System

Abstract

A system is provided.SOLUTION: A system comprising: means for tokenizing a right to use a generative artificial intelligence model; means for issuing the token as a smart contract on a blockchain; means for providing a platform for trading the issued token; means for trading the token through the platform; means for receiving a request for the token holder to use the generative artificial intelligence model; and means for operating the generative artificial intelligence model based on the request and providing a use result to the holder.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] The development and use of current artificial intelligence models, particularly generative AI models, requires enormous amounts of capital, and there are limited means of efficiently raising this capital. Furthermore, a mechanism for easily and safely trading rights to use generative AI models on the market has yet to be fully established. This hinders the widespread adoption of AI technology and the efficiency of fundraising. Furthermore, there is a lack of an automated platform for efficiently managing and administering tokenized rights to use the models. Therefore, the present invention aims to provide a mechanism for efficiently and safely trading rights to use generative AI models on the market as tokens, thereby facilitating fundraising for AI companies and increasing convenience for token users. [Means for solving the problem]

[0005] The present invention provides a means for tokenizing the right to use a generative AI model and a means for issuing the token as a smart contract on a blockchain. It also provides a platform for buying and selling issued tokens, facilitating the buying and selling of tokens through the platform. Furthermore, it develops a system that includes a means for receiving requests from token holders to use a generative AI model, operating the generative AI model based on the requests, and providing the holder with the results of use. This platform has the functions of managing token buying and selling history and calculating fees, as well as recording holder usage history and automatically calculating applicable fees. This enables AI companies to quickly raise funds, allows individual investors to easily invest in AI technology, and allows token holders to efficiently use AI models.

[0006] A "generative artificial intelligence model" is an artificial intelligence algorithm or system that has the ability to generate new data based on input data.

[0007] "Right of Use" refers to the right to use a specific generative AI model for one's own purposes for a certain period of time or a certain number of times.

[0008] A "token" is a digital asset that can be traded on a blockchain and represents a specific right of use or value.

[0009] A "smart contract" is a self-executing program deployed on a blockchain that automatically executes transactions or operations when certain conditions are met.

[0010] "Platform" means a system or environment that allows users to access and use certain services or functions.

[0011] "Trading History" refers to all transaction records and details relating to the buying and selling of Tokens.

[0012] "Fees" means expenses incurred in the transaction of funds or services as compensation for brokering or managing the transaction.

[0013] "Usage results" refers to the generated data and output obtained after using a generative artificial intelligence model.

[0014] "Usage History" refers to a record of how a token holder has used a generative AI model.

[0015] "Blockchain" is a method of recording digital transactions based on distributed ledger technology, which ensures security and transparency. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[0038] System Overview

[0039] The system consists of the following main components:

[0040] 1. Registration portal for AI companies

[0041] 2. Token Generation and Management Server

[0042] 3. Trading platform for users

[0043] 4. Generative AI Model Operation Server

[0044] Registration portal for AI companies

[0045] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[0046] Token Generation and Management Server

[0047] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0048] User-friendly trading platform

[0049] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0050] Program processing

[0051] Token Purchase

[0052] When a user purchases a token, the following happens:

[0053] 1. The user logs in to the platform and selects the tokens they wish to purchase.

[0054] 2. The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[0055] 3. The server checks the balance of the user's wallet and, if the transaction conditions are met, executes the smart contract to transfer the tokens to the user's wallet.

[0056] 4. The server notifies the user of the success of the transaction, and a message indicating the purchase is complete is displayed on the terminal.

[0057] Token Sale

[0058] When a user sells their tokens, the following happens:

[0059] 1. A user submits a request to sell their tokens.

[0060] 2. The server publishes the request on the trading platform and waits for other users to purchase.

[0061] 3. When another user wants to make a purchase, the server executes a smart contract and distributes tokens and funds to each wallet.

[0062] 4. The server notifies the selling user of the success of the transaction, and a message indicating the sale is complete is displayed on the terminal.

[0063] Token holders' right to use AI

[0064] When a user uses a token to access a generative AI model, the following happens:

[0065] 1. The user accesses a dedicated portal and submits a request for use.

[0066] 2. The server analyzes the request and sends the data to the generative artificial intelligence model.

[0067] 3. The server receives the generated results and provides them to the user.

[0068] 4. The server records the token usage history and calculates the applicable fees.

[0069] Specific examples

[0070] For example, suppose a user holds a token for an image-generating AI model and wants to use it to generate a new image. The user enters an image generation request into the portal and specifies the required parameters (e.g., the theme and style of the image they want to generate). The server receives the request and sends the data to the generative AI model. The model generates a new image, and the result is sent to the user via the server. This process ensures that the entire platform operates securely and efficiently.

[0071] This system will enable AI companies to raise funds quickly, allow individual investors to easily invest in AI technology, and allow token holders to efficiently use AI models, greatly improving convenience.

[0072] The processing flow will be explained below.

[0073] Token purchase processing steps

[0074] Step 1:

[0075] Users log in to the platform and select the tokens they wish to purchase.

[0076] Step 2:

[0077] The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[0078] Step 3:

[0079] The server checks the user's wallet balance and sees if the desired purchase amount is available in the wallet.

[0080] Step 4:

[0081] The server generates a smart contract for token trading based on the user's request and deploys it on the blockchain.

[0082] Step 5:

[0083] The server executes the smart contract and transfers the purchased tokens to the user's wallet.

[0084] Step 6:

[0085] The server confirms the success of the transaction and sends a transaction completion notification to the user's terminal.

[0086] Step 7:

[0087] A message will appear on your device confirming your purchase.

[0088] Token sale processing steps

[0089] Step 1:

[0090] Users submit a request to sell their tokens to the platform.

[0091] Step 2:

[0092] The server receives the sale request and publishes it on the trading platform.

[0093] Step 3:

[0094] Another user accesses the platform and views the published sale request.

[0095] Step 4:

[0096] Send a request to buy tokens that another user wants to sell.

[0097] Step 5:

[0098] The server transfers tokens and transaction amounts between wallets through smart contracts.

[0099] Step 6:

[0100] The server confirms the success of the transaction and sends a notice of completion of the sale to the terminals of the selling user and the purchasing user.

[0101] Step 7:

[0102] A message will appear on the terminal indicating that the sale is complete.

[0103] Processing steps for token holders to exercise their AI usage rights

[0104] Step 1:

[0105] Users have a token and access a dedicated portal.

[0106] Step 2:

[0107] To use the generative artificial intelligence model, the user inputs the necessary request information (input data, parameters, etc.).

[0108] Step 3:

[0109] The user sends a request and notifies the server.

[0110] Step 4:

[0111] The server analyzes the request content and sends input data to the generative artificial intelligence model.

[0112] Step 5:

[0113] Generative artificial intelligence models generate new data (e.g., generated images) based on input data.

[0114] Step 6:

[0115] The server receives the generated results and provides them to the user.

[0116] Step 7:

[0117] The server records the token usage history and calculates the applicable fees.

[0118] Step 8:

[0119] The server sends the usage results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[0120] Example: Using an image generation AI model

[0121] Step 1:

[0122] Users hold tokens for the image-generating AI model and access a dedicated portal.

[0123] Step 2:

[0124] Users enter image generation requests on the portal and specify the theme and style of the image they want to generate.

[0125] Step 3:

[0126] The user sends a request and notifies the server.

[0127] Step 4:

[0128] The server analyzes the request and sends input data to the image generation AI model.

[0129] Step 5:

[0130] An image generation AI model generates new images based on input data.

[0131] Step 6:

[0132] The server receives the resulting image and provides it to the user.

[0133] Step 7:

[0134] The server records the token usage history and calculates the applicable fees.

[0135] Step 8:

[0136] The server sends the generated results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[0137] In this way, users can efficiently and safely buy and sell tokens and utilize generative AI models.

[0138] Example 1

[0139] 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."

[0140] Currently, there is no efficient and transparent way to manage and operate the rights to use generative AI models. In particular, there is a lack of a reliable platform for tokenizing the rights to use AI models and trading them on the market. This makes it difficult for AI companies to raise funds quickly and creates high barriers for individual investors to access these technologies. Furthermore, there is a lack of convenience as there is no way for token holders to properly use AI models.

[0141] 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.

[0142] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for generating and deploying the token as a smart contract on a blockchain; means for listing the issued token on a trading platform and enabling trading; means for buying and selling the token through the trading platform; means for receiving a request from the token holder to use the generative AI model; means for transmitting data to the generative AI model based on the request and providing the holder with the generation results; means for operating the generative AI model based on the content of the request and providing the holder with the usage results; and means for notifying the token purchaser or seller of the success of the transaction. This enables the right to use a generative AI model to be tokenized efficiently and transparently, allowing it to be safely bought, sold, and used in the market.

[0143] A "generative artificial intelligence model" is an artificial intelligence algorithm or program that can generate data based on user requests.

[0144] "Tokenization" is the process of converting certain rights or assets into digital tokens.

[0145] "Blockchain" is a technology that uses distributed ledger technology to manage transaction data in a transparent and immutable manner.

[0146] A "smart contract" is a self-executing contract program that runs on a blockchain and is a mechanism that automatically executes a contract when certain conditions are met.

[0147] "Trading Platform" means an online system or website through which users can buy and sell digital tokens.

[0148] A "request" is an act in which a user requests a specific service or data generation from a generative artificial intelligence model.

[0149] "Metadata" is data containing detailed information related to a token or digital asset, such as issuance quantity, price, terms of use, etc.

[0150] A "wallet" is a software tool or application used to securely store and transact digital tokens.

[0151] "Trading History" means the records of purchases and sales of Tokens made on the Trading Platform.

[0152] "Fees" are costs incurred in connection with the purchase, sale, and use of tokens, and are collected by the platform or operator.

[0153] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[0154] System Overview

[0155] The system consists of the following main components:

[0156] 1. Registration portal for AI companies

[0157] 2. Token Generation and Management Server

[0158] 3. Trading platform for users

[0159] 4. Generative AI Model Operation Server

[0160] Registration portal for AI companies

[0161] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. This registered information is managed by a server.

[0162] Token Generation and Management Server

[0163] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0164] User-friendly trading platform

[0165] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0166] Generative AI model operation server

[0167] When a user uses a token to access a generative AI model, they access a dedicated portal and submit a request. The server analyzes the request and sends the data to the generative AI model. The server receives the generated results and provides them to the user. The server records the token usage history and calculates the applicable fees.

[0168] Specific examples

[0169] For example, suppose a user has a token for an image generation AI model and wants to use it to generate a new image. The user accesses the portal, enters a request for image generation, and specifies the theme and style of the image they want to generate. An example of a specific prompt is as follows:

[0170] "Generate a beach scene illuminated by a summer sunset."

[0171] "Please create a cyberpunk-style illustration with a futuristic city theme."

[0172] The server receives the request and sends the data to the generative AI model, which then generates a new image and sends the result to the user via the server. This process ensures the entire platform operates safely and efficiently.

[0173] This system will enable AI companies to raise funds quickly, individual investors to easily access AI technology, and token holders to efficiently use generative AI models, greatly improving convenience.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The user (AI company) registers model information

[0177] Input: The user enters information such as the name, version, and license terms of the AI ​​model.

[0178] Server Actions: The server receives the entered information, verifies the data for accuracy and completeness, and prepares this information for storage in a database.

[0179] Output: The server stores the information in a database and sends a "Registration complete" message to the device.

[0180] Specific operation: The user clicks the "Enter model information" button. The server verifies the information and registers it in the database. The terminal displays the message "Registration completed."

[0181] Step 2:

[0182] Server-generated tokens

[0183] Input: Token generation request from AI company.

[0184] Server operation: The server receives the request, generates a smart contract using the blockchain API, sets the token metadata in the smart contract, and deploys it.

[0185] Output: Issued tokens are generated and listed on the exchange platform.

[0186] How it works: When the server receives a "token generation" request, it uses the blockchain API to create a smart contract and deploy it, which generates a token and lists it on the platform.

[0187] Step 3:

[0188] User purchase of tokens

[0189] Input: The user enters the number of tokens they wish to purchase and the price.

[0190] Server operation: The server checks the user's wallet balance and verifies whether the transaction conditions are met. If the conditions are met, it executes the smart contract to transfer the tokens to the user's wallet.

[0191] Output: A message indicating purchase is complete is displayed on the terminal.

[0192] Specific operation: The user clicks the "Purchase" button. The server calls the wallet API to check the balance, activates the smart contract to transfer the tokens, and displays a "Purchase Complete" message on the terminal.

[0193] Step 4:

[0194] Token sales by users

[0195] Input: Enter a request to sell the tokens the user holds.

[0196] Server operation: The server receives the sale request and publishes it on the trading platform. If a user wishes to purchase, it executes the smart contract and distributes the tokens and funds to each wallet.

[0197] Output: A message indicating the sale is complete is displayed on the terminal.

[0198] Specific operation: The user clicks the "Sell" button. The server publishes the sale request to the trading platform, and if a buyer appears, it launches the smart contract, distributes tokens and funds, and displays a "Sell Complete" message on the terminal.

[0199] Step 5:

[0200] Token holders' right to use AI

[0201] Input: The user enters a usage request and prompt.

[0202] Server operation: The server analyzes the request content and sends the data to the generative AI model. It receives the generated results and provides them to the user. It records the token usage history and calculates the applicable fees.

[0203] Output: The generated results are displayed on the terminal.

[0204] Specific operation: The user clicks the "Send request to AI model" button. The server sends the prompt to the AI ​​model. The generated result is received and displayed on the device. The usage history is recorded and fees are calculated.

[0205] (Application example 1)

[0206] 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."

[0207] There is a need to provide a system for the efficient and transparent use of generative AI models. However, current technology lacks a platform that can properly manage and securely trade the rights to use generative AI models. Furthermore, content distribution services face challenges in maintaining the quality of generated content and responding promptly to user requests.

[0208] 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.

[0209] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for issuing tokens as smart contracts on distributed ledger technology; means for providing a platform for buying and selling the issued tokens; means for buying and selling the tokens through the platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; means for the usage results to include content related to a content distribution service; and means for generating the content based on the tokens held by the user. This enables efficient management and safe trading of the right to use the tokenized generative AI model, as well as high-quality content distribution.

[0210] A "generative artificial intelligence model" refers to an artificial intelligence technology that can automatically generate new content and data based on input data.

[0211] "Right of use" refers to the right to use a specific service or technology (in this case, a generative artificial intelligence model) for a certain period of time or a certain number of times.

[0212] "Tokenization" refers to the process of converting digital assets or rights into digital vouchers called tokens and managing them on a blockchain.

[0213] "Distributed ledger technology" refers to technology that stores digital data on a distributed network and manages transaction histories and contracts transparently and securely.

[0214] A "smart contract" refers to a contract whose terms are written in program code and are executed automatically.

[0215] "Platform" refers to a system or environment that serves as the foundation for providing a specific purpose or service.

[0216] A "request" refers to an instruction that a user gives to a system requesting a specific action or service.

[0217] "Content distribution service" refers to an online service that provides users with digital content such as text, images, audio, and video.

[0218] "Content" refers to information, data, media or other digital material organized in a particular format or subject.

[0219] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where it can be bought and sold on the market. Specific embodiments for implementing this invention will be described below.

[0220] System Configuration

[0221] The system consists of the following main components:

[0222] 1. Registration Portal:

[0223] This is a portal where AI companies can register their rights to use generative AI models. AI companies can enter their model information here and make a request to tokenize their usage rights.

[0224] 2. Token Generation and Management Server:

[0225] This server receives tokenization requests from AI companies, creates and deploys smart contracts on distributed ledger technology, and issues tokens according to the smart contract, managing the token metadata (issuance amount, price, terms of use, etc.).

[0226] 3. Trading Platform:

[0227] Users can buy and sell tokens through this platform. When purchasing tokens, the server checks the user's wallet balance and transfers the tokens if the required funds are available. When a sell request is made, the server publishes the request and mediates the transaction with the user who wishes to buy.

[0228] 4. Generative AI model operation server:

[0229] When a user uses a token to utilize a generative artificial intelligence model, this server receives the request, operates the generative artificial intelligence model, and provides the utilization results to the user.

[0230] Program processing

[0231] The system allows users to use a generative artificial intelligence model on a smartphone or other device to generate content related to a content distribution service.

[0232] Hardware and software:

[0233] Hardware:

[0234] Smartphone, blockchain node server, AI model server

[0235] software:

[0236] Python 3.x, Requests library, Web3 library, blockchain node (e.g., Ethereum), generative AI model service (e.g., GPT-3)

[0237] Data processing and calculation:

[0238] 1. Initialize your wallet:

[0239] The user's wallet is initialized using eth_account and web3.

[0240] 2. Check token balance:

[0241] The user's wallet address is used to check the token balance on the blockchain, which determines whether the user can spend the tokens.

[0242] 3. AI-generated request:

[0243] A prompt from the user is sent to the generative AI model to generate content. For example, a prompt could be, "Generate ideas for a blog post. The topic is technology."

[0244] 4. Providing generated results:

[0245] The generated content is provided to users through a server, allowing users to instantly obtain high-quality content.

[0246] Specific examples

[0247] For example, if a user wants to generate ideas for a blog post, they might enter a prompt like this:

[0248] Example prompt sentence:

[0249] "Generate ideas for blog posts on technology."

[0250] Based on this request, the generative AI model generates high-quality blog post ideas related to the specified topic and provides the results to the user via the server, allowing the user to generate content quickly and efficiently using their own tokens.

[0251] This system tokenizes the rights to use generative AI models, providing a platform for secure transactions and greatly improving the convenience of users to generate high-quality content.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1:

[0254] A user launches the smartphone app and confirms their right to use the generative AI model they own. They then log in to their wallet and check the balance of their tokens. At this time, the wallet address is entered, and the number of tokens held is displayed as the output.

[0255] Step 2:

[0256] When a user wants to use a generative AI model, tokens are required, so the user must ensure that they have sufficient token balance. The server obtains the current token balance using distributed ledger technology based on the user's wallet address and displays the token balance as the output.

[0257] Step 3:

[0258] The user creates a prompt to use the generative AI model and enters it into the application's input form. For example, the user might enter a prompt such as, "Generate ideas for a blog post. The topic is technology." The entered prompt is then sent to the server.

[0259] Step 4:

[0260] The server receives the user's prompt sentence and sends it to the generative AI model. This inputs data from the server to the generative AI model, which then begins generating content based on the input data. The server then sends request data including the prompt sentence to the generative AI model and receives the generated content data.

[0261] Step 5:

[0262] The generative AI model generates content based on the received prompt. Specifically, digital content such as text and images is generated according to the theme and style associated with the input prompt. The data generated is determined by the AI ​​model's calculations.

[0263] Step 6:

[0264] The server analyzes the content received from the generative AI model and converts it into a format to be provided to the user. The converted data is then output from the server to the user's device (smartphone).

[0265] Step 7:

[0266] Finally, the generated content is displayed on the user's smartphone. The user can review and use the generated content (e.g., blog post ideas and images). The user can also view the generated results and, if necessary, generate more content by entering additional prompts.

[0267] Through this series of steps, users can use their tokens to effectively utilize generative AI models to quickly generate high-quality content.

[0268] 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.

