system

Abstract

Provide a system. [Solution] A means for a work provider to register on a dedicated platform and upload work data; The server stores the uploaded artwork data in a database and trains the generative model. a means for a user to input a work creation request in text form; a means for the server to cause the generative model to generate a work in a particular style based on a user's request; a means by which the server stores and provides the generated work to users; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional digital art generation systems, users have limited means to easily obtain customized works based on the style of a specific artist, resulting in problems such as inconsistent quality and long production times. In addition, creators of artworks have limited opportunities to utilize their own style and techniques to generate new revenue. This makes it difficult to match the needs of users and creators, preventing effective service provision. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, it provides a means for a work provider to register on a dedicated platform and upload work data. Next, it provides a means for a server to store the uploaded work data in a database and train a generative model. Furthermore, it provides a means for a user to input a work generation request in text format, and it provides a means for the server to generate a work in a specific style based on the generative model. Finally, by constructing a system including a means for the server to store the generated work and provide it to the user, it is possible to quickly provide high-quality digital artworks that meet the user's needs. In addition, it improves user convenience by including a means for providing a link that allows the user to download the generated work and a means for notifying the user of the download link.

[0006] "Work Provider" means an individual or organization that provides their own work data to a dedicated platform in order to generate a digital artwork.

[0007] The "dedicated platform" is an online system where content providers can upload their own content and users can make content requests.

[0008] "Work data" refers to digital files such as images, illustrations, paintings, etc. uploaded by work providers.

[0009] A "server" is a computer system that manages and stores uploaded work data and provides training data to the generative model.

[0010] "Database" refers to a system for storing and managing uploaded and generated work data.

[0011] A "generative model" is an artificial intelligence model that learns from uploaded artwork data and generates new digital artworks based on user requests.

[0012] "User" means an individual or organization that makes a work request in text format through the dedicated platform.

[0013] A "request" is information that a user provides to a generative model by entering the content of the desired work in text format.

[0014] A "generated work" is a new digital artwork created by a generative model based on a request.

[0015] "Link" means a URL or other connection through which a User may download a Generated Work.

[0016] "Notification" refers to an alert or message that notifies the user when the generated work is complete and provides a download link. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] System Overview

[0039] The present invention relates to a digital art generation system, and in particular to an embodiment of a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[0040] Artist registration and work submission

[0041] 1. Artist registration

[0042] The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[0043] The server receives the entered information and stores it in a database.

[0044] The server will send a registration confirmation email to the artist to notify them that registration is complete.

[0045] 2. Providing works

[0046] The artist logs into the platform.

[0047] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[0048] The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[0049] The server adds the artwork file to the training dataset of the generative model and retrains the generative model.

[0050] User requests and creation

[0051] 1. User Login

[0052] A user visits the platform's login page and enters their email address and password.

[0053] The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[0054] 2. Work Request

[0055] User clicks the "New Request" button from the dashboard.

[0056] The user inputs the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[0057] If the user has the option to select a particular artist, they do so.

[0058] 3. Request Processing

[0059] The user clicks the "Submit Request" button.

[0060] The server receives the request, generates a request ID and saves it in the database.

[0061] The server forwards the request to the generative model, which generates the artwork in the specified style.

[0062] 4. AI-generated artwork

[0063] The server provides the request content to the generative model to start processing.

[0064] A generative model generates artwork in the style of a specific artist based on a user request.

[0065] The server receives the generated work and stores it in a database.

[0066] 5. Providing works

[0067] The server generates a URL for the generated work and associates it with the user request ID.

[0068] The device will notify the user that "generation is complete" and provide a download link.

[0069] The user clicks on the provided link to download the generated work.

[0070] Specific examples

[0071] For example, Artist A uploads a work to the platform, and the work is trained by the AI ​​model. User B logs in and requests, "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the AI ​​model, which generates a work based on Artist A's style. The generated work is saved by the server and notified to User B. User B can then click the download link to retrieve the work.

[0072] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[0073] The processing flow will be explained below.

[0074] Artist registration and work submission processing

[0075] Artist Registration

[0076] Step 1:

[0077] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[0078] Step 2:

[0079] The server receives the entered information and stores it in a database.

[0080] Step 3:

[0081] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[0082] Work provided

[0083] Step 1:

[0084] The artist logs into the platform.

[0085] Step 2:

[0086] The artist selects a menu item to upload a new work file.

[0087] Step 3:

[0088] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[0089] Step 4:

[0090] The server validates the uploaded file (checks the file format, size, etc.).

[0091] Step 5:

[0092] The server stores successfully validated files in the database and registers their metadata (e.g. title, description, tags).

[0093] Step 6:

[0094] The server adds the newly saved artwork file to the training dataset of the generative model and begins the retraining process of the generative model.

[0095] Handling user requests and creations

[0096] User Login

[0097] Step 1:

[0098] A user visits the platform's login page and enters their email address and password.

[0099] Step 2:

[0100] The server performs the authentication process and, if the entered information is correct, redirects the user to the dashboard.

[0101] Work request

[0102] Step 1:

[0103] User clicks the "New Request" button from the dashboard.

[0104] Step 2:

[0105] If the user enters the request in text format and selects a specific artist, set that option.

[0106] Step 3:

[0107] The user clicks the "Submit Request" button.

[0108] Request Processing

[0109] Step 1:

[0110] The server receives the request.

[0111] Step 2:

[0112] The server generates a request ID and stores it in the database along with the request content.

[0113] Step 3:

[0114] The server forwards the request ID and request content to the generated model.

[0115] Artwork generation by AI

[0116] Step 1:

[0117] The server provides the request content to the generative model to start processing.

[0118] Step 2:

[0119] A generative model analyzes the user's request and generates a work in the style of a specific artist.

[0120] Step 3:

[0121] The server receives the generated work and stores it in a database.

[0122] Work provided

[0123] Step 1:

[0124] The server generates a URL for the generated work and associates it with the request ID.

[0125] Step 2:

[0126] The device will notify the user that "generation is complete" and provide a download link.

[0127] Step 3:

[0128] The user clicks on the provided link to download the generated work.

[0129] In this way, customized digital artwork that reflects the artist's style is quickly provided based on the user's request.

[0130] Example 1

[0131] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] Conventional digital art generation systems have struggled to efficiently generate works that reflect an artist's style. Furthermore, there are limited means for users to quickly obtain customized works based on their desired style and content. Therefore, there is a need for a digital art generation and distribution system that satisfies both artists and users.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0134] In this invention, the server includes: a means for a work provider to register on the dedicated platform and input necessary information; a means for the server to receive the input information and save it in a database; a means for the server to send a registration confirmation notification and notify the completion of registration; a means for the work provider to log in to the platform and upload a work file; a means for the server to verify the uploaded work file and save it in a database; a means for the server to add the saved file to a training dataset for a generative model and perform re-training; a means for a user to log in to the platform and input a request; a means for the server to receive the input request and forward it to a generative model to generate a work in a specific style; a means for the server to receive the generated work and save it in a database; and a means for the terminal to notify the user that the generated work has been provided. This enables customized works that reflect the artist's style to be quickly generated and provided.

[0135] "Artwork provider" refers to an artist who registers and provides their own work on the digital art generation platform.

[0136] "Platform" means a web application or software that allows Work Providers to register and upload Works and Users to submit Requests.

[0137] "Required information" refers to basic personal information such as name, email address, and password that the work provider enters when registering on the dedicated platform.

[0138] "Server" refers to a computing device that receives and processes input data from external clients, stores it in a database, validates the data, and launches and manages generative models.

[0139] "Database" refers to a software management system that systematically stores information received by the server.

[0140] "Registration confirmation notification" refers to an email sent by the server to confirm the information registered by the work provider on the dedicated platform.

[0141] "Login" refers to the operation by which work providers and users complete the authentication procedures required to access the dedicated platform.

[0142] "Work files" are image files in JPEG or PNG format that are treated as digital art.

[0143] "Verification" refers to the process in which the server checks whether the uploaded work file is in the specified format and size.

[0144] A "generative model" is an algorithm or program that uses machine learning techniques to generate new digital art from input data.

[0145] A "training dataset" is a collection of original artwork data that a generative model needs to generate new artworks.

[0146] "Retraining" is the process of retraining a generative model by adding new data to improve the model's performance.

[0147] A "request" is a request that a user inputs in text form the content and style of the work they wish to create and sends it to the server.

[0148] "Generated Work" means digital art generated by a generative model in a particular style based on a user request.

[0149] "Providing" means that the server notifies and distributes the generated work to the user in a usable form.

[0150] This invention relates to a digital art generation system, and specifically describes a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[0151] System Overview

[0152] This system consists of a dedicated platform, a server, a database, a generative model, and a user interface. The dedicated platform is implemented as a web application and can be accessed by content providers and users.

[0153] Hardware and software used

[0154] Server: A server equipped with high-performance computing resources (e.g., AWS (registered trademark) EC2, Google (registered trademark) Cloud Compute Engine)

[0155] Database: Relational database such as MySQL (registered trademark), PostgreSQL, etc.

[0156] Generative models: Generative models using machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch (e.g., StyleGAN)

[0157] Authentication system: User authentication using OAuth and JWT

[0158] Notification system: SMTP server, WebSocket, Firebase Cloud Messaging (FCM)

[0159] Artist registration and work submission

[0160] 1. The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[0161] Example: Open your browser, access the "Register" page, and enter the required information.

[0162] 2. The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[0163] Example: The server receives form data, validates it using Python's validation library, and saves it to MySQL.

[0164] 3. The server sends a registration confirmation notification to the artist via the SMTP server, notifying them that registration is complete.

[0165] Example: Send a confirmation email using the SMTP client library.

[0166] 4. The user (artist) logs in to the platform, selects and uploads the work file.

[0167] Example: Select an image file (JPEG, PNG) from the file selection dialog and click the "Upload" button.

[0168] 5. The server validates the uploaded file (checks the format and size) and stores files that pass validation in the database and storage.

[0169] Example: Use the PIL library to check the format of an image file and save it if there are no problems.

[0170] 6. The server adds the saved file to the training dataset for the generative model and starts retraining the generative model.

[0171] Example: Run a Python script to add new training data to a generative model and retrain it.

[0172] User requests and creation

[0173] 1. The user visits the platform's login page and enters their email address and password.

[0174] Example: Open the "Login" page in your browser and fill in the login form.

[0175] 2. The server authenticates the user and, if successful, displays the dashboard page.

[0176] Example: Verify user information using the authentication API and issue a JWT token.

[0177] 3. The user clicks the "New Request" button and enters the request in text format (e.g., "I want you to draw a scene with a cat at the seaside"), and optionally selects a specific artist.

[0178] Example: Click the "New Request" button on the dashboard and enter the request details.

[0179] 4. The user clicks the "Send Request" button to send the request to the server.

[0180] Example: Check the form and press the "Submit Request" button.

[0181] 5. The server receives the request, generates a request ID, and saves it in the database.

[0182] Example: Save the request contents to the database using the INSERT statement.

[0183] 6. The server forwards the request to the generative model, which generates the artwork in the specified style.

[0184] Example: Call the Generative Model API and send the request in JSON format.

[0185] 7. The generative model generates artwork in a specific style based on the request.

[0186] Example: A generative model generates an image based on a request.

[0187] 8. The server receives the generated work and stores it in a database.

[0188] Example: Save the received image file and save its URL in a database.

[0189] 9. The device will notify the user that "Generation is complete" and provide a download link for the work.

[0190] Example: Using WebSocket to send real-time notifications and display links.

[0191] 10. The user clicks on the provided link to download the generated work.

[0192] Example: Click on a link and download the image via your browser.

[0193] Specific examples

[0194] For example, an artist uploads a new piece of work to the platform, which is then trained by the generative model. A user then logs in and submits a request, such as "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the generative model, which then generates a work based on the artist's style. The generated work is then saved on the server and notified to the user. The user can then download the work by clicking on the provided link.

[0195] Prompt Sentence Examples

[0196] Example prompt 1: "Please draw a scene with a cat at the seaside in Artist A's style."

[0197] Example prompt 2: "Add a sunset to an autumn mountain scene" in the style of Artist B.

[0198] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[0199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0200] Step 1:

[0201] The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[0202] Input: Name, Email Address, Password

[0203] Output: Information sent to the server

[0204] Specific actions: The artist fills out the form in the browser and clicks the "Register" button.

[0205] Step 2:

[0206] The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[0207] Input: Artist registration information

[0208] Output: Save result to database (success / failure)

[0209] Specific operation: The server checks the received data using a validation library and saves it to MySQL using an INSERT statement.

[0210] Step 3:

[0211] The server sends a registration confirmation notice to the artist using an SMTP server, notifying him or her that registration has been completed.

[0212] Input: Contents of registration confirmation email (artist information)

[0213] Output: Confirmation email sending result (success / failure)

[0214] Specific operation: The server uses an SMTP client to send a confirmation email to the artist.

[0215] Step 4:

[0216] The user (artist) logs in to the platform, selects and uploads the work file.

[0217] Input: Email address, password, work file

[0218] Output: The file is sent to the server.

[0219] Specific operation: The artist enters their email address and password into the browser form, logs in, and then uploads the artwork file.

[0220] Step 5:

[0221] The server validates the uploaded files (checks the format and size) and stores the files that pass the validation in the database and storage.

[0222] Input: Work file

[0223] Output: Verification result, save result (success / failure)

[0224] Specific operation: Uses the PIL library to check the format and size of the image file, and if correct, saves it to the file system and database.

[0225] Step 6:

[0226] The server adds the saved file to the training dataset of the generative model and starts retraining.

[0227] Input: New work file

[0228] Output: Retraining started (success / failure)

[0229] What happens: The server runs a Python script to add new data to a generative model (e.g., StyleGAN) and retrain it.

[0230] Step 7:

[0231] A user visits the platform's login page and enters their email address and password.

[0232] Input: Email address, password

[0233] Output: Authentication result (success / failure)

[0234] Specific behavior: The user fills in the login form in the browser and clicks the "Login" button.

[0235] Step 8:

[0236] The server performs user authentication processing, and if successful, displays the dashboard page.

[0237] Input: Email address, password

[0238] Output: Token issued, dashboard page displayed

[0239] Specific operation: Uses the authentication API to verify user information, issue a JWT token, and display the dashboard page.

[0240] Step 9:

[0241] The user clicks the "New Request" button, enters the request in text format, and optionally selects a specific artist.

[0242] Input: Request details, select specific artist

[0243] Output: The request is sent to the server

[0244] Specific operation: The user clicks the "New Request" button on the dashboard and enters the request details.

[0245] Step 10:

[0246] The user clicks the "Send Request" button to send the request to the server.

[0247] Input: Request details, select specific artist

[0248] Output: The request is sent to the server

[0249] Specific operation: The user checks the request details and presses the "Submit Request" button.

[0250] Step 11:

[0251] The server receives the request, generates a request ID and saves it in the database.

[0252] Input: Request content

[0253] Output: Request ID, result saved to database (success / failure)

[0254] Specific operation: The server saves the request content in the database using an INSERT statement and generates a request ID.

[0255] Step 12:

[0256] The server forwards the request to the generative model, which generates the artwork in the specified style.

[0257] Input: Request details, Request ID

[0258] Output: The generated work

[0259] Specific operation: Makes an API call to the generative model and sends the request content in JSON format.

[0260] Step 13:

[0261] A generative model generates artwork in a specific style based on a request.

[0262] Input: Request content

[0263] Output: The generated work

[0264] Specific operation: The generative model generates digital art in response to user requests.

[0265] Step 14:

[0266] The server receives the generated work and stores it in a database.

[0267] Input: Generated artwork

[0268] Output: Save result to database (success / failure)

[0269] Specific operation: Save the received image file and save its URL in the database.

[0270] Step 15:

[0271] The device will notify the user that "creation is complete" and provide a download link for the work.

[0272] Input: URL of the generated work

[0273] Output: User notification, download link

[0274] Specific behavior: Uses WebSocket to send real-time notifications and display links.

[0275] Step 16:

[0276] The user clicks on the provided link to download the generated work.

[0277] Input: Download link

[0278] Output: Download the generated work

[0279] What happens: Click the link and your browser will download the work file.

[0280] (Application example 1)

[0281] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0282] Demand for digital art is growing in modern society, especially for customized art based on the style of a specific artist. However, existing systems lack sufficient means for users to quickly generate and acquire art in a specific artist's style, potentially resulting in a poor user experience. Furthermore, artists lack efficient ways to monetize their work on digital platforms. Given this situation, there is a need for an efficient system that allows users to instantly generate, purchase, and download customized art using a generative AI model that has learned the style of a specific artist.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0284] In this invention, the server includes: means for a work provider to register on a dedicated platform and upload work data; means for the server to store the uploaded work data in a database and train a generative model; means for a user to input a work generation request in text format; means for the server to cause the generative model to generate a work in a specific style based on the user's request; means for the server to store the generated work and provide it to the user; and means for a user to request digital art generated in the style of a specific artist from a mobile device and instantly preview, purchase, and download it. This allows users to quickly and efficiently generate and obtain customized art in the style of a specific artist, and also allows artists to gain new revenue opportunities using their own style.

[0285] "Artwork provider" means a person or legal entity that registers their information on a dedicated platform and uploads artwork data for the purpose of providing artwork.

[0286] "Platform" means a specific digital environment or web service used by Artwork Providers to register and upload Artworks and by Users to request and download Artworks.

[0287] "Artwork Data" means the digital file format (e.g., JPEG, PNG, etc.) of the artwork that is used as a dataset to train a generative model.

[0288] "Server" refers to a computer system that operates and manages the dedicated platform, stores data in a database, and generates and provides artworks using generative models.

[0289] The "database" is a system for storing and managing uploaded artwork data, generated artworks, and user request information.

[0290] A "generative model" is an artificial intelligence (AI) model that learns the style of a particular artist and generates new artwork based on user requests.

[0291] "User" means a person who accesses the dedicated platform, inputs a request for artwork creation in text format, and downloads the generated artwork.

[0292] The "means for inputting a request for creation of a work in text format" refers to an interface for a user to input the request content in text format in order to request the creation of a specific artwork on the dedicated platform.

[0293] A "particular style" refers to the characteristic expression techniques and design elements of a particular artist, and serves as a standard for generating artwork based on that.

[0294] "Mobile device" refers to a portable electronic device such as a smartphone or tablet that allows users to access the platform and request and download artworks.

[0295] "Preview" is a feature that allows you to check the generated artwork before purchasing or downloading it.

[0296] "Purchase" means that the user pays a certain amount of money to acquire ownership of the generated artwork.

[0297] "Downloading" refers to the act of saving a digital file of the generated artwork to a user's device via the Internet.

[0298] The present invention relates to a system that uses a generative AI model that has learned the style of a specific artist to quickly generate and provide digital art in accordance with a user's request. Specific embodiments are described below.

[0299] System Overview

[0300] The system consists of a set of platforms where art providers can register their artworks and provide datasets for training generative AI models, and users can generate and acquire artworks in the style of specific artists. The main components include a server, database, generative AI model, and user interface.

[0301] Hardware and software used

[0302] Hardware:

[0303] Server: A high-performance computer for processing data and running generative models.

[0304] Mobile Device: The device, such as a smartphone or tablet, that a user uses to request and download art.

[0305] software:

[0306] Python: The main programming language.

[0307] FastAPI: API server framework.

[0308] SQLModel: ORM for database operations.

[0309] PIL: Image manipulation library.

[0310] PyTorch: A deep learning library.

[0311] Transformers: Using CLIP models.

[0312] Specific steps

[0313] Registration of work providers

[0314] 1. The creator of the work accesses the dedicated platform and enters the required information such as name, email address, and password on the registration page.

[0315] 2. The server receives the input information and stores it in a database.

[0316] 3. The server sends a registration confirmation email to notify the work provider that registration is complete.

[0317] 4. The artwork provider logs into the system and uploads the artwork data file (e.g., JPEG, PNG, etc.).

[0318] 5. The server validates the format and size of the uploaded file, stores it in the database, and adds it to the training dataset for the generative model for retraining.

[0319] User requests and submissions

[0320] 1. The user accesses the login page of the dedicated platform and enters their email address and password for authentication.

[0321] 2. The user enters a text request for art creation, such as "Please draw a scene with a cat at the beach."

[0322] 3. The server receives the request, generates a request ID and saves it in the database.

[0323] 4. The server provides the request to the generative model, which then begins generating the artwork.

[0324] 5. The generative model generates a work based on the user's request, based on the style of a specific artist, and returns the result to the server.

[0325] 6. The server stores the generated work in a database and generates a link.

[0326] 7. The device will notify the user that "Generation is complete" and provide a download link.

[0327] 8. The user can click on the provided link to download the generated artwork.

[0328] Examples of prompt statements

[0329] An example of a user-entered prompt would be a text request such as "Please draw a scene with a cat at the beach."

