System

Abstract

To provide a platform for enabling a right holder to create and sell a generation AI model by utilizing his or her own right object, and to obtain profits.SOLUTION: A system comprising: means for storing enrollment information input by a user in a database; means for a registered user to input login information and authenticate; means for a user to upload his or her copyrighted material to a server; means for converting the uploaded copyrighted material into training data; means for creating a generative AI model based on the training data; means for generating answers to user questions using the generative AI model; and means for selling the generative AI model to other users to manage and distribute revenue.SELECTED DRAWING: Figure 1

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] Generative AI models are useful, but copyright issues can arise when their training data is copyrighted. Current systems make it difficult for users to use legitimate copyrighted material as training data when using generative AI models. Furthermore, there are limited means for monetizing these models without infringing on their own rights. This invention solves these issues by providing a platform that allows rights holders to create and sell generative AI models using their own copyrighted material and generate revenue. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: A system including a means for saving registration information entered by a user in a database, a means for a registered user to enter and authenticate login information, a means for a user to upload his or her own copyrighted material to a server, a means for converting the uploaded copyrighted material into training data, a means for creating a generative AI model based on the training data, a means for generating answers to user questions using the generative AI model, and a means for selling the generative AI model to other users and managing and distributing the profits makes it possible to create and sell generative AI models while avoiding copyright issues.

[0006] "User registration information" refers to personal information such as name, email address, and password that a user enters to use the system.

[0007] A "database" is a system for storing and managing registration information, learning data, generative AI models, revenue information, etc.

[0008] "Authentication" is the process of checking the login information (email address and password) entered by the user against a database to confirm that the user is a legitimate user.

[0009] "Rights" refers to copyrighted content such as documents, images, audio, and video that is owned by an author or rights holder.

[0010] A "server" is a set of computers or systems that receives and processes requests from users.

[0011] "Training Data" means data used by a generative AI model for training, including data that has been analyzed or converted from a copyrighted work.

[0012] A "generative AI model" is an artificial intelligence model that is trained based on learning data and generates answers to user questions.

[0013] "Sales procedure" refers to the process of processing purchase procedures and payments to provide generative AI models to other users.

[0014] "Revenue management" is the process of aggregating revenue generated from the sale of generative AI models and distributing it to rights holders. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that allows users to create generative AI models using their own proprietary content and sell them to other users to earn revenue. This system includes the following processes.

[0037] User registration and login functionality

[0038] 1. The user enters registration information such as name, email address, and password.

[0039] 2. The device sends the entered registration information to the server.

[0040] 3. The server stores the received registration information in a database.

[0041] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[0042] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[0043] Uploading copyrighted material and creating learning data

[0044] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[0045] 2. The device sends the selected file to the server.

[0046] 3. The server saves the received file in storage and analyzes it.

[0047] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[0048] Use and sale of generative AI models

[0049] 1. The user enters a question using the generative AI model and sends it to the server.

[0050] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[0051] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[0052] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[0053] Revenue sharing and management

[0054] 1. The server aggregates the sales and revenue data of the generated AI model.

[0055] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[0056] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[0057] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[0058] 5. The server notifies the user that the withdrawal process is complete.

[0059] Specific examples

[0060] For example, a writer (User A) uploads his or her own work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for a generative AI model. User A then uses the generative AI model to generate responses to questions about his or her own work. Another user, User B, likes this generative AI model and proceeds with a purchase. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[0061] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. This system is particularly useful as a means of effectively integrating copyright management and AI technology.

[0062] The processing flow will be explained below.

[0063] User registration and login functionality

[0064] User Registration

[0065] Step 1:

[0066] A user fills out a web form with registration information such as name, email address, and password.

[0067] Step 2:

[0068] The terminal transmits the entered registration information to the server.

[0069] Specific operation: Sends input data to the server via an HTTP POST request.

[0070] Step 3:

[0071] The server stores the received registration information in a database.

[0072] Specific behavior: Executes an SQL query and inserts user information into the database.

[0073] Step 4:

[0074] The server returns a registration success message to the terminal.

[0075] Specific operation: A "Registration completed" message is returned in the HTTP response.

[0076] User Login

[0077] Step 1:

[0078] The user enters their email address and password into the login form.

[0079] Step 2:

[0080] The device sends the login information to the server.

[0081] Specific behavior: Sends login information to the server via an HTTP POST request.

[0082] Step 3:

[0083] The server checks the entered information against a database and authenticates it.

[0084] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[0085] Step 4:

[0086] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[0087] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[0088] Uploading copyrighted material and creating learning data

[0089] Uploading Rights Material

[0090] Step 1:

[0091] The user selects the rights material file and clicks the upload button.

[0092] Step 2:

[0093] The terminal transmits the selected file to the server.

[0094] Specific operation: Send the file data in the body of the HTTP POST request.

[0095] Step 3:

[0096] The server stores the received file in storage.

[0097] Specific behavior: Saves a file to a specific directory on the server.

[0098] Creating training data

[0099] Step 1:

[0100] The server analyzes the uploaded files and converts them into learning data.

[0101] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[0102] Step 2:

[0103] The server incorporates the training data into the generated AI model.

[0104] Specific operation: Convert data format and feed it to AI model.

[0105] Step 3:

[0106] The server notifies the user that the model has finished training.

[0107] Specific operation: Notify the completion of learning using HTTP response or push notification.

[0108] Use and sale of generative AI models

[0109] Using generative AI models

[0110] Step 1:

[0111] The user enters a question using the generative AI model.

[0112] Step 2:

[0113] The device sends the question to the server.

[0114] Specific behavior: Send a HTTP POST request with the question in the body.

[0115] Step 3:

[0116] The server inputs the question into a generative AI model, which generates an answer.

[0117] What it does: Input a question into a generative AI model and process the resulting answer.

[0118] Step 4:

[0119] The server generates a response and sends it back to the terminal.

[0120] Specific operation: Response data is returned in the HTTP response.

[0121] Selling generative AI models

[0122] Step 1:

[0123] Another user selects a generative AI model and proceeds to the purchase page.

[0124] Step 2:

[0125] The terminal inputs payment information and sends it to the server.

[0126] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[0127] Step 3:

[0128] The server processes the payment information and completes the sale.

[0129] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[0130] Step 4:

[0131] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[0132] Specific behavior: Update the database and add permissions to the user account.

[0133] Revenue sharing and management

[0134] Revenue Management

[0135] Step 1:

[0136] The server collects sales and revenue data for the generated AI model.

[0137] Specific operation: Retrieves sales records from the database and aggregates sales data.

[0138] Step 2:

[0139] The server performs the calculations to distribute revenue to rights holders.

[0140] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[0141] Step 3:

[0142] The server reflects the revenue data in the user profile.

[0143] What it does: Updates each user's profile and makes their earnings information visible.

[0144] Withdrawing earnings

[0145] Step 1:

[0146] The user performs a withdrawal operation from the earnings screen.

[0147] Step 2:

[0148] The terminal sends a withdrawal request to the server.

[0149] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[0150] Step 3:

[0151] The server processes the withdrawal request and initiates the transfer.

[0152] Specific operation: Call the payment API and transfer money using the specified payment method.

[0153] Step 4:

[0154] The server notifies the user that the withdrawal process is complete.

[0155] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[0156] Example 1

[0157] 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."

[0158] In recent years, new business models using generative AI models have been gaining attention. However, there is a need for a system that allows users to smoothly execute the entire process of creating generative AI models using their own digital content and selling them to other users. In conventional systems, digital content management, training data generation, generative AI model sales, and revenue management are often performed on separate platforms, resulting in complex operations and reduced efficiency. Furthermore, there is a need for easy and transparent revenue distribution and withdrawal procedures. The objective of the present invention is to solve these problems and provide a system that allows users to efficiently earn revenue without infringing on rights.

[0159] 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.

[0160] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter login information and generate an authentication token, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to prompt sentences entered by users using the generative AI model, and means for selling the generative AI model to other users and managing and distributing sales data and revenue. This allows users to efficiently perform tasks from managing their digital content to withdrawing revenue within a single system.

[0161] "User" means any person or entity that uses the System, enters registration information to create an account, and uploads digital content or purchases generative AI models.

[0162] "Database" means an information storage system for storing and managing user registration information, uploaded digital content, sales revenue data, etc.

[0163] "Login Information" means the authentication information a user uses to access a system, typically consisting of an email address and password.

[0164] An "authentication token" is temporary authentication information issued to a user after they successfully log in to a system, and is used to identify the user in subsequent requests.

[0165] "Digital content" refers to the rightful property uploaded by a user, such as data files such as text files, image files, and audio files.

[0166] "Training Data" means data used to create a generative AI model, extracted from uploaded digital content.

[0167] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers in natural language in response to prompts from users.

[0168] A "prompt" is a sentence that explains the question or instruction that a user enters into a generative AI model.

[0169] "Sales Data" refers to revenue information recorded as a result of sales of generative AI models.

[0170] "Revenue" means the monetary benefits derived from the sale of a generative AI model, which are subject to distribution among users.

[0171] This invention is a system that allows users to create generative AI models using their own digital content and sell them to other users to earn revenue. This system enables users, terminals, and servers to work together to achieve the following functions:

[0172] First, the user uses a device (e.g., PC or smartphone) to enter registration information such as name, email address, and password. The device then sends the entered information to the server via an HTTP POST request, which the server receives and stores in a database (e.g., MySQL). At this point, a confirmation email of completed registration is automatically sent to the user's email address.

[0173] Next, when a user logs in to the system, they enter their email address and password. The device sends this information to the server, which then checks the corresponding user information in its database. If authentication is successful, the server generates an authentication token and returns it to the device, which then stores it in its local storage.

[0174] After logging in to the system, the user uploads digital content (e.g., text files, image files) to the server. The device sends the selected file to the server, which then stores the data in storage (e.g., Amazon S3). The server analyzes the uploaded digital content and converts it into text data. This converted text data is used as training data to create a generative AI model (e.g., GPT-3).

[0175] The generated AI model has the ability to generate answers in natural language in response to prompts entered by the user. For example, when a user enters a question such as "What is the main theme of this novel?", the server uses the generative AI model to generate the appropriate answer and sends it back to the device. The device then displays the generated answer to the user.

[0176] When another user purchases a generative AI model, the purchase process is carried out from the terminal. The user enters purchase information (e.g., credit card information) and sends it to the server, which then completes the payment through a payment service (e.g., Stripe). Once payment is complete, the server grants the purchasing user access to the generative AI model.

[0177] Regarding revenue management, the server aggregates the sales data and revenue data of the generated AI model and reflects it in the user profile. The user clicks the "Withdraw" button on the revenue screen and sends a withdrawal request to the server from their device. The server processes the request and transfers the money to the user through a payment service (e.g., PayPal). Once the withdrawal process is complete, the server notifies the user.

[0178] As a concrete example, a writer user uploads a PDF file of their own work, a novel, and creates a generative AI model based on this. The user uses the generative AI model to generate responses to questions such as, "What is the main theme of this novel?" Another user purchases this generative AI model and enters their payment information to complete the payment, granting the purchasing user the right to use the generative AI model. Revenues are then distributed appropriately through the server, and the user can withdraw their own revenues.

[0179] This system allows users to create generative AI models using digital content without infringing on copyrights, and efficiently generate revenue. Furthermore, the entire process is completed within a single system, significantly reducing operational complexity.

[0180] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0181] System program processing steps

[0182] Step 1: User Registration

[0183] 1. The user enters registration information such as name, email address, and password.

[0184] Input: Name, Email Address, Password

[0185] Output: The entered registration information

[0186] Specific action: Enter information into a web form and click the submit button.

[0187] 2. The device sends the entered registration information to the server via an HTTP POST request.

[0188] Input: The registration information entered

[0189] Output: Server request

[0190] Specific behavior: Creating and sending an HTTP request.

[0191] 3. The server stores the received registration information in a database.

[0192] Input: Received registration information

[0193] Output: Database update

[0194] Specific actions: Generates SQL queries and inserts data into a database.

[0195] 4. The server sends a confirmation email to the user's email address confirming registration.

[0196] Input: User's email address

[0197] Output:Confirmation email

[0198] Specific operation: A confirmation email is sent via the mail server.

[0199] Step 2: Log in

[0200] 1. The user enters their email address and password and submits the login information.

[0201] Input: Email address, password

[0202] Output: The entered login information

[0203] Specific action: Enter information into a web form and click the submit button.

[0204] 2. The device sends the entered login information to the server via an HTTP POST request.

[0205] Input: The login information entered

[0206] Output: Server request

[0207] Specific behavior: Creating and sending an HTTP request.

[0208] 3. The server retrieves the user information from the database and verifies the email address and password.

[0209] Input: Login information, database user information

[0210] Output: Authentication result

[0211] Specific behavior: Executes an SQL query to retrieve and collate user information.

[0212] 4. The server generates an authentication token and returns it to the device in JSON format.

[0213] Input: Authentication success information

[0214] Output: Authentication token

[0215] Specific operation: Generates an authentication token and returns it in the HTTP response.

[0216] 5. The device stores the authentication token in local storage and uses it for subsequent requests.

[0217] Input: Authentication Token

[0218] Output: Stored authentication token

[0219] Specific operation: The authentication token is saved in the browser's local storage.

[0220] Step 3: Upload your digital content

[0221] 1. The user selects digital content (e.g., a PDF file of a novel).

[0222] Input: Digital content files

[0223] Output: Selected file path

[0224] Specific operation: Opens a file selection dialog and selects a file.

[0225] 2. The device uploads the selected file to the server via an HTTP POST request.

[0226] Input: Selected file path

[0227] Output: Server request

[0228] Specific operation: Generates an HTTP request and sends the file to the server.

[0229] 3. The server saves the received file to storage (e.g. Amazon S3).

[0230] Input: Received file data

[0231] Output: Saved file path

[0232] Specific operation: Calls the storage API and saves the file.

[0233] 4. The server notifies the user that the save is complete.

[0234] Input: Saved file path

[0235] Output: Notification message

[0236] Specific operation: Sends HTTP responses and email notifications.

[0237] Step 4: Creating training data

[0238] 1. The server analyzes the file stored in the storage and converts it into text data (e.g., converting PDF to text).

[0239] Input: Saved file path

[0240] Output: Text data

[0241] Specific operation: Extracts text data using a PDF parsing library.

[0242] 2. The server converts the text data into an appropriate format (e.g., JSON) and saves it as training data for the generative AI model.

[0243] Input: Text data

[0244] Output: Formatted training data

[0245] Specific operation: Format text data into JSON format and save it in the database.

[0246] 3. The server stores the meta information of the converted learning data in a database.

[0247] Input: Formatted training data

[0248] Output: Database update

[0249] Specific operation: Generates an SQL query to insert meta information into the database.

[0250] Step 5: Use and sell the generative AI model

[0251] 1. The user inputs a question (prompt sentence) using the generative AI model.

[0252] Input: prompt statement

[0253] Output: The prompt text entered

[0254] Specific action: Enter a question into the web form and click the submit button.

[0255] 2. The device sends the question to the server via an HTTP POST request.

[0256] Input: The prompt text entered

[0257] Output: Server request

[0258] Specific operations: Generate an HTTP request and send a question to the server.

[0259] 3. The server receives the question and calls the corresponding generative AI model (e.g., GPT-3).

[0260] Input: Question, Generative AI Model

[0261] Output: Response data

[0262] What it does: Generate answers using a generative AI model.

[0263] 4. The server generates the answer from the AI ​​model and sends it to the device in JSON format.

[0264] Input: Answer data

[0265] Output: Answer response

[0266] Specific operation: The response data is converted into JSON format and sent as an HTTP response.

[0267] 5. The device displays the received answer to the user.

[0268] Input: Answer response

[0269] Output: Displayed answer

[0270] Specific action: Display the answer on a web page.

[0271] 6. Other users can purchase the generative AI model by clicking the "Purchase" button on the details page.

[0272] Input: Purchase Request

[0273] Output: Purchase procedure information

[0274] Specific operation: Click the purchase button to display the purchase procedure screen.

[0275] 7. The device enters the purchase information and sends it to the server via an HTTP POST request.

[0276] Input: Purchase procedure information

[0277] Output: Server request

[0278] Specific actions: Enter purchase information and click the submit button.

[0279] 8. The server processes the payment information and completes the payment through a payment service (e.g., Stripe).

[0280] Input: Payment information

[0281] Output: Payment completed

[0282] Specific operation: Call the payment API and process the payment.

[0283] 9. The server confirms the purchase and grants the purchasing user access to the generated AI model.

[0284] Input: Payment completion information

[0285] Output: Permission granted

[0286] Specific Actions: Update user profile and grant access privileges.

[0287] 10. The device displays a notification to the user that the purchase is complete.

[0288] Input: Purchase completion information

[0289] Output: Notification message

[0290] Specific operation: Display a purchase completion message.

[0291] Step 6: Revenue sharing and management

[0292] 1. The server aggregates the sales and revenue data of the generated AI model from the database.

[0293] Input: Sales data, revenue data

[0294] Output: Aggregation results

[0295] Specific behavior: Executes SQL queries and aggregates data.

[0296] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[0297] Input: Aggregation results

[0298] Output: Updated user profile

[0299] Specific Actions: Perform revenue share calculations and update user profiles.

[0300] 3. The server will notify the rights holder of the revenue details by email.

[0301] Input: Revenue sharing information

[0302] Output: Notification email

[0303] Specific operation: Revenue details are notified via the mail server.

[0304] 4. The user opens the earnings screen and clicks the "Withdraw" button.

[0305] Input: Withdrawal request

[0306] Output: Withdrawal procedure information

[0307] Specific actions: Click the Withdraw button to begin the withdrawal process.

[0308] 5. The terminal sends a withdrawal request to the server via an HTTP POST request.

[0309] Input: Withdrawal procedure information

[0310] Output: Server request

[0311] Specific actions: Enter withdrawal information and click the submit button.

[0312] 6. The server processes the withdrawal request and sends the money to the user via a payment service (e.g., PayPal).

[0313] Input: Withdrawal request information

[0314] Output: Transfer completed

[0315] Specific operation: Call the payment API and process the transfer.

[0316] 7. The server notifies the user by email that the withdrawal process is complete.

[0317] Input: Remittance completion information

[0318] Output: Notification email

[0319] Specific behavior: Sends notifications via a mail server.

[0320] (Application example 1)

[0321] 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."

[0322] Conventional content distribution services lack a mechanism for generating new content from digital content owned by users and generating revenue from it. Furthermore, there is no established method for properly managing and distributing the generated content and for fairly distributing revenue, which means that users are unable to effectively utilize their own creations.

[0323] 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.

[0324] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for providing newly generated content in a content distribution service, which allows users to earn revenue by using new content generated based on their own digital content.

[0325] "User" means an individual or legal entity that registers on the content distribution service platform and uploads digital content or uses a generative AI model.

[0326] "Registration Information" refers to information provided by a User when registering for the Service for the first time, including, for example, name, email address, and password.

[0327] "Authentication" is the process of verifying that a user is a legitimate user based on the login information they provide.

[0328] "Digital Content" means data in electronic form that is owned by a user, including, but not limited to, text, images, audio, etc.

[0329] A "server" is a computer system that stores, processes, and distributes data over the Internet.

[0330] "Uploading" is the process by which a user sends their digital content from a terminal to a server for storage.

[0331] "Training Data" means data extracted from uploaded digital content that is used to train a generative AI model.

[0332] A "generative AI model" is an artificial intelligence model that is trained based on learning data and generates answers to user questions and new content.

[0333] A "question" is an inquiry that a user inputs to a generated AI model.

[0334] An "answer" is the response that a generative AI model generates in response to a user's question.

[0335] "Sales" is the process of providing a generative AI model to other users and receiving payment in return.

[0336] "Revenue" means the financial benefit derived from the sale of a generative AI model.

[0337] "Management and distribution" refers to the process of aggregating the revenue generated from the sale of generative AI models and distributing it to relevant users in appropriate proportions.

[0338] A "content distribution service" is an online service for creating, sharing, buying, selling, and distributing digital content.

[0339] "Newly Generated Content" is new digital content that is automatically generated based on a user's digital content and a generative AI model.

[0340] The embodiment of the present invention is realized by constructing a system that includes three main elements: a server, a terminal, and a user.

[0341] Program Overview and Processing

[0342] 1. Registration and login functionality

[0343] The user uses their own device to enter registration information such as name, email address, and password. This information is sent from the device to the server, where it is stored in a database. After completing registration, the user enters their email address and password into the login form and sends it back to the server. The server verifies the entered information, generates an authentication token, and sends it back to the device.

[0344] 2. Uploading copyrighted material and creating training data

[0345] Users upload their digital content (e.g., text, images, audio, etc.) from their devices to the server, which stores the uploaded digital content and converts it into an appropriate format. The server then uses this data to train a generative AI model.

[0346] 3. Use and sale of generative AI models

[0347] The generated AI model can be used by a user by inputting a question using a device. The server receives the question, generates an answer using the corresponding generative AI model, and sends it back to the device. This generative AI model can also be purchased by other users. The server processes payment information, completes the purchase procedure, and grants the purchasing user access to the generative AI model.