[0269] The present invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. Furthermore, this system is combined with an emotion engine that recognizes user emotions and has the function of adjusting the output of the generative AI model based on the user's emotions.

[0270] System Overview

[0271] The system consists of the following main components:

[0272] 1. Registration portal for AI companies

[0273] 2. Token Generation and Management Server

[0274] 3. Trading platform for users

[0275] 4. Generative AI Model Operation Server

[0276] 5. Emotion Engine

[0277] Registration portal for AI companies

[0278] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[0279] Token Generation and Management Server

[0280] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0281] User-friendly trading platform

[0282] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0283] Emotion Engine

[0284] The server is equipped with an emotion engine that recognizes the user's emotions. This engine analyzes emotions from the user's input data and interactions, and reflects this data in the operation of the generative AI model.

[0285] Generative AI model operation server

[0286] The server operates the generative AI model based on user requests and provides the results. It also receives emotion data from the emotion engine and optimizes the model's output based on the user's emotions.

[0287] Program processing

[0288] Emotion Engine Process

[0289] When a user accesses the platform and uses a generative AI model, the following occurs:

[0290] 1. A user logs into the platform and the emotion engine monitors the user's input and interactions.

[0291] 2. The emotion engine extracts emotional data from the user's text input, facial expressions, and voice.

[0292] 3. The emotion engine sends the analyzed emotion data to the server in real time.

[0293] 4. The server adjusts the parameters of the generative AI model based on the emotion data.

[0294] Emotion-driven generation

[0295] As a concrete example, consider a case where a user uses a token to access an image-generating AI model:

[0296] 1. The user accesses a dedicated portal and specifies the theme and style of the image they want to generate.

[0297] 2. The emotion engine analyzes the user's emotions (e.g., happy, sad, surprised).

[0298] 3. The server adjusts the settings of the image generation AI model based on the emotion data.

[0299] For example, if the user is recognized as "happy," the parameters are set so that an image with bright and vivid colors is generated.

[0300] 4. The image generation AI model generates a new image based on the adjusted parameters.

[0301] 5. The server receives the resulting image and provides it to the user.

[0302] 6. The server records the token usage history and calculates the applicable fees.

[0303] Specific examples

[0304] This explains the process by which a user trains a generative AI model using the emotion engine and uses tokens:

[0305] 1. A user logs into the portal and their emotions are monitored in real time by the emotion engine.

[0306] 2. If the user inputs a request for image generation and indicates an excited emotion, the emotion engine sends the emotion data to the server.

[0307] 3. The server changes the color and content settings of the image generation AI model based on the emotion data.

[0308] 4. The image generation AI model generates an image using parameters according to the user's emotions and provides the image to the user via the server.

[0309] 5. This allows users to get customized output tailored to their individual emotional state.

[0310] In this way, the system takes into account user sentiment and provides more personalized output from the generative AI model. It also efficiently manages token trading and usage history.

[0311] The processing flow will be explained below.

[0312] Emotion Engine Process

[0313] Collaboration with emotion engine

[0314] Step 1:

[0315] Users log in to the platform and access a dedicated portal.

[0316] Step 2:

[0317] The emotion engine monitors user input and interactions and collects emotion data in real time.

[0318] Step 3:

[0319] The emotion engine analyzes emotions from the user's character input, facial expression analysis, voice input, etc., and generates emotion data.

[0320] Step 4:

[0321] The emotion engine transmits the analyzed emotion data to the server.

[0322] Emotion-driven generation

[0323] Examples of using image generation AI models

[0324] Step 1:

[0325] The user logs into the specialized portal and opens a request form for image generation.

[0326] Step 2:

[0327] The user inputs the theme (e.g., "landscape" or "portrait") and style (e.g., "realistic" or "abstract") of the image they want to generate.

[0328] Step 3:

[0329] An emotion engine analyzes the user's current emotional state (e.g., "happy," "sad," "excited").

[0330] Step 4:

[0331] The emotion engine transmits emotion data to the server in real time.

[0332] Step 5:

[0333] The server adjusts the setting parameters of the generative artificial intelligence model based on the emotion data.

[0334] For example, if the user is expressing a "happy" emotion, the colors of the generated image are brightened and the overall tone is adjusted to a positive one.

[0335] Step 6:

[0336] The server sends the adjusted setting parameters to the image generation AI model.

[0337] Step 7:

[0338] The image generation AI model generates new images based on user requests and parameter settings from the server.

[0339] Step 8:

[0340] The image generated by the image generation AI model is sent back to the server.

[0341] Step 9:

[0342] The server transmits the generated image received to the user's terminal for providing it to the user.

[0343] Step 10:

[0344] The server records the token usage history and calculates the applicable fees.

[0345] Step 11:

[0346] The terminal will display the generated image and details about the token usage.

[0347] Specific examples

[0348] Actual user experience

[0349] Step 1:

[0350] Users hold tokens for the image-generating AI model and log in to the platform to use it.

[0351] Step 2:

[0352] A user inputs a request for image generation into the portal, specifying a theme such as "downtown sunset."

[0353] Step 3:

[0354] The emotion engine detects excited emotions based on the user's voice input and facial recognition data.

[0355] Step 4:

[0356] The emotion engine sends the analysis results to the server in real time.

[0357] Step 5:

[0358] The server adjusts the color and design settings of the image-generating AI model based on data from the emotion engine.

[0359] Step 6:

[0360] The server sends the adjustment parameters to the image generation AI model and instructs the model to generate an image.

[0361] Step 7:

[0362] The image generation AI model generates colorful and vibrant images according to the received parameters.

[0363] Step 8:

[0364] The image generation AI model sends the generated images to the server.

[0365] Step 9:

[0366] The server sends the generated image to the user's terminal and updates the token usage history.

[0367] Step 10:

[0368] The terminal displays a generated image of a colorful downtown sunset, along with token usage history and fee information.

[0369] In this way, by collecting user emotional data and reflecting it in the generative AI model, personalized results can be generated for each individual user. Token trading and usage history can also be managed efficiently.

[0370] Example 2

[0371] 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."

[0372] Conventional generative AI model systems lacked efficient methods for managing model usage rights and the ability to personalize output based on user sentiment. Furthermore, there was a need for an efficient and transparent system for the buying and selling of tokenized usage rights, record-keeping, and fee calculation.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for tokenizing the right to use the generative AI model; means for issuing the token as a smart contract on a blockchain; means for providing an electronic platform for buying and selling the issued token; means for buying and selling the token through the electronic platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; emotion recognition means for analyzing user emotions; and means for adjusting the parameters of the generative AI model based on the user's emotion data. This enables efficient management of the right to use the generative AI model and personalized output based on the user's emotion data. Furthermore, token buying and selling, history management, and fee calculation can be performed efficiently and transparently.

[0374] A "generative artificial intelligence model" is an artificial intelligence system that can automatically generate new data and information based on input data.

[0375] A "token" is a type of digital asset issued using blockchain technology that represents a specific right of use or value.

[0376] "Blockchain" is a secure, tamper-resistant digital distributed ledger technology and a data structure for continuously recording transaction history and information.

[0377] A "smart contract" refers to a contract or program that is automatically executed on a blockchain when certain conditions are met.

[0378] An "electronic platform" refers to an online service or system provided via a computer network, which serves as a foundation for users to carry out certain operations or transactions.

[0379] "Emotion recognition means" is a technology for analyzing data such as a user's facial expressions, voice, and text input, and identifying the user's emotional state.

[0380] "Means for adjusting parameters" refers to techniques for changing the operating settings of a system or model based on acquired data and conditions.

[0381] A "request" refers to a user's request to the system for a specific operation or service to be provided.

[0382] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. It also incorporates an emotion engine that recognizes user emotions and adjusts the output of the generative AI model based on the user's emotions.

[0383] Configuring Components

[0384] The system consists of the following main components:

[0385] 1. Registration portal for AI companies

[0386] 2. Token Generation and Management Server

[0387] 3. Trading platform for users

[0388] 4. Generative AI Model Operation Server

[0389] 5. Emotion Engine

[0390] Registration portal for AI companies

[0391] Users (AI companies) access a registration portal and register for the right to use a generative AI model. In this portal, companies enter model information (type, terms of use, fees, etc.) and request tokenization. The entered information is managed by the server.

[0392] Token Generation and Management Server

[0393] The server receives tokenization requests from AI companies, creates smart contracts on the blockchain, and issues tokens. The smart contracts contain metadata such as the token type, quantity, price, and terms of use. The issued tokens are published on the trading platform and can be bought and sold. This process ensures transparent and secure management of tokenized usage rights.

[0394] User-friendly trading platform

[0395] Users buy and sell tokens through this platform. When purchasing, the server checks the user's wallet balance and transfers the tokens to the user's wallet if the balance is sufficient. In the case of a sell request, the server also receives the request and mediates the transaction. This platform also has the function of managing token buying and selling history and fee calculation.

[0396] Emotion Engine

[0397] The emotion engine analyzes the user's input data (text, facial expressions, voice, etc.) and recognizes the user's emotions. The analysis results are sent to the server, which then uses the data to adjust the parameters of the generative AI model. For example, if the user expresses the emotion "happy," the settings are changed to brighten the color tone of the generated image.

[0398] Generative AI model operation server

[0399] The generative AI model operation server operates the generative AI model based on user requests. Specifically, it generates appropriate output according to the theme and style specified by the user. In this process, emotional data from the emotion engine is used to adjust the model parameters. The generated results are provided to the user via the server.

[0400] Specific examples

[0401] Consider the case where a user makes an image generation request:

[0402] 1. The user accesses a dedicated portal and enters the theme and style of the image they want to generate.

[0403] 2. The emotion engine analyzes user input and interactions to extract emotional data.

[0404] 3. The server adjusts the parameters of the image generation AI model based on the emotion data.

[0405] For example, if the user is recognized as "excited," the image color tone and content are set to be vivid.

[0406] 4. The image generation AI model generates a new image based on the adjusted parameters.

[0407] 5. The server receives the resulting image and provides it to the user.

[0408] This allows users to obtain customized output according to their individual emotional state. An example of a specific prompt could be a request such as "A beautiful view of the sunset." The system aims to personalize the output based on the user's emotions and provide more satisfying results.

[0409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0410] Step 1:

[0411] Users (AI companies) access the registration portal and enter information about their generative AI models, including the company name, model type, terms of use, and fees.

[0412] Input: Company name, model type, terms of use, price, etc.

[0413] Output: Model registration request

[0414] The server receives this input data and stores it in a database.

[0415] Specific operations: Stores the information in the database and sends a notification to the user that registration is complete.

[0416] Step 2:

[0417] The user (AI company) sends a tokenization request.

[0418] Input: Tokenization request

[0419] Output: Instructions for generating a token

[0420] The server accepts the request and creates a smart contract on the blockchain.

[0421] Specific operation: The smart contract describes the type, quantity, price, and terms of use of the token, and then deploys it to the blockchain.

[0422] Step 3:

[0423] The server issues the tokens and publishes them on the trading platform.

[0424] Input: Smart contract, token metadata

[0425] Output: Issued tokens, public data on the trading platform

[0426] Specific operations: Issue tokens and process information disclosure to the trading platform.

[0427] Step 4:

[0428] A user accesses the trading platform and requests to purchase tokens.

[0429] Input: Token purchase request

[0430] Output: Token purchase successful message, wallet updated

[0431] The server checks the user's wallet balance and transfers the tokens to the user's wallet if the purchase conditions are met.

[0432] Specific operations: Check wallet balance, transfer tokens, and update transaction history.

[0433] Step 5:

[0434] A user requests the use of a generative artificial intelligence model.

[0435] Input: Request to use the generative AI model, prompt (e.g., "A beautiful view of the sunset")

[0436] Output: Usage results (generated images, etc.)

[0437] The server receives the request and launches the generative AI model.

[0438] Specific operation: Launches the generative AI model and generates data based on the specified prompt.

[0439] Step 6:

[0440] The emotion engine analyzes user input and interactions to extract emotional data.

[0441] Input: User text input, facial expression data, voice data

[0442] Output: Emotion data (e.g. "Excited")

[0443] Specific operations: Perform natural language processing, image analysis, and voice analysis to analyze input data and identify emotions.

[0444] Step 7:

[0445] The emotion engine sends the analysis results to the server in real time.

[0446] Input: Emotion data

[0447] Output: Instruction to transfer emotion data to the server

[0448] Specific operation: The emotion data transfer protocol is used to process the data sent to the server.

[0449] Step 8:

[0450] The server adjusts the parameters of the generative AI model based on the emotion data.

[0451] Input: Emotion data

[0452] Output: Adjusted generation parameters

[0453] Specific operation: Optimize the model parameters based on the emotion data and change the settings to generate output that corresponds to the emotion.

[0454] Step 9:

[0455] The generative artificial intelligence model generates an output based on the adjusted parameters.

[0456] Input: Adjusted generation parameters

[0457] Output: Generated results (e.g., image data)

[0458] Specific operation: The generative AI model generates new data (e.g., images or text) and returns the generated results to the server.

[0459] Step 10:

[0460] The server receives the generated results and provides them to the user.

[0461] Input: Generated result (e.g., image data)

[0462] Output: Data provided to the user

[0463] Specific operation: Processing is performed to display or provide the generated output data to the user through the user interface.

[0464] (Application example 2)

[0465] 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."

[0466] Conventional generative AI models provide uniform output without considering the user's emotional state, resulting in a lack of personalization for individual users. Furthermore, there is no mechanism in place to safely and efficiently buy and sell model usage rights on the market, leading to inappropriate usage and rights management issues. Furthermore, there is a lack of efficient means for managing usage history and calculating fees.

[0467] 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 tokenizing the usage rights of the generative AI model; means for issuing the tokens as smart contracts on the blockchain; means having an emotion engine that analyzes user emotions; means for adjusting the generative AI model based on emotion data analyzed by the emotion engine; means for providing a platform for buying and selling the issued tokens; means for receiving a request from the token holder to use the generative AI model; and means for operating the generative AI model based on the request and providing the holder with the usage results. This makes it possible to output a generative AI model personalized to the user's emotional state, and enables the safe and efficient buying and selling and management of usage rights.

[0468] A "generative artificial intelligence model" is a machine learning algorithm that generates new information and content based on data.

[0469] "Tokenization" refers to the technology or process that represents digital assets or rights as tokens and makes them tradable on the blockchain.

[0470] "Blockchain" is a distributed ledger technology, a system that records transaction history in a linked chain.

[0471] A "smart contract" is a program that runs automatically on a blockchain and expresses contract terms as program code.

[0472] An "emotion engine" is a technology or software that analyzes emotions from a user's facial expressions, voice, text input, etc., and reflects that data in a generative artificial intelligence model.

[0473] "Platform" means an online service or system that allows users to buy, sell, manage, and use Tokens.

[0474] A "token holder" is a person or company that owns a token that represents the right to use a particular generative AI model.

[0475] "Means for receiving a request" refers to an interface or system for receiving a request from a user to use a generative artificial intelligence model.

[0476] The embodiment of the present invention is configured as a system that operates by integrating various hardware and software. The following is a specific method for realizing this system.

[0477] System Overview

[0478] The system mainly consists of the following components:

[0479] 1. Generative AI Model

[0480] 2. Tokenization and Smart Contracts

[0481] 3. Sentiment Analysis Engine

[0482] 4. User Interface and Platform

[0483] 5. Server data processing and management functions

[0484] Hardware and software used

[0485] Hardware: Smartphone (camera, microphone, display), server (high-performance processor, storage)

[0486] software:

[0487] Sentiment analysis engine: Google Cloud Vision API, TensorFlow

[0488] Generative AI models: OpenAI GPT-4, DALL-E, music generative AI

[0489] Token Management and Trading: Ethereum Blockchain, Wallet Applications

[0490] User Interface:React Native

[0491] Program processing

[0492] 1. User login and initial settings

[0493] When a user logs in to the system, the camera and microphone are first activated for facial recognition and voice analysis. This data is analyzed in real time using the Google Cloud Vision API and TensorFlow to extract the user's emotional data. The emotional data is then sent to the server and added to the user's profile.

[0494] 2. Emotion data analysis and transmission

[0495] The emotion analysis engine analyzes the user's emotion data collected in real time, and the server adjusts the parameters of the generative AI model based on the analysis results.

[0496] 3. Token Issuance and Management

[0497] The server tokenizes the right to use the generative AI model and issues it as a smart contract on the blockchain. The issued tokens can be bought and sold through the user interface, and the token trading history and ownership status are managed by the Ethereum blockchain and wallet application.

[0498] 4. Content Creation and Delivery

[0499] When a user submits a request to use a specific generative AI model, the server generates a prompt based on the emotion data and applies it to the generative AI model. For example, the prompt might look like this:

[0500] The sentiment analysis engine detected "sad." Please generate a kind message to comfort the user when they feel sad.

[0501] The server uses this prompt to send a request to the generative artificial intelligence model and provides the generated content (text, images, music, etc.) to the user.

[0502] Specific examples

[0503] For example, if a user has had a fight with a friend and is feeling sad, the camera analyzes their facial expression and recognizes the emotion "sad." Based on this data, the server uses OpenAI GPT-4 to generate a kind message and then DALL-E to generate a comforting image. These contents are then delivered to the user's smartphone.

[0504] As described above, the present invention provides the output of a personalized generative artificial intelligence model that is tailored to the emotional state of the user, and realizes a system for safely and efficiently buying, selling, and managing usage rights.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] When a user logs in to a device, the device's camera and microphone are activated, allowing the user's face and voice data to be collected in real time. The inputs are camera footage and audio data, which are then analyzed for facial expressions and voice tone using the Google Cloud Vision API and TensorFlow. The output is the user's emotion data captured in real time.

[0508] Step 2:

[0509] The server receives emotion data sent from the device. This emotion data is stored in the user's profile and updates the database based on the user's emotional state. The input is the analyzed emotion data, which is then written to the database. The output is the updated user profile.

[0510] Step 3:

[0511] The user sends a request from their device to use a specific generative AI model. This request includes the type and theme of the content they want to generate. The input is the user's request data, and a prompt sentence is generated based on this. The output is the prompt sentence.

[0512] Step 4:

[0513] The server adjusts the settings of the generative AI model based on the emotional data analyzed by the emotion engine. For example, if the user's emotional state is analyzed as "sad," the parameters of the generative AI model are set to generate a comforting message. The inputs are the analyzed emotional data and the initial settings of the generative model, and the model parameters are adjusted based on these. The output is the adjusted generative AI model.

[0514] Step 5:

[0515] The server inputs a prompt sentence into the adjusted generative AI model, runs the model, and obtains generated content. Specifically, OpenAI GPT-4 generates text, DALL-E generates images, and music is generated using a music generation AI. The inputs are the prompt sentence and the adjusted generative AI model, and a wide variety of content is generated by the operation of the model. The output is the various generated content.

[0516] Step 6:

[0517] The server provides the generated content to the terminal. The user receives the generated content, such as text, images, and music, through the terminal. The input is the generated content from the server, which is provided to the user. The output is the content displayed on the user's terminal.

[0518] Step 7:

[0519] Through the token management system, users can buy and sell the right to use generative AI models. The server manages transactions and wallets on the blockchain, and updates token ownership status in response to user buy and sell requests. The inputs are buy and sell requests from users and token information, and the output is updated token ownership status and transaction history.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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."