[0330] This system allows users to easily request customized art by specifying the style of a specific artist, and also allows artists to utilize their own style to gain new revenue opportunities.

[0331] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0332] Step 1:

[0333] The server receives the registration information of the work provider.

[0334] Input: Name of the creator, email address, password

[0335] How it works: The server stores the information you entered in a database and sends you a confirmation email.

[0336] Output: Registration completion notification

[0337] Step 2:

[0338] The server receives artwork data uploaded from the artwork provider.

[0339] Input: Artwork file (JPEG, PNG, etc.)

[0340] Action: The server validates the file format and size, saves it to the database, adds it to the training dataset of the generative model, and retrains it.

[0341] Output: File verification results, re-learning completion notification

[0342] Step 3:

[0343] The user accesses the dedicated platform and logs in.

[0344] Input: User's email address, password

[0345] How it works: The server handles authentication and, if successful, redirects the user to the dashboard.

[0346] Output: Login success message, dashboard

[0347] Step 4:

[0348] The user inputs a work creation request in text format.

[0349] Input: Request for creation (e.g., "I want you to draw a scene with a cat at the seaside")

[0350] How it works: The server receives the request, generates a request ID, and saves it in the database.

[0351] Output: Request ID, request save completion notification

[0352] Step 5:

[0353] The server provides the request to the generative model and begins generating the artwork.

[0354] Input: User request, style model of a specific artist

[0355] How it works: The generative model generates artwork in a specific style based on the request and the style model.

[0356] Output: Generated artwork

[0357] Step 6:

[0358] The server stores the generated work in a database and generates a link.

[0359] Input: Generated artwork, request ID

[0360] How it works: The server stores the generated work in a database, associates it with a request ID, and generates a download link.

[0361] Output: Download link

[0362] Step 7:

[0363] The device will notify the user of the completion of generation and provide a download link.

[0364] Input: Download link

[0365] What it does: The device notifies the user that "Generation is complete" and provides a download link for the generated work.

[0366] Output: Generation completion notification, download link

[0367] Step 8:

[0368] The user clicks on the provided link to download the work.

[0369] Input: Download link

[0370] How it works: The user clicks on the link and the generated artwork downloads to their device.

[0371] Output: Downloaded artwork

[0372] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0373] System Overview

[0374] This invention relates to a digital art generation system, and in particular to an embodiment of a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[0375] Artist registration and work submission

[0376] Artist Registration

[0377] 1. The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[0378] 2. The server receives the entered information and stores it in a database.

[0379] 3. The server automatically sends a registration confirmation email to the artist, notifying them that registration is complete.

[0380] Work provided

[0381] 1. Artist logs in to the platform.

[0382] 2. The artist selects the menu to upload a new work file.

[0383] 3. The artist uploads the artwork file (e.g. JPEG, PNG, etc.) to the platform.

[0384] 4. The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[0385] 5. The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[0386] User requests and creation

[0387] User Login

[0388] 1. The user visits the platform's login page and enters their email address and password.

[0389] 2. The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[0390] Work request

[0391] 1. User clicks the "New Request" button from the dashboard.

[0392] 2. The user enters the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[0393] 3. If there is an option for users to select a specific artist, set that option.

[0394] 4. The server uses an emotion engine to analyze emotions from the text entered by the user.

[0395] 5. The server adjusts and optimizes the request content based on the emotion analysis results obtained from the emotion engine.

[0396] Request Processing

[0397] 1. The server provides the tailored request content to the generative model and starts processing.

[0398] 2. The generative model analyzes the user's request and generates a work in the style of a specific artist.

[0399] 3. The server receives the generated work and stores it in a database.

[0400] Work provided

[0401] 1. The server generates a URL for the generated work and associates it with the request ID.

[0402] 2. The device notifies the user that "Generation is complete" and provides a download link.

[0403] 3. The user clicks on the provided link to download the generated work.

[0404] Specific examples

[0405] For example, artist A uploads his or her work to the platform, and the work is trained by the generative model. User B logs in and inputs a request, saying, "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and uses an emotion engine to analyze User B's emotions. If an emotion such as "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to User B. User B can then click the download link to obtain the generated work.

[0406] This system allows users to quickly obtain customized digital artwork based on their emotions, and also provides artists with new revenue opportunities.

[0407] The processing flow will be explained below.

[0408] Artist registration and work submission processing

[0409] Artist Registration

[0410] Step 1:

[0411] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[0412] Step 2:

[0413] The server receives the entered information and stores it in a database.

[0414] Step 3:

[0415] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[0416] Work provided

[0417] Step 1:

[0418] The artist logs into the platform.

[0419] Step 2:

[0420] The artist selects a menu item to upload a new work file.

[0421] Step 3:

[0422] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[0423] Step 4:

[0424] The server validates the uploaded file (file type, size, etc.) and stores it in the database.

[0425] Step 5:

[0426] The server registers the metadata (e.g., title, description, tags) of the artwork files stored in the database.

[0427] Step 6:

[0428] The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[0429] Handling user requests and creations

[0430] User Login

[0431] Step 1:

[0432] A user visits the platform's login page and enters their email address and password.

[0433] Step 2:

[0434] The server handles the authentication process and redirects the user to the dashboard if the entered information is correct.

[0435] Work request

[0436] Step 1:

[0437] User clicks the "New Request" button from the dashboard.

[0438] Step 2:

[0439] If the user enters the request in text format and selects a specific artist, set that option.

[0440] Step 3:

[0441] The user clicks the "Submit Request" button.

[0442] Request Processing

[0443] Step 1:

[0444] The server receives the request, generates a request ID and saves it in the database.

[0445] Step 2:

[0446] The server uses an emotion engine to analyze the user's emotion from the text included in the request.

[0447] Step 3:

[0448] The server optimizes the request content and makes appropriate adjustments based on the analysis results of the emotion engine.

[0449] Artwork generation by AI

[0450] Step 1:

[0451] The server provides the tailored request content to the generative model to initiate the generation process.

[0452] Step 2:

[0453] The generative model generates artwork in the style of a specific artist based on tailored requests.

[0454] Step 3:

[0455] The server receives the generated work and stores it in a database.

[0456] Work provided

[0457] Step 1:

[0458] The server generates a URL for the generated work and associates it with the request ID.

[0459] Step 2:

[0460] The device will notify the user that "generation is complete" and provide a download link.

[0461] Step 3:

[0462] The user clicks on the provided link to download the generated work.

[0463] Specific examples

[0464] Specific examples of artist registration

[0465] Step 1:

[0466] Artist A accesses the dedicated platform, enters the necessary information, and completes registration.

[0467] Step 2:

[0468] The server saves Artist A's information in a database and automatically sends a registration confirmation email.

[0469] Examples of work provided

[0470] Step 1:

[0471] Artist A logs in to the platform.

[0472] Step 2:

[0473] Artist A uploads a new work and the server validates the file.

[0474] Step 3:

[0475] The server stores the successfully verified file in the database and registers the metadata.

[0476] Step 4:

[0477] The server provides the artwork file to the generative model, and the AI ​​model begins retraining.

[0478] Request and creation examples

[0479] Step 1:

[0480] User B logs in to the platform and fills in the request form, saying, "I would like you to draw a scene of a cat standing with a sunset in the background."

[0481] Step 2:

[0482] The server receives the request and performs emotion analysis using an emotion engine.

[0483] Step 3:

[0484] The server optimizes the request based on the analysis results and provides it to the generative model to start the generation process.

[0485] Step 4:

[0486] The generative model generates artwork in the style of a particular artist, which is then stored on a server.

[0487] Step 5:

[0488] The device notifies User B and provides a download link.

[0489] Step 6:

[0490] User B clicks the link to download the generated work.

[0491] In this way, users can quickly obtain customized digital artwork based on their emotions, and artists can gain new revenue opportunities.

[0492] Example 2

[0493] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0494] While conventional digital art generation systems have the technology to generate artworks using the artist's style, it has been difficult to generate customized artworks that reflect the user's emotions. Furthermore, because the user's input request is passed directly to the generative model, the artwork generated does not necessarily reflect the user's intentions or emotions. This makes it difficult to increase user satisfaction.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0496] In this invention, the server includes a means for performing sentiment analysis based on the user's request and optimizing the request content, a means for causing the generative model to generate a work in a specific style based on the user's request, and a means for generating a URL for the generated work and associating it with the request ID, thereby generating a customized artwork that reflects the user's sentiment and increasing user satisfaction.

[0497] "Work provider" refers to an individual or organization that registers on the dedicated platform and provides work data used to generate digital art.

[0498] "Dedicated Platform" means a web-based system through which work providers upload work data and users input work generation requests.

[0499] A "server" is a computer system that processes data received from work providers and users and performs various functions for work generation.

[0500] "Artwork data" is a general term for image files and related data uploaded by the creator of the work to train a generative model of digital art.

[0501] A "database" is a system for systematically storing uploaded work data, created works, user request information, etc., and for efficiently managing and searching such data.

[0502] A "generative model" is a machine learning model that learns the style of a particular artist and generates new artwork based on user requests.

[0503] "User" means an individual or entity that requests the creation of digital art using the Dedicated Platform.

[0504] The "request content" is text data that describes the specific requirements for the digital art that the user wants to create.

[0505] "Sentiment analysis" is the process of detecting emotions from the request content entered by the user and optimizing the request content based on those emotions.

[0506] "URL" is the address on the Internet for accessing the created work, and is provided to the user by the server.

[0507] A "request ID" is an identification number assigned to uniquely identify each request.

[0508] MODE FOR CARRYING OUT THE INVENTION

[0509] This invention relates to a system for generating digital art, specifically a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[0510] System program processing

[0511] The system uses the following hardware and software:

[0512] Server: A central computer system that stores and processes work data and request data. Data is managed using an SQL or NoSQL database.

[0513] Terminal: A device used by artists and users to access the dedicated platform using a web browser.

[0514] Generative models: For example, using machine learning models such as Stable Diffusion to generate artwork in the style of a particular artist.

[0515] Sentiment engine: Analyzes emotions from user input text using natural language processing techniques, such as Transformer-based models (e.g., BERT).

[0516] Artist registration and work submission

[0517] Artists access the dedicated platform and register by entering required information such as their name, email address, and password. The server receives this information and stores it in a database. A registration confirmation email is automatically sent to the artist, who clicks on a link to complete the registration. After logging in, the artist uploads their artwork data. The server then verifies the uploaded data and adds it to the training dataset for the generative model.

[0518] Training a generative model

[0519] Based on the uploaded artwork data, the generative model learns the style of a specific artist, which is then stored in a database and used to generate artwork.

[0520] User requests and creation

[0521] Users access the dedicated platform and log in by entering their login information. After logging in, they can enter their request in text format and specify a specific artist. The server receives this request and performs emotional analysis using an emotion engine.

[0522] For example, if the prompt is "Please draw a scene of a cat standing by the sea in the sunset. Please create a calm atmosphere," the emotion engine will detect the emotion "calm" and optimize the request. Based on the optimized request, the generative model will generate a work, which will then be stored in a database by the server.

[0523] Providing generated works

[0524] The server generates a URL for the generated work and associates it with the request ID. A notification containing this URL is sent to the device, and the user can click the download link to download the work.

[0525] This system allows users to quickly obtain digital artworks that are customized to their emotions, and also allows artists to efficiently offer their works and gain new revenue opportunities.

[0526] Specific examples

[0527] For example, an artist uploads their work to the platform, and the work is trained by the generative model. A user logs in and inputs a request, such as "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and analyzes the user's emotions using an emotion engine. If the emotion of "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to the user. The user can obtain the generated work by clicking a download link.

[0528] Specific embodiments of the present invention allow users to easily obtain customized digital artwork based on their emotions.

[0529] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0530] Step 1: Artist Registration

[0531] The artist accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password. The server receives the entered information and stores it in a database. The server automatically sends a registration confirmation email, and the artist completes the registration by clicking the link in the confirmation email.

[0532] Input: Artist name, email address, password

[0533] Output: The artist's information is saved in the database and a confirmation email is sent.

[0534] Step 2: Submit your work

[0535] An artist logs in to the platform and selects a menu to upload their artwork file. Once the artist uploads the artwork file (e.g., JPEG, PNG, etc.), the server validates the uploaded file and stores it in a database. The server then adds the artwork file to the training dataset for the generative model and begins the training process.

[0536] Input: Artist's artwork file

[0537] Output: Work files stored in a database, training dataset for generative models

[0538] Step 3: User Login

[0539] The user accesses the platform's login page and enters their email address and password. The server performs authentication and, if successful, redirects the user to the dashboard. The device displays a message to the user indicating successful login.

[0540] Input: Email address, Password

[0541] Output: Login successful message

[0542] Step 4: Submit a request

[0543] The user clicks the "New Request" button on the dashboard and enters the request in text format (e.g., "I want you to paint a scene of a cat standing with a sunset in the background"). A specific artist can be selected if necessary. The server receives and stores the input. The server uses an emotion engine to analyze the emotion from the request and optimizes the request based on the results.

[0544] Input: Request content in text format

[0545] Output: Sentiment analysis results, optimized request content

[0546] Step 5: Request Processing

[0547] The server provides the tailored request to the generative model to initiate processing. The generative model analyzes the request and generates a work based on the style of the specific artist. The server receives the generated work and stores it in a database.

[0548] Input: Optimized request content

[0549] Output: Generated artwork file

[0550] Step 6: Submit your work

[0551] The server generates a URL for the generated work and associates it with the request ID. The device notifies the user that "generation is complete" and provides a download link. The user clicks the provided link to download the generated work.

[0552] Input: Generated artwork file

[0553] Output: Download link for the work, Download work

[0554] By following these steps, users can easily obtain customized digital artworks, and this system is beneficial for both artists and users.

[0555] (Application example 2)

[0556] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0557] Conventional digital art creation systems generate artwork based solely on a simple text request entered by the user, making it difficult to provide customized artwork that reflects the user's emotions and mood. Therefore, there is a need to effectively create and provide digital artwork that truly resonates with users. The present invention solves this problem and provides a more personalized digital art creation system that reflects the user's emotions.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0559] In this invention, the server includes a means for generating a work based on the emotion analysis result, a means for presenting a preview image of the generated work and offering the option of purchasing or downloading, and a means for storing the generated work and providing it to the user, thereby enabling the generation and provision of a customized digital artwork that reflects the emotion of the user.

[0560] A "work provider" is an entity that uploads their own work data to the dedicated platform.

[0561] The "dedicated platform" is an online system where creators can register and upload their work data and train the generative model.

[0562] "Work data" refers to digital content such as image files and text data that work providers upload to the platform.

[0563] The "server" is a central processing unit that stores artwork data in a database and trains the generative model.

[0564] A "database" is a storage device for storing uploaded work data and created works.

[0565] A "generative model" is an artificial intelligence model that learns from uploaded artwork data and generates digital artworks based on user requests.

[0566] "User" is the entity that enters a request to generate a digital artwork and receives the results.

[0567] An “artwork generation request” is a textual instruction that a user inputs into a Generative Model to generate a particular artwork.

[0568] "Emotion analysis results" are analysis results based on requests and emotions entered by users, and are data used to instruct the generative model.

[0569] A "preview image" is an image that displays a part or the whole of a generated digital artwork for the user to check.

[0570] "Purchase or Download Option" means the option that a user can choose to purchase or download the generated digital artwork for free.

[0571] "Generated Works" are digital artworks created by generative models based on user requests and sentiment analysis.

[0572] The present invention provides a specific system for generating and providing customized digital artworks that reflect a user's emotions. The system includes a means for an artwork provider to register and upload artwork data to a dedicated platform, a means for a server to store the uploaded artwork data in a database and train a generative model, a means for a user to input an artwork generation request, a means for the server to cause the generative model to generate an artwork based on the user's request and the results of emotion analysis, a means for presenting a preview image of the generated artwork and offering the option of purchasing or downloading, and a means for the server to store the generated artwork and provide it to the user.

[0573] Hardware and Software Configuration

[0574] server

[0575] The servers are equipped with high-performance hardware such as Intel Xeon processors and NVIDIA GPUs, and run generative and sentiment analysis models using software libraries such as Python, TensorFlow, and PyTorch.

[0576] The database can be a relational or NoSQL database such as PostgreSQL or MongoDB.

[0577] User terminal

[0578] User devices are smartphones or PCs, and access the platform using a dedicated application or web browser.

[0579] The smartphone application is implemented as an iOS or ANDROID (registered trademark) app.

[0580] Sentiment Analysis and Generative Models

[0581] Emotion Analysis: Emotion recognition uses Python and the EmotionRecognition library to analyze emotional states from data input in text format.

[0582] Generative Modeling: Generative modeling uses Hugging Face's Transformers library and is performed by an AI model trained in the style of a specific artist.

[0583] Operating Procedure

[0584] Artist registration and work submission

[0585] 1. The artist registers an account on the dedicated platform and uploads their work data.

[0586] 2. The server verifies the uploaded artwork data, stores it in a database, and trains the generative model.

[0587] User requests and creation

[0588] 1. The user logs in to the dedicated app and enters a request to create a work in text format.

[0589] 2. The server uses an emotion engine to analyze emotions from the user's input.

[0590] 3. Generate prompts based on the sentiment analysis results and provide instructions to the generative model.

[0591] 4. The generative model generates a digital artwork in a specific style based on the user's request and the results of sentiment analysis.

[0592] Product preview and offering

[0593] 1. The server generates a preview image of the created work and presents it to the user.

[0594] 2. Users can preview the work and choose to purchase it or download it for free.

[0595] 3. The server stores the generated work in a database and provides a download link.

[0596] Specific examples

[0597] For example, if a user inputs "I feel tired" and requests "I want a drawing of a scene with a cat standing with a sunset in the background," the server sends this to the emotion engine and generates the "tired" emotion as an analysis result.

[0598] User sentiment: "I feel tired"

[0599] User request: "I want you to draw a scene of a cat standing with a sunset in the background."

[0600] Generated prompt: "Create a tired themed artwork of a cat standing against a sunset background in the style of Artist A."

[0601] A prompt is generated. The generative model generates a work based on this prompt and provides it to the user. This system makes it possible to quickly and effectively generate and provide customized digital artworks that reflect the user's emotions.

[0602] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0603] Step 1:

[0604] Artist Registration

[0605] Artists access the dedicated platform and create an account by entering the required information, such as their name, email address, and password. The server receives the information entered and stores it in a database. A registration confirmation email is automatically generated and sent to the artist.

[0606] Input: Artist information such as name, email address, and password

[0607] Output: Artist information stored in the database and a confirmation email

[0608] Step 2:

[0609] Work provided

[0610] Artists log in to the platform and upload artwork files (JPEG, PNG, etc.) from their account management page. The server verifies the uploaded file format and size, stores it in a database, and adds the saved artwork data to the training dataset of the generative model, starting the model retraining process.

[0611] Input: Artist's work file

[0612] Output: Artwork data stored in a database and a retrained generative model

[0613] Step 3:

[0614] User Login

[0615] A user accesses the platform through a dedicated app or web browser and enters their email address and password. The server performs the authentication process, and if authentication is successful, redirects the user to the dashboard.

[0616] Input: User's email address and password

[0617] Output: Authentication result and redirect to dashboard

[0618] Step 4:

[0619] Enter your work request

[0620] Users can click the New Request button on the dashboard and enter a text-based production request, with the option to select a specific artist.

[0621] Input: User text request and artist selection

[0622] Output: The request data sent to the server

[0623] Step 5:

[0624] Emotion analysis

[0625] The server sends the request data to the emotion engine, which analyzes the user's input to determine the emotion. The emotion engine analyzes the text and identifies the dominant emotion (e.g., happy, sad, tired, etc.).

[0626] Input: User request text

[0627] Output: Emotion analysis results

[0628] Step 6:

[0629] Prompt Generation

[0630] The server creates prompts for the generative model based on the results of sentiment analysis and the user's request. For example, based on the request "I feel tired" and "A scene of a cat standing against a sunset," it generates a prompt such as "Create a tired-themed artwork of a cat standing against a sunset background in the style of Artist A."

[0631] Input: Sentiment analysis results and user requests

[0632] Output: Prompts for the generated model

[0633] Step 7:

[0634] Work generation

[0635] The server provides prompts to the generative model, which then generates the digital artwork. The generative model uses the learnings of a particular artist as a style to generate the digital artwork.

[0636] Input: Prompt for the generative model

[0637] Output: Generated digital artwork

[0638] Step 8:

[0639] Preview display

[0640] The server generates a preview image of the generated work and presents it to the user, who can then view the preview and select the option to purchase or download.

[0641] Input: Generated digital artwork

[0642] Output: Provide preview images and options to the user

[0643] Step 9:

[0644] Work provided

[0645] The server stores the generated work in a database and generates a download link, which is then sent to the user, who can then download or purchase the generated work.

