this digital asset on an online NFT marketplace. There, the necessary metadata is configured, and it is made public.

[0245] Step 7:

[0246] Users can view the generated digital art and check its sales status on the marketplace using their devices.

[0247] Step 8:

[0248] The server uses sales management tools to properly manage the revenue generated from sold NFTs. Revenue is distributed to stakeholders or allocated to designated projects based on established rules.

[0249] Step 9:

[0250] Users can check their sales usage and point update information on their devices. This makes the entire process transparent and easy to manage.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] Traditional image generation and sales systems have faced challenges in efficiency and automation in individual image generation, digital asset registration, and sales. In particular, it is currently difficult for users to efficiently list digital images they have generated individually on the market and to properly manage and distribute profits.

[0254] 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.

[0255] In this invention, the server includes an information input means for receiving theme and style instructions, including prompt text; an information processing means for generating images using a generation AI model based on the instructions; and an asset management means for registering the generated images as digital information and recording related information. This makes it possible for users to easily specify themes and styles, immediately register the generated images as digital information, and efficiently and automatically list them on the market.

[0256] A "prompt statement" is a set of instructions used by the user to describe the theme and style of the image they want to generate.

[0257] An "information input means" is an interface that has the function of receiving instructions from the user.

[0258] A "generative AI model" is a technology that uses artificial intelligence algorithms, such as deep learning techniques, to generate images based on input prompt text.

[0259] "Information processing means" refers to a server component that has the function of generating images using a generation AI model based on the received prompt message.

[0260] "Digital information" refers to assets that are registered and managed electronically, including generated images.

[0261] An "asset management system" is a system that has the function of registering generated digital information and managing and tracking related information.

[0262] An "information provision means" is a component that has the function of putting digital information on the market and enables online information distribution.

[0263] A "profit management system" is a system that manages the profits obtained from the sale of digital information and distributes them based on a pre-established mechanism.

[0264] This invention details an embodiment of a system that allows users to easily generate images with specified themes and styles, and register and list them as digital information. Specifically, it comprises information input means, information processing means, asset management means, information provision means, and profit management means.

[0265] The user first accesses the information input method using a terminal. Here, they input instructions regarding the theme and style of the image they want to generate as prompt text. A concrete example of a prompt text might be "a night view of a cyberpunk futuristic city."

[0266] The terminal sends the input prompt message to the server. The server utilizes information processing tools and generates an image using a generative AI model. The generative AI model incorporates deep learning technology and generates images using the latest technology based on the input prompt message.

[0267] The generated images are registered as digital information by the server using asset management tools. This process records associated metadata along with the generated images, enabling later tracking and management.

[0268] Next, the server uses information provision methods to list digital information on the marketplace. Specifically, it uses a dedicated API to publish image listing information on the marketplace.

[0269] If a sale is completed, the server processes the profits using profit management mechanisms. A portion of the profits is distributed to the user or specific project. In this way, users can sell their work on the market and take advantage of the automatically configured profit-sharing function.

[0270] As described above, in order to implement the invention, it is necessary to properly operate the information input means, the generation AI model, and the information processing means, and to manage digital information and provide it appropriately to the market. This will enable users to easily and efficiently generate and sell digital art.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user accesses the terminal's user interface and enters prompt text regarding the theme and style of the image they want to generate. Specifically, the user enters the theme "Cyberpunk futuristic city night view" into the text box on the screen and clicks the "Submit" button. The input is the prompt text, and the output is the submitted prompt text.

[0274] Step 2:

[0275] The terminal sends the entered prompt message to the server. Specifically, the terminal sends the prompt message to the server via the network. The input is the prompt message received from the user, and the output is the prompt message passed to the server.

[0276] Step 3:

[0277] The server analyzes the received prompt and issues instructions to the generative AI model. Specifically, the server uses a natural language processing engine to analyze the prompt text and provides instructions to the generative AI model that reflect the theme and style. The input is the prompt text, and the output is the instructions to the generative AI model.

[0278] Step 4:

[0279] The server generates images using a generative AI model. Specifically, the generative AI model utilizes deep learning techniques to generate images based on the analysis results. The input is instructions for the generative AI model, and the output is the generated image.

[0280] Step 5:

[0281] The server registers the generated images as digital information and records related information. Specifically, the server hashes the image files and stores them in a database along with metadata such as creation date and theme. The input is the generated image, and the output is the registered digital information.

[0282] Step 6:

[0283] The server uses an information provision method to list digital information on the marketplace. Specifically, the server connects to the marketplace's API and registers product information. The input is the registered digital information, and the output is the product information listed on the marketplace.

[0284] Step 7:

[0285] If a sale is concluded, the server processes the profit obtained using the profit management means. As a specific operation, the server distributes the revenue based on the settings via the payment system. The input is the sales information in the market, and the output is the profit distributed to users, etc.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In existing image generation platforms, there was a problem that it was difficult to immediately put the generated images on the market, and users could not obtain an experience of interacting with the visual information in real time. Also, the procedures for selling the generated digital art were complicated, and there was a lack of flexibility in the redistribution of profits.

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

[0290] In this invention, the server includes information acquisition means for inputting an instruction from a user, visual generation device means for generating visual information based on the instruction, asset registration means for registering the generated visual information as a digital asset, market posting means for posting the digital asset on the information market in an exchangeable state, profit management means for managing and redistributing the profit obtained by the exchange of the digital asset, and visual output means for displaying the visual information generated in real time through the user's line-of-sight device and immediately putting it on the information market. Thereby, the user can immediately put the generated visual information on the market and obtain a real-time experience.

[0291] The "information acquisition means" refers to a device such as an input device or an interface for receiving an instruction from a user.

[0292] A "visual generation device means" is a processing device for generating visual information or images based on user instructions.

[0293] An "asset registration system" is a system for registering generated visual information as a digital asset in a database.

[0294] "Market listing method" refers to a method for displaying generated digital assets on an online marketplace in an exchangeable state.

[0295] A "revenue management mechanism" is a management system for managing and redistributing revenue obtained from the sale or exchange of digital assets.

[0296] A "visual output means" is a device that displays visual information generated in real time through the user's eye-tracking device, enabling immediate listing on the information market.

[0297] A system for carrying out this invention includes a process in which a user intuitively inputs instructions through an eye-tracking device, and generates and manages visual information using a generative AI model.

[0298] First, the user uses smart glasses or other eye-tracking devices to input prompts as instructions. This input is received by an information acquisition mechanism. Specifically, instructions can be easily entered using methods such as voice recognition or touch gestures.

[0299] Next, the terminal sends the received prompt message to the server. The server uses a visual generation device to generate visual information based on a generation AI model. This model incorporates deep learning technology and generates high-quality images based on the style and theme desired by the user.

[0300] The generated visual information is registered in a database as a digital asset by the asset registration means. Then, using the market posting means, the digital asset is posted on an online market in a state where it can be exchanged. As a result, the user can immediately put up for sale the generated image and access many buyers.

[0301] Furthermore, the revenue management means manages the revenue obtained from sales and exchanges and redistributes it to users and related parties. This system supports flexible settings for redistributing revenue according to specific purposes.

[0302] As a specific example, when a user inputs a prompt sentence such as "A futuristic city night view" with a gaze device, the server generates an image of a futuristic city night view based on that instruction and registers it as an NFT. Then, it is immediately put up for sale on an online market, and when it is sold, the revenue is appropriately managed.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user uses a gaze device to input a prompt sentence such as "A futuristic city night view". The gaze device obtains the user's instruction via voice recognition or gestures and transmits that input to the terminal.

[0306] Step 2:

[0307] The terminal transmits the received prompt sentence to the server. Here, the terminal converts the prompt sentence into an appropriate format and prepares a data packet for transmission to the server via the communication module.

[0308] Step 3:

[0309] The server causes the visual generation device to process the received prompt message. In this process, a generative AI model is used to generate visual information based on the input prompt. Specifically, the generative AI model uses a deep learning algorithm to predict visual information and outputs it as a final image.

[0310] Step 4:

[0311] The generated visual information is registered as a digital asset in the database by the asset registration mechanism on the server. The server adds relevant metadata and stores the digital asset in an identifiable format.

[0312] Step 5:

[0313] The server uses marketplace listing methods to list registered digital assets on the online marketplace. Here, an API is used to send detailed asset information to the marketplace platform, making it immediately available for listing.