[0536] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[0537] System Overview

[0538] The system consists of the following main components:

[0539] 1. Registration portal for AI companies

[0540] 2. Token Generation and Management Server

[0541] 3. Trading platform for users

[0542] 4. Generative AI Model Operation Server

[0543] Registration portal for AI companies

[0544] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[0545] Token Generation and Management Server

[0546] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0547] User-friendly trading platform

[0548] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0549] Program processing

[0550] Token Purchase

[0551] When a user purchases a token, the following happens:

[0552] 1. The user logs in to the platform and selects the tokens they wish to purchase.

[0553] 2. The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[0554] 3. The server checks the balance of the user's wallet and, if the transaction conditions are met, executes the smart contract to transfer the tokens to the user's wallet.

[0555] 4. The server notifies the user of the success of the transaction, and a message indicating the purchase is complete is displayed on the terminal.

[0556] Token Sale

[0557] When a user sells their tokens, the following happens:

[0558] 1. A user submits a request to sell their tokens.

[0559] 2. The server publishes the request on the trading platform and waits for other users to purchase.

[0560] 3. When another user wants to make a purchase, the server executes a smart contract and distributes tokens and funds to each wallet.

[0561] 4. The server notifies the selling user of the success of the transaction, and a message indicating the sale is complete is displayed on the terminal.

[0562] Token holders' right to use AI

[0563] When a user uses a token to access a generative AI model, the following happens:

[0564] 1. The user accesses a dedicated portal and submits a request for use.

[0565] 2. The server analyzes the request and sends the data to the generative artificial intelligence model.

[0566] 3. The server receives the generated results and provides them to the user.

[0567] 4. The server records the token usage history and calculates the applicable fees.

[0568] Specific examples

[0569] For example, suppose a user holds a token for an image-generating AI model and wants to use it to generate a new image. The user enters an image generation request into the portal and specifies the required parameters (e.g., the theme and style of the image they want to generate). The server receives the request and sends the data to the generative AI model. The model generates a new image, and the result is sent to the user via the server. This process ensures that the entire platform operates securely and efficiently.

[0570] This system will enable AI companies to raise funds quickly, allow individual investors to easily invest in AI technology, and allow token holders to efficiently use AI models, greatly improving convenience.

[0571] The processing flow will be explained below.

[0572] Token purchase processing steps

[0573] Step 1:

[0574] Users log in to the platform and select the tokens they wish to purchase.

[0575] Step 2:

[0576] The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[0577] Step 3:

[0578] The server checks the user's wallet balance and sees if the desired purchase amount is available in the wallet.

[0579] Step 4:

[0580] The server generates a smart contract for token trading based on the user's request and deploys it on the blockchain.

[0581] Step 5:

[0582] The server executes the smart contract and transfers the purchased tokens to the user's wallet.

[0583] Step 6:

[0584] The server confirms the success of the transaction and sends a transaction completion notification to the user's terminal.

[0585] Step 7:

[0586] A message will appear on your device confirming your purchase.

[0587] Token sale processing steps

[0588] Step 1:

[0589] Users submit a request to sell their tokens to the platform.

[0590] Step 2:

[0591] The server receives the sale request and publishes it on the trading platform.

[0592] Step 3:

[0593] Another user accesses the platform and views the published sale request.

[0594] Step 4:

[0595] Send a request to buy tokens that another user wants to sell.

[0596] Step 5:

[0597] The server transfers tokens and transaction amounts between wallets through smart contracts.

[0598] Step 6:

[0599] The server confirms the success of the transaction and sends a notice of completion of the sale to the terminals of the selling user and the purchasing user.

[0600] Step 7:

[0601] A message will appear on the terminal indicating that the sale is complete.

[0602] Processing steps for token holders to exercise their AI usage rights

[0603] Step 1:

[0604] Users have a token and access a dedicated portal.

[0605] Step 2:

[0606] To use the generative artificial intelligence model, the user inputs the necessary request information (input data, parameters, etc.).

[0607] Step 3:

[0608] The user sends a request and notifies the server.

[0609] Step 4:

[0610] The server analyzes the request content and sends input data to the generative artificial intelligence model.

[0611] Step 5:

[0612] Generative artificial intelligence models generate new data (e.g., generated images) based on input data.

[0613] Step 6:

[0614] The server receives the generated results and provides them to the user.

[0615] Step 7:

[0616] The server records the token usage history and calculates the applicable fees.

[0617] Step 8:

[0618] The server sends the usage results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[0619] Example: Using an image generation AI model

[0620] Step 1:

[0621] Users hold tokens for the image-generating AI model and access a dedicated portal.

[0622] Step 2:

[0623] Users enter image generation requests on the portal and specify the theme and style of the image they want to generate.

[0624] Step 3:

[0625] The user sends a request and notifies the server.

[0626] Step 4:

[0627] The server analyzes the request and sends input data to the image generation AI model.

[0628] Step 5:

[0629] An image generation AI model generates new images based on input data.

[0630] Step 6:

[0631] The server receives the resulting image and provides it to the user.

[0632] Step 7:

[0633] The server records the token usage history and calculates the applicable fees.

[0634] Step 8:

[0635] The server sends the generated results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[0636] In this way, users can efficiently and safely buy and sell tokens and utilize generative AI models.

[0637] Example 1

[0638] 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."

[0639] Currently, there is no efficient and transparent way to manage and operate the rights to use generative AI models. In particular, there is a lack of a reliable platform for tokenizing the rights to use AI models and trading them on the market. This makes it difficult for AI companies to raise funds quickly and creates high barriers for individual investors to access these technologies. Furthermore, there is a lack of convenience as there is no way for token holders to properly use AI models.

[0640] 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.

[0641] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for generating and deploying the token as a smart contract on a blockchain; means for listing the issued token on a trading platform and enabling trading; means for buying and selling the token through the trading platform; means for receiving a request from the token holder to use the generative AI model; means for transmitting data to the generative AI model based on the request and providing the holder with the generation results; means for operating the generative AI model based on the content of the request and providing the holder with the usage results; and means for notifying the token purchaser or seller of the success of the transaction. This enables the right to use a generative AI model to be tokenized efficiently and transparently, allowing it to be safely bought, sold, and used in the market.

[0642] A "generative artificial intelligence model" is an artificial intelligence algorithm or program that can generate data based on user requests.

[0643] "Tokenization" is the process of converting certain rights or assets into digital tokens.

[0644] "Blockchain" is a technology that uses distributed ledger technology to manage transaction data in a transparent and immutable manner.

[0645] A "smart contract" is a self-executing contract program that runs on a blockchain and is a mechanism that automatically executes a contract when certain conditions are met.

[0646] "Trading Platform" means an online system or website through which users can buy and sell digital tokens.

[0647] A "request" is an act in which a user requests a specific service or data generation from a generative artificial intelligence model.

[0648] "Metadata" is data containing detailed information related to a token or digital asset, such as issuance quantity, price, terms of use, etc.

[0649] A "wallet" is a software tool or application used to securely store and transact digital tokens.

[0650] "Trading History" means the records of purchases and sales of Tokens made on the Trading Platform.

[0651] "Fees" are costs incurred in connection with the purchase, sale, and use of tokens, and are collected by the platform or operator.

[0652] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[0653] System Overview

[0654] The system consists of the following main components:

[0655] 1. Registration portal for AI companies

[0656] 2. Token Generation and Management Server

[0657] 3. Trading platform for users

[0658] 4. Generative AI Model Operation Server

[0659] Registration portal for AI companies

[0660] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. This registered information is managed by a server.

[0661] Token Generation and Management Server

[0662] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0663] User-friendly trading platform

[0664] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0665] Generative AI model operation server

[0666] When a user uses a token to access a generative AI model, they access a dedicated portal and submit a request. The server analyzes the request and sends the data to the generative AI model. The server receives the generated results and provides them to the user. The server records the token usage history and calculates the applicable fees.

[0667] Specific examples

[0668] For example, suppose a user has a token for an image generation AI model and wants to use it to generate a new image. The user accesses the portal, enters a request for image generation, and specifies the theme and style of the image they want to generate. An example of a specific prompt is as follows:

[0669] "Generate a beach scene illuminated by a summer sunset."

[0670] "Please create a cyberpunk-style illustration with a futuristic city theme."

[0671] The server receives the request and sends the data to the generative AI model, which then generates a new image and sends the result to the user via the server. This process ensures the entire platform operates safely and efficiently.

[0672] This system will enable AI companies to raise funds quickly, individual investors to easily access AI technology, and token holders to efficiently use generative AI models, greatly improving convenience.

[0673] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0674] Step 1:

[0675] The user (AI company) registers model information

[0676] Input: The user enters information such as the name, version, and license terms of the AI ​​model.

[0677] Server Actions: The server receives the entered information, verifies the data for accuracy and completeness, and prepares this information for storage in a database.

[0678] Output: The server stores the information in a database and sends a "Registration complete" message to the device.

[0679] Specific operation: The user clicks the "Enter model information" button. The server verifies the information and registers it in the database. The terminal displays the message "Registration completed."

[0680] Step 2:

[0681] Server-generated tokens

[0682] Input: Token generation request from AI company.

[0683] Server operation: The server receives the request, generates a smart contract using the blockchain API, sets the token metadata in the smart contract, and deploys it.

[0684] Output: Issued tokens are generated and listed on the exchange platform.

[0685] How it works: When the server receives a "token generation" request, it uses the blockchain API to create a smart contract and deploy it, which generates a token and lists it on the platform.

[0686] Step 3:

[0687] User purchase of tokens

[0688] Input: The user enters the number of tokens they wish to purchase and the price.

[0689] Server operation: The server checks the user's wallet balance and verifies whether the transaction conditions are met. If the conditions are met, it executes the smart contract to transfer the tokens to the user's wallet.

[0690] Output: A message indicating purchase is complete is displayed on the terminal.

[0691] Specific operation: The user clicks the "Purchase" button. The server calls the wallet API to check the balance, activates the smart contract to transfer the tokens, and displays a "Purchase Complete" message on the terminal.

[0692] Step 4:

[0693] Token sales by users

[0694] Input: Enter a request to sell the tokens the user holds.

[0695] Server operation: The server receives the sale request and publishes it on the trading platform. If a user wishes to purchase, it executes the smart contract and distributes the tokens and funds to each wallet.

[0696] Output: A message indicating the sale is complete is displayed on the terminal.

[0697] Specific operation: The user clicks the "Sell" button. The server publishes the sale request to the trading platform, and if a buyer appears, it launches the smart contract, distributes tokens and funds, and displays a "Sell Complete" message on the terminal.

[0698] Step 5:

[0699] Token holders' right to use AI

[0700] Input: The user enters a usage request and prompt.

[0701] Server operation: The server analyzes the request content and sends the data to the generative AI model. It receives the generated results and provides them to the user. It records the token usage history and calculates the applicable fees.

[0702] Output: The generated results are displayed on the terminal.

[0703] Specific operation: The user clicks the "Send request to AI model" button. The server sends the prompt to the AI ​​model. The generated result is received and displayed on the device. The usage history is recorded and fees are calculated.

[0704] (Application example 1)

[0705] 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."

[0706] There is a need to provide a system for the efficient and transparent use of generative AI models. However, current technology lacks a platform that can properly manage and securely trade the rights to use generative AI models. Furthermore, content distribution services face challenges in maintaining the quality of generated content and responding promptly to user requests.

[0707] 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.

[0708] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for issuing tokens as smart contracts on distributed ledger technology; means for providing a platform for buying and selling the issued tokens; means for buying and selling the tokens through the platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; means for the usage results to include content related to a content distribution service; and means for generating the content based on the tokens held by the user. This enables efficient management and safe trading of the right to use the tokenized generative AI model, as well as high-quality content distribution.

[0709] A "generative artificial intelligence model" refers to an artificial intelligence technology that can automatically generate new content and data based on input data.

[0710] "Right of use" refers to the right to use a specific service or technology (in this case, a generative artificial intelligence model) for a certain period of time or a certain number of times.

[0711] "Tokenization" refers to the process of converting digital assets or rights into digital vouchers called tokens and managing them on a blockchain.

[0712] "Distributed ledger technology" refers to technology that stores digital data on a distributed network and manages transaction histories and contracts transparently and securely.

[0713] A "smart contract" refers to a contract whose terms are written in program code and are executed automatically.

[0714] "Platform" refers to a system or environment that serves as the foundation for providing a specific purpose or service.

[0715] A "request" refers to an instruction that a user gives to a system requesting a specific action or service.

[0716] "Content distribution service" refers to an online service that provides users with digital content such as text, images, audio, and video.

[0717] "Content" refers to information, data, media or other digital material organized in a particular format or subject.

[0718] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where it can be bought and sold on the market. Specific embodiments for implementing this invention will be described below.

[0719] System Configuration

[0720] The system consists of the following main components:

[0721] 1. Registration Portal:

[0722] This is a portal where AI companies can register their rights to use generative AI models. AI companies can enter their model information here and make a request to tokenize their usage rights.

[0723] 2. Token Generation and Management Server:

[0724] This server receives tokenization requests from AI companies, creates and deploys smart contracts on distributed ledger technology, and issues tokens according to the smart contract, managing the token metadata (issuance amount, price, terms of use, etc.).

[0725] 3. Trading Platform:

[0726] Users can buy and sell tokens through this platform. When purchasing tokens, the server checks the user's wallet balance and transfers the tokens if the required funds are available. When a sell request is made, the server publishes the request and mediates the transaction with the user who wishes to buy.

[0727] 4. Generative AI model operation server:

[0728] When a user uses a token to utilize a generative artificial intelligence model, this server receives the request, operates the generative artificial intelligence model, and provides the utilization results to the user.

[0729] Program processing

[0730] The system allows users to use a generative artificial intelligence model on a smartphone or other device to generate content related to a content distribution service.

[0731] Hardware and software:

[0732] Hardware:

[0733] Smartphone, blockchain node server, AI model server

[0734] software:

[0735] Python 3.x, Requests library, Web3 library, blockchain node (e.g., Ethereum), generative AI model service (e.g., GPT-3)

[0736] Data processing and calculation:

[0737] 1. Initialize your wallet:

[0738] The user's wallet is initialized using eth_account and web3.

[0739] 2. Check token balance:

[0740] The user's wallet address is used to check the token balance on the blockchain, which determines whether the user can spend the tokens.

[0741] 3. AI-generated request:

[0742] A prompt from the user is sent to the generative AI model to generate content. For example, a prompt could be, "Generate ideas for a blog post. The topic is technology."

[0743] 4. Providing generated results:

[0744] The generated content is provided to users through a server, allowing users to instantly obtain high-quality content.

[0745] Specific examples

[0746] For example, if a user wants to generate ideas for a blog post, they might enter a prompt like this:

[0747] Example prompt sentence:

[0748] "Generate ideas for blog posts on technology."

[0749] Based on this request, the generative AI model generates high-quality blog post ideas related to the specified topic and provides the results to the user via the server, allowing the user to generate content quickly and efficiently using their own tokens.

[0750] This system tokenizes the rights to use generative AI models, providing a platform for secure transactions and greatly improving the convenience of users to generate high-quality content.

[0751] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0752] Step 1:

[0753] A user launches the smartphone app and confirms their right to use the generative AI model they own. They then log in to their wallet and check the balance of their tokens. At this time, the wallet address is entered, and the number of tokens held is displayed as the output.

[0754] Step 2:

[0755] When a user wants to use a generative AI model, tokens are required, so the user must ensure that they have sufficient token balance. The server obtains the current token balance using distributed ledger technology based on the user's wallet address and displays the token balance as the output.

[0756] Step 3:

[0757] The user creates a prompt to use the generative AI model and enters it into the application's input form. For example, the user might enter a prompt such as, "Generate ideas for a blog post. The topic is technology." The entered prompt is then sent to the server.

[0758] Step 4:

[0759] The server receives the user's prompt sentence and sends it to the generative AI model. This inputs data from the server to the generative AI model, which then begins generating content based on the input data. The server then sends request data including the prompt sentence to the generative AI model and receives the generated content data.

[0760] Step 5:

[0761] The generative AI model generates content based on the received prompt. Specifically, digital content such as text and images is generated according to the theme and style associated with the input prompt. The data generated is determined by the AI ​​model's calculations.

[0762] Step 6:

[0763] The server analyzes the content received from the generative AI model and converts it into a format to be provided to the user. The converted data is then output from the server to the user's device (smartphone).

[0764] Step 7:

[0765] Finally, the generated content is displayed on the user's smartphone. The user can review and use the generated content (e.g., blog post ideas and images). The user can also view the generated results and, if necessary, generate more content by entering additional prompts.

[0766] Through this series of steps, users can use their tokens to effectively utilize generative AI models to quickly generate high-quality content.

[0767] 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.

[0768] The present invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. Furthermore, this system is combined with an emotion engine that recognizes user emotions and has the function of adjusting the output of the generative AI model based on the user's emotions.

[0769] System Overview

[0770] The system consists of the following main components:

[0771] 1. Registration portal for AI companies

[0772] 2. Token Generation and Management Server

[0773] 3. Trading platform for users

[0774] 4. Generative AI Model Operation Server

[0775] 5. Emotion Engine

[0776] Registration portal for AI companies

[0777] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[0778] Token Generation and Management Server

[0779] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[0780] User-friendly trading platform

[0781] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[0782] Emotion Engine

[0783] The server is equipped with an emotion engine that recognizes the user's emotions. This engine analyzes emotions from the user's input data and interactions, and reflects this data in the operation of the generative AI model.

[0784] Generative AI model operation server

[0785] The server operates the generative AI model based on user requests and provides the results. It also receives emotion data from the emotion engine and optimizes the model's output based on the user's emotions.

[0786] Program processing

[0787] Emotion Engine Process

[0788] When a user accesses the platform and uses a generative AI model, the following occurs:

[0789] 1. A user logs into the platform and the emotion engine monitors the user's input and interactions.

[0790] 2. The emotion engine extracts emotional data from the user's text input, facial expressions, and voice.

[0791] 3. The emotion engine sends the analyzed emotion data to the server in real time.

[0792] 4. The server adjusts the parameters of the generative AI model based on the emotion data.

[0793] Emotion-driven generation

[0794] As a concrete example, consider a case where a user uses a token to access an image-generating AI model:

[0795] 1. The user accesses a dedicated portal and specifies the theme and style of the image they want to generate.

[0796] 2. The emotion engine analyzes the user's emotions (e.g., happy, sad, surprised).

[0797] 3. The server adjusts the settings of the image generation AI model based on the emotion data.

[0798] For example, if the user is recognized as "happy," the parameters are set so that an image with bright and vivid colors is generated.

[0799] 4. The image generation AI model generates a new image based on the adjusted parameters.

[0800] 5. The server receives the resulting image and provides it to the user.

[0801] 6. The server records the token usage history and calculates the applicable fees.

[0802] Specific examples

[0803] This explains the process by which a user trains a generative AI model using the emotion engine and uses tokens:

[0804] 1. A user logs into the portal and their emotions are monitored in real time by the emotion engine.