[0646] Input: Generated digital artwork

[0647] Output: Download link and notification

[0648] This allows for the rapid creation and effective provision of customized digital artworks that reflect the user's emotions.

[0649] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0650] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0652] [Second embodiment]

[0653] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0654] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0655] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0657] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0659] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0660] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0661] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0662] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0663] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0664] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0665] System Overview

[0666] The present invention relates to a digital art generation system, and in particular to an embodiment of a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[0667] Artist registration and work submission

[0668] 1. Artist registration

[0669] The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[0670] The server receives the entered information and stores it in a database.

[0671] The server will send a registration confirmation email to the artist to notify them that registration is complete.

[0672] 2. Providing works

[0673] The artist logs into the platform.

[0674] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[0675] The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[0676] The server adds the artwork file to the training dataset of the generative model and retrains the generative model.

[0677] User requests and creation

[0678] 1. User Login

[0679] A user visits the platform's login page and enters their email address and password.

[0680] The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[0681] 2. Work Request

[0682] User clicks the "New Request" button from the dashboard.

[0683] The user inputs the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[0684] If the user has the option to select a particular artist, they do so.

[0685] 3. Request Processing

[0686] The user clicks the "Submit Request" button.

[0687] The server receives the request, generates a request ID and saves it in the database.

[0688] The server forwards the request to the generative model, which generates the artwork in the specified style.

[0689] 4. AI-generated artwork

[0690] The server provides the request content to the generative model to start processing.

[0691] A generative model generates artwork in the style of a specific artist based on a user request.

[0692] The server receives the generated work and stores it in a database.

[0693] 5. Providing works

[0694] The server generates a URL for the generated work and associates it with the user request ID.

[0695] The device will notify the user that "generation is complete" and provide a download link.

[0696] The user clicks on the provided link to download the generated work.

[0697] Specific examples

[0698] For example, Artist A uploads a work to the platform, and the work is trained by the AI ​​model. User B logs in and requests, "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the AI ​​model, which generates a work based on Artist A's style. The generated work is saved by the server and notified to User B. User B can then click the download link to retrieve the work.

[0699] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[0700] The processing flow will be explained below.

[0701] Artist registration and work submission processing

[0702] Artist Registration

[0703] Step 1:

[0704] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[0705] Step 2:

[0706] The server receives the entered information and stores it in a database.

[0707] Step 3:

[0708] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[0709] Work provided

[0710] Step 1:

[0711] The artist logs into the platform.

[0712] Step 2:

[0713] The artist selects a menu item to upload a new work file.

[0714] Step 3:

[0715] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[0716] Step 4:

[0717] The server validates the uploaded file (checks the file format, size, etc.).

[0718] Step 5:

[0719] The server stores successfully validated files in the database and registers their metadata (e.g. title, description, tags).

[0720] Step 6:

[0721] The server adds the newly saved artwork file to the training dataset of the generative model and begins the retraining process of the generative model.

[0722] Handling user requests and creations

[0723] User Login

[0724] Step 1:

[0725] A user visits the platform's login page and enters their email address and password.

[0726] Step 2:

[0727] The server performs the authentication process and, if the entered information is correct, redirects the user to the dashboard.

[0728] Work request

[0729] Step 1:

[0730] User clicks the "New Request" button from the dashboard.

[0731] Step 2:

[0732] If the user enters the request in text format and selects a specific artist, set that option.

[0733] Step 3:

[0734] The user clicks the "Submit Request" button.

[0735] Request Processing

[0736] Step 1:

[0737] The server receives the request.

[0738] Step 2:

[0739] The server generates a request ID and stores it in the database along with the request content.

[0740] Step 3:

[0741] The server forwards the request ID and request content to the generated model.

[0742] Artwork generation by AI

[0743] Step 1:

[0744] The server provides the request content to the generative model to start processing.

[0745] Step 2:

[0746] A generative model analyzes the user's request and generates a work in the style of a specific artist.

[0747] Step 3:

[0748] The server receives the generated work and stores it in a database.

[0749] Work provided

[0750] Step 1:

[0751] The server generates a URL for the generated work and associates it with the request ID.

[0752] Step 2:

[0753] The device will notify the user that "generation is complete" and provide a download link.

[0754] Step 3:

[0755] The user clicks on the provided link to download the generated work.

[0756] In this way, customized digital artwork that reflects the artist's style is quickly provided based on the user's request.

[0757] Example 1

[0758] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] Conventional digital art generation systems have struggled to efficiently generate works that reflect an artist's style. Furthermore, there are limited means for users to quickly obtain customized works based on their desired style and content. Therefore, there is a need for a digital art generation and distribution system that satisfies both artists and users.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0761] In this invention, the server includes: a means for a work provider to register on the dedicated platform and input necessary information; a means for the server to receive the input information and save it in a database; a means for the server to send a registration confirmation notification and notify the completion of registration; a means for the work provider to log in to the platform and upload a work file; a means for the server to verify the uploaded work file and save it in a database; a means for the server to add the saved file to a training dataset for a generative model and perform re-training; a means for a user to log in to the platform and input a request; a means for the server to receive the input request and forward it to a generative model to generate a work in a specific style; a means for the server to receive the generated work and save it in a database; and a means for the terminal to notify the user that the generated work has been provided. This enables customized works that reflect the artist's style to be quickly generated and provided.

[0762] "Artwork provider" refers to an artist who registers and provides their own work on the digital art generation platform.

[0763] "Platform" means a web application or software that allows Work Providers to register and upload Works and Users to submit Requests.

[0764] "Required information" refers to basic personal information such as name, email address, and password that the work provider enters when registering on the dedicated platform.

[0765] "Server" refers to a computing device that receives and processes input data from external clients, stores it in a database, validates the data, and launches and manages generative models.

[0766] "Database" refers to a software management system that systematically stores information received by the server.

[0767] "Registration confirmation notification" refers to an email sent by the server to confirm the information registered by the work provider on the dedicated platform.

[0768] "Login" refers to the operation by which work providers and users complete the authentication procedures required to access the dedicated platform.

[0769] "Work files" are image files in JPEG or PNG format that are treated as digital art.

[0770] "Verification" refers to the process in which the server checks whether the uploaded work file is in the specified format and size.

[0771] A "generative model" is an algorithm or program that uses machine learning techniques to generate new digital art from input data.

[0772] A "training dataset" is a collection of original artwork data that a generative model needs to generate new artworks.

[0773] "Retraining" is the process of retraining a generative model by adding new data to improve the model's performance.

[0774] A "request" is a request that a user inputs in text form the content and style of the work they wish to create and sends it to the server.

[0775] "Generated Work" means digital art generated by a generative model in a particular style based on a user request.

[0776] "Providing" means that the server notifies and distributes the generated work to the user in a usable form.

[0777] This invention relates to a digital art generation system, and specifically describes a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[0778] System Overview

[0779] This system consists of a dedicated platform, a server, a database, a generative model, and a user interface. The dedicated platform is implemented as a web application and can be accessed by content providers and users.

[0780] Hardware and software used

[0781] Server: A server with high-performance computing resources (e.g., AWS EC2, Google Cloud Compute Engine)

[0782] Database: Relational database such as MySQL or PostgreSQL

[0783] Generative models: Generative models using machine learning frameworks such as TensorFlow and PyTorch (e.g., StyleGAN)

[0784] Authentication system: User authentication using OAuth and JWT

[0785] Notification system: SMTP server, WebSocket, Firebase Cloud Messaging (FCM)

[0786] Artist registration and work submission

[0787] 1. The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[0788] Example: Open your browser, access the "Register" page, and enter the required information.

[0789] 2. The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[0790] Example: The server receives form data, validates it using Python's validation library, and saves it to MySQL.

[0791] 3. The server sends a registration confirmation notification to the artist via the SMTP server, notifying them that registration is complete.

[0792] Example: Send a confirmation email using the SMTP client library.

[0793] 4. The user (artist) logs in to the platform, selects and uploads the work file.

[0794] Example: Select an image file (JPEG, PNG) from the file selection dialog and click the "Upload" button.

[0795] 5. The server validates the uploaded file (checks the format and size) and stores files that pass validation in the database and storage.

[0796] Example: Use the PIL library to check the format of an image file and save it if there are no problems.

[0797] 6. The server adds the saved file to the training dataset for the generative model and starts retraining the generative model.

[0798] Example: Run a Python script to add new training data to a generative model and retrain it.

[0799] User requests and creation

[0800] 1. The user visits the platform's login page and enters their email address and password.

[0801] Example: Open the "Login" page in your browser and fill in the login form.

[0802] 2. The server authenticates the user and, if successful, displays the dashboard page.

[0803] Example: Verify user information using the authentication API and issue a JWT token.

[0804] 3. The user clicks the "New Request" button and enters the request in text format (e.g., "I want you to draw a scene with a cat at the seaside"), and optionally selects a specific artist.

[0805] Example: Click the "New Request" button on the dashboard and enter the request details.

[0806] 4. The user clicks the "Send Request" button to send the request to the server.

[0807] Example: Check the form and press the "Submit Request" button.

[0808] 5. The server receives the request, generates a request ID, and saves it in the database.

[0809] Example: Save the request contents to the database using the INSERT statement.

[0810] 6. The server forwards the request to the generative model, which generates the artwork in the specified style.

[0811] Example: Call the Generative Model API and send the request in JSON format.

[0812] 7. The generative model generates artwork in a specific style based on the request.

[0813] Example: A generative model generates an image based on a request.

[0814] 8. The server receives the generated work and stores it in a database.

[0815] Example: Save the received image file and save its URL in a database.

[0816] 9. The device will notify the user that "Generation is complete" and provide a download link for the work.

[0817] Example: Using WebSocket to send real-time notifications and display links.

[0818] 10. The user clicks on the provided link to download the generated work.

[0819] Example: Click on a link and download the image via your browser.

[0820] Specific examples

[0821] For example, an artist uploads a new piece of work to the platform, which is then trained by the generative model. A user then logs in and submits a request, such as "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the generative model, which then generates a work based on the artist's style. The generated work is then saved on the server and notified to the user. The user can then download the work by clicking on the provided link.

[0822] Prompt Sentence Examples

[0823] Example prompt 1: "Please draw a scene with a cat at the seaside in Artist A's style."

[0824] Example prompt 2: "Add a sunset to an autumn mountain scene" in the style of Artist B.

[0825] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[0826] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0827] Step 1:

[0828] The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[0829] Input: Name, Email Address, Password

[0830] Output: Information sent to the server

[0831] Specific actions: The artist fills out the form in the browser and clicks the "Register" button.

[0832] Step 2:

[0833] The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[0834] Input: Artist registration information

[0835] Output: Save result to database (success / failure)

[0836] Specific operation: The server checks the received data using a validation library and saves it to MySQL using an INSERT statement.

[0837] Step 3:

[0838] The server sends a registration confirmation notice to the artist using an SMTP server, notifying him or her that registration has been completed.

[0839] Input: Contents of registration confirmation email (artist information)

[0840] Output: Confirmation email sending result (success / failure)

[0841] Specific operation: The server uses an SMTP client to send a confirmation email to the artist.

[0842] Step 4:

[0843] The user (artist) logs in to the platform, selects and uploads the work file.

[0844] Input: Email address, password, work file

[0845] Output: The file is sent to the server.

[0846] Specific operation: The artist enters their email address and password into the browser form, logs in, and then uploads the artwork file.

[0847] Step 5:

[0848] The server validates the uploaded files (checks the format and size) and stores the files that pass the validation in the database and storage.

[0849] Input: Work file

[0850] Output: Verification result, save result (success / failure)

[0851] Specific operation: Uses the PIL library to check the format and size of the image file, and if correct, saves it to the file system and database.

[0852] Step 6:

[0853] The server adds the saved file to the training dataset of the generative model and starts retraining.

[0854] Input: New work file

[0855] Output: Retraining started (success / failure)

[0856] What happens: The server runs a Python script to add new data to a generative model (e.g., StyleGAN) and retrain it.

[0857] Step 7:

[0858] A user visits the platform's login page and enters their email address and password.

[0859] Input: Email address, password

[0860] Output: Authentication result (success / failure)

[0861] Specific behavior: The user fills in the login form in the browser and clicks the "Login" button.

[0862] Step 8:

[0863] The server performs user authentication processing, and if successful, displays the dashboard page.

[0864] Input: Email address, password

[0865] Output: Token issued, dashboard page displayed

[0866] Specific operation: Uses the authentication API to verify user information, issue a JWT token, and display the dashboard page.

[0867] Step 9:

[0868] The user clicks the "New Request" button, enters the request in text format, and optionally selects a specific artist.

[0869] Input: Request details, select specific artist

[0870] Output: The request is sent to the server

[0871] Specific operation: The user clicks the "New Request" button on the dashboard and enters the request details.

[0872] Step 10:

[0873] The user clicks the "Send Request" button to send the request to the server.

[0874] Input: Request details, select specific artist

[0875] Output: The request is sent to the server

[0876] Specific operation: The user checks the request details and presses the "Submit Request" button.

[0877] Step 11:

[0878] The server receives the request, generates a request ID and saves it in the database.

[0879] Input: Request content

[0880] Output: Request ID, result saved to database (success / failure)

[0881] Specific operation: The server saves the request content in the database using an INSERT statement and generates a request ID.

[0882] Step 12:

[0883] The server forwards the request to the generative model, which generates the artwork in the specified style.

[0884] Input: Request details, Request ID

[0885] Output: The generated work

[0886] Specific operation: Makes an API call to the generative model and sends the request content in JSON format.

[0887] Step 13:

[0888] A generative model generates artwork in a specific style based on a request.

[0889] Input: Request content

[0890] Output: The generated work

[0891] Specific operation: The generative model generates digital art in response to user requests.

[0892] Step 14:

[0893] The server receives the generated work and stores it in a database.

[0894] Input: Generated artwork

[0895] Output: Save result to database (success / failure)

[0896] Specific operation: Save the received image file and save its URL in the database.

[0897] Step 15:

[0898] The device will notify the user that "creation is complete" and provide a download link for the work.

[0899] Input: URL of the generated work

[0900] Output: User notification, download link

[0901] Specific behavior: Uses WebSocket to send real-time notifications and display links.

[0902] Step 16:

[0903] The user clicks on the provided link to download the generated work.

[0904] Input: Download link

[0905] Output: Download the generated work

[0906] What happens: Click the link and your browser will download the work file.

[0907] (Application example 1)

[0908] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0909] Demand for digital art is growing in modern society, especially for customized art based on the style of a specific artist. However, existing systems lack sufficient means for users to quickly generate and acquire art in a specific artist's style, potentially resulting in a poor user experience. Furthermore, artists lack efficient ways to monetize their work on digital platforms. Given this situation, there is a need for an efficient system that allows users to instantly generate, purchase, and download customized art using a generative AI model that has learned the style of a specific artist.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0911] In this invention, the server includes: means for a work provider to register on a dedicated platform and upload work data; means for the server to store the uploaded work data in a database and train a generative model; means for a user to input a work generation request in text format; means for the server to cause the generative model to generate a work in a specific style based on the user's request; means for the server to store the generated work and provide it to the user; and means for a user to request digital art generated in the style of a specific artist from a mobile device and instantly preview, purchase, and download it. This allows users to quickly and efficiently generate and obtain customized art in the style of a specific artist, and also allows artists to gain new revenue opportunities using their own style.

[0912] "Artwork provider" means a person or legal entity that registers their information on a dedicated platform and uploads artwork data for the purpose of providing artwork.

[0913] "Platform" means a specific digital environment or web service used by Artwork Providers to register and upload Artworks and by Users to request and download Artworks.

[0914] "Artwork Data" means the digital file format (e.g., JPEG, PNG, etc.) of the artwork that is used as a dataset to train a generative model.

[0915] "Server" refers to a computer system that operates and manages the dedicated platform, stores data in a database, and generates and provides artworks using generative models.

[0916] The "database" is a system for storing and managing uploaded artwork data, generated artworks, and user request information.

[0917] A "generative model" is an artificial intelligence (AI) model that learns the style of a particular artist and generates new artwork based on user requests.

[0918] "User" means a person who accesses the dedicated platform, inputs a request for artwork creation in text format, and downloads the generated artwork.

[0919] The "means for inputting a request for creation of a work in text format" refers to an interface for a user to input the request content in text format in order to request the creation of a specific artwork on the dedicated platform.

[0920] A "particular style" refers to the characteristic expression techniques and design elements of a particular artist, and serves as a standard for generating artwork based on that.

[0921] "Mobile device" refers to a portable electronic device such as a smartphone or tablet that allows users to access the platform and request and download artworks.

[0922] "Preview" is a feature that allows you to check the generated artwork before purchasing or downloading it.

[0923] "Purchase" means that the user pays a certain amount of money to acquire ownership of the generated artwork.

[0924] "Downloading" refers to the act of saving a digital file of the generated artwork to a user's device via the Internet.

[0925] The present invention relates to a system that uses a generative AI model that has learned the style of a specific artist to quickly generate and provide digital art in accordance with a user's request. Specific embodiments are described below.

[0926] System Overview

[0927] The system consists of a set of platforms where art providers can register their artworks and provide datasets for training generative AI models, and users can generate and acquire artworks in the style of specific artists. The main components include a server, database, generative AI model, and user interface.

[0928] Hardware and software used

[0929] Hardware:

[0930] Server: A high-performance computer for processing data and running generative models.

[0931] Mobile Device: The device, such as a smartphone or tablet, that a user uses to request and download art.

[0932] software:

[0933] Python: The main programming language.

[0934] FastAPI: API server framework.

[0935] SQLModel: ORM for database operations.

[0936] PIL: Image manipulation library.

[0937] PyTorch: A deep learning library.

[0938] Transformers: Using CLIP models.

[0939] Specific steps

[0940] Registration of work providers

[0941] 1. The creator of the work accesses the dedicated platform and enters the required information such as name, email address, and password on the registration page.

[0942] 2. The server receives the input information and stores it in a database.

[0943] 3. The server sends a registration confirmation email to notify the work provider that registration is complete.

[0944] 4. The artwork provider logs into the system and uploads the artwork data file (e.g., JPEG, PNG, etc.).

[0945] 5. The server validates the format and size of the uploaded file, stores it in the database, and adds it to the training dataset for the generative model for retraining.

[0946] User requests and submissions

[0947] 1. The user accesses the login page of the dedicated platform and enters their email address and password for authentication.

[0948] 2. The user enters a text request for art creation, such as "Please draw a scene with a cat at the beach."

[0949] 3. The server receives the request, generates a request ID and saves it in the database.

[0950] 4. The server provides the request to the generative model, which then begins generating the artwork.

[0951] 5. The generative model generates a work based on the user's request, based on the style of a specific artist, and returns the result to the server.

[0952] 6. The server stores the generated work in a database and generates a link.

[0953] 7. The device will notify the user that "Generation is complete" and provide a download link.

[0954] 8. The user can click on the provided link to download the generated artwork.

[0955] Examples of prompt statements

[0956] An example of a user-entered prompt would be a text request such as "Please draw a scene with a cat at the beach."

[0957] This system allows users to easily request customized art by specifying the style of a specific artist, and also allows artists to utilize their own style to gain new revenue opportunities.

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] The server receives the registration information of the work provider.

[0961] Input: Name of the creator, email address, password

[0962] How it works: The server stores the information you entered in a database and sends you a confirmation email.

[0963] Output: Registration completion notification

[0964] Step 2:

[0965] The server receives artwork data uploaded from the artwork provider.

[0966] Input: Artwork file (JPEG, PNG, etc.)

[0967] Action: The server validates the file format and size, saves it to the database, adds it to the training dataset of the generative model, and retrains it.

[0968] Output: File verification results, re-learning completion notification

[0969] Step 3:

[0970] The user accesses the dedicated platform and logs in.

[0971] Input: User's email address, password

[0972] How it works: The server handles authentication and, if successful, redirects the user to the dashboard.

[0973] Output: Login success message, dashboard

[0974] Step 4:

[0975] The user inputs a work creation request in text format.

[0976] Input: Request for creation (e.g., "I want you to draw a scene with a cat at the seaside")

[0977] How it works: The server receives the request, generates a request ID, and saves it in the database.

[0978] Output: Request ID, request save completion notification

[0979] Step 5:

[0980] The server provides the request to the generative model and begins generating the artwork.

[0981] Input: User request, style model of a specific artist

[0982] How it works: The generative model generates artwork in a specific style based on the request and the style model.

[0983] Output: Generated artwork

[0984] Step 6:

[0985] The server stores the generated work in a database and generates a link.

[0986] Input: Generated artwork, request ID

[0987] How it works: The server stores the generated work in a database, associates it with a request ID, and generates a download link.

[0988] Output: Download link

[0989] Step 7:

[0990] The device will notify the user of the completion of generation and provide a download link.

[0991] Input: Download link

[0992] What it does: The device notifies the user that "Generation is complete" and provides a download link for the generated work.