[0314] Step 6:

[0315] After a sale is completed, the server manages the revenue generated through revenue management mechanisms. It aggregates sales information, distributes the revenue to users and other stakeholders according to predetermined ratios, and allocates it to specific purposes as needed.

[0316] Through the steps described above, a system is realized that allows users to intuitively generate and sell images through eye-tracking devices.

[0317] 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.

[0318] In embodiments of the present invention, a system specialized in image generation and sales is combined with an emotion engine that recognizes and utilizes user emotions. As a result, this system can provide a more personalized digital art generation experience.

[0319] When a user accesses the user interface via a device, a prompt for sentiment analysis is displayed. When the user enters text in response to the prompt, the device sends this information to the server. The sentiment engine on the server analyzes this text to recognize the user's emotional state.

[0320] The recognized emotions are reflected in the image generation process of the image generation device. Specifically, the selection of themes, colors, and styles based on the emotional state is automatically adjusted, resulting in more personalized digital art. This process produces artwork that matches the user's current emotions and preferences, leading to greater satisfaction.

[0321] The generated digital art is registered as a digital asset by the server. Once registration is complete, it is published on an online marketplace using a marketplace registration method, and sales begin.

[0322] Furthermore, the server also has the ability to provide feedback to the user through an emotion engine. This feedback includes information about how the generated art reflects the user's emotional state, offering the user a new experience of enjoying a visual representation of their own emotional state.

[0323] As a concrete example, if a user inputs text indicating they are feeling "happy today," the emotion engine recognizes this state as "joy." Based on this information, the server instructs the image generator to use a bright and colorful theme and style. As a result, the generated digital art visually expresses the user's feeling of "joy." This art is immediately put on the market as an NFT, allowing the user's emotional value to be evaluated in a new way.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user uses a terminal to access the user interface and enter text in response to prompts for sentiment analysis.

[0327] Step 2:

[0328] The terminal sends the entered text data to the server. The transmission is performed via a secure protocol.

[0329] Step 3:

[0330] The emotion engine on the server analyzes the received text data to recognize the user's emotional state. Natural language processing techniques are used for this analysis.

[0331] Step 4:

[0332] Based on the emotional state recognized by the server, it provides instructions to the image generator regarding theme, color, and style. These instructions are provided as settings optimized for the emotional state.

[0333] Step 5:

[0334] An image generation device generates digital art based on instructions from a server. The generation process utilizes machine learning models to create designs that reflect the user's emotions.

[0335] Step 6:

[0336] The generated digital art is temporarily stored on the server. It is then authenticated and registered as a digital asset using a registration system.

[0337] Step 7:

[0338] The server utilizes marketplace registration methods to list authenticated digital assets on online NFT marketplaces. Sales begin once the necessary metadata settings are complete.

[0339] Step 8:

[0340] Users can view the content and sales status of the digitally generated art on their devices. This allows users to understand how the art reflects their own emotions.

[0341] Step 9:

[0342] The server uses a sales management system to track revenue from sold digital art. Revenue is distributed according to settings, and is distributed to specific uses or users.

[0343] Step 10:

[0344] Users receive sales data and feedback on their devices, confirming that the art is a visual representation based on emotional states.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] Conventional digital art generation systems have struggled to create works that reflect individual user emotions and preferences, resulting in insufficient personalization of the user experience. Furthermore, the manual nature of sales and revenue management for generated works leads to inefficiencies and cumbersome processes.

[0348] 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.

[0349] In this invention, the server includes an information acquisition means for receiving emotion-based instructions from a user, an image generation means for generating images based on the emotion information, and an information management means for registering the generated images as digital information. This enables the generation of personalized digital art that responds to the user's emotions, as well as efficient sales and revenue management.

[0350] "Information acquisition means" refers to a configuration equipped with functions for collecting and analyzing emotionally-based instructions from users.

[0351] The "image generation means" is a configuration for executing the process of creating a digital image based on acquired emotional information.

[0352] "Information management means" refers to a configuration for securely storing and registering generated digital images as digital information.

[0353] A "marketplace listing method" is a configuration that presents registered digital information to an online marketplace, making it available for sale.

[0354] An "income management system" is a configuration equipped with functions for tracking, managing, and appropriately redistributing income obtained from the sale of digital information.

[0355] To implement this invention, a user accesses the system via a terminal and provides emotion-based input. When the user inputs their emotional state in text format, the terminal sends this information to the server. The server first uses an emotion analysis engine to analyze this text information. Natural language processing libraries (e.g., Python's NLTK or Hugging Face's Transformers) can be used for emotion analysis.

[0356] The server issues commands to the image generation device based on the emotion data obtained through emotion analysis. This device is equipped with a function to automatically generate specific images corresponding to emotions using a generative AI model (e.g., DALL-E or StyleGAN). For example, if "joy" is detected, the generative AI model is programmed to create an image with a bright, positive tone. As a concrete example, if the user enters "I'm having fun today" as text, the emotion engine recognizes this as "joy" and instructs the image generation device to create a digital image with colors and style appropriate to that emotional state.

[0357] The generated digital images are registered as digital information by the server. This registration is performed using information management means, and encryption is applied as needed, utilizing blockchain technology to prevent tampering. Through marketplace means, the registered images are put up for sale on the online marketplace and become immediately available for purchase. Sales management and revenue tracking are handled by revenue management means, and the revenue obtained can be reinvested for specified purposes.

[0358] An example of a prompt in this system is the text, "Tell me how you're feeling today. Based on your feelings, I will generate a special piece of art just for you." This allows users to easily communicate their emotions to the system and experience personalized digital art.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The user accesses the system using a terminal. The user uses the prompts displayed on the screen to input text about their emotions. The input is given as a sentence expressing a specific emotion, such as "I'm having a good day." The terminal prepares this input data and sends it to the server.

[0362] Step 2:

[0363] The server passes the received text data to the sentiment analysis engine. The text data, as input, is analyzed using a natural language processing library. The sentiment analysis engine extracts keywords and context related to emotions from the text and outputs emotion labels such as "joy" and "sadness." This process automatically categorizes emotions.

[0364] Step 3:

[0365] The server issues generation instructions to the image generator based on the analyzed emotion data. Based on the emotion labels as input, the generation AI model sets color and style parameters and generates an appropriate image. In this step, the generated image is concretely visualized and output as digital art.

[0366] Step 4:

[0367] The generated digital art is registered as digital information by the server using information management tools. The generated images used as input are encrypted and securely stored on the server. Blockchain technology is utilized at this stage to prevent tampering with the digital information.

[0368] Step 5:

[0369] The server lists registered digital art on an online marketplace through a marketplace mechanism. The digital information used as input is transferred to the digital marketplace and becomes available for purchase. This makes the generated art accessible to a wide range of users.

[0370] Step 6:

[0371] The server manages sales revenue using revenue management systems and allocates it to designated uses. Revenue data obtained from market sales is tracked and recorded. Revenue is redistributed and returned to digital art creators based on automatically set rules.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0374] Currently, in the creation and sale of personalized digital art, it is difficult to analyze users' emotions in real time and provide digital representations that reflect them. This is because there is no system that automatically reflects specific art styles or themes based on the user's emotional state. As a result, there is a challenge in being able to quickly and efficiently provide digital content that matches the user's emotions.

[0375] 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.

[0376] In this invention, the server includes an information input device means for receiving information input from a user, an emotion analysis device means for analyzing the user's emotional state, and an image generation device means for generating an image based on the emotional state. This makes it possible to generate and provide customized digital art that corresponds to the emotions input by the user.

[0377] An "information input device means" is a device that receives information regarding the user's emotions and instructions, and serves as an interface connecting the user and the system.

[0378] An "emotion analysis device" is a device that analyzes the user's emotional state from the information received, and provides information necessary for the next processing based on the emotional data.

[0379] An "image generation device means" is a device that generates images using appropriate themes and styles based on an analyzed emotional state.

[0380] A "recording device" is a device that saves generated images in a digital format in preparation for later use and distribution.

[0381] A "distribution registration device" is a device that registers recorded digital formats for distribution in the market and enables their distribution.

[0382] A "revenue management device" is a device for tracking revenue obtained from transactions of distributed digital formats and for distributing it appropriately.