[0805] 2. If the user inputs a request for image generation and indicates an excited emotion, the emotion engine sends the emotion data to the server.

[0806] 3. The server changes the color and content settings of the image generation AI model based on the emotion data.

[0807] 4. The image generation AI model generates an image using parameters according to the user's emotions and provides the image to the user via the server.

[0808] 5. This allows users to get customized output tailored to their individual emotional state.

[0809] In this way, the system takes into account user sentiment and provides more personalized output from the generative AI model. It also efficiently manages token trading and usage history.

[0810] The processing flow will be explained below.

[0811] Emotion Engine Process

[0812] Collaboration with emotion engine

[0813] Step 1:

[0814] Users log in to the platform and access a dedicated portal.

[0815] Step 2:

[0816] The emotion engine monitors user input and interactions and collects emotion data in real time.

[0817] Step 3:

[0818] The emotion engine analyzes emotions from the user's character input, facial expression analysis, voice input, etc., and generates emotion data.

[0819] Step 4:

[0820] The emotion engine transmits the analyzed emotion data to the server.

[0821] Emotion-driven generation

[0822] Examples of using image generation AI models

[0823] Step 1:

[0824] The user logs into the specialized portal and opens a request form for image generation.

[0825] Step 2:

[0826] The user inputs the theme (e.g., "landscape" or "portrait") and style (e.g., "realistic" or "abstract") of the image they want to generate.

[0827] Step 3:

[0828] An emotion engine analyzes the user's current emotional state (e.g., "happy," "sad," "excited").

[0829] Step 4:

[0830] The emotion engine transmits emotion data to the server in real time.

[0831] Step 5:

[0832] The server adjusts the setting parameters of the generative artificial intelligence model based on the emotion data.

[0833] For example, if the user is expressing a "happy" emotion, the colors of the generated image are brightened and the overall tone is adjusted to a positive one.

[0834] Step 6:

[0835] The server sends the adjusted setting parameters to the image generation AI model.

[0836] Step 7:

[0837] The image generation AI model generates new images based on user requests and parameter settings from the server.

[0838] Step 8:

[0839] The image generated by the image generation AI model is sent back to the server.

[0840] Step 9:

[0841] The server transmits the generated image received to the user's terminal for providing it to the user.

[0842] Step 10:

[0843] The server records the token usage history and calculates the applicable fees.

[0844] Step 11:

[0845] The terminal will display the generated image and details about the token usage.

[0846] Specific examples

[0847] Actual user experience

[0848] Step 1:

[0849] Users hold tokens for the image-generating AI model and log in to the platform to use it.

[0850] Step 2:

[0851] A user inputs a request for image generation into the portal, specifying a theme such as "downtown sunset."

[0852] Step 3:

[0853] The emotion engine detects excited emotions based on the user's voice input and facial recognition data.

[0854] Step 4:

[0855] The emotion engine sends the analysis results to the server in real time.

[0856] Step 5:

[0857] The server adjusts the color and design settings of the image-generating AI model based on data from the emotion engine.

[0858] Step 6:

[0859] The server sends the adjustment parameters to the image generation AI model and instructs the model to generate an image.

[0860] Step 7:

[0861] The image generation AI model generates colorful and vibrant images according to the received parameters.

[0862] Step 8:

[0863] The image generation AI model sends the generated images to the server.

[0864] Step 9:

[0865] The server sends the generated image to the user's terminal and updates the token usage history.

[0866] Step 10:

[0867] The terminal displays a generated image of a colorful downtown sunset, along with token usage history and fee information.

[0868] In this way, by collecting user emotional data and reflecting it in the generative AI model, personalized results can be generated for each individual user. Token trading and usage history can also be managed efficiently.

[0869] Example 2

[0870] 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."

[0871] Conventional generative AI model systems lacked efficient methods for managing model usage rights and the ability to personalize output based on user sentiment. Furthermore, there was a need for an efficient and transparent system for the buying and selling of tokenized usage rights, record-keeping, and fee calculation.

[0872] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for tokenizing the right to use the generative AI model; means for issuing the token as a smart contract on a blockchain; means for providing an electronic platform for buying and selling the issued token; means for buying and selling the token through the electronic platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; emotion recognition means for analyzing user emotions; and means for adjusting the parameters of the generative AI model based on the user's emotion data. This enables efficient management of the right to use the generative AI model and personalized output based on the user's emotion data. Furthermore, token buying and selling, history management, and fee calculation can be performed efficiently and transparently.

[0873] A "generative artificial intelligence model" is an artificial intelligence system that can automatically generate new data and information based on input data.

[0874] A "token" is a type of digital asset issued using blockchain technology that represents a specific right of use or value.

[0875] "Blockchain" is a secure, tamper-resistant digital distributed ledger technology and a data structure for continuously recording transaction history and information.

[0876] A "smart contract" refers to a contract or program that is automatically executed on a blockchain when certain conditions are met.

[0877] An "electronic platform" refers to an online service or system provided via a computer network, which serves as a foundation for users to carry out certain operations or transactions.

[0878] "Emotion recognition means" is a technology for analyzing data such as a user's facial expressions, voice, and text input, and identifying the user's emotional state.

[0879] "Means for adjusting parameters" refers to techniques for changing the operating settings of a system or model based on acquired data and conditions.

[0880] A "request" refers to a user's request to the system for a specific operation or service to be provided.

[0881] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. It also incorporates an emotion engine that recognizes user emotions and adjusts the output of the generative AI model based on the user's emotions.

[0882] Configuring Components

[0883] The system consists of the following main components:

[0884] 1. Registration portal for AI companies

[0885] 2. Token Generation and Management Server

[0886] 3. Trading platform for users

[0887] 4. Generative AI Model Operation Server

[0888] 5. Emotion Engine

[0889] Registration portal for AI companies

[0890] Users (AI companies) access a registration portal and register for the right to use a generative AI model. In this portal, companies enter model information (type, terms of use, fees, etc.) and request tokenization. The entered information is managed by the server.

[0891] Token Generation and Management Server

[0892] The server receives tokenization requests from AI companies, creates smart contracts on the blockchain, and issues tokens. The smart contracts contain metadata such as the token type, quantity, price, and terms of use. The issued tokens are published on the trading platform and can be bought and sold. This process ensures transparent and secure management of tokenized usage rights.

[0893] User-friendly trading platform

[0894] Users buy and sell tokens through this platform. When purchasing, the server checks the user's wallet balance and transfers the tokens to the user's wallet if the balance is sufficient. In the case of a sell request, the server also receives the request and mediates the transaction. This platform also has the function of managing token buying and selling history and fee calculation.

[0895] Emotion Engine

[0896] The emotion engine analyzes the user's input data (text, facial expressions, voice, etc.) and recognizes the user's emotions. The analysis results are sent to the server, which then uses the data to adjust the parameters of the generative AI model. For example, if the user expresses the emotion "happy," the settings are changed to brighten the color tone of the generated image.

[0897] Generative AI model operation server

[0898] The generative AI model operation server operates the generative AI model based on user requests. Specifically, it generates appropriate output according to the theme and style specified by the user. In this process, emotional data from the emotion engine is used to adjust the model parameters. The generated results are provided to the user via the server.

[0899] Specific examples

[0900] Consider the case where a user makes an image generation request:

[0901] 1. The user accesses a dedicated portal and enters the theme and style of the image they want to generate.

[0902] 2. The emotion engine analyzes user input and interactions to extract emotional data.

[0903] 3. The server adjusts the parameters of the image generation AI model based on the emotion data.

[0904] For example, if the user is recognized as "excited," the image color tone and content are set to be vivid.

[0905] 4. The image generation AI model generates a new image based on the adjusted parameters.

[0906] 5. The server receives the resulting image and provides it to the user.

[0907] This allows users to obtain customized output according to their individual emotional state. An example of a specific prompt could be a request such as "A beautiful view of the sunset." The system aims to personalize the output based on the user's emotions and provide more satisfying results.

[0908] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0909] Step 1:

[0910] Users (AI companies) access the registration portal and enter information about their generative AI models, including the company name, model type, terms of use, and fees.

[0911] Input: Company name, model type, terms of use, price, etc.

[0912] Output: Model registration request

[0913] The server receives this input data and stores it in a database.

[0914] Specific operations: Stores the information in the database and sends a notification to the user that registration is complete.

[0915] Step 2:

[0916] The user (AI company) sends a tokenization request.

[0917] Input: Tokenization request

[0918] Output: Instructions for generating a token

[0919] The server accepts the request and creates a smart contract on the blockchain.

[0920] Specific operation: The smart contract describes the type, quantity, price, and terms of use of the token, and then deploys it to the blockchain.

[0921] Step 3:

[0922] The server issues the tokens and publishes them on the trading platform.

[0923] Input: Smart contract, token metadata

[0924] Output: Issued tokens, public data on the trading platform

[0925] Specific operations: Issue tokens and process information disclosure to the trading platform.

[0926] Step 4:

[0927] A user accesses the trading platform and requests to purchase tokens.

[0928] Input: Token purchase request

[0929] Output: Token purchase successful message, wallet updated

[0930] The server checks the user's wallet balance and transfers the tokens to the user's wallet if the purchase conditions are met.

[0931] Specific operations: Check wallet balance, transfer tokens, and update transaction history.

[0932] Step 5:

[0933] A user requests the use of a generative artificial intelligence model.

[0934] Input: Request to use the generative AI model, prompt (e.g., "A beautiful view of the sunset")

[0935] Output: Usage results (generated images, etc.)

[0936] The server receives the request and launches the generative AI model.

[0937] Specific operation: Launches the generative AI model and generates data based on the specified prompt.

[0938] Step 6:

[0939] The emotion engine analyzes user input and interactions to extract emotional data.

[0940] Input: User text input, facial expression data, voice data

[0941] Output: Emotion data (e.g. "Excited")

[0942] Specific operations: Perform natural language processing, image analysis, and voice analysis to analyze input data and identify emotions.

[0943] Step 7:

[0944] The emotion engine sends the analysis results to the server in real time.

[0945] Input: Emotion data

[0946] Output: Instruction to transfer emotion data to the server

[0947] Specific operation: The emotion data transfer protocol is used to process the data sent to the server.

[0948] Step 8:

[0949] The server adjusts the parameters of the generative AI model based on the emotion data.

[0950] Input: Emotion data

[0951] Output: Adjusted generation parameters

[0952] Specific operation: Optimize the model parameters based on the emotion data and change the settings to generate output that corresponds to the emotion.

[0953] Step 9:

[0954] The generative artificial intelligence model generates an output based on the adjusted parameters.

[0955] Input: Adjusted generation parameters

[0956] Output: Generated results (e.g., image data)

[0957] Specific operation: The generative AI model generates new data (e.g., images or text) and returns the generated results to the server.

[0958] Step 10:

[0959] The server receives the generated results and provides them to the user.

[0960] Input: Generated result (e.g., image data)

[0961] Output: Data provided to the user

[0962] Specific operation: Processing is performed to display or provide the generated output data to the user through the user interface.

[0963] (Application example 2)

[0964] 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."

[0965] Conventional generative AI models provide uniform output without considering the user's emotional state, resulting in a lack of personalization for individual users. Furthermore, there is no mechanism in place to safely and efficiently buy and sell model usage rights on the market, leading to inappropriate usage and rights management issues. Furthermore, there is a lack of efficient means for managing usage history and calculating fees.

[0966] 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 tokenizing the usage rights of the generative AI model; means for issuing the tokens as smart contracts on the blockchain; means having an emotion engine that analyzes user emotions; means for adjusting the generative AI model based on emotion data analyzed by the emotion engine; means for providing a platform for buying and selling the issued tokens; means for receiving a request from the token holder to use the generative AI model; and means for operating the generative AI model based on the request and providing the holder with the usage results. This makes it possible to output a generative AI model personalized to the user's emotional state, and enables the safe and efficient buying and selling and management of usage rights.

[0967] A "generative artificial intelligence model" is a machine learning algorithm that generates new information and content based on data.

[0968] "Tokenization" refers to the technology or process that represents digital assets or rights as tokens and makes them tradable on the blockchain.

[0969] "Blockchain" is a distributed ledger technology, a system that records transaction history in a linked chain.

[0970] A "smart contract" is a program that runs automatically on a blockchain and expresses contract terms as program code.

[0971] An "emotion engine" is a technology or software that analyzes emotions from a user's facial expressions, voice, text input, etc., and reflects that data in a generative artificial intelligence model.

[0972] "Platform" means an online service or system that allows users to buy, sell, manage, and use Tokens.

[0973] A "token holder" is a person or company that owns a token that represents the right to use a particular generative AI model.

[0974] "Means for receiving a request" refers to an interface or system for receiving a request from a user to use a generative artificial intelligence model.

[0975] The embodiment of the present invention is configured as a system that operates by integrating various hardware and software. The following is a specific method for realizing this system.

[0976] System Overview

[0977] The system mainly consists of the following components:

[0978] 1. Generative AI Model

[0979] 2. Tokenization and Smart Contracts

[0980] 3. Sentiment Analysis Engine

[0981] 4. User Interface and Platform

[0982] 5. Server data processing and management functions

[0983] Hardware and software used

[0984] Hardware: Smartphone (camera, microphone, display), server (high-performance processor, storage)

[0985] software:

[0986] Sentiment analysis engine: Google Cloud Vision API, TensorFlow

[0987] Generative AI models: OpenAI GPT-4, DALL-E, music generative AI

[0988] Token Management and Trading: Ethereum Blockchain, Wallet Applications

[0989] User Interface:React Native

[0990] Program processing

[0991] 1. User login and initial settings

[0992] When a user logs in to the system, the camera and microphone are first activated for facial recognition and voice analysis. This data is analyzed in real time using the Google Cloud Vision API and TensorFlow to extract the user's emotional data. The emotional data is then sent to the server and added to the user's profile.

[0993] 2. Emotion data analysis and transmission

[0994] The emotion analysis engine analyzes the user's emotion data collected in real time, and the server adjusts the parameters of the generative AI model based on the analysis results.

[0995] 3. Token Issuance and Management

[0996] The server tokenizes the right to use the generative AI model and issues it as a smart contract on the blockchain. The issued tokens can be bought and sold through the user interface, and the token trading history and ownership status are managed by the Ethereum blockchain and wallet application.

[0997] 4. Content Creation and Delivery

[0998] When a user submits a request to use a specific generative AI model, the server generates a prompt based on the emotion data and applies it to the generative AI model. For example, the prompt might look like this:

[0999] The sentiment analysis engine detected "sad." Please generate a kind message to comfort the user when they feel sad.

[1000] The server uses this prompt to send a request to the generative artificial intelligence model and provides the generated content (text, images, music, etc.) to the user.

[1001] Specific examples

[1002] For example, if a user has had a fight with a friend and is feeling sad, the camera analyzes their facial expression and recognizes the emotion "sad." Based on this data, the server uses OpenAI GPT-4 to generate a kind message and then DALL-E to generate a comforting image. These contents are then delivered to the user's smartphone.

[1003] As described above, the present invention provides the output of a personalized generative artificial intelligence model that is tailored to the emotional state of the user, and realizes a system for safely and efficiently buying, selling, and managing usage rights.

[1004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1005] Step 1:

[1006] When a user logs in to a device, the device's camera and microphone are activated, allowing the user's face and voice data to be collected in real time. The inputs are camera footage and audio data, which are then analyzed for facial expressions and voice tone using the Google Cloud Vision API and TensorFlow. The output is the user's emotion data captured in real time.

[1007] Step 2:

[1008] The server receives emotion data sent from the device. This emotion data is stored in the user's profile and updates the database based on the user's emotional state. The input is the analyzed emotion data, which is then written to the database. The output is the updated user profile.

[1009] Step 3:

[1010] The user sends a request from their device to use a specific generative AI model. This request includes the type and theme of the content they want to generate. The input is the user's request data, and a prompt sentence is generated based on this. The output is the prompt sentence.

[1011] Step 4:

[1012] The server adjusts the settings of the generative AI model based on the emotional data analyzed by the emotion engine. For example, if the user's emotional state is analyzed as "sad," the parameters of the generative AI model are set to generate a comforting message. The inputs are the analyzed emotional data and the initial settings of the generative model, and the model parameters are adjusted based on these. The output is the adjusted generative AI model.

[1013] Step 5:

[1014] The server inputs a prompt sentence into the adjusted generative AI model, runs the model, and obtains generated content. Specifically, OpenAI GPT-4 generates text, DALL-E generates images, and music is generated using a music generation AI. The inputs are the prompt sentence and the adjusted generative AI model, and a wide variety of content is generated by the operation of the model. The output is the various generated content.

[1015] Step 6:

[1016] The server provides the generated content to the terminal. The user receives the generated content, such as text, images, and music, through the terminal. The input is the generated content from the server, which is provided to the user. The output is the content displayed on the user's terminal.

[1017] Step 7:

[1018] Through the token management system, users can buy and sell the right to use generative AI models. The server manages transactions and wallets on the blockchain, and updates token ownership status in response to user buy and sell requests. The inputs are buy and sell requests from users and token information, and the output is updated token ownership status and transaction history.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] [Third embodiment]

[1023] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1024] 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.

[1025] 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).

[1026] 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.

[1027] 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.

[1028] 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).

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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."

[1035] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[1036] System Overview

[1037] The system consists of the following main components:

[1038] 1. Registration portal for AI companies

[1039] 2. Token Generation and Management Server

[1040] 3. Trading platform for users

[1041] 4. Generative AI Model Operation Server

[1042] Registration portal for AI companies

[1043] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[1044] Token Generation and Management Server

[1045] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1046] User-friendly trading platform

[1047] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1048] Program processing

[1049] Token Purchase

[1050] When a user purchases a token, the following happens:

[1051] 1. The user logs in to the platform and selects the tokens they wish to purchase.

[1052] 2. The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[1053] 3. The server checks the balance of the user's wallet and, if the transaction conditions are met, executes the smart contract to transfer the tokens to the user's wallet.

[1054] 4. The server notifies the user of the success of the transaction, and a message indicating the purchase is complete is displayed on the terminal.

[1055] Token Sale

[1056] When a user sells their tokens, the following happens:

[1057] 1. A user submits a request to sell their tokens.

[1058] 2. The server publishes the request on the trading platform and waits for other users to purchase.

[1059] 3. When another user wants to make a purchase, the server executes a smart contract and distributes tokens and funds to each wallet.

[1060] 4. The server notifies the selling user of the success of the transaction, and a message indicating the sale is complete is displayed on the terminal.

[1061] Token holders' right to use AI

[1062] When a user uses a token to access a generative AI model, the following happens:

[1063] 1. The user accesses a dedicated portal and submits a request for use.

[1064] 2. The server analyzes the request and sends the data to the generative artificial intelligence model.

[1065] 3. The server receives the generated results and provides them to the user.

[1066] 4. The server records the token usage history and calculates the applicable fees.

[1067] Specific examples

[1068] For example, suppose a user holds a token for an image-generating AI model and wants to use it to generate a new image. The user enters an image generation request into the portal and specifies the required parameters (e.g., the theme and style of the image they want to generate). The server receives the request and sends the data to the generative AI model. The model generates a new image, and the result is sent to the user via the server. This process ensures that the entire platform operates securely and efficiently.