[0993] Output: Generation completion notification, download link

[0994] Step 8:

[0995] The user clicks on the provided link to download the work.

[0996] Input: Download link

[0997] How it works: The user clicks on the link and the generated artwork downloads to their device.

[0998] Output: Downloaded artwork

[0999] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1000] System Overview

[1001] This invention relates to a digital art generation system, and in particular to an embodiment of a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[1002] Artist registration and work submission

[1003] Artist Registration

[1004] 1. The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[1005] 2. The server receives the entered information and stores it in a database.

[1006] 3. The server automatically sends a registration confirmation email to the artist, notifying them that registration is complete.

[1007] Work provided

[1008] 1. Artist logs in to the platform.

[1009] 2. The artist selects the menu to upload a new work file.

[1010] 3. The artist uploads the artwork file (e.g. JPEG, PNG, etc.) to the platform.

[1011] 4. The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[1012] 5. The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[1013] User requests and creation

[1014] User Login

[1015] 1. The user visits the platform's login page and enters their email address and password.

[1016] 2. The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[1017] Work request

[1018] 1. User clicks the "New Request" button from the dashboard.

[1019] 2. The user enters the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[1020] 3. If there is an option for users to select a specific artist, set that option.

[1021] 4. The server uses an emotion engine to analyze emotions from the text entered by the user.

[1022] 5. The server adjusts and optimizes the request content based on the emotion analysis results obtained from the emotion engine.

[1023] Request Processing

[1024] 1. The server provides the tailored request content to the generative model and starts processing.

[1025] 2. The generative model analyzes the user's request and generates a work in the style of a specific artist.

[1026] 3. The server receives the generated work and stores it in a database.

[1027] Work provided

[1028] 1. The server generates a URL for the generated work and associates it with the request ID.

[1029] 2. The device notifies the user that "Generation is complete" and provides a download link.

[1030] 3. The user clicks on the provided link to download the generated work.

[1031] Specific examples

[1032] For example, artist A uploads his or her work to the platform, and the work is trained by the generative model. User B logs in and inputs a request, saying, "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and uses an emotion engine to analyze User B's emotions. If an emotion such as "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to User B. User B can then click the download link to obtain the generated work.

[1033] This system allows users to quickly obtain customized digital artwork based on their emotions, and also provides artists with new revenue opportunities.

[1034] The processing flow will be explained below.

[1035] Artist registration and work submission processing

[1036] Artist Registration

[1037] Step 1:

[1038] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[1039] Step 2:

[1040] The server receives the entered information and stores it in a database.

[1041] Step 3:

[1042] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[1043] Work provided

[1044] Step 1:

[1045] The artist logs into the platform.

[1046] Step 2:

[1047] The artist selects a menu item to upload a new work file.

[1048] Step 3:

[1049] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1050] Step 4:

[1051] The server validates the uploaded file (file type, size, etc.) and stores it in the database.

[1052] Step 5:

[1053] The server registers the metadata (e.g., title, description, tags) of the artwork files stored in the database.

[1054] Step 6:

[1055] The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[1056] Handling user requests and creations

[1057] User Login

[1058] Step 1:

[1059] A user visits the platform's login page and enters their email address and password.

[1060] Step 2:

[1061] The server handles the authentication process and redirects the user to the dashboard if the entered information is correct.

[1062] Work request

[1063] Step 1:

[1064] User clicks the "New Request" button from the dashboard.

[1065] Step 2:

[1066] If the user enters the request in text format and selects a specific artist, set that option.

[1067] Step 3:

[1068] The user clicks the "Submit Request" button.

[1069] Request Processing

[1070] Step 1:

[1071] The server receives the request, generates a request ID and saves it in the database.

[1072] Step 2:

[1073] The server uses an emotion engine to analyze the user's emotion from the text included in the request.

[1074] Step 3:

[1075] The server optimizes the request content and makes appropriate adjustments based on the analysis results of the emotion engine.

[1076] Artwork generation by AI

[1077] Step 1:

[1078] The server provides the tailored request content to the generative model to initiate the generation process.

[1079] Step 2:

[1080] The generative model generates artwork in the style of a specific artist based on tailored requests.

[1081] Step 3:

[1082] The server receives the generated work and stores it in a database.

[1083] Work provided

[1084] Step 1:

[1085] The server generates a URL for the generated work and associates it with the request ID.

[1086] Step 2:

[1087] The device will notify the user that "generation is complete" and provide a download link.

[1088] Step 3:

[1089] The user clicks on the provided link to download the generated work.

[1090] Specific examples

[1091] Specific examples of artist registration

[1092] Step 1:

[1093] Artist A accesses the dedicated platform, enters the necessary information, and completes registration.

[1094] Step 2:

[1095] The server saves Artist A's information in a database and automatically sends a registration confirmation email.

[1096] Examples of work provided

[1097] Step 1:

[1098] Artist A logs in to the platform.

[1099] Step 2:

[1100] Artist A uploads a new work and the server validates the file.

[1101] Step 3:

[1102] The server stores the successfully verified file in the database and registers the metadata.

[1103] Step 4:

[1104] The server provides the artwork file to the generative model, and the AI ​​model begins retraining.

[1105] Request and creation examples

[1106] Step 1:

[1107] User B logs in to the platform and fills in the request form, saying, "I would like you to draw a scene of a cat standing with a sunset in the background."

[1108] Step 2:

[1109] The server receives the request and performs emotion analysis using an emotion engine.

[1110] Step 3:

[1111] The server optimizes the request based on the analysis results and provides it to the generative model to start the generation process.

[1112] Step 4:

[1113] The generative model generates artwork in the style of a particular artist, which is then stored on a server.

[1114] Step 5:

[1115] The device notifies User B and provides a download link.

[1116] Step 6:

[1117] User B clicks the link to download the generated work.

[1118] In this way, users can quickly obtain customized digital artwork based on their emotions, and artists can gain new revenue opportunities.

[1119] Example 2

[1120] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1121] While conventional digital art generation systems have the technology to generate artworks using the artist's style, it has been difficult to generate customized artworks that reflect the user's emotions. Furthermore, because the user's input request is passed directly to the generative model, the artwork generated does not necessarily reflect the user's intentions or emotions. This makes it difficult to increase user satisfaction.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1123] In this invention, the server includes a means for performing sentiment analysis based on the user's request and optimizing the request content, a means for causing the generative model to generate a work in a specific style based on the user's request, and a means for generating a URL for the generated work and associating it with the request ID, thereby generating a customized artwork that reflects the user's sentiment and increasing user satisfaction.

[1124] "Work provider" refers to an individual or organization that registers on the dedicated platform and provides work data used to generate digital art.

[1125] "Dedicated Platform" means a web-based system through which work providers upload work data and users input work generation requests.

[1126] A "server" is a computer system that processes data received from work providers and users and performs various functions for work generation.

[1127] "Artwork data" is a general term for image files and related data uploaded by the creator of the work to train a generative model of digital art.

[1128] A "database" is a system for systematically storing uploaded work data, created works, user request information, etc., and for efficiently managing and searching such data.

[1129] A "generative model" is a machine learning model that learns the style of a particular artist and generates new artwork based on user requests.

[1130] "User" means an individual or entity that requests the creation of digital art using the Dedicated Platform.

[1131] The "request content" is text data that describes the specific requirements for the digital art that the user wants to create.

[1132] "Sentiment analysis" is the process of detecting emotions from the request content entered by the user and optimizing the request content based on those emotions.

[1133] "URL" is the address on the Internet for accessing the created work, and is provided to the user by the server.

[1134] A "request ID" is an identification number assigned to uniquely identify each request.

[1135] MODE FOR CARRYING OUT THE INVENTION

[1136] This invention relates to a system for generating digital art, specifically a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[1137] System program processing

[1138] The system uses the following hardware and software:

[1139] Server: A central computer system that stores and processes work data and request data. Data is managed using an SQL or NoSQL database.

[1140] Terminal: A device used by artists and users to access the dedicated platform using a web browser.

[1141] Generative models: For example, using machine learning models such as Stable Diffusion to generate artwork in the style of a particular artist.

[1142] Sentiment engine: Analyzes emotions from user input text using natural language processing techniques, such as Transformer-based models (e.g., BERT).

[1143] Artist registration and work submission

[1144] Artists access the dedicated platform and register by entering required information such as their name, email address, and password. The server receives this information and stores it in a database. A registration confirmation email is automatically sent to the artist, who clicks on a link to complete the registration. After logging in, the artist uploads their artwork data. The server then verifies the uploaded data and adds it to the training dataset for the generative model.

[1145] Training a generative model

[1146] Based on the uploaded artwork data, the generative model learns the style of a specific artist, which is then stored in a database and used to generate artwork.

[1147] User requests and creation

[1148] Users access the dedicated platform and log in by entering their login information. After logging in, they can enter their request in text format and specify a specific artist. The server receives this request and performs emotional analysis using an emotion engine.

[1149] For example, if the prompt is "Please draw a scene of a cat standing by the sea in the sunset. Please create a calm atmosphere," the emotion engine will detect the emotion "calm" and optimize the request. Based on the optimized request, the generative model will generate a work, which will then be stored in a database by the server.

[1150] Providing generated works

[1151] The server generates a URL for the generated work and associates it with the request ID. A notification containing this URL is sent to the device, and the user can click the download link to download the work.

[1152] This system allows users to quickly obtain digital artworks that are customized to their emotions, and also allows artists to efficiently offer their works and gain new revenue opportunities.

[1153] Specific examples

[1154] For example, an artist uploads their work to the platform, and the work is trained by the generative model. A user logs in and inputs a request, such as "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and analyzes the user's emotions using an emotion engine. If the emotion of "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to the user. The user can obtain the generated work by clicking a download link.

[1155] Specific embodiments of the present invention allow users to easily obtain customized digital artwork based on their emotions.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1: Artist Registration

[1158] The artist accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password. The server receives the entered information and stores it in a database. The server automatically sends a registration confirmation email, and the artist completes the registration by clicking the link in the confirmation email.

[1159] Input: Artist name, email address, password

[1160] Output: The artist's information is saved in the database and a confirmation email is sent.

[1161] Step 2: Submit your work

[1162] An artist logs in to the platform and selects a menu to upload their artwork file. Once the artist uploads the artwork file (e.g., JPEG, PNG, etc.), the server validates the uploaded file and stores it in a database. The server then adds the artwork file to the training dataset for the generative model and begins the training process.

[1163] Input: Artist's artwork file

[1164] Output: Work files stored in a database, training dataset for generative models

[1165] Step 3: User Login

[1166] The user accesses the platform's login page and enters their email address and password. The server performs authentication and, if successful, redirects the user to the dashboard. The device displays a message to the user indicating successful login.

[1167] Input: Email address, Password

[1168] Output: Login successful message

[1169] Step 4: Submit a request

[1170] The user clicks the "New Request" button on the dashboard and enters the request in text format (e.g., "I want you to paint a scene of a cat standing with a sunset in the background"). A specific artist can be selected if necessary. The server receives and stores the input. The server uses an emotion engine to analyze the emotion from the request and optimizes the request based on the results.

[1171] Input: Request content in text format

[1172] Output: Sentiment analysis results, optimized request content

[1173] Step 5: Request Processing

[1174] The server provides the tailored request to the generative model to initiate processing. The generative model analyzes the request and generates a work based on the style of the specific artist. The server receives the generated work and stores it in a database.

[1175] Input: Optimized request content

[1176] Output: Generated artwork file

[1177] Step 6: Submit your work

[1178] The server generates a URL for the generated work and associates it with the request ID. The device notifies the user that "generation is complete" and provides a download link. The user clicks the provided link to download the generated work.

[1179] Input: Generated artwork file

[1180] Output: Download link for the work, Download work

[1181] By following these steps, users can easily obtain customized digital artworks, and this system is beneficial for both artists and users.

[1182] (Application example 2)

[1183] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1184] Conventional digital art creation systems generate artwork based solely on a simple text request entered by the user, making it difficult to provide customized artwork that reflects the user's emotions and mood. Therefore, there is a need to effectively create and provide digital artwork that truly resonates with users. The present invention solves this problem and provides a more personalized digital art creation system that reflects the user's emotions.

[1185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1186] In this invention, the server includes a means for generating a work based on the emotion analysis result, a means for presenting a preview image of the generated work and offering the option of purchasing or downloading, and a means for storing the generated work and providing it to the user, thereby enabling the generation and provision of a customized digital artwork that reflects the emotion of the user.

[1187] A "work provider" is an entity that uploads their own work data to the dedicated platform.

[1188] The "dedicated platform" is an online system where creators can register and upload their work data and train the generative model.

[1189] "Work data" refers to digital content such as image files and text data that work providers upload to the platform.

[1190] The "server" is a central processing unit that stores artwork data in a database and trains the generative model.

[1191] A "database" is a storage device for storing uploaded work data and created works.

[1192] A "generative model" is an artificial intelligence model that learns from uploaded artwork data and generates digital artworks based on user requests.

[1193] "User" is the entity that enters a request to generate a digital artwork and receives the results.

[1194] An “artwork generation request” is a textual instruction that a user inputs into a Generative Model to generate a particular artwork.

[1195] "Emotion analysis results" are analysis results based on requests and emotions entered by users, and are data used to instruct the generative model.

[1196] A "preview image" is an image that displays a part or the whole of a generated digital artwork for the user to check.

[1197] "Purchase or Download Option" means the option that a user can choose to purchase or download the generated digital artwork for free.

[1198] "Generated Works" are digital artworks created by generative models based on user requests and sentiment analysis.

[1199] The present invention provides a specific system for generating and providing customized digital artworks that reflect a user's emotions. The system includes a means for an artwork provider to register and upload artwork data to a dedicated platform, a means for a server to store the uploaded artwork data in a database and train a generative model, a means for a user to input an artwork generation request, a means for the server to cause the generative model to generate an artwork based on the user's request and the results of emotion analysis, a means for presenting a preview image of the generated artwork and offering the option of purchasing or downloading, and a means for the server to store the generated artwork and provide it to the user.

[1200] Hardware and Software Configuration

[1201] server

[1202] The servers are equipped with high-performance hardware such as Intel Xeon processors and NVIDIA GPUs, and run generative and sentiment analysis models using software libraries such as Python, TensorFlow, and PyTorch.

[1203] The database can be a relational or NoSQL database such as PostgreSQL or MongoDB.

[1204] User terminal

[1205] User devices are smartphones or PCs, and access the platform using a dedicated application or web browser.

[1206] Smartphone applications are implemented as iOS or Android apps.

[1207] Sentiment Analysis and Generative Models

[1208] Emotion Analysis: Emotion recognition uses Python and the EmotionRecognition library to analyze emotional states from data input in text format.

[1209] Generative Modeling: Generative modeling uses Hugging Face's Transformers library and is performed by an AI model trained in the style of a specific artist.

[1210] Operating Procedure

[1211] Artist registration and work submission

[1212] 1. The artist registers an account on the dedicated platform and uploads their work data.

[1213] 2. The server verifies the uploaded artwork data, stores it in a database, and trains the generative model.

[1214] User requests and creation

[1215] 1. The user logs in to the dedicated app and enters a request to create a work in text format.

[1216] 2. The server uses an emotion engine to analyze emotions from the user's input.

[1217] 3. Generate prompts based on the sentiment analysis results and provide instructions to the generative model.

[1218] 4. The generative model generates a digital artwork in a specific style based on the user's request and the results of sentiment analysis.

[1219] Product preview and offering

[1220] 1. The server generates a preview image of the created work and presents it to the user.

[1221] 2. Users can preview the work and choose to purchase it or download it for free.

[1222] 3. The server stores the generated work in a database and provides a download link.

[1223] Specific examples

[1224] For example, if a user inputs "I feel tired" and requests "I want a drawing of a scene with a cat standing with a sunset in the background," the server sends this to the emotion engine and generates the "tired" emotion as an analysis result.

[1225] User sentiment: "I feel tired"

[1226] User request: "I want you to draw a scene of a cat standing with a sunset in the background."

[1227] Generated prompt: "Create a tired themed artwork of a cat standing against a sunset background in the style of Artist A."

[1228] A prompt is generated. The generative model generates a work based on this prompt and provides it to the user. This system makes it possible to quickly and effectively generate and provide customized digital artworks that reflect the user's emotions.

[1229] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1230] Step 1:

[1231] Artist Registration

[1232] Artists access the dedicated platform and create an account by entering the required information, such as their name, email address, and password. The server receives the information entered and stores it in a database. A registration confirmation email is automatically generated and sent to the artist.

[1233] Input: Artist information such as name, email address, and password

[1234] Output: Artist information stored in the database and a confirmation email

[1235] Step 2:

[1236] Work provided

[1237] Artists log in to the platform and upload artwork files (JPEG, PNG, etc.) from their account management page. The server verifies the uploaded file format and size, stores it in a database, and adds the saved artwork data to the training dataset of the generative model, starting the model retraining process.

[1238] Input: Artist's work file

[1239] Output: Artwork data stored in a database and a retrained generative model

[1240] Step 3:

[1241] User Login

[1242] A user accesses the platform through a dedicated app or web browser and enters their email address and password. The server performs the authentication process, and if authentication is successful, redirects the user to the dashboard.

[1243] Input: User's email address and password

[1244] Output: Authentication result and redirect to dashboard

[1245] Step 4:

[1246] Enter your work request

[1247] Users can click the New Request button on the dashboard and enter a text-based production request, with the option to select a specific artist.

[1248] Input: User text request and artist selection

[1249] Output: The request data sent to the server

[1250] Step 5:

[1251] Emotion analysis

[1252] The server sends the request data to the emotion engine, which analyzes the user's input to determine the emotion. The emotion engine analyzes the text and identifies the dominant emotion (e.g., happy, sad, tired, etc.).

[1253] Input: User request text

[1254] Output: Emotion analysis results

[1255] Step 6:

[1256] Prompt Generation

[1257] The server creates prompts for the generative model based on the results of sentiment analysis and the user's request. For example, based on the request "I feel tired" and "A scene of a cat standing against a sunset," it generates a prompt such as "Create a tired-themed artwork of a cat standing against a sunset background in the style of Artist A."

[1258] Input: Sentiment analysis results and user requests

[1259] Output: Prompts for the generated model

[1260] Step 7:

[1261] Work generation

[1262] The server provides prompts to the generative model, which then generates the digital artwork. The generative model uses the learnings of a particular artist as a style to generate the digital artwork.

[1263] Input: Prompt for the generative model

[1264] Output: Generated digital artwork

[1265] Step 8:

[1266] Preview display

[1267] The server generates a preview image of the generated work and presents it to the user, who can then view the preview and select the option to purchase or download.

[1268] Input: Generated digital artwork

[1269] Output: Provide preview images and options to the user

[1270] Step 9:

[1271] Work provided

[1272] The server stores the generated work in a database and generates a download link, which is then sent to the user, who can then download or purchase the generated work.

[1273] Input: Generated digital artwork

[1274] Output: Download link and notification

[1275] This allows for the rapid creation and effective provision of customized digital artworks that reflect the user's emotions.

[1276] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1277] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1278] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1279] [Third embodiment]

[1280] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1281] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1282] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1283] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1284] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1285] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1286] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1287] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1288] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1289] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1290] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1291] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1292] System Overview

[1293] The present invention relates to a digital art generation system, and in particular to an embodiment of a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[1294] Artist registration and work submission

[1295] 1. Artist registration

[1296] The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[1297] The server receives the entered information and stores it in a database.

[1298] The server will send a registration confirmation email to the artist to notify them that registration is complete.

[1299] 2. Providing works

[1300] The artist logs into the platform.

[1301] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1302] The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[1303] The server adds the artwork file to the training dataset of the generative model and retrains the generative model.

[1304] User requests and creation

[1305] 1. User Login

[1306] A user visits the platform's login page and enters their email address and password.

[1307] The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[1308] 2. Work Request

[1309] User clicks the "New Request" button from the dashboard.

[1310] The user inputs the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[1311] If the user has the option to select a particular artist, they do so.

[1312] 3. Request Processing

[1313] The user clicks the "Submit Request" button.

[1314] The server receives the request, generates a request ID and saves it in the database.

[1315] The server forwards the request to the generative model, which generates the artwork in the specified style.

[1316] 4. AI-generated artwork

[1317] The server provides the request content to the generative model to start processing.

[1318] A generative model generates artwork in the style of a specific artist based on a user request.

[1319] The server receives the generated work and stores it in a database.

[1320] 5. Providing works

[1321] The server generates a URL for the generated work and associates it with the user request ID.

[1322] The device will notify the user that "generation is complete" and provide a download link.

[1323] The user clicks on the provided link to download the generated work.

[1324] Specific examples

[1325] For example, Artist A uploads a work to the platform, and the work is trained by the AI ​​model. User B logs in and requests, "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the AI ​​model, which generates a work based on Artist A's style. The generated work is saved by the server and notified to User B. User B can then click the download link to retrieve the work.