[0383] To implement this invention, the user first uses an information terminal such as a smartphone or smart glasses to input information about their emotions via an information input device. The text information entered by the user is sent to a server. This server is equipped with an emotion analysis device that analyzes the entered text and identifies the user's emotional state.

[0384] The analyzed emotional information is then sent to an image generation device. This device selects the optimal theme, colors, and style based on the aforementioned emotional state and generates personalized digital art using a generative AI model. This generation process can utilize, for example, the OpenAI API.

[0385] The generated digital art is recorded in digital format by a recording device and then listed on an online marketplace using a distribution registration device. This distribution registration device may utilize blockchain technology, allowing for the secure distribution of artwork.

[0386] Furthermore, the revenue management system tracks revenues earned based on market transactions and distributes them as needed. The revenue management system utilizes cloud-based accounting software to efficiently manage revenues.

[0387] For example, if a user enters the text "Today was a good day," the emotion analysis device identifies this as "happiness," and as a result, the image generation device generates bright, happy-looking artwork. This generated artwork is immediately put up for sale on the online marketplace by the distribution registration device.

[0388] An example of a prompt might be, "Generate a digital artwork with bright colors based on the following emotion: 'happiness'." In this way, a digital artwork tailored to the user's emotions is provided in real time.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user inputs information about their emotions in text format via the terminal's information input device. This input is intended to accurately reflect the user's current emotional state. The entered text data is sent to the server when it is ready for subsequent processing.

[0392] Step 2:

[0393] The server processes the received text data using an emotion analysis device to identify the user's emotional state. This device uses natural language processing technology to extract emotions from the text and classify them into emotional categories such as "happiness" or "sadness." The input is text data, and the output is the analyzed emotional state.

[0394] Step 3:

[0395] The identified emotional state is then sent to an image generation device. Based on the analysis results, the server uses a generative AI model to generate an appropriate image. The generative AI model selects a theme, colors, and style that match the emotional state, creating customized digital art. The input is emotional state data, and the output is the generated digital art.

[0396] Step 4:

[0397] The generated digital art is stored by a recording device on the server. This digital format will later serve as the basis for its distribution in the market. Here, the digital art data as output is stored.

[0398] Step 5:

[0399] The saved digital art is registered on the market via a distribution and registration device. The server uses blockchain technology to securely register the generated digital art and make it available for distribution. The input is the saved digital art data, and the output is the digital art that has been registered on the market.

[0400] Step 6:

[0401] When a trade takes place in the market, the revenue management device collects the transaction data and manages the resulting profits. This device tracks profits in real time and distributes them as needed. The input is transaction data, and the output is a profit distribution instruction.

[0402] 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.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] As embodiments of the present invention, each element constituting the image generation and sales platform will be described.

[0419] The user first accesses the user interface using a terminal. There, they provide instructions by entering prompts regarding themes and styles for image generation.

[0420] The terminal sends the input prompt to the server, which processes it using an image generation device. Specifically, the server analyzes the received prompt and provides appropriate instructions to the image generation AI algorithm. This AI algorithm uses deep learning technology and other methods to generate digital art that meets the user's wishes.

[0421] The generated images are registered as digital assets by the server using a registration system. At this time, related metadata is also recorded, facilitating subsequent tracking and sales management.

[0422] Next, the server uses a marketplace registration mechanism to list the created digital assets on an online NFT marketplace. This gives users the opportunity to sell their work to a wide range of buyers.

[0423] Once a sale is completed, the server processes the revenue using sales management tools. A portion of the revenue is either returned to the digital asset issuer, depending on the settings, or allocated to specific projects or points programs.

[0424] As a concrete example, if a user wants to create art on the theme of "futuristic city," they input this theme through their device. The server processes the input using an image generation device and generates an image of a futuristic city. This digital art is immediately converted into an NFT and listed on an online marketplace. Subsequently, the revenue from sales is managed for a specified purpose and used to achieve the ultimate goal in line with the user's intentions.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user uses a terminal to access the user interface and enter prompts for image generation.

[0428] Step 2:

[0429] The terminal sends the entered prompt to the server. Here, the data is transmitted using a secure communication method.

[0430] Step 3:

[0431] The server receives a prompt and starts the image generation device. It processes the received prompt appropriately using a text analysis algorithm and prepares it to be passed to the generation AI.

[0432] Step 4:

[0433] The server uses image generation AI to generate images based on prompts. This process utilizes deep learning technology to create new digital images based on the selected theme.

[0434] Step 5:

[0435] The generated images are temporarily stored on the server and then registered as digital assets using a registration mechanism. Blockchain technology is used for registration to ensure transparency and traceability.

[0436] Step 6:

[0437] The server uses a marketplace registration system to list this digital asset on an online NFT marketplace. There, the necessary metadata is configured, and it is made public.

[0438] Step 7:

[0439] Users can view the generated digital art and check its sales status on the marketplace using their devices.

[0440] Step 8:

[0441] The server uses sales management tools to properly manage the revenue generated from sold NFTs. Revenue is distributed to stakeholders or allocated to designated projects based on established rules.

[0442] Step 9:

[0443] Users can check their sales usage and point update information on their devices. This makes the entire process transparent and easy to manage.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] Traditional image generation and sales systems have faced challenges in efficiency and automation in individual image generation, digital asset registration, and sales. In particular, it is currently difficult for users to efficiently list digital images they have generated individually on the market and to properly manage and distribute profits.

[0447] 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.

[0448] In this invention, the server includes an information input means for receiving theme and style instructions, including prompt text; an information processing means for generating images using a generation AI model based on the instructions; and an asset management means for registering the generated images as digital information and recording related information. This makes it possible for users to easily specify themes and styles, immediately register the generated images as digital information, and efficiently and automatically list them on the market.

[0449] A "prompt statement" is a set of instructions used by the user to describe the theme and style of the image they want to generate.

[0450] An "information input means" is an interface that has the function of receiving instructions from the user.

[0451] A "generative AI model" is a technology that uses artificial intelligence algorithms, such as deep learning techniques, to generate images based on input prompt text.

[0452] "Information processing means" refers to a server component that has the function of generating images using a generation AI model based on the received prompt message.

[0453] "Digital information" refers to assets that are registered and managed electronically, including generated images.

[0454] An "asset management system" is a system that has the function of registering generated digital information and managing and tracking related information.

[0455] An "information provision means" is a component that has the function of putting digital information on the market and enables online information distribution.

[0456] A "profit management system" is a system that manages the profits obtained from the sale of digital information and distributes them based on a pre-established mechanism.

[0457] This invention details an embodiment of a system that allows users to easily generate images with specified themes and styles, and register and list them as digital information. Specifically, it comprises information input means, information processing means, asset management means, information provision means, and profit management means.

[0458] The user first accesses the information input method using a terminal. Here, they input instructions regarding the theme and style of the image they want to generate as prompt text. A concrete example of a prompt text might be "a night view of a cyberpunk futuristic city."

[0459] The terminal sends the input prompt message to the server. The server utilizes information processing tools and generates an image using a generative AI model. The generative AI model incorporates deep learning technology and generates images using the latest technology based on the input prompt message.

[0460] The generated images are registered as digital information by the server using asset management tools. This process records associated metadata along with the generated images, enabling later tracking and management.

[0461] Next, the server uses information provision methods to list digital information on the marketplace. Specifically, it uses a dedicated API to publish image listing information on the marketplace.

[0462] If a sale is completed, the server processes the profits using profit management mechanisms. A portion of the profits is distributed to the user or specific project. In this way, users can sell their work on the market and take advantage of the automatically configured profit-sharing function.

[0463] As described above, in order to implement the invention, it is necessary to properly operate the information input means, the generation AI model, and the information processing means, and to manage digital information and provide it appropriately to the market. This will enable users to easily and efficiently generate and sell digital art.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user accesses the terminal's user interface and enters prompt text regarding the theme and style of the image they want to generate. Specifically, the user enters the theme "Cyberpunk futuristic city night view" into the text box on the screen and clicks the "Submit" button. The input is the prompt text, and the output is the submitted prompt text.

[0467] Step 2:

[0468] The terminal sends the entered prompt message to the server. Specifically, the terminal sends the prompt message to the server via the network. The input is the prompt message received from the user, and the output is the prompt message passed to the server.