[0348] 4. Revenue sharing and management

[0349] The server aggregates the sales data and revenue data of the generative AI model and distributes the revenue fairly to the rights holders. When a user submits a request to withdraw revenue, the server processes the withdrawal request and transfers the money to the user via the specified payment method. A notification of the completion of the withdrawal process is also sent from the server to the user.

[0350] Hardware and software used

[0351] Hardware: Smartphone (iOS or Android device)

[0352] Server: AWS or Google Cloud Platform

[0353] Database: MySQL or PostgreSQL

[0354] AI models: Generative AI models such as GPT-3 and BERT

[0355] Frontend: React Native for mobile app development

[0356] Backend: Node.js and Express framework

[0357] Specific examples

[0358] Example 1: Writer usage scenario

[0359] A writer uploads a short story they have written to an app from their smartphone. The server then analyzes the story and saves it as text data. A generative AI model is trained based on this data. The writer uses the generative AI model to generate responses to questions about their story. For example, if the question is "Who is the first character the protagonist meets in the story?", the generative AI model will respond with "The first character the protagonist meets is A."

[0360] Prompt Sentence Examples

[0361] "In the novel 'Star Messenger,' what is the name of the main character?"

[0362] With the system configuration described above, users can generate new content based on their own digital content and generate revenue from it.

[0363] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0364] Step 1:

[0365] A user inputs registration information such as name, email address, and password from a terminal. The terminal sends this input information to a server. The server stores the received information in a database and creates a profile for the registered user. The input includes name, email address, and password, and the output is the registered user information stored in the database.

[0366] Step 2:

[0367] A user who has completed registration enters their email address and password into the login form and sends them from the device to the server. The server checks the registration information against the database, and if it matches, generates an authentication token and sends it back to the device. The input includes the email address and password, and the output is the authentication token sent to the device.

[0368] Step 3:

[0369] The user selects their own digital content (text, images, audio, etc.) from the device and clicks the upload button. The device sends the selected file to the server. The server saves the received digital content in storage and begins data analysis. The input is the digital content file, and the output is the file saved in storage and the data to be analyzed.

[0370] Step 4:

[0371] The server converts the uploaded digital content into training data. Specifically, it converts the text and image formats into a format that is easy for the AI ​​model to learn, and stores the data in a database. The input is the uploaded digital content file, and the output is the converted training data.

[0372] Step 5:

[0373] The server creates a generative AI model based on the training data. To train the AI ​​model, the server runs an efficient learning process using many datasets. The input is the transformed training data, and the output is the generated AI model.

[0374] Step 6:

[0375] The user inputs a question from the device using a generative AI model. The device sends the question to the server, which then generates an answer using the corresponding generative AI model and sends it back to the device. The input is the question entered by the user, and the output is the answer returned by the server.

[0376] Step 7:

[0377] Another user executes the process of purchasing a generative AI model. The user completes the purchase procedure for the generative AI model from their device, and the device sends payment information to the server. The server processes the payment information, completes the purchase procedure, and grants the purchasing user access rights to the generative AI model. The input is the purchase procedure and payment information, and the output is a purchase completion notification and access rights to the generative AI model.

[0378] Step 8:

[0379] The server aggregates the sales data and revenue data of the generated AI model and distributes the revenue to the rights holder. When a user operates the revenue screen to withdraw revenue, the terminal sends a withdrawal request to the server. The server processes the withdrawal request and transfers the money to the user using the specified payment method. The server notifies the user that the withdrawal process is complete. The input is the withdrawal request, and the output is the withdrawal completion notification and the transfer.

[0380] 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.

[0381] The present invention combines a system that allows users to create generative AI models using their own proprietary property and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system includes the following processes.

[0382] User registration and login functionality

[0383] 1. A user fills out a web form with registration information such as name, email address, and password.

[0384] 2. The device sends the entered registration information to the server.

[0385] 3. The server stores the received registration information in a database.

[0386] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[0387] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[0388] Uploading copyrighted material and creating learning data

[0389] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[0390] 2. The device sends the selected file to the server.

[0391] 3. The server saves the received file in storage and analyzes it.

[0392] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[0393] Use and sale of generative AI models

[0394] 1. The user enters a question using the generative AI model and sends it to the server.

[0395] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[0396] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[0397] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[0398] Revenue sharing and management

[0399] 1. The server aggregates the sales and revenue data of the generated AI model.

[0400] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[0401] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[0402] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[0403] 5. The server notifies the user that the withdrawal process is complete.

[0404] Incorporating an emotion engine

[0405] 1. The device will be equipped with an emotion engine that will recognize the user's emotions in real time. The emotion engine will analyze the user's facial expressions, tone of voice, etc.

[0406] 2. The device sends the emotion data to the server.

[0407] 3. Based on the emotion data received by the server, the response of the generative AI model is adjusted and optimized.

[0408] 4. The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[0409] Specific examples

[0410] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about his or her work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends his or her emotional state to the server. The server optimizes the response based on his or her emotional state and provides it to User A. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[0411] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience by providing responses according to the user's emotional state.

[0412] The processing flow will be explained below.

[0413] User registration and login functionality

[0414] User Registration

[0415] Step 1:

[0416] A user fills out a web form with registration information such as name, email address, and password.

[0417] Step 2:

[0418] The terminal transmits the entered registration information to the server.

[0419] Specific operation: Sends input data to the server via an HTTP POST request.

[0420] Step 3:

[0421] The server stores the received registration information in a database.

[0422] Specific behavior: Executes an SQL query and inserts user information into the database.

[0423] Step 4:

[0424] The server returns a registration success message to the terminal.

[0425] Specific operation: A "Registration completed" message is returned in the HTTP response.

[0426] User Login

[0427] Step 1:

[0428] The user enters their email address and password into the login form.

[0429] Step 2:

[0430] The device sends the login information to the server.

[0431] Specific behavior: Sends login information to the server via an HTTP POST request.

[0432] Step 3:

[0433] The server checks the entered information against a database and authenticates it.

[0434] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[0435] Step 4:

[0436] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[0437] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[0438] Uploading copyrighted material and creating learning data

[0439] Uploading Rights Material

[0440] Step 1:

[0441] The user selects the rights material file and clicks the upload button.

[0442] Step 2:

[0443] The terminal transmits the selected file to the server.

[0444] Specific operation: Send the file data in the body of the HTTP POST request.

[0445] Step 3:

[0446] The server stores the received file in storage.

[0447] Specific behavior: Saves a file to a specific directory on the server.

[0448] Creating training data

[0449] Step 1:

[0450] The server analyzes the uploaded files and converts them into learning data.

[0451] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[0452] Step 2:

[0453] The server incorporates the training data into the generated AI model.

[0454] Specific operation: Convert data format and feed it to AI model.

[0455] Step 3:

[0456] The server notifies the user that the model has finished training.

[0457] Specific operation: Notify the completion of learning using HTTP response or push notification.

[0458] Use and sale of generative AI models

[0459] Using generative AI models

[0460] Step 1:

[0461] The user enters a question using the generative AI model.

[0462] Step 2:

[0463] The device sends the question to the server.

[0464] Specific behavior: Send a HTTP POST request with the question in the body.

[0465] Step 3:

[0466] The server inputs the question into a generative AI model, which generates an answer.

[0467] What it does: Input a question into a generative AI model and process the resulting answer.

[0468] Step 4:

[0469] The server generates a response and sends it back to the terminal.

[0470] Specific operation: Response data is returned in the HTTP response.

[0471] Selling generative AI models

[0472] Step 1:

[0473] Another user selects a generative AI model and proceeds to the purchase page.

[0474] Step 2:

[0475] The terminal inputs payment information and sends it to the server.

[0476] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[0477] Step 3:

[0478] The server processes the payment information and completes the sale.

[0479] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[0480] Step 4:

[0481] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[0482] Specific behavior: Update the database and add permissions to the user account.

[0483] Revenue sharing and management

[0484] Revenue Management

[0485] Step 1:

[0486] The server collects sales and revenue data for the generated AI model.

[0487] Specific operation: Retrieves sales records from the database and aggregates sales data.

[0488] Step 2:

[0489] The server performs the calculations to distribute revenue to rights holders.

[0490] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[0491] Step 3:

[0492] The server reflects the revenue data in the user profile.

[0493] What it does: Updates each user's profile and makes their earnings information visible.

[0494] Withdrawing earnings

[0495] Step 1:

[0496] The user performs a withdrawal operation from the earnings screen.

[0497] Step 2:

[0498] The terminal sends a withdrawal request to the server.

[0499] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[0500] Step 3:

[0501] The server processes the withdrawal request and initiates the transfer.

[0502] Specific operation: Call the payment API and transfer money using the specified payment method.

[0503] Step 4:

[0504] The server notifies the user that the withdrawal process is complete.

[0505] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[0506] Incorporating an emotion engine

[0507] Emotion recognition with emotion engine

[0508] Step 1:

[0509] A user inputs a question using a terminal equipped with an emotion engine.

[0510] Step 2:

[0511] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[0512] Specific operations: Captures data through the camera and microphone and runs emotion analysis algorithms.

[0513] Step 3:

[0514] The device transmits the emotion data to the server.

[0515] Specific operation: Sends emotion data in the body of an HTTP POST request.

[0516] Optimizing responses using emotional data

[0517] Step 1:

[0518] The server adjusts the response of the generative AI model based on the emotional data it receives.

[0519] What it does: Analyzes emotional data and adjusts response content and tone appropriately.

[0520] Step 2:

[0521] The server generates a response and sends it back to the terminal.

[0522] Specific operation: Returns an answer optimized based on emotional data in the HTTP response.

[0523] Specific examples

[0524] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about the work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends the emotional state to the server. The server optimizes the response based on User A's emotional state and provides it in a form appropriate to that emotion. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from purchases by other users to User A.

[0525] This invention provides a new means of generating revenue by creating and selling generative AI models without infringing copyrights. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience based on the user's emotional state. This system effectively integrates copyright management and AI technology, providing users with opportunities for advanced interaction.

[0526] Example 2

[0527] 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."

[0528] Conventional generative AI model creation systems have the problem of requiring users to easily use their own proprietary content to create generative AI models and then sell them to other users through a complicated process. Another issue is that the responses of generative AI models do not adapt to the user's emotional state, resulting in a uniform, non-personalized user experience. This requires a great deal of effort from users in the process of creating and selling generative AI models, making it difficult to maximize revenue.

[0529] 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.

[0530] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own rights to the server, means for converting the uploaded rights into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for recognizing user emotions and adjusting and optimizing the responses of the generative AI model based on the emotion data. This allows users to easily and efficiently import and sell rights into the generative AI model, and enables personalized responses to be provided based on the user's emotional state.

[0531] "User" means a person or organization that uses the System to enter registration information, upload Rights, and create and sell Generative AI Models.

[0532] "Rights" refers to intellectual property such as text, images, audio, and video that users upload to the system.

[0533] A "server" is a hardware and software system that receives, stores, and analyzes data sent by users, and creates and manages generative AI models.

[0534] "Terminal" means an electronic device such as a computer, smartphone, or tablet that a User uses to enter registration information and upload rights material.

[0535] A "database" is an information management system installed on a server for storing user registration information and data related to rights.

[0536] "Training data" refers to data that has been converted and formatted based on the copyrighted material uploaded by the user for the purpose of being used by the generative AI model for training.

[0537] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers to user questions.

[0538] An "authentication token" is a temporary identifier issued based on authentication information when a user logs in to a system.

[0539] An "emotion engine" is software that recognizes a user's emotions in real time and analyzes that emotional data.

[0540] "Revenue management" refers to a system that aggregates sales and revenue data from generative AI models and distributes them appropriately.

[0541] "Personalization" refers to the technique of adjusting the output of a system according to the individual preferences and feelings of the user.

[0542] MODE FOR CARRYING OUT THE INVENTION

[0543] The present invention combines a system that allows users to create generative AI models using their own rights and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system allows users to easily and efficiently import and sell their rights into generative AI models, and can provide personalized responses based on the user's emotional state.

[0544] Hardware and Software Configuration

[0545] The system utilizes a server, terminals, a database, an emotion engine, and a generative AI model. Specifically, each component plays the following role:

[0546] 1. Server:

[0547] Receive, store and analyze registration information and property data.

[0548] Use a database management system (e.g., MySQL, PostgreSQL).

[0549] Amazon S3 is used as cloud storage to permanently store data.

[0550] Apache Tika is used to convert rights materials in various formats into text data.

[0551] Generate training data and incorporate it into a generative AI model (e.g., GPT-3, BERT).

[0552] It also manages response generation, payment processing, and revenue sharing.

[0553] 2. Terminal:

[0554] Submits the registration information entered by the user via a web form to a server.

[0555] The right object is sent to the server via the file upload function.

[0556] A webcam and microphone are used to obtain the data (facial expressions and tone of voice) required for the emotion engine.

[0557] 3. Database:

[0558] The server stores and manages the received user information and rights information.

[0559] 4. Emotion Engine:

[0560] It recognizes the user's emotional state and transmits that data to a server.

[0561] Analyzes the user's facial expressions and tone of voice in real time.

[0562] Specific examples

[0563] For example, let's consider the case where User A, an author, uploads his or her work (a PDF file of a novel) to this system. User A accesses the system from a web browser on a device (PC or smartphone). User A enters registration information such as name, email address, and password, and sends it to the server. The server receives this information and stores it in a database.

[0564] User A then logs in, uses the upload function to select the novel PDF file "novel.pdf," and sends it to the server. The server saves the file to Amazon S3 and converts it to text data using Apache Tika. The converted text data is prepared as training data and fed into the generative AI model.

[0565] User A uses the generative AI model to input the prompt, "What action will the protagonist of this novel take next?" The server uses the generative AI model to generate a response, and returns the answer, "The protagonist decides to go on an adventure."

[0566] Furthermore, to analyze User A's emotional state, the device's camera and microphone are used to collect facial expressions and tone of voice. The emotion engine analyzes this data and sends it to the server. The server optimizes responses based on the emotional data and provides personalized responses to User A.

[0567] Another user, User B, may like this generative AI model and proceed with the purchase. After completing the payment procedure, User B obtains access to the generative AI model and can similarly enter questions and get answers. The revenue is appropriately calculated by the server and distributed to User A.

[0568] Prompt Sentence Examples

[0569] "Tell me what action the protagonist of this novel will take next."

[0570] "Generate a story related to this image."

[0571] "Use the emotion engine to generate a message based on my current emotions."

[0572] This allows the present invention to provide new revenue streams for users and a more personalized user experience using emotion recognition technology.

[0573] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0574] System program processing flow

[0575] User registration and login functionality

[0576] Step 1:

[0577] A user fills out a web form with registration information such as name, email address, and password.

[0578] Input: Name, email address, and password entered by the user into a web form

[0579] Output: Registration information

[0580] Specific operation: User A opens a website in a browser and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[0581] Step 2:

[0582] The terminal transmits the entered registration information to the server.

[0583] Input: Registration information entered by the user into a web form

[0584] Output: Sent to the server as an HTTP POST request

[0585] Specific operation: User A's browser sends the entered information.

[0586] Step 3:

[0587] The server stores the received registration information in a database.

[0588] Input: Registration information received by the server

[0589] Output: Save to database

[0590] Specific operation: The server stores the received information in MySQL and returns status code 200 if successful.

[0591] Step 4:

[0592] After completing registration, the user enters their email address and password into the login form and sends it to the server.

[0593] Input: Email address and password entered in the login form

[0594] Output: Sent to the server as an HTTP POST request

[0595] Specific operation: User A enters "taro@example.com" and "password123" into the login form.

[0596] Step 5:

[0597] The server verifies the entered email address and password, generates an authentication token, and returns it to the terminal.

[0598] Input: Email address and password received by the server

[0599] Output: Authentication token

[0600] Specific operation: The server checks the email address and password against the database, and if successful, generates and returns a JWT token.

[0601] Uploading copyrighted material and creating learning data

[0602] Step 1:

[0603] The user selects the right material (for example, a file such as text or an image) and uploads it to the server.

[0604] Input: Rights file

[0605] Output: User selection

[0606] Specific operation: User A selects the novel PDF file "novel.pdf".

[0607] Step 2:

[0608] The terminal transmits the selected file to the server.

[0609] Input: Selected files

[0610] Output: Upload to server

[0611] Specific behavior: The browser uploads the selected PDF file to the server.

[0612] Step 3:

[0613] The server stores the received files in cloud storage and performs format conversion and data cleansing as necessary.

[0614] Input: Received file

[0615] Output: Saving to cloud storage and format conversion

[0616] Specific operation: The server saves the received PDF file in Amazon S3 and converts it into text data using Apache Tika.

[0617] Step 4:

[0618] The server converts the rights into an appropriate format as learning data and incorporates it into the generative AI model.

[0619] Input: Converted text data

[0620] Output: Input to generative AI model as training data

[0621] What it does: The converted text data is fed into a generative AI model such as GPT-3 or BERT to begin the learning process.

[0622] Use and sale of generative AI models

[0623] Step 1:

[0624] The user inputs a question using the generative AI model and sends it to the server.

[0625] Input: User question

[0626] Output: Send a query to the server

[0627] Specific operation: User A enters the question "What will the protagonist of this novel do next?" and presses the send button.

[0628] Step 2:

[0629] The server receives the question, generates an answer using an appropriate generative AI model, and sends it to the device.

[0630] Input: User question

[0631] Output: The generated answer

[0632] Specific operation: The server inputs the question into the generation AI model, which generates the answer "The protagonist decides to go on an adventure."

[0633] Step 3:

[0634] Other users can purchase the generative AI model by completing the purchase process on their device.

[0635] Input: Purchase procedure information

[0636] Output: Purchase information sent to the server

[0637] Specific behavior: User B enters credit card information into the payment form and makes a purchase.

[0638] Step 4:

[0639] The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[0640] Input: Payment information

[0641] Output: Permission granted

[0642] Specific operation: After the payment is confirmed, the server sends User B an access key for the generated AI model.

[0643] Revenue sharing and management

[0644] Step 1:

[0645] The server aggregates the sales and revenue data generated by the AI ​​model.

[0646] Input: Sales data, revenue data

[0647] Output: Aggregated data

[0648] Specific operation: The server executes the revenue aggregation batch at 00:00 every day and aggregates sales information from the database.

[0649] Step 2:

[0650] The server calculates the distribution and reflects it in the user profile.

[0651] Input: Aggregated data

[0652] Output: Update user profile

[0653] What it does: Updates rightsholder profiles and displays revenue information.

[0654] Step 3:

[0655] The user submits a revenue withdrawal request.

[0656] Input: Withdrawal request

[0657] Output: Request sent to server

[0658] Specific operation: User A presses the "Withdraw" button on the revenue screen and enters the withdrawal amount.

[0659] Step 4:

[0660] The server processes the withdrawal request.

[0661] Input: Withdrawal request

[0662] Output: Transfer to user account

[0663] What happens: The server processes the withdrawal request and transfers the funds to the user's account using the PayPal API.

[0664] Step 5:

[0665] The server will acknowledge completion.

[0666] Input: Withdrawal result

[0667] Output: Completion notification

[0668] Specific operation: After the withdrawal is completed, the server sends a notification email to User A.

[0669] Incorporating an emotion engine

[0670] Step 1:

[0671] The device is equipped with an emotion engine that recognizes the user's emotions.

[0672] Input: User facial expressions and tone of voice

[0673] Output: Emotion data

[0674] Specific operation: Uses the device's camera and microphone to collect the user's facial expressions and tone of voice.

[0675] Step 2:

[0676] The device transmits the emotion data to the server.

[0677] Input: Emotion data

[0678] output: Send to the server

[0679] Specific operation: The device sends emotion data to the server in real time via WebSocket.

[0680] Step 3:

[0681] The server adjusts and optimizes the response of the generative AI model based on the emotion data.

[0682] Input: Emotion data

[0683] output: the optimized response

[0684] Specific operation: The server analyzes the emotional data, determines that the user is in a happy state, and generates a positive response.

[0685] Step 4:

[0686] The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[0687] Input: Optimized response

[0688] output:Returning the answer to the user

[0689] Specific Action: The server generates a positive response, "That's great!", and sends it back to the user.

[0690] As a result, through the above processing steps, a system is realized in which users can easily incorporate their rights into a generative AI model and obtain personalized responses.

[0691] (Application example 2)

[0692] 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."

[0693] Generative models using modern AI technology require a large amount of data, making it difficult to personalize them based on user emotions and interactions. In particular, when selling generative AI models to other users, determining how the buyer will actually use the model and whether the model's responses adapt to the user's emotions are important issues. Furthermore, autonomous vehicles require real-time emotion recognition to ensure passenger safety and comfort. However, with conventional systems, it has been technically difficult to recognize user emotions and incorporate their feedback into generative AI models.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for saving registration information entered by a user in a database; means for a registered user to enter and authenticate login information; means for a user to upload their own rights to the server; means for converting the uploaded rights into training data; means for creating a generative AI model based on the training data; means for generating answers to user questions using the generative AI model; means for selling the generative AI model to other users and managing and distributing revenue; means for the terminal to recognize the user's emotions in real time and analyze the emotional data; and means for transmitting the analyzed emotional data to the server and adjusting the response of the generative AI model. This enables personalization of the generative AI model based on the user's emotional state and real-time emotion recognition to ensure passenger safety and comfort.