[1326] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[1327] The processing flow will be explained below.

[1328] Artist registration and work submission processing

[1329] Artist Registration

[1330] Step 1:

[1331] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[1332] Step 2:

[1333] The server receives the entered information and stores it in a database.

[1334] Step 3:

[1335] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[1336] Work provided

[1337] Step 1:

[1338] The artist logs into the platform.

[1339] Step 2:

[1340] The artist selects a menu item to upload a new work file.

[1341] Step 3:

[1342] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1343] Step 4:

[1344] The server validates the uploaded file (checks the file format, size, etc.).

[1345] Step 5:

[1346] The server stores successfully validated files in the database and registers their metadata (e.g. title, description, tags).

[1347] Step 6:

[1348] The server adds the newly saved artwork file to the training dataset of the generative model and begins the retraining process of the generative model.

[1349] Handling user requests and creations

[1350] User Login

[1351] Step 1:

[1352] A user visits the platform's login page and enters their email address and password.

[1353] Step 2:

[1354] The server performs the authentication process and, if the entered information is correct, redirects the user to the dashboard.

[1355] Work request

[1356] Step 1:

[1357] User clicks the "New Request" button from the dashboard.

[1358] Step 2:

[1359] If the user enters the request in text format and selects a specific artist, set that option.

[1360] Step 3:

[1361] The user clicks the "Submit Request" button.

[1362] Request Processing

[1363] Step 1:

[1364] The server receives the request.

[1365] Step 2:

[1366] The server generates a request ID and stores it in the database along with the request content.

[1367] Step 3:

[1368] The server forwards the request ID and request content to the generated model.

[1369] Artwork generation by AI

[1370] Step 1:

[1371] The server provides the request content to the generative model to start processing.

[1372] Step 2:

[1373] A generative model analyzes the user's request and generates a work in the style of a specific artist.

[1374] Step 3:

[1375] The server receives the generated work and stores it in a database.

[1376] Work provided

[1377] Step 1:

[1378] The server generates a URL for the generated work and associates it with the request ID.

[1379] Step 2:

[1380] The device will notify the user that "generation is complete" and provide a download link.

[1381] Step 3:

[1382] The user clicks on the provided link to download the generated work.

[1383] In this way, customized digital artwork that reflects the artist's style is quickly provided based on the user's request.

[1384] Example 1

[1385] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1386] Conventional digital art generation systems have struggled to efficiently generate works that reflect an artist's style. Furthermore, there are limited means for users to quickly obtain customized works based on their desired style and content. Therefore, there is a need for a digital art generation and distribution system that satisfies both artists and users.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1388] In this invention, the server includes: a means for a work provider to register on the dedicated platform and input necessary information; a means for the server to receive the input information and save it in a database; a means for the server to send a registration confirmation notification and notify the completion of registration; a means for the work provider to log in to the platform and upload a work file; a means for the server to verify the uploaded work file and save it in a database; a means for the server to add the saved file to a training dataset for a generative model and perform re-training; a means for a user to log in to the platform and input a request; a means for the server to receive the input request and forward it to a generative model to generate a work in a specific style; a means for the server to receive the generated work and save it in a database; and a means for the terminal to notify the user that the generated work has been provided. This enables customized works that reflect the artist's style to be quickly generated and provided.

[1389] "Artwork provider" refers to an artist who registers and provides their own work on the digital art generation platform.

[1390] "Platform" means a web application or software that allows Work Providers to register and upload Works and Users to submit Requests.

[1391] "Required information" refers to basic personal information such as name, email address, and password that the work provider enters when registering on the dedicated platform.

[1392] "Server" refers to a computing device that receives and processes input data from external clients, stores it in a database, validates the data, and launches and manages generative models.

[1393] "Database" refers to a software management system that systematically stores information received by the server.

[1394] "Registration confirmation notification" refers to an email sent by the server to confirm the information registered by the work provider on the dedicated platform.

[1395] "Login" refers to the operation by which work providers and users complete the authentication procedures required to access the dedicated platform.

[1396] "Work files" are image files in JPEG or PNG format that are treated as digital art.

[1397] "Verification" refers to the process in which the server checks whether the uploaded work file is in the specified format and size.

[1398] A "generative model" is an algorithm or program that uses machine learning techniques to generate new digital art from input data.

[1399] A "training dataset" is a collection of original artwork data that a generative model needs to generate new artworks.

[1400] "Retraining" is the process of retraining a generative model by adding new data to improve the model's performance.

[1401] A "request" is a request that a user inputs in text form the content and style of the work they wish to create and sends it to the server.

[1402] "Generated Work" means digital art generated by a generative model in a particular style based on a user request.

[1403] "Providing" means that the server notifies and distributes the generated work to the user in a usable form.

[1404] This invention relates to a digital art generation system, and specifically describes a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[1405] System Overview

[1406] This system consists of a dedicated platform, a server, a database, a generative model, and a user interface. The dedicated platform is implemented as a web application and can be accessed by content providers and users.

[1407] Hardware and software used

[1408] Server: A server with high-performance computing resources (e.g., AWS EC2, Google Cloud Compute Engine)

[1409] Database: Relational database such as MySQL or PostgreSQL

[1410] Generative models: Generative models using machine learning frameworks such as TensorFlow and PyTorch (e.g., StyleGAN)

[1411] Authentication system: User authentication using OAuth and JWT

[1412] Notification system: SMTP server, WebSocket, Firebase Cloud Messaging (FCM)

[1413] Artist registration and work submission

[1414] 1. The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[1415] Example: Open your browser, access the "Register" page, and enter the required information.

[1416] 2. The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[1417] Example: The server receives form data, validates it using Python's validation library, and saves it to MySQL.

[1418] 3. The server sends a registration confirmation notification to the artist via the SMTP server, notifying them that registration is complete.

[1419] Example: Send a confirmation email using the SMTP client library.

[1420] 4. The user (artist) logs in to the platform, selects and uploads the work file.

[1421] Example: Select an image file (JPEG, PNG) from the file selection dialog and click the "Upload" button.

[1422] 5. The server validates the uploaded file (checks the format and size) and stores files that pass validation in the database and storage.

[1423] Example: Use the PIL library to check the format of an image file and save it if there are no problems.

[1424] 6. The server adds the saved file to the training dataset for the generative model and starts retraining the generative model.

[1425] Example: Run a Python script to add new training data to a generative model and retrain it.

[1426] User requests and creation

[1427] 1. The user visits the platform's login page and enters their email address and password.

[1428] Example: Open the "Login" page in your browser and fill in the login form.

[1429] 2. The server authenticates the user and, if successful, displays the dashboard page.

[1430] Example: Verify user information using the authentication API and issue a JWT token.

[1431] 3. The user clicks the "New Request" button and enters the request in text format (e.g., "I want you to draw a scene with a cat at the seaside"), and optionally selects a specific artist.

[1432] Example: Click the "New Request" button on the dashboard and enter the request details.

[1433] 4. The user clicks the "Send Request" button to send the request to the server.

[1434] Example: Check the form and press the "Submit Request" button.

[1435] 5. The server receives the request, generates a request ID, and saves it in the database.

[1436] Example: Save the request contents to the database using the INSERT statement.

[1437] 6. The server forwards the request to the generative model, which generates the artwork in the specified style.

[1438] Example: Call the Generative Model API and send the request in JSON format.

[1439] 7. The generative model generates artwork in a specific style based on the request.

[1440] Example: A generative model generates an image based on a request.

[1441] 8. The server receives the generated work and stores it in a database.

[1442] Example: Save the received image file and save its URL in a database.

[1443] 9. The device will notify the user that "Generation is complete" and provide a download link for the work.

[1444] Example: Using WebSocket to send real-time notifications and display links.

[1445] 10. The user clicks on the provided link to download the generated work.

[1446] Example: Click on a link and download the image via your browser.

[1447] Specific examples

[1448] For example, an artist uploads a new piece of work to the platform, which is then trained by the generative model. A user then logs in and submits a request, such as "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the generative model, which then generates a work based on the artist's style. The generated work is then saved on the server and notified to the user. The user can then download the work by clicking on the provided link.

[1449] Prompt Sentence Examples

[1450] Example prompt 1: "Please draw a scene with a cat at the seaside in Artist A's style."

[1451] Example prompt 2: "Add a sunset to an autumn mountain scene" in the style of Artist B.

[1452] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[1453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1454] Step 1:

[1455] The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[1456] Input: Name, Email Address, Password

[1457] Output: Information sent to the server

[1458] Specific actions: The artist fills out the form in the browser and clicks the "Register" button.

[1459] Step 2:

[1460] The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[1461] Input: Artist registration information

[1462] Output: Save result to database (success / failure)

[1463] Specific operation: The server checks the received data using a validation library and saves it to MySQL using an INSERT statement.

[1464] Step 3:

[1465] The server sends a registration confirmation notice to the artist using an SMTP server, notifying him or her that registration has been completed.

[1466] Input: Contents of registration confirmation email (artist information)

[1467] Output: Confirmation email sending result (success / failure)

[1468] Specific operation: The server uses an SMTP client to send a confirmation email to the artist.

[1469] Step 4:

[1470] The user (artist) logs in to the platform, selects and uploads the work file.

[1471] Input: Email address, password, work file

[1472] Output: The file is sent to the server.

[1473] Specific operation: The artist enters their email address and password into the browser form, logs in, and then uploads the artwork file.

[1474] Step 5:

[1475] The server validates the uploaded files (checks the format and size) and stores the files that pass the validation in the database and storage.

[1476] Input: Work file

[1477] Output: Verification result, save result (success / failure)

[1478] Specific operation: Uses the PIL library to check the format and size of the image file, and if correct, saves it to the file system and database.

[1479] Step 6:

[1480] The server adds the saved file to the training dataset of the generative model and starts retraining.

[1481] Input: New work file

[1482] Output: Retraining started (success / failure)

[1483] What happens: The server runs a Python script to add new data to a generative model (e.g., StyleGAN) and retrain it.

[1484] Step 7:

[1485] A user visits the platform's login page and enters their email address and password.

[1486] Input: Email address, password

[1487] Output: Authentication result (success / failure)

[1488] Specific behavior: The user fills in the login form in the browser and clicks the "Login" button.

[1489] Step 8:

[1490] The server performs user authentication processing, and if successful, displays the dashboard page.

[1491] Input: Email address, password

[1492] Output: Token issued, dashboard page displayed

[1493] Specific operation: Uses the authentication API to verify user information, issue a JWT token, and display the dashboard page.

[1494] Step 9:

[1495] The user clicks the "New Request" button, enters the request in text format, and optionally selects a specific artist.

[1496] Input: Request details, select specific artist

[1497] Output: The request is sent to the server

[1498] Specific operation: The user clicks the "New Request" button on the dashboard and enters the request details.

[1499] Step 10:

[1500] The user clicks the "Send Request" button to send the request to the server.

[1501] Input: Request details, select specific artist

[1502] Output: The request is sent to the server

[1503] Specific operation: The user checks the request details and presses the "Submit Request" button.

[1504] Step 11:

[1505] The server receives the request, generates a request ID and saves it in the database.

[1506] Input: Request content

[1507] Output: Request ID, result saved to database (success / failure)

[1508] Specific operation: The server saves the request content in the database using an INSERT statement and generates a request ID.

[1509] Step 12:

[1510] The server forwards the request to the generative model, which generates the artwork in the specified style.

[1511] Input: Request details, Request ID

[1512] Output: The generated work

[1513] Specific operation: Makes an API call to the generative model and sends the request content in JSON format.

[1514] Step 13:

[1515] A generative model generates artwork in a specific style based on a request.

[1516] Input: Request content

[1517] Output: The generated work

[1518] Specific operation: The generative model generates digital art in response to user requests.

[1519] Step 14:

[1520] The server receives the generated work and stores it in a database.

[1521] Input: Generated artwork

[1522] Output: Save result to database (success / failure)

[1523] Specific operation: Save the received image file and save its URL in the database.

[1524] Step 15:

[1525] The device will notify the user that "creation is complete" and provide a download link for the work.

[1526] Input: URL of the generated work

[1527] Output: User notification, download link

[1528] Specific behavior: Uses WebSocket to send real-time notifications and display links.

[1529] Step 16:

[1530] The user clicks on the provided link to download the generated work.

[1531] Input: Download link

[1532] Output: Download the generated work

[1533] What happens: Click the link and your browser will download the work file.

[1534] (Application example 1)

[1535] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1536] Demand for digital art is growing in modern society, especially for customized art based on the style of a specific artist. However, existing systems lack sufficient means for users to quickly generate and acquire art in a specific artist's style, potentially resulting in a poor user experience. Furthermore, artists lack efficient ways to monetize their work on digital platforms. Given this situation, there is a need for an efficient system that allows users to instantly generate, purchase, and download customized art using a generative AI model that has learned the style of a specific artist.

[1537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1538] In this invention, the server includes: means for a work provider to register on a dedicated platform and upload work data; means for the server to store the uploaded work data in a database and train a generative model; means for a user to input a work generation request in text format; means for the server to cause the generative model to generate a work in a specific style based on the user's request; means for the server to store the generated work and provide it to the user; and means for a user to request digital art generated in the style of a specific artist from a mobile device and instantly preview, purchase, and download it. This allows users to quickly and efficiently generate and obtain customized art in the style of a specific artist, and also allows artists to gain new revenue opportunities using their own style.

[1539] "Artwork provider" means a person or legal entity that registers their information on a dedicated platform and uploads artwork data for the purpose of providing artwork.

[1540] "Platform" means a specific digital environment or web service used by Artwork Providers to register and upload Artworks and by Users to request and download Artworks.

[1541] "Artwork Data" means the digital file format (e.g., JPEG, PNG, etc.) of the artwork that is used as a dataset to train a generative model.

[1542] "Server" refers to a computer system that operates and manages the dedicated platform, stores data in a database, and generates and provides artworks using generative models.

[1543] The "database" is a system for storing and managing uploaded artwork data, generated artworks, and user request information.

[1544] A "generative model" is an artificial intelligence (AI) model that learns the style of a particular artist and generates new artwork based on user requests.

[1545] "User" means a person who accesses the dedicated platform, inputs a request for artwork creation in text format, and downloads the generated artwork.

[1546] The "means for inputting a request for creation of a work in text format" refers to an interface for a user to input the request content in text format in order to request the creation of a specific artwork on the dedicated platform.

[1547] A "particular style" refers to the characteristic expression techniques and design elements of a particular artist, and serves as a standard for generating artwork based on that.

[1548] "Mobile device" refers to a portable electronic device such as a smartphone or tablet that allows users to access the platform and request and download artworks.

[1549] "Preview" is a feature that allows you to check the generated artwork before purchasing or downloading it.

[1550] "Purchase" means that the user pays a certain amount of money to acquire ownership of the generated artwork.

[1551] "Downloading" refers to the act of saving a digital file of the generated artwork to a user's device via the Internet.

[1552] The present invention relates to a system that uses a generative AI model that has learned the style of a specific artist to quickly generate and provide digital art in accordance with a user's request. Specific embodiments are described below.

[1553] System Overview

[1554] The system consists of a set of platforms where art providers can register their artworks and provide datasets for training generative AI models, and users can generate and acquire artworks in the style of specific artists. The main components include a server, database, generative AI model, and user interface.

[1555] Hardware and software used

[1556] Hardware:

[1557] Server: A high-performance computer for processing data and running generative models.

[1558] Mobile Device: The device, such as a smartphone or tablet, that a user uses to request and download art.

[1559] software:

[1560] Python: The main programming language.

[1561] FastAPI: API server framework.

[1562] SQLModel: ORM for database operations.

[1563] PIL: Image manipulation library.

[1564] PyTorch: A deep learning library.

[1565] Transformers: Using CLIP models.

[1566] Specific steps

[1567] Registration of work providers

[1568] 1. The creator of the work accesses the dedicated platform and enters the required information such as name, email address, and password on the registration page.

[1569] 2. The server receives the input information and stores it in a database.

[1570] 3. The server sends a registration confirmation email to notify the work provider that registration is complete.

[1571] 4. The artwork provider logs into the system and uploads the artwork data file (e.g., JPEG, PNG, etc.).

[1572] 5. The server validates the format and size of the uploaded file, stores it in the database, and adds it to the training dataset for the generative model for retraining.

[1573] User requests and submissions

[1574] 1. The user accesses the login page of the dedicated platform and enters their email address and password for authentication.

[1575] 2. The user enters a text request for art creation, such as "Please draw a scene with a cat at the beach."

[1576] 3. The server receives the request, generates a request ID and saves it in the database.

[1577] 4. The server provides the request to the generative model, which then begins generating the artwork.

[1578] 5. The generative model generates a work based on the user's request, based on the style of a specific artist, and returns the result to the server.

[1579] 6. The server stores the generated work in a database and generates a link.

[1580] 7. The device will notify the user that "Generation is complete" and provide a download link.

[1581] 8. The user can click on the provided link to download the generated artwork.

[1582] Examples of prompt statements

[1583] An example of a user-entered prompt would be a text request such as "Please draw a scene with a cat at the beach."

[1584] This system allows users to easily request customized art by specifying the style of a specific artist, and also allows artists to utilize their own style to gain new revenue opportunities.

[1585] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1586] Step 1:

[1587] The server receives the registration information of the work provider.

[1588] Input: Name of the creator, email address, password

[1589] How it works: The server stores the information you entered in a database and sends you a confirmation email.

[1590] Output: Registration completion notification

[1591] Step 2:

[1592] The server receives artwork data uploaded from the artwork provider.

[1593] Input: Artwork file (JPEG, PNG, etc.)

[1594] Action: The server validates the file format and size, saves it to the database, adds it to the training dataset of the generative model, and retrains it.

[1595] Output: File verification results, re-learning completion notification

[1596] Step 3:

[1597] The user accesses the dedicated platform and logs in.

[1598] Input: User's email address, password

[1599] How it works: The server handles authentication and, if successful, redirects the user to the dashboard.

[1600] Output: Login success message, dashboard

[1601] Step 4:

[1602] The user inputs a work creation request in text format.

[1603] Input: Request for creation (e.g., "I want you to draw a scene with a cat at the seaside")

[1604] How it works: The server receives the request, generates a request ID, and saves it in the database.

[1605] Output: Request ID, request save completion notification

[1606] Step 5:

[1607] The server provides the request to the generative model and begins generating the artwork.

[1608] Input: User request, style model of a specific artist

[1609] How it works: The generative model generates artwork in a specific style based on the request and the style model.

[1610] Output: Generated artwork

[1611] Step 6:

[1612] The server stores the generated work in a database and generates a link.

[1613] Input: Generated artwork, request ID

[1614] How it works: The server stores the generated work in a database, associates it with a request ID, and generates a download link.

[1615] Output: Download link

[1616] Step 7:

[1617] The device will notify the user of the completion of generation and provide a download link.

[1618] Input: Download link

[1619] What it does: The device notifies the user that "Generation is complete" and provides a download link for the generated work.

[1620] Output: Generation completion notification, download link

[1621] Step 8:

[1622] The user clicks on the provided link to download the work.

[1623] Input: Download link

[1624] How it works: The user clicks on the link and the generated artwork downloads to their device.

[1625] Output: Downloaded artwork

[1626] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1627] System Overview

[1628] This invention relates to a digital art generation system, and in particular to an embodiment of a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[1629] Artist registration and work submission

[1630] Artist Registration

[1631] 1. The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[1632] 2. The server receives the entered information and stores it in a database.

[1633] 3. The server automatically sends a registration confirmation email to the artist, notifying them that registration is complete.

[1634] Work provided

[1635] 1. Artist logs in to the platform.

[1636] 2. The artist selects the menu to upload a new work file.

[1637] 3. The artist uploads the artwork file (e.g. JPEG, PNG, etc.) to the platform.

[1638] 4. The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[1639] 5. The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[1640] User requests and creation

[1641] User Login

[1642] 1. The user visits the platform's login page and enters their email address and password.

[1643] 2. The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[1644] Work request

[1645] 1. User clicks the "New Request" button from the dashboard.

[1646] 2. The user enters the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[1647] 3. If there is an option for users to select a specific artist, set that option.

[1648] 4. The server uses an emotion engine to analyze emotions from the text entered by the user.

[1649] 5. The server adjusts and optimizes the request content based on the emotion analysis results obtained from the emotion engine.

[1650] Request Processing

[1651] 1. The server provides the tailored request content to the generative model and starts processing.

[1652] 2. The generative model analyzes the user's request and generates a work in the style of a specific artist.

[1653] 3. The server receives the generated work and stores it in a database.

[1654] Work provided

[1655] 1. The server generates a URL for the generated work and associates it with the request ID.

[1656] 2. The device notifies the user that "Generation is complete" and provides a download link.

[1657] 3. The user clicks on the provided link to download the generated work.