[0469] Step 3:

[0470] The server analyzes the received prompt and issues instructions to the generative AI model. Specifically, the server uses a natural language processing engine to analyze the prompt text and provides instructions to the generative AI model that reflect the theme and style. The input is the prompt text, and the output is the instructions to the generative AI model.

[0471] Step 4:

[0472] The server generates images using a generative AI model. Specifically, the generative AI model utilizes deep learning techniques to generate images based on the analysis results. The input is instructions for the generative AI model, and the output is the generated image.

[0473] Step 5:

[0474] The server registers the generated images as digital information and records related information. Specifically, the server hashes the image files and stores them in a database along with metadata such as creation date and theme. The input is the generated image, and the output is the registered digital information.

[0475] Step 6:

[0476] The server uses an information provision method to list digital information on the marketplace. Specifically, the server connects to the marketplace's API and registers product information. The input is the registered digital information, and the output is the product information listed on the marketplace.

[0477] Step 7:

[0478] If a sale is completed, the server processes the profits using profit management tools. Specifically, the server distributes revenue through a payment system based on predefined settings. The input is sales information from the market, and the output is the profits already distributed to users, etc.

[0479] (Application Example 1)

[0480] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] Existing image generation platforms have faced challenges such as the difficulty of immediately listing generated images on the market, preventing users from experiencing real-time interaction with the visual information. Furthermore, the procedures for selling generated digital art are cumbersome, and revenue redistribution lacks flexibility.

[0482] 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.

[0483] In this invention, the server includes information acquisition means for inputting instructions from a user, visual generation device means for generating visual information based on the instructions, asset registration means for registering the generated visual information as a digital asset, market listing means for listing the digital asset in an exchangeable state on an information market, revenue management means for managing and redistributing the revenue obtained from the exchange of the digital asset, and visual output means for displaying the visual information generated in real time through the user's eye-tracking device and immediately listing it on the information market. This enables the user to immediately list the generated visual information on the market and obtain a real-time experience.

[0484] "Information acquisition means" refers to devices such as input devices or interfaces for receiving instructions from the user.

[0485] A "visual generation device means" is a processing device for generating visual information or images based on user instructions.

[0486] An "asset registration system" is a system for registering generated visual information as a digital asset in a database.

[0487] "Market listing method" refers to a method for displaying generated digital assets on an online marketplace in an exchangeable state.

[0488] A "revenue management mechanism" is a management system for managing and redistributing revenue obtained from the sale or exchange of digital assets.

[0489] A "visual output means" is a device that displays visual information generated in real time through the user's eye-tracking device, enabling immediate listing on the information market.

[0490] A system for carrying out this invention includes a process in which a user intuitively inputs instructions through an eye-tracking device, and generates and manages visual information using a generative AI model.

[0491] First, the user uses smart glasses or other eye-tracking devices to input prompts as instructions. This input is received by an information acquisition mechanism. Specifically, instructions can be easily entered using methods such as voice recognition or touch gestures.

[0492] Next, the terminal sends the received prompt message to the server. The server uses a visual generation device to generate visual information based on a generation AI model. This model incorporates deep learning technology and generates high-quality images based on the style and theme desired by the user.

[0493] The generated visual information is registered as a digital asset in a database using an asset registration system. Subsequently, the digital asset is listed on an online marketplace in an exchangeable state using a marketplace listing system. This allows users to immediately list their generated images and access them from a large number of buyers.

[0494] Furthermore, the revenue management system manages the revenue generated from sales and exchanges and redistributes it to users and stakeholders. This system supports flexible settings for redistributing revenue according to specific purposes.

[0495] As a concrete example, when a user inputs a prompt such as "a futuristic city night view" using an eye-tracking device, the server generates an image of a futuristic city night view based on that instruction and registers it as an NFT. It is then immediately put up for sale on an online marketplace, and when it is sold, the revenue is appropriately managed.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The user uses an eye-tracking device to input a prompt such as "futuristic city night view." The eye-tracking device receives the user's instructions via voice recognition or gestures and sends the input to the terminal.

[0499] Step 2:

[0500] The terminal sends the received prompt message to the server. Here, the terminal converts the prompt message into the appropriate format and prepares a data packet to be sent to the server via the communication module.

[0501] Step 3:

[0502] The server causes the visual generation device to process the received prompt message. In this process, a generative AI model is used to generate visual information based on the input prompt. Specifically, the generative AI model uses a deep learning algorithm to predict visual information and outputs it as a final image.

[0503] Step 4:

[0504] The generated visual information is registered as a digital asset in the database by the asset registration mechanism on the server. The server adds relevant metadata and stores the digital asset in an identifiable format.

[0505] Step 5:

[0506] The server uses marketplace listing methods to list registered digital assets on the online marketplace. Here, an API is used to send detailed asset information to the marketplace platform, making it immediately available for listing.

[0507] Step 6:

[0508] After a sale is completed, the server manages the revenue generated through revenue management mechanisms. It aggregates sales information, distributes the revenue to users and other stakeholders according to predetermined ratios, and allocates it to specific purposes as needed.

[0509] Through the steps described above, a system is realized that allows users to intuitively generate and sell images through eye-tracking devices.

[0510] 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.

[0511] In embodiments of the present invention, a system specialized in image generation and sales is combined with an emotion engine that recognizes and utilizes user emotions. As a result, this system can provide a more personalized digital art generation experience.

[0512] When a user accesses the user interface via a device, a prompt for sentiment analysis is displayed. When the user enters text in response to the prompt, the device sends this information to the server. The sentiment engine on the server analyzes this text to recognize the user's emotional state.

[0513] The recognized emotions are reflected in the image generation process of the image generation device. Specifically, the selection of themes, colors, and styles based on the emotional state is automatically adjusted, resulting in more personalized digital art. This process produces artwork that matches the user's current emotions and preferences, leading to greater satisfaction.

[0514] The generated digital art is registered as a digital asset by the server. Once registration is complete, it is published on an online marketplace using a marketplace registration method, and sales begin.

[0515] Furthermore, the server also has the ability to provide feedback to the user through an emotion engine. This feedback includes information about how the generated art reflects the user's emotional state, offering the user a new experience of enjoying a visual representation of their own emotional state.

[0516] As a concrete example, if a user inputs text indicating they are feeling "happy today," the emotion engine recognizes this state as "joy." Based on this information, the server instructs the image generator to use a bright and colorful theme and style. As a result, the generated digital art visually expresses the user's feeling of "joy." This art is immediately put on the market as an NFT, allowing the user's emotional value to be evaluated in a new way.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The user uses a terminal to access the user interface and enter text in response to prompts for sentiment analysis.

[0520] Step 2:

[0521] The terminal sends the entered text data to the server. The transmission is performed via a secure protocol.

[0522] Step 3:

[0523] The emotion engine on the server analyzes the received text data to recognize the user's emotional state. Natural language processing techniques are used for this analysis.

[0524] Step 4:

[0525] Based on the emotional state recognized by the server, it provides instructions to the image generator regarding theme, color, and style. These instructions are provided as settings optimized for the emotional state.

[0526] Step 5:

[0527] An image generation device generates digital art based on instructions from a server. The generation process utilizes machine learning models to create designs that reflect the user's emotions.

[0528] Step 6:

[0529] The generated digital art is temporarily stored on the server. It is then authenticated and registered as a digital asset using a registration system.

[0530] Step 7:

[0531] The server utilizes marketplace registration methods to list authenticated digital assets on online NFT marketplaces. Sales begin once the necessary metadata settings are complete.

[0532] Step 8:

[0533] Users can view the content and sales status of the digitally generated art on their devices. This allows users to understand how the art reflects their own emotions.

[0534] Step 9:

[0535] The server uses a sales management system to track revenue from sold digital art. Revenue is distributed according to settings, and is distributed to specific uses or users.

[0536] Step 10:

[0537] Users receive sales data and feedback on their devices, confirming that the art is a visual representation based on emotional states.

[0538] (Example 2)

[0539] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] Conventional digital art generation systems have struggled to create works that reflect individual user emotions and preferences, resulting in insufficient personalization of the user experience. Furthermore, the manual nature of sales and revenue management for generated works leads to inefficiencies and cumbersome processes.

[0541] 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.