[0695] "User" means a person who uploads copyrighted material and creates and sells generative AI models.

[0696] "Registration information" refers to information such as name, email address, and password that a user provides to the system.

[0697] "Database" means a storage device where the System stores registration information and other data.

[0698] "Login Information" means the combination of email address and password used by a User to access the System.

[0699] "Authentication" is the process by which a system verifies a user's login information and grants access.

[0700] "Rights" refer to intellectual property such as text and images owned by the user.

[0701] A "server" is a central computing device on which a system runs.

[0702] "Uploading" is the act of a user transmitting his / her own copyrighted material to a server.

[0703] "Training data" is data used to create a generative AI model.

[0704] A "generative AI model" is an artificial intelligence model that generates answers to questions based on training data.

[0705] The "emotion engine" is software that recognizes the user's emotions in real time and analyzes the data.

[0706] "Emotion data" is data relating to the user's emotional state obtained by the emotion engine.

[0707] "Revenue" is the profit earned from selling generative AI models to other users.

[0708] "Distribution" is the act of allocating earned revenue to rights holders.

[0709] "Real-time" refers to instantly recognizing and processing user actions and emotions.

[0710] MODE FOR CARRYING OUT THE INVENTION

[0711] The present invention combines a system that allows users to create generative AI models based on their own property and sell them to other users to earn revenue, with an emotion engine that recognizes user emotions. The system includes user registration, uploading property, creating generative AI models, and selling and managing generative AI models.

[0712] System Overview

[0713] Users create an account by filling out a web form. The registration information is stored in a database. When logging in, users enter their email address and password, which is then authenticated by the server.

[0714] Uploading copyrighted material is done by users sending intellectual property such as text and images to the server. The server receives this and converts it into training data. A generative AI model is created based on this training data, allowing users to generate appropriate answers to their own questions. This generative AI model can then be sold to other users.

[0715] Incorporating an emotion engine

[0716] The device is equipped with an emotion engine that recognizes the user's emotions in real time. This emotion engine analyzes facial expressions, tone of voice, etc. The emotion data is sent to a server, which adjusts and optimizes the response of the generative AI model based on the user's emotional state.

[0717] Hardware and software used

[0718] Camera: Uses the smartphone's built-in camera to capture the user's facial expressions.

[0719] Emotion Recognition Model: Uses an emotion recognition model built with Keras and TensorFlow.

[0720] dlib library: A library for face detection.

[0721] Server: A central server for processing and managing the database, emotion engine, and generative AI models.

[0722] Data acquisition and processing

[0723] The camera captures the user's facial expressions, and the captured image is cropped using the dlib library, then fed into an emotion recognition model to analyze the user's emotions in real time.

[0724] Data processing and transmission

[0725] The analyzed emotion data is sent to a server, which receives it and adjusts the generative AI model's response to the user's emotional state. For example, if a passenger feels stressed in an autonomous vehicle, the vehicle's infotainment system will adjust to provide a comfortable environment.

[0726] Specific examples

[0727] For example, if the emotion engine detects that a passenger is feeling stressed, it can change the interior lighting to a warmer color, play relaxing music, and even change the vehicle's driving mode to make the journey smoother and more relaxing.

[0728] Prompt Sentence Examples

[0729] "How can I build an application to analyze passenger facial expressions and change infotainment system settings based on their emotions?"

[0730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0731] Step 1:

[0732] A user enters registration information such as name, email address, and password into a web form and submits it. Input: Name, email address, password. Data processing: Registration information is confirmed and saved in a database. Output: Notification of successful registration.

[0733] Step 2:

[0734] A registered user enters their email address and password into the login form and sends it to the server. Input: Email address, password. Data calculation: The server compares the entered email address and password with the information in the database and generates an authentication token. Output: Authentication token issued. If authentication is successful, the user is notified that login is complete.

[0735] Step 3:

[0736] The user selects their own copyrighted material (for example, text or images) and sends it to the server via an upload form. Input: Copyrighted material file. Data processing: The server saves the received file in storage and converts it into learning data. Output: Notification that copyrighted material upload is complete.

[0737] Step 4:

[0738] The server analyzes the uploaded rights object and converts it into training data for the generative AI model. Input: Rights object file. Data calculation: Converts the rights object into an appropriate format and adds it to the training dataset for the generative AI model. Output: Notification that the training data is ready.

[0739] Step 5:

[0740] The server creates a generative AI model based on the training data. Input: Training data. Data calculation: The server builds and trains a generative AI model. Output: Notification that the generative AI model has been created.

[0741] Step 6:

[0742] The user inputs a question using the generative AI model and sends it to the server. Input: Question in text format. Data calculation: The server uses the generative AI model to generate an answer corresponding to the question. Output: Answer text.

[0743] Step 7:

[0744] The device uses a camera to capture facial expressions and tone of voice to recognize the user's emotions in real time. Input: User's facial image and voice data. Data processing: The emotion engine analyzes facial expressions and tone of voice to generate emotion data. Output: Emotion data.

[0745] Step 8:

[0746] The device sends the collected emotion data to the server. Input: Emotion data. Data calculation: The server reflects the received emotion data in the model response and optimizes it. Output: Optimized response data.

[0747] Step 9:

[0748] The server adjusts the generated answer based on the user's emotional state and sends it back to the device. Input: Optimized answer data. Data processing: The server adjusts the answer data based on the user's emotions. Output: Adjusted answer text.

[0749] Step 10:

[0750] A purchase procedure is carried out so that other users can purchase the generative AI model. Input: Purchase information. Data calculation: The server processes the payment information and completes the purchase procedure. Output: Granting access rights to the generative AI model and notification of purchase completion.

[0751] Step 11:

[0752] The server aggregates the sales data and revenue data of the generated AI model and distributes revenue to sellers. Input: Sales data, revenue data. Data calculation: The server calculates revenue and distributes it to sellers. Output: Notification of revenue distribution.

[0753] Step 12:

[0754] To withdraw revenue, the user operates the revenue screen and sends a withdrawal request to the server. Input: Withdrawal request. Data calculation: The server processes the withdrawal request and transfers the money to the user using the specified payment method. Output: Notification of withdrawal completion.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] [Second embodiment]

[0759] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0760] 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.

[0761] 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).

[0762] 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.

[0763] 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.

[0764] 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).

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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."

[0771] The present invention is a system that allows users to create generative AI models using their own proprietary content and sell them to other users to earn revenue. This system includes the following processes.

[0772] User registration and login functionality

[0773] 1. The user enters registration information such as name, email address, and password.

[0774] 2. The device sends the entered registration information to the server.

[0775] 3. The server stores the received registration information in a database.

[0776] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[0777] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[0778] Uploading copyrighted material and creating learning data

[0779] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[0780] 2. The device sends the selected file to the server.

[0781] 3. The server saves the received file in storage and analyzes it.

[0782] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[0783] Use and sale of generative AI models

[0784] 1. The user enters a question using the generative AI model and sends it to the server.

[0785] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[0786] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[0787] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[0788] Revenue sharing and management

[0789] 1. The server aggregates the sales and revenue data of the generated AI model.

[0790] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[0791] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[0792] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[0793] 5. The server notifies the user that the withdrawal process is complete.

[0794] Specific examples

[0795] For example, a writer (User A) uploads his or her own work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for a generative AI model. User A then uses the generative AI model to generate responses to questions about his or her own work. Another user, User B, likes this generative AI model and proceeds with a purchase. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[0796] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. This system is particularly useful as a means of effectively integrating copyright management and AI technology.

[0797] The processing flow will be explained below.

[0798] User registration and login functionality

[0799] User Registration

[0800] Step 1:

[0801] A user fills out a web form with registration information such as name, email address, and password.

[0802] Step 2:

[0803] The terminal transmits the entered registration information to the server.

[0804] Specific operation: Sends input data to the server via an HTTP POST request.

[0805] Step 3:

[0806] The server stores the received registration information in a database.

[0807] Specific behavior: Executes an SQL query and inserts user information into the database.

[0808] Step 4:

[0809] The server returns a registration success message to the terminal.

[0810] Specific operation: A "Registration completed" message is returned in the HTTP response.

[0811] User Login

[0812] Step 1:

[0813] The user enters their email address and password into the login form.

[0814] Step 2:

[0815] The device sends the login information to the server.

[0816] Specific behavior: Sends login information to the server via an HTTP POST request.

[0817] Step 3:

[0818] The server checks the entered information against a database and authenticates it.

[0819] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[0820] Step 4:

[0821] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[0822] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[0823] Uploading copyrighted material and creating learning data

[0824] Uploading Rights Material

[0825] Step 1:

[0826] The user selects the rights material file and clicks the upload button.

[0827] Step 2:

[0828] The terminal transmits the selected file to the server.

[0829] Specific operation: Send the file data in the body of the HTTP POST request.

[0830] Step 3:

[0831] The server stores the received file in storage.

[0832] Specific behavior: Saves a file to a specific directory on the server.

[0833] Creating training data

[0834] Step 1:

[0835] The server analyzes the uploaded files and converts them into learning data.

[0836] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[0837] Step 2:

[0838] The server incorporates the training data into the generated AI model.

[0839] Specific operation: Convert data format and feed it to AI model.

[0840] Step 3:

[0841] The server notifies the user that the model has finished training.

[0842] Specific operation: Notify the completion of learning using HTTP response or push notification.

[0843] Use and sale of generative AI models

[0844] Using generative AI models

[0845] Step 1:

[0846] The user enters a question using the generative AI model.

[0847] Step 2:

[0848] The device sends the question to the server.

[0849] Specific behavior: Send a HTTP POST request with the question in the body.

[0850] Step 3:

[0851] The server inputs the question into a generative AI model, which generates an answer.

[0852] What it does: Input a question into a generative AI model and process the resulting answer.

[0853] Step 4:

[0854] The server generates a response and sends it back to the terminal.

[0855] Specific operation: Response data is returned in the HTTP response.

[0856] Selling generative AI models

[0857] Step 1:

[0858] Another user selects a generative AI model and proceeds to the purchase page.

[0859] Step 2:

[0860] The terminal inputs payment information and sends it to the server.

[0861] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[0862] Step 3:

[0863] The server processes the payment information and completes the sale.

[0864] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[0865] Step 4:

[0866] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[0867] Specific behavior: Update the database and add permissions to the user account.

[0868] Revenue sharing and management

[0869] Revenue Management

[0870] Step 1:

[0871] The server collects sales and revenue data for the generated AI model.

[0872] Specific operation: Retrieves sales records from the database and aggregates sales data.

[0873] Step 2:

[0874] The server performs the calculations to distribute revenue to rights holders.

[0875] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[0876] Step 3:

[0877] The server reflects the revenue data in the user profile.

[0878] What it does: Updates each user's profile and makes their earnings information visible.

[0879] Withdrawing earnings

[0880] Step 1:

[0881] The user performs a withdrawal operation from the earnings screen.

[0882] Step 2:

[0883] The terminal sends a withdrawal request to the server.

[0884] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[0885] Step 3:

[0886] The server processes the withdrawal request and initiates the transfer.

[0887] Specific operation: Call the payment API and transfer money using the specified payment method.

[0888] Step 4:

[0889] The server notifies the user that the withdrawal process is complete.

[0890] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[0891] Example 1

[0892] 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."

[0893] In recent years, new business models using generative AI models have been gaining attention. However, there is a need for a system that allows users to smoothly execute the entire process of creating generative AI models using their own digital content and selling them to other users. In conventional systems, digital content management, training data generation, generative AI model sales, and revenue management are often performed on separate platforms, resulting in complex operations and reduced efficiency. Furthermore, there is a need for easy and transparent revenue distribution and withdrawal procedures. The objective of the present invention is to solve these problems and provide a system that allows users to efficiently earn revenue without infringing on rights.

[0894] 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.

[0895] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter login information and generate an authentication token, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to prompt sentences entered by users using the generative AI model, and means for selling the generative AI model to other users and managing and distributing sales data and revenue. This allows users to efficiently perform tasks from managing their digital content to withdrawing revenue within a single system.

[0896] "User" means any person or entity that uses the System, enters registration information to create an account, and uploads digital content or purchases generative AI models.

[0897] "Database" means an information storage system for storing and managing user registration information, uploaded digital content, sales revenue data, etc.

[0898] "Login Information" means the authentication information a user uses to access a system, typically consisting of an email address and password.

[0899] An "authentication token" is temporary authentication information issued to a user after they successfully log in to a system, and is used to identify the user in subsequent requests.

[0900] "Digital content" refers to the rightful property uploaded by a user, such as data files such as text files, image files, and audio files.

[0901] "Training Data" means data used to create a generative AI model, extracted from uploaded digital content.

[0902] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers in natural language in response to prompts from users.

[0903] A "prompt" is a sentence that explains the question or instruction that a user enters into a generative AI model.

[0904] "Sales Data" refers to revenue information recorded as a result of sales of generative AI models.

[0905] "Revenue" means the monetary benefits derived from the sale of a generative AI model, which are subject to distribution among users.

[0906] This invention is a system that allows users to create generative AI models using their own digital content and sell them to other users to earn revenue. This system enables users, terminals, and servers to work together to achieve the following functions:

[0907] First, the user uses a device (e.g., PC or smartphone) to enter registration information such as name, email address, and password. The device then sends the entered information to the server via an HTTP POST request, which the server receives and stores in a database (e.g., MySQL). At this point, a confirmation email of completed registration is automatically sent to the user's email address.

[0908] Next, when a user logs in to the system, they enter their email address and password. The device sends this information to the server, which then checks the corresponding user information in its database. If authentication is successful, the server generates an authentication token and returns it to the device, which then stores it in its local storage.

[0909] After logging in to the system, the user uploads digital content (e.g., text files, image files) to the server. The device sends the selected file to the server, which then stores the data in storage (e.g., Amazon S3). The server analyzes the uploaded digital content and converts it into text data. This converted text data is used as training data to create a generative AI model (e.g., GPT-3).

[0910] The generated AI model has the ability to generate answers in natural language in response to prompts entered by the user. For example, when a user enters a question such as "What is the main theme of this novel?", the server uses the generative AI model to generate the appropriate answer and sends it back to the device. The device then displays the generated answer to the user.

[0911] When another user purchases a generative AI model, the purchase process is carried out from the terminal. The user enters purchase information (e.g., credit card information) and sends it to the server, which then completes the payment through a payment service (e.g., Stripe). Once payment is complete, the server grants the purchasing user access to the generative AI model.

[0912] Regarding revenue management, the server aggregates the sales data and revenue data of the generated AI model and reflects it in the user profile. The user clicks the "Withdraw" button on the revenue screen and sends a withdrawal request to the server from their device. The server processes the request and transfers the money to the user through a payment service (e.g., PayPal). Once the withdrawal process is complete, the server notifies the user.

[0913] As a concrete example, a writer user uploads a PDF file of their own work, a novel, and creates a generative AI model based on this. The user uses the generative AI model to generate responses to questions such as, "What is the main theme of this novel?" Another user purchases this generative AI model and enters their payment information to complete the payment, granting the purchasing user the right to use the generative AI model. Revenues are then distributed appropriately through the server, and the user can withdraw their own revenues.

[0914] This system allows users to create generative AI models using digital content without infringing on copyrights, and efficiently generate revenue. Furthermore, the entire process is completed within a single system, significantly reducing operational complexity.

[0915] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0916] System program processing steps

[0917] Step 1: User Registration

[0918] 1. The user enters registration information such as name, email address, and password.

[0919] Input: Name, Email Address, Password

[0920] Output: The entered registration information

[0921] Specific action: Enter information into a web form and click the submit button.

[0922] 2. The device sends the entered registration information to the server via an HTTP POST request.

[0923] Input: The registration information entered

[0924] Output: Server request

[0925] Specific behavior: Creating and sending an HTTP request.

[0926] 3. The server stores the received registration information in a database.

[0927] Input: Received registration information

[0928] Output: Database update

[0929] Specific actions: Generates SQL queries and inserts data into a database.

[0930] 4. The server sends a confirmation email to the user's email address confirming registration.

[0931] Input: User's email address

[0932] Output:Confirmation email

[0933] Specific operation: A confirmation email is sent via the mail server.

[0934] Step 2: Log in

[0935] 1. The user enters their email address and password and submits the login information.

[0936] Input: Email address, password

[0937] Output: The entered login information

[0938] Specific action: Enter information into a web form and click the submit button.

[0939] 2. The device sends the entered login information to the server via an HTTP POST request.

[0940] Input: The login information entered

[0941] Output: Server request

[0942] Specific behavior: Creating and sending an HTTP request.

[0943] 3. The server retrieves the user information from the database and verifies the email address and password.

[0944] Input: Login information, database user information

[0945] Output: Authentication result

[0946] Specific behavior: Executes an SQL query to retrieve and collate user information.

[0947] 4. The server generates an authentication token and returns it to the device in JSON format.

[0948] Input: Authentication success information

[0949] Output: Authentication token

[0950] Specific operation: Generates an authentication token and returns it in the HTTP response.

[0951] 5. The device stores the authentication token in local storage and uses it for subsequent requests.

[0952] Input: Authentication Token

[0953] Output: Stored authentication token

[0954] Specific operation: The authentication token is saved in the browser's local storage.

[0955] Step 3: Upload your digital content

[0956] 1. The user selects digital content (e.g., a PDF file of a novel).

[0957] Input: Digital content files

[0958] Output: Selected file path

[0959] Specific operation: Opens a file selection dialog and selects a file.

[0960] 2. The device uploads the selected file to the server via an HTTP POST request.

[0961] Input: Selected file path

[0962] Output: Server request

[0963] Specific operation: Generates an HTTP request and sends the file to the server.

[0964] 3. The server saves the received file to storage (e.g. Amazon S3).

[0965] Input: Received file data

[0966] Output: Saved file path

[0967] Specific operation: Calls the storage API and saves the file.

[0968] 4. The server notifies the user that the save is complete.

[0969] Input: Saved file path

[0970] Output: Notification message

[0971] Specific operation: Sends HTTP responses and email notifications.

[0972] Step 4: Creating training data

[0973] 1. The server analyzes the file stored in the storage and converts it into text data (e.g., converting PDF to text).

[0974] Input: Saved file path

[0975] Output: Text data

[0976] Specific operation: Extracts text data using a PDF parsing library.

[0977] 2. The server converts the text data into an appropriate format (e.g., JSON) and saves it as training data for the generative AI model.

[0978] Input: Text data

[0979] Output: Formatted training data

[0980] Specific operation: Format text data into JSON format and save it in the database.

[0981] 3. The server stores the meta information of the converted learning data in a database.

[0982] Input: Formatted training data

[0983] Output: Database update

[0984] Specific operation: Generates an SQL query to insert meta information into the database.

[0985] Step 5: Use and sell the generative AI model

[0986] 1. The user inputs a question (prompt sentence) using the generative AI model.

[0987] Input: prompt statement

[0988] Output: The prompt text entered

[0989] Specific action: Enter a question into the web form and click the submit button.

[0990] 2. The device sends the question to the server via an HTTP POST request.

[0991] Input: The prompt text entered

[0992] Output: Server request

[0993] Specific operations: Generate an HTTP request and send a question to the server.

[0994] 3. The server receives the question and calls the corresponding generative AI model (e.g., GPT-3).

[0995] Input: Question, Generative AI Model

[0996] Output: Response data

[0997] What it does: Generate answers using a generative AI model.

[0998] 4. The server generates the answer from the AI ​​model and sends it to the device in JSON format.

[0999] Input: Answer data

[1000] Output: Answer response

[1001] Specific operation: The response data is converted into JSON format and sent as an HTTP response.

[1002] 5. The device displays the received answer to the user.

[1003] Input: Answer response

[1004] Output: Displayed answer

[1005] Specific action: Display the answer on a web page.

[1006] 6. Other users can purchase the generative AI model by clicking the "Purchase" button on the details page.

[1007] Input: Purchase Request

[1008] Output: Purchase procedure information

[1009] Specific operation: Click the purchase button to display the purchase procedure screen.

[1010] 7. The device enters the purchase information and sends it to the server via an HTTP POST request.

[1011] Input: Purchase procedure information

[1012] Output: Server request

[1013] Specific actions: Enter purchase information and click the submit button.

[1014] 8. The server processes the payment information and completes the payment through a payment service (e.g., Stripe).

[1015] Input: Payment information

[1016] Output: Payment completed

[1017] Specific operation: Call the payment API and process the payment.

[1018] 9. The server confirms the purchase and grants the purchasing user access to the generated AI model.

[1019] Input: Payment completion information

[1020] Output: Permission granted

[1021] Specific Actions: Update user profile and grant access privileges.