[1658] Specific examples

[1659] For example, artist A uploads his or her work to the platform, and the work is trained by the generative model. User B logs in and inputs a request, saying, "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and uses an emotion engine to analyze User B's emotions. If an emotion such as "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to User B. User B can then click the download link to obtain the generated work.

[1660] This system allows users to quickly obtain customized digital artwork based on their emotions, and also provides artists with new revenue opportunities.

[1661] The processing flow will be explained below.

[1662] Artist registration and work submission processing

[1663] Artist Registration

[1664] Step 1:

[1665] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[1666] Step 2:

[1667] The server receives the entered information and stores it in a database.

[1668] Step 3:

[1669] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[1670] Work provided

[1671] Step 1:

[1672] The artist logs into the platform.

[1673] Step 2:

[1674] The artist selects a menu item to upload a new work file.

[1675] Step 3:

[1676] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1677] Step 4:

[1678] The server validates the uploaded file (file type, size, etc.) and stores it in the database.

[1679] Step 5:

[1680] The server registers the metadata (e.g., title, description, tags) of the artwork files stored in the database.

[1681] Step 6:

[1682] The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[1683] Handling user requests and creations

[1684] User Login

[1685] Step 1:

[1686] A user visits the platform's login page and enters their email address and password.

[1687] Step 2:

[1688] The server handles the authentication process and redirects the user to the dashboard if the entered information is correct.

[1689] Work request

[1690] Step 1:

[1691] User clicks the "New Request" button from the dashboard.

[1692] Step 2:

[1693] If the user enters the request in text format and selects a specific artist, set that option.

[1694] Step 3:

[1695] The user clicks the "Submit Request" button.

[1696] Request Processing

[1697] Step 1:

[1698] The server receives the request, generates a request ID and saves it in the database.

[1699] Step 2:

[1700] The server uses an emotion engine to analyze the user's emotion from the text included in the request.

[1701] Step 3:

[1702] The server optimizes the request content and makes appropriate adjustments based on the analysis results of the emotion engine.

[1703] Artwork generation by AI

[1704] Step 1:

[1705] The server provides the tailored request content to the generative model to initiate the generation process.

[1706] Step 2:

[1707] The generative model generates artwork in the style of a specific artist based on tailored requests.

[1708] Step 3:

[1709] The server receives the generated work and stores it in a database.

[1710] Work provided

[1711] Step 1:

[1712] The server generates a URL for the generated work and associates it with the request ID.

[1713] Step 2:

[1714] The device will notify the user that "generation is complete" and provide a download link.

[1715] Step 3:

[1716] The user clicks on the provided link to download the generated work.

[1717] Specific examples

[1718] Specific examples of artist registration

[1719] Step 1:

[1720] Artist A accesses the dedicated platform, enters the necessary information, and completes registration.

[1721] Step 2:

[1722] The server saves Artist A's information in a database and automatically sends a registration confirmation email.

[1723] Examples of work provided

[1724] Step 1:

[1725] Artist A logs in to the platform.

[1726] Step 2:

[1727] Artist A uploads a new work and the server validates the file.

[1728] Step 3:

[1729] The server stores the successfully verified file in the database and registers the metadata.

[1730] Step 4:

[1731] The server provides the artwork file to the generative model, and the AI ​​model begins retraining.

[1732] Request and creation examples

[1733] Step 1:

[1734] User B logs in to the platform and fills in the request form, saying, "I would like you to draw a scene of a cat standing with a sunset in the background."

[1735] Step 2:

[1736] The server receives the request and performs emotion analysis using an emotion engine.

[1737] Step 3:

[1738] The server optimizes the request based on the analysis results and provides it to the generative model to start the generation process.

[1739] Step 4:

[1740] The generative model generates artwork in the style of a particular artist, which is then stored on a server.

[1741] Step 5:

[1742] The device notifies User B and provides a download link.

[1743] Step 6:

[1744] User B clicks the link to download the generated work.

[1745] In this way, users can quickly obtain customized digital artwork based on their emotions, and artists can gain new revenue opportunities.

[1746] Example 2

[1747] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1748] While conventional digital art generation systems have the technology to generate artworks using the artist's style, it has been difficult to generate customized artworks that reflect the user's emotions. Furthermore, because the user's input request is passed directly to the generative model, the artwork generated does not necessarily reflect the user's intentions or emotions. This makes it difficult to increase user satisfaction.

[1749] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1750] In this invention, the server includes a means for performing sentiment analysis based on the user's request and optimizing the request content, a means for causing the generative model to generate a work in a specific style based on the user's request, and a means for generating a URL for the generated work and associating it with the request ID, thereby generating a customized artwork that reflects the user's sentiment and increasing user satisfaction.

[1751] "Work provider" refers to an individual or organization that registers on the dedicated platform and provides work data used to generate digital art.

[1752] "Dedicated Platform" means a web-based system through which work providers upload work data and users input work generation requests.

[1753] A "server" is a computer system that processes data received from work providers and users and performs various functions for work generation.

[1754] "Artwork data" is a general term for image files and related data uploaded by the creator of the work to train a generative model of digital art.

[1755] A "database" is a system for systematically storing uploaded work data, created works, user request information, etc., and for efficiently managing and searching such data.

[1756] A "generative model" is a machine learning model that learns the style of a particular artist and generates new artwork based on user requests.

[1757] "User" means an individual or entity that requests the creation of digital art using the Dedicated Platform.

[1758] The "request content" is text data that describes the specific requirements for the digital art that the user wants to create.

[1759] "Sentiment analysis" is the process of detecting emotions from the request content entered by the user and optimizing the request content based on those emotions.

[1760] "URL" is the address on the Internet for accessing the created work, and is provided to the user by the server.

[1761] A "request ID" is an identification number assigned to uniquely identify each request.

[1762] MODE FOR CARRYING OUT THE INVENTION

[1763] This invention relates to a system for generating digital art, specifically a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[1764] System program processing

[1765] The system uses the following hardware and software:

[1766] Server: A central computer system that stores and processes work data and request data. Data is managed using an SQL or NoSQL database.

[1767] Terminal: A device used by artists and users to access the dedicated platform using a web browser.

[1768] Generative models: For example, using machine learning models such as Stable Diffusion to generate artwork in the style of a particular artist.

[1769] Sentiment engine: Analyzes emotions from user input text using natural language processing techniques, such as Transformer-based models (e.g., BERT).

[1770] Artist registration and work submission

[1771] Artists access the dedicated platform and register by entering required information such as their name, email address, and password. The server receives this information and stores it in a database. A registration confirmation email is automatically sent to the artist, who clicks on a link to complete the registration. After logging in, the artist uploads their artwork data. The server then verifies the uploaded data and adds it to the training dataset for the generative model.

[1772] Training a generative model

[1773] Based on the uploaded artwork data, the generative model learns the style of a specific artist, which is then stored in a database and used to generate artwork.

[1774] User requests and creation

[1775] Users access the dedicated platform and log in by entering their login information. After logging in, they can enter their request in text format and specify a specific artist. The server receives this request and performs emotional analysis using an emotion engine.

[1776] For example, if the prompt is "Please draw a scene of a cat standing by the sea in the sunset. Please create a calm atmosphere," the emotion engine will detect the emotion "calm" and optimize the request. Based on the optimized request, the generative model will generate a work, which will then be stored in a database by the server.

[1777] Providing generated works

[1778] The server generates a URL for the generated work and associates it with the request ID. A notification containing this URL is sent to the device, and the user can click the download link to download the work.

[1779] This system allows users to quickly obtain digital artworks that are customized to their emotions, and also allows artists to efficiently offer their works and gain new revenue opportunities.

[1780] Specific examples

[1781] For example, an artist uploads their work to the platform, and the work is trained by the generative model. A user logs in and inputs a request, such as "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and analyzes the user's emotions using an emotion engine. If the emotion of "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to the user. The user can obtain the generated work by clicking a download link.

[1782] Specific embodiments of the present invention allow users to easily obtain customized digital artwork based on their emotions.

[1783] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1784] Step 1: Artist Registration

[1785] The artist accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password. The server receives the entered information and stores it in a database. The server automatically sends a registration confirmation email, and the artist completes the registration by clicking the link in the confirmation email.

[1786] Input: Artist name, email address, password

[1787] Output: The artist's information is saved in the database and a confirmation email is sent.

[1788] Step 2: Submit your work

[1789] An artist logs in to the platform and selects a menu to upload their artwork file. Once the artist uploads the artwork file (e.g., JPEG, PNG, etc.), the server validates the uploaded file and stores it in a database. The server then adds the artwork file to the training dataset for the generative model and begins the training process.

[1790] Input: Artist's artwork file

[1791] Output: Work files stored in a database, training dataset for generative models

[1792] Step 3: User Login

[1793] The user accesses the platform's login page and enters their email address and password. The server performs authentication and, if successful, redirects the user to the dashboard. The device displays a message to the user indicating successful login.

[1794] Input: Email address, Password

[1795] Output: Login successful message

[1796] Step 4: Submit a request

[1797] The user clicks the "New Request" button on the dashboard and enters the request in text format (e.g., "I want you to paint a scene of a cat standing with a sunset in the background"). A specific artist can be selected if necessary. The server receives and stores the input. The server uses an emotion engine to analyze the emotion from the request and optimizes the request based on the results.

[1798] Input: Request content in text format

[1799] Output: Sentiment analysis results, optimized request content

[1800] Step 5: Request Processing

[1801] The server provides the tailored request to the generative model to initiate processing. The generative model analyzes the request and generates a work based on the style of the specific artist. The server receives the generated work and stores it in a database.

[1802] Input: Optimized request content

[1803] Output: Generated artwork file

[1804] Step 6: Submit your work

[1805] The server generates a URL for the generated work and associates it with the request ID. The device notifies the user that "generation is complete" and provides a download link. The user clicks the provided link to download the generated work.

[1806] Input: Generated artwork file

[1807] Output: Download link for the work, Download work

[1808] By following these steps, users can easily obtain customized digital artworks, and this system is beneficial for both artists and users.

[1809] (Application example 2)

[1810] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1811] Conventional digital art creation systems generate artwork based solely on a simple text request entered by the user, making it difficult to provide customized artwork that reflects the user's emotions and mood. Therefore, there is a need to effectively create and provide digital artwork that truly resonates with users. The present invention solves this problem and provides a more personalized digital art creation system that reflects the user's emotions.

[1812] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1813] In this invention, the server includes a means for generating a work based on the emotion analysis result, a means for presenting a preview image of the generated work and offering the option of purchasing or downloading, and a means for storing the generated work and providing it to the user, thereby enabling the generation and provision of a customized digital artwork that reflects the emotion of the user.

[1814] A "work provider" is an entity that uploads their own work data to the dedicated platform.

[1815] The "dedicated platform" is an online system where creators can register and upload their work data and train the generative model.

[1816] "Work data" refers to digital content such as image files and text data that work providers upload to the platform.

[1817] The "server" is a central processing unit that stores artwork data in a database and trains the generative model.

[1818] A "database" is a storage device for storing uploaded work data and created works.

[1819] A "generative model" is an artificial intelligence model that learns from uploaded artwork data and generates digital artworks based on user requests.

[1820] "User" is the entity that enters a request to generate a digital artwork and receives the results.

[1821] An “artwork generation request” is a textual instruction that a user inputs into a Generative Model to generate a particular artwork.

[1822] "Emotion analysis results" are analysis results based on requests and emotions entered by users, and are data used to instruct the generative model.

[1823] A "preview image" is an image that displays a part or the whole of a generated digital artwork for the user to check.

[1824] "Purchase or Download Option" means the option that a user can choose to purchase or download the generated digital artwork for free.

[1825] "Generated Works" are digital artworks created by generative models based on user requests and sentiment analysis.

[1826] The present invention provides a specific system for generating and providing customized digital artworks that reflect a user's emotions. The system includes a means for an artwork provider to register and upload artwork data to a dedicated platform, a means for a server to store the uploaded artwork data in a database and train a generative model, a means for a user to input an artwork generation request, a means for the server to cause the generative model to generate an artwork based on the user's request and the results of emotion analysis, a means for presenting a preview image of the generated artwork and offering the option of purchasing or downloading, and a means for the server to store the generated artwork and provide it to the user.

[1827] Hardware and Software Configuration

[1828] server

[1829] The servers are equipped with high-performance hardware such as Intel Xeon processors and NVIDIA GPUs, and run generative and sentiment analysis models using software libraries such as Python, TensorFlow, and PyTorch.

[1830] The database can be a relational or NoSQL database such as PostgreSQL or MongoDB.

[1831] User terminal

[1832] User devices are smartphones or PCs, and access the platform using a dedicated application or web browser.

[1833] Smartphone applications are implemented as iOS or Android apps.

[1834] Sentiment Analysis and Generative Models

[1835] Emotion Analysis: Emotion recognition uses Python and the EmotionRecognition library to analyze emotional states from data input in text format.

[1836] Generative Modeling: Generative modeling uses Hugging Face's Transformers library and is performed by an AI model trained in the style of a specific artist.

[1837] Operating Procedure

[1838] Artist registration and work submission

[1839] 1. The artist registers an account on the dedicated platform and uploads their work data.

[1840] 2. The server verifies the uploaded artwork data, stores it in a database, and trains the generative model.

[1841] User requests and creation

[1842] 1. The user logs in to the dedicated app and enters a request to create a work in text format.

[1843] 2. The server uses an emotion engine to analyze emotions from the user's input.

[1844] 3. Generate prompts based on the sentiment analysis results and provide instructions to the generative model.

[1845] 4. The generative model generates a digital artwork in a specific style based on the user's request and the results of sentiment analysis.

[1846] Product preview and offering

[1847] 1. The server generates a preview image of the created work and presents it to the user.

[1848] 2. Users can preview the work and choose to purchase it or download it for free.

[1849] 3. The server stores the generated work in a database and provides a download link.

[1850] Specific examples

[1851] For example, if a user inputs "I feel tired" and requests "I want a drawing of a scene with a cat standing with a sunset in the background," the server sends this to the emotion engine and generates the "tired" emotion as an analysis result.

[1852] User sentiment: "I feel tired"

[1853] User request: "I want you to draw a scene of a cat standing with a sunset in the background."

[1854] Generated prompt: "Create a tired themed artwork of a cat standing against a sunset background in the style of Artist A."

[1855] A prompt is generated. The generative model generates a work based on this prompt and provides it to the user. This system makes it possible to quickly and effectively generate and provide customized digital artworks that reflect the user's emotions.

[1856] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1857] Step 1:

[1858] Artist Registration

[1859] Artists access the dedicated platform and create an account by entering the required information, such as their name, email address, and password. The server receives the information entered and stores it in a database. A registration confirmation email is automatically generated and sent to the artist.

[1860] Input: Artist information such as name, email address, and password

[1861] Output: Artist information stored in the database and a confirmation email

[1862] Step 2:

[1863] Work provided

[1864] Artists log in to the platform and upload artwork files (JPEG, PNG, etc.) from their account management page. The server verifies the uploaded file format and size, stores it in a database, and adds the saved artwork data to the training dataset of the generative model, starting the model retraining process.

[1865] Input: Artist's work file

[1866] Output: Artwork data stored in a database and a retrained generative model

[1867] Step 3:

[1868] User Login

[1869] A user accesses the platform through a dedicated app or web browser and enters their email address and password. The server performs the authentication process, and if authentication is successful, redirects the user to the dashboard.

[1870] Input: User's email address and password

[1871] Output: Authentication result and redirect to dashboard

[1872] Step 4:

[1873] Enter your work request

[1874] Users can click the New Request button on the dashboard and enter a text-based production request, with the option to select a specific artist.

[1875] Input: User text request and artist selection

[1876] Output: The request data sent to the server

[1877] Step 5:

[1878] Emotion analysis

[1879] The server sends the request data to the emotion engine, which analyzes the user's input to determine the emotion. The emotion engine analyzes the text and identifies the dominant emotion (e.g., happy, sad, tired, etc.).

[1880] Input: User request text

[1881] Output: Emotion analysis results

[1882] Step 6:

[1883] Prompt Generation

[1884] The server creates prompts for the generative model based on the results of sentiment analysis and the user's request. For example, based on the request "I feel tired" and "A scene of a cat standing against a sunset," it generates a prompt such as "Create a tired-themed artwork of a cat standing against a sunset background in the style of Artist A."

[1885] Input: Sentiment analysis results and user requests

[1886] Output: Prompts for the generated model

[1887] Step 7:

[1888] Work generation

[1889] The server provides prompts to the generative model, which then generates the digital artwork. The generative model uses the learnings of a particular artist as a style to generate the digital artwork.

[1890] Input: Prompt for the generative model

[1891] Output: Generated digital artwork

[1892] Step 8:

[1893] Preview display

[1894] The server generates a preview image of the generated work and presents it to the user, who can then view the preview and select the option to purchase or download.

[1895] Input: Generated digital artwork

[1896] Output: Provide preview images and options to the user

[1897] Step 9:

[1898] Work provided

[1899] The server stores the generated work in a database and generates a download link, which is then sent to the user, who can then download or purchase the generated work.

[1900] Input: Generated digital artwork

[1901] Output: Download link and notification

[1902] This allows for the rapid creation and effective provision of customized digital artworks that reflect the user's emotions.

[1903] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1904] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1905] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1906] [Fourth embodiment]

[1907] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1908] 7, a 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.

[1909] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1910] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1911] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1912] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1913] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1914] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1915] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1916] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1917] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1918] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1919] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1920] System Overview

[1921] The present invention relates to a digital art generation system, and in particular to an embodiment of a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[1922] Artist registration and work submission

[1923] 1. Artist registration

[1924] The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[1925] The server receives the entered information and stores it in a database.

[1926] The server will send a registration confirmation email to the artist to notify them that registration is complete.

[1927] 2. Providing works

[1928] The artist logs into the platform.

[1929] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1930] The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[1931] The server adds the artwork file to the training dataset of the generative model and retrains the generative model.

[1932] User requests and creation

[1933] 1. User Login

[1934] A user visits the platform's login page and enters their email address and password.

[1935] The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[1936] 2. Work Request

[1937] User clicks the "New Request" button from the dashboard.

[1938] The user inputs the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[1939] If the user has the option to select a particular artist, they do so.

[1940] 3. Request Processing

[1941] The user clicks the "Submit Request" button.

[1942] The server receives the request, generates a request ID and saves it in the database.

[1943] The server forwards the request to the generative model, which generates the artwork in the specified style.

[1944] 4. AI-generated artwork

[1945] The server provides the request content to the generative model to start processing.

[1946] A generative model generates artwork in the style of a specific artist based on a user request.

[1947] The server receives the generated work and stores it in a database.

[1948] 5. Providing works

[1949] The server generates a URL for the generated work and associates it with the user request ID.

[1950] The device will notify the user that "generation is complete" and provide a download link.

[1951] The user clicks on the provided link to download the generated work.

[1952] Specific examples

[1953] For example, Artist A uploads a work to the platform, and the work is trained by the AI ​​model. User B logs in and requests, "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the AI ​​model, which generates a work based on Artist A's style. The generated work is saved by the server and notified to User B. User B can then click the download link to retrieve the work.

[1954] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[1955] The processing flow will be explained below.

[1956] Artist registration and work submission processing

[1957] Artist Registration

[1958] Step 1:

[1959] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[1960] Step 2:

[1961] The server receives the entered information and stores it in a database.

[1962] Step 3:

[1963] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[1964] Work provided

[1965] Step 1:

[1966] The artist logs into the platform.

[1967] Step 2:

[1968] The artist selects a menu item to upload a new work file.

[1969] Step 3:

[1970] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[1971] Step 4:

[1972] The server validates the uploaded file (checks the file format, size, etc.).

[1973] Step 5:

[1974] The server stores successfully validated files in the database and registers their metadata (e.g. title, description, tags).

[1975] Step 6:

[1976] The server adds the newly saved artwork file to the training dataset of the generative model and begins the retraining process of the generative model.

[1977] Handling user requests and creations

[1978] User Login

[1979] Step 1:

[1980] A user visits the platform's login page and enters their email address and password.

[1981] Step 2:

[1982] The server performs the authentication process and, if the entered information is correct, redirects the user to the dashboard.

[1983] Work request

[1984] Step 1:

[1985] User clicks the "New Request" button from the dashboard.

[1986] Step 2:

[1987] If the user enters the request in text format and selects a specific artist, set that option.

[1988] Step 3:

[1989] The user clicks the "Submit Request" button.

[1990] Request Processing

[1991] Step 1:

[1992] The server receives the request.

[1993] Step 2:

[1994] The server generates a request ID and stores it in the database along with the request content.

[1995] Step 3:

[1996] The server forwards the request ID and request content to the generated model.

[1997] Artwork generation by AI

[1998] Step 1:

[1999] The server provides the request content to the generative model to start processing.

[2000] Step 2:

[2001] A generative model analyzes the user's request and generates a work in the style of a specific artist.