[0542] In this invention, the server includes an information acquisition means for receiving emotion-based instructions from a user, an image generation means for generating images based on the emotion information, and an information management means for registering the generated images as digital information. This enables the generation of personalized digital art that responds to the user's emotions, as well as efficient sales and revenue management.

[0543] "Information acquisition means" refers to a configuration equipped with functions for collecting and analyzing emotionally-based instructions from users.

[0544] The "image generation means" is a configuration for executing the process of creating a digital image based on acquired emotional information.

[0545] "Information management means" refers to a configuration for securely storing and registering generated digital images as digital information.

[0546] A "marketplace listing method" is a configuration that presents registered digital information to an online marketplace, making it available for sale.

[0547] An "income management system" is a configuration equipped with functions for tracking, managing, and appropriately redistributing income obtained from the sale of digital information.

[0548] To implement this invention, a user accesses the system via a terminal and provides emotion-based input. When the user inputs their emotional state in text format, the terminal sends this information to the server. The server first uses an emotion analysis engine to analyze this text information. Natural language processing libraries (e.g., Python's NLTK or Hugging Face's Transformers) can be used for emotion analysis.

[0549] The server issues commands to the image generation device based on the emotion data obtained through emotion analysis. This device is equipped with a function to automatically generate specific images corresponding to emotions using a generative AI model (e.g., DALL-E or StyleGAN). For example, if "joy" is detected, the generative AI model is programmed to create an image with a bright, positive tone. As a concrete example, if the user enters "I'm having fun today" as text, the emotion engine recognizes this as "joy" and instructs the image generation device to create a digital image with colors and style appropriate to that emotional state.

[0550] The generated digital images are registered as digital information by the server. This registration is performed using information management means, and encryption is applied as needed, utilizing blockchain technology to prevent tampering. Through marketplace means, the registered images are put up for sale on the online marketplace and become immediately available for purchase. Sales management and revenue tracking are handled by revenue management means, and the revenue obtained can be reinvested for specified purposes.

[0551] An example of a prompt in this system is the text, "Tell me how you're feeling today. Based on your feelings, I will generate a special piece of art just for you." This allows users to easily communicate their emotions to the system and experience personalized digital art.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The user accesses the system using a terminal. The user uses the prompts displayed on the screen to input text about their emotions. The input is given as a sentence expressing a specific emotion, such as "I'm having a good day." The terminal prepares this input data and sends it to the server.

[0555] Step 2:

[0556] The server passes the received text data to the sentiment analysis engine. The text data, as input, is analyzed using a natural language processing library. The sentiment analysis engine extracts keywords and context related to emotions from the text and outputs emotion labels such as "joy" and "sadness." This process automatically categorizes emotions.

[0557] Step 3:

[0558] The server issues generation instructions to the image generator based on the analyzed emotion data. Based on the emotion labels as input, the generation AI model sets color and style parameters and generates an appropriate image. In this step, the generated image is concretely visualized and output as digital art.

[0559] Step 4:

[0560] The generated digital art is registered as digital information by the server using information management tools. The generated images used as input are encrypted and securely stored on the server. Blockchain technology is utilized at this stage to prevent tampering with the digital information.

[0561] Step 5:

[0562] The server lists registered digital art on an online marketplace through a marketplace mechanism. The digital information used as input is transferred to the digital marketplace and becomes available for purchase. This makes the generated art accessible to a wide range of users.

[0563] Step 6:

[0564] The server manages sales revenue using revenue management systems and allocates it to designated uses. Revenue data obtained from market sales is tracked and recorded. Revenue is redistributed and returned to digital art creators based on automatically set rules.

[0565] (Application Example 2)

[0566] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0567] Currently, in the creation and sale of personalized digital art, it is difficult to analyze users' emotions in real time and provide digital representations that reflect them. This is because there is no system that automatically reflects specific art styles or themes based on the user's emotional state. As a result, there is a challenge in being able to quickly and efficiently provide digital content that matches the user's emotions.

[0568] 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.

[0569] In this invention, the server includes an information input device means for receiving information input from a user, an emotion analysis device means for analyzing the user's emotional state, and an image generation device means for generating an image based on the emotional state. This makes it possible to generate and provide customized digital art that corresponds to the emotions input by the user.

[0570] An "information input device means" is a device that receives information regarding the user's emotions and instructions, and serves as an interface connecting the user and the system.

[0571] An "emotion analysis device" is a device that analyzes the user's emotional state from the information received, and provides information necessary for the next processing based on the emotional data.

[0572] An "image generation device means" is a device that generates images using appropriate themes and styles based on an analyzed emotional state.

[0573] A "recording device" is a device that saves generated images in a digital format in preparation for later use and distribution.

[0574] A "distribution registration device" is a device that registers recorded digital formats for distribution in the market and enables their distribution.

[0575] A "revenue management device" is a device for tracking revenue obtained from transactions of distributed digital formats and for distributing it appropriately.

[0576] To implement this invention, the user first uses an information terminal such as a smartphone or smart glasses to input information about their emotions via an information input device. The text information entered by the user is sent to a server. This server is equipped with an emotion analysis device that analyzes the entered text and identifies the user's emotional state.

[0577] The analyzed emotional information is then sent to an image generation device. This device selects the optimal theme, colors, and style based on the aforementioned emotional state and generates personalized digital art using a generative AI model. This generation process can utilize, for example, the OpenAI API.

[0578] The generated digital art is recorded in digital format by a recording device and then listed on an online marketplace using a distribution registration device. This distribution registration device may utilize blockchain technology, allowing for the secure distribution of artwork.

[0579] Furthermore, the revenue management system tracks revenues earned based on market transactions and distributes them as needed. The revenue management system utilizes cloud-based accounting software to efficiently manage revenues.

[0580] For example, if a user enters the text "Today was a good day," the emotion analysis device identifies this as "happiness," and as a result, the image generation device generates bright, happy-looking artwork. This generated artwork is immediately put up for sale on the online marketplace by the distribution registration device.

[0581] An example of a prompt might be, "Generate a digital artwork with bright colors based on the following emotion: 'happiness'." In this way, a digital artwork tailored to the user's emotions is provided in real time.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The user inputs information about their emotions in text format via the terminal's information input device. This input is intended to accurately reflect the user's current emotional state. The entered text data is sent to the server when it is ready for subsequent processing.

[0585] Step 2:

[0586] The server processes the received text data using an emotion analysis device to identify the user's emotional state. This device uses natural language processing technology to extract emotions from the text and classify them into emotional categories such as "happiness" or "sadness." The input is text data, and the output is the analyzed emotional state.

[0587] Step 3:

[0588] The identified emotional state is then sent to an image generation device. Based on the analysis results, the server uses a generative AI model to generate an appropriate image. The generative AI model selects a theme, colors, and style that match the emotional state, creating customized digital art. The input is emotional state data, and the output is the generated digital art.

[0589] Step 4:

[0590] The generated digital art is stored by a recording device on the server. This digital format will later serve as the basis for its distribution in the market. Here, the digital art data as output is stored.

[0591] Step 5:

[0592] The saved digital art is registered on the market via a distribution and registration device. The server uses blockchain technology to securely register the generated digital art and make it available for distribution. The input is the saved digital art data, and the output is the digital art that has been registered on the market.

[0593] Step 6:

[0594] When a trade takes place in the market, the revenue management device collects the transaction data and manages the resulting profits. This device tracks profits in real time and distributes them as needed. The input is transaction data, and the output is a profit distribution instruction.

[0595] 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.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] As embodiments of the present invention, each element constituting the image generation and sales platform will be described.

[0613] The user first accesses the user interface using a terminal. There, they provide instructions by entering prompts regarding themes and styles for image generation.

[0614] The terminal sends the input prompt to the server, which processes it using an image generation device. Specifically, the server analyzes the received prompt and provides appropriate instructions to the image generation AI algorithm. This AI algorithm uses deep learning technology and other methods to generate digital art that meets the user's wishes.

[0615] The generated images are registered as digital assets by the server using a registration system. At this time, related metadata is also recorded, facilitating subsequent tracking and sales management.

[0616] Next, the server uses a marketplace registration mechanism to list the created digital assets on an online NFT marketplace. This gives users the opportunity to sell their work to a wide range of buyers.