[1022] 10. The device displays a notification to the user that the purchase is complete.

[1023] Input: Purchase completion information

[1024] Output: Notification message

[1025] Specific operation: Display a purchase completion message.

[1026] Step 6: Revenue sharing and management

[1027] 1. The server aggregates the sales and revenue data of the generated AI model from the database.

[1028] Input: Sales data, revenue data

[1029] Output: Aggregation results

[1030] Specific behavior: Executes SQL queries and aggregates data.

[1031] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[1032] Input: Aggregation results

[1033] Output: Updated user profile

[1034] Specific Actions: Perform revenue share calculations and update user profiles.

[1035] 3. The server will notify the rights holder of the revenue details by email.

[1036] Input: Revenue sharing information

[1037] Output: Notification email

[1038] Specific operation: Revenue details are notified via the mail server.

[1039] 4. The user opens the earnings screen and clicks the "Withdraw" button.

[1040] Input: Withdrawal request

[1041] Output: Withdrawal procedure information

[1042] Specific actions: Click the Withdraw button to begin the withdrawal process.

[1043] 5. The terminal sends a withdrawal request to the server via an HTTP POST request.

[1044] Input: Withdrawal procedure information

[1045] Output: Server request

[1046] Specific actions: Enter withdrawal information and click the submit button.

[1047] 6. The server processes the withdrawal request and sends the money to the user via a payment service (e.g., PayPal).

[1048] Input: Withdrawal request information

[1049] Output: Transfer completed

[1050] Specific operation: Call the payment API and process the transfer.

[1051] 7. The server notifies the user by email that the withdrawal process is complete.

[1052] Input: Remittance completion information

[1053] Output: Notification email

[1054] Specific behavior: Sends notifications via a mail server.

[1055] (Application example 1)

[1056] 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."

[1057] Conventional content distribution services lack a mechanism for generating new content from digital content owned by users and generating revenue from it. Furthermore, there is no established method for properly managing and distributing the generated content and for fairly distributing revenue, which means that users are unable to effectively utilize their own creations.

[1058] 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.

[1059] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for providing newly generated content in a content distribution service, which allows users to earn revenue by using new content generated based on their own digital content.

[1060] "User" means an individual or legal entity that registers on the content distribution service platform and uploads digital content or uses a generative AI model.

[1061] "Registration Information" refers to information provided by a User when registering for the Service for the first time, including, for example, name, email address, and password.

[1062] "Authentication" is the process of verifying that a user is a legitimate user based on the login information they provide.

[1063] "Digital Content" means data in electronic form that is owned by a user, including, but not limited to, text, images, audio, etc.

[1064] A "server" is a computer system that stores, processes, and distributes data over the Internet.

[1065] "Uploading" is the process by which a user sends their digital content from a terminal to a server for storage.

[1066] "Training Data" means data extracted from uploaded digital content that is used to train a generative AI model.

[1067] A "generative AI model" is an artificial intelligence model that is trained based on learning data and generates answers to user questions and new content.

[1068] A "question" is an inquiry that a user inputs to a generated AI model.

[1069] An "answer" is the response that a generative AI model generates in response to a user's question.

[1070] "Sales" is the process of providing a generative AI model to other users and receiving payment in return.

[1071] "Revenue" means the financial benefit derived from the sale of a generative AI model.

[1072] "Management and distribution" refers to the process of aggregating the revenue generated from the sale of generative AI models and distributing it to relevant users in appropriate proportions.

[1073] A "content distribution service" is an online service for creating, sharing, buying, selling, and distributing digital content.

[1074] "Newly Generated Content" is new digital content that is automatically generated based on a user's digital content and a generative AI model.

[1075] The embodiment of the present invention is realized by constructing a system that includes three main elements: a server, a terminal, and a user.

[1076] Program Overview and Processing

[1077] 1. Registration and login functionality

[1078] The user uses their own device to enter registration information such as name, email address, and password. This information is sent from the device to the server, where it is stored in a database. After completing registration, the user enters their email address and password into the login form and sends it back to the server. The server verifies the entered information, generates an authentication token, and sends it back to the device.

[1079] 2. Uploading copyrighted material and creating training data

[1080] Users upload their digital content (e.g., text, images, audio, etc.) from their devices to the server, which stores the uploaded digital content and converts it into an appropriate format. The server then uses this data to train a generative AI model.

[1081] 3. Use and sale of generative AI models

[1082] The generated AI model can be used by a user by inputting a question using a device. The server receives the question, generates an answer using the corresponding generative AI model, and sends it back to the device. This generative AI model can also be purchased by other users. The server processes payment information, completes the purchase procedure, and grants the purchasing user access to the generative AI model.

[1083] 4. Revenue sharing and management

[1084] The server aggregates the sales data and revenue data of the generative AI model and distributes the revenue fairly to the rights holders. When a user submits a request to withdraw revenue, the server processes the withdrawal request and transfers the money to the user via the specified payment method. A notification of the completion of the withdrawal process is also sent from the server to the user.

[1085] Hardware and software used

[1086] Hardware: Smartphone (iOS or Android device)

[1087] Server: AWS or Google Cloud Platform

[1088] Database: MySQL or PostgreSQL

[1089] AI models: Generative AI models such as GPT-3 and BERT

[1090] Frontend: React Native for mobile app development

[1091] Backend: Node.js and Express framework

[1092] Specific examples

[1093] Example 1: Writer usage scenario

[1094] A writer uploads a short story they have written to an app from their smartphone. The server then analyzes the story and saves it as text data. A generative AI model is trained based on this data. The writer uses the generative AI model to generate responses to questions about their story. For example, if the question is "Who is the first character the protagonist meets in the story?", the generative AI model will respond with "The first character the protagonist meets is A."

[1095] Prompt Sentence Examples

[1096] "In the novel 'Star Messenger,' what is the name of the main character?"

[1097] With the system configuration described above, users can generate new content based on their own digital content and generate revenue from it.

[1098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1099] Step 1:

[1100] A user inputs registration information such as name, email address, and password from a terminal. The terminal sends this input information to a server. The server stores the received information in a database and creates a profile for the registered user. The input includes name, email address, and password, and the output is the registered user information stored in the database.

[1101] Step 2:

[1102] A user who has completed registration enters their email address and password into the login form and sends them from the device to the server. The server checks the registration information against the database, and if it matches, generates an authentication token and sends it back to the device. The input includes the email address and password, and the output is the authentication token sent to the device.

[1103] Step 3:

[1104] The user selects their own digital content (text, images, audio, etc.) from the device and clicks the upload button. The device sends the selected file to the server. The server saves the received digital content in storage and begins data analysis. The input is the digital content file, and the output is the file saved in storage and the data to be analyzed.

[1105] Step 4:

[1106] The server converts the uploaded digital content into training data. Specifically, it converts the text and image formats into a format that is easy for the AI ​​model to learn, and stores the data in a database. The input is the uploaded digital content file, and the output is the converted training data.

[1107] Step 5:

[1108] The server creates a generative AI model based on the training data. To train the AI ​​model, the server runs an efficient learning process using many datasets. The input is the transformed training data, and the output is the generated AI model.

[1109] Step 6:

[1110] The user inputs a question from the device using a generative AI model. The device sends the question to the server, which then generates an answer using the corresponding generative AI model and sends it back to the device. The input is the question entered by the user, and the output is the answer returned by the server.

[1111] Step 7:

[1112] Another user executes the process of purchasing a generative AI model. The user completes the purchase procedure for the generative AI model from their device, and the device sends payment information to the server. The server processes the payment information, completes the purchase procedure, and grants the purchasing user access rights to the generative AI model. The input is the purchase procedure and payment information, and the output is a purchase completion notification and access rights to the generative AI model.

[1113] Step 8:

[1114] The server aggregates the sales data and revenue data of the generated AI model and distributes the revenue to the rights holder. When a user operates the revenue screen to withdraw revenue, the terminal sends a withdrawal request to the server. The server processes the withdrawal request and transfers the money to the user using the specified payment method. The server notifies the user that the withdrawal process is complete. The input is the withdrawal request, and the output is the withdrawal completion notification and the transfer.

[1115] 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.

[1116] The present invention combines a system that allows users to create generative AI models using their own proprietary property and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system includes the following processes.

[1117] User registration and login functionality

[1118] 1. A user fills out a web form with registration information such as name, email address, and password.

[1119] 2. The device sends the entered registration information to the server.

[1120] 3. The server stores the received registration information in a database.

[1121] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[1122] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[1123] Uploading copyrighted material and creating learning data

[1124] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[1125] 2. The device sends the selected file to the server.

[1126] 3. The server saves the received file in storage and analyzes it.

[1127] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[1128] Use and sale of generative AI models

[1129] 1. The user enters a question using the generative AI model and sends it to the server.

[1130] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[1131] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[1132] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[1133] Revenue sharing and management

[1134] 1. The server aggregates the sales and revenue data of the generated AI model.

[1135] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[1136] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[1137] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[1138] 5. The server notifies the user that the withdrawal process is complete.

[1139] Incorporating an emotion engine

[1140] 1. The device will be equipped with an emotion engine that will recognize the user's emotions in real time. The emotion engine will analyze the user's facial expressions, tone of voice, etc.

[1141] 2. The device sends the emotion data to the server.

[1142] 3. Based on the emotion data received by the server, the response of the generative AI model is adjusted and optimized.

[1143] 4. The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[1144] Specific examples

[1145] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about his or her work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends his or her emotional state to the server. The server optimizes the response based on his or her emotional state and provides it to User A. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[1146] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience by providing responses according to the user's emotional state.

[1147] The processing flow will be explained below.

[1148] User registration and login functionality

[1149] User Registration

[1150] Step 1:

[1151] A user fills out a web form with registration information such as name, email address, and password.

[1152] Step 2:

[1153] The terminal transmits the entered registration information to the server.

[1154] Specific operation: Sends input data to the server via an HTTP POST request.

[1155] Step 3:

[1156] The server stores the received registration information in a database.

[1157] Specific behavior: Executes an SQL query and inserts user information into the database.

[1158] Step 4:

[1159] The server returns a registration success message to the terminal.

[1160] Specific operation: A "Registration completed" message is returned in the HTTP response.

[1161] User Login

[1162] Step 1:

[1163] The user enters their email address and password into the login form.

[1164] Step 2:

[1165] The device sends the login information to the server.

[1166] Specific behavior: Sends login information to the server via an HTTP POST request.

[1167] Step 3:

[1168] The server checks the entered information against a database and authenticates it.

[1169] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[1170] Step 4:

[1171] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[1172] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[1173] Uploading copyrighted material and creating learning data

[1174] Uploading Rights Material

[1175] Step 1:

[1176] The user selects the rights material file and clicks the upload button.

[1177] Step 2:

[1178] The terminal transmits the selected file to the server.

[1179] Specific operation: Send the file data in the body of the HTTP POST request.

[1180] Step 3:

[1181] The server stores the received file in storage.

[1182] Specific behavior: Saves a file to a specific directory on the server.

[1183] Creating training data

[1184] Step 1:

[1185] The server analyzes the uploaded files and converts them into learning data.

[1186] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[1187] Step 2:

[1188] The server incorporates the training data into the generated AI model.

[1189] Specific operation: Convert data format and feed it to AI model.

[1190] Step 3:

[1191] The server notifies the user that the model has finished training.

[1192] Specific operation: Notify the completion of learning using HTTP response or push notification.

[1193] Use and sale of generative AI models

[1194] Using generative AI models

[1195] Step 1:

[1196] The user enters a question using the generative AI model.

[1197] Step 2:

[1198] The device sends the question to the server.

[1199] Specific behavior: Send a HTTP POST request with the question in the body.

[1200] Step 3:

[1201] The server inputs the question into a generative AI model, which generates an answer.

[1202] What it does: Input a question into a generative AI model and process the resulting answer.

[1203] Step 4:

[1204] The server generates a response and sends it back to the terminal.

[1205] Specific operation: Response data is returned in the HTTP response.

[1206] Selling generative AI models

[1207] Step 1:

[1208] Another user selects a generative AI model and proceeds to the purchase page.

[1209] Step 2:

[1210] The terminal inputs payment information and sends it to the server.

[1211] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[1212] Step 3:

[1213] The server processes the payment information and completes the sale.

[1214] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[1215] Step 4:

[1216] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[1217] Specific behavior: Update the database and add permissions to the user account.

[1218] Revenue sharing and management

[1219] Revenue Management

[1220] Step 1:

[1221] The server collects sales and revenue data for the generated AI model.

[1222] Specific operation: Retrieves sales records from the database and aggregates sales data.

[1223] Step 2:

[1224] The server performs the calculations to distribute revenue to rights holders.

[1225] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[1226] Step 3:

[1227] The server reflects the revenue data in the user profile.

[1228] What it does: Updates each user's profile and makes their earnings information visible.

[1229] Withdrawing earnings

[1230] Step 1:

[1231] The user performs a withdrawal operation from the earnings screen.

[1232] Step 2:

[1233] The terminal sends a withdrawal request to the server.

[1234] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[1235] Step 3:

[1236] The server processes the withdrawal request and initiates the transfer.

[1237] Specific operation: Call the payment API and transfer money using the specified payment method.

[1238] Step 4:

[1239] The server notifies the user that the withdrawal process is complete.

[1240] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[1241] Incorporating an emotion engine

[1242] Emotion recognition with emotion engine

[1243] Step 1:

[1244] A user inputs a question using a terminal equipped with an emotion engine.

[1245] Step 2:

[1246] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[1247] Specific operations: Captures data through the camera and microphone and runs emotion analysis algorithms.

[1248] Step 3:

[1249] The device transmits the emotion data to the server.

[1250] Specific operation: Sends emotion data in the body of an HTTP POST request.

[1251] Optimizing responses using emotional data

[1252] Step 1:

[1253] The server adjusts the response of the generative AI model based on the emotional data it receives.

[1254] What it does: Analyzes emotional data and adjusts response content and tone appropriately.

[1255] Step 2:

[1256] The server generates a response and sends it back to the terminal.

[1257] Specific operation: Returns an answer optimized based on emotional data in the HTTP response.

[1258] Specific examples

[1259] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about the work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends the emotional state to the server. The server optimizes the response based on User A's emotional state and provides it in a form appropriate to that emotion. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from purchases by other users to User A.

[1260] This invention provides a new means of generating revenue by creating and selling generative AI models without infringing copyrights. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience based on the user's emotional state. This system effectively integrates copyright management and AI technology, providing users with opportunities for advanced interaction.

[1261] Example 2

[1262] 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."

[1263] Conventional generative AI model creation systems have the problem of requiring users to easily use their own proprietary content to create generative AI models and then sell them to other users through a complicated process. Another issue is that the responses of generative AI models do not adapt to the user's emotional state, resulting in a uniform, non-personalized user experience. This requires a great deal of effort from users in the process of creating and selling generative AI models, making it difficult to maximize revenue.

[1264] 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.

[1265] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own rights to the server, means for converting the uploaded rights into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for recognizing user emotions and adjusting and optimizing the responses of the generative AI model based on the emotion data. This allows users to easily and efficiently import and sell rights into the generative AI model, and enables personalized responses to be provided based on the user's emotional state.

[1266] "User" means a person or organization that uses the System to enter registration information, upload Rights, and create and sell Generative AI Models.

[1267] "Rights" refers to intellectual property such as text, images, audio, and video that users upload to the system.

[1268] A "server" is a hardware and software system that receives, stores, and analyzes data sent by users, and creates and manages generative AI models.

[1269] "Terminal" means an electronic device such as a computer, smartphone, or tablet that a User uses to enter registration information and upload rights material.

[1270] A "database" is an information management system installed on a server for storing user registration information and data related to rights.

[1271] "Training data" refers to data that has been converted and formatted based on the copyrighted material uploaded by the user for the purpose of being used by the generative AI model for training.

[1272] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers to user questions.

[1273] An "authentication token" is a temporary identifier issued based on authentication information when a user logs in to a system.

[1274] An "emotion engine" is software that recognizes a user's emotions in real time and analyzes that emotional data.

[1275] "Revenue management" refers to a system that aggregates sales and revenue data from generative AI models and distributes them appropriately.

[1276] "Personalization" refers to the technique of adjusting the output of a system according to the individual preferences and feelings of the user.

[1277] MODE FOR CARRYING OUT THE INVENTION

[1278] The present invention combines a system that allows users to create generative AI models using their own rights and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system allows users to easily and efficiently import and sell their rights into generative AI models, and can provide personalized responses based on the user's emotional state.

[1279] Hardware and Software Configuration

[1280] The system utilizes a server, terminals, a database, an emotion engine, and a generative AI model. Specifically, each component plays the following role:

[1281] 1. Server:

[1282] Receive, store and analyze registration information and property data.

[1283] Use a database management system (e.g., MySQL, PostgreSQL).

[1284] Amazon S3 is used as cloud storage to permanently store data.

[1285] Apache Tika is used to convert rights materials in various formats into text data.

[1286] Generate training data and incorporate it into a generative AI model (e.g., GPT-3, BERT).

[1287] It also manages response generation, payment processing, and revenue sharing.

[1288] 2. Terminal:

[1289] Submits the registration information entered by the user via a web form to a server.

[1290] The right object is sent to the server via the file upload function.

[1291] A webcam and microphone are used to obtain the data (facial expressions and tone of voice) required for the emotion engine.

[1292] 3. Database:

[1293] The server stores and manages the received user information and rights information.

[1294] 4. Emotion Engine:

[1295] It recognizes the user's emotional state and transmits that data to a server.

[1296] Analyzes the user's facial expressions and tone of voice in real time.

[1297] Specific examples

[1298] For example, let's consider the case where User A, an author, uploads his or her work (a PDF file of a novel) to this system. User A accesses the system from a web browser on a device (PC or smartphone). User A enters registration information such as name, email address, and password, and sends it to the server. The server receives this information and stores it in a database.

[1299] User A then logs in, uses the upload function to select the novel PDF file "novel.pdf," and sends it to the server. The server saves the file to Amazon S3 and converts it to text data using Apache Tika. The converted text data is prepared as training data and fed into the generative AI model.

[1300] User A uses the generative AI model to input the prompt, "What action will the protagonist of this novel take next?" The server uses the generative AI model to generate a response, and returns the answer, "The protagonist decides to go on an adventure."

[1301] Furthermore, to analyze User A's emotional state, the device's camera and microphone are used to collect facial expressions and tone of voice. The emotion engine analyzes this data and sends it to the server. The server optimizes responses based on the emotional data and provides personalized responses to User A.

[1302] Another user, User B, may like this generative AI model and proceed with the purchase. After completing the payment procedure, User B obtains access to the generative AI model and can similarly enter questions and get answers. The revenue is appropriately calculated by the server and distributed to User A.

[1303] Prompt Sentence Examples

[1304] "Tell me what action the protagonist of this novel will take next."

[1305] "Generate a story related to this image."

[1306] "Use the emotion engine to generate a message based on my current emotions."

[1307] This allows the present invention to provide new revenue streams for users and a more personalized user experience using emotion recognition technology.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] System program processing flow

[1310] User registration and login functionality

[1311] Step 1:

[1312] A user fills out a web form with registration information such as name, email address, and password.

[1313] Input: Name, email address, and password entered by the user into a web form

[1314] Output: Registration information

[1315] Specific operation: User A opens a website in a browser and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[1316] Step 2:

[1317] The terminal transmits the entered registration information to the server.

[1318] Input: Registration information entered by the user into a web form

[1319] Output: Sent to the server as an HTTP POST request

[1320] Specific operation: User A's browser sends the entered information.

[1321] Step 3:

[1322] The server stores the received registration information in a database.

[1323] Input: Registration information received by the server

[1324] Output: Save to database

[1325] Specific operation: The server stores the received information in MySQL and returns status code 200 if successful.

[1326] Step 4:

[1327] After completing registration, the user enters their email address and password into the login form and sends it to the server.

[1328] Input: Email address and password entered in the login form

[1329] Output: Sent to the server as an HTTP POST request

[1330] Specific operation: User A enters "taro@example.com" and "password123" into the login form.

[1331] Step 5:

[1332] The server verifies the entered email address and password, generates an authentication token, and returns it to the terminal.

[1333] Input: Email address and password received by the server

[1334] Output: Authentication token

[1335] Specific operation: The server checks the email address and password against the database, and if successful, generates and returns a JWT token.

[1336] Uploading copyrighted material and creating learning data

[1337] Step 1:

[1338] The user selects the right material (for example, a file such as text or an image) and uploads it to the server.

[1339] Input: Rights file

[1340] Output: User selection

[1341] Specific operation: User A selects the novel PDF file "novel.pdf".

[1342] Step 2:

[1343] The terminal transmits the selected file to the server.

[1344] Input: Selected files

[1345] Output: Upload to server

[1346] Specific behavior: The browser uploads the selected PDF file to the server.

[1347] Step 3:

[1348] The server stores the received files in cloud storage and performs format conversion and data cleansing as necessary.