[1069] This system will enable AI companies to raise funds quickly, allow individual investors to easily invest in AI technology, and allow token holders to efficiently use AI models, greatly improving convenience.

[1070] The processing flow will be explained below.

[1071] Token purchase processing steps

[1072] Step 1:

[1073] Users log in to the platform and select the tokens they wish to purchase.

[1074] Step 2:

[1075] The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[1076] Step 3:

[1077] The server checks the user's wallet balance and sees if the desired purchase amount is available in the wallet.

[1078] Step 4:

[1079] The server generates a smart contract for token trading based on the user's request and deploys it on the blockchain.

[1080] Step 5:

[1081] The server executes the smart contract and transfers the purchased tokens to the user's wallet.

[1082] Step 6:

[1083] The server confirms the success of the transaction and sends a transaction completion notification to the user's terminal.

[1084] Step 7:

[1085] A message will appear on your device confirming your purchase.

[1086] Token sale processing steps

[1087] Step 1:

[1088] Users submit a request to sell their tokens to the platform.

[1089] Step 2:

[1090] The server receives the sale request and publishes it on the trading platform.

[1091] Step 3:

[1092] Another user accesses the platform and views the published sale request.

[1093] Step 4:

[1094] Send a request to buy tokens that another user wants to sell.

[1095] Step 5:

[1096] The server transfers tokens and transaction amounts between wallets through smart contracts.

[1097] Step 6:

[1098] The server confirms the success of the transaction and sends a notice of completion of the sale to the terminals of the selling user and the purchasing user.

[1099] Step 7:

[1100] A message will appear on the terminal indicating that the sale is complete.

[1101] Processing steps for token holders to exercise their AI usage rights

[1102] Step 1:

[1103] Users have a token and access a dedicated portal.

[1104] Step 2:

[1105] To use the generative artificial intelligence model, the user inputs the necessary request information (input data, parameters, etc.).

[1106] Step 3:

[1107] The user sends a request and notifies the server.

[1108] Step 4:

[1109] The server analyzes the request content and sends input data to the generative artificial intelligence model.

[1110] Step 5:

[1111] Generative artificial intelligence models generate new data (e.g., generated images) based on input data.

[1112] Step 6:

[1113] The server receives the generated results and provides them to the user.

[1114] Step 7:

[1115] The server records the token usage history and calculates the applicable fees.

[1116] Step 8:

[1117] The server sends the usage results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[1118] Example: Using an image generation AI model

[1119] Step 1:

[1120] Users hold tokens for the image-generating AI model and access a dedicated portal.

[1121] Step 2:

[1122] Users enter image generation requests on the portal and specify the theme and style of the image they want to generate.

[1123] Step 3:

[1124] The user sends a request and notifies the server.

[1125] Step 4:

[1126] The server analyzes the request and sends input data to the image generation AI model.

[1127] Step 5:

[1128] An image generation AI model generates new images based on input data.

[1129] Step 6:

[1130] The server receives the resulting image and provides it to the user.

[1131] Step 7:

[1132] The server records the token usage history and calculates the applicable fees.

[1133] Step 8:

[1134] The server sends the generated results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[1135] In this way, users can efficiently and safely buy and sell tokens and utilize generative AI models.

[1136] Example 1

[1137] 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."

[1138] Currently, there is no efficient and transparent way to manage and operate the rights to use generative AI models. In particular, there is a lack of a reliable platform for tokenizing the rights to use AI models and trading them on the market. This makes it difficult for AI companies to raise funds quickly and creates high barriers for individual investors to access these technologies. Furthermore, there is a lack of convenience as there is no way for token holders to properly use AI models.

[1139] 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.

[1140] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for generating and deploying the token as a smart contract on a blockchain; means for listing the issued token on a trading platform and enabling trading; means for buying and selling the token through the trading platform; means for receiving a request from the token holder to use the generative AI model; means for transmitting data to the generative AI model based on the request and providing the holder with the generation results; means for operating the generative AI model based on the content of the request and providing the holder with the usage results; and means for notifying the token purchaser or seller of the success of the transaction. This enables the right to use a generative AI model to be tokenized efficiently and transparently, allowing it to be safely bought, sold, and used in the market.

[1141] A "generative artificial intelligence model" is an artificial intelligence algorithm or program that can generate data based on user requests.

[1142] "Tokenization" is the process of converting certain rights or assets into digital tokens.

[1143] "Blockchain" is a technology that uses distributed ledger technology to manage transaction data in a transparent and immutable manner.

[1144] A "smart contract" is a self-executing contract program that runs on a blockchain and is a mechanism that automatically executes a contract when certain conditions are met.

[1145] "Trading Platform" means an online system or website through which users can buy and sell digital tokens.

[1146] A "request" is an act in which a user requests a specific service or data generation from a generative artificial intelligence model.

[1147] "Metadata" is data containing detailed information related to a token or digital asset, such as issuance quantity, price, terms of use, etc.

[1148] A "wallet" is a software tool or application used to securely store and transact digital tokens.

[1149] "Trading History" means the records of purchases and sales of Tokens made on the Trading Platform.

[1150] "Fees" are costs incurred in connection with the purchase, sale, and use of tokens, and are collected by the platform or operator.

[1151] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[1152] System Overview

[1153] The system consists of the following main components:

[1154] 1. Registration portal for AI companies

[1155] 2. Token Generation and Management Server

[1156] 3. Trading platform for users

[1157] 4. Generative AI Model Operation Server

[1158] Registration portal for AI companies

[1159] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. This registered information is managed by a server.

[1160] Token Generation and Management Server

[1161] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1162] User-friendly trading platform

[1163] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1164] Generative AI model operation server

[1165] When a user uses a token to access a generative AI model, they access a dedicated portal and submit a request. The server analyzes the request and sends the data to the generative AI model. The server receives the generated results and provides them to the user. The server records the token usage history and calculates the applicable fees.

[1166] Specific examples

[1167] For example, suppose a user has a token for an image generation AI model and wants to use it to generate a new image. The user accesses the portal, enters a request for image generation, and specifies the theme and style of the image they want to generate. An example of a specific prompt is as follows:

[1168] "Generate a beach scene illuminated by a summer sunset."

[1169] "Please create a cyberpunk-style illustration with a futuristic city theme."

[1170] The server receives the request and sends the data to the generative AI model, which then generates a new image and sends the result to the user via the server. This process ensures the entire platform operates safely and efficiently.

[1171] This system will enable AI companies to raise funds quickly, individual investors to easily access AI technology, and token holders to efficiently use generative AI models, greatly improving convenience.

[1172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] The user (AI company) registers model information

[1175] Input: The user enters information such as the name, version, and license terms of the AI ​​model.

[1176] Server Actions: The server receives the entered information, verifies the data for accuracy and completeness, and prepares this information for storage in a database.

[1177] Output: The server stores the information in a database and sends a "Registration complete" message to the device.

[1178] Specific operation: The user clicks the "Enter model information" button. The server verifies the information and registers it in the database. The terminal displays the message "Registration completed."

[1179] Step 2:

[1180] Server-generated tokens

[1181] Input: Token generation request from AI company.

[1182] Server operation: The server receives the request, generates a smart contract using the blockchain API, sets the token metadata in the smart contract, and deploys it.

[1183] Output: Issued tokens are generated and listed on the exchange platform.

[1184] How it works: When the server receives a "token generation" request, it uses the blockchain API to create a smart contract and deploy it, which generates a token and lists it on the platform.

[1185] Step 3:

[1186] User purchase of tokens

[1187] Input: The user enters the number of tokens they wish to purchase and the price.

[1188] Server operation: The server checks the user's wallet balance and verifies whether the transaction conditions are met. If the conditions are met, it executes the smart contract to transfer the tokens to the user's wallet.

[1189] Output: A message indicating purchase is complete is displayed on the terminal.

[1190] Specific operation: The user clicks the "Purchase" button. The server calls the wallet API to check the balance, activates the smart contract to transfer the tokens, and displays a "Purchase Complete" message on the terminal.

[1191] Step 4:

[1192] Token sales by users

[1193] Input: Enter a request to sell the tokens the user holds.

[1194] Server operation: The server receives the sale request and publishes it on the trading platform. If a user wishes to purchase, it executes the smart contract and distributes the tokens and funds to each wallet.

[1195] Output: A message indicating the sale is complete is displayed on the terminal.

[1196] Specific operation: The user clicks the "Sell" button. The server publishes the sale request to the trading platform, and if a buyer appears, it launches the smart contract, distributes tokens and funds, and displays a "Sell Complete" message on the terminal.

[1197] Step 5:

[1198] Token holders' right to use AI

[1199] Input: The user enters a usage request and prompt.

[1200] Server operation: The server analyzes the request content and sends the data to the generative AI model. It receives the generated results and provides them to the user. It records the token usage history and calculates the applicable fees.

[1201] Output: The generated results are displayed on the terminal.

[1202] Specific operation: The user clicks the "Send request to AI model" button. The server sends the prompt to the AI ​​model. The generated result is received and displayed on the device. The usage history is recorded and fees are calculated.

[1203] (Application example 1)

[1204] 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."

[1205] There is a need to provide a system for the efficient and transparent use of generative AI models. However, current technology lacks a platform that can properly manage and securely trade the rights to use generative AI models. Furthermore, content distribution services face challenges in maintaining the quality of generated content and responding promptly to user requests.

[1206] 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.

[1207] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for issuing tokens as smart contracts on distributed ledger technology; means for providing a platform for buying and selling the issued tokens; means for buying and selling the tokens through the platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; means for the usage results to include content related to a content distribution service; and means for generating the content based on the tokens held by the user. This enables efficient management and safe trading of the right to use the tokenized generative AI model, as well as high-quality content distribution.

[1208] A "generative artificial intelligence model" refers to an artificial intelligence technology that can automatically generate new content and data based on input data.

[1209] "Right of use" refers to the right to use a specific service or technology (in this case, a generative artificial intelligence model) for a certain period of time or a certain number of times.

[1210] "Tokenization" refers to the process of converting digital assets or rights into digital vouchers called tokens and managing them on a blockchain.

[1211] "Distributed ledger technology" refers to technology that stores digital data on a distributed network and manages transaction histories and contracts transparently and securely.

[1212] A "smart contract" refers to a contract whose terms are written in program code and are executed automatically.

[1213] "Platform" refers to a system or environment that serves as the foundation for providing a specific purpose or service.

[1214] A "request" refers to an instruction that a user gives to a system requesting a specific action or service.

[1215] "Content distribution service" refers to an online service that provides users with digital content such as text, images, audio, and video.

[1216] "Content" refers to information, data, media or other digital material organized in a particular format or subject.

[1217] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where it can be bought and sold on the market. Specific embodiments for implementing this invention will be described below.

[1218] System Configuration

[1219] The system consists of the following main components:

[1220] 1. Registration Portal:

[1221] This is a portal where AI companies can register their rights to use generative AI models. AI companies can enter their model information here and make a request to tokenize their usage rights.

[1222] 2. Token Generation and Management Server:

[1223] This server receives tokenization requests from AI companies, creates and deploys smart contracts on distributed ledger technology, and issues tokens according to the smart contract, managing the token metadata (issuance amount, price, terms of use, etc.).

[1224] 3. Trading Platform:

[1225] Users can buy and sell tokens through this platform. When purchasing tokens, the server checks the user's wallet balance and transfers the tokens if the required funds are available. When a sell request is made, the server publishes the request and mediates the transaction with the user who wishes to buy.

[1226] 4. Generative AI model operation server:

[1227] When a user uses a token to utilize a generative artificial intelligence model, this server receives the request, operates the generative artificial intelligence model, and provides the utilization results to the user.

[1228] Program processing

[1229] The system allows users to use a generative artificial intelligence model on a smartphone or other device to generate content related to a content distribution service.

[1230] Hardware and software:

[1231] Hardware:

[1232] Smartphone, blockchain node server, AI model server

[1233] software:

[1234] Python 3.x, Requests library, Web3 library, blockchain node (e.g., Ethereum), generative AI model service (e.g., GPT-3)

[1235] Data processing and calculation:

[1236] 1. Initialize your wallet:

[1237] The user's wallet is initialized using eth_account and web3.

[1238] 2. Check token balance:

[1239] The user's wallet address is used to check the token balance on the blockchain, which determines whether the user can spend the tokens.

[1240] 3. AI-generated request:

[1241] A prompt from the user is sent to the generative AI model to generate content. For example, a prompt could be, "Generate ideas for a blog post. The topic is technology."

[1242] 4. Providing generated results:

[1243] The generated content is provided to users through a server, allowing users to instantly obtain high-quality content.

[1244] Specific examples

[1245] For example, if a user wants to generate ideas for a blog post, they might enter a prompt like this:

[1246] Example prompt sentence:

[1247] "Generate ideas for blog posts on technology."

[1248] Based on this request, the generative AI model generates high-quality blog post ideas related to the specified topic and provides the results to the user via the server, allowing the user to generate content quickly and efficiently using their own tokens.

[1249] This system tokenizes the rights to use generative AI models, providing a platform for secure transactions and greatly improving the convenience of users to generate high-quality content.

[1250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1251] Step 1:

[1252] A user launches the smartphone app and confirms their right to use the generative AI model they own. They then log in to their wallet and check the balance of their tokens. At this time, the wallet address is entered, and the number of tokens held is displayed as the output.

[1253] Step 2:

[1254] When a user wants to use a generative AI model, tokens are required, so the user must ensure that they have sufficient token balance. The server obtains the current token balance using distributed ledger technology based on the user's wallet address and displays the token balance as the output.

[1255] Step 3:

[1256] The user creates a prompt to use the generative AI model and enters it into the application's input form. For example, the user might enter a prompt such as, "Generate ideas for a blog post. The topic is technology." The entered prompt is then sent to the server.

[1257] Step 4:

[1258] The server receives the user's prompt sentence and sends it to the generative AI model. This inputs data from the server to the generative AI model, which then begins generating content based on the input data. The server then sends request data including the prompt sentence to the generative AI model and receives the generated content data.

[1259] Step 5:

[1260] The generative AI model generates content based on the received prompt. Specifically, digital content such as text and images is generated according to the theme and style associated with the input prompt. The data generated is determined by the AI ​​model's calculations.

[1261] Step 6:

[1262] The server analyzes the content received from the generative AI model and converts it into a format to be provided to the user. The converted data is then output from the server to the user's device (smartphone).

[1263] Step 7:

[1264] Finally, the generated content is displayed on the user's smartphone. The user can review and use the generated content (e.g., blog post ideas and images). The user can also view the generated results and, if necessary, generate more content by entering additional prompts.

[1265] Through this series of steps, users can use their tokens to effectively utilize generative AI models to quickly generate high-quality content.

[1266] 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.

[1267] The present invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. Furthermore, this system is combined with an emotion engine that recognizes user emotions and has the function of adjusting the output of the generative AI model based on the user's emotions.

[1268] System Overview

[1269] The system consists of the following main components:

[1270] 1. Registration portal for AI companies

[1271] 2. Token Generation and Management Server

[1272] 3. Trading platform for users

[1273] 4. Generative AI Model Operation Server

[1274] 5. Emotion Engine

[1275] Registration portal for AI companies

[1276] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[1277] Token Generation and Management Server

[1278] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1279] User-friendly trading platform

[1280] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1281] Emotion Engine

[1282] The server is equipped with an emotion engine that recognizes the user's emotions. This engine analyzes emotions from the user's input data and interactions, and reflects this data in the operation of the generative AI model.

[1283] Generative AI model operation server

[1284] The server operates the generative AI model based on user requests and provides the results. It also receives emotion data from the emotion engine and optimizes the model's output based on the user's emotions.

[1285] Program processing

[1286] Emotion Engine Process

[1287] When a user accesses the platform and uses a generative AI model, the following occurs:

[1288] 1. A user logs into the platform and the emotion engine monitors the user's input and interactions.

[1289] 2. The emotion engine extracts emotional data from the user's text input, facial expressions, and voice.

[1290] 3. The emotion engine sends the analyzed emotion data to the server in real time.

[1291] 4. The server adjusts the parameters of the generative AI model based on the emotion data.

[1292] Emotion-driven generation

[1293] As a concrete example, consider a case where a user uses a token to access an image-generating AI model:

[1294] 1. The user accesses a dedicated portal and specifies the theme and style of the image they want to generate.

[1295] 2. The emotion engine analyzes the user's emotions (e.g., happy, sad, surprised).

[1296] 3. The server adjusts the settings of the image generation AI model based on the emotion data.

[1297] For example, if the user is recognized as "happy," the parameters are set so that an image with bright and vivid colors is generated.

[1298] 4. The image generation AI model generates a new image based on the adjusted parameters.

[1299] 5. The server receives the resulting image and provides it to the user.

[1300] 6. The server records the token usage history and calculates the applicable fees.

[1301] Specific examples

[1302] This explains the process by which a user trains a generative AI model using the emotion engine and uses tokens:

[1303] 1. A user logs into the portal and their emotions are monitored in real time by the emotion engine.

[1304] 2. If the user inputs a request for image generation and indicates an excited emotion, the emotion engine sends the emotion data to the server.

[1305] 3. The server changes the color and content settings of the image generation AI model based on the emotion data.

[1306] 4. The image generation AI model generates an image using parameters according to the user's emotions and provides the image to the user via the server.

[1307] 5. This allows users to get customized output tailored to their individual emotional state.

[1308] In this way, the system takes into account user sentiment and provides more personalized output from the generative AI model. It also efficiently manages token trading and usage history.

[1309] The processing flow will be explained below.

[1310] Emotion Engine Process

[1311] Collaboration with emotion engine

[1312] Step 1:

[1313] Users log in to the platform and access a dedicated portal.

[1314] Step 2:

[1315] The emotion engine monitors user input and interactions and collects emotion data in real time.

[1316] Step 3:

[1317] The emotion engine analyzes emotions from the user's character input, facial expression analysis, voice input, etc., and generates emotion data.

[1318] Step 4:

[1319] The emotion engine transmits the analyzed emotion data to the server.

[1320] Emotion-driven generation

[1321] Examples of using image generation AI models

[1322] Step 1:

[1323] The user logs into the specialized portal and opens a request form for image generation.

[1324] Step 2:

[1325] The user inputs the theme (e.g., "landscape" or "portrait") and style (e.g., "realistic" or "abstract") of the image they want to generate.

[1326] Step 3:

[1327] An emotion engine analyzes the user's current emotional state (e.g., "happy," "sad," "excited").

[1328] Step 4:

[1329] The emotion engine transmits emotion data to the server in real time.

[1330] Step 5:

[1331] The server adjusts the setting parameters of the generative artificial intelligence model based on the emotion data.

[1332] For example, if the user is expressing a "happy" emotion, the colors of the generated image are brightened and the overall tone is adjusted to a positive one.

[1333] Step 6:

[1334] The server sends the adjusted setting parameters to the image generation AI model.

[1335] Step 7:

[1336] The image generation AI model generates new images based on user requests and parameter settings from the server.

[1337] Step 8:

[1338] The image generated by the image generation AI model is sent back to the server.

[1339] Step 9:

[1340] The server transmits the generated image received to the user's terminal for providing it to the user.