[0617] Once a sale is completed, the server processes the revenue using sales management tools. A portion of the revenue is either returned to the digital asset issuer, depending on the settings, or allocated to specific projects or points programs.

[0618] As a concrete example, if a user wants to create art on the theme of "futuristic city," they input this theme through their device. The server processes the input using an image generation device and generates an image of a futuristic city. This digital art is immediately converted into an NFT and listed on an online marketplace. Subsequently, the revenue from sales is managed for a specified purpose and used to achieve the ultimate goal in line with the user's intentions.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The user uses a terminal to access the user interface and enter prompts for image generation.

[0622] Step 2:

[0623] The terminal sends the entered prompt to the server. Here, the data is transmitted using a secure communication method.

[0624] Step 3:

[0625] The server receives a prompt and starts the image generation device. It processes the received prompt appropriately using a text analysis algorithm and prepares it to be passed to the generation AI.

[0626] Step 4:

[0627] The server uses image generation AI to generate images based on prompts. This process utilizes deep learning technology to create new digital images based on the selected theme.

[0628] Step 5:

[0629] The generated images are temporarily stored on the server and then registered as digital assets using a registration mechanism. Blockchain technology is used for registration to ensure transparency and traceability.

[0630] Step 6:

[0631] The server uses a marketplace registration system to list this digital asset on an online NFT marketplace. There, the necessary metadata is configured, and it is made public.

[0632] Step 7:

[0633] Users can view the generated digital art and check its sales status on the marketplace using their devices.

[0634] Step 8:

[0635] The server uses sales management tools to properly manage the revenue generated from sold NFTs. Revenue is distributed to stakeholders or allocated to designated projects based on established rules.

[0636] Step 9:

[0637] Users can check their sales usage and point update information on their devices. This makes the entire process transparent and easy to manage.

[0638] (Example 1)

[0639] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] Traditional image generation and sales systems have faced challenges in efficiency and automation in individual image generation, digital asset registration, and sales. In particular, it is currently difficult for users to efficiently list digital images they have generated individually on the market and to properly manage and distribute profits.

[0641] 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.

[0642] In this invention, the server includes an information input means for receiving theme and style instructions, including prompt text; an information processing means for generating images using a generation AI model based on the instructions; and an asset management means for registering the generated images as digital information and recording related information. This makes it possible for users to easily specify themes and styles, immediately register the generated images as digital information, and efficiently and automatically list them on the market.

[0643] A "prompt statement" is a set of instructions used by the user to describe the theme and style of the image they want to generate.

[0644] An "information input means" is an interface that has the function of receiving instructions from the user.

[0645] A "generative AI model" is a technology that uses artificial intelligence algorithms, such as deep learning techniques, to generate images based on input prompt text.

[0646] "Information processing means" refers to a server component that has the function of generating images using a generation AI model based on the received prompt message.

[0647] "Digital information" refers to assets that are registered and managed electronically, including generated images.

[0648] An "asset management system" is a system that has the function of registering generated digital information and managing and tracking related information.

[0649] An "information provision means" is a component that has the function of putting digital information on the market and enables online information distribution.

[0650] A "profit management system" is a system that manages the profits obtained from the sale of digital information and distributes them based on a pre-established mechanism.

[0651] This invention details an embodiment of a system that allows users to easily generate images with specified themes and styles, and register and list them as digital information. Specifically, it comprises information input means, information processing means, asset management means, information provision means, and profit management means.

[0652] The user first accesses the information input method using a terminal. Here, they input instructions regarding the theme and style of the image they want to generate as prompt text. A concrete example of a prompt text might be "a night view of a cyberpunk futuristic city."

[0653] The terminal sends the input prompt message to the server. The server utilizes information processing tools and generates an image using a generative AI model. The generative AI model incorporates deep learning technology and generates images using the latest technology based on the input prompt message.

[0654] The generated images are registered as digital information by the server using asset management tools. This process records associated metadata along with the generated images, enabling later tracking and management.

[0655] Next, the server uses information provision methods to list digital information on the marketplace. Specifically, it uses a dedicated API to publish image listing information on the marketplace.

[0656] If a sale is completed, the server processes the profits using profit management mechanisms. A portion of the profits is distributed to the user or specific project. In this way, users can sell their work on the market and take advantage of the automatically configured profit-sharing function.

[0657] As described above, in order to implement the invention, it is necessary to properly operate the information input means, the generation AI model, and the information processing means, and to manage digital information and provide it appropriately to the market. This will enable users to easily and efficiently generate and sell digital art.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The user accesses the terminal's user interface and enters prompt text regarding the theme and style of the image they want to generate. Specifically, the user enters the theme "Cyberpunk futuristic city night view" into the text box on the screen and clicks the "Submit" button. The input is the prompt text, and the output is the submitted prompt text.

[0661] Step 2:

[0662] The terminal sends the entered prompt message to the server. Specifically, the terminal sends the prompt message to the server via the network. The input is the prompt message received from the user, and the output is the prompt message passed to the server.

[0663] Step 3:

[0664] The server analyzes the received prompt and issues instructions to the generative AI model. Specifically, the server uses a natural language processing engine to analyze the prompt text and provides instructions to the generative AI model that reflect the theme and style. The input is the prompt text, and the output is the instructions to the generative AI model.

[0665] Step 4:

[0666] The server generates images using a generative AI model. Specifically, the generative AI model utilizes deep learning techniques to generate images based on the analysis results. The input is instructions for the generative AI model, and the output is the generated image.

[0667] Step 5:

[0668] The server registers the generated images as digital information and records related information. Specifically, the server hashes the image files and stores them in a database along with metadata such as creation date and theme. The input is the generated image, and the output is the registered digital information.

[0669] Step 6:

[0670] The server uses an information provision method to list digital information on the marketplace. Specifically, the server connects to the marketplace's API and registers product information. The input is the registered digital information, and the output is the product information listed on the marketplace.

[0671] Step 7:

[0672] If a sale is completed, the server processes the profits using profit management tools. Specifically, the server distributes revenue through a payment system based on predefined settings. The input is sales information from the market, and the output is the profits already distributed to users, etc.

[0673] (Application Example 1)

[0674] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] Existing image generation platforms have faced challenges such as the difficulty of immediately listing generated images on the market, preventing users from experiencing real-time interaction with the visual information. Furthermore, the procedures for selling generated digital art are cumbersome, and revenue redistribution lacks flexibility.

[0676] 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.

[0677] In this invention, the server includes information acquisition means for inputting instructions from a user, visual generation device means for generating visual information based on the instructions, asset registration means for registering the generated visual information as a digital asset, market listing means for listing the digital asset in an exchangeable state on an information market, revenue management means for managing and redistributing the revenue obtained from the exchange of the digital asset, and visual output means for displaying the visual information generated in real time through the user's eye-tracking device and immediately listing it on the information market. This enables the user to immediately list the generated visual information on the market and obtain a real-time experience.

[0678] "Information acquisition means" refers to devices such as input devices or interfaces for receiving instructions from the user.

[0679] A "visual generation device means" is a processing device for generating visual information or images based on user instructions.

[0680] An "asset registration system" is a system for registering generated visual information as a digital asset in a database.

[0681] "Market listing method" refers to a method for displaying generated digital assets on an online marketplace in an exchangeable state.

[0682] A "revenue management mechanism" is a management system for managing and redistributing revenue obtained from the sale or exchange of digital assets.

[0683] A "visual output means" is a device that displays visual information generated in real time through the user's eye-tracking device, enabling immediate listing on the information market.

[0684] A system for carrying out this invention includes a process in which a user intuitively inputs instructions through an eye-tracking device, and generates and manages visual information using a generative AI model.

[0685] First, the user uses smart glasses or other eye-tracking devices to input prompts as instructions. This input is received by an information acquisition mechanism. Specifically, instructions can be easily entered using methods such as voice recognition or touch gestures.

[0686] Next, the terminal sends the received prompt message to the server. The server uses a visual generation device to generate visual information based on a generation AI model. This model incorporates deep learning technology and generates high-quality images based on the style and theme desired by the user.

[0687] The generated visual information is registered as a digital asset in a database using an asset registration system. Subsequently, the digital asset is listed on an online marketplace in an exchangeable state using a marketplace listing system. This allows users to immediately list their generated images and access them from a large number of buyers.