[2002] Step 3:

[2003] The server receives the generated work and stores it in a database.

[2004] Work provided

[2005] Step 1:

[2006] The server generates a URL for the generated work and associates it with the request ID.

[2007] Step 2:

[2008] The device will notify the user that "generation is complete" and provide a download link.

[2009] Step 3:

[2010] The user clicks on the provided link to download the generated work.

[2011] In this way, customized digital artwork that reflects the artist's style is quickly provided based on the user's request.

[2012] Example 1

[2013] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2014] Conventional digital art generation systems have struggled to efficiently generate works that reflect an artist's style. Furthermore, there are limited means for users to quickly obtain customized works based on their desired style and content. Therefore, there is a need for a digital art generation and distribution system that satisfies both artists and users.

[2015] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2016] In this invention, the server includes: a means for a work provider to register on the dedicated platform and input necessary information; a means for the server to receive the input information and save it in a database; a means for the server to send a registration confirmation notification and notify the completion of registration; a means for the work provider to log in to the platform and upload a work file; a means for the server to verify the uploaded work file and save it in a database; a means for the server to add the saved file to a training dataset for a generative model and perform re-training; a means for a user to log in to the platform and input a request; a means for the server to receive the input request and forward it to a generative model to generate a work in a specific style; a means for the server to receive the generated work and save it in a database; and a means for the terminal to notify the user that the generated work has been provided. This enables customized works that reflect the artist's style to be quickly generated and provided.

[2017] "Artwork provider" refers to an artist who registers and provides their own work on the digital art generation platform.

[2018] "Platform" means a web application or software that allows Work Providers to register and upload Works and Users to submit Requests.

[2019] "Required information" refers to basic personal information such as name, email address, and password that the work provider enters when registering on the dedicated platform.

[2020] "Server" refers to a computing device that receives and processes input data from external clients, stores it in a database, validates the data, and launches and manages generative models.

[2021] "Database" refers to a software management system that systematically stores information received by the server.

[2022] "Registration confirmation notification" refers to an email sent by the server to confirm the information registered by the work provider on the dedicated platform.

[2023] "Login" refers to the operation by which work providers and users complete the authentication procedures required to access the dedicated platform.

[2024] "Work files" are image files in JPEG or PNG format that are treated as digital art.

[2025] "Verification" refers to the process in which the server checks whether the uploaded work file is in the specified format and size.

[2026] A "generative model" is an algorithm or program that uses machine learning techniques to generate new digital art from input data.

[2027] A "training dataset" is a collection of original artwork data that a generative model needs to generate new artworks.

[2028] "Retraining" is the process of retraining a generative model by adding new data to improve the model's performance.

[2029] A "request" is a request that a user inputs in text form the content and style of the work they wish to create and sends it to the server.

[2030] "Generated Work" means digital art generated by a generative model in a particular style based on a user request.

[2031] "Providing" means that the server notifies and distributes the generated work to the user in a usable form.

[2032] This invention relates to a digital art generation system, and specifically describes a system that generates and provides works based on user requests using a generation model that has learned the artist's style.

[2033] System Overview

[2034] This system consists of a dedicated platform, a server, a database, a generative model, and a user interface. The dedicated platform is implemented as a web application and can be accessed by content providers and users.

[2035] Hardware and software used

[2036] Server: A server with high-performance computing resources (e.g., AWS EC2, Google Cloud Compute Engine)

[2037] Database: Relational database such as MySQL or PostgreSQL

[2038] Generative models: Generative models using machine learning frameworks such as TensorFlow and PyTorch (e.g., StyleGAN)

[2039] Authentication system: User authentication using OAuth and JWT

[2040] Notification system: SMTP server, WebSocket, Firebase Cloud Messaging (FCM)

[2041] Artist registration and work submission

[2042] 1. The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[2043] Example: Open your browser, access the "Register" page, and enter the required information.

[2044] 2. The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[2045] Example: The server receives form data, validates it using Python's validation library, and saves it to MySQL.

[2046] 3. The server sends a registration confirmation notification to the artist via the SMTP server, notifying them that registration is complete.

[2047] Example: Send a confirmation email using the SMTP client library.

[2048] 4. The user (artist) logs in to the platform, selects and uploads the work file.

[2049] Example: Select an image file (JPEG, PNG) from the file selection dialog and click the "Upload" button.

[2050] 5. The server validates the uploaded file (checks the format and size) and stores files that pass validation in the database and storage.

[2051] Example: Use the PIL library to check the format of an image file and save it if there are no problems.

[2052] 6. The server adds the saved file to the training dataset for the generative model and starts retraining the generative model.

[2053] Example: Run a Python script to add new training data to a generative model and retrain it.

[2054] User requests and creation

[2055] 1. The user visits the platform's login page and enters their email address and password.

[2056] Example: Open the "Login" page in your browser and fill in the login form.

[2057] 2. The server authenticates the user and, if successful, displays the dashboard page.

[2058] Example: Verify user information using the authentication API and issue a JWT token.

[2059] 3. The user clicks the "New Request" button and enters the request in text format (e.g., "I want you to draw a scene with a cat at the seaside"), and optionally selects a specific artist.

[2060] Example: Click the "New Request" button on the dashboard and enter the request details.

[2061] 4. The user clicks the "Send Request" button to send the request to the server.

[2062] Example: Check the form and press the "Submit Request" button.

[2063] 5. The server receives the request, generates a request ID, and saves it in the database.

[2064] Example: Save the request contents to the database using the INSERT statement.

[2065] 6. The server forwards the request to the generative model, which generates the artwork in the specified style.

[2066] Example: Call the Generative Model API and send the request in JSON format.

[2067] 7. The generative model generates artwork in a specific style based on the request.

[2068] Example: A generative model generates an image based on a request.

[2069] 8. The server receives the generated work and stores it in a database.

[2070] Example: Save the received image file and save its URL in a database.

[2071] 9. The device will notify the user that "Generation is complete" and provide a download link for the work.

[2072] Example: Using WebSocket to send real-time notifications and display links.

[2073] 10. The user clicks on the provided link to download the generated work.

[2074] Example: Click on a link and download the image via your browser.

[2075] Specific examples

[2076] For example, an artist uploads a new piece of work to the platform, which is then trained by the generative model. A user then logs in and submits a request, such as "Please draw people dancing in an autumn landscape." The server receives this request and provides it to the generative model, which then generates a work based on the artist's style. The generated work is then saved on the server and notified to the user. The user can then download the work by clicking on the provided link.

[2077] Prompt Sentence Examples

[2078] Example prompt 1: "Please draw a scene with a cat at the seaside in Artist A's style."

[2079] Example prompt 2: "Add a sunset to an autumn mountain scene" in the style of Artist B.

[2080] This system allows artists to create new digital art in their own style and allows users to quickly obtain customized works in their desired style.

[2081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2082] Step 1:

[2083] The user (artist) accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password.

[2084] Input: Name, Email Address, Password

[2085] Output: Information sent to the server

[2086] Specific actions: The artist fills out the form in the browser and clicks the "Register" button.

[2087] Step 2:

[2088] The server receives the input information, performs formal validation, and if validation is successful, stores the information in the database.

[2089] Input: Artist registration information

[2090] Output: Save result to database (success / failure)

[2091] Specific operation: The server checks the received data using a validation library and saves it to MySQL using an INSERT statement.

[2092] Step 3:

[2093] The server sends a registration confirmation notice to the artist using an SMTP server, notifying him or her that registration has been completed.

[2094] Input: Contents of registration confirmation email (artist information)

[2095] Output: Confirmation email sending result (success / failure)

[2096] Specific operation: The server uses an SMTP client to send a confirmation email to the artist.

[2097] Step 4:

[2098] The user (artist) logs in to the platform, selects and uploads the work file.

[2099] Input: Email address, password, work file

[2100] Output: The file is sent to the server.

[2101] Specific operation: The artist enters their email address and password into the browser form, logs in, and then uploads the artwork file.

[2102] Step 5:

[2103] The server validates the uploaded files (checks the format and size) and stores the files that pass the validation in the database and storage.

[2104] Input: Work file

[2105] Output: Verification result, save result (success / failure)

[2106] Specific operation: Uses the PIL library to check the format and size of the image file, and if correct, saves it to the file system and database.

[2107] Step 6:

[2108] The server adds the saved file to the training dataset of the generative model and starts retraining.

[2109] Input: New work file

[2110] Output: Retraining started (success / failure)

[2111] What happens: The server runs a Python script to add new data to a generative model (e.g., StyleGAN) and retrain it.

[2112] Step 7:

[2113] A user visits the platform's login page and enters their email address and password.

[2114] Input: Email address, password

[2115] Output: Authentication result (success / failure)

[2116] Specific behavior: The user fills in the login form in the browser and clicks the "Login" button.

[2117] Step 8:

[2118] The server performs user authentication processing, and if successful, displays the dashboard page.

[2119] Input: Email address, password

[2120] Output: Token issued, dashboard page displayed

[2121] Specific operation: Uses the authentication API to verify user information, issue a JWT token, and display the dashboard page.

[2122] Step 9:

[2123] The user clicks the "New Request" button, enters the request in text format, and optionally selects a specific artist.

[2124] Input: Request details, select specific artist

[2125] Output: The request is sent to the server

[2126] Specific operation: The user clicks the "New Request" button on the dashboard and enters the request details.

[2127] Step 10:

[2128] The user clicks the "Send Request" button to send the request to the server.

[2129] Input: Request details, select specific artist

[2130] Output: The request is sent to the server

[2131] Specific operation: The user checks the request details and presses the "Submit Request" button.

[2132] Step 11:

[2133] The server receives the request, generates a request ID and saves it in the database.

[2134] Input: Request content

[2135] Output: Request ID, result saved to database (success / failure)

[2136] Specific operation: The server saves the request content in the database using an INSERT statement and generates a request ID.

[2137] Step 12:

[2138] The server forwards the request to the generative model, which generates the artwork in the specified style.

[2139] Input: Request details, Request ID

[2140] Output: The generated work

[2141] Specific operation: Makes an API call to the generative model and sends the request content in JSON format.

[2142] Step 13:

[2143] A generative model generates artwork in a specific style based on a request.

[2144] Input: Request content

[2145] Output: The generated work

[2146] Specific operation: The generative model generates digital art in response to user requests.

[2147] Step 14:

[2148] The server receives the generated work and stores it in a database.

[2149] Input: Generated artwork

[2150] Output: Save result to database (success / failure)

[2151] Specific operation: Save the received image file and save its URL in the database.

[2152] Step 15:

[2153] The device will notify the user that "creation is complete" and provide a download link for the work.

[2154] Input: URL of the generated work

[2155] Output: User notification, download link

[2156] Specific behavior: Uses WebSocket to send real-time notifications and display links.

[2157] Step 16:

[2158] The user clicks on the provided link to download the generated work.

[2159] Input: Download link

[2160] Output: Download the generated work

[2161] What happens: Click the link and your browser will download the work file.

[2162] (Application example 1)

[2163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2164] Demand for digital art is growing in modern society, especially for customized art based on the style of a specific artist. However, existing systems lack sufficient means for users to quickly generate and acquire art in a specific artist's style, potentially resulting in a poor user experience. Furthermore, artists lack efficient ways to monetize their work on digital platforms. Given this situation, there is a need for an efficient system that allows users to instantly generate, purchase, and download customized art using a generative AI model that has learned the style of a specific artist.

[2165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2166] In this invention, the server includes: means for a work provider to register on a dedicated platform and upload work data; means for the server to store the uploaded work data in a database and train a generative model; means for a user to input a work generation request in text format; means for the server to cause the generative model to generate a work in a specific style based on the user's request; means for the server to store the generated work and provide it to the user; and means for a user to request digital art generated in the style of a specific artist from a mobile device and instantly preview, purchase, and download it. This allows users to quickly and efficiently generate and obtain customized art in the style of a specific artist, and also allows artists to gain new revenue opportunities using their own style.

[2167] "Artwork provider" means a person or legal entity that registers their information on a dedicated platform and uploads artwork data for the purpose of providing artwork.

[2168] "Platform" means a specific digital environment or web service used by Artwork Providers to register and upload Artworks and by Users to request and download Artworks.

[2169] "Artwork Data" means the digital file format (e.g., JPEG, PNG, etc.) of the artwork that is used as a dataset to train a generative model.

[2170] "Server" refers to a computer system that operates and manages the dedicated platform, stores data in a database, and generates and provides artworks using generative models.

[2171] The "database" is a system for storing and managing uploaded artwork data, generated artworks, and user request information.

[2172] A "generative model" is an artificial intelligence (AI) model that learns the style of a particular artist and generates new artwork based on user requests.

[2173] "User" means a person who accesses the dedicated platform, inputs a request for artwork creation in text format, and downloads the generated artwork.

[2174] The "means for inputting a request for creation of a work in text format" refers to an interface for a user to input the request content in text format in order to request the creation of a specific artwork on the dedicated platform.

[2175] A "particular style" refers to the characteristic expression techniques and design elements of a particular artist, and serves as a standard for generating artwork based on that.

[2176] "Mobile device" refers to a portable electronic device such as a smartphone or tablet that allows users to access the platform and request and download artworks.

[2177] "Preview" is a feature that allows you to check the generated artwork before purchasing or downloading it.

[2178] "Purchase" means that the user pays a certain amount of money to acquire ownership of the generated artwork.

[2179] "Downloading" refers to the act of saving a digital file of the generated artwork to a user's device via the Internet.

[2180] The present invention relates to a system that uses a generative AI model that has learned the style of a specific artist to quickly generate and provide digital art in accordance with a user's request. Specific embodiments are described below.

[2181] System Overview

[2182] The system consists of a set of platforms where art providers can register their artworks and provide datasets for training generative AI models, and users can generate and acquire artworks in the style of specific artists. The main components include a server, database, generative AI model, and user interface.

[2183] Hardware and software used

[2184] Hardware:

[2185] Server: A high-performance computer for processing data and running generative models.

[2186] Mobile Device: The device, such as a smartphone or tablet, that a user uses to request and download art.

[2187] software:

[2188] Python: The main programming language.

[2189] FastAPI: API server framework.

[2190] SQLModel: ORM for database operations.

[2191] PIL: Image manipulation library.

[2192] PyTorch: A deep learning library.

[2193] Transformers: Using CLIP models.

[2194] Specific steps

[2195] Registration of work providers

[2196] 1. The creator of the work accesses the dedicated platform and enters the required information such as name, email address, and password on the registration page.

[2197] 2. The server receives the input information and stores it in a database.

[2198] 3. The server sends a registration confirmation email to notify the work provider that registration is complete.

[2199] 4. The artwork provider logs into the system and uploads the artwork data file (e.g., JPEG, PNG, etc.).

[2200] 5. The server validates the format and size of the uploaded file, stores it in the database, and adds it to the training dataset for the generative model for retraining.

[2201] User requests and submissions

[2202] 1. The user accesses the login page of the dedicated platform and enters their email address and password for authentication.

[2203] 2. The user enters a text request for art creation, such as "Please draw a scene with a cat at the beach."

[2204] 3. The server receives the request, generates a request ID and saves it in the database.

[2205] 4. The server provides the request to the generative model, which then begins generating the artwork.

[2206] 5. The generative model generates a work based on the user's request, based on the style of a specific artist, and returns the result to the server.

[2207] 6. The server stores the generated work in a database and generates a link.

[2208] 7. The device will notify the user that "Generation is complete" and provide a download link.

[2209] 8. The user can click on the provided link to download the generated artwork.

[2210] Examples of prompt statements

[2211] An example of a user-entered prompt would be a text request such as "Please draw a scene with a cat at the beach."

[2212] This system allows users to easily request customized art by specifying the style of a specific artist, and also allows artists to utilize their own style to gain new revenue opportunities.

[2213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2214] Step 1:

[2215] The server receives the registration information of the work provider.

[2216] Input: Name of the creator, email address, password

[2217] How it works: The server stores the information you entered in a database and sends you a confirmation email.

[2218] Output: Registration completion notification

[2219] Step 2:

[2220] The server receives artwork data uploaded from the artwork provider.

[2221] Input: Artwork file (JPEG, PNG, etc.)

[2222] Action: The server validates the file format and size, saves it to the database, adds it to the training dataset of the generative model, and retrains it.

[2223] Output: File verification results, re-learning completion notification

[2224] Step 3:

[2225] The user accesses the dedicated platform and logs in.

[2226] Input: User's email address, password

[2227] How it works: The server handles authentication and, if successful, redirects the user to the dashboard.

[2228] Output: Login success message, dashboard

[2229] Step 4:

[2230] The user inputs a work creation request in text format.

[2231] Input: Request for creation (e.g., "I want you to draw a scene with a cat at the seaside")

[2232] How it works: The server receives the request, generates a request ID, and saves it in the database.

[2233] Output: Request ID, request save completion notification

[2234] Step 5:

[2235] The server provides the request to the generative model and begins generating the artwork.

[2236] Input: User request, style model of a specific artist

[2237] How it works: The generative model generates artwork in a specific style based on the request and the style model.

[2238] Output: Generated artwork

[2239] Step 6:

[2240] The server stores the generated work in a database and generates a link.

[2241] Input: Generated artwork, request ID

[2242] How it works: The server stores the generated work in a database, associates it with a request ID, and generates a download link.

[2243] Output: Download link

[2244] Step 7:

[2245] The device will notify the user of the completion of generation and provide a download link.

[2246] Input: Download link

[2247] What it does: The device notifies the user that "Generation is complete" and provides a download link for the generated work.

[2248] Output: Generation completion notification, download link

[2249] Step 8:

[2250] The user clicks on the provided link to download the work.

[2251] Input: Download link

[2252] How it works: The user clicks on the link and the generated artwork downloads to their device.

[2253] Output: Downloaded artwork

[2254] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2255] System Overview

[2256] This invention relates to a digital art generation system, and in particular to an embodiment of a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[2257] Artist registration and work submission

[2258] Artist Registration

[2259] 1. The artist accesses the registration page of the dedicated platform and enters the required information (name, email address, password, etc.).

[2260] 2. The server receives the entered information and stores it in a database.

[2261] 3. The server automatically sends a registration confirmation email to the artist, notifying them that registration is complete.

[2262] Work provided

[2263] 1. Artist logs in to the platform.

[2264] 2. The artist selects the menu to upload a new work file.

[2265] 3. The artist uploads the artwork file (e.g. JPEG, PNG, etc.) to the platform.

[2266] 4. The server validates the uploaded file (file format, size, etc.) and stores it in the database.

[2267] 5. The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[2268] User requests and creation

[2269] User Login

[2270] 1. The user visits the platform's login page and enters their email address and password.

[2271] 2. The server performs the authentication process and redirects the user to the dashboard if authentication is successful.

[2272] Work request

[2273] 1. User clicks the "New Request" button from the dashboard.

[2274] 2. The user enters the request in text format (e.g., "Please draw a scene with a cat at the seaside").

[2275] 3. If there is an option for users to select a specific artist, set that option.

[2276] 4. The server uses an emotion engine to analyze emotions from the text entered by the user.

[2277] 5. The server adjusts and optimizes the request content based on the emotion analysis results obtained from the emotion engine.

[2278] Request Processing

[2279] 1. The server provides the tailored request content to the generative model and starts processing.

[2280] 2. The generative model analyzes the user's request and generates a work in the style of a specific artist.

[2281] 3. The server receives the generated work and stores it in a database.

[2282] Work provided

[2283] 1. The server generates a URL for the generated work and associates it with the request ID.

[2284] 2. The device notifies the user that "Generation is complete" and provides a download link.

[2285] 3. The user clicks on the provided link to download the generated work.

[2286] Specific examples

[2287] For example, artist A uploads his or her work to the platform, and the work is trained by the generative model. User B logs in and inputs a request, saying, "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and uses an emotion engine to analyze User B's emotions. If an emotion such as "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to User B. User B can then click the download link to obtain the generated work.

[2288] This system allows users to quickly obtain customized digital artwork based on their emotions, and also provides artists with new revenue opportunities.

[2289] The processing flow will be explained below.

[2290] Artist registration and work submission processing

[2291] Artist Registration

[2292] Step 1:

[2293] The artist accesses the registration page on the dedicated platform and enters the required information such as name, email address, and password.

[2294] Step 2:

[2295] The server receives the entered information and stores it in a database.

[2296] Step 3:

[2297] The server will automatically send a registration confirmation email to notify the artist that registration is complete.

[2298] Work provided

[2299] Step 1:

[2300] The artist logs into the platform.

[2301] Step 2:

[2302] The artist selects a menu item to upload a new work file.

[2303] Step 3:

[2304] Artists upload their artwork files (e.g. JPEG, PNG, etc.) to the platform.

[2305] Step 4:

[2306] The server validates the uploaded file (file type, size, etc.) and stores it in the database.

[2307] Step 5:

[2308] The server registers the metadata (e.g., title, description, tags) of the artwork files stored in the database.

[2309] Step 6:

[2310] The server adds the artwork file to the training dataset of the generative model and starts the retraining process of the generative model.

[2311] Handling user requests and creations

[2312] User Login

[2313] Step 1:

[2314] A user visits the platform's login page and enters their email address and password.

[2315] Step 2:

[2316] The server handles the authentication process and redirects the user to the dashboard if the entered information is correct.

[2317] Work request

[2318] Step 1:

[2319] User clicks the "New Request" button from the dashboard.

[2320] Step 2:

[2321] If the user enters the request in text format and selects a specific artist, set that option.

[2322] Step 3:

[2323] The user clicks the "Submit Request" button.

[2324] Request Processing

[2325] Step 1:

[2326] The server receives the request, generates a request ID and saves it in the database.

[2327] Step 2:

[2328] The server uses an emotion engine to analyze the user's emotion from the text included in the request.

[2329] Step 3:

[2330] The server optimizes the request content and makes appropriate adjustments based on the analysis results of the emotion engine.

[2331] Artwork generation by AI

[2332] Step 1:

[2333] The server provides the tailored request content to the generative model to initiate the generation process.

[2334] Step 2:

[2335] The generative model generates artwork in the style of a specific artist based on tailored requests.

[2336] Step 3:

[2337] The server receives the generated work and stores it in a database.

[2338] Work provided

[2339] Step 1:

[2340] The server generates a URL for the generated work and associates it with the request ID.

[2341] Step 2:

[2342] The device will notify the user that "generation is complete" and provide a download link.

[2343] Step 3:

[2344] The user clicks on the provided link to download the generated work.

[2345] Specific examples

[2346] Specific examples of artist registration

[2347] Step 1:

[2348] Artist A accesses the dedicated platform, enters the necessary information, and completes registration.

[2349] Step 2:

[2350] The server saves Artist A's information in a database and automatically sends a registration confirmation email.

[2351] Examples of work provided

[2352] Step 1:

[2353] Artist A logs in to the platform.

[2354] Step 2:

[2355] Artist A uploads a new work and the server validates the file.

[2356] Step 3:

[2357] The server stores the successfully verified file in the database and registers the metadata.

[2358] Step 4:

[2359] The server provides the artwork file to the generative model, and the AI ​​model begins retraining.

[2360] Request and creation examples

[2361] Step 1:

[2362] User B logs in to the platform and fills in the request form, saying, "I would like you to draw a scene of a cat standing with a sunset in the background."

[2363] Step 2:

[2364] The server receives the request and performs emotion analysis using an emotion engine.

[2365] Step 3:

[2366] The server optimizes the request based on the analysis results and provides it to the generative model to start the generation process.

[2367] Step 4:

[2368] The generative model generates artwork in the style of a particular artist, which is then stored on a server.

[2369] Step 5:

[2370] The device notifies User B and provides a download link.

[2371] Step 6:

[2372] User B clicks the link to download the generated work.

[2373] In this way, users can quickly obtain customized digital artwork based on their emotions, and artists can gain new revenue opportunities.

[2374] Example 2

[2375] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2376] While conventional digital art generation systems have the technology to generate artworks using the artist's style, it has been difficult to generate customized artworks that reflect the user's emotions. Furthermore, because the user's input request is passed directly to the generative model, the artwork generated does not necessarily reflect the user's intentions or emotions. This makes it difficult to increase user satisfaction.

[2377] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2378] In this invention, the server includes a means for performing sentiment analysis based on the user's request and optimizing the request content, a means for causing the generative model to generate a work in a specific style based on the user's request, and a means for generating a URL for the generated work and associating it with the request ID, thereby generating a customized artwork that reflects the user's sentiment and increasing user satisfaction.

[2379] "Work provider" refers to an individual or organization that registers on the dedicated platform and provides work data used to generate digital art.

[2380] "Dedicated Platform" means a web-based system through which work providers upload work data and users input work generation requests.

[2381] A "server" is a computer system that processes data received from work providers and users and performs various functions for work generation.

[2382] "Artwork data" is a general term for image files and related data uploaded by the creator of the work to train a generative model of digital art.

[2383] A "database" is a system for systematically storing uploaded work data, created works, user request information, etc., and for efficiently managing and searching such data.

[2384] A "generative model" is a machine learning model that learns the style of a particular artist and generates new artwork based on user requests.

[2385] "User" means an individual or entity that requests the creation of digital art using the Dedicated Platform.

[2386] The "request content" is text data that describes the specific requirements for the digital art that the user wants to create.

[2387] "Sentiment analysis" is the process of detecting emotions from the request content entered by the user and optimizing the request content based on those emotions.

[2388] "URL" is the address on the Internet for accessing the created work, and is provided to the user by the server.

[2389] A "request ID" is an identification number assigned to uniquely identify each request.

[2390] MODE FOR CARRYING OUT THE INVENTION

[2391] This invention relates to a system for generating digital art, specifically a system that combines a generative model that learns the artist's style with an emotion engine that recognizes the user's emotions. This system allows users to easily obtain digital artworks customized based on their own emotions.

[2392] System program processing

[2393] The system uses the following hardware and software:

[2394] Server: A central computer system that stores and processes work data and request data. Data is managed using an SQL or NoSQL database.

[2395] Terminal: A device used by artists and users to access the dedicated platform using a web browser.

[2396] Generative models: For example, using machine learning models such as Stable Diffusion to generate artwork in the style of a particular artist.

[2397] Sentiment engine: Analyzes emotions from user input text using natural language processing techniques, such as Transformer-based models (e.g., BERT).

[2398] Artist registration and work submission

[2399] Artists access the dedicated platform and register by entering required information such as their name, email address, and password. The server receives this information and stores it in a database. A registration confirmation email is automatically sent to the artist, who clicks on a link to complete the registration. After logging in, the artist uploads their artwork data. The server then verifies the uploaded data and adds it to the training dataset for the generative model.

[2400] Training a generative model

[2401] Based on the uploaded artwork data, the generative model learns the style of a specific artist, which is then stored in a database and used to generate artwork.

[2402] User requests and creation

[2403] Users access the dedicated platform and log in by entering their login information. After logging in, they can enter their request in text format and specify a specific artist. The server receives this request and performs emotional analysis using an emotion engine.

[2404] For example, if the prompt is "Please draw a scene of a cat standing by the sea in the sunset. Please create a calm atmosphere," the emotion engine will detect the emotion "calm" and optimize the request. Based on the optimized request, the generative model will generate a work, which will then be stored in a database by the server.

[2405] Providing generated works

[2406] The server generates a URL for the generated work and associates it with the request ID. A notification containing this URL is sent to the device, and the user can click the download link to download the work.

[2407] This system allows users to quickly obtain digital artworks that are customized to their emotions, and also allows artists to efficiently offer their works and gain new revenue opportunities.

[2408] Specific examples

[2409] For example, an artist uploads their work to the platform, and the work is trained by the generative model. A user logs in and inputs a request, such as "Please paint a scene of a cat standing with a sunset in the background." The server receives this request and analyzes the user's emotions using an emotion engine. If the emotion of "calm" is detected based on the analysis results, the color tone and atmosphere of the work are adjusted. The generative model generates a work based on this adjusted request. The generated work is saved by the server and notified to the user. The user can obtain the generated work by clicking a download link.

[2410] Specific embodiments of the present invention allow users to easily obtain customized digital artwork based on their emotions.

[2411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2412] Step 1: Artist Registration

[2413] The artist accesses the registration page of the dedicated platform and enters the required information such as name, email address, and password. The server receives the entered information and stores it in a database. The server automatically sends a registration confirmation email, and the artist completes the registration by clicking the link in the confirmation email.

[2414] Input: Artist name, email address, password

[2415] Output: The artist's information is saved in the database and a confirmation email is sent.

[2416] Step 2: Submit your work

[2417] An artist logs in to the platform and selects a menu to upload their artwork file. Once the artist uploads the artwork file (e.g., JPEG, PNG, etc.), the server validates the uploaded file and stores it in a database. The server then adds the artwork file to the training dataset for the generative model and begins the training process.

[2418] Input: Artist's artwork file

[2419] Output: Work files stored in a database, training dataset for generative models

[2420] Step 3: User Login

[2421] The user accesses the platform's login page and enters their email address and password. The server performs authentication and, if successful, redirects the user to the dashboard. The device displays a message to the user indicating successful login.

[2422] Input: Email address, Password

[2423] Output: Login successful message

[2424] Step 4: Submit a request

[2425] The user clicks the "New Request" button on the dashboard and enters the request in text format (e.g., "I want you to paint a scene of a cat standing with a sunset in the background"). A specific artist can be selected if necessary. The server receives and stores the input. The server uses an emotion engine to analyze the emotion from the request and optimizes the request based on the results.

[2426] Input: Request content in text format

[2427] Output: Sentiment analysis results, optimized request content

[2428] Step 5: Request Processing

[2429] The server provides the tailored request to the generative model to initiate processing. The generative model analyzes the request and generates a work based on the style of the specific artist. The server receives the generated work and stores it in a database.

[2430] Input: Optimized request content

[2431] Output: Generated artwork file

[2432] Step 6: Submit your work

[2433] The server generates a URL for the generated work and associates it with the request ID. The device notifies the user that "generation is complete" and provides a download link. The user clicks the provided link to download the generated work.

[2434] Input: Generated artwork file

[2435] Output: Download link for the work, Download work

[2436] By following these steps, users can easily obtain customized digital artworks, and this system is beneficial for both artists and users.

[2437] (Application example 2)

[2438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2439] Conventional digital art creation systems generate artwork based solely on a simple text request entered by the user, making it difficult to provide customized artwork that reflects the user's emotions and mood. Therefore, there is a need to effectively create and provide digital artwork that truly resonates with users. The present invention solves this problem and provides a more personalized digital art creation system that reflects the user's emotions.

[2440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2441] In this invention, the server includes a means for generating a work based on the emotion analysis result, a means for presenting a preview image of the generated work and offering the option of purchasing or downloading, and a means for storing the generated work and providing it to the user, thereby enabling the generation and provision of a customized digital artwork that reflects the emotion of the user.

[2442] A "work provider" is an entity that uploads their own work data to the dedicated platform.

[2443] The "dedicated platform" is an online system where creators can register and upload their work data and train the generative model.

[2444] "Work data" refers to digital content such as image files and text data that work providers upload to the platform.

[2445] The "server" is a central processing unit that stores artwork data in a database and trains the generative model.

[2446] A "database" is a storage device for storing uploaded work data and created works.

[2447] A "generative model" is an artificial intelligence model that learns from uploaded artwork data and generates digital artworks based on user requests.

[2448] "User" is the entity that enters a request to generate a digital artwork and receives the results.

[2449] An “artwork generation request” is a textual instruction that a user inputs into a Generative Model to generate a particular artwork.

[2450] "Emotion analysis results" are analysis results based on requests and emotions entered by users, and are data used to instruct the generative model.

[2451] A "preview image" is an image that displays a part or the whole of a generated digital artwork for the user to check.

[2452] "Purchase or Download Option" means the option that a user can choose to purchase or download the generated digital artwork for free.

[2453] "Generated Works" are digital artworks created by generative models based on user requests and sentiment analysis.

[2454] The present invention provides a specific system for generating and providing customized digital artworks that reflect a user's emotions. The system includes a means for an artwork provider to register and upload artwork data to a dedicated platform, a means for a server to store the uploaded artwork data in a database and train a generative model, a means for a user to input an artwork generation request, a means for the server to cause the generative model to generate an artwork based on the user's request and the results of emotion analysis, a means for presenting a preview image of the generated artwork and offering the option of purchasing or downloading, and a means for the server to store the generated artwork and provide it to the user.

[2455] Hardware and Software Configuration

[2456] server

[2457] The servers are equipped with high-performance hardware such as Intel Xeon processors and NVIDIA GPUs, and run generative and sentiment analysis models using software libraries such as Python, TensorFlow, and PyTorch.

[2458] The database can be a relational or NoSQL database such as PostgreSQL or MongoDB.

[2459] User terminal

[2460] User devices are smartphones or PCs, and access the platform using a dedicated application or web browser.

[2461] Smartphone applications are implemented as iOS or Android apps.

[2462] Sentiment Analysis and Generative Models

[2463] Emotion Analysis: Emotion recognition uses Python and the EmotionRecognition library to analyze emotional states from data input in text format.

[2464] Generative Modeling: Generative modeling uses Hugging Face's Transformers library and is performed by an AI model trained in the style of a specific artist.

[2465] Operating Procedure

[2466] Artist registration and work submission

[2467] 1. The artist registers an account on the dedicated platform and uploads their work data.

[2468] 2. The server verifies the uploaded artwork data, stores it in a database, and trains the generative model.

[2469] User requests and creation

[2470] 1. The user logs in to the dedicated app and enters a request to create a work in text format.

[2471] 2. The server uses an emotion engine to analyze emotions from the user's input.

[2472] 3. Generate prompts based on the sentiment analysis results and provide instructions to the generative model.

[2473] 4. The generative model generates a digital artwork in a specific style based on the user's request and the results of sentiment analysis.

[2474] Product preview and offering

[2475] 1. The server generates a preview image of the created work and presents it to the user.

[2476] 2. Users can preview the work and choose to purchase it or download it for free.

[2477] 3. The server stores the generated work in a database and provides a download link.

[2478] Specific examples

[2479] For example, if a user inputs "I feel tired" and requests "I want a drawing of a scene with a cat standing with a sunset in the background," the server sends this to the emotion engine and generates the "tired" emotion as an analysis result.

[2480] User sentiment: "I feel tired"

[2481] User request: "I want you to draw a scene of a cat standing with a sunset in the background."

[2482] Generated prompt: "Create a tired themed artwork of a cat standing against a sunset background in the style of Artist A."

[2483] A prompt is generated. The generative model generates a work based on this prompt and provides it to the user. This system makes it possible to quickly and effectively generate and provide customized digital artworks that reflect the user's emotions.

[2484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2485] Step 1:

[2486] Artist Registration

[2487] Artists access the dedicated platform and create an account by entering the required information, such as their name, email address, and password. The server receives the information entered and stores it in a database. A registration confirmation email is automatically generated and sent to the artist.

[2488] Input: Artist information such as name, email address, and password

[2489] Output: Artist information stored in the database and a confirmation email

[2490] Step 2:

[2491] Work provided

[2492] Artists log in to the platform and upload artwork files (JPEG, PNG, etc.) from their account management page. The server verifies the uploaded file format and size, stores it in a database, and adds the saved artwork data to the training dataset of the generative model, starting the model retraining process.

[2493] Input: Artist's work file

[2494] Output: Artwork data stored in a database and a retrained generative model

[2495] Step 3:

[2496] User Login

[2497] A user accesses the platform through a dedicated app or web browser and enters their email address and password. The server performs the authentication process, and if authentication is successful, redirects the user to the dashboard.

[2498] Input: User's email address and password

[2499] Output: Authentication result and redirect to dashboard

[2500] Step 4:

[2501] Enter your work request

[2502] Users can click the New Request button on the dashboard and enter a text-based production request, with the option to select a specific artist.

[2503] Input: User text request and artist selection

[2504] Output: The request data sent to the server

[2505] Step 5:

[2506] Emotion analysis

[2507] The server sends the request data to the emotion engine, which analyzes the user's input to determine the emotion. The emotion engine analyzes the text and identifies the dominant emotion (e.g., happy, sad, tired, etc.).

[2508] Input: User request text

[2509] Output: Emotion analysis results

[2510] Step 6:

[2511] Prompt Generation

[2512] The server creates prompts for the generative model based on the results of sentiment analysis and the user's request. For example, based on the request "I feel tired" and "A scene of a cat standing against a sunset," it generates a prompt such as "Create a tired-themed artwork of a cat standing against a sunset background in the style of Artist A."

[2513] Input: Sentiment analysis results and user requests

[2514] Output: Prompts for the generated model

[2515] Step 7:

[2516] Work generation

[2517] The server provides prompts to the generative model, which then generates the digital artwork. The generative model uses the learnings of a particular artist as a style to generate the digital artwork.

[2518] Input: Prompt for the generative model

[2519] Output: Generated digital artwork

[2520] Step 8:

[2521] Preview display

[2522] The server generates a preview image of the generated work and presents it to the user, who can then view the preview and select the option to purchase or download.

[2523] Input: Generated digital artwork

[2524] Output: Provide preview images and options to the user

[2525] Step 9:

[2526] Work provided

[2527] The server stores the generated work in a database and generates a download link, which is then sent to the user, who can then download or purchase the generated work.

[2528] Input: Generated digital artwork

[2529] Output: Download link and notification

[2530] This allows for the rapid creation and effective provision of customized digital artworks that reflect the user's emotions.

[2531] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2532] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2533] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2534] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2535] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2536] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2537] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2538] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2539] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2540] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2541] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2542] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2543] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2544] 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.

[2545] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2546] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2547] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2548] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2549] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2550] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2551] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2552] The following is further disclosed regarding the above embodiment.

[2553] (Claim 1)

[2554] A means for creators to register on a dedicated platform and upload their work data,

[2555] The server stores the uploaded artwork data in a database and trains the generative model.

[2556] a means for a user to input a work creation request in text form;

[2557] a means for the server to cause the generative model to generate a work in a particular style based on a user's request;

[2558] a means by which the server stores and provides the generated work to users;

[2559] A system including:

[2560] (Claim 2)

[2561] 10. The system of claim 1, further comprising means for providing a link that enables a user to download the generated work.

[2562] (Claim 3)

[2563] 10. The system of claim 1, further comprising means for notifying a download link for the generated work.

[2564] "Example 1"

[2565] (Claim 1)

[2566] A means for content providers to register on a dedicated platform and enter the necessary information,

[2567] A means for the server to receive the input information and store it in a database;

[2568] a means for the server to send a registration confirmation notice to indicate completion of registration;

[2569] A means for the work provider to log in to the platform and upload the work file;

[2570] The server has a means for verifying the uploaded artwork files and storing them in a database;

[2571] A means for the server to add the saved file to the training dataset of the generative model and perform retraining;

[2572] A means for users to log in to the platform and enter requests;

[2573] a means for the server to receive input requests and forward them to the generative model to generate artwork in a particular style;

[2574] means for the server to receive the generated work and store it in a database;

[2575] means for notifying a user of the availability of the terminal-generated work;

[2576] A system including:

[2577] (Claim 2)

[2578] 10. The system of claim 1, further comprising means for providing a link that enables a user to download the generated work.

[2579] (Claim 3)

[2580] 10. The system of claim 1, further comprising means for notifying a download link for the generated work.

[2581] "Application Example 1"

[2582] (Claim 1)

[2583] A means for creators to register on a dedicated platform and upload their work data,

[2584] The server stores the uploaded artwork data in a database and trains the generative model.

[2585] a means for a user to input a work creation request in text form;

[2586] a means for the server to cause the generative model to generate a work in a particular style based on a user's request;

[2587] a means by which the server stores and provides the generated work to users;

[2588] A means for users to request, instantly preview, purchase, and download digital art created in the style of a specific artist from their mobile device;

[2589] A system including:

[2590] (Claim 2)

[2591] 10. The system of claim 1, further comprising means for providing a link that enables a user to download the generated work.

[2592] (Claim 3)

[2593] 10. The system of claim 1, further comprising means for notifying a download link for the generated work.

[2594] "Example 2: Combining Emotion Engines"

[2595] (Claim 1)

[2596] A means for creators to register on a dedicated platform and upload their work data,

[2597] The server stores the uploaded artwork data in a database and trains the generative model.

[2598] a means for a user to input a work creation request in text form;

[2599] A means for the server to perform sentiment analysis based on the user's request and optimize the request content;

[2600] a means for the server to cause the generative model to generate a work in a particular style based on a user's request;

[2601] a means by which the server stores and provides the generated work to users;

[2602] A system including:

[2603] (Claim 2)

[2604] 10. The system of claim 1, further comprising means for the server to generate a URL for the generated work and associate it with the request ID.

[2605] (Claim 3)

[2606] 10. The system of claim 1, further comprising means for notifying a download link for the generated work.

[2607] "Application example 2 when combining emotion engines"

[2608] (Claim 1)

[2609] A means for creators to register on a dedicated platform and upload their work data,

[2610] The server stores the uploaded artwork data in a database and trains the generative model.

[2611] a means for a user to input a work creation request in text form;

[2612] A means ...

Claims

1. A means for creators to register on a dedicated platform and upload their work data, The server stores the uploaded artwork data in a database and trains the generative model. a means for a user to input a work creation request in text form; a means for the server to cause the generative model to generate a work in a particular style based on a user's request; a means by which the server stores and provides the generated work to users; A system including:

2. 10. The system of claim 1, further comprising means for providing a link that enables a user to download the generated work.

3. The system of claim 1 , further comprising means for notifying a download link for the created work.

Images