system

Abstract

We provide a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. [Solution] The system includes a user registration means accessible by users via a web browser, a login means for authenticating registered user information accessible by users, a tool provision means for constructing an AI model based on data provided by the user, an evaluation means for evaluating the constructed AI model, a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results, a sales information input means for selling the generated AI model on the platform, a sales page generation means for generating and publishing a sales page based on the sales information, a profit distribution means for distributing profits earned from the sold AI model to users, and a report generation means for generating a profit revenue report.

Description

[Technical Field]

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

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

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

[0004] In recent years, the development and use of artificial intelligence (AI) models has progressed rapidly, but there are limited ways for ordinary individuals to create AI models that mimic their own thinking and make money by publishing those models. Furthermore, there is no comprehensive platform that allows users to easily go through the entire process of creating, evaluating, modifying, and selling AI models. This poses a challenge, making it difficult for ordinary users with low technical skills to use AI technology. [Means for solving the problem]

[0005] The present invention provides a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. Specifically, the system includes a user registration means accessible by users via a web browser, a login means for authenticating registered user information, a tool provision means for building AI models based on data provided by users, an evaluation means for evaluating the built AI models, a correction and retraining means for modifying and retraining the AI ​​models based on the evaluation results, a sales information input means for selling the created AI models on a platform, a sales page generation means for generating and publishing sales pages based on the sales information, a profit distribution means for distributing profits earned from sold AI models to users, and a report generation means for generating a profit report on the profits. This allows ordinary users to easily create AI models and earn revenue by selling them.

[0006] "User registration means" is a function that allows a user to access the system, enter registration information, and create a new account.

[0007] The "login means" is a function that authenticates a user using registered user information and allows the user to access the system if the authentication is successful.

[0008] "Tool provision means" is a function that allows users to input data based on their own thoughts and build an AI model.

[0009] "Evaluation means" is a function that measures the performance and accuracy of the constructed AI model and displays the results.

[0010] "Modification and retraining means" is a function for modifying the model based on the evaluation results of the AI ​​model and retraining it.

[0011] The "sales information input means" is a function that allows users to input the information (price, description, etc.) necessary to sell a completed AI model.

[0012] The "sales page generation means" is a function that automatically generates a sales page for an AI model based on sales information and publishes it on the platform.

[0013] The "profit distribution means" is a function that allows the system to calculate and distribute the profits earned from the sold AI models to users.

[0014] The "report generation means" is a function that aggregates the sales status and revenue of the AI ​​model and displays it to the user as a revenue report. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention describes a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling the models. This system includes the following components:

[0037] System configuration

[0038] 1. User registration method

[0039] 2. Login method

[0040] 3. Tool Provision Method

[0041] 4. Evaluation Methods

[0042] 5. Corrective and retraining measures

[0043] 6. Sales information input method

[0044] 7. Sales Page Generation Method

[0045] 8. Profit distribution means

[0046] 9. Report Generation Methods

[0047] Detailed system description

[0048] User registration method

[0049] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0050] Login method

[0051] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0052] Tool provision method

[0053] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. After inputting the data, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[0054] Evaluation methods

[0055] The server evaluates the generated AI model and notifies the user of the results, which are then displayed on the device for the user to review.

[0056] Corrective and retraining measures

[0057] The user inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The device sends the modified data to the server, which then performs retraining. The generated new model is then reevaluated, and the results are notified to the user.

[0058] Sales information input method

[0059] To sell a completed AI model, the user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0060] Sales page generation method

[0061] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0062] profit distribution means

[0063] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[0064] Report Generation Method

[0065] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0066] Specific examples

[0067] As a specific example of use, consider a scenario in which a user creates and sells an AI model that reflects their own expertise. For example, suppose a user uses their knowledge of finance to create an AI model for stock price prediction. The user inputs data through the system to generate the AI ​​model. The generated model is evaluated and modified as necessary. The final completed AI model is sold on the platform, and users can earn revenue by purchasing it from other users.

[0068] As described above, the present invention is an excellent system that allows users to easily create AI models and earn revenue through a series of processes including evaluation, modification, retraining, and sales.

[0069] The processing flow will be explained below.

[0070] User Registration and Login

[0071] Step 1:

[0072] The user accesses the system via a web browser and clicks the "Register" button.

[0073] Step 2:

[0074] The terminal displays a user registration form.

[0075] Step 3:

[0076] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[0077] Step 4:

[0078] The terminal transmits the entered registration information to the server.

[0079] Step 5:

[0080] The server stores the received registration information in a database and automatically sends a confirmation email.

[0081] Step 6:

[0082] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[0083] Step 7:

[0084] The user enters their email address and password on the login page and clicks the "Login" button.

[0085] Step 8:

[0086] The terminal sends the login information to the server.

[0087] Step 9:

[0088] The server checks the database to see if the entered email address and password match.

[0089] Step 10:

[0090] If the authentication is successful, the server displays the user's dashboard on the terminal.

[0091] Providing AI creation tools

[0092] Step 11:

[0093] The user clicks the "Create AI" button on the dashboard.

[0094] Step 12:

[0095] The device displays the interface of the AI ​​creation tool.

[0096] Step 13:

[0097] The user inputs data based on their own thoughts (e.g., text data) and clicks the "Save" button.

[0098] Step 14:

[0099] The device temporarily stores the entered data and provides a "Start Training" button.

[0100] Training an AI model

[0101] Step 15:

[0102] The user clicks the "Start Training" button.

[0103] Step 16:

[0104] The terminal transmits the input data to the server.

[0105] Step 17:

[0106] The server begins training the AI ​​model based on the received data.

[0107] Step 18:

[0108] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[0109] Evaluating and correcting AI models

[0110] Step 19:

[0111] The server transmits the evaluation results to the terminal and notifies the user.

[0112] Step 20:

[0113] The terminal displays the evaluation results to the user.

[0114] Step 21:

[0115] The user inputs data to modify the model based on the evaluation results and clicks the "Retrain" button.

[0116] Step 22:

[0117] The terminal transmits the corrected data to the server.

[0118] Step 23:

[0119] The server retrains the modified model and recalculates the evaluation results.

[0120] Step 24:

[0121] The server sends the new evaluation results to the terminal and notifies the user.

[0122] Selling AI models

[0123] Step 25:

[0124] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[0125] Step 26:

[0126] The terminal transmits the sales information to the server.

[0127] Step 27:

[0128] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[0129] Step 28:

[0130] The server notifies the terminal that the sales page has been published and displays it to the user.

[0131] Profit sharing and reporting

[0132] Step 29:

[0133] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[0134] Step 30:

[0135] The server processes the payment through a payment system.

[0136] Step 31:

[0137] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[0138] Step 32:

[0139] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0140] This completes a series of steps that allows users to create and sell AI models that mimic their own thinking and earn revenue.

[0141] Example 1

[0142] 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."

[0143] In conventional AI model generation systems, the process required for individuals to create AI models that reflect their own thinking and sell them to generate revenue was complex and often required specialized knowledge. Furthermore, the process of evaluating, correcting, retraining, and selling the models was not efficient, placing a heavy burden on users and making it difficult to monetize.

[0144] 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.

[0145] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing a generative AI model based on data provided by the user; an evaluation means for evaluating the constructed generative AI model; a correction and retraining means for correcting and retraining the generative AI model based on the evaluation results; a sales information input means for selling the generated generative AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold generative AI model to users; and a report generation means for generating a profit report of the profits. This enables individuals to easily create, modify, and sell AI models and earn profits thereby.

[0146] The "user registration means" is a means by which a user accesses the system via a web browser and registers with the system by entering information such as name, email address, and password.

[0147] "Login means" refers to the means by which a registered user logs into the system by entering an email address and password.

[0148] "Tool provision means" refers to a means of providing an interface and functionality for building a generative AI model based on data provided by the user.

[0149] The "evaluation means" is a means for evaluating the constructed generative AI model and notifying the user of the results.

[0150] "Modification and retraining means" refers to a means for modifying the generative AI model based on the evaluation results and retraining it.

[0151] The "sales information input means" is a means for a user to input a price and description for selling a generated AI model.

[0152] The "sales page generation means" is a means for automatically generating a sales page based on sales information and publishing it on the platform.

[0153] "Profit sharing means" means a means for calculating and distributing profits earned from the sold generative AI models to users.

[0154] The "report generation means" is a means for generating profit revenue reports and displaying them on the user's dashboard.

[0155] "Payment processing means" refers to a means for processing payments from potential purchasers regarding the sale of AI models.

[0156] The "notification means" is a means for notifying the user's device of the evaluation results of the AI ​​model generated by the user.

[0157] The present invention is a system that allows users to create AI models that mimic their own thinking and earn revenue by selling those models. This system is implemented using the following hardware and software.

[0158] Hardware and software used

[0159] Hardware

[0160] Server: Includes database, AI training and evaluation system, and payment processing system

[0161] Terminal (user device): PC, smartphone, tablet, etc.

[0162] software

[0163] Web browser

[0164] Database management system (e.g., MySQL (registered trademark))

[0165] AI training platform (e.g., TENSORFLOW (registered trademark), PyTorch)

[0166] Program processing

[0167] User registration method

[0168] The user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters information such as name, email address, and password into the form and submits the registration information. The server saves the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0169] Login method

[0170] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then authenticates the user by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0171] Tool provision method

[0172] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. The user then clicks the "Start Training" button to send the data to the server. The server uses the received data to start training the AI ​​model and saves the generated model.

[0173] Evaluation methods

[0174] The server evaluates the generated AI model and notifies the user of the evaluation results, which are then displayed on the device for the user to review.

[0175] Corrective and retraining measures

[0176] The user modifies the model based on the evaluation results and retrains it by inputting new data. The device then sends the modified data to the server, which then retrains it. The generated new model is then reevaluated, and the results are notified to the user.

[0177] Sales information input method

[0178] The user clicks the "Sell" button and enters the price and description of the AI ​​model, and the device sends this information to the server.

[0179] Sales page generation method

[0180] The server automatically generates a sales page based on the received sales information and publishes it on the platform, and a publication notification is sent to the user.

[0181] profit distribution means

[0182] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system, calculates the profit, and distributes it to the user after deducting a commission fee.

[0183] Report Generation Method

[0184] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0185] Specific examples

[0186] Example of user registration procedure

[0187] The user accesses the system's URL and clicks the "Register" button on the page that appears. The terminal displays a form for entering name, email address, and password. The user enters the information and clicks the "Submit" button. The server receives the entered information and stores it in a database. The server automatically sends a confirmation email and displays a message to the user saying "Registration completed."

[0188] Specific examples of AI model creation

[0189] The user clicks the "Create AI" button on the dashboard. The terminal displays the AI ​​creation interface, allowing the user to enter text data. The user enters text data related to asset management into the system and clicks the "Start Training" button. The server receives the entered data and begins training the AI ​​model. The server saves the trained model and notifies the user of the generated results.