[1341] Step 10:

[1342] The server records the token usage history and calculates the applicable fees.

[1343] Step 11:

[1344] The terminal will display the generated image and details about the token usage.

[1345] Specific examples

[1346] Actual user experience

[1347] Step 1:

[1348] Users hold tokens for the image-generating AI model and log in to the platform to use it.

[1349] Step 2:

[1350] A user inputs a request for image generation into the portal, specifying a theme such as "downtown sunset."

[1351] Step 3:

[1352] The emotion engine detects excited emotions based on the user's voice input and facial recognition data.

[1353] Step 4:

[1354] The emotion engine sends the analysis results to the server in real time.

[1355] Step 5:

[1356] The server adjusts the color and design settings of the image-generating AI model based on data from the emotion engine.

[1357] Step 6:

[1358] The server sends the adjustment parameters to the image generation AI model and instructs the model to generate an image.

[1359] Step 7:

[1360] The image generation AI model generates colorful and vibrant images according to the received parameters.

[1361] Step 8:

[1362] The image generation AI model sends the generated images to the server.

[1363] Step 9:

[1364] The server sends the generated image to the user's terminal and updates the token usage history.

[1365] Step 10:

[1366] The terminal displays a generated image of a colorful downtown sunset, along with token usage history and fee information.

[1367] In this way, by collecting user emotional data and reflecting it in the generative AI model, personalized results can be generated for each individual user. Token trading and usage history can also be managed efficiently.

[1368] Example 2

[1369] 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."

[1370] Conventional generative AI model systems lacked efficient methods for managing model usage rights and the ability to personalize output based on user sentiment. Furthermore, there was a need for an efficient and transparent system for the buying and selling of tokenized usage rights, record-keeping, and fee calculation.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for tokenizing the right to use the generative AI model; means for issuing the token as a smart contract on a blockchain; means for providing an electronic platform for buying and selling the issued token; means for buying and selling the token through the electronic platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; emotion recognition means for analyzing user emotions; and means for adjusting the parameters of the generative AI model based on the user's emotion data. This enables efficient management of the right to use the generative AI model and personalized output based on the user's emotion data. Furthermore, token buying and selling, history management, and fee calculation can be performed efficiently and transparently.

[1372] A "generative artificial intelligence model" is an artificial intelligence system that can automatically generate new data and information based on input data.

[1373] A "token" is a type of digital asset issued using blockchain technology that represents a specific right of use or value.

[1374] "Blockchain" is a secure, tamper-resistant digital distributed ledger technology and a data structure for continuously recording transaction history and information.

[1375] A "smart contract" refers to a contract or program that is automatically executed on a blockchain when certain conditions are met.

[1376] An "electronic platform" refers to an online service or system provided via a computer network, which serves as a foundation for users to carry out certain operations or transactions.

[1377] "Emotion recognition means" is a technology for analyzing data such as a user's facial expressions, voice, and text input, and identifying the user's emotional state.

[1378] "Means for adjusting parameters" refers to techniques for changing the operating settings of a system or model based on acquired data and conditions.

[1379] A "request" refers to a user's request to the system for a specific operation or service to be provided.

[1380] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. It also incorporates an emotion engine that recognizes user emotions and adjusts the output of the generative AI model based on the user's emotions.

[1381] Configuring Components

[1382] The system consists of the following main components:

[1383] 1. Registration portal for AI companies

[1384] 2. Token Generation and Management Server

[1385] 3. Trading platform for users

[1386] 4. Generative AI Model Operation Server

[1387] 5. Emotion Engine

[1388] Registration portal for AI companies

[1389] Users (AI companies) access a registration portal and register for the right to use a generative AI model. In this portal, companies enter model information (type, terms of use, fees, etc.) and request tokenization. The entered information is managed by the server.

[1390] Token Generation and Management Server

[1391] The server receives tokenization requests from AI companies, creates smart contracts on the blockchain, and issues tokens. The smart contracts contain metadata such as the token type, quantity, price, and terms of use. The issued tokens are published on the trading platform and can be bought and sold. This process ensures transparent and secure management of tokenized usage rights.

[1392] User-friendly trading platform

[1393] Users buy and sell tokens through this platform. When purchasing, the server checks the user's wallet balance and transfers the tokens to the user's wallet if the balance is sufficient. In the case of a sell request, the server also receives the request and mediates the transaction. This platform also has the function of managing token buying and selling history and fee calculation.

[1394] Emotion Engine

[1395] The emotion engine analyzes the user's input data (text, facial expressions, voice, etc.) and recognizes the user's emotions. The analysis results are sent to the server, which then uses the data to adjust the parameters of the generative AI model. For example, if the user expresses the emotion "happy," the settings are changed to brighten the color tone of the generated image.

[1396] Generative AI model operation server

[1397] The generative AI model operation server operates the generative AI model based on user requests. Specifically, it generates appropriate output according to the theme and style specified by the user. In this process, emotional data from the emotion engine is used to adjust the model parameters. The generated results are provided to the user via the server.

[1398] Specific examples

[1399] Consider the case where a user makes an image generation request:

[1400] 1. The user accesses a dedicated portal and enters the theme and style of the image they want to generate.

[1401] 2. The emotion engine analyzes user input and interactions to extract emotional data.

[1402] 3. The server adjusts the parameters of the image generation AI model based on the emotion data.

[1403] For example, if the user is recognized as "excited," the image color tone and content are set to be vivid.

[1404] 4. The image generation AI model generates a new image based on the adjusted parameters.

[1405] 5. The server receives the resulting image and provides it to the user.

[1406] This allows users to obtain customized output according to their individual emotional state. An example of a specific prompt could be a request such as "A beautiful view of the sunset." The system aims to personalize the output based on the user's emotions and provide more satisfying results.

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1:

[1409] Users (AI companies) access the registration portal and enter information about their generative AI models, including the company name, model type, terms of use, and fees.

[1410] Input: Company name, model type, terms of use, price, etc.

[1411] Output: Model registration request

[1412] The server receives this input data and stores it in a database.

[1413] Specific operations: Stores the information in the database and sends a notification to the user that registration is complete.

[1414] Step 2:

[1415] The user (AI company) sends a tokenization request.

[1416] Input: Tokenization request

[1417] Output: Instructions for generating a token

[1418] The server accepts the request and creates a smart contract on the blockchain.

[1419] Specific operation: The smart contract describes the type, quantity, price, and terms of use of the token, and then deploys it to the blockchain.

[1420] Step 3:

[1421] The server issues the tokens and publishes them on the trading platform.

[1422] Input: Smart contract, token metadata

[1423] Output: Issued tokens, public data on the trading platform

[1424] Specific operations: Issue tokens and process information disclosure to the trading platform.

[1425] Step 4:

[1426] A user accesses the trading platform and requests to purchase tokens.

[1427] Input: Token purchase request

[1428] Output: Token purchase successful message, wallet updated

[1429] The server checks the user's wallet balance and transfers the tokens to the user's wallet if the purchase conditions are met.

[1430] Specific operations: Check wallet balance, transfer tokens, and update transaction history.

[1431] Step 5:

[1432] A user requests the use of a generative artificial intelligence model.

[1433] Input: Request to use the generative AI model, prompt (e.g., "A beautiful view of the sunset")

[1434] Output: Usage results (generated images, etc.)

[1435] The server receives the request and launches the generative AI model.

[1436] Specific operation: Launches the generative AI model and generates data based on the specified prompt.

[1437] Step 6:

[1438] The emotion engine analyzes user input and interactions to extract emotional data.

[1439] Input: User text input, facial expression data, voice data

[1440] Output: Emotion data (e.g. "Excited")

[1441] Specific operations: Perform natural language processing, image analysis, and voice analysis to analyze input data and identify emotions.

[1442] Step 7:

[1443] The emotion engine sends the analysis results to the server in real time.

[1444] Input: Emotion data

[1445] Output: Instruction to transfer emotion data to the server

[1446] Specific operation: The emotion data transfer protocol is used to process the data sent to the server.

[1447] Step 8:

[1448] The server adjusts the parameters of the generative AI model based on the emotion data.

[1449] Input: Emotion data

[1450] Output: Adjusted generation parameters

[1451] Specific operation: Optimize the model parameters based on the emotion data and change the settings to generate output that corresponds to the emotion.

[1452] Step 9:

[1453] The generative artificial intelligence model generates an output based on the adjusted parameters.

[1454] Input: Adjusted generation parameters

[1455] Output: Generated results (e.g., image data)

[1456] Specific operation: The generative AI model generates new data (e.g., images or text) and returns the generated results to the server.

[1457] Step 10:

[1458] The server receives the generated results and provides them to the user.

[1459] Input: Generated result (e.g., image data)

[1460] Output: Data provided to the user

[1461] Specific operation: Processing is performed to display or provide the generated output data to the user through the user interface.

[1462] (Application example 2)

[1463] 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."

[1464] Conventional generative AI models provide uniform output without considering the user's emotional state, resulting in a lack of personalization for individual users. Furthermore, there is no mechanism in place to safely and efficiently buy and sell model usage rights on the market, leading to inappropriate usage and rights management issues. Furthermore, there is a lack of efficient means for managing usage history and calculating fees.

[1465] 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 tokenizing the usage rights of the generative AI model; means for issuing the tokens as smart contracts on the blockchain; means having an emotion engine that analyzes user emotions; means for adjusting the generative AI model based on emotion data analyzed by the emotion engine; means for providing a platform for buying and selling the issued tokens; means for receiving a request from the token holder to use the generative AI model; and means for operating the generative AI model based on the request and providing the holder with the usage results. This makes it possible to output a generative AI model personalized to the user's emotional state, and enables the safe and efficient buying and selling and management of usage rights.

[1466] A "generative artificial intelligence model" is a machine learning algorithm that generates new information and content based on data.

[1467] "Tokenization" refers to the technology or process that represents digital assets or rights as tokens and makes them tradable on the blockchain.

[1468] "Blockchain" is a distributed ledger technology, a system that records transaction history in a linked chain.

[1469] A "smart contract" is a program that runs automatically on a blockchain and expresses contract terms as program code.

[1470] An "emotion engine" is a technology or software that analyzes emotions from a user's facial expressions, voice, text input, etc., and reflects that data in a generative artificial intelligence model.

[1471] "Platform" means an online service or system that allows users to buy, sell, manage, and use Tokens.

[1472] A "token holder" is a person or company that owns a token that represents the right to use a particular generative AI model.

[1473] "Means for receiving a request" refers to an interface or system for receiving a request from a user to use a generative artificial intelligence model.

[1474] The embodiment of the present invention is configured as a system that operates by integrating various hardware and software. The following is a specific method for realizing this system.

[1475] System Overview

[1476] The system mainly consists of the following components:

[1477] 1. Generative AI Model

[1478] 2. Tokenization and Smart Contracts

[1479] 3. Sentiment Analysis Engine

[1480] 4. User Interface and Platform

[1481] 5. Server data processing and management functions

[1482] Hardware and software used

[1483] Hardware: Smartphone (camera, microphone, display), server (high-performance processor, storage)

[1484] software:

[1485] Sentiment analysis engine: Google Cloud Vision API, TensorFlow

[1486] Generative AI models: OpenAI GPT-4, DALL-E, music generative AI

[1487] Token Management and Trading: Ethereum Blockchain, Wallet Applications

[1488] User Interface:React Native

[1489] Program processing

[1490] 1. User login and initial settings

[1491] When a user logs in to the system, the camera and microphone are first activated for facial recognition and voice analysis. This data is analyzed in real time using the Google Cloud Vision API and TensorFlow to extract the user's emotional data. The emotional data is then sent to the server and added to the user's profile.

[1492] 2. Emotion data analysis and transmission

[1493] The emotion analysis engine analyzes the user's emotion data collected in real time, and the server adjusts the parameters of the generative AI model based on the analysis results.

[1494] 3. Token Issuance and Management

[1495] The server tokenizes the right to use the generative AI model and issues it as a smart contract on the blockchain. The issued tokens can be bought and sold through the user interface, and the token trading history and ownership status are managed by the Ethereum blockchain and wallet application.

[1496] 4. Content Creation and Delivery

[1497] When a user submits a request to use a specific generative AI model, the server generates a prompt based on the emotion data and applies it to the generative AI model. For example, the prompt might look like this:

[1498] The sentiment analysis engine detected "sad." Please generate a kind message to comfort the user when they feel sad.

[1499] The server uses this prompt to send a request to the generative artificial intelligence model and provides the generated content (text, images, music, etc.) to the user.

[1500] Specific examples

[1501] For example, if a user has had a fight with a friend and is feeling sad, the camera analyzes their facial expression and recognizes the emotion "sad." Based on this data, the server uses OpenAI GPT-4 to generate a kind message and then DALL-E to generate a comforting image. These contents are then delivered to the user's smartphone.

[1502] As described above, the present invention provides the output of a personalized generative artificial intelligence model that is tailored to the emotional state of the user, and realizes a system for safely and efficiently buying, selling, and managing usage rights.

[1503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1504] Step 1:

[1505] When a user logs in to a device, the device's camera and microphone are activated, allowing the user's face and voice data to be collected in real time. The inputs are camera footage and audio data, which are then analyzed for facial expressions and voice tone using the Google Cloud Vision API and TensorFlow. The output is the user's emotion data captured in real time.

[1506] Step 2:

[1507] The server receives emotion data sent from the device. This emotion data is stored in the user's profile and updates the database based on the user's emotional state. The input is the analyzed emotion data, which is then written to the database. The output is the updated user profile.

[1508] Step 3:

[1509] The user sends a request from their device to use a specific generative AI model. This request includes the type and theme of the content they want to generate. The input is the user's request data, and a prompt sentence is generated based on this. The output is the prompt sentence.

[1510] Step 4:

[1511] The server adjusts the settings of the generative AI model based on the emotional data analyzed by the emotion engine. For example, if the user's emotional state is analyzed as "sad," the parameters of the generative AI model are set to generate a comforting message. The inputs are the analyzed emotional data and the initial settings of the generative model, and the model parameters are adjusted based on these. The output is the adjusted generative AI model.

[1512] Step 5:

[1513] The server inputs a prompt sentence into the adjusted generative AI model, runs the model, and obtains generated content. Specifically, OpenAI GPT-4 generates text, DALL-E generates images, and music is generated using a music generation AI. The inputs are the prompt sentence and the adjusted generative AI model, and a wide variety of content is generated by the operation of the model. The output is the various generated content.

[1514] Step 6:

[1515] The server provides the generated content to the terminal. The user receives the generated content, such as text, images, and music, through the terminal. The input is the generated content from the server, which is provided to the user. The output is the content displayed on the user's terminal.

[1516] Step 7:

[1517] Through the token management system, users can buy and sell the right to use generative AI models. The server manages transactions and wallets on the blockchain, and updates token ownership status in response to user buy and sell requests. The inputs are buy and sell requests from users and token information, and the output is updated token ownership status and transaction history.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] [Fourth embodiment]

[1522] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1523] 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.

[1524] 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).

[1525] 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.

[1526] 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.

[1527] 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).

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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."

[1535] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[1536] System Overview

[1537] The system consists of the following main components:

[1538] 1. Registration portal for AI companies

[1539] 2. Token Generation and Management Server

[1540] 3. Trading platform for users

[1541] 4. Generative AI Model Operation Server

[1542] Registration portal for AI companies

[1543] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[1544] Token Generation and Management Server

[1545] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1546] User-friendly trading platform

[1547] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1548] Program processing

[1549] Token Purchase

[1550] When a user purchases a token, the following happens:

[1551] 1. The user logs in to the platform and selects the tokens they wish to purchase.

[1552] 2. The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[1553] 3. The server checks the balance of the user's wallet and, if the transaction conditions are met, executes the smart contract to transfer the tokens to the user's wallet.

[1554] 4. The server notifies the user of the success of the transaction, and a message indicating the purchase is complete is displayed on the terminal.

[1555] Token Sale

[1556] When a user sells their tokens, the following happens:

[1557] 1. A user submits a request to sell their tokens.

[1558] 2. The server publishes the request on the trading platform and waits for other users to purchase.

[1559] 3. When another user wants to make a purchase, the server executes a smart contract and distributes tokens and funds to each wallet.

[1560] 4. The server notifies the selling user of the success of the transaction, and a message indicating the sale is complete is displayed on the terminal.

[1561] Token holders' right to use AI

[1562] When a user uses a token to access a generative AI model, the following happens:

[1563] 1. The user accesses a dedicated portal and submits a request for use.

[1564] 2. The server analyzes the request and sends the data to the generative artificial intelligence model.

[1565] 3. The server receives the generated results and provides them to the user.

[1566] 4. The server records the token usage history and calculates the applicable fees.

[1567] Specific examples

[1568] For example, suppose a user holds a token for an image-generating AI model and wants to use it to generate a new image. The user enters an image generation request into the portal and specifies the required parameters (e.g., the theme and style of the image they want to generate). The server receives the request and sends the data to the generative AI model. The model generates a new image, and the result is sent to the user via the server. This process ensures that the entire platform operates securely and efficiently.

[1569] This system will enable AI companies to raise funds quickly, allow individual investors to easily invest in AI technology, and allow token holders to efficiently use AI models, greatly improving convenience.

[1570] The processing flow will be explained below.

[1571] Token purchase processing steps

[1572] Step 1:

[1573] Users log in to the platform and select the tokens they wish to purchase.

[1574] Step 2:

[1575] The user enters the number of tokens they wish to purchase and the price, and submits a purchase request.

[1576] Step 3:

[1577] The server checks the user's wallet balance and sees if the desired purchase amount is available in the wallet.

[1578] Step 4:

[1579] The server generates a smart contract for token trading based on the user's request and deploys it on the blockchain.

[1580] Step 5:

[1581] The server executes the smart contract and transfers the purchased tokens to the user's wallet.

[1582] Step 6:

[1583] The server confirms the success of the transaction and sends a transaction completion notification to the user's terminal.

[1584] Step 7:

[1585] A message will appear on your device confirming your purchase.

[1586] Token sale processing steps

[1587] Step 1:

[1588] Users submit a request to sell their tokens to the platform.

[1589] Step 2:

[1590] The server receives the sale request and publishes it on the trading platform.

[1591] Step 3:

[1592] Another user accesses the platform and views the published sale request.

[1593] Step 4:

[1594] Send a request to buy tokens that another user wants to sell.

[1595] Step 5:

[1596] The server transfers tokens and transaction amounts between wallets through smart contracts.

[1597] Step 6:

[1598] The server confirms the success of the transaction and sends a notice of completion of the sale to the terminals of the selling user and the purchasing user.

[1599] Step 7:

[1600] A message will appear on the terminal indicating that the sale is complete.

[1601] Processing steps for token holders to exercise their AI usage rights

[1602] Step 1:

[1603] Users have a token and access a dedicated portal.

[1604] Step 2:

[1605] To use the generative artificial intelligence model, the user inputs the necessary request information (input data, parameters, etc.).

[1606] Step 3:

[1607] The user sends a request and notifies the server.

[1608] Step 4:

[1609] The server analyzes the request content and sends input data to the generative artificial intelligence model.

[1610] Step 5:

[1611] Generative artificial intelligence models generate new data (e.g., generated images) based on input data.