[1349] Input: Received file

[1350] Output: Saving to cloud storage and format conversion

[1351] Specific operation: The server saves the received PDF file in Amazon S3 and converts it into text data using Apache Tika.

[1352] Step 4:

[1353] The server converts the rights into an appropriate format as learning data and incorporates it into the generative AI model.

[1354] Input: Converted text data

[1355] Output: Input to generative AI model as training data

[1356] What it does: The converted text data is fed into a generative AI model such as GPT-3 or BERT to begin the learning process.

[1357] Use and sale of generative AI models

[1358] Step 1:

[1359] The user inputs a question using the generative AI model and sends it to the server.

[1360] Input: User question

[1361] Output: Send a query to the server

[1362] Specific operation: User A enters the question "What will the protagonist of this novel do next?" and presses the send button.

[1363] Step 2:

[1364] The server receives the question, generates an answer using an appropriate generative AI model, and sends it to the device.

[1365] Input: User question

[1366] Output: The generated answer

[1367] Specific operation: The server inputs the question into the generation AI model, which generates the answer "The protagonist decides to go on an adventure."

[1368] Step 3:

[1369] Other users can purchase the generative AI model by completing the purchase process on their device.

[1370] Input: Purchase procedure information

[1371] Output: Purchase information sent to the server

[1372] Specific behavior: User B enters credit card information into the payment form and makes a purchase.

[1373] Step 4:

[1374] The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[1375] Input: Payment information

[1376] Output: Permission granted

[1377] Specific operation: After the payment is confirmed, the server sends User B an access key for the generated AI model.

[1378] Revenue sharing and management

[1379] Step 1:

[1380] The server aggregates the sales and revenue data generated by the AI ​​model.

[1381] Input: Sales data, revenue data

[1382] Output: Aggregated data

[1383] Specific operation: The server executes the revenue aggregation batch at 00:00 every day and aggregates sales information from the database.

[1384] Step 2:

[1385] The server calculates the distribution and reflects it in the user profile.

[1386] Input: Aggregated data

[1387] Output: Update user profile

[1388] What it does: Updates rightsholder profiles and displays revenue information.

[1389] Step 3:

[1390] The user submits a revenue withdrawal request.

[1391] Input: Withdrawal request

[1392] Output: Request sent to server

[1393] Specific operation: User A presses the "Withdraw" button on the revenue screen and enters the withdrawal amount.

[1394] Step 4:

[1395] The server processes the withdrawal request.

[1396] Input: Withdrawal request

[1397] Output: Transfer to user account

[1398] What happens: The server processes the withdrawal request and transfers the funds to the user's account using the PayPal API.

[1399] Step 5:

[1400] The server will acknowledge completion.

[1401] Input: Withdrawal result

[1402] Output: Completion notification

[1403] Specific operation: After the withdrawal is completed, the server sends a notification email to User A.

[1404] Incorporating an emotion engine

[1405] Step 1:

[1406] The device is equipped with an emotion engine that recognizes the user's emotions.

[1407] Input: User facial expressions and tone of voice

[1408] Output: Emotion data

[1409] Specific operation: Uses the device's camera and microphone to collect the user's facial expressions and tone of voice.

[1410] Step 2:

[1411] The device transmits the emotion data to the server.

[1412] Input: Emotion data

[1413] output: Send to the server

[1414] Specific operation: The device sends emotion data to the server in real time via WebSocket.

[1415] Step 3:

[1416] The server adjusts and optimizes the response of the generative AI model based on the emotion data.

[1417] Input: Emotion data

[1418] output: the optimized response

[1419] Specific operation: The server analyzes the emotional data, determines that the user is in a happy state, and generates a positive response.

[1420] Step 4:

[1421] The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[1422] Input: Optimized response

[1423] output:Returning the answer to the user

[1424] Specific Action: The server generates a positive response, "That's great!", and sends it back to the user.

[1425] As a result, through the above processing steps, a system is realized in which users can easily incorporate their rights into a generative AI model and obtain personalized responses.

[1426] (Application example 2)

[1427] 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."

[1428] Generative models using modern AI technology require a large amount of data, making it difficult to personalize them based on user emotions and interactions. In particular, when selling generative AI models to other users, determining how the buyer will actually use the model and whether the model's responses adapt to the user's emotions are important issues. Furthermore, autonomous vehicles require real-time emotion recognition to ensure passenger safety and comfort. However, with conventional systems, it has been technically difficult to recognize user emotions and incorporate their feedback into generative AI models.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for saving registration information entered by a user in a database; means for a registered user to enter and authenticate login information; means for a user to upload their own rights to the server; means for converting the uploaded rights into training data; means for creating a generative AI model based on the training data; means for generating answers to user questions using the generative AI model; means for selling the generative AI model to other users and managing and distributing revenue; means for the terminal to recognize the user's emotions in real time and analyze the emotional data; and means for transmitting the analyzed emotional data to the server and adjusting the response of the generative AI model. This enables personalization of the generative AI model based on the user's emotional state and real-time emotion recognition to ensure passenger safety and comfort.

[1430] "User" means a person who uploads copyrighted material and creates and sells generative AI models.

[1431] "Registration information" refers to information such as name, email address, and password that a user provides to the system.

[1432] "Database" means a storage device where the System stores registration information and other data.

[1433] "Login Information" means the combination of email address and password used by a User to access the System.

[1434] "Authentication" is the process by which a system verifies a user's login information and grants access.

[1435] "Rights" refer to intellectual property such as text and images owned by the user.

[1436] A "server" is a central computing device on which a system runs.

[1437] "Uploading" is the act of a user transmitting his / her own copyrighted material to a server.

[1438] "Training data" is data used to create a generative AI model.

[1439] A "generative AI model" is an artificial intelligence model that generates answers to questions based on training data.

[1440] The "emotion engine" is software that recognizes the user's emotions in real time and analyzes the data.

[1441] "Emotion data" is data relating to the user's emotional state obtained by the emotion engine.

[1442] "Revenue" is the profit earned from selling generative AI models to other users.

[1443] "Distribution" is the act of allocating earned revenue to rights holders.

[1444] "Real-time" refers to instantly recognizing and processing user actions and emotions.

[1445] MODE FOR CARRYING OUT THE INVENTION

[1446] The present invention combines a system that allows users to create generative AI models based on their own property and sell them to other users to earn revenue, with an emotion engine that recognizes user emotions. The system includes user registration, uploading property, creating generative AI models, and selling and managing generative AI models.

[1447] System Overview

[1448] Users create an account by filling out a web form. The registration information is stored in a database. When logging in, users enter their email address and password, which is then authenticated by the server.

[1449] Uploading copyrighted material is done by users sending intellectual property such as text and images to the server. The server receives this and converts it into training data. A generative AI model is created based on this training data, allowing users to generate appropriate answers to their own questions. This generative AI model can then be sold to other users.

[1450] Incorporating an emotion engine

[1451] The device is equipped with an emotion engine that recognizes the user's emotions in real time. This emotion engine analyzes facial expressions, tone of voice, etc. The emotion data is sent to a server, which adjusts and optimizes the response of the generative AI model based on the user's emotional state.

[1452] Hardware and software used

[1453] Camera: Uses the smartphone's built-in camera to capture the user's facial expressions.

[1454] Emotion Recognition Model: Uses an emotion recognition model built with Keras and TensorFlow.

[1455] dlib library: A library for face detection.

[1456] Server: A central server for processing and managing the database, emotion engine, and generative AI models.

[1457] Data acquisition and processing

[1458] The camera captures the user's facial expressions, and the captured image is cropped using the dlib library, then fed into an emotion recognition model to analyze the user's emotions in real time.

[1459] Data processing and transmission

[1460] The analyzed emotion data is sent to a server, which receives it and adjusts the generative AI model's response to the user's emotional state. For example, if a passenger feels stressed in an autonomous vehicle, the vehicle's infotainment system will adjust to provide a comfortable environment.

[1461] Specific examples

[1462] For example, if the emotion engine detects that a passenger is feeling stressed, it can change the interior lighting to a warmer color, play relaxing music, and even change the vehicle's driving mode to make the journey smoother and more relaxing.

[1463] Prompt Sentence Examples

[1464] "How can I build an application to analyze passenger facial expressions and change infotainment system settings based on their emotions?"

[1465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1466] Step 1:

[1467] A user enters registration information such as name, email address, and password into a web form and submits it. Input: Name, email address, password. Data processing: Registration information is confirmed and saved in a database. Output: Notification of successful registration.

[1468] Step 2:

[1469] A registered user enters their email address and password into the login form and sends it to the server. Input: Email address, password. Data calculation: The server compares the entered email address and password with the information in the database and generates an authentication token. Output: Authentication token issued. If authentication is successful, the user is notified that login is complete.

[1470] Step 3:

[1471] The user selects their own copyrighted material (for example, text or images) and sends it to the server via an upload form. Input: Copyrighted material file. Data processing: The server saves the received file in storage and converts it into learning data. Output: Notification that copyrighted material upload is complete.

[1472] Step 4:

[1473] The server analyzes the uploaded rights object and converts it into training data for the generative AI model. Input: Rights object file. Data calculation: Converts the rights object into an appropriate format and adds it to the training dataset for the generative AI model. Output: Notification that the training data is ready.

[1474] Step 5:

[1475] The server creates a generative AI model based on the training data. Input: Training data. Data calculation: The server builds and trains a generative AI model. Output: Notification that the generative AI model has been created.

[1476] Step 6:

[1477] The user inputs a question using the generative AI model and sends it to the server. Input: Question in text format. Data calculation: The server uses the generative AI model to generate an answer corresponding to the question. Output: Answer text.

[1478] Step 7:

[1479] The device uses a camera to capture facial expressions and tone of voice to recognize the user's emotions in real time. Input: User's facial image and voice data. Data processing: The emotion engine analyzes facial expressions and tone of voice to generate emotion data. Output: Emotion data.

[1480] Step 8:

[1481] The device sends the collected emotion data to the server. Input: Emotion data. Data calculation: The server reflects the received emotion data in the model response and optimizes it. Output: Optimized response data.

[1482] Step 9:

[1483] The server adjusts the generated answer based on the user's emotional state and sends it back to the device. Input: Optimized answer data. Data processing: The server adjusts the answer data based on the user's emotions. Output: Adjusted answer text.

[1484] Step 10:

[1485] A purchase procedure is carried out so that other users can purchase the generative AI model. Input: Purchase information. Data calculation: The server processes the payment information and completes the purchase procedure. Output: Granting access rights to the generative AI model and notification of purchase completion.

[1486] Step 11:

[1487] The server aggregates the sales data and revenue data of the generated AI model and distributes revenue to sellers. Input: Sales data, revenue data. Data calculation: The server calculates revenue and distributes it to sellers. Output: Notification of revenue distribution.

[1488] Step 12:

[1489] To withdraw revenue, the user operates the revenue screen and sends a withdrawal request to the server. Input: Withdrawal request. Data calculation: The server processes the withdrawal request and transfers the money to the user using the specified payment method. Output: Notification of withdrawal completion.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] [Third embodiment]

[1494] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1495] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1496] 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).

[1497] 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.

[1498] 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.

[1499] 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).

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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."

[1506] The present invention is a system that allows users to create generative AI models using their own proprietary content and sell them to other users to earn revenue. This system includes the following processes.

[1507] User registration and login functionality

[1508] 1. The user enters registration information such as name, email address, and password.

[1509] 2. The device sends the entered registration information to the server.

[1510] 3. The server stores the received registration information in a database.

[1511] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[1512] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[1513] Uploading copyrighted material and creating learning data

[1514] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[1515] 2. The device sends the selected file to the server.

[1516] 3. The server saves the received file in storage and analyzes it.

[1517] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[1518] Use and sale of generative AI models

[1519] 1. The user enters a question using the generative AI model and sends it to the server.

[1520] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[1521] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[1522] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[1523] Revenue sharing and management

[1524] 1. The server aggregates the sales and revenue data of the generated AI model.

[1525] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[1526] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[1527] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[1528] 5. The server notifies the user that the withdrawal process is complete.

[1529] Specific examples

[1530] For example, a writer (User A) uploads his or her own work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for a generative AI model. User A then uses the generative AI model to generate responses to questions about his or her own work. Another user, User B, likes this generative AI model and proceeds with a purchase. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[1531] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. This system is particularly useful as a means of effectively integrating copyright management and AI technology.

[1532] The processing flow will be explained below.

[1533] User registration and login functionality

[1534] User Registration

[1535] Step 1:

[1536] A user fills out a web form with registration information such as name, email address, and password.

[1537] Step 2:

[1538] The terminal transmits the entered registration information to the server.

[1539] Specific operation: Sends input data to the server via an HTTP POST request.

[1540] Step 3:

[1541] The server stores the received registration information in a database.

[1542] Specific behavior: Executes an SQL query and inserts user information into the database.

[1543] Step 4:

[1544] The server returns a registration success message to the terminal.

[1545] Specific operation: A "Registration completed" message is returned in the HTTP response.

[1546] User Login

[1547] Step 1:

[1548] The user enters their email address and password into the login form.

[1549] Step 2:

[1550] The device sends the login information to the server.

[1551] Specific behavior: Sends login information to the server via an HTTP POST request.

[1552] Step 3:

[1553] The server checks the entered information against a database and authenticates it.

[1554] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[1555] Step 4:

[1556] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[1557] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[1558] Uploading copyrighted material and creating learning data

[1559] Uploading Rights Material

[1560] Step 1:

[1561] The user selects the rights material file and clicks the upload button.

[1562] Step 2:

[1563] The terminal transmits the selected file to the server.

[1564] Specific operation: Send the file data in the body of the HTTP POST request.

[1565] Step 3:

[1566] The server stores the received file in storage.

[1567] Specific behavior: Saves a file to a specific directory on the server.

[1568] Creating training data

[1569] Step 1:

[1570] The server analyzes the uploaded files and converts them into learning data.

[1571] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[1572] Step 2:

[1573] The server incorporates the training data into the generated AI model.

[1574] Specific operation: Convert data format and feed it to AI model.

[1575] Step 3:

[1576] The server notifies the user that the model has finished training.

[1577] Specific operation: Notify the completion of learning using HTTP response or push notification.

[1578] Use and sale of generative AI models

[1579] Using generative AI models

[1580] Step 1:

[1581] The user enters a question using the generative AI model.

[1582] Step 2:

[1583] The device sends the question to the server.

[1584] Specific behavior: Send a HTTP POST request with the question in the body.

[1585] Step 3:

[1586] The server inputs the question into a generative AI model, which generates an answer.

[1587] What it does: Input a question into a generative AI model and process the resulting answer.

[1588] Step 4:

[1589] The server generates a response and sends it back to the terminal.

[1590] Specific operation: Response data is returned in the HTTP response.

[1591] Selling generative AI models

[1592] Step 1:

[1593] Another user selects a generative AI model and proceeds to the purchase page.

[1594] Step 2:

[1595] The terminal inputs payment information and sends it to the server.

[1596] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[1597] Step 3:

[1598] The server processes the payment information and completes the sale.

[1599] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[1600] Step 4:

[1601] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[1602] Specific behavior: Update the database and add permissions to the user account.

[1603] Revenue sharing and management

[1604] Revenue Management

[1605] Step 1:

[1606] The server collects sales and revenue data for the generated AI model.

[1607] Specific operation: Retrieves sales records from the database and aggregates sales data.

[1608] Step 2:

[1609] The server performs the calculations to distribute revenue to rights holders.

[1610] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[1611] Step 3:

[1612] The server reflects the revenue data in the user profile.

[1613] What it does: Updates each user's profile and makes their earnings information visible.

[1614] Withdrawing earnings

[1615] Step 1:

[1616] The user performs a withdrawal operation from the earnings screen.

[1617] Step 2:

[1618] The terminal sends a withdrawal request to the server.

[1619] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[1620] Step 3:

[1621] The server processes the withdrawal request and initiates the transfer.

[1622] Specific operation: Call the payment API and transfer money using the specified payment method.

[1623] Step 4:

[1624] The server notifies the user that the withdrawal process is complete.

[1625] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[1626] Example 1

[1627] 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."

[1628] In recent years, new business models using generative AI models have been gaining attention. However, there is a need for a system that allows users to smoothly execute the entire process of creating generative AI models using their own digital content and selling them to other users. In conventional systems, digital content management, training data generation, generative AI model sales, and revenue management are often performed on separate platforms, resulting in complex operations and reduced efficiency. Furthermore, there is a need for easy and transparent revenue distribution and withdrawal procedures. The objective of the present invention is to solve these problems and provide a system that allows users to efficiently earn revenue without infringing on rights.

[1629] 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.

[1630] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter login information and generate an authentication token, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to prompt sentences entered by users using the generative AI model, and means for selling the generative AI model to other users and managing and distributing sales data and revenue. This allows users to efficiently perform tasks from managing their digital content to withdrawing revenue within a single system.

[1631] "User" means any person or entity that uses the System, enters registration information to create an account, and uploads digital content or purchases generative AI models.

[1632] "Database" means an information storage system for storing and managing user registration information, uploaded digital content, sales revenue data, etc.

[1633] "Login Information" means the authentication information a user uses to access a system, typically consisting of an email address and password.

[1634] An "authentication token" is temporary authentication information issued to a user after they successfully log in to a system, and is used to identify the user in subsequent requests.

[1635] "Digital content" refers to the rightful property uploaded by a user, such as data files such as text files, image files, and audio files.

[1636] "Training Data" means data used to create a generative AI model, extracted from uploaded digital content.

[1637] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers in natural language in response to prompts from users.

[1638] A "prompt" is a sentence that explains the question or instruction that a user enters into a generative AI model.

[1639] "Sales Data" refers to revenue information recorded as a result of sales of generative AI models.

[1640] "Revenue" means the monetary benefits derived from the sale of a generative AI model, which are subject to distribution among users.

[1641] This invention is a system that allows users to create generative AI models using their own digital content and sell them to other users to earn revenue. This system enables users, terminals, and servers to work together to achieve the following functions:

[1642] First, the user uses a device (e.g., PC or smartphone) to enter registration information such as name, email address, and password. The device then sends the entered information to the server via an HTTP POST request, which the server receives and stores in a database (e.g., MySQL). At this point, a confirmation email of completed registration is automatically sent to the user's email address.

[1643] Next, when a user logs in to the system, they enter their email address and password. The device sends this information to the server, which then checks the corresponding user information in its database. If authentication is successful, the server generates an authentication token and returns it to the device, which then stores it in its local storage.

[1644] After logging in to the system, the user uploads digital content (e.g., text files, image files) to the server. The device sends the selected file to the server, which then stores the data in storage (e.g., Amazon S3). The server analyzes the uploaded digital content and converts it into text data. This converted text data is used as training data to create a generative AI model (e.g., GPT-3).

[1645] The generated AI model has the ability to generate answers in natural language in response to prompts entered by the user. For example, when a user enters a question such as "What is the main theme of this novel?", the server uses the generative AI model to generate the appropriate answer and sends it back to the device. The device then displays the generated answer to the user.

[1646] When another user purchases a generative AI model, the purchase process is carried out from the terminal. The user enters purchase information (e.g., credit card information) and sends it to the server, which then completes the payment through a payment service (e.g., Stripe). Once payment is complete, the server grants the purchasing user access to the generative AI model.

[1647] Regarding revenue management, the server aggregates the sales data and revenue data of the generated AI model and reflects it in the user profile. The user clicks the "Withdraw" button on the revenue screen and sends a withdrawal request to the server from their device. The server processes the request and transfers the money to the user through a payment service (e.g., PayPal). Once the withdrawal process is complete, the server notifies the user.

[1648] As a concrete example, a writer user uploads a PDF file of their own work, a novel, and creates a generative AI model based on this. The user uses the generative AI model to generate responses to questions such as, "What is the main theme of this novel?" Another user purchases this generative AI model and enters their payment information to complete the payment, granting the purchasing user the right to use the generative AI model. Revenues are then distributed appropriately through the server, and the user can withdraw their own revenues.

[1649] This system allows users to create generative AI models using digital content without infringing on copyrights, and efficiently generate revenue. Furthermore, the entire process is completed within a single system, significantly reducing operational complexity.

[1650] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1651] System program processing steps

[1652] Step 1: User Registration

[1653] 1. The user enters registration information such as name, email address, and password.

[1654] Input: Name, Email Address, Password

[1655] Output: The entered registration information

[1656] Specific action: Enter information into a web form and click the submit button.

[1657] 2. The device sends the entered registration information to the server via an HTTP POST request.

[1658] Input: The registration information entered

[1659] Output: Server request

[1660] Specific behavior: Creating and sending an HTTP request.

[1661] 3. The server stores the received registration information in a database.

[1662] Input: Received registration information

[1663] Output: Database update

[1664] Specific actions: Generates SQL queries and inserts data into a database.

[1665] 4. The server sends a confirmation email to the user's email address confirming registration.

[1666] Input: User's email address

[1667] Output:Confirmation email

[1668] Specific operation: A confirmation email is sent via the mail server.

[1669] Step 2: Log in

[1670] 1. The user enters their email address and password and submits the login information.