[0688] Furthermore, the revenue management system manages the revenue generated from sales and exchanges and redistributes it to users and stakeholders. This system supports flexible settings for redistributing revenue according to specific purposes.

[0689] As a concrete example, when a user inputs a prompt such as "a futuristic city night view" using an eye-tracking device, the server generates an image of a futuristic city night view based on that instruction and registers it as an NFT. It is then immediately put up for sale on an online marketplace, and when it is sold, the revenue is appropriately managed.

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] The user uses an eye-tracking device to input a prompt such as "futuristic city night view." The eye-tracking device receives the user's instructions via voice recognition or gestures and sends the input to the terminal.

[0693] Step 2:

[0694] The terminal sends the received prompt message to the server. Here, the terminal converts the prompt message into the appropriate format and prepares a data packet to be sent to the server via the communication module.

[0695] Step 3:

[0696] The server causes the visual generation device to process the received prompt message. In this process, a generative AI model is used to generate visual information based on the input prompt. Specifically, the generative AI model uses a deep learning algorithm to predict visual information and outputs it as a final image.

[0697] Step 4:

[0698] The generated visual information is registered as a digital asset in the database by the asset registration mechanism on the server. The server adds relevant metadata and stores the digital asset in an identifiable format.

[0699] Step 5:

[0700] The server uses marketplace listing methods to list registered digital assets on the online marketplace. Here, an API is used to send detailed asset information to the marketplace platform, making it immediately available for listing.

[0701] Step 6:

[0702] After a sale is completed, the server manages the revenue generated through revenue management mechanisms. It aggregates sales information, distributes the revenue to users and other stakeholders according to predetermined ratios, and allocates it to specific purposes as needed.

[0703] Through the steps described above, a system is realized that allows users to intuitively generate and sell images through eye-tracking devices.

[0704] 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.

[0705] In embodiments of the present invention, a system specialized in image generation and sales is combined with an emotion engine that recognizes and utilizes user emotions. As a result, this system can provide a more personalized digital art generation experience.

[0706] When a user accesses the user interface via a device, a prompt for sentiment analysis is displayed. When the user enters text in response to the prompt, the device sends this information to the server. The sentiment engine on the server analyzes this text to recognize the user's emotional state.

[0707] The recognized emotions are reflected in the image generation process of the image generation device. Specifically, the selection of themes, colors, and styles based on the emotional state is automatically adjusted, resulting in more personalized digital art. This process produces artwork that matches the user's current emotions and preferences, leading to greater satisfaction.

[0708] The generated digital art is registered as a digital asset by the server. Once registration is complete, it is published on an online marketplace using a marketplace registration method, and sales begin.

[0709] Furthermore, the server also has the ability to provide feedback to the user through an emotion engine. This feedback includes information about how the generated art reflects the user's emotional state, offering the user a new experience of enjoying a visual representation of their own emotional state.

[0710] As a concrete example, if a user inputs text indicating they are feeling "happy today," the emotion engine recognizes this state as "joy." Based on this information, the server instructs the image generator to use a bright and colorful theme and style. As a result, the generated digital art visually expresses the user's feeling of "joy." This art is immediately put on the market as an NFT, allowing the user's emotional value to be evaluated in a new way.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The user uses a terminal to access the user interface and enter text in response to prompts for sentiment analysis.

[0714] Step 2:

[0715] The terminal sends the entered text data to the server. The transmission is performed via a secure protocol.

[0716] Step 3:

[0717] The emotion engine on the server analyzes the received text data to recognize the user's emotional state. Natural language processing techniques are used for this analysis.

[0718] Step 4:

[0719] Based on the emotional state recognized by the server, it provides instructions to the image generator regarding theme, color, and style. These instructions are provided as settings optimized for the emotional state.

[0720] Step 5:

[0721] An image generation device generates digital art based on instructions from a server. The generation process utilizes machine learning models to create designs that reflect the user's emotions.

[0722] Step 6:

[0723] The generated digital art is temporarily stored on the server. It is then authenticated and registered as a digital asset using a registration system.

[0724] Step 7:

[0725] The server utilizes marketplace registration methods to list authenticated digital assets on online NFT marketplaces. Sales begin once the necessary metadata settings are complete.

[0726] Step 8:

[0727] Users can view the content and sales status of the digitally generated art on their devices. This allows users to understand how the art reflects their own emotions.

[0728] Step 9:

[0729] The server uses a sales management system to track revenue from sold digital art. Revenue is distributed according to settings, and is distributed to specific uses or users.

[0730] Step 10:

[0731] Users receive sales data and feedback on their devices, confirming that the art is a visual representation based on emotional states.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Conventional digital art generation systems have struggled to create works that reflect individual user emotions and preferences, resulting in insufficient personalization of the user experience. Furthermore, the manual nature of sales and revenue management for generated works leads to inefficiencies and cumbersome processes.

[0735] 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.

[0736] In this invention, the server includes an information acquisition means for receiving emotion-based instructions from a user, an image generation means for generating images based on the emotion information, and an information management means for registering the generated images as digital information. This enables the generation of personalized digital art that responds to the user's emotions, as well as efficient sales and revenue management.

[0737] "Information acquisition means" refers to a configuration equipped with functions for collecting and analyzing emotionally-based instructions from users.

[0738] The "image generation means" is a configuration for executing the process of creating a digital image based on acquired emotional information.

[0739] "Information management means" refers to a configuration for securely storing and registering generated digital images as digital information.

[0740] A "marketplace listing method" is a configuration that presents registered digital information to an online marketplace, making it available for sale.

[0741] An "income management system" is a configuration equipped with functions for tracking, managing, and appropriately redistributing income obtained from the sale of digital information.

[0742] To implement this invention, a user accesses the system via a terminal and provides emotion-based input. When the user inputs their emotional state in text format, the terminal sends this information to the server. The server first uses an emotion analysis engine to analyze this text information. Natural language processing libraries (e.g., Python's NLTK or Hugging Face's Transformers) can be used for emotion analysis.

[0743] The server issues commands to the image generation device based on the emotion data obtained through emotion analysis. This device is equipped with a function to automatically generate specific images corresponding to emotions using a generative AI model (e.g., DALL-E or StyleGAN). For example, if "joy" is detected, the generative AI model is programmed to create an image with a bright, positive tone. As a concrete example, if the user enters "I'm having fun today" as text, the emotion engine recognizes this as "joy" and instructs the image generation device to create a digital image with colors and style appropriate to that emotional state.

[0744] The generated digital images are registered as digital information by the server. This registration is performed using information management means, and encryption is applied as needed, utilizing blockchain technology to prevent tampering. Through marketplace means, the registered images are put up for sale on the online marketplace and become immediately available for purchase. Sales management and revenue tracking are handled by revenue management means, and the revenue obtained can be reinvested for specified purposes.

[0745] An example of a prompt in this system is the text, "Tell me how you're feeling today. Based on your feelings, I will generate a special piece of art just for you." This allows users to easily communicate their emotions to the system and experience personalized digital art.

[0746] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0747] Step 1:

[0748] The user accesses the system using a terminal. The user uses the prompts displayed on the screen to input text about their emotions. The input is given as a sentence expressing a specific emotion, such as "I'm having a good day." The terminal prepares this input data and sends it to the server.

[0749] Step 2:

[0750] The server passes the received text data to the sentiment analysis engine. The text data, as input, is analyzed using a natural language processing library. The sentiment analysis engine extracts keywords and context related to emotions from the text and outputs emotion labels such as "joy" and "sadness." This process automatically categorizes emotions.

[0751] Step 3:

[0752] The server issues generation instructions to the image generator based on the analyzed emotion data. Based on the emotion labels as input, the generation AI model sets color and style parameters and generates an appropriate image. In this step, the generated image is concretely visualized and output as digital art.

[0753] Step 4:

[0754] The generated digital art is registered as digital information by the server using information management tools. The generated images used as input are encrypted and securely stored on the server. Blockchain technology is utilized at this stage to prevent tampering with the digital information.

[0755] Step 5:

[0756] The server lists registered digital art on an online marketplace through a marketplace mechanism. The digital information used as input is transferred to the digital marketplace and becomes available for purchase. This makes the generated art accessible to a wide range of users.

[0757] Step 6:

[0758] The server manages sales revenue using revenue management systems and allocates it to designated uses. Revenue data obtained from market sales is tracked and recorded. Revenue is redistributed and returned to digital art creators based on automatically set rules.