[1612] Step 6:

[1613] The server receives the generated results and provides them to the user.

[1614] Step 7:

[1615] The server records the token usage history and calculates the applicable fees.

[1616] Step 8:

[1617] The server sends the usage results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[1618] Example: Using an image generation AI model

[1619] Step 1:

[1620] Users hold tokens for the image-generating AI model and access a dedicated portal.

[1621] Step 2:

[1622] Users enter image generation requests on the portal and specify the theme and style of the image they want to generate.

[1623] Step 3:

[1624] The user sends a request and notifies the server.

[1625] Step 4:

[1626] The server analyzes the request and sends input data to the image generation AI model.

[1627] Step 5:

[1628] An image generation AI model generates new images based on input data.

[1629] Step 6:

[1630] The server receives the resulting image and provides it to the user.

[1631] Step 7:

[1632] The server records the token usage history and calculates the applicable fees.

[1633] Step 8:

[1634] The server sends the generated results and usage history to the user's terminal, and a message indicating completion of usage is displayed on the terminal.

[1635] In this way, users can efficiently and safely buy and sell tokens and utilize generative AI models.

[1636] Example 1

[1637] 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."

[1638] Currently, there is no efficient and transparent way to manage and operate the rights to use generative AI models. In particular, there is a lack of a reliable platform for tokenizing the rights to use AI models and trading them on the market. This makes it difficult for AI companies to raise funds quickly and creates high barriers for individual investors to access these technologies. Furthermore, there is a lack of convenience as there is no way for token holders to properly use AI models.

[1639] 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.

[1640] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for generating and deploying the token as a smart contract on a blockchain; means for listing the issued token on a trading platform and enabling trading; means for buying and selling the token through the trading platform; means for receiving a request from the token holder to use the generative AI model; means for transmitting data to the generative AI model based on the request and providing the holder with the generation results; means for operating the generative AI model based on the content of the request and providing the holder with the usage results; and means for notifying the token purchaser or seller of the success of the transaction. This enables the right to use a generative AI model to be tokenized efficiently and transparently, allowing it to be safely bought, sold, and used in the market.

[1641] A "generative artificial intelligence model" is an artificial intelligence algorithm or program that can generate data based on user requests.

[1642] "Tokenization" is the process of converting certain rights or assets into digital tokens.

[1643] "Blockchain" is a technology that uses distributed ledger technology to manage transaction data in a transparent and immutable manner.

[1644] A "smart contract" is a self-executing contract program that runs on a blockchain and is a mechanism that automatically executes a contract when certain conditions are met.

[1645] "Trading Platform" means an online system or website through which users can buy and sell digital tokens.

[1646] A "request" is an act in which a user requests a specific service or data generation from a generative artificial intelligence model.

[1647] "Metadata" is data containing detailed information related to a token or digital asset, such as issuance quantity, price, terms of use, etc.

[1648] A "wallet" is a software tool or application used to securely store and transact digital tokens.

[1649] "Trading History" means the records of purchases and sales of Tokens made on the Trading Platform.

[1650] "Fees" are costs incurred in connection with the purchase, sale, and use of tokens, and are collected by the platform or operator.

[1651] This invention provides a system that tokenizes the rights to use generative AI models and provides a platform where they can be traded on the market. This system uses blockchain technology to efficiently and transparently issue, trade, and manage the use of tokens.

[1652] System Overview

[1653] The system consists of the following main components:

[1654] 1. Registration portal for AI companies

[1655] 2. Token Generation and Management Server

[1656] 3. Trading platform for users

[1657] 4. Generative AI Model Operation Server

[1658] Registration portal for AI companies

[1659] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. This registered information is managed by a server.

[1660] Token Generation and Management Server

[1661] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1662] User-friendly trading platform

[1663] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1664] Generative AI model operation server

[1665] When a user uses a token to access a generative AI model, they access a dedicated portal and submit a request. The server analyzes the request and sends the data to the generative AI model. The server receives the generated results and provides them to the user. The server records the token usage history and calculates the applicable fees.

[1666] Specific examples

[1667] For example, suppose a user has a token for an image generation AI model and wants to use it to generate a new image. The user accesses the portal, enters a request for image generation, and specifies the theme and style of the image they want to generate. An example of a specific prompt is as follows:

[1668] "Generate a beach scene illuminated by a summer sunset."

[1669] "Please create a cyberpunk-style illustration with a futuristic city theme."

[1670] The server receives the request and sends the data to the generative AI model, which then generates a new image and sends the result to the user via the server. This process ensures the entire platform operates safely and efficiently.

[1671] This system will enable AI companies to raise funds quickly, individual investors to easily access AI technology, and token holders to efficiently use generative AI models, greatly improving convenience.

[1672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1673] Step 1:

[1674] The user (AI company) registers model information

[1675] Input: The user enters information such as the name, version, and license terms of the AI ​​model.

[1676] Server Actions: The server receives the entered information, verifies the data for accuracy and completeness, and prepares this information for storage in a database.

[1677] Output: The server stores the information in a database and sends a "Registration complete" message to the device.

[1678] Specific operation: The user clicks the "Enter model information" button. The server verifies the information and registers it in the database. The terminal displays the message "Registration completed."

[1679] Step 2:

[1680] Server-generated tokens

[1681] Input: Token generation request from AI company.

[1682] Server operation: The server receives the request, generates a smart contract using the blockchain API, sets the token metadata in the smart contract, and deploys it.

[1683] Output: Issued tokens are generated and listed on the exchange platform.

[1684] How it works: When the server receives a "token generation" request, it uses the blockchain API to create a smart contract and deploy it, which generates a token and lists it on the platform.

[1685] Step 3:

[1686] User purchase of tokens

[1687] Input: The user enters the number of tokens they wish to purchase and the price.

[1688] Server operation: The server checks the user's wallet balance and verifies whether the transaction conditions are met. If the conditions are met, it executes the smart contract to transfer the tokens to the user's wallet.

[1689] Output: A message indicating purchase is complete is displayed on the terminal.

[1690] Specific operation: The user clicks the "Purchase" button. The server calls the wallet API to check the balance, activates the smart contract to transfer the tokens, and displays a "Purchase Complete" message on the terminal.

[1691] Step 4:

[1692] Token sales by users

[1693] Input: Enter a request to sell the tokens the user holds.

[1694] Server operation: The server receives the sale request and publishes it on the trading platform. If a user wishes to purchase, it executes the smart contract and distributes the tokens and funds to each wallet.

[1695] Output: A message indicating the sale is complete is displayed on the terminal.

[1696] Specific operation: The user clicks the "Sell" button. The server publishes the sale request to the trading platform, and if a buyer appears, it launches the smart contract, distributes tokens and funds, and displays a "Sell Complete" message on the terminal.

[1697] Step 5:

[1698] Token holders' right to use AI

[1699] Input: The user enters a usage request and prompt.

[1700] Server operation: The server analyzes the request content and sends the data to the generative AI model. It receives the generated results and provides them to the user. It records the token usage history and calculates the applicable fees.

[1701] Output: The generated results are displayed on the terminal.

[1702] Specific operation: The user clicks the "Send request to AI model" button. The server sends the prompt to the AI ​​model. The generated result is received and displayed on the device. The usage history is recorded and fees are calculated.

[1703] (Application example 1)

[1704] 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."

[1705] There is a need to provide a system for the efficient and transparent use of generative AI models. However, current technology lacks a platform that can properly manage and securely trade the rights to use generative AI models. Furthermore, content distribution services face challenges in maintaining the quality of generated content and responding promptly to user requests.

[1706] 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.

[1707] In this invention, the server includes: means for tokenizing the right to use a generative AI model; means for issuing tokens as smart contracts on distributed ledger technology; means for providing a platform for buying and selling the issued tokens; means for buying and selling the tokens through the platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; means for the usage results to include content related to a content distribution service; and means for generating the content based on the tokens held by the user. This enables efficient management and safe trading of the right to use the tokenized generative AI model, as well as high-quality content distribution.

[1708] A "generative artificial intelligence model" refers to an artificial intelligence technology that can automatically generate new content and data based on input data.

[1709] "Right of use" refers to the right to use a specific service or technology (in this case, a generative artificial intelligence model) for a certain period of time or a certain number of times.

[1710] "Tokenization" refers to the process of converting digital assets or rights into digital vouchers called tokens and managing them on a blockchain.

[1711] "Distributed ledger technology" refers to technology that stores digital data on a distributed network and manages transaction histories and contracts transparently and securely.

[1712] A "smart contract" refers to a contract whose terms are written in program code and are executed automatically.

[1713] "Platform" refers to a system or environment that serves as the foundation for providing a specific purpose or service.

[1714] A "request" refers to an instruction that a user gives to a system requesting a specific action or service.

[1715] "Content distribution service" refers to an online service that provides users with digital content such as text, images, audio, and video.

[1716] "Content" refers to information, data, media or other digital material organized in a particular format or subject.

[1717] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where it can be bought and sold on the market. Specific embodiments for implementing this invention will be described below.

[1718] System Configuration

[1719] The system consists of the following main components:

[1720] 1. Registration Portal:

[1721] This is a portal where AI companies can register their rights to use generative AI models. AI companies can enter their model information here and make a request to tokenize their usage rights.

[1722] 2. Token Generation and Management Server:

[1723] This server receives tokenization requests from AI companies, creates and deploys smart contracts on distributed ledger technology, and issues tokens according to the smart contract, managing the token metadata (issuance amount, price, terms of use, etc.).

[1724] 3. Trading Platform:

[1725] Users can buy and sell tokens through this platform. When purchasing tokens, the server checks the user's wallet balance and transfers the tokens if the required funds are available. When a sell request is made, the server publishes the request and mediates the transaction with the user who wishes to buy.

[1726] 4. Generative AI model operation server:

[1727] When a user uses a token to utilize a generative artificial intelligence model, this server receives the request, operates the generative artificial intelligence model, and provides the utilization results to the user.

[1728] Program processing

[1729] The system allows users to use a generative artificial intelligence model on a smartphone or other device to generate content related to a content distribution service.

[1730] Hardware and software:

[1731] Hardware:

[1732] Smartphone, blockchain node server, AI model server

[1733] software:

[1734] Python 3.x, Requests library, Web3 library, blockchain node (e.g., Ethereum), generative AI model service (e.g., GPT-3)

[1735] Data processing and calculation:

[1736] 1. Initialize your wallet:

[1737] The user's wallet is initialized using eth_account and web3.

[1738] 2. Check token balance:

[1739] The user's wallet address is used to check the token balance on the blockchain, which determines whether the user can spend the tokens.

[1740] 3. AI-generated request:

[1741] A prompt from the user is sent to the generative AI model to generate content. For example, a prompt could be, "Generate ideas for a blog post. The topic is technology."

[1742] 4. Providing generated results:

[1743] The generated content is provided to users through a server, allowing users to instantly obtain high-quality content.

[1744] Specific examples

[1745] For example, if a user wants to generate ideas for a blog post, they might enter a prompt like this:

[1746] Example prompt sentence:

[1747] "Generate ideas for blog posts on technology."

[1748] Based on this request, the generative AI model generates high-quality blog post ideas related to the specified topic and provides the results to the user via the server, allowing the user to generate content quickly and efficiently using their own tokens.

[1749] This system tokenizes the rights to use generative AI models, providing a platform for secure transactions and greatly improving the convenience of users to generate high-quality content.

[1750] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1751] Step 1:

[1752] A user launches the smartphone app and confirms their right to use the generative AI model they own. They then log in to their wallet and check the balance of their tokens. At this time, the wallet address is entered, and the number of tokens held is displayed as the output.

[1753] Step 2:

[1754] When a user wants to use a generative AI model, tokens are required, so the user must ensure that they have sufficient token balance. The server obtains the current token balance using distributed ledger technology based on the user's wallet address and displays the token balance as the output.

[1755] Step 3:

[1756] The user creates a prompt to use the generative AI model and enters it into the application's input form. For example, the user might enter a prompt such as, "Generate ideas for a blog post. The topic is technology." The entered prompt is then sent to the server.

[1757] Step 4:

[1758] The server receives the user's prompt sentence and sends it to the generative AI model. This inputs data from the server to the generative AI model, which then begins generating content based on the input data. The server then sends request data including the prompt sentence to the generative AI model and receives the generated content data.

[1759] Step 5:

[1760] The generative AI model generates content based on the received prompt. Specifically, digital content such as text and images is generated according to the theme and style associated with the input prompt. The data generated is determined by the AI ​​model's calculations.

[1761] Step 6:

[1762] The server analyzes the content received from the generative AI model and converts it into a format to be provided to the user. The converted data is then output from the server to the user's device (smartphone).

[1763] Step 7:

[1764] Finally, the generated content is displayed on the user's smartphone. The user can review and use the generated content (e.g., blog post ideas and images). The user can also view the generated results and, if necessary, generate more content by entering additional prompts.

[1765] Through this series of steps, users can use their tokens to effectively utilize generative AI models to quickly generate high-quality content.

[1766] 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.

[1767] The present invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. Furthermore, this system is combined with an emotion engine that recognizes user emotions and has the function of adjusting the output of the generative AI model based on the user's emotions.

[1768] System Overview

[1769] The system consists of the following main components:

[1770] 1. Registration portal for AI companies

[1771] 2. Token Generation and Management Server

[1772] 3. Trading platform for users

[1773] 4. Generative AI Model Operation Server

[1774] 5. Emotion Engine

[1775] Registration portal for AI companies

[1776] This is a portal where users (AI companies) can register their rights to use generative AI models. Here, AI companies input their model information and make a request to tokenize their usage rights. The registered information is managed by a server.

[1777] Token Generation and Management Server

[1778] The server receives a tokenization request from the AI ​​company and creates and deploys a smart contract on the blockchain. Tokens are issued according to this smart contract, and token metadata (issue amount, price, terms of use, etc.) is managed. The issued tokens are published on the trading platform and can be bought and sold.

[1779] User-friendly trading platform

[1780] Users buy and sell tokens through this platform. When a user purchases tokens, the server checks the user's wallet balance and transfers the tokens if the necessary funds are available. When a sale request is made, the server publishes the request and mediates the transaction with the user who wishes to purchase.

[1781] Emotion Engine

[1782] The server is equipped with an emotion engine that recognizes the user's emotions. This engine analyzes emotions from the user's input data and interactions, and reflects this data in the operation of the generative AI model.

[1783] Generative AI model operation server

[1784] The server operates the generative AI model based on user requests and provides the results. It also receives emotion data from the emotion engine and optimizes the model's output based on the user's emotions.

[1785] Program processing

[1786] Emotion Engine Process

[1787] When a user accesses the platform and uses a generative AI model, the following occurs:

[1788] 1. A user logs into the platform and the emotion engine monitors the user's input and interactions.

[1789] 2. The emotion engine extracts emotional data from the user's text input, facial expressions, and voice.

[1790] 3. The emotion engine sends the analyzed emotion data to the server in real time.

[1791] 4. The server adjusts the parameters of the generative AI model based on the emotion data.

[1792] Emotion-driven generation

[1793] As a concrete example, consider a case where a user uses a token to access an image-generating AI model:

[1794] 1. The user accesses a dedicated portal and specifies the theme and style of the image they want to generate.

[1795] 2. The emotion engine analyzes the user's emotions (e.g., happy, sad, surprised).

[1796] 3. The server adjusts the settings of the image generation AI model based on the emotion data.

[1797] For example, if the user is recognized as "happy," the parameters are set so that an image with bright and vivid colors is generated.

[1798] 4. The image generation AI model generates a new image based on the adjusted parameters.

[1799] 5. The server receives the resulting image and provides it to the user.

[1800] 6. The server records the token usage history and calculates the applicable fees.

[1801] Specific examples

[1802] This explains the process by which a user trains a generative AI model using the emotion engine and uses tokens:

[1803] 1. A user logs into the portal and their emotions are monitored in real time by the emotion engine.

[1804] 2. If the user inputs a request for image generation and indicates an excited emotion, the emotion engine sends the emotion data to the server.

[1805] 3. The server changes the color and content settings of the image generation AI model based on the emotion data.

[1806] 4. The image generation AI model generates an image using parameters according to the user's emotions and provides the image to the user via the server.

[1807] 5. This allows users to get customized output tailored to their individual emotional state.

[1808] In this way, the system takes into account user sentiment and provides more personalized output from the generative AI model. It also efficiently manages token trading and usage history.

[1809] The processing flow will be explained below.

[1810] Emotion Engine Process

[1811] Collaboration with emotion engine

[1812] Step 1:

[1813] Users log in to the platform and access a dedicated portal.

[1814] Step 2:

[1815] The emotion engine monitors user input and interactions and collects emotion data in real time.

[1816] Step 3:

[1817] The emotion engine analyzes emotions from the user's character input, facial expression analysis, voice input, etc., and generates emotion data.

[1818] Step 4:

[1819] The emotion engine transmits the analyzed emotion data to the server.

[1820] Emotion-driven generation

[1821] Examples of using image generation AI models

[1822] Step 1:

[1823] The user logs into the specialized portal and opens a request form for image generation.

[1824] Step 2:

[1825] The user inputs the theme (e.g., "landscape" or "portrait") and style (e.g., "realistic" or "abstract") of the image they want to generate.

[1826] Step 3:

[1827] An emotion engine analyzes the user's current emotional state (e.g., "happy," "sad," "excited").

[1828] Step 4:

[1829] The emotion engine transmits emotion data to the server in real time.

[1830] Step 5:

[1831] The server adjusts the setting parameters of the generative artificial intelligence model based on the emotion data.

[1832] For example, if the user is expressing a "happy" emotion, the colors of the generated image are brightened and the overall tone is adjusted to a positive one.

[1833] Step 6:

[1834] The server sends the adjusted setting parameters to the image generation AI model.

[1835] Step 7:

[1836] The image generation AI model generates new images based on user requests and parameter settings from the server.

[1837] Step 8:

[1838] The image generated by the image generation AI model is sent back to the server.

[1839] Step 9:

[1840] The server transmits the generated image received to the user's terminal for providing it to the user.

[1841] Step 10:

[1842] The server records the token usage history and calculates the applicable fees.

[1843] Step 11:

[1844] The terminal will display the generated image and details about the token usage.

[1845] Specific examples

[1846] Actual user experience

[1847] Step 1:

[1848] Users hold tokens for the image-generating AI model and log in to the platform to use it.

[1849] Step 2:

[1850] A user inputs a request for image generation into the portal, specifying a theme such as "downtown sunset."

[1851] Step 3:

[1852] The emotion engine detects excited emotions based on the user's voice input and facial recognition data.

[1853] Step 4:

[1854] The emotion engine sends the analysis results to the server in real time.

[1855] Step 5:

[1856] The server adjusts the color and design settings of the image-generating AI model based on data from the emotion engine.

[1857] Step 6:

[1858] The server sends the adjustment parameters to the image generation AI model and instructs the model to generate an image.

[1859] Step 7:

[1860] The image generation AI model generates colorful and vibrant images according to the received parameters.

[1861] Step 8:

[1862] The image generation AI model sends the generated images to the server.

[1863] Step 9:

[1864] The server sends the generated image to the user's terminal and updates the token usage history.

[1865] Step 10:

[1866] The terminal displays a generated image of a colorful downtown sunset, along with token usage history and fee information.