[1671] Input: Email address, password

[1672] Output: The entered login information

[1673] Specific action: Enter information into a web form and click the submit button.

[1674] 2. The device sends the entered login information to the server via an HTTP POST request.

[1675] Input: The login information entered

[1676] Output: Server request

[1677] Specific behavior: Creating and sending an HTTP request.

[1678] 3. The server retrieves the user information from the database and verifies the email address and password.

[1679] Input: Login information, database user information

[1680] Output: Authentication result

[1681] Specific behavior: Executes an SQL query to retrieve and collate user information.

[1682] 4. The server generates an authentication token and returns it to the device in JSON format.

[1683] Input: Authentication success information

[1684] Output: Authentication token

[1685] Specific operation: Generates an authentication token and returns it in the HTTP response.

[1686] 5. The device stores the authentication token in local storage and uses it for subsequent requests.

[1687] Input: Authentication Token

[1688] Output: Stored authentication token

[1689] Specific operation: The authentication token is saved in the browser's local storage.

[1690] Step 3: Upload your digital content

[1691] 1. The user selects digital content (e.g., a PDF file of a novel).

[1692] Input: Digital content files

[1693] Output: Selected file path

[1694] Specific operation: Opens a file selection dialog and selects a file.

[1695] 2. The device uploads the selected file to the server via an HTTP POST request.

[1696] Input: Selected file path

[1697] Output: Server request

[1698] Specific operation: Generates an HTTP request and sends the file to the server.

[1699] 3. The server saves the received file to storage (e.g. Amazon S3).

[1700] Input: Received file data

[1701] Output: Saved file path

[1702] Specific operation: Calls the storage API and saves the file.

[1703] 4. The server notifies the user that the save is complete.

[1704] Input: Saved file path

[1705] Output: Notification message

[1706] Specific operation: Sends HTTP responses and email notifications.

[1707] Step 4: Creating training data

[1708] 1. The server analyzes the file stored in the storage and converts it into text data (e.g., converting PDF to text).

[1709] Input: Saved file path

[1710] Output: Text data

[1711] Specific operation: Extracts text data using a PDF parsing library.

[1712] 2. The server converts the text data into an appropriate format (e.g., JSON) and saves it as training data for the generative AI model.

[1713] Input: Text data

[1714] Output: Formatted training data

[1715] Specific operation: Format text data into JSON format and save it in the database.

[1716] 3. The server stores the meta information of the converted learning data in a database.

[1717] Input: Formatted training data

[1718] Output: Database update

[1719] Specific operation: Generates an SQL query to insert meta information into the database.

[1720] Step 5: Use and sell the generative AI model

[1721] 1. The user inputs a question (prompt sentence) using the generative AI model.

[1722] Input: prompt statement

[1723] Output: The prompt text entered

[1724] Specific action: Enter a question into the web form and click the submit button.

[1725] 2. The device sends the question to the server via an HTTP POST request.

[1726] Input: The prompt text entered

[1727] Output: Server request

[1728] Specific operations: Generate an HTTP request and send a question to the server.

[1729] 3. The server receives the question and calls the corresponding generative AI model (e.g., GPT-3).

[1730] Input: Question, Generative AI Model

[1731] Output: Response data

[1732] What it does: Generate answers using a generative AI model.

[1733] 4. The server generates the answer from the AI ​​model and sends it to the device in JSON format.

[1734] Input: Answer data

[1735] Output: Answer response

[1736] Specific operation: The response data is converted into JSON format and sent as an HTTP response.

[1737] 5. The device displays the received answer to the user.

[1738] Input: Answer response

[1739] Output: Displayed answer

[1740] Specific action: Display the answer on a web page.

[1741] 6. Other users can purchase the generative AI model by clicking the "Purchase" button on the details page.

[1742] Input: Purchase Request

[1743] Output: Purchase procedure information

[1744] Specific operation: Click the purchase button to display the purchase procedure screen.

[1745] 7. The device enters the purchase information and sends it to the server via an HTTP POST request.

[1746] Input: Purchase procedure information

[1747] Output: Server request

[1748] Specific actions: Enter purchase information and click the submit button.

[1749] 8. The server processes the payment information and completes the payment through a payment service (e.g., Stripe).

[1750] Input: Payment information

[1751] Output: Payment completed

[1752] Specific operation: Call the payment API and process the payment.

[1753] 9. The server confirms the purchase and grants the purchasing user access to the generated AI model.

[1754] Input: Payment completion information

[1755] Output: Permission granted

[1756] Specific Actions: Update user profile and grant access privileges.

[1757] 10. The device displays a notification to the user that the purchase is complete.

[1758] Input: Purchase completion information

[1759] Output: Notification message

[1760] Specific operation: Display a purchase completion message.

[1761] Step 6: Revenue sharing and management

[1762] 1. The server aggregates the sales and revenue data of the generated AI model from the database.

[1763] Input: Sales data, revenue data

[1764] Output: Aggregation results

[1765] Specific behavior: Executes SQL queries and aggregates data.

[1766] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[1767] Input: Aggregation results

[1768] Output: Updated user profile

[1769] Specific Actions: Perform revenue share calculations and update user profiles.

[1770] 3. The server will notify the rights holder of the revenue details by email.

[1771] Input: Revenue sharing information

[1772] Output: Notification email

[1773] Specific operation: Revenue details are notified via the mail server.

[1774] 4. The user opens the earnings screen and clicks the "Withdraw" button.

[1775] Input: Withdrawal request

[1776] Output: Withdrawal procedure information

[1777] Specific actions: Click the Withdraw button to begin the withdrawal process.

[1778] 5. The terminal sends a withdrawal request to the server via an HTTP POST request.

[1779] Input: Withdrawal procedure information

[1780] Output: Server request

[1781] Specific actions: Enter withdrawal information and click the submit button.

[1782] 6. The server processes the withdrawal request and sends the money to the user via a payment service (e.g., PayPal).

[1783] Input: Withdrawal request information

[1784] Output: Transfer completed

[1785] Specific operation: Call the payment API and process the transfer.

[1786] 7. The server notifies the user by email that the withdrawal process is complete.

[1787] Input: Remittance completion information

[1788] Output: Notification email

[1789] Specific behavior: Sends notifications via a mail server.

[1790] (Application example 1)

[1791] 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."

[1792] Conventional content distribution services lack a mechanism for generating new content from digital content owned by users and generating revenue from it. Furthermore, there is no established method for properly managing and distributing the generated content and for fairly distributing revenue, which means that users are unable to effectively utilize their own creations.

[1793] 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.

[1794] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for providing newly generated content in a content distribution service, which allows users to earn revenue by using new content generated based on their own digital content.

[1795] "User" means an individual or legal entity that registers on the content distribution service platform and uploads digital content or uses a generative AI model.

[1796] "Registration Information" refers to information provided by a User when registering for the Service for the first time, including, for example, name, email address, and password.

[1797] "Authentication" is the process of verifying that a user is a legitimate user based on the login information they provide.

[1798] "Digital Content" means data in electronic form that is owned by a user, including, but not limited to, text, images, audio, etc.

[1799] A "server" is a computer system that stores, processes, and distributes data over the Internet.

[1800] "Uploading" is the process by which a user sends their digital content from a terminal to a server for storage.

[1801] "Training Data" means data extracted from uploaded digital content that is used to train a generative AI model.

[1802] A "generative AI model" is an artificial intelligence model that is trained based on learning data and generates answers to user questions and new content.

[1803] A "question" is an inquiry that a user inputs to a generated AI model.

[1804] An "answer" is the response that a generative AI model generates in response to a user's question.

[1805] "Sales" is the process of providing a generative AI model to other users and receiving payment in return.

[1806] "Revenue" means the financial benefit derived from the sale of a generative AI model.

[1807] "Management and distribution" refers to the process of aggregating the revenue generated from the sale of generative AI models and distributing it to relevant users in appropriate proportions.

[1808] A "content distribution service" is an online service for creating, sharing, buying, selling, and distributing digital content.

[1809] "Newly Generated Content" is new digital content that is automatically generated based on a user's digital content and a generative AI model.

[1810] The embodiment of the present invention is realized by constructing a system that includes three main elements: a server, a terminal, and a user.

[1811] Program Overview and Processing

[1812] 1. Registration and login functionality

[1813] The user uses their own device to enter registration information such as name, email address, and password. This information is sent from the device to the server, where it is stored in a database. After completing registration, the user enters their email address and password into the login form and sends it back to the server. The server verifies the entered information, generates an authentication token, and sends it back to the device.

[1814] 2. Uploading copyrighted material and creating training data

[1815] Users upload their digital content (e.g., text, images, audio, etc.) from their devices to the server, which stores the uploaded digital content and converts it into an appropriate format. The server then uses this data to train a generative AI model.

[1816] 3. Use and sale of generative AI models

[1817] The generated AI model can be used by a user by inputting a question using a device. The server receives the question, generates an answer using the corresponding generative AI model, and sends it back to the device. This generative AI model can also be purchased by other users. The server processes payment information, completes the purchase procedure, and grants the purchasing user access to the generative AI model.

[1818] 4. Revenue sharing and management

[1819] The server aggregates the sales data and revenue data of the generative AI model and distributes the revenue fairly to the rights holders. When a user submits a request to withdraw revenue, the server processes the withdrawal request and transfers the money to the user via the specified payment method. A notification of the completion of the withdrawal process is also sent from the server to the user.

[1820] Hardware and software used

[1821] Hardware: Smartphone (iOS or Android device)

[1822] Server: AWS or Google Cloud Platform

[1823] Database: MySQL or PostgreSQL

[1824] AI models: Generative AI models such as GPT-3 and BERT

[1825] Frontend: React Native for mobile app development

[1826] Backend: Node.js and Express framework

[1827] Specific examples

[1828] Example 1: Writer usage scenario

[1829] A writer uploads a short story they have written to an app from their smartphone. The server then analyzes the story and saves it as text data. A generative AI model is trained based on this data. The writer uses the generative AI model to generate responses to questions about their story. For example, if the question is "Who is the first character the protagonist meets in the story?", the generative AI model will respond with "The first character the protagonist meets is A."

[1830] Prompt Sentence Examples

[1831] "In the novel 'Star Messenger,' what is the name of the main character?"

[1832] With the system configuration described above, users can generate new content based on their own digital content and generate revenue from it.

[1833] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1834] Step 1:

[1835] A user inputs registration information such as name, email address, and password from a terminal. The terminal sends this input information to a server. The server stores the received information in a database and creates a profile for the registered user. The input includes name, email address, and password, and the output is the registered user information stored in the database.

[1836] Step 2:

[1837] A user who has completed registration enters their email address and password into the login form and sends them from the device to the server. The server checks the registration information against the database, and if it matches, generates an authentication token and sends it back to the device. The input includes the email address and password, and the output is the authentication token sent to the device.

[1838] Step 3:

[1839] The user selects their own digital content (text, images, audio, etc.) from the device and clicks the upload button. The device sends the selected file to the server. The server saves the received digital content in storage and begins data analysis. The input is the digital content file, and the output is the file saved in storage and the data to be analyzed.

[1840] Step 4:

[1841] The server converts the uploaded digital content into training data. Specifically, it converts the text and image formats into a format that is easy for the AI ​​model to learn, and stores the data in a database. The input is the uploaded digital content file, and the output is the converted training data.

[1842] Step 5:

[1843] The server creates a generative AI model based on the training data. To train the AI ​​model, the server runs an efficient learning process using many datasets. The input is the transformed training data, and the output is the generated AI model.

[1844] Step 6:

[1845] The user inputs a question from the device using a generative AI model. The device sends the question to the server, which then generates an answer using the corresponding generative AI model and sends it back to the device. The input is the question entered by the user, and the output is the answer returned by the server.

[1846] Step 7:

[1847] Another user executes the process of purchasing a generative AI model. The user completes the purchase procedure for the generative AI model from their device, and the device sends payment information to the server. The server processes the payment information, completes the purchase procedure, and grants the purchasing user access rights to the generative AI model. The input is the purchase procedure and payment information, and the output is a purchase completion notification and access rights to the generative AI model.

[1848] Step 8:

[1849] The server aggregates the sales data and revenue data of the generated AI model and distributes the revenue to the rights holder. When a user operates the revenue screen to withdraw revenue, the terminal sends a withdrawal request to the server. The server processes the withdrawal request and transfers the money to the user using the specified payment method. The server notifies the user that the withdrawal process is complete. The input is the withdrawal request, and the output is the withdrawal completion notification and the transfer.

[1850] 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.

[1851] The present invention combines a system that allows users to create generative AI models using their own proprietary property and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system includes the following processes.

[1852] User registration and login functionality

[1853] 1. A user fills out a web form with registration information such as name, email address, and password.

[1854] 2. The device sends the entered registration information to the server.

[1855] 3. The server stores the received registration information in a database.

[1856] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[1857] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[1858] Uploading copyrighted material and creating learning data

[1859] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[1860] 2. The device sends the selected file to the server.

[1861] 3. The server saves the received file in storage and analyzes it.

[1862] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[1863] Use and sale of generative AI models

[1864] 1. The user enters a question using the generative AI model and sends it to the server.

[1865] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[1866] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[1867] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[1868] Revenue sharing and management

[1869] 1. The server aggregates the sales and revenue data of the generated AI model.

[1870] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[1871] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[1872] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[1873] 5. The server notifies the user that the withdrawal process is complete.

[1874] Incorporating an emotion engine

[1875] 1. The device will be equipped with an emotion engine that will recognize the user's emotions in real time. The emotion engine will analyze the user's facial expressions, tone of voice, etc.

[1876] 2. The device sends the emotion data to the server.

[1877] 3. Based on the emotion data received by the server, the response of the generative AI model is adjusted and optimized.

[1878] 4. The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[1879] Specific examples

[1880] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about his or her work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends his or her emotional state to the server. The server optimizes the response based on his or her emotional state and provides it to User A. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[1881] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience by providing responses according to the user's emotional state.

[1882] The processing flow will be explained below.

[1883] User registration and login functionality

[1884] User Registration

[1885] Step 1:

[1886] A user fills out a web form with registration information such as name, email address, and password.

[1887] Step 2:

[1888] The terminal transmits the entered registration information to the server.

[1889] Specific operation: Sends input data to the server via an HTTP POST request.

[1890] Step 3:

[1891] The server stores the received registration information in a database.

[1892] Specific behavior: Executes an SQL query and inserts user information into the database.

[1893] Step 4:

[1894] The server returns a registration success message to the terminal.

[1895] Specific operation: A "Registration completed" message is returned in the HTTP response.

[1896] User Login

[1897] Step 1:

[1898] The user enters their email address and password into the login form.

[1899] Step 2:

[1900] The device sends the login information to the server.

[1901] Specific behavior: Sends login information to the server via an HTTP POST request.

[1902] Step 3:

[1903] The server checks the entered information against a database and authenticates it.

[1904] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[1905] Step 4:

[1906] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[1907] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[1908] Uploading copyrighted material and creating learning data

[1909] Uploading Rights Material

[1910] Step 1:

[1911] The user selects the rights material file and clicks the upload button.

[1912] Step 2:

[1913] The terminal transmits the selected file to the server.

[1914] Specific operation: Send the file data in the body of the HTTP POST request.

[1915] Step 3:

[1916] The server stores the received file in storage.

[1917] Specific behavior: Saves a file to a specific directory on the server.

[1918] Creating training data

[1919] Step 1:

[1920] The server analyzes the uploaded files and converts them into learning data.

[1921] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[1922] Step 2:

[1923] The server incorporates the training data into the generated AI model.

[1924] Specific operation: Convert data format and feed it to AI model.

[1925] Step 3:

[1926] The server notifies the user that the model has finished training.

[1927] Specific operation: Notify the completion of learning using HTTP response or push notification.

[1928] Use and sale of generative AI models

[1929] Using generative AI models

[1930] Step 1:

[1931] The user enters a question using the generative AI model.

[1932] Step 2:

[1933] The device sends the question to the server.

[1934] Specific behavior: Send a HTTP POST request with the question in the body.

[1935] Step 3:

[1936] The server inputs the question into a generative AI model, which generates an answer.

[1937] What it does: Input a question into a generative AI model and process the resulting answer.

[1938] Step 4:

[1939] The server generates a response and sends it back to the terminal.

[1940] Specific operation: Response data is returned in the HTTP response.

[1941] Selling generative AI models

[1942] Step 1:

[1943] Another user selects a generative AI model and proceeds to the purchase page.

[1944] Step 2:

[1945] The terminal inputs payment information and sends it to the server.

[1946] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[1947] Step 3:

[1948] The server processes the payment information and completes the sale.

[1949] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[1950] Step 4:

[1951] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[1952] Specific behavior: Update the database and add permissions to the user account.

[1953] Revenue sharing and management

[1954] Revenue Management

[1955] Step 1:

[1956] The server collects sales and revenue data for the generated AI model.

[1957] Specific operation: Retrieves sales records from the database and aggregates sales data.

[1958] Step 2:

[1959] The server performs the calculations to distribute revenue to rights holders.

[1960] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[1961] Step 3:

[1962] The server reflects the revenue data in the user profile.

[1963] What it does: Updates each user's profile and makes their earnings information visible.

[1964] Withdrawing earnings

[1965] Step 1:

[1966] The user performs a withdrawal operation from the earnings screen.

[1967] Step 2:

[1968] The terminal sends a withdrawal request to the server.

[1969] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[1970] Step 3:

[1971] The server processes the withdrawal request and initiates the transfer.

[1972] Specific operation: Call the payment API and transfer money using the specified payment method.

[1973] Step 4:

[1974] The server notifies the user that the withdrawal process is complete.

[1975] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[1976] Incorporating an emotion engine

[1977] Emotion recognition with emotion engine

[1978] Step 1:

[1979] A user inputs a question using a terminal equipped with an emotion engine.

[1980] Step 2:

[1981] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data.

[1982] Specific operations: Captures data through the camera and microphone and runs emotion analysis algorithms.

[1983] Step 3:

[1984] The device transmits the emotion data to the server.

[1985] Specific operation: Sends emotion data in the body of an HTTP POST request.

[1986] Optimizing responses using emotional data

[1987] Step 1:

[1988] The server adjusts the response of the generative AI model based on the emotional data it receives.

[1989] What it does: Analyzes emotional data and adjusts response content and tone appropriately.

[1990] Step 2:

[1991] The server generates a response and sends it back to the terminal.

[1992] Specific operation: Returns an answer optimized based on emotional data in the HTTP response.

[1993] Specific examples

[1994] For example, a writer (User A) uploads his or her work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for the generative AI model. User A then uses the generative AI model to generate responses to questions about the work. At this time, the emotion engine analyzes User A's facial expressions and tone of voice and sends the emotional state to the server. The server optimizes the response based on User A's emotional state and provides it in a form appropriate to that emotion. Another User B also likes this generative AI model and completes the purchase process. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from purchases by other users to User A.

[1995] This invention provides a new means of generating revenue by creating and selling generative AI models without infringing copyrights. Furthermore, by incorporating an emotion engine, it is possible to provide a more personalized experience based on the user's emotional state. This system effectively integrates copyright management and AI technology, providing users with opportunities for advanced interaction.

[1996] Example 2

[1997] 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."

[1998] Conventional generative AI model creation systems have the problem of requiring users to easily use their own proprietary content to create generative AI models and then sell them to other users through a complicated process. Another issue is that the responses of generative AI models do not adapt to the user's emotional state, resulting in a uniform, non-personalized user experience. This requires a great deal of effort from users in the process of creating and selling generative AI models, making it difficult to maximize revenue.

[1999] 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.

[2000] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own rights to the server, means for converting the uploaded rights into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for recognizing user emotions and adjusting and optimizing the responses of the generative AI model based on the emotion data. This allows users to easily and efficiently import and sell rights into the generative AI model, and enables personalized responses to be provided based on the user's emotional state.

[2001] "User" means a person or organization that uses the System to enter registration information, upload Rights, and create and sell Generative AI Models.

[2002] "Rights" refers to intellectual property such as text, images, audio, and video that users upload to the system.

[2003] A "server" is a hardware and software system that receives, stores, and analyzes data sent by users, and creates and manages generative AI models.

[2004] "Terminal" means an electronic device such as a computer, smartphone, or tablet that a User uses to enter registration information and upload rights material.

[2005] A "database" is an information management system installed on a server for storing user registration information and data related to rights.

[2006] "Training data" refers to data that has been converted and formatted based on the copyrighted material uploaded by the user for the purpose of being used by the generative AI model for training.

[2007] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers to user questions.

[2008] An "authentication token" is a temporary identifier issued based on authentication information when a user logs in to a system.

[2009] An "emotion engine" is software that recognizes a user's emotions in real time and analyzes that emotional data.

[2010] "Revenue management" refers to a system that aggregates sales and revenue data from generative AI models and distributes them appropriately.

[2011] "Personalization" refers to the technique of adjusting the output of a system according to the individual preferences and feelings of the user.

[2012] MODE FOR CARRYING OUT THE INVENTION

[2013] The present invention combines a system that allows users to create generative AI models using their own rights and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system allows users to easily and efficiently import and sell their rights into generative AI models, and can provide personalized responses based on the user's emotional state.

[2014] Hardware and Software Configuration

[2015] The system utilizes a server, terminals, a database, an emotion engine, and a generative AI model. Specifically, each component plays the following role:

[2016] 1. Server:

[2017] Receive, store and analyze registration information and property data.

[2018] Use a database management system (e.g., MySQL, PostgreSQL).

[2019] Amazon S3 is used as cloud storage to permanently store data.

[2020] Apache Tika is used to convert rights materials in various formats into text data.

[2021] Generate training data and incorporate it into a generative AI model (e.g., GPT-3, BERT).

[2022] It also manages response generation, payment processing, and revenue sharing.

[2023] 2. Terminal:

[2024] Submits the registration information entered by the user via a web form to a server.

[2025] The right object is sent to the server via the file upload function.

[2026] A webcam and microphone are used to obtain the data (facial expressions and tone of voice) required for the emotion engine.

[2027] 3. Database:

[2028] The server stores and manages the received user information and rights information.

[2029] 4. Emotion Engine:

[2030] It recognizes the user's emotional state and transmits that data to a server.

[2031] Analyzes the user's facial expressions and tone of voice in real time.

[2032] Specific examples

[2033] For example, let's consider the case where User A, an author, uploads his or her work (a PDF file of a novel) to this system. User A accesses the system from a web browser on a device (PC or smartphone). User A enters registration information such as name, email address, and password, and sends it to the server. The server receives this information and stores it in a database.

[2034] User A then logs in, uses the upload function to select the novel PDF file "novel.pdf," and sends it to the server. The server saves the file to Amazon S3 and converts it to text data using Apache Tika. The converted text data is prepared as training data and fed into the generative AI model.

[2035] User A uses the generative AI model to input the prompt, "What action will the protagonist of this novel take next?" The server uses the generative AI model to generate a response, and returns the answer, "The protagonist decides to go on an adventure."

[2036] Furthermore, to analyze User A's emotional state, the device's camera and microphone are used to collect facial expressions and tone of voice. The emotion engine analyzes this data and sends it to the server. The server optimizes responses based on the emotional data and provides personalized responses to User A.

[2037] Another user, User B, may like this generative AI model and proceed with the purchase. After completing the payment procedure, User B obtains access to the generative AI model and can similarly enter questions and get answers. The revenue is appropriately calculated by the server and distributed to User A.

[2038] Prompt Sentence Examples

[2039] "Tell me what action the protagonist of this novel will take next."

[2040] "Generate a story related to this image."

[2041] "Use the emotion engine to generate a message based on my current emotions."

[2042] This allows the present invention to provide new revenue streams for users and a more personalized user experience using emotion recognition technology.

[2043] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2044] System program processing flow

[2045] User registration and login functionality

[2046] Step 1:

[2047] A user fills out a web form with registration information such as name, email address, and password.

[2048] Input: Name, email address, and password entered by the user into a web form

[2049] Output: Registration information

[2050] Specific operation: User A opens a website in a browser and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[2051] Step 2:

[2052] The terminal transmits the entered registration information to the server.

[2053] Input: Registration information entered by the user into a web form

[2054] Output: Sent to the server as an HTTP POST request

[2055] Specific operation: User A's browser sends the entered information.

[2056] Step 3:

[2057] The server stores the received registration information in a database.

[2058] Input: Registration information received by the server

[2059] Output: Save to database

[2060] Specific operation: The server stores the received information in MySQL and returns status code 200 if successful.

[2061] Step 4:

[2062] After completing registration, the user enters their email address and password into the login form and sends it to the server.

[2063] Input: Email address and password entered in the login form

[2064] Output: Sent to the server as an HTTP POST request

[2065] Specific operation: User A enters "taro@example.com" and "password123" into the login form.

[2066] Step 5:

[2067] The server verifies the entered email address and password, generates an authentication token, and returns it to the terminal.

[2068] Input: Email address and password received by the server

[2069] Output: Authentication token

[2070] Specific operation: The server checks the email address and password against the database, and if successful, generates and returns a JWT token.

[2071] Uploading copyrighted material and creating learning data

[2072] Step 1:

[2073] The user selects the right material (for example, a file such as text or an image) and uploads it to the server.

[2074] Input: Rights file

[2075] Output: User selection

[2076] Specific operation: User A selects the novel PDF file "novel.pdf".

[2077] Step 2:

[2078] The terminal transmits the selected file to the server.

[2079] Input: Selected files

[2080] Output: Upload to server

[2081] Specific behavior: The browser uploads the selected PDF file to the server.

[2082] Step 3:

[2083] The server stores the received files in cloud storage and performs format conversion and data cleansing as necessary.

[2084] Input: Received file

[2085] Output: Saving to cloud storage and format conversion

[2086] Specific operation: The server saves the received PDF file in Amazon S3 and converts it into text data using Apache Tika.

[2087] Step 4:

[2088] The server converts the rights into an appropriate format as learning data and incorporates it into the generative AI model.

[2089] Input: Converted text data

[2090] Output: Input to generative AI model as training data

[2091] What it does: The converted text data is fed into a generative AI model such as GPT-3 or BERT to begin the learning process.

[2092] Use and sale of generative AI models

[2093] Step 1:

[2094] The user inputs a question using the generative AI model and sends it to the server.

[2095] Input: User question

[2096] Output: Send a query to the server

[2097] Specific operation: User A enters the question "What will the protagonist of this novel do next?" and presses the send button.

[2098] Step 2:

[2099] The server receives the question, generates an answer using an appropriate generative AI model, and sends it to the device.

[2100] Input: User question

[2101] Output: The generated answer

[2102] Specific operation: The server inputs the question into the generation AI model, which generates the answer "The protagonist decides to go on an adventure."

[2103] Step 3:

[2104] Other users can purchase the generative AI model by completing the purchase process on their device.

[2105] Input: Purchase procedure information

[2106] Output: Purchase information sent to the server

[2107] Specific behavior: User B enters credit card information into the payment form and makes a purchase.

[2108] Step 4:

[2109] The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[2110] Input: Payment information

[2111] Output: Permission granted

[2112] Specific operation: After the payment is confirmed, the server sends User B an access key for the generated AI model.

[2113] Revenue sharing and management

[2114] Step 1:

[2115] The server aggregates the sales and revenue data generated by the AI ​​model.

[2116] Input: Sales data, revenue data

[2117] Output: Aggregated data

[2118] Specific operation: The server executes the revenue aggregation batch at 00:00 every day and aggregates sales information from the database.

[2119] Step 2:

[2120] The server calculates the distribution and reflects it in the user profile.

[2121] Input: Aggregated data

[2122] Output: Update user profile

[2123] What it does: Updates rightsholder profiles and displays revenue information.

[2124] Step 3:

[2125] The user submits a revenue withdrawal request.

[2126] Input: Withdrawal request

[2127] Output: Request sent to server

[2128] Specific operation: User A presses the "Withdraw" button on the revenue screen and enters the withdrawal amount.

[2129] Step 4:

[2130] The server processes the withdrawal request.

[2131] Input: Withdrawal request

[2132] Output: Transfer to user account

[2133] What happens: The server processes the withdrawal request and transfers the funds to the user's account using the PayPal API.

[2134] Step 5:

[2135] The server will acknowledge completion.

[2136] Input: Withdrawal result

[2137] Output: Completion notification

[2138] Specific operation: After the withdrawal is completed, the server sends a notification email to User A.

[2139] Incorporating an emotion engine

[2140] Step 1:

[2141] The device is equipped with an emotion engine that recognizes the user's emotions.

[2142] Input: User facial expressions and tone of voice

[2143] Output: Emotion data

[2144] Specific operation: Uses the device's camera and microphone to collect the user's facial expressions and tone of voice.

[2145] Step 2:

[2146] The device transmits the emotion data to the server.

[2147] Input: Emotion data

[2148] output: Send to the server

[2149] Specific operation: The device sends emotion data to the server in real time via WebSocket.

[2150] Step 3:

[2151] The server adjusts and optimizes the response of the generative AI model based on the emotion data.

[2152] Input: Emotion data

[2153] output: the optimized response

[2154] Specific operation: The server analyzes the emotional data, determines that the user is in a happy state, and generates a positive response.

[2155] Step 4:

[2156] The server returns the generated answer to the terminal in a form that is appropriate for the user's emotional state.

[2157] Input: Optimized response

[2158] output:Returning the answer to the user

[2159] Specific Action: The server generates a positive response, "That's great!", and sends it back to the user.

[2160] As a result, through the above processing steps, a system is realized in which users can easily incorporate their rights into a generative AI model and obtain personalized responses.

[2161] (Application example 2)

[2162] 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."

[2163] Generative models using modern AI technology require a large amount of data, making it difficult to personalize them based on user emotions and interactions. In particular, when selling generative AI models to other users, determining how the buyer will actually use the model and whether the model's responses adapt to the user's emotions are important issues. Furthermore, autonomous vehicles require real-time emotion recognition to ensure passenger safety and comfort. However, with conventional systems, it has been technically difficult to recognize user emotions and incorporate their feedback into generative AI models.

[2164] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for saving registration information entered by a user in a database; means for a registered user to enter and authenticate login information; means for a user to upload their own rights to the server; means for converting the uploaded rights into training data; means for creating a generative AI model based on the training data; means for generating answers to user questions using the generative AI model; means for selling the generative AI model to other users and managing and distributing revenue; means for the terminal to recognize the user's emotions in real time and analyze the emotional data; and means for transmitting the analyzed emotional data to the server and adjusting the response of the generative AI model. This enables personalization of the generative AI model based on the user's emotional state and real-time emotion recognition to ensure passenger safety and comfort.

[2165] "User" means a person who uploads copyrighted material and creates and sells generative AI models.

[2166] "Registration information" refers to information such as name, email address, and password that a user provides to the system.

[2167] "Database" means a storage device where the System stores registration information and other data.

[2168] "Login Information" means the combination of email address and password used by a User to access the System.

[2169] "Authentication" is the process by which a system verifies a user's login information and grants access.

[2170] "Rights" refer to intellectual property such as text and images owned by the user.

[2171] A "server" is a central computing device on which a system runs.

[2172] "Uploading" is the act of a user transmitting his / her own copyrighted material to a server.

[2173] "Training data" is data used to create a generative AI model.

[2174] A "generative AI model" is an artificial intelligence model that generates answers to questions based on training data.

[2175] The "emotion engine" is software that recognizes the user's emotions in real time and analyzes the data.

[2176] "Emotion data" is data relating to the user's emotional state obtained by the emotion engine.

[2177] "Revenue" is the profit earned from selling generative AI models to other users.

[2178] "Distribution" is the act of allocating earned revenue to rights holders.

[2179] "Real-time" refers to instantly recognizing and processing user actions and emotions.

[2180] MODE FOR CARRYING OUT THE INVENTION

[2181] The present invention combines a system that allows users to create generative AI models based on their own property and sell them to other users to earn revenue, with an emotion engine that recognizes user emotions. The system includes user registration, uploading property, creating generative AI models, and selling and managing generative AI models.

[2182] System Overview

[2183] Users create an account by filling out a web form. The registration information is stored in a database. When logging in, users enter their email address and password, which is then authenticated by the server.

[2184] Uploading copyrighted material is done by users sending intellectual property such as text and images to the server. The server receives this and converts it into training data. A generative AI model is created based on this training data, allowing users to generate appropriate answers to their own questions. This generative AI model can then be sold to other users.

[2185] Incorporating an emotion engine

[2186] The device is equipped with an emotion engine that recognizes the user's emotions in real time. This emotion engine analyzes facial expressions, tone of voice, etc. The emotion data is sent to a server, which adjusts and optimizes the response of the generative AI model based on the user's emotional state.

[2187] Hardware and software used

[2188] Camera: Uses the smartphone's built-in camera to capture the user's facial expressions.

[2189] Emotion Recognition Model: Uses an emotion recognition model built with Keras and TensorFlow.

[2190] dlib library: A library for face detection.

[2191] Server: A central server for processing and managing the database, emotion engine, and generative AI models.

[2192] Data acquisition and processing

[2193] The camera captures the user's facial expressions, and the captured image is cropped using the dlib library, then fed into an emotion recognition model to analyze the user's emotions in real time.

[2194] Data processing and transmission

[2195] The analyzed emotion data is sent to a server, which receives it and adjusts the generative AI model's response to the user's emotional state. For example, if a passenger feels stressed in an autonomous vehicle, the vehicle's infotainment system will adjust to provide a comfortable environment.

[2196] Specific examples

[2197] For example, if the emotion engine detects that a passenger is feeling stressed, it can change the interior lighting to a warmer color, play relaxing music, and even change the vehicle's driving mode to make the journey smoother and more relaxing.

[2198] Prompt Sentence Examples

[2199] "How can I build an application to analyze passenger facial expressions and change infotainment system settings based on their emotions?"

[2200] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2201] Step 1:

[2202] A user enters registration information such as name, email address, and password into a web form and submits it. Input: Name, email address, password. Data processing: Registration information is confirmed and saved in a database. Output: Notification of successful registration.

[2203] Step 2:

[2204] A registered user enters their email address and password into the login form and sends it to the server. Input: Email address, password. Data calculation: The server compares the entered email address and password with the information in the database and generates an authentication token. Output: Authentication token issued. If authentication is successful, the user is notified that login is complete.

[2205] Step 3:

[2206] The user selects their own copyrighted material (for example, text or images) and sends it to the server via an upload form. Input: Copyrighted material file. Data processing: The server saves the received file in storage and converts it into learning data. Output: Notification that copyrighted material upload is complete.

[2207] Step 4:

[2208] The server analyzes the uploaded rights object and converts it into training data for the generative AI model. Input: Rights object file. Data calculation: Converts the rights object into an appropriate format and adds it to the training dataset for the generative AI model. Output: Notification that the training data is ready.

[2209] Step 5:

[2210] The server creates a generative AI model based on the training data. Input: Training data. Data calculation: The server builds and trains a generative AI model. Output: Notification that the generative AI model has been created.

[2211] Step 6:

[2212] The user inputs a question using the generative AI model and sends it to the server. Input: Question in text format. Data calculation: The server uses the generative AI model to generate an answer corresponding to the question. Output: Answer text.

[2213] Step 7:

[2214] The device uses a camera to capture facial expressions and tone of voice to recognize the user's emotions in real time. Input: User's facial image and voice data. Data processing: The emotion engine analyzes facial expressions and tone of voice to generate emotion data. Output: Emotion data.

[2215] Step 8:

[2216] The device sends the collected emotion data to the server. Input: Emotion data. Data calculation: The server reflects the received emotion data in the model response and optimizes it. Output: Optimized response data.

[2217] Step 9:

[2218] The server adjusts the generated answer based on the user's emotional state and sends it back to the device. Input: Optimized answer data. Data processing: The server adjusts the answer data based on the user's emotions. Output: Adjusted answer text.

[2219] Step 10:

[2220] A purchase procedure is carried out so that other users can purchase the generative AI model. Input: Purchase information. Data calculation: The server processes the payment information and completes the purchase procedure. Output: Granting access rights to the generative AI model and notification of purchase completion.

[2221] Step 11:

[2222] The server aggregates the sales data and revenue data of the generated AI model and distributes revenue to sellers. Input: Sales data, revenue data. Data calculation: The server calculates revenue and distributes it to sellers. Output: Notification of revenue distribution.

[2223] Step 12:

[2224] To withdraw revenue, the user operates the revenue screen and sends a withdrawal request to the server. Input: Withdrawal request. Data calculation: The server processes the withdrawal request and transfers the money to the user using the specified payment method. Output: Notification of withdrawal completion.

[2225] 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.

[2226] 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.

[2227] 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.

[2228] [Fourth embodiment]

[2229] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2230] 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.

[2231] 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).

[2232] 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.

[2233] 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.

[2234] 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).

[2235] 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.

[2236] 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.

[2237] 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.

[2238] 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.

[2239] 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.

[2240] 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.

[2241] 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."

[2242] The present invention is a system that allows users to create generative AI models using their own proprietary content and sell them to other users to earn revenue. This system includes the following processes.

[2243] User registration and login functionality

[2244] 1. The user enters registration information such as name, email address, and password.

[2245] 2. The device sends the entered registration information to the server.

[2246] 3. The server stores the received registration information in a database.

[2247] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[2248] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[2249] Uploading copyrighted material and creating learning data

[2250] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[2251] 2. The device sends the selected file to the server.

[2252] 3. The server saves the received file in storage and analyzes it.

[2253] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[2254] Use and sale of generative AI models

[2255] 1. The user enters a question using the generative AI model and sends it to the server.

[2256] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[2257] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[2258] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[2259] Revenue sharing and management

[2260] 1. The server aggregates the sales and revenue data of the generated AI model.

[2261] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[2262] 3. The user withdraws their earnings by operating the earnings screen and sending a withdrawal request from their terminal.

[2263] 4. The server processes the withdrawal request and sends the money to the user via the specified payment method.

[2264] 5. The server notifies the user that the withdrawal process is complete.

[2265] Specific examples

[2266] For example, a writer (User A) uploads his or her own work (a PDF file of a novel). The server converts this PDF file into text data and uses it as training data for a generative AI model. User A then uses the generative AI model to generate responses to questions about his or her own work. Another user, User B, likes this generative AI model and proceeds with a purchase. The server completes the payment and grants User B the right to use the generative AI model. The server appropriately calculates and distributes the revenue earned from other users' purchases to User A.

[2267] This invention allows users to create and sell generative AI models without infringing on copyrights, thereby generating new revenue. This system is particularly useful as a means of effectively integrating copyright management and AI technology.

[2268] The processing flow will be explained below.

[2269] User registration and login functionality

[2270] User Registration

[2271] Step 1:

[2272] A user fills out a web form with registration information such as name, email address, and password.

[2273] Step 2:

[2274] The terminal transmits the entered registration information to the server.

[2275] Specific operation: Sends input data to the server via an HTTP POST request.

[2276] Step 3:

[2277] The server stores the received registration information in a database.

[2278] Specific behavior: Executes an SQL query and inserts user information into the database.

[2279] Step 4:

[2280] The server returns a registration success message to the terminal.

[2281] Specific operation: A "Registration completed" message is returned in the HTTP response.

[2282] User Login

[2283] Step 1:

[2284] The user enters their email address and password into the login form.

[2285] Step 2:

[2286] The device sends the login information to the server.

[2287] Specific behavior: Sends login information to the server via an HTTP POST request.

[2288] Step 3:

[2289] The server checks the entered information against a database and authenticates it.

[2290] Specific behavior: Runs an SQL query to find login information that matches the registration information.

[2291] Step 4:

[2292] If the server is successful in the authentication, it generates an authentication token and returns it to the terminal.

[2293] Specific operation: Generates a JWT and returns the token in the HTTP response. If authentication fails, returns an error message.

[2294] Uploading copyrighted material and creating learning data

[2295] Uploading Rights Material

[2296] Step 1:

[2297] The user selects the rights material file and clicks the upload button.

[2298] Step 2:

[2299] The terminal transmits the selected file to the server.

[2300] Specific operation: Send the file data in the body of the HTTP POST request.

[2301] Step 3:

[2302] The server stores the received file in storage.

[2303] Specific behavior: Saves a file to a specific directory on the server.

[2304] Creating training data

[2305] Step 1:

[2306] The server analyzes the uploaded files and converts them into learning data.

[2307] Specific operation: For PDF files, text extraction is performed, and for image files, image recognition technology is used.

[2308] Step 2:

[2309] The server incorporates the training data into the generated AI model.

[2310] Specific operation: Convert data format and feed it to AI model.

[2311] Step 3:

[2312] The server notifies the user that the model has finished training.

[2313] Specific operation: Notify the completion of learning using HTTP response or push notification.

[2314] Use and sale of generative AI models

[2315] Using generative AI models

[2316] Step 1:

[2317] The user enters a question using the generative AI model.

[2318] Step 2:

[2319] The device sends the question to the server.

[2320] Specific behavior: Send a HTTP POST request with the question in the body.

[2321] Step 3:

[2322] The server inputs the question into a generative AI model, which generates an answer.

[2323] What it does: Input a question into a generative AI model and process the resulting answer.

[2324] Step 4:

[2325] The server generates a response and sends it back to the terminal.

[2326] Specific operation: Response data is returned in the HTTP response.

[2327] Selling generative AI models

[2328] Step 1:

[2329] Another user selects a generative AI model and proceeds to the purchase page.

[2330] Step 2:

[2331] The terminal inputs payment information and sends it to the server.

[2332] Specific operation: Enter credit card information etc. and send it via HTTP POST request.

[2333] Step 3:

[2334] The server processes the payment information and completes the sale.

[2335] Specific behavior: Calls the payment gateway API to confirm the payment and saves the sales record in the database.

[2336] Step 4:

[2337] After the server completes the purchase, it grants the purchasing user access to the generated AI model.

[2338] Specific behavior: Update the database and add permissions to the user account.