[0759] (Application Example 2)

[0760] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0761] Currently, in the creation and sale of personalized digital art, it is difficult to analyze users' emotions in real time and provide digital representations that reflect them. This is because there is no system that automatically reflects specific art styles or themes based on the user's emotional state. As a result, there is a challenge in being able to quickly and efficiently provide digital content that matches the user's emotions.

[0762] 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.

[0763] In this invention, the server includes an information input device means for receiving information input from a user, an emotion analysis device means for analyzing the user's emotional state, and an image generation device means for generating an image based on the emotional state. This makes it possible to generate and provide customized digital art that corresponds to the emotions input by the user.

[0764] An "information input device means" is a device that receives information regarding the user's emotions and instructions, and serves as an interface connecting the user and the system.

[0765] An "emotion analysis device" is a device that analyzes the user's emotional state from the information received, and provides information necessary for the next processing based on the emotional data.

[0766] An "image generation device means" is a device that generates images using appropriate themes and styles based on an analyzed emotional state.

[0767] A "recording device" is a device that saves generated images in a digital format in preparation for later use and distribution.

[0768] A "distribution registration device" is a device that registers recorded digital formats for distribution in the market and enables their distribution.

[0769] A "revenue management device" is a device for tracking revenue obtained from transactions of distributed digital formats and for distributing it appropriately.

[0770] To implement this invention, the user first uses an information terminal such as a smartphone or smart glasses to input information about their emotions via an information input device. The text information entered by the user is sent to a server. This server is equipped with an emotion analysis device that analyzes the entered text and identifies the user's emotional state.

[0771] The analyzed emotional information is then sent to an image generation device. This device selects the optimal theme, colors, and style based on the aforementioned emotional state and generates personalized digital art using a generative AI model. This generation process can utilize, for example, the OpenAI API.

[0772] The generated digital art is recorded in digital format by a recording device and then listed on an online marketplace using a distribution registration device. This distribution registration device may utilize blockchain technology, allowing for the secure distribution of artwork.

[0773] Furthermore, the revenue management system tracks revenues earned based on market transactions and distributes them as needed. The revenue management system utilizes cloud-based accounting software to efficiently manage revenues.

[0774] For example, if a user enters the text "Today was a good day," the emotion analysis device identifies this as "happiness," and as a result, the image generation device generates bright, happy-looking artwork. This generated artwork is immediately put up for sale on the online marketplace by the distribution registration device.

[0775] An example of a prompt might be, "Generate a digital artwork with bright colors based on the following emotion: 'happiness'." In this way, a digital artwork tailored to the user's emotions is provided in real time.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The user inputs information about their emotions in text format via the terminal's information input device. This input is intended to accurately reflect the user's current emotional state. The entered text data is sent to the server when it is ready for subsequent processing.

[0779] Step 2:

[0780] The server processes the received text data using an emotion analysis device to identify the user's emotional state. This device uses natural language processing technology to extract emotions from the text and classify them into emotional categories such as "happiness" or "sadness." The input is text data, and the output is the analyzed emotional state.

[0781] Step 3:

[0782] The identified emotional state is then sent to an image generation device. Based on the analysis results, the server uses a generative AI model to generate an appropriate image. The generative AI model selects a theme, colors, and style that match the emotional state, creating customized digital art. The input is emotional state data, and the output is the generated digital art.

[0783] Step 4:

[0784] The generated digital art is stored by a recording device on the server. This digital format will later serve as the basis for its distribution in the market. Here, the digital art data as output is stored.

[0785] Step 5:

[0786] The saved digital art is registered on the market via a distribution and registration device. The server uses blockchain technology to securely register the generated digital art and make it available for distribution. The input is the saved digital art data, and the output is the digital art that has been registered on the market.

[0787] Step 6:

[0788] When a trade takes place in the market, the revenue management device collects the transaction data and manages the resulting profits. This device tracks profits in real time and distributes them as needed. The input is transaction data, and the output is a profit distribution instruction.

[0789] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0790] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0791] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0792] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0793] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0794] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0795] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0796] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0797] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0798] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0799] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0800] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0801] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0802] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0803] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0804] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0805] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0806] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0807] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0808] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0809] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] A user interface means for receiving instructions from the user,

[0813] An image generation apparatus means that generates an image based on the aforementioned instructions,

[0814] A registration method for registering the generated image as a digital asset,

[0815] A marketplace registration means for listing the aforementioned digital assets on the market in a state where they can be sold,

[0816] A system including a sales management means for managing and returning sales obtained from the sale of the aforementioned digital assets.

[0817] (Claim 2)

[0818] The system according to claim 1, characterized in that the user interface means has a function to receive prompt instructions.

[0819] (Claim 3)

[0820] The system according to claim 1, characterized in that the sales management means has a setting function for allocating sales to specific uses.

[0821] "Example 1"

[0822] (Claim 1)

[0823] An information input means that accepts theme and style instructions, including prompt text,

[0824] Information processing means that generates an image using a generation AI model based on the aforementioned instructions,

[0825] An asset management system that registers generated images as digital information and records related information,

[0826] Information provision means for registering the aforementioned digital information in a marketable, exchangeable state,

[0827] A system including a profit management means for managing the profits obtained from the exchange of the aforementioned digital information and distributing them based on a pre-set mechanism.

[0828] (Claim 2)

[0829] The system according to claim 1, characterized in that the information input means has a function to accept prompt messages.

[0830] (Claim 3)

[0831] The system according to claim 1, characterized in that the profit management means has a setting function for distributing profits to specific uses.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] A means of acquiring information for inputting user instructions,

[0835] A visual generation device means that generates visual information based on the aforementioned instructions,

[0836] An asset registration means for registering generated visual information as a digital asset,

[0837] A market listing means for listing the aforementioned digital assets in an exchangeable state on the information market,

[0838] A revenue management means for managing and redistributing the revenue obtained from the exchange of the aforementioned digital assets,

[0839] A system including a visual output means for displaying visual information generated in real time through a user's eye-tracking device and immediately listing it on the information market.

[0840] (Claim 2)

[0841] The system according to claim 1, characterized in that the information acquisition means has a function to receive instruction text.

[0842] (Claim 3)

[0843] The system according to claim 1, characterized in that the revenue management means has a setting function for redistributing revenue for a specific purpose.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A means of obtaining information that accepts emotion-based instructions from users,

[0847] Image generation means for generating an image based on the aforementioned emotional information,

[0848] An information management means for registering the generated image as digital information,

[0849] A means of making the aforementioned digital information available to the market in a marketable state,

[0850] A system including an income management means for managing and distributing income obtained from the sale of the aforementioned digital information.

[0851] (Claim 2)

[0852] The system according to claim 1, characterized in that the information acquisition means has a function to accept emotional input in the form of a prompt message.

[0853] (Claim 3)

[0854] The system according to claim 1, characterized in that the income management means has a setting function for allocating income to specific uses.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] An information input device means for receiving information input from a user,

[0858] An emotion analysis device means for analyzing the user's emotional state based on the aforementioned information,

[0859] An image generation apparatus means that generates an image based on the aforementioned emotional state,

[0860] A recording means for registering the generated image as a digital recording medium,

[0861] A distribution registration means for listing the aforementioned digital recording medium on the market in a state where it can be distributed,

[0862] A system including a revenue management device means for managing and distributing revenue obtained from the trading of the aforementioned digital recording media.

[0863] (Claim 2)

[0864] The system according to claim 1, characterized in that the information input device means has a function to receive prompts that reflect emotions.

[0865] (Claim 3)

[0866] The system according to claim 1, characterized in that the revenue management device means has a setting function for distributing revenue to specific roles. [Explanation of symbols]

[0867] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A user interface means for receiving instructions from the user, An image generation apparatus means that generates an image based on the aforementioned instructions, A registration method for registering the generated image as a digital asset, A marketplace registration means for listing the aforementioned digital assets on the market in a state where they can be sold, A system including a sales management means for managing and returning sales obtained from the sale of the aforementioned digital assets.

2. The system according to claim 1, characterized in that the user interface means has a function to receive prompt instructions.

3. The system according to claim 1, characterized in that the sales management means has a setting function for allocating sales to specific uses.

Images