[1867] In this way, by collecting user emotional data and reflecting it in the generative AI model, personalized results can be generated for each individual user. Token trading and usage history can also be managed efficiently.

[1868] Example 2

[1869] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1870] Conventional generative AI model systems lacked efficient methods for managing model usage rights and the ability to personalize output based on user sentiment. Furthermore, there was a need for an efficient and transparent system for the buying and selling of tokenized usage rights, record-keeping, and fee calculation.

[1871] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for tokenizing the right to use the generative AI model; means for issuing the token as a smart contract on a blockchain; means for providing an electronic platform for buying and selling the issued token; means for buying and selling the token through the electronic platform; means for receiving a request from the token holder to use the generative AI model; means for operating the generative AI model based on the request and providing the holder with the usage results; emotion recognition means for analyzing user emotions; and means for adjusting the parameters of the generative AI model based on the user's emotion data. This enables efficient management of the right to use the generative AI model and personalized output based on the user's emotion data. Furthermore, token buying and selling, history management, and fee calculation can be performed efficiently and transparently.

[1872] A "generative artificial intelligence model" is an artificial intelligence system that can automatically generate new data and information based on input data.

[1873] A "token" is a type of digital asset issued using blockchain technology that represents a specific right of use or value.

[1874] "Blockchain" is a secure, tamper-resistant digital distributed ledger technology and a data structure for continuously recording transaction history and information.

[1875] A "smart contract" refers to a contract or program that is automatically executed on a blockchain when certain conditions are met.

[1876] An "electronic platform" refers to an online service or system provided via a computer network, which serves as a foundation for users to carry out certain operations or transactions.

[1877] "Emotion recognition means" is a technology for analyzing data such as a user's facial expressions, voice, and text input, and identifying the user's emotional state.

[1878] "Means for adjusting parameters" refers to techniques for changing the operating settings of a system or model based on acquired data and conditions.

[1879] A "request" refers to a user's request to the system for a specific operation or service to be provided.

[1880] This invention provides a system that tokenizes the right to use a generative AI model and provides a platform where the tokens can be traded on the market. It also incorporates an emotion engine that recognizes user emotions and adjusts the output of the generative AI model based on the user's emotions.

[1881] Configuring Components

[1882] The system consists of the following main components:

[1883] 1. Registration portal for AI companies

[1884] 2. Token Generation and Management Server

[1885] 3. Trading platform for users

[1886] 4. Generative AI Model Operation Server

[1887] 5. Emotion Engine

[1888] Registration portal for AI companies

[1889] Users (AI companies) access a registration portal and register for the right to use a generative AI model. In this portal, companies enter model information (type, terms of use, fees, etc.) and request tokenization. The entered information is managed by the server.

[1890] Token Generation and Management Server

[1891] The server receives tokenization requests from AI companies, creates smart contracts on the blockchain, and issues tokens. The smart contracts contain metadata such as the token type, quantity, price, and terms of use. The issued tokens are published on the trading platform and can be bought and sold. This process ensures transparent and secure management of tokenized usage rights.

[1892] User-friendly trading platform

[1893] Users buy and sell tokens through this platform. When purchasing, the server checks the user's wallet balance and transfers the tokens to the user's wallet if the balance is sufficient. In the case of a sell request, the server also receives the request and mediates the transaction. This platform also has the function of managing token buying and selling history and fee calculation.

[1894] Emotion Engine

[1895] The emotion engine analyzes the user's input data (text, facial expressions, voice, etc.) and recognizes the user's emotions. The analysis results are sent to the server, which then uses the data to adjust the parameters of the generative AI model. For example, if the user expresses the emotion "happy," the settings are changed to brighten the color tone of the generated image.

[1896] Generative AI model operation server

[1897] The generative AI model operation server operates the generative AI model based on user requests. Specifically, it generates appropriate output according to the theme and style specified by the user. In this process, emotional data from the emotion engine is used to adjust the model parameters. The generated results are provided to the user via the server.

[1898] Specific examples

[1899] Consider the case where a user makes an image generation request:

[1900] 1. The user accesses a dedicated portal and enters the theme and style of the image they want to generate.

[1901] 2. The emotion engine analyzes user input and interactions to extract emotional data.

[1902] 3. The server adjusts the parameters of the image generation AI model based on the emotion data.

[1903] For example, if the user is recognized as "excited," the image color tone and content are set to be vivid.

[1904] 4. The image generation AI model generates a new image based on the adjusted parameters.

[1905] 5. The server receives the resulting image and provides it to the user.

[1906] This allows users to obtain customized output according to their individual emotional state. An example of a specific prompt could be a request such as "A beautiful view of the sunset." The system aims to personalize the output based on the user's emotions and provide more satisfying results.

[1907] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1908] Step 1:

[1909] Users (AI companies) access the registration portal and enter information about their generative AI models, including the company name, model type, terms of use, and fees.

[1910] Input: Company name, model type, terms of use, price, etc.

[1911] Output: Model registration request

[1912] The server receives this input data and stores it in a database.

[1913] Specific operations: Stores the information in the database and sends a notification to the user that registration is complete.

[1914] Step 2:

[1915] The user (AI company) sends a tokenization request.

[1916] Input: Tokenization request

[1917] Output: Instructions for generating a token

[1918] The server accepts the request and creates a smart contract on the blockchain.

[1919] Specific operation: The smart contract describes the type, quantity, price, and terms of use of the token, and then deploys it to the blockchain.

[1920] Step 3:

[1921] The server issues the tokens and publishes them on the trading platform.

[1922] Input: Smart contract, token metadata

[1923] Output: Issued tokens, public data on the trading platform

[1924] Specific operations: Issue tokens and process information disclosure to the trading platform.

[1925] Step 4:

[1926] A user accesses the trading platform and requests to purchase tokens.

[1927] Input: Token purchase request

[1928] Output: Token purchase successful message, wallet updated

[1929] The server checks the user's wallet balance and transfers the tokens to the user's wallet if the purchase conditions are met.

[1930] Specific operations: Check wallet balance, transfer tokens, and update transaction history.

[1931] Step 5:

[1932] A user requests the use of a generative artificial intelligence model.

[1933] Input: Request to use the generative AI model, prompt (e.g., "A beautiful view of the sunset")

[1934] Output: Usage results (generated images, etc.)

[1935] The server receives the request and launches the generative AI model.

[1936] Specific operation: Launches the generative AI model and generates data based on the specified prompt.

[1937] Step 6:

[1938] The emotion engine analyzes user input and interactions to extract emotional data.

[1939] Input: User text input, facial expression data, voice data

[1940] Output: Emotion data (e.g. "Excited")

[1941] Specific operations: Perform natural language processing, image analysis, and voice analysis to analyze input data and identify emotions.

[1942] Step 7:

[1943] The emotion engine sends the analysis results to the server in real time.

[1944] Input: Emotion data

[1945] Output: Instruction to transfer emotion data to the server

[1946] Specific operation: The emotion data transfer protocol is used to process the data sent to the server.

[1947] Step 8:

[1948] The server adjusts the parameters of the generative AI model based on the emotion data.

[1949] Input: Emotion data

[1950] Output: Adjusted generation parameters

[1951] Specific operation: Optimize the model parameters based on the emotion data and change the settings to generate output that corresponds to the emotion.

[1952] Step 9:

[1953] The generative artificial intelligence model generates an output based on the adjusted parameters.

[1954] Input: Adjusted generation parameters

[1955] Output: Generated results (e.g., image data)

[1956] Specific operation: The generative AI model generates new data (e.g., images or text) and returns the generated results to the server.

[1957] Step 10:

[1958] The server receives the generated results and provides them to the user.

[1959] Input: Generated result (e.g., image data)

[1960] Output: Data provided to the user

[1961] Specific operation: Processing is performed to display or provide the generated output data to the user through the user interface.

[1962] (Application example 2)

[1963] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1964] Conventional generative AI models provide uniform output without considering the user's emotional state, resulting in a lack of personalization for individual users. Furthermore, there is no mechanism in place to safely and efficiently buy and sell model usage rights on the market, leading to inappropriate usage and rights management issues. Furthermore, there is a lack of efficient means for managing usage history and calculating fees.

[1965] 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 tokenizing the usage rights of the generative AI model; means for issuing the tokens as smart contracts on the blockchain; means having an emotion engine that analyzes user emotions; means for adjusting the generative AI model based on emotion data analyzed by the emotion engine; means for providing a platform for buying and selling the issued tokens; means for receiving a request from the token holder to use the generative AI model; and means for operating the generative AI model based on the request and providing the holder with the usage results. This makes it possible to output a generative AI model personalized to the user's emotional state, and enables the safe and efficient buying and selling and management of usage rights.

[1966] A "generative artificial intelligence model" is a machine learning algorithm that generates new information and content based on data.

[1967] "Tokenization" refers to the technology or process that represents digital assets or rights as tokens and makes them tradable on the blockchain.

[1968] "Blockchain" is a distributed ledger technology, a system that records transaction history in a linked chain.

[1969] A "smart contract" is a program that runs automatically on a blockchain and expresses contract terms as program code.

[1970] An "emotion engine" is a technology or software that analyzes emotions from a user's facial expressions, voice, text input, etc., and reflects that data in a generative artificial intelligence model.

[1971] "Platform" means an online service or system that allows users to buy, sell, manage, and use Tokens.

[1972] A "token holder" is a person or company that owns a token that represents the right to use a particular generative AI model.

[1973] "Means for receiving a request" refers to an interface or system for receiving a request from a user to use a generative artificial intelligence model.

[1974] The embodiment of the present invention is configured as a system that operates by integrating various hardware and software. The following is a specific method for realizing this system.

[1975] System Overview

[1976] The system mainly consists of the following components:

[1977] 1. Generative AI Model

[1978] 2. Tokenization and Smart Contracts

[1979] 3. Sentiment Analysis Engine

[1980] 4. User Interface and Platform

[1981] 5. Server data processing and management functions

[1982] Hardware and software used

[1983] Hardware: Smartphone (camera, microphone, display), server (high-performance processor, storage)

[1984] software:

[1985] Sentiment analysis engine: Google Cloud Vision API, TensorFlow

[1986] Generative AI models: OpenAI GPT-4, DALL-E, music generative AI

[1987] Token Management and Trading: Ethereum Blockchain, Wallet Applications

[1988] User Interface:React Native

[1989] Program processing

[1990] 1. User login and initial settings

[1991] When a user logs in to the system, the camera and microphone are first activated for facial recognition and voice analysis. This data is analyzed in real time using the Google Cloud Vision API and TensorFlow to extract the user's emotional data. The emotional data is then sent to the server and added to the user's profile.

[1992] 2. Emotion data analysis and transmission

[1993] The emotion analysis engine analyzes the user's emotion data collected in real time, and the server adjusts the parameters of the generative AI model based on the analysis results.

[1994] 3. Token Issuance and Management

[1995] The server tokenizes the right to use the generative AI model and issues it as a smart contract on the blockchain. The issued tokens can be bought and sold through the user interface, and the token trading history and ownership status are managed by the Ethereum blockchain and wallet application.

[1996] 4. Content Creation and Delivery

[1997] When a user submits a request to use a specific generative AI model, the server generates a prompt based on the emotion data and applies it to the generative AI model. For example, the prompt might look like this:

[1998] The sentiment analysis engine detected "sad." Please generate a kind message to comfort the user when they feel sad.

[1999] The server uses this prompt to send a request to the generative artificial intelligence model and provides the generated content (text, images, music, etc.) to the user.

[2000] Specific examples

[2001] For example, if a user has had a fight with a friend and is feeling sad, the camera analyzes their facial expression and recognizes the emotion "sad." Based on this data, the server uses OpenAI GPT-4 to generate a kind message and then DALL-E to generate a comforting image. These contents are then delivered to the user's smartphone.

[2002] As described above, the present invention provides the output of a personalized generative artificial intelligence model that is tailored to the emotional state of the user, and realizes a system for safely and efficiently buying, selling, and managing usage rights.

[2003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2004] Step 1:

[2005] When a user logs in to a device, the device's camera and microphone are activated, allowing the user's face and voice data to be collected in real time. The inputs are camera footage and audio data, which are then analyzed for facial expressions and voice tone using the Google Cloud Vision API and TensorFlow. The output is the user's emotion data captured in real time.

[2006] Step 2:

[2007] The server receives emotion data sent from the device. This emotion data is stored in the user's profile and updates the database based on the user's emotional state. The input is the analyzed emotion data, which is then written to the database. The output is the updated user profile.

[2008] Step 3:

[2009] The user sends a request from their device to use a specific generative AI model. This request includes the type and theme of the content they want to generate. The input is the user's request data, and a prompt sentence is generated based on this. The output is the prompt sentence.

[2010] Step 4:

[2011] The server adjusts the settings of the generative AI model based on the emotional data analyzed by the emotion engine. For example, if the user's emotional state is analyzed as "sad," the parameters of the generative AI model are set to generate a comforting message. The inputs are the analyzed emotional data and the initial settings of the generative model, and the model parameters are adjusted based on these. The output is the adjusted generative AI model.

[2012] Step 5:

[2013] The server inputs a prompt sentence into the adjusted generative AI model, runs the model, and obtains generated content. Specifically, OpenAI GPT-4 generates text, DALL-E generates images, and music is generated using a music generation AI. The inputs are the prompt sentence and the adjusted generative AI model, and a wide variety of content is generated by the operation of the model. The output is the various generated content.

[2014] Step 6:

[2015] The server provides the generated content to the terminal. The user receives the generated content, such as text, images, and music, through the terminal. The input is the generated content from the server, which is provided to the user. The output is the content displayed on the user's terminal.

[2016] Step 7:

[2017] Through the token management system, users can buy and sell the right to use generative AI models. The server manages transactions and wallets on the blockchain, and updates token ownership status in response to user buy and sell requests. The inputs are buy and sell requests from users and token information, and the output is updated token ownership status and transaction history.

[2018] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2019] 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.

[2020] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2021] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2022] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2023] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2024] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2025] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2026] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2027] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2028] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2029] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2030] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2031] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2032] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2033] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2034] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2035] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2036] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2037] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2038] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2039] The following is further disclosed regarding the above embodiment.

[2040] (Claim 1)

[2041] A means to tokenize the right to use generative AI models,

[2042] means for issuing said tokens as smart contracts on a blockchain;

[2043] A means of providing a platform for buying and selling issued tokens;

[2044] means for buying and selling said tokens through said platform;

[2045] means for receiving a request for the token holder to use the generative artificial intelligence model;

[2046] means for operating the generative artificial intelligence model based on the request and providing the owner with a usage result;

[2047] A system including:

[2048] (Claim 2)

[2049] 2. The system according to claim 1, wherein the platform has a means for managing the trading history of the tokens and calculating fees.

[2050] (Claim 3)

[2051] 2. The system according to claim 1, further comprising means for recording the holder's usage history and automatically calculating applicable fees.

[2052] "Example 1"

[2053] (Claim 1)

[2054] A means to tokenize the right to use generative AI models,

[2055] means for generating and deploying said tokens as smart contracts on a blockchain;

[2056] A means to list issued tokens on a trading platform and make them available for buying and selling;

[2057] means for buying and selling said tokens through said trading platform;

[2058] means for receiving a request for the token holder to use the generative artificial intelligence model;

[2059] means for transmitting data to the generative artificial intelligence model based on the request and providing the generated results to the holder;

[2060] means for operating a generative artificial intelligence model based on the content of the request and providing the owner with the results of use;

[2061] means for notifying the token buyer or seller of the success of said transaction;

[2062] A system including:

[2063] (Claim 2)

[2064] 2. The system according to claim 1, wherein the platform has a means for managing the trading history of the tokens and calculating fees.

[2065] (Claim 3)

[2066] 2. The system according to claim 1, further comprising means for recording the holder's usage history and automatically calculating applicable fees.

[2067] "Application Example 1"

[2068] (Claim 1)

[2069] A means to tokenize the right to use generative AI models,

[2070] A means for issuing the tokens as smart contracts on a distributed ledger technology;

[2071] A means of providing a platform for buying and selling issued tokens;

[2072] means for buying and selling said tokens through said platform;

[2073] means for receiving a request for the token holder to use the generative artificial intelligence model;

[2074] means for operating the generative artificial intelligence model based on the request and providing the owner with a usage result;

[2075] a means for the usage result to include content related to the content distribution service;

[2076] A means for generating the content based on the tokens held by the user;

[2077] A system including:

[2078] (Claim 2)

[2079] 2. The system according to claim 1, wherein the platform has a means for managing the trading history of the tokens and calculating fees.

[2080] (Claim 3)

[2081] 2. The system according to claim 1, further comprising means for recording the holder's usage history and automatically calculating applicable fees.

[2082] "Example 2: Combining Emotion Engines"

[2083] (Claim 1)

[2084] A means to tokenize the right to use generative AI models,

[2085] means for issuing said tokens as smart contracts on a blockchain;

[2086] means for providing an electronic platform for buying and selling the issued tokens;

[2087] means for buying and selling said tokens through said electronic platform;

[2088] means for receiving a request for the token holder to use the generative artificial intelligence model;

[2089] means for operating the generative artificial intelligence model based on the request and providing the owner with a usage result;

[2090] emotion recognition means for analyzing the emotion of a user;

[2091] means for adjusting parameters of the generative artificial intelligence model based on emotion data of the user;

[2092] A system including:

[2093] (Claim 2)

[2094] 2. The system of claim 1, wherein the electronic platform has a means for managing the trading history of the tokens and calculating fees.

[2095] (Claim 3)

[2096] 2. The system of claim 1, further comprising means for recording the token holder's usage history and automatically calculating applicable fees.

[2097] "Application example 2 when combining emotion engines"

[2098] (Claim 1)

[2099] A means to tokenize the right to use generative AI models,

[2100] means for issuing said tokens as smart contracts on a blockchain;

[2101] A means of providing a platform for buying and selling issued tokens;

[2102] means for buying and selling said tokens through said platform;

[2103] means for receiving a request for the token holder to use the generative artificial intelligence model;

[2104] means for analyzing the emotions of a user, the means including an emotion engine;

[2105] means for adjusting a generative artificial intelligence model based on emotion data analyzed by the emotion engine;

[2106] means for operating the generative artificial intelligence model based on the request and providing the owner with a usage result;

[2107] A system including:

[2108] (Claim 2)

[2109] 2. The system according to claim 1, wherein the platform has a means for managing the trading history of the tokens and calculating fees.

[2110] (Claim 3)

[2111] 2. The system according to claim 1, further comprising means for recording the holder's usage history and automatically calculating applicable fees. [Explanation of symbols]

[2112] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to tokenize the right to use generative AI models, means for issuing said tokens as smart contracts on a blockchain; A means of providing a platform for buying and selling issued tokens; means for buying and selling said tokens through said platform; means for receiving a request for the token holder to use the generative artificial intelligence model; means for operating the generative artificial intelligence model based on the request and providing the owner with a usage result; A system including:

2. The system according to claim 1 , wherein the platform has a means for managing a trading history of the tokens and calculating a commission.

3. 2. The system according to claim 1, further comprising means for recording the holder's usage history and automatically calculating applicable fees.

Images