[2339] Revenue sharing and management

[2340] Revenue Management

[2341] Step 1:

[2342] The server collects sales and revenue data for the generated AI model.

[2343] Specific operation: Retrieves sales records from the database and aggregates sales data.

[2344] Step 2:

[2345] The server performs the calculations to distribute revenue to rights holders.

[2346] Specific operation: Calculate the revenue of each rights holder and update the revenue distribution table.

[2347] Step 3:

[2348] The server reflects the revenue data in the user profile.

[2349] What it does: Updates each user's profile and makes their earnings information visible.

[2350] Withdrawing earnings

[2351] Step 1:

[2352] The user performs a withdrawal operation from the earnings screen.

[2353] Step 2:

[2354] The terminal sends a withdrawal request to the server.

[2355] Specific operation: Enter the withdrawal amount and send it via HTTP POST request.

[2356] Step 3:

[2357] The server processes the withdrawal request and initiates the transfer.

[2358] Specific operation: Call the payment API and transfer money using the specified payment method.

[2359] Step 4:

[2360] The server notifies the user that the withdrawal process is complete.

[2361] Specific operation: Notify the completion of withdrawal via HTTP response or push notification.

[2362] Example 1

[2363] 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."

[2364] In recent years, new business models using generative AI models have been gaining attention. However, there is a need for a system that allows users to smoothly execute the entire process of creating generative AI models using their own digital content and selling them to other users. In conventional systems, digital content management, training data generation, generative AI model sales, and revenue management are often performed on separate platforms, resulting in complex operations and reduced efficiency. Furthermore, there is a need for easy and transparent revenue distribution and withdrawal procedures. The objective of the present invention is to solve these problems and provide a system that allows users to efficiently earn revenue without infringing on rights.

[2365] 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.

[2366] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter login information and generate an authentication token, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to prompt sentences entered by users using the generative AI model, and means for selling the generative AI model to other users and managing and distributing sales data and revenue. This allows users to efficiently perform tasks from managing their digital content to withdrawing revenue within a single system.

[2367] "User" means any person or entity that uses the System, enters registration information to create an account, and uploads digital content or purchases generative AI models.

[2368] "Database" means an information storage system for storing and managing user registration information, uploaded digital content, sales revenue data, etc.

[2369] "Login Information" means the authentication information a user uses to access a system, typically consisting of an email address and password.

[2370] An "authentication token" is temporary authentication information issued to a user after they successfully log in to a system, and is used to identify the user in subsequent requests.

[2371] "Digital content" refers to the rightful property uploaded by a user, such as data files such as text files, image files, and audio files.

[2372] "Training Data" means data used to create a generative AI model, extracted from uploaded digital content.

[2373] A "generative AI model" is an artificial intelligence model that is built based on training data and generates answers in natural language in response to prompts from users.

[2374] A "prompt" is a sentence that explains the question or instruction that a user enters into a generative AI model.

[2375] "Sales Data" refers to revenue information recorded as a result of sales of generative AI models.

[2376] "Revenue" means the monetary benefits derived from the sale of a generative AI model, which are subject to distribution among users.

[2377] This invention is a system that allows users to create generative AI models using their own digital content and sell them to other users to earn revenue. This system enables users, terminals, and servers to work together to achieve the following functions:

[2378] First, the user uses a device (e.g., PC or smartphone) to enter registration information such as name, email address, and password. The device then sends the entered information to the server via an HTTP POST request, which the server receives and stores in a database (e.g., MySQL). At this point, a confirmation email of completed registration is automatically sent to the user's email address.

[2379] Next, when a user logs in to the system, they enter their email address and password. The device sends this information to the server, which then checks the corresponding user information in its database. If authentication is successful, the server generates an authentication token and returns it to the device, which then stores it in its local storage.

[2380] After logging in to the system, the user uploads digital content (e.g., text files, image files) to the server. The device sends the selected file to the server, which then stores the data in storage (e.g., Amazon S3). The server analyzes the uploaded digital content and converts it into text data. This converted text data is used as training data to create a generative AI model (e.g., GPT-3).

[2381] The generated AI model has the ability to generate answers in natural language in response to prompts entered by the user. For example, when a user enters a question such as "What is the main theme of this novel?", the server uses the generative AI model to generate the appropriate answer and sends it back to the device. The device then displays the generated answer to the user.

[2382] When another user purchases a generative AI model, the purchase process is carried out from the terminal. The user enters purchase information (e.g., credit card information) and sends it to the server, which then completes the payment through a payment service (e.g., Stripe). Once payment is complete, the server grants the purchasing user access to the generative AI model.

[2383] Regarding revenue management, the server aggregates the sales data and revenue data of the generated AI model and reflects it in the user profile. The user clicks the "Withdraw" button on the revenue screen and sends a withdrawal request to the server from their device. The server processes the request and transfers the money to the user through a payment service (e.g., PayPal). Once the withdrawal process is complete, the server notifies the user.

[2384] As a concrete example, a writer user uploads a PDF file of their own work, a novel, and creates a generative AI model based on this. The user uses the generative AI model to generate responses to questions such as, "What is the main theme of this novel?" Another user purchases this generative AI model and enters their payment information to complete the payment, granting the purchasing user the right to use the generative AI model. Revenues are then distributed appropriately through the server, and the user can withdraw their own revenues.

[2385] This system allows users to create generative AI models using digital content without infringing on copyrights, and efficiently generate revenue. Furthermore, the entire process is completed within a single system, significantly reducing operational complexity.

[2386] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2387] System program processing steps

[2388] Step 1: User Registration

[2389] 1. The user enters registration information such as name, email address, and password.

[2390] Input: Name, Email Address, Password

[2391] Output: The entered registration information

[2392] Specific action: Enter information into a web form and click the submit button.

[2393] 2. The device sends the entered registration information to the server via an HTTP POST request.

[2394] Input: The registration information entered

[2395] Output: Server request

[2396] Specific behavior: Creating and sending an HTTP request.

[2397] 3. The server stores the received registration information in a database.

[2398] Input: Received registration information

[2399] Output: Database update

[2400] Specific actions: Generates SQL queries and inserts data into a database.

[2401] 4. The server sends a confirmation email to the user's email address confirming registration.

[2402] Input: User's email address

[2403] Output:Confirmation email

[2404] Specific operation: A confirmation email is sent via the mail server.

[2405] Step 2: Log in

[2406] 1. The user enters their email address and password and submits the login information.

[2407] Input: Email address, password

[2408] Output: The entered login information

[2409] Specific action: Enter information into a web form and click the submit button.

[2410] 2. The device sends the entered login information to the server via an HTTP POST request.

[2411] Input: The login information entered

[2412] Output: Server request

[2413] Specific behavior: Creating and sending an HTTP request.

[2414] 3. The server retrieves the user information from the database and verifies the email address and password.

[2415] Input: Login information, database user information

[2416] Output: Authentication result

[2417] Specific behavior: Executes an SQL query to retrieve and collate user information.

[2418] 4. The server generates an authentication token and returns it to the device in JSON format.

[2419] Input: Authentication success information

[2420] Output: Authentication token

[2421] Specific operation: Generates an authentication token and returns it in the HTTP response.

[2422] 5. The device stores the authentication token in local storage and uses it for subsequent requests.

[2423] Input: Authentication Token

[2424] Output: Stored authentication token

[2425] Specific operation: The authentication token is saved in the browser's local storage.

[2426] Step 3: Upload your digital content

[2427] 1. The user selects digital content (e.g., a PDF file of a novel).

[2428] Input: Digital content files

[2429] Output: Selected file path

[2430] Specific operation: Opens a file selection dialog and selects a file.

[2431] 2. The device uploads the selected file to the server via an HTTP POST request.

[2432] Input: Selected file path

[2433] Output: Server request

[2434] Specific operation: Generates an HTTP request and sends the file to the server.

[2435] 3. The server saves the received file to storage (e.g. Amazon S3).

[2436] Input: Received file data

[2437] Output: Saved file path

[2438] Specific operation: Calls the storage API and saves the file.

[2439] 4. The server notifies the user that the save is complete.

[2440] Input: Saved file path

[2441] Output: Notification message

[2442] Specific operation: Sends HTTP responses and email notifications.

[2443] Step 4: Creating training data

[2444] 1. The server analyzes the file stored in the storage and converts it into text data (e.g., converting PDF to text).

[2445] Input: Saved file path

[2446] Output: Text data

[2447] Specific operation: Extracts text data using a PDF parsing library.

[2448] 2. The server converts the text data into an appropriate format (e.g., JSON) and saves it as training data for the generative AI model.

[2449] Input: Text data

[2450] Output: Formatted training data

[2451] Specific operation: Format text data into JSON format and save it in the database.

[2452] 3. The server stores the meta information of the converted learning data in a database.

[2453] Input: Formatted training data

[2454] Output: Database update

[2455] Specific operation: Generates an SQL query to insert meta information into the database.

[2456] Step 5: Use and sell the generative AI model

[2457] 1. The user inputs a question (prompt sentence) using the generative AI model.

[2458] Input: prompt statement

[2459] Output: The prompt text entered

[2460] Specific action: Enter a question into the web form and click the submit button.

[2461] 2. The device sends the question to the server via an HTTP POST request.

[2462] Input: The prompt text entered

[2463] Output: Server request

[2464] Specific operations: Generate an HTTP request and send a question to the server.

[2465] 3. The server receives the question and calls the corresponding generative AI model (e.g., GPT-3).

[2466] Input: Question, Generative AI Model

[2467] Output: Response data

[2468] What it does: Generate answers using a generative AI model.

[2469] 4. The server generates the answer from the AI ​​model and sends it to the device in JSON format.

[2470] Input: Answer data

[2471] Output: Answer response

[2472] Specific operation: The response data is converted into JSON format and sent as an HTTP response.

[2473] 5. The device displays the received answer to the user.

[2474] Input: Answer response

[2475] Output: Displayed answer

[2476] Specific action: Display the answer on a web page.

[2477] 6. Other users can purchase the generative AI model by clicking the "Purchase" button on the details page.

[2478] Input: Purchase Request

[2479] Output: Purchase procedure information

[2480] Specific operation: Click the purchase button to display the purchase procedure screen.

[2481] 7. The device enters the purchase information and sends it to the server via an HTTP POST request.

[2482] Input: Purchase procedure information

[2483] Output: Server request

[2484] Specific actions: Enter purchase information and click the submit button.

[2485] 8. The server processes the payment information and completes the payment through a payment service (e.g., Stripe).

[2486] Input: Payment information

[2487] Output: Payment completed

[2488] Specific operation: Call the payment API and process the payment.

[2489] 9. The server confirms the purchase and grants the purchasing user access to the generated AI model.

[2490] Input: Payment completion information

[2491] Output: Permission granted

[2492] Specific Actions: Update user profile and grant access privileges.

[2493] 10. The device displays a notification to the user that the purchase is complete.

[2494] Input: Purchase completion information

[2495] Output: Notification message

[2496] Specific operation: Display a purchase completion message.

[2497] Step 6: Revenue sharing and management

[2498] 1. The server aggregates the sales and revenue data of the generated AI model from the database.

[2499] Input: Sales data, revenue data

[2500] Output: Aggregation results

[2501] Specific behavior: Executes SQL queries and aggregates data.

[2502] 2. The server calculates the revenue distribution to the rights holders and reflects it in the user profile.

[2503] Input: Aggregation results

[2504] Output: Updated user profile

[2505] Specific Actions: Perform revenue share calculations and update user profiles.

[2506] 3. The server will notify the rights holder of the revenue details by email.

[2507] Input: Revenue sharing information

[2508] Output: Notification email

[2509] Specific operation: Revenue details are notified via the mail server.

[2510] 4. The user opens the earnings screen and clicks the "Withdraw" button.

[2511] Input: Withdrawal request

[2512] Output: Withdrawal procedure information

[2513] Specific actions: Click the Withdraw button to begin the withdrawal process.

[2514] 5. The terminal sends a withdrawal request to the server via an HTTP POST request.

[2515] Input: Withdrawal procedure information

[2516] Output: Server request

[2517] Specific actions: Enter withdrawal information and click the submit button.

[2518] 6. The server processes the withdrawal request and sends the money to the user via a payment service (e.g., PayPal).

[2519] Input: Withdrawal request information

[2520] Output: Transfer completed

[2521] Specific operation: Call the payment API and process the transfer.

[2522] 7. The server notifies the user by email that the withdrawal process is complete.

[2523] Input: Remittance completion information

[2524] Output: Notification email

[2525] Specific behavior: Sends notifications via a mail server.

[2526] (Application example 1)

[2527] 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."

[2528] Conventional content distribution services lack a mechanism for generating new content from digital content owned by users and generating revenue from it. Furthermore, there is no established method for properly managing and distributing the generated content and for fairly distributing revenue, which means that users are unable to effectively utilize their own creations.

[2529] 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.

[2530] In this invention, the server includes means for saving registration information entered by users in a database, means for registered users to enter and authenticate login information, means for users to upload their own digital content to the server, means for converting the uploaded digital content into training data, means for creating a generative AI model based on the training data, means for generating answers to user questions using the generative AI model, means for selling the generative AI model to other users and managing and distributing revenue, and means for providing newly generated content in a content distribution service, which allows users to earn revenue by using new content generated based on their own digital content.

[2531] "User" means an individual or legal entity that registers on the content distribution service platform and uploads digital content or uses a generative AI model.

[2532] "Registration Information" refers to information provided by a User when registering for the Service for the first time, including, for example, name, email address, and password.

[2533] "Authentication" is the process of verifying that a user is a legitimate user based on the login information they provide.

[2534] "Digital Content" means data in electronic form that is owned by a user, including, but not limited to, text, images, audio, etc.

[2535] A "server" is a computer system that stores, processes, and distributes data over the Internet.

[2536] "Uploading" is the process by which a user sends their digital content from a terminal to a server for storage.

[2537] "Training Data" means data extracted from uploaded digital content that is used to train a generative AI model.

[2538] A "generative AI model" is an artificial intelligence model that is trained based on learning data and generates answers to user questions and new content.

[2539] A "question" is an inquiry that a user inputs to a generated AI model.

[2540] An "answer" is the response that a generative AI model generates in response to a user's question.

[2541] "Sales" is the process of providing a generative AI model to other users and receiving payment in return.

[2542] "Revenue" means the financial benefit derived from the sale of a generative AI model.

[2543] "Management and distribution" refers to the process of aggregating the revenue generated from the sale of generative AI models and distributing it to relevant users in appropriate proportions.

[2544] A "content distribution service" is an online service for creating, sharing, buying, selling, and distributing digital content.

[2545] "Newly Generated Content" is new digital content that is automatically generated based on a user's digital content and a generative AI model.

[2546] The embodiment of the present invention is realized by constructing a system that includes three main elements: a server, a terminal, and a user.

[2547] Program Overview and Processing

[2548] 1. Registration and login functionality

[2549] The user uses their own device to enter registration information such as name, email address, and password. This information is sent from the device to the server, where it is stored in a database. After completing registration, the user enters their email address and password into the login form and sends it back to the server. The server verifies the entered information, generates an authentication token, and sends it back to the device.

[2550] 2. Uploading copyrighted material and creating training data

[2551] Users upload their digital content (e.g., text, images, audio, etc.) from their devices to the server, which stores the uploaded digital content and converts it into an appropriate format. The server then uses this data to train a generative AI model.

[2552] 3. Use and sale of generative AI models

[2553] The generated AI model can be used by a user by inputting a question using a device. The server receives the question, generates an answer using the corresponding generative AI model, and sends it back to the device. This generative AI model can also be purchased by other users. The server processes payment information, completes the purchase procedure, and grants the purchasing user access to the generative AI model.

[2554] 4. Revenue sharing and management

[2555] The server aggregates the sales data and revenue data of the generative AI model and distributes the revenue fairly to the rights holders. When a user submits a request to withdraw revenue, the server processes the withdrawal request and transfers the money to the user via the specified payment method. A notification of the completion of the withdrawal process is also sent from the server to the user.

[2556] Hardware and software used

[2557] Hardware: Smartphone (iOS or Android device)

[2558] Server: AWS or Google Cloud Platform

[2559] Database: MySQL or PostgreSQL

[2560] AI models: Generative AI models such as GPT-3 and BERT

[2561] Frontend: React Native for mobile app development

[2562] Backend: Node.js and Express framework

[2563] Specific examples

[2564] Example 1: Writer usage scenario

[2565] A writer uploads a short story they have written to an app from their smartphone. The server then analyzes the story and saves it as text data. A generative AI model is trained based on this data. The writer uses the generative AI model to generate responses to questions about their story. For example, if the question is "Who is the first character the protagonist meets in the story?", the generative AI model will respond with "The first character the protagonist meets is A."

[2566] Prompt Sentence Examples

[2567] "In the novel 'Star Messenger,' what is the name of the main character?"

[2568] With the system configuration described above, users can generate new content based on their own digital content and generate revenue from it.

[2569] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2570] Step 1:

[2571] A user inputs registration information such as name, email address, and password from a terminal. The terminal sends this input information to a server. The server stores the received information in a database and creates a profile for the registered user. The input includes name, email address, and password, and the output is the registered user information stored in the database.

[2572] Step 2:

[2573] A user who has completed registration enters their email address and password into the login form and sends them from the device to the server. The server checks the registration information against the database, and if it matches, generates an authentication token and sends it back to the device. The input includes the email address and password, and the output is the authentication token sent to the device.

[2574] Step 3:

[2575] The user selects their own digital content (text, images, audio, etc.) from the device and clicks the upload button. The device sends the selected file to the server. The server saves the received digital content in storage and begins data analysis. The input is the digital content file, and the output is the file saved in storage and the data to be analyzed.

[2576] Step 4:

[2577] The server converts the uploaded digital content into training data. Specifically, it converts the text and image formats into a format that is easy for the AI ​​model to learn, and stores the data in a database. The input is the uploaded digital content file, and the output is the converted training data.

[2578] Step 5:

[2579] The server creates a generative AI model based on the training data. To train the AI ​​model, the server runs an efficient learning process using many datasets. The input is the transformed training data, and the output is the generated AI model.

[2580] Step 6:

[2581] The user inputs a question from the device using a generative AI model. The device sends the question to the server, which then generates an answer using the corresponding generative AI model and sends it back to the device. The input is the question entered by the user, and the output is the answer returned by the server.

[2582] Step 7:

[2583] Another user executes the process of purchasing a generative AI model. The user completes the purchase procedure for the generative AI model from their device, and the device sends payment information to the server. The server processes the payment information, completes the purchase procedure, and grants the purchasing user access rights to the generative AI model. The input is the purchase procedure and payment information, and the output is a purchase completion notification and access rights to the generative AI model.

[2584] Step 8:

[2585] The server aggregates the sales data and revenue data of the generated AI model and distributes the revenue to the rights holder. When a user operates the revenue screen to withdraw revenue, the terminal sends a withdrawal request to the server. The server processes the withdrawal request and transfers the money to the user using the specified payment method. The server notifies the user that the withdrawal process is complete. The input is the withdrawal request, and the output is the withdrawal completion notification and the transfer.

[2586] 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.

[2587] The present invention combines a system that allows users to create generative AI models using their own proprietary property and sell them to other users to earn revenue, with an emotion engine that recognizes the user's emotions. This system includes the following processes.

[2588] User registration and login functionality

[2589] 1. A user fills out a web form with registration information such as name, email address, and password.

[2590] 2. The device sends the entered registration information to the server.

[2591] 3. The server stores the received registration information in a database.

[2592] 4. After completing registration, the user enters their email address and password into the login form and submits it to the server.

[2593] 5. The server verifies the entered email address and password, generates an authentication token, and returns it to the device.

[2594] Uploading copyrighted material and creating learning data

[2595] 1. The user selects their own copyrighted material (e.g., text, image files, etc.) and uploads it to the server.

[2596] 2. The device sends the selected file to the server.

[2597] 3. The server saves the received file in storage and analyzes it.

[2598] 4. The server converts the rights into an appropriate format as training data and incorporates it into the generative AI model.

[2599] Use and sale of generative AI models

[2600] 1. The user enters a question using the generative AI model and sends it to the server.

[2601] 2. The server receives the question, generates an answer using the corresponding generative AI model, and sends it to the device.

[2602] 3. Other users can purchase the generative AI model by completing the purchase process on their device.

[2603] 4. The server processes the payment information and completes the purchase. After the purchase is complete, the server grants the purchasing user access to the generated AI model.

[2604] Reve...

Claims

1. means for storing the registration information entered by the user in a database; a means for registered users to enter and authenticate their login information; A means for users to upload their own property to the server; A means for converting the uploaded copyrighted material into learning data; A means for creating a generative AI model based on the training data; A means for generating an answer to a user's question using a generative AI model; A means to sell generative AI models to other users and manage and distribute revenue; and A system including:

2. The system of claim 1, wherein learning data for incorporation into the generative AI model is extracted from uploaded rights materials of various formats.

3. The system of claim 1, which manages payment procedures for the sale and purchase of generative AI models.

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