[0190] Prompt Sentence Examples

[0191] "Use your financial knowledge to create and sell an AI model for predicting stock prices. Please provide a detailed description of the steps from user registration to sales."

[0192] As described above, the present invention is a system that enables individuals to easily create, modify, and sell AI models, thereby earning revenue.

[0193] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0194] Step 1:

[0195] A user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters their name, email address, and password. The entered information is sent to the server, which stores the received information in a database and automatically sends a confirmation email to complete the registration. The input is user information (name, email address, password), and the output is the user information stored in the database and a confirmation email.

[0196] Step 2:

[0197] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then references the database to authenticate the user. If authentication is successful, the server displays the user's dashboard. The input is login information (email address, password), and the output is the authentication result and the dashboard display.

[0198] Step 3:

[0199] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, and the user inputs data (e.g., text data) that mimics their thoughts. Once the input is complete, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model using the received data and saves the generated model. The input is the training data (text data), and the output is the generated AI model.

[0200] Step 4:

[0201] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal so that the user can check them. The input is the generated AI model, and the output is the evaluation results.

[0202] Step 5:

[0203] The user modifies the model based on the evaluation results and inputs new data. When the user clicks the "Start Training" button, the device sends the modified data to the server. The server then retrains, reevaluates the generated new model, and notifies the user of the results. The input is the modified data, and the output is the reevaluated AI model and its evaluation results.

[0204] Step 6:

[0205] The user clicks the "Sell" button and enters the price and description of the AI ​​model. The device sends this information to the server. The server automatically generates a sales page based on the received sales information and publishes it on the platform. A publication notification is sent to the user. The input is sales information (price, description), and the output is the published sales page.

[0206] Step 7:

[0207] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. The server calculates profits and distributes them to the user after deducting a fee. The input is the purchase transaction, and the output is payment processing and profit distribution.

[0208] Step 8:

[0209] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard. The input is the purchase data and profit sharing data, and the output is the revenue report.

[0210] (Application example 1)

[0211] 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."

[0212] Conventional AI model creation systems lacked the functionality to allow users to easily create models that reflect their own thoughts and preferences, and to sell those models to other users. Furthermore, they lacked the functionality to utilize the created AI models for content recommendations, which prevented them from improving user convenience. This made it difficult to provide users with a personalized experience.

[0213] 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.

[0214] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a recommendation means for using the generated AI model to recommend content; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; and a report generation means for generating a profit report. This enables users to easily create AI models that reflect their own thoughts and preferences, use the models to recommend personalized content, and sell them to other users.

[0215] The "user registration means" is a means by which a user accesses the system via a web browser, enters information such as name, email address, and password, and registers.

[0216] A "login means" is a means for a user to enter an email address and password for authentication when accessing a system.

[0217] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[0218] "Evaluation means" refers to a means for evaluating the constructed AI model and notifying the user of the evaluation results.

[0219] "Modification and retraining measures" are measures to modify the AI ​​model based on the evaluation results and retrain it using new data.

[0220] A "recommendation method" is a method of using the generated AI model to recommend content that matches the user's preferences.

[0221] The "sales information input means" is a means for inputting information such as price and description in order to sell the generated AI model on the platform.

[0222] The "sales page generation means" is a means for automatically generating and publishing a sales page based on sales information.

[0223] A "profit distribution method" is a method for calculating profits earned from sold AI models and distributing them to users after deducting fees.

[0224] The "report generating means" is a means for generating a profit income report and providing it to the user.

[0225] This invention provides a system that allows users to create an AI model that reflects their own thoughts and preferences, and use that model for content recommendation and sales. Specific embodiments of this system are described below.

[0226] The system's components are mainly related to the server, terminals, and users. Specifically, it consists of a user registration means, a login means, a tool provision means, an evaluation means, a correction and retraining means, a recommendation means, a sales information input means, a sales page generation means, a profit distribution means, and a report generation means.

[0227] A user accesses the system via a web browser, enters their name, email address, password, etc. into the user registration form, and submits it to the server. The server saves the registration information in a database and automatically sends a confirmation email to complete the user registration. Next, the user enters their email address and password on the login page, and the server authenticates the information by referencing it in the database. If authentication is successful, the user's dashboard is displayed.

[0228] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data reflecting their thoughts and preferences (e.g., music preference list, viewing history, etc.). After inputting the data, the user clicks the "Start Training" button, and the server begins training the AI ​​model based on the received data and saves the generated model.

[0229] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal and can be viewed by the user. The user then inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The server retrains the AI ​​model based on the modified data, reevaluates the generated new model, and notifies the user of the results.

[0230] The generated AI model is used to recommend content (music, videos, articles, etc.) that matches the user's preferences. The recommendation tool allows users to receive personalized content recommendations. To sell the completed AI model, users click the "Sell" button and enter sales information such as price and description. This information is sent to the server, and a sales page is automatically generated and published.

[0231] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit and distributes the remainder to the user after deducting a certain fee. The server also generates a revenue report based on the purchase data and profit distribution details, and displays it on the user's dashboard.

[0232] As a concrete example, consider the case where a user creates a "music recommendation AI model." The user inputs their favorite music list and listening history, and trains the AI ​​model based on that data. The completed model recommends new music that matches the user's tastes. An example of a prompt sentence would be, "The user inputs their favorite music list and trains the AI ​​model based on that data. The completed model recommends new music that is close to the user's tastes."

[0233] The hardware used is a server (e.g., Amazon Web Services, Google® Cloud Platform), and the software used is a web framework (e.g., Flask), a database (e.g., MySQL, PostgreSQL), and a library for training AI models (e.g., TensorFlow, PyTorch).

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] A user accesses the system via a web browser, enters information such as name, email address, and password into the user registration form, and sends it to the server. The server receives this information and stores it in a database. In this process, the input is the user's personal information, and the output is the registration information stored in the database.

[0237] Step 2:

[0238] A user enters their email address and password on the login page and clicks the login button. The server checks this information against the database and, if authentication is successful, displays the user's dashboard. The input is an email address and password, and the output is the authentication result and the display of the dashboard.

[0239] Step 3:

[0240] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data that reflects their thoughts and preferences (e.g., music preference list, listening history, etc.). The input is the user's data, and the output is the transmission of the data to the server.

[0241] Step 4:

[0242] When the user clicks the "Start Training" button, the server starts training the AI ​​model based on the received data. Libraries such as TensorFlow and PyTorch are used for training. In this process, the input is the user data, and the output is the generated AI model.

[0243] Step 5:

[0244] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation uses model accuracy and performance indicators. The input is the generated AI model, and the output is a notification of the evaluation results.

[0245] Step 6:

[0246] The user inputs data to modify the model based on the evaluation results and performs retraining. The server receives the modified data and performs retraining. In this process, the input is the modified data and the output is an improved AI model.

[0247] Step 7:

[0248] The generated AI model is used to recommend content (e.g., music, videos, articles, etc.) that matches the user's preferences. The server uses a recommendation algorithm to extract content and suggest it to the user. The input is the generated AI model, and the output is the recommended content.

[0249] Step 8:

[0250] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description. The server receives this information and automatically generates and publishes a sales page. The input is the sales information, and the output is the published sales page.

[0251] Step 9:

[0252] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once the payment is completed, the server calculates the profit, deducts the fee, and distributes the remainder to the user. The input is the purchase information, and the output is the payment processing result and the distribution of the profit.

[0253] Step 10:

[0254] The server generates a revenue report based on the purchase data and profit sharing information and displays it on the user's dashboard. The input is the purchase data and profit sharing information, and the output is the revenue report.

[0255] 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.

[0256] This invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thoughts and earn revenue by selling those models, and includes the following components:

[0257] System configuration

[0258] 1. User registration method

[0259] 2. Login method

[0260] 3. Tool Provision Method

[0261] 4. Evaluation Methods

[0262] 5. Corrective and retraining measures

[0263] 6. Sales information input method

[0264] 7. Sales Page Generation Method

[0265] 8. Profit distribution means

[0266] 9. Report Generation Methods

[0267] 10. Emotion Engine

[0268] Detailed system description

[0269] User registration method

[0270] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0271] Login method

[0272] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0273] Tool provision method

[0274] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics their thoughts. After inputting the data, the user clicks the "Save" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[0275] Emotion Engine

[0276] The server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine uses natural language processing technology to analyze the user's emotions and adjusts the training data for the AI ​​model based on the analysis results. For example, if the data entered by the user contains a lot of positive emotions, the AI ​​model will be trained to emphasize positive data based on that information.

[0277] Evaluation methods

[0278] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[0279] Corrective and retraining measures

[0280] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[0281] Sales information input method

[0282] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0283] Sales page generation method

[0284] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0285] profit distribution means

[0286] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[0287] Report Generation Method

[0288] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0289] Specific examples

[0290] As a specific example of use, consider a scenario in which a user uses the emotion engine to create an AI model that reflects their own expertise. For example, suppose a user creates an AI model based on customer service emotion analysis. The user inputs customer text data through the system, and the emotion engine analyzes the data to recognize positive or negative emotions. The AI ​​model is trained based on this emotion information, enabling it to respond quickly to changes in customer emotion. The completed AI model can be sold on the platform, and users can earn revenue when other users purchase it.

[0291] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[0292] The processing flow will be explained below.

[0293] User registration and login process

[0294] Step 1:

[0295] The user accesses the system via a web browser and clicks the "Register" button.

[0296] Step 2:

[0297] The terminal displays a user registration form.

[0298] Step 3:

[0299] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[0300] Step 4:

[0301] The terminal transmits the entered registration information to the server.

[0302] Step 5:

[0303] The server stores the received registration information in a database and automatically sends a confirmation email.

[0304] Step 6:

[0305] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[0306] Step 7:

[0307] The user enters their email address and password on the login page and clicks the "Login" button.

[0308] Step 8:

[0309] The terminal sends the login information to the server.

[0310] Step 9:

[0311] The server checks the database to see if the entered email address and password match.

[0312] Step 10:

[0313] If the authentication is successful, the server displays the user's dashboard on the terminal.

[0314] Processing related to the provision of AI creation tools

[0315] Step 11:

[0316] The user clicks the "Create AI" button on the dashboard.

[0317] Step 12:

[0318] The device displays the interface of the AI ​​creation tool.

[0319] Step 13:

[0320] The user inputs text data based on their own thoughts and clicks the "Save" button.

[0321] Step 14:

[0322] The device temporarily stores the entered data and provides a "Start Training" button.

[0323] Processing for training AI models

[0324] Step 15:

[0325] The user clicks the "Start Training" button.

[0326] Step 16:

[0327] The terminal transmits the input data to the server.

[0328] Step 17:

[0329] The server begins training the AI ​​model based on the received data.

[0330] Step 18:

[0331] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[0332] Emotion engine processing

[0333] Step 19:

[0334] The server passes the data entered by the user to the emotion engine, which recognizes the emotion.

[0335] Step 20:

[0336] The emotion engine uses natural language processing technology to analyze the user's emotions from the input data.

[0337] Step 21:

[0338] The emotion engine passes the analysis results to a server, which generates instructions for adjusting the training data for the AI ​​model.

[0339] AI model evaluation and correction process

[0340] Step 22:

[0341] The server notifies the user based on the evaluation results and emotion engine information.

[0342] Step 23:

[0343] The terminal displays the evaluation results to the user.

[0344] Step 24:

[0345] The user modifies the model based on the evaluation results and clicks the "Retrain" button.

[0346] Step 25:

[0347] The terminal transmits the corrected data to the server.

[0348] Step 26:

[0349] The server retrains the modified AI model and calculates the evaluation results again.

[0350] Step 27:

[0351] The server sends the new evaluation results to the terminal and notifies the user.

[0352] Processing of AI model sales

[0353] Step 28:

[0354] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[0355] Step 29:

[0356] The terminal transmits the sales information to the server.

[0357] Step 30:

[0358] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[0359] Step 31:

[0360] The server notifies the terminal that the sales page has been published and displays it to the user.

[0361] Profit sharing and reporting process

[0362] Step 32:

[0363] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[0364] Step 33:

[0365] The server processes the payment through a payment system.

[0366] Step 34:

[0367] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[0368] Step 35:

[0369] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0370] This completes a series of steps that allows users to use the emotion engine to create and sell AI models that mimic their own thoughts and earn revenue.

[0371] Example 2

[0372] 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."

[0373] Conventional AI model creation and sales systems have had the problem that it is difficult for users to create models that take into account their own thoughts and emotions. It is also difficult for users to evaluate the emotional balance of the AI ​​models they have created. Furthermore, the process of selling the created models and earning revenue is cumbersome. There was a need to solve these issues and provide a more user-friendly and efficient system.

[0374] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0375] In this invention, the server includes a user registration means accessible by a user via a web browser, a login means for authenticating registered user information accessible by the user, a tool provision means for constructing an AI model based on data provided by the user, an evaluation means for evaluating the constructed AI model, a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results, a sales information input means for selling the generated AI model on the platform, a sales page generation means for generating and publishing a sales page based on the sales information, a profit distribution means for distributing profits earned from the sold AI model to users, a report generation means for generating a profit report, and an emotion engine for analyzing emotions using natural language processing. This enables users to create, evaluate, sell, and distribute profits from AI models that take emotions into account.

[0376] The "user registration means" is a function that allows a user to access the system via a web browser, enter their own information, and register with the system.

[0377] The "login means" is a function that allows a user to access the system based on registered user information.

[0378] "Tool provision means" is a function that provides interfaces and tools for building AI models based on data provided by the user.

[0379] "Evaluation means" is a function for evaluating the performance and quality of the constructed AI model.

[0380] "Modification and retraining means" is a function for modifying the AI ​​model based on the evaluation results and retraining it.

[0381] The "sales information input means" is a function for inputting information for selling the generated AI model.

[0382] The "sales page generation means" is a function for automatically generating and publishing a sales page based on input sales information.

[0383] "Profit distribution means" is a function for distributing profits earned from sold AI models to users.

[0384] The "report generation means" is a function for generating a profit income report and providing it to the user.

[0385] The "emotion engine" is a function that uses natural language processing to analyze the emotions in input data and optimize the training data for AI models.

[0386] The present invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. Specific embodiments are described below.

[0387] System Components

[0388] This system mainly consists of the following components:

[0389] 1. User registration method

[0390] 2. Login method

[0391] 3. Tool Provision Method

[0392] 4. Evaluation Methods

[0393] 5. Corrective and retraining measures

[0394] 6. Sales information input method

[0395] 7. Sales Page Generation Method

[0396] 8. Profit distribution means

[0397] 9. Report Generation Methods

[0398] 10. Emotion Engine

[0399] User registration method

[0400] The user accesses the system via a web browser (e.g., GOOGLE CHROME (registered trademark), Mozilla Firefox) and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as their name, email address, and password into the form, which is then sent to the server. The server stores the received registration information in a database (e.g., MySQL, PostgreSQL), automatically sends a confirmation email, and registration is complete.

[0401] Login method

[0402] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0403] Tool provision method

[0404] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data that mimics their thoughts (e.g., text data such as "The weather is very nice today, and I'm feeling very good."). After inputting the data, the user clicks the "Save" button to send the data to the server. The server uses the received data to begin training an AI model using natural language processing tools (e.g., NLTK, spaCy) and machine learning tools (e.g., TensorFlow, PyTorch), and stores the generated model in a database.

[0405] Emotion Engine

[0406] A distinctive feature of this system is that the server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine analyzes the user's emotions using natural language processing techniques (e.g., BERT, GPT-3 (registered trademark)) and adjusts the training data for the AI ​​model based on the analysis results.

[0407] Evaluation methods

[0408] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[0409] Corrective and retraining measures

[0410] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[0411] Sales information input method

[0412] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0413] Sales page generation method

[0414] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0415] profit distribution means

[0416] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe). Once payment is completed, the server calculates the profit and distributes the remainder to the user after deducting a certain fee.

[0417] Report Generation Method

[0418] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0419] Specific examples

[0420] Consider a scenario where a user is creating an AI model based on customer service sentiment analysis. The user inputs customer text data (e.g., "The customer is satisfied with the service") through the system, and the sentiment engine analyzes the data to recognize positive or negative sentiment. The AI ​​model is trained based on this emotional information and can quickly respond to changes in customer sentiment. The completed AI model can be sold on the platform, and other users can purchase it, generating revenue for the user.

[0421] Prompt Sentence Examples

[0422] "Please enter text data. Example: 'The weather is very nice today and I'm feeling great.'"

[0423] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[0424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0425] Processing Steps

[0426] Step 1: User Registration

[0427] The user opens a web browser to the system's home page and clicks the "Register" button.

[0428] The terminal displays a user registration form, where the user enters information such as name, email address, and password, and clicks the "Submit" button.

[0429] The server receives the entered information, stores it in a database, and automatically sends a confirmation email to notify the user that their registration is complete.

[0430] Input: User's name, email address, and password

[0431] Data processing: Save input information to a database

[0432] Output: Sending a confirmation email and notifying the completion of registration

[0433] Step 2: Log in

[0434] The user enters their email address and password on the login page and clicks the "Login" button.

[0435] The terminal sends the entered information to the server, which then refers to a database and performs authentication.

[0436] If authentication is successful, the server displays the user's dashboard.

[0437] Input: Email address, Password

[0438] Data calculation: Authentication in the database based on input information

[0439] Output: Display dashboard

[0440] Step 3: Creating an AI model

[0441] The user clicks the "Create AI" button on the dashboard.

[0442] The device displays an interface for creating AI, and the user inputs data that mimics their thoughts. For example, they input text data such as, "The weather is very nice today, and I'm in a great mood."

[0443] The user clicks the "Save" button to send the data to the server.

[0444] The server uses natural language processing tools to analyze the text based on the received data and begins training the AI ​​model. The generated model is then stored in a database.

[0445] Input: User's text data

[0446] Data processing: Text analysis using natural language processing

[0447] Data Computing: Generating training data and training AI models

[0448] Output: Save the trained AI model

[0449] Step 4: Analysis by the Emotion Engine

[0450] The server inputs the training data into the emotion engine and performs emotion analysis.

[0451] The sentiment engine uses natural language processing techniques to identify sentiment within the data and calculate the percentage of positive and negative sentiment.

[0452] Based on the results of the sentiment analysis, the server optimizes the training data for the AI ​​model.

[0453] Input: Training data

[0454] Data Computation: Extracting Emotional Information through Sentiment Analysis

[0455] Output: Optimized training data

[0456] Step 5: Evaluate the model

[0457] The server performs evaluation tests on the generated AI model.

[0458] The evaluation results are displayed on the terminal, allowing the user to check the evaluation results.

[0459] Emotional balance is also evaluated using an emotion engine.

[0460] Input: AI model

[0461] Data calculation: Evaluation test administration and emotional balance measurement

[0462] Output: Display of evaluation results

[0463] Step 6: Modify and retrain the model

[0464] If the user is not satisfied with the evaluation results, he or she can re-enter the corrected data and click the "Retrain" button.

[0465] The terminal transmits the corrected data to the server.

[0466] The server retrains the AI ​​model based on the new data and generates a new model.

[0467] The evaluation results for the new model will be displayed on the device again.

[0468] Input: Correction data

[0469] Data Calculation: Retraining

[0470] Output: Re-evaluated AI model and results display

[0471] Step 7: Enter sales information

[0472] To sell a completed AI model, users click the "Sell" button and enter sales information such as price and description.

[0473] This information is sent to a server and recorded in a database.

[0474] Input: Sales information such as price, description, etc.

[0475] Data processing: Sales information stored in a database

[0476] Output: Sales information stored in a database

[0477] Step 8: Generate a sales page

[0478] The server automatically generates a sales page based on the entered sales information.

[0479] Once the sales page is published, the server notifies the user.

[0480] Input: Sales information

[0481] Data processing: Sales page generation

[0482] Output: Published sales page and notification

[0483] Step 9: Profit sharing

[0484] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe).

[0485] Once payment is completed, the server deducts the fee, calculates the profit, and distributes the remainder to the user.

[0486] Input: Purchase Information

[0487] Data calculation: settlement processing and profit calculation

[0488] Output: Profit sharing

[0489] Step 10: Generate reports

[0490] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0491] Input: Purchase data, profit sharing information

[0492] Data Processing: Report Generation

[0493] Output: View the revenue report

[0494] (Application example 2)

[0495] 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."

[0496] In modern e-commerce, it is difficult for users to select products that best suit their preferences and needs from the wide variety of products available. This is particularly true when review information is scattered and often lacks reliability. Furthermore, when users make purchasing decisions based on product reviews, it is difficult to accurately reflect the impact of the review content on the user's emotions. Therefore, it is important to improve the user experience and stimulate purchasing motivation by suggesting products that are appropriate for each user based on sentiment analysis.

[0497] The identification processing by the identification 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: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; a report generation means for generating a profit report; a sentiment analysis means for analyzing the sentiment of product reviews when the user views them; and a product suggestion means for making customized product suggestions to the user based on the sentiment analysis results. This allows users to receive product suggestions that match their preferences and needs through the sentiment analysis of reviews, thereby improving their purchasing experience.

[0498] The "user registration means" is a means by which a user accesses the system via a web browser and registers by entering personal information such as name, email address, and password.

[0499] "Login means" refers to the means by which a user accesses the system using a registered email address and password and is authenticated.

[0500] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[0501] "Evaluation means" refers to a means for evaluating the constructed AI model and providing the evaluation results to the user.

[0502] "Modification and retraining means" refers to a means of modifying an AI model based on the evaluation results and retraining it.

[0503] The "sales information input means" is a means for inputting information for selling the generated AI model on the platform.

[0504] The "sales page generation means" is a means for automatically generating and publishing a sales page based on input sales information.

[0505] A "profit distribution method" is a method for distributing profits earned from sold AI models to users after deducting a certain fee.

[0506] The "report generating means" is a means for generating a profit income report and providing it to the user.

[0507] The "sentiment analysis means" is a means for analyzing the sentiment of a product review when the user views the review and determining whether the sentiment is positive or negative.

[0508] The "product suggestion means" is a means for making customized product suggestions to the user based on the emotion analysis results.

[0509] The present invention combines an emotion engine with a system that allows users to create AI models that mimic their own thoughts and sell those models. Specific embodiments of the present invention will be described below.

[0510] 1. System Overview

[0511] This system includes means for user registration, login, tool provision, evaluation, correction and retraining, sales information input, sales page generation, profit distribution, report generation, sentiment analysis, and product suggestion. By using this system, users can easily create and sell AI models that reflect their own emotions.

[0512] 2. Hardware and Software Used

[0513] Hardware: Smartphone (iOS, ANDROID (registered trademark))

[0514] software:

[0515] Frontend: React Native

[0516] Backend: Node.js, Express.js

[0517] Database: MongoDB

[0518] Emotion Engine: Google Cloud Natural Language API

[0519] Payment system: Stripe API

[0520] 3. Processing Flow

[0521] User Registration

[0522] Users access the system via a web browser and enter the required information (name, email address, password) using the user registration method. The entered information is stored in a MongoDB database via a Node.js server.

[0523] Log in

[0524] The user enters their email address and password on the login page, and is authenticated again via the Node.js server. If authentication is successful, the dashboard is displayed.

[0525] Creating an AI model

[0526] Users use the provided tools to input data based on their own thoughts and build an AI model, which is then sent to the server and analyzed through the emotion engine, after which the AI ​​model is trained.

[0527] Sentiment analysis and product recommendations

[0528] When a user browses a product review, the review's text data undergoes sentiment analysis using the Google Cloud Natural Language API. Based on the results of this analysis, the product suggestion tool creates a customized list of products to suggest to the user.

[0529] Sales and Revenue Sharing

[0530] Completed AI models are sold on the platform through the sales information input means. The sales page generation means automatically generates and publishes a sales page. Profits from the sold AI models are distributed to users after deducting fees using the Stripe API, and a profit revenue report is generated.

[0531] 4. Examples and prompts

[0532] As a concrete example, let's consider a case where a user values ​​positive reviews. For example, if the user enters the following prompt, the AI ​​will suggest suitable products for the user.

[0533] Example of input prompt:

[0534] "Generate the following product suggestions based on recent purchase history and sentiment analysis of reviews. Users prefer positive reviews, so list products that match that."

[0535] Purchase History:

[0536] Product A (positive evaluation)

[0537] Product B (negative rating)

[0538] Review sentiment analysis results:

[0539] Product C: 80% of reviews contain positive sentiment

[0540] Product D: 50% of reviews contain negative sentiment

[0541] Using this example prompt sentence, the system can suggest an appropriate product (e.g., product C) to the user.

[0542] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0543] Step 1:

[0544] User Registration:

[0545] A user accesses the registration page via a web browser and enters their name, email address, and password. The input information is sent from the terminal to the Node.js server. The server stores the received information in a MongoDB database. Once the storage is complete, a confirmation email is sent. Input: User information. Output: Data saved in MongoDB, confirmation email sent.

[0546] Step 2:

[0547] Login:

[0548] The user enters their email address and password on the login page and sends them again from their terminal to the Node.js server. The server performs authentication by referencing the MongoDB database. If authentication is successful, the user's dashboard is displayed. Input: Email address, password. Output: Dashboard displayed when authentication is successful.

[0549] Step 3:

[0550] Creating an AI model:

[0551] Users input data on the dashboard and create an AI model using the tools provided. The input data is sent from the device to the server and analyzed using the emotion engine (Google Cloud Natural Language API). The analysis results are used to train the AI ​​model on the server. Input: User data (text, etc.). Output: Trained AI model.

[0552] Step 4:

[0553] Assessment and Remediation / Retraining:

[0554] The server evaluates the trained AI model and notifies the device of the evaluation results. The user modifies the model based on the evaluation results and retrains it. The modified data is sent back to the server, and retraining is performed. Input: Modified data. Output: Retrained AI model, notification of evaluation results.

[0555] Step 5:

[0556] Sentiment analysis and product recommendations:

[0557] When a user browses a product review, the review's text data is sent from the device to the server. The server performs sentiment analysis using the Google Cloud Natural Language API. The analysis results are stored in a database, and customized product suggestions are generated based on the results. Input: Review text data. Output: Sentiment analysis results, customized product suggestions.

[0558] Step 6:

[0559] Sales and Revenue Sharing:

[0560] The user enters sales information for the completed AI model and sends it from the terminal to the server. The server automatically generates and publishes a sales page, and the AI ​​model is sold. The sales data is processed by the Stripe API, and profits are calculated after deducting fees. This profit is distributed to the user, and a revenue report is generated. Input: Sales information, payment information. Output: Automatically generated sales page, revenue report.

[0561] Step 7:

[0562] Notifications and History Management:

[0563] The evaluation results and revenue information of the AI ​​model generated by the user are notified to the device and can be viewed by the user. In addition, past purchase history and recommendation history are stored in a MongoDB database and can be viewed by the user on their personal page. Input: Evaluation results, revenue information. Output: Notification, display of history information.

[0564] 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.

[0565] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0566] 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.

[0567] [Second embodiment]

[0568] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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."

[0580] This invention describes a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling the models. This system includes the following components:

[0581] System configuration

[0582] 1. User registration method

[0583] 2. Login method

[0584] 3. Tool Provision Method

[0585] 4. Evaluation Methods

[0586] 5. Corrective and retraining measures

[0587] 6. Sales information input method

[0588] 7. Sales Page Generation Method

[0589] 8. Profit distribution means

[0590] 9. Report Generation Methods

[0591] Detailed system description

[0592] User registration method

[0593] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0594] Login method

[0595] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0596] Tool provision method

[0597] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. After inputting the data, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[0598] Evaluation methods

[0599] The server evaluates the generated AI model and notifies the user of the results, which are then displayed on the device for the user to review.

[0600] Corrective and retraining measures

[0601] The user inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The device sends the modified data to the server, which then performs retraining. The generated new model is then reevaluated, and the results are notified to the user.

[0602] Sales information input method

[0603] To sell a completed AI model, the user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0604] Sales page generation method

[0605] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0606] profit distribution means

[0607] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[0608] Report Generation Method

[0609] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0610] Specific examples

[0611] As a specific example of use, consider a scenario in which a user creates and sells an AI model that reflects their own expertise. For example, suppose a user uses their knowledge of finance to create an AI model for stock price prediction. The user inputs data through the system to generate the AI ​​model. The generated model is evaluated and modified as necessary. The final completed AI model is sold on the platform, and users can earn revenue by purchasing it from other users.

[0612] As described above, the present invention is an excellent system that allows users to easily create AI models and earn revenue through a series of processes including evaluation, modification, retraining, and sales.

[0613] The processing flow will be explained below.

[0614] User Registration and Login

[0615] Step 1:

[0616] The user accesses the system via a web browser and clicks the "Register" button.

[0617] Step 2:

[0618] The terminal displays a user registration form.

[0619] Step 3:

[0620] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[0621] Step 4:

[0622] The terminal transmits the entered registration information to the server.

[0623] Step 5:

[0624] The server stores the received registration information in a database and automatically sends a confirmation email.

[0625] Step 6:

[0626] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[0627] Step 7:

[0628] The user enters their email address and password on the login page and clicks the "Login" button.

[0629] Step 8:

[0630] The terminal sends the login information to the server.

[0631] Step 9:

[0632] The server checks the database to see if the entered email address and password match.

[0633] Step 10:

[0634] If the authentication is successful, the server displays the user's dashboard on the terminal.

[0635] Providing AI creation tools

[0636] Step 11:

[0637] The user clicks the "Create AI" button on the dashboard.

[0638] Step 12:

[0639] The device displays the interface of the AI ​​creation tool.

[0640] Step 13:

[0641] The user inputs data based on their own thoughts (e.g., text data) and clicks the "Save" button.

[0642] Step 14:

[0643] The device temporarily stores the entered data and provides a "Start Training" button.

[0644] Training an AI model

[0645] Step 15:

[0646] The user clicks the "Start Training" button.

[0647] Step 16:

[0648] The terminal transmits the input data to the server.

[0649] Step 17:

[0650] The server begins training the AI ​​model based on the received data.

[0651] Step 18:

[0652] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[0653] Evaluating and correcting AI models

[0654] Step 19:

[0655] The server transmits the evaluation results to the terminal and notifies the user.

[0656] Step 20:

[0657] The terminal displays the evaluation results to the user.

[0658] Step 21:

[0659] The user inputs data to modify the model based on the evaluation results and clicks the "Retrain" button.

[0660] Step 22:

[0661] The terminal transmits the corrected data to the server.

[0662] Step 23:

[0663] The server retrains the modified model and recalculates the evaluation results.

[0664] Step 24:

[0665] The server sends the new evaluation results to the terminal and notifies the user.

[0666] Selling AI models

[0667] Step 25:

[0668] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[0669] Step 26:

[0670] The terminal transmits the sales information to the server.

[0671] Step 27:

[0672] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[0673] Step 28:

[0674] The server notifies the terminal that the sales page has been published and displays it to the user.

[0675] Profit sharing and reporting

[0676] Step 29:

[0677] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[0678] Step 30:

[0679] The server processes the payment through a payment system.

[0680] Step 31:

[0681] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[0682] Step 32:

[0683] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0684] This completes a series of steps that allows users to create and sell AI models that mimic their own thinking and earn revenue.

[0685] Example 1

[0686] 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."

[0687] In conventional AI model generation systems, the process required for individuals to create AI models that reflect their own thinking and sell them to generate revenue was complex and often required specialized knowledge. Furthermore, the process of evaluating, correcting, retraining, and selling the models was not efficient, placing a heavy burden on users and making it difficult to monetize.

[0688] 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.

[0689] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing a generative AI model based on data provided by the user; an evaluation means for evaluating the constructed generative AI model; a correction and retraining means for correcting and retraining the generative AI model based on the evaluation results; a sales information input means for selling the generated generative AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold generative AI model to users; and a report generation means for generating a profit report of the profits. This enables individuals to easily create, modify, and sell AI models and earn profits thereby.

[0690] The "user registration means" is a means by which a user accesses the system via a web browser and registers with the system by entering information such as name, email address, and password.

[0691] "Login means" refers to the means by which a registered user logs into the system by entering an email address and password.

[0692] "Tool provision means" refers to a means of providing an interface and functionality for building a generative AI model based on data provided by the user.

[0693] The "evaluation means" is a means for evaluating the constructed generative AI model and notifying the user of the results.

[0694] "Modification and retraining means" refers to a means for modifying the generative AI model based on the evaluation results and retraining it.

[0695] The "sales information input means" is a means for a user to input a price and description for selling a generated AI model.

[0696] The "sales page generation means" is a means for automatically generating a sales page based on sales information and publishing it on the platform.

[0697] "Profit sharing means" means a means for calculating and distributing profits earned from the sold generative AI models to users.

[0698] The "report generation means" is a means for generating profit revenue reports and displaying them on the user's dashboard.

[0699] "Payment processing means" refers to a means for processing payments from potential purchasers regarding the sale of AI models.

[0700] The "notification means" is a means for notifying the user's device of the evaluation results of the AI ​​model generated by the user.

[0701] The present invention is a system that allows users to create AI models that mimic their own thinking and earn revenue by selling those models. This system is implemented using the following hardware and software.

[0702] Hardware and software used

[0703] Hardware

[0704] Server: Includes database, AI training and evaluation system, and payment processing system

[0705] Terminal (user device): PC, smartphone, tablet, etc.

[0706] software

[0707] Web browser

[0708] Database management system (e.g. MySQL)

[0709] AI training platforms (e.g. TensorFlow, PyTorch)

[0710] Program processing

[0711] User registration method

[0712] The user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters information such as name, email address, and password into the form and submits the registration information. The server saves the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0713] Login method

[0714] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then authenticates the user by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0715] Tool provision method

[0716] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. The user then clicks the "Start Training" button to send the data to the server. The server uses the received data to start training the AI ​​model and saves the generated model.

[0717] Evaluation methods

[0718] The server evaluates the generated AI model and notifies the user of the evaluation results, which are then displayed on the device for the user to review.

[0719] Corrective and retraining measures

[0720] The user modifies the model based on the evaluation results and retrains it by inputting new data. The device then sends the modified data to the server, which then retrains it. The generated new model is then reevaluated, and the results are notified to the user.

[0721] Sales information input method

[0722] The user clicks the "Sell" button and enters the price and description of the AI ​​model, and the device sends this information to the server.

[0723] Sales page generation method

[0724] The server automatically generates a sales page based on the received sales information and publishes it on the platform, and a publication notification is sent to the user.

[0725] profit distribution means

[0726] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system, calculates the profit, and distributes it to the user after deducting a commission fee.

[0727] Report Generation Method

[0728] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0729] Specific examples

[0730] Example of user registration procedure

[0731] The user accesses the system's URL and clicks the "Register" button on the page that appears. The terminal displays a form for entering name, email address, and password. The user enters the information and clicks the "Submit" button. The server receives the entered information and stores it in a database. The server automatically sends a confirmation email and displays a message to the user saying "Registration completed."

[0732] Specific examples of AI model creation

[0733] The user clicks the "Create AI" button on the dashboard. The terminal displays the AI ​​creation interface, allowing the user to enter text data. The user enters text data related to asset management into the system and clicks the "Start Training" button. The server receives the entered data and begins training the AI ​​model. The server saves the trained model and notifies the user of the generated results.

[0734] Prompt Sentence Examples

[0735] "Use your financial knowledge to create and sell an AI model for predicting stock prices. Please provide a detailed description of the steps from user registration to sales."

[0736] As described above, the present invention is a system that enables individuals to easily create, modify, and sell AI models, thereby earning revenue.

[0737] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] A user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters their name, email address, and password. The entered information is sent to the server, which stores the received information in a database and automatically sends a confirmation email to complete the registration. The input is user information (name, email address, password), and the output is the user information stored in the database and a confirmation email.

[0740] Step 2:

[0741] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then references the database to authenticate the user. If authentication is successful, the server displays the user's dashboard. The input is login information (email address, password), and the output is the authentication result and the dashboard display.

[0742] Step 3:

[0743] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, and the user inputs data (e.g., text data) that mimics their thoughts. Once the input is complete, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model using the received data and saves the generated model. The input is the training data (text data), and the output is the generated AI model.

[0744] Step 4:

[0745] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal so that the user can check them. The input is the generated AI model, and the output is the evaluation results.

[0746] Step 5:

[0747] The user modifies the model based on the evaluation results and inputs new data. When the user clicks the "Start Training" button, the device sends the modified data to the server. The server then retrains, reevaluates the generated new model, and notifies the user of the results. The input is the modified data, and the output is the reevaluated AI model and its evaluation results.

[0748] Step 6:

[0749] The user clicks the "Sell" button and enters the price and description of the AI ​​model. The device sends this information to the server. The server automatically generates a sales page based on the received sales information and publishes it on the platform. A publication notification is sent to the user. The input is sales information (price, description), and the output is the published sales page.

[0750] Step 7:

[0751] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. The server calculates profits and distributes them to the user after deducting a fee. The input is the purchase transaction, and the output is payment processing and profit distribution.

[0752] Step 8:

[0753] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard. The input is the purchase data and profit sharing data, and the output is the revenue report.

[0754] (Application example 1)

[0755] 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."

[0756] Conventional AI model creation systems lacked the functionality to allow users to easily create models that reflect their own thoughts and preferences, and to sell those models to other users. Furthermore, they lacked the functionality to utilize the created AI models for content recommendations, which prevented them from improving user convenience. This made it difficult to provide users with a personalized experience.

[0757] 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.

[0758] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a recommendation means for using the generated AI model to recommend content; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; and a report generation means for generating a profit report. This enables users to easily create AI models that reflect their own thoughts and preferences, use the models to recommend personalized content, and sell them to other users.

[0759] The "user registration means" is a means by which a user accesses the system via a web browser, enters information such as name, email address, and password, and registers.

[0760] A "login means" is a means for a user to enter an email address and password for authentication when accessing a system.

[0761] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[0762] "Evaluation means" refers to a means for evaluating the constructed AI model and notifying the user of the evaluation results.

[0763] "Modification and retraining measures" are measures to modify the AI ​​model based on the evaluation results and retrain it using new data.

[0764] A "recommendation method" is a method of using the generated AI model to recommend content that matches the user's preferences.

[0765] The "sales information input means" is a means for inputting information such as price and description in order to sell the generated AI model on the platform.

[0766] The "sales page generation means" is a means for automatically generating and publishing a sales page based on sales information.

[0767] A "profit distribution method" is a method for calculating profits earned from sold AI models and distributing them to users after deducting fees.

[0768] The "report generating means" is a means for generating a profit income report and providing it to the user.

[0769] This invention provides a system that allows users to create an AI model that reflects their own thoughts and preferences, and use that model for content recommendation and sales. Specific embodiments of this system are described below.

[0770] The system's components are mainly related to the server, terminals, and users. Specifically, it consists of a user registration means, a login means, a tool provision means, an evaluation means, a correction and retraining means, a recommendation means, a sales information input means, a sales page generation means, a profit distribution means, and a report generation means.

[0771] A user accesses the system via a web browser, enters their name, email address, password, etc. into the user registration form, and submits it to the server. The server saves the registration information in a database and automatically sends a confirmation email to complete the user registration. Next, the user enters their email address and password on the login page, and the server authenticates the information by referencing it in the database. If authentication is successful, the user's dashboard is displayed.

[0772] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data reflecting their thoughts and preferences (e.g., music preference list, viewing history, etc.). After inputting the data, the user clicks the "Start Training" button, and the server begins training the AI ​​model based on the received data and saves the generated model.

[0773] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal and can be viewed by the user. The user then inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The server retrains the AI ​​model based on the modified data, reevaluates the generated new model, and notifies the user of the results.

[0774] The generated AI model is used to recommend content (music, videos, articles, etc.) that matches the user's preferences. The recommendation tool allows users to receive personalized content recommendations. To sell the completed AI model, users click the "Sell" button and enter sales information such as price and description. This information is sent to the server, and a sales page is automatically generated and published.

[0775] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit and distributes the remainder to the user after deducting a certain fee. The server also generates a revenue report based on the purchase data and profit distribution details, and displays it on the user's dashboard.

[0776] As a concrete example, consider the case where a user creates a "music recommendation AI model." The user inputs their favorite music list and listening history, and trains the AI ​​model based on that data. The completed model recommends new music that matches the user's tastes. An example of a prompt sentence would be, "The user inputs their favorite music list and trains the AI ​​model based on that data. The completed model recommends new music that is close to the user's tastes."

[0777] The hardware used is a server (e.g., Amazon Web Services, Google Cloud Platform), and the software used is a web framework (e.g., Flask), a database (e.g., MySQL, PostgreSQL), and a library for training AI models (e.g., TensorFlow, PyTorch).

[0778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0779] Step 1:

[0780] A user accesses the system via a web browser, enters information such as name, email address, and password into the user registration form, and sends it to the server. The server receives this information and stores it in a database. In this process, the input is the user's personal information, and the output is the registration information stored in the database.

[0781] Step 2:

[0782] A user enters their email address and password on the login page and clicks the login button. The server checks this information against the database and, if authentication is successful, displays the user's dashboard. The input is an email address and password, and the output is the authentication result and the display of the dashboard.

[0783] Step 3:

[0784] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data that reflects their thoughts and preferences (e.g., music preference list, listening history, etc.). The input is the user's data, and the output is the transmission of the data to the server.

[0785] Step 4:

[0786] When the user clicks the "Start Training" button, the server starts training the AI ​​model based on the received data. Libraries such as TensorFlow and PyTorch are used for training. In this process, the input is the user data, and the output is the generated AI model.

[0787] Step 5:

[0788] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation uses model accuracy and performance indicators. The input is the generated AI model, and the output is a notification of the evaluation results.

[0789] Step 6:

[0790] The user inputs data to modify the model based on the evaluation results and performs retraining. The server receives the modified data and performs retraining. In this process, the input is the modified data and the output is an improved AI model.

[0791] Step 7:

[0792] The generated AI model is used to recommend content (e.g., music, videos, articles, etc.) that matches the user's preferences. The server uses a recommendation algorithm to extract content and suggest it to the user. The input is the generated AI model, and the output is the recommended content.

[0793] Step 8:

[0794] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description. The server receives this information and automatically generates and publishes a sales page. The input is the sales information, and the output is the published sales page.

[0795] Step 9:

[0796] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once the payment is completed, the server calculates the profit, deducts the fee, and distributes the remainder to the user. The input is the purchase information, and the output is the payment processing result and the distribution of the profit.

[0797] Step 10:

[0798] The server generates a revenue report based on the purchase data and profit sharing information and displays it on the user's dashboard. The input is the purchase data and profit sharing information, and the output is the revenue report.

[0799] 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.

[0800] This invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thoughts and earn revenue by selling those models, and includes the following components:

[0801] System configuration

[0802] 1. User registration method

[0803] 2. Login method

[0804] 3. Tool Provision Method

[0805] 4. Evaluation Methods

[0806] 5. Corrective and retraining measures

[0807] 6. Sales information input method

[0808] 7. Sales Page Generation Method

[0809] 8. Profit distribution means

[0810] 9. Report Generation Methods

[0811] 10. Emotion Engine

[0812] Detailed system description

[0813] User registration method

[0814] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[0815] Login method

[0816] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0817] Tool provision method

[0818] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics their thoughts. After inputting the data, the user clicks the "Save" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[0819] Emotion Engine

[0820] The server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine uses natural language processing technology to analyze the user's emotions and adjusts the training data for the AI ​​model based on the analysis results. For example, if the data entered by the user contains a lot of positive emotions, the AI ​​model will be trained to emphasize positive data based on that information.

[0821] Evaluation methods

[0822] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[0823] Corrective and retraining measures

[0824] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[0825] Sales information input method

[0826] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0827] Sales page generation method

[0828] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0829] profit distribution means

[0830] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[0831] Report Generation Method

[0832] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0833] Specific examples

[0834] As a specific example of use, consider a scenario in which a user uses the emotion engine to create an AI model that reflects their own expertise. For example, suppose a user creates an AI model based on customer service emotion analysis. The user inputs customer text data through the system, and the emotion engine analyzes the data to recognize positive or negative emotions. The AI ​​model is trained based on this emotion information, enabling it to respond quickly to changes in customer emotion. The completed AI model can be sold on the platform, and users can earn revenue when other users purchase it.

[0835] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[0836] The processing flow will be explained below.

[0837] User registration and login process

[0838] Step 1:

[0839] The user accesses the system via a web browser and clicks the "Register" button.

[0840] Step 2:

[0841] The terminal displays a user registration form.

[0842] Step 3:

[0843] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[0844] Step 4:

[0845] The terminal transmits the entered registration information to the server.

[0846] Step 5:

[0847] The server stores the received registration information in a database and automatically sends a confirmation email.

[0848] Step 6:

[0849] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[0850] Step 7:

[0851] The user enters their email address and password on the login page and clicks the "Login" button.

[0852] Step 8:

[0853] The terminal sends the login information to the server.

[0854] Step 9:

[0855] The server checks the database to see if the entered email address and password match.

[0856] Step 10:

[0857] If the authentication is successful, the server displays the user's dashboard on the terminal.

[0858] Processing related to the provision of AI creation tools

[0859] Step 11:

[0860] The user clicks the "Create AI" button on the dashboard.

[0861] Step 12:

[0862] The device displays the interface of the AI ​​creation tool.

[0863] Step 13:

[0864] The user inputs text data based on their own thoughts and clicks the "Save" button.

[0865] Step 14:

[0866] The device temporarily stores the entered data and provides a "Start Training" button.

[0867] Processing for training AI models

[0868] Step 15:

[0869] The user clicks the "Start Training" button.

[0870] Step 16:

[0871] The terminal transmits the input data to the server.

[0872] Step 17:

[0873] The server begins training the AI ​​model based on the received data.

[0874] Step 18:

[0875] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[0876] Emotion engine processing

[0877] Step 19:

[0878] The server passes the data entered by the user to the emotion engine, which recognizes the emotion.

[0879] Step 20:

[0880] The emotion engine uses natural language processing technology to analyze the user's emotions from the input data.

[0881] Step 21:

[0882] The emotion engine passes the analysis results to a server, which generates instructions for adjusting the training data for the AI ​​model.

[0883] AI model evaluation and correction process

[0884] Step 22:

[0885] The server notifies the user based on the evaluation results and emotion engine information.

[0886] Step 23:

[0887] The terminal displays the evaluation results to the user.

[0888] Step 24:

[0889] The user modifies the model based on the evaluation results and clicks the "Retrain" button.

[0890] Step 25:

[0891] The terminal transmits the corrected data to the server.

[0892] Step 26:

[0893] The server retrains the modified AI model and calculates the evaluation results again.

[0894] Step 27:

[0895] The server sends the new evaluation results to the terminal and notifies the user.

[0896] Processing of AI model sales

[0897] Step 28:

[0898] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[0899] Step 29:

[0900] The terminal transmits the sales information to the server.

[0901] Step 30:

[0902] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[0903] Step 31:

[0904] The server notifies the terminal that the sales page has been published and displays it to the user.

[0905] Profit sharing and reporting process

[0906] Step 32:

[0907] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[0908] Step 33:

[0909] The server processes the payment through a payment system.

[0910] Step 34:

[0911] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[0912] Step 35:

[0913] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0914] This completes a series of steps that allows users to use the emotion engine to create and sell AI models that mimic their own thoughts and earn revenue.

[0915] Example 2

[0916] 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."

[0917] Conventional AI model creation and sales systems have had the problem that it is difficult for users to create models that take into account their own thoughts and emotions. It is also difficult for users to evaluate the emotional balance of the AI ​​models they have created. Furthermore, the process of selling the created models and earning revenue is cumbersome. There was a need to solve these issues and provide a more user-friendly and efficient system.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0919] In this invention, the server includes a user registration means accessible by a user via a web browser, a login means for authenticating registered user information accessible by the user, a tool provision means for constructing an AI model based on data provided by the user, an evaluation means for evaluating the constructed AI model, a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results, a sales information input means for selling the generated AI model on the platform, a sales page generation means for generating and publishing a sales page based on the sales information, a profit distribution means for distributing profits earned from the sold AI model to users, a report generation means for generating a profit report, and an emotion engine for analyzing emotions using natural language processing. This enables users to create, evaluate, sell, and distribute profits from AI models that take emotions into account.

[0920] The "user registration means" is a function that allows a user to access the system via a web browser, enter their own information, and register with the system.

[0921] The "login means" is a function that allows a user to access the system based on registered user information.

[0922] "Tool provision means" is a function that provides interfaces and tools for building AI models based on data provided by the user.

[0923] "Evaluation means" is a function for evaluating the performance and quality of the constructed AI model.

[0924] "Modification and retraining means" is a function for modifying the AI ​​model based on the evaluation results and retraining it.

[0925] The "sales information input means" is a function for inputting information for selling the generated AI model.

[0926] The "sales page generation means" is a function for automatically generating and publishing a sales page based on input sales information.

[0927] "Profit distribution means" is a function for distributing profits earned from sold AI models to users.

[0928] The "report generation means" is a function for generating a profit income report and providing it to the user.

[0929] The "emotion engine" is a function that uses natural language processing to analyze the emotions in input data and optimize the training data for AI models.

[0930] The present invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. Specific embodiments are described below.

[0931] System Components

[0932] This system mainly consists of the following components:

[0933] 1. User registration method

[0934] 2. Login method

[0935] 3. Tool Provision Method

[0936] 4. Evaluation Methods

[0937] 5. Corrective and retraining measures

[0938] 6. Sales information input method

[0939] 7. Sales Page Generation Method

[0940] 8. Profit distribution means

[0941] 9. Report Generation Methods

[0942] 10. Emotion Engine

[0943] User registration method

[0944] The user accesses the system via a web browser (e.g., Google Chrome, Mozilla Firefox) and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as their name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database (e.g., MySQL, PostgreSQL), automatically sends a confirmation email, and registration is complete.

[0945] Login method

[0946] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[0947] Tool provision method

[0948] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data that mimics their thoughts (e.g., text data such as "The weather is very nice today, and I'm feeling very good."). After inputting the data, the user clicks the "Save" button to send the data to the server. The server uses the received data to begin training an AI model using natural language processing tools (e.g., NLTK, spaCy) and machine learning tools (e.g., TensorFlow, PyTorch), and stores the generated model in a database.

[0949] Emotion Engine

[0950] A distinctive feature of this system is that the server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine analyzes the user's emotions using natural language processing techniques (e.g., BERT, GPT-3), and adjusts the training data for the AI ​​model based on the analysis results.

[0951] Evaluation methods

[0952] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[0953] Corrective and retraining measures

[0954] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[0955] Sales information input method

[0956] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[0957] Sales page generation method

[0958] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[0959] profit distribution means

[0960] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe). Once payment is completed, the server calculates the profit and distributes the remainder to the user after deducting a certain fee.

[0961] Report Generation Method

[0962] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[0963] Specific examples

[0964] Consider a scenario where a user is creating an AI model based on customer service sentiment analysis. The user inputs customer text data (e.g., "The customer is satisfied with the service") through the system, and the sentiment engine analyzes the data to recognize positive or negative sentiment. The AI ​​model is trained based on this emotional information and can quickly respond to changes in customer sentiment. The completed AI model can be sold on the platform, and other users can purchase it, generating revenue for the user.

[0965] Prompt Sentence Examples

[0966] "Please enter text data. Example: 'The weather is very nice today and I'm feeling great.'"

[0967] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[0968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0969] Processing Steps

[0970] Step 1: User Registration

[0971] The user opens a web browser to the system's home page and clicks the "Register" button.

[0972] The terminal displays a user registration form, where the user enters information such as name, email address, and password, and clicks the "Submit" button.

[0973] The server receives the entered information, stores it in a database, and automatically sends a confirmation email to notify the user that their registration is complete.

[0974] Input: User's name, email address, and password

[0975] Data processing: Save input information to a database

[0976] Output: Sending a confirmation email and notifying the completion of registration

[0977] Step 2: Log in

[0978] The user enters their email address and password on the login page and clicks the "Login" button.

[0979] The terminal sends the entered information to the server, which then refers to a database and performs authentication.

[0980] If authentication is successful, the server displays the user's dashboard.

[0981] Input: Email address, Password

[0982] Data calculation: Authentication in the database based on input information

[0983] Output: Display dashboard

[0984] Step 3: Creating an AI model

[0985] The user clicks the "Create AI" button on the dashboard.

[0986] The device displays an interface for creating AI, and the user inputs data that mimics their thoughts. For example, they input text data such as, "The weather is very nice today, and I'm in a great mood."

[0987] The user clicks the "Save" button to send the data to the server.

[0988] The server uses natural language processing tools to analyze the text based on the received data and begins training the AI ​​model. The generated model is then stored in a database.

[0989] Input: User's text data

[0990] Data processing: Text analysis using natural language processing

[0991] Data Computing: Generating training data and training AI models

[0992] Output: Save the trained AI model

[0993] Step 4: Analysis by the Emotion Engine

[0994] The server inputs the training data into the emotion engine and performs emotion analysis.

[0995] The sentiment engine uses natural language processing techniques to identify sentiment within the data and calculate the percentage of positive and negative sentiment.

[0996] Based on the results of the sentiment analysis, the server optimizes the training data for the AI ​​model.

[0997] Input: Training data

[0998] Data Computation: Extracting Emotional Information through Sentiment Analysis

[0999] Output: Optimized training data

[1000] Step 5: Evaluate the model

[1001] The server performs evaluation tests on the generated AI model.

[1002] The evaluation results are displayed on the terminal, allowing the user to check the evaluation results.

[1003] Emotional balance is also evaluated using an emotion engine.

[1004] Input: AI model

[1005] Data calculation: Evaluation test administration and emotional balance measurement

[1006] Output: Display of evaluation results

[1007] Step 6: Modify and retrain the model

[1008] If the user is not satisfied with the evaluation results, he or she can re-enter the corrected data and click the "Retrain" button.

[1009] The terminal transmits the corrected data to the server.

[1010] The server retrains the AI ​​model based on the new data and generates a new model.

[1011] The evaluation results for the new model will be displayed on the device again.

[1012] Input: Correction data

[1013] Data Calculation: Retraining

[1014] Output: Re-evaluated AI model and results display

[1015] Step 7: Enter sales information

[1016] To sell a completed AI model, users click the "Sell" button and enter sales information such as price and description.

[1017] This information is sent to a server and recorded in a database.

[1018] Input: Sales information such as price, description, etc.

[1019] Data processing: Sales information stored in a database

[1020] Output: Sales information stored in a database

[1021] Step 8: Generate a sales page

[1022] The server automatically generates a sales page based on the entered sales information.

[1023] Once the sales page is published, the server notifies the user.

[1024] Input: Sales information

[1025] Data processing: Sales page generation

[1026] Output: Published sales page and notification

[1027] Step 9: Profit sharing

[1028] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe).

[1029] Once payment is completed, the server deducts the fee, calculates the profit, and distributes the remainder to the user.

[1030] Input: Purchase Information

[1031] Data calculation: settlement processing and profit calculation

[1032] Output: Profit sharing

[1033] Step 10: Generate reports

[1034] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1035] Input: Purchase data, profit sharing information

[1036] Data Processing: Report Generation

[1037] Output: View the revenue report

[1038] (Application example 2)

[1039] 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."

[1040] In modern e-commerce, it is difficult for users to select products that best suit their preferences and needs from the wide variety of products available. This is particularly true when review information is scattered and often lacks reliability. Furthermore, when users make purchasing decisions based on product reviews, it is difficult to accurately reflect the impact of the review content on the user's emotions. Therefore, it is important to improve the user experience and stimulate purchasing motivation by suggesting products that are appropriate for each user based on sentiment analysis.

[1041] The identification processing by the identification 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: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; a report generation means for generating a profit report; a sentiment analysis means for analyzing the sentiment of product reviews when the user views them; and a product suggestion means for making customized product suggestions to the user based on the sentiment analysis results. This allows users to receive product suggestions that match their preferences and needs through the sentiment analysis of reviews, thereby improving their purchasing experience.

[1042] The "user registration means" is a means by which a user accesses the system via a web browser and registers by entering personal information such as name, email address, and password.

[1043] "Login means" refers to the means by which a user accesses the system using a registered email address and password and is authenticated.

[1044] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[1045] "Evaluation means" refers to a means for evaluating the constructed AI model and providing the evaluation results to the user.

[1046] "Modification and retraining means" refers to a means of modifying an AI model based on the evaluation results and retraining it.

[1047] The "sales information input means" is a means for inputting information for selling the generated AI model on the platform.

[1048] The "sales page generation means" is a means for automatically generating and publishing a sales page based on input sales information.

[1049] A "profit distribution method" is a method for distributing profits earned from sold AI models to users after deducting a certain fee.

[1050] The "report generating means" is a means for generating a profit income report and providing it to the user.

[1051] The "sentiment analysis means" is a means for analyzing the sentiment of a product review when the user views the review and determining whether the sentiment is positive or negative.

[1052] The "product suggestion means" is a means for making customized product suggestions to the user based on the emotion analysis results.

[1053] The present invention combines an emotion engine with a system that allows users to create AI models that mimic their own thoughts and sell those models. Specific embodiments of the present invention will be described below.

[1054] 1. System Overview

[1055] This system includes means for user registration, login, tool provision, evaluation, correction and retraining, sales information input, sales page generation, profit distribution, report generation, sentiment analysis, and product suggestion. By using this system, users can easily create and sell AI models that reflect their own emotions.

[1056] 2. Hardware and Software Used

[1057] Hardware: Smartphone (iOS, Android)

[1058] software:

[1059] Frontend: React Native

[1060] Backend: Node.js, Express.js

[1061] Database: MongoDB

[1062] Emotion Engine: Google Cloud Natural Language API

[1063] Payment system: Stripe API

[1064] 3. Processing Flow

[1065] User Registration

[1066] Users access the system via a web browser and enter the required information (name, email address, password) using the user registration method. The entered information is stored in a MongoDB database via a Node.js server.

[1067] Log in

[1068] The user enters their email address and password on the login page, and is authenticated again via the Node.js server. If authentication is successful, the dashboard is displayed.

[1069] Creating an AI model

[1070] Users use the provided tools to input data based on their own thoughts and build an AI model, which is then sent to the server and analyzed through the emotion engine, after which the AI ​​model is trained.

[1071] Sentiment analysis and product recommendations

[1072] When a user browses a product review, the review's text data undergoes sentiment analysis using the Google Cloud Natural Language API. Based on the results of this analysis, the product suggestion tool creates a customized list of products to suggest to the user.

[1073] Sales and Revenue Sharing

[1074] Completed AI models are sold on the platform through the sales information input means. The sales page generation means automatically generates and publishes a sales page. Profits from the sold AI models are distributed to users after deducting fees using the Stripe API, and a profit revenue report is generated.

[1075] 4. Examples and prompts

[1076] As a concrete example, let's consider a case where a user values ​​positive reviews. For example, if the user enters the following prompt, the AI ​​will suggest suitable products for the user.

[1077] Example of input prompt:

[1078] "Generate the following product suggestions based on recent purchase history and sentiment analysis of reviews. Users prefer positive reviews, so list products that match that."

[1079] Purchase History:

[1080] Product A (positive evaluation)

[1081] Product B (negative rating)

[1082] Review sentiment analysis results:

[1083] Product C: 80% of reviews contain positive sentiment

[1084] Product D: 50% of reviews contain negative sentiment

[1085] Using this example prompt sentence, the system can suggest an appropriate product (e.g., product C) to the user.

[1086] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1087] Step 1:

[1088] User Registration:

[1089] A user accesses the registration page via a web browser and enters their name, email address, and password. The input information is sent from the terminal to the Node.js server. The server stores the received information in a MongoDB database. Once the storage is complete, a confirmation email is sent. Input: User information. Output: Data saved in MongoDB, confirmation email sent.

[1090] Step 2:

[1091] Login:

[1092] The user enters their email address and password on the login page and sends them again from their terminal to the Node.js server. The server performs authentication by referencing the MongoDB database. If authentication is successful, the user's dashboard is displayed. Input: Email address, password. Output: Dashboard displayed when authentication is successful.

[1093] Step 3:

[1094] Creating an AI model:

[1095] Users input data on the dashboard and create an AI model using the tools provided. The input data is sent from the device to the server and analyzed using the emotion engine (Google Cloud Natural Language API). The analysis results are used to train the AI ​​model on the server. Input: User data (text, etc.). Output: Trained AI model.

[1096] Step 4:

[1097] Assessment and Remediation / Retraining:

[1098] The server evaluates the trained AI model and notifies the device of the evaluation results. The user modifies the model based on the evaluation results and retrains it. The modified data is sent back to the server, and retraining is performed. Input: Modified data. Output: Retrained AI model, notification of evaluation results.

[1099] Step 5:

[1100] Sentiment analysis and product recommendations:

[1101] When a user browses a product review, the review's text data is sent from the device to the server. The server performs sentiment analysis using the Google Cloud Natural Language API. The analysis results are stored in a database, and customized product suggestions are generated based on the results. Input: Review text data. Output: Sentiment analysis results, customized product suggestions.

[1102] Step 6:

[1103] Sales and Revenue Sharing:

[1104] The user enters sales information for the completed AI model and sends it from the terminal to the server. The server automatically generates and publishes a sales page, and the AI ​​model is sold. The sales data is processed by the Stripe API, and profits are calculated after deducting fees. This profit is distributed to the user, and a revenue report is generated. Input: Sales information, payment information. Output: Automatically generated sales page, revenue report.

[1105] Step 7:

[1106] Notifications and History Management:

[1107] The evaluation results and revenue information of the AI ​​model generated by the user are notified to the device and can be viewed by the user. In addition, past purchase history and recommendation history are stored in a MongoDB database and can be viewed by the user on their personal page. Input: Evaluation results, revenue information. Output: Notification, display of history information.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] [Third embodiment]

[1112] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1114] 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).

[1115] 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.

[1116] 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.

[1117] 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).

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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.

[1123] 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."

[1124] This invention describes a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling the models. This system includes the following components:

[1125] System configuration

[1126] 1. User registration method

[1127] 2. Login method

[1128] 3. Tool Provision Method

[1129] 4. Evaluation Methods

[1130] 5. Corrective and retraining measures

[1131] 6. Sales information input method

[1132] 7. Sales Page Generation Method

[1133] 8. Profit distribution means

[1134] 9. Report Generation Methods

[1135] Detailed system description

[1136] User registration method

[1137] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1138] Login method

[1139] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1140] Tool provision method

[1141] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. After inputting the data, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[1142] Evaluation methods

[1143] The server evaluates the generated AI model and notifies the user of the results, which are then displayed on the device for the user to review.

[1144] Corrective and retraining measures

[1145] The user inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The device sends the modified data to the server, which then performs retraining. The generated new model is then reevaluated, and the results are notified to the user.

[1146] Sales information input method

[1147] To sell a completed AI model, the user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[1148] Sales page generation method

[1149] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[1150] profit distribution means

[1151] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[1152] Report Generation Method

[1153] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1154] Specific examples

[1155] As a specific example of use, consider a scenario in which a user creates and sells an AI model that reflects their own expertise. For example, suppose a user uses their knowledge of finance to create an AI model for stock price prediction. The user inputs data through the system to generate the AI ​​model. The generated model is evaluated and modified as necessary. The final completed AI model is sold on the platform, and users can earn revenue by purchasing it from other users.

[1156] As described above, the present invention is an excellent system that allows users to easily create AI models and earn revenue through a series of processes including evaluation, modification, retraining, and sales.

[1157] The processing flow will be explained below.

[1158] User Registration and Login

[1159] Step 1:

[1160] The user accesses the system via a web browser and clicks the "Register" button.

[1161] Step 2:

[1162] The terminal displays a user registration form.

[1163] Step 3:

[1164] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[1165] Step 4:

[1166] The terminal transmits the entered registration information to the server.

[1167] Step 5:

[1168] The server stores the received registration information in a database and automatically sends a confirmation email.

[1169] Step 6:

[1170] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[1171] Step 7:

[1172] The user enters their email address and password on the login page and clicks the "Login" button.

[1173] Step 8:

[1174] The terminal sends the login information to the server.

[1175] Step 9:

[1176] The server checks the database to see if the entered email address and password match.

[1177] Step 10:

[1178] If the authentication is successful, the server displays the user's dashboard on the terminal.

[1179] Providing AI creation tools

[1180] Step 11:

[1181] The user clicks the "Create AI" button on the dashboard.

[1182] Step 12:

[1183] The device displays the interface of the AI ​​creation tool.

[1184] Step 13:

[1185] The user inputs data based on their own thoughts (e.g., text data) and clicks the "Save" button.

[1186] Step 14:

[1187] The device temporarily stores the entered data and provides a "Start Training" button.

[1188] Training an AI model

[1189] Step 15:

[1190] The user clicks the "Start Training" button.

[1191] Step 16:

[1192] The terminal transmits the input data to the server.

[1193] Step 17:

[1194] The server begins training the AI ​​model based on the received data.

[1195] Step 18:

[1196] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[1197] Evaluating and correcting AI models

[1198] Step 19:

[1199] The server transmits the evaluation results to the terminal and notifies the user.

[1200] Step 20:

[1201] The terminal displays the evaluation results to the user.

[1202] Step 21:

[1203] The user inputs data to modify the model based on the evaluation results and clicks the "Retrain" button.

[1204] Step 22:

[1205] The terminal transmits the corrected data to the server.

[1206] Step 23:

[1207] The server retrains the modified model and recalculates the evaluation results.

[1208] Step 24:

[1209] The server sends the new evaluation results to the terminal and notifies the user.

[1210] Selling AI models

[1211] Step 25:

[1212] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[1213] Step 26:

[1214] The terminal transmits the sales information to the server.

[1215] Step 27:

[1216] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[1217] Step 28:

[1218] The server notifies the terminal that the sales page has been published and displays it to the user.

[1219] Profit sharing and reporting

[1220] Step 29:

[1221] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[1222] Step 30:

[1223] The server processes the payment through a payment system.

[1224] Step 31:

[1225] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[1226] Step 32:

[1227] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1228] This completes a series of steps that allows users to create and sell AI models that mimic their own thinking and earn revenue.

[1229] Example 1

[1230] 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."

[1231] In conventional AI model generation systems, the process required for individuals to create AI models that reflect their own thinking and sell them to generate revenue was complex and often required specialized knowledge. Furthermore, the process of evaluating, correcting, retraining, and selling the models was not efficient, placing a heavy burden on users and making it difficult to monetize.

[1232] 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.

[1233] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing a generative AI model based on data provided by the user; an evaluation means for evaluating the constructed generative AI model; a correction and retraining means for correcting and retraining the generative AI model based on the evaluation results; a sales information input means for selling the generated generative AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold generative AI model to users; and a report generation means for generating a profit report of the profits. This enables individuals to easily create, modify, and sell AI models and earn profits thereby.

[1234] The "user registration means" is a means by which a user accesses the system via a web browser and registers with the system by entering information such as name, email address, and password.

[1235] "Login means" refers to the means by which a registered user logs into the system by entering an email address and password.

[1236] "Tool provision means" refers to a means of providing an interface and functionality for building a generative AI model based on data provided by the user.

[1237] The "evaluation means" is a means for evaluating the constructed generative AI model and notifying the user of the results.

[1238] "Modification and retraining means" refers to a means for modifying the generative AI model based on the evaluation results and retraining it.

[1239] The "sales information input means" is a means for a user to input a price and description for selling a generated AI model.

[1240] The "sales page generation means" is a means for automatically generating a sales page based on sales information and publishing it on the platform.

[1241] "Profit sharing means" means a means for calculating and distributing profits earned from the sold generative AI models to users.

[1242] The "report generation means" is a means for generating profit revenue reports and displaying them on the user's dashboard.

[1243] "Payment processing means" refers to a means for processing payments from potential purchasers regarding the sale of AI models.

[1244] The "notification means" is a means for notifying the user's device of the evaluation results of the AI ​​model generated by the user.

[1245] The present invention is a system that allows users to create AI models that mimic their own thinking and earn revenue by selling those models. This system is implemented using the following hardware and software.

[1246] Hardware and software used

[1247] Hardware

[1248] Server: Includes database, AI training and evaluation system, and payment processing system

[1249] Terminal (user device): PC, smartphone, tablet, etc.

[1250] software

[1251] Web browser

[1252] Database management system (e.g. MySQL)

[1253] AI training platforms (e.g. TensorFlow, PyTorch)

[1254] Program processing

[1255] User registration method

[1256] The user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters information such as name, email address, and password into the form and submits the registration information. The server saves the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1257] Login method

[1258] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then authenticates the user by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1259] Tool provision method

[1260] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. The user then clicks the "Start Training" button to send the data to the server. The server uses the received data to start training the AI ​​model and saves the generated model.

[1261] Evaluation methods

[1262] The server evaluates the generated AI model and notifies the user of the evaluation results, which are then displayed on the device for the user to review.

[1263] Corrective and retraining measures

[1264] The user modifies the model based on the evaluation results and retrains it by inputting new data. The device then sends the modified data to the server, which then retrains it. The generated new model is then reevaluated, and the results are notified to the user.

[1265] Sales information input method

[1266] The user clicks the "Sell" button and enters the price and description of the AI ​​model, and the device sends this information to the server.

[1267] Sales page generation method

[1268] The server automatically generates a sales page based on the received sales information and publishes it on the platform, and a publication notification is sent to the user.

[1269] profit distribution means

[1270] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system, calculates the profit, and distributes it to the user after deducting a commission fee.

[1271] Report Generation Method

[1272] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1273] Specific examples

[1274] Example of user registration procedure

[1275] The user accesses the system's URL and clicks the "Register" button on the page that appears. The terminal displays a form for entering name, email address, and password. The user enters the information and clicks the "Submit" button. The server receives the entered information and stores it in a database. The server automatically sends a confirmation email and displays a message to the user saying "Registration completed."

[1276] Specific examples of AI model creation

[1277] The user clicks the "Create AI" button on the dashboard. The terminal displays the AI ​​creation interface, allowing the user to enter text data. The user enters text data related to asset management into the system and clicks the "Start Training" button. The server receives the entered data and begins training the AI ​​model. The server saves the trained model and notifies the user of the generated results.

[1278] Prompt Sentence Examples

[1279] "Use your financial knowledge to create and sell an AI model for predicting stock prices. Please provide a detailed description of the steps from user registration to sales."

[1280] As described above, the present invention is a system that enables individuals to easily create, modify, and sell AI models, thereby earning revenue.

[1281] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1282] Step 1:

[1283] A user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters their name, email address, and password. The entered information is sent to the server, which stores the received information in a database and automatically sends a confirmation email to complete the registration. The input is user information (name, email address, password), and the output is the user information stored in the database and a confirmation email.

[1284] Step 2:

[1285] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then references the database to authenticate the user. If authentication is successful, the server displays the user's dashboard. The input is login information (email address, password), and the output is the authentication result and the dashboard display.

[1286] Step 3:

[1287] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, and the user inputs data (e.g., text data) that mimics their thoughts. Once the input is complete, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model using the received data and saves the generated model. The input is the training data (text data), and the output is the generated AI model.

[1288] Step 4:

[1289] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal so that the user can check them. The input is the generated AI model, and the output is the evaluation results.

[1290] Step 5:

[1291] The user modifies the model based on the evaluation results and inputs new data. When the user clicks the "Start Training" button, the device sends the modified data to the server. The server then retrains, reevaluates the generated new model, and notifies the user of the results. The input is the modified data, and the output is the reevaluated AI model and its evaluation results.

[1292] Step 6:

[1293] The user clicks the "Sell" button and enters the price and description of the AI ​​model. The device sends this information to the server. The server automatically generates a sales page based on the received sales information and publishes it on the platform. A publication notification is sent to the user. The input is sales information (price, description), and the output is the published sales page.

[1294] Step 7:

[1295] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. The server calculates profits and distributes them to the user after deducting a fee. The input is the purchase transaction, and the output is payment processing and profit distribution.

[1296] Step 8:

[1297] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard. The input is the purchase data and profit sharing data, and the output is the revenue report.

[1298] (Application example 1)

[1299] 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."

[1300] Conventional AI model creation systems lacked the functionality to allow users to easily create models that reflect their own thoughts and preferences, and to sell those models to other users. Furthermore, they lacked the functionality to utilize the created AI models for content recommendations, which prevented them from improving user convenience. This made it difficult to provide users with a personalized experience.

[1301] 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.

[1302] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a recommendation means for using the generated AI model to recommend content; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; and a report generation means for generating a profit report. This enables users to easily create AI models that reflect their own thoughts and preferences, use the models to recommend personalized content, and sell them to other users.

[1303] The "user registration means" is a means by which a user accesses the system via a web browser, enters information such as name, email address, and password, and registers.

[1304] A "login means" is a means for a user to enter an email address and password for authentication when accessing a system.

[1305] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[1306] "Evaluation means" refers to a means for evaluating the constructed AI model and notifying the user of the evaluation results.

[1307] "Modification and retraining measures" are measures to modify the AI ​​model based on the evaluation results and retrain it using new data.

[1308] A "recommendation method" is a method of using the generated AI model to recommend content that matches the user's preferences.

[1309] The "sales information input means" is a means for inputting information such as price and description in order to sell the generated AI model on the platform.

[1310] The "sales page generation means" is a means for automatically generating and publishing a sales page based on sales information.

[1311] A "profit distribution method" is a method for calculating profits earned from sold AI models and distributing them to users after deducting fees.

[1312] The "report generating means" is a means for generating a profit income report and providing it to the user.

[1313] This invention provides a system that allows users to create an AI model that reflects their own thoughts and preferences, and use that model for content recommendation and sales. Specific embodiments of this system are described below.

[1314] The system's components are mainly related to the server, terminals, and users. Specifically, it consists of a user registration means, a login means, a tool provision means, an evaluation means, a correction and retraining means, a recommendation means, a sales information input means, a sales page generation means, a profit distribution means, and a report generation means.

[1315] A user accesses the system via a web browser, enters their name, email address, password, etc. into the user registration form, and submits it to the server. The server saves the registration information in a database and automatically sends a confirmation email to complete the user registration. Next, the user enters their email address and password on the login page, and the server authenticates the information by referencing it in the database. If authentication is successful, the user's dashboard is displayed.

[1316] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data reflecting their thoughts and preferences (e.g., music preference list, viewing history, etc.). After inputting the data, the user clicks the "Start Training" button, and the server begins training the AI ​​model based on the received data and saves the generated model.

[1317] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal and can be viewed by the user. The user then inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The server retrains the AI ​​model based on the modified data, reevaluates the generated new model, and notifies the user of the results.

[1318] The generated AI model is used to recommend content (music, videos, articles, etc.) that matches the user's preferences. The recommendation tool allows users to receive personalized content recommendations. To sell the completed AI model, users click the "Sell" button and enter sales information such as price and description. This information is sent to the server, and a sales page is automatically generated and published.

[1319] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit and distributes the remainder to the user after deducting a certain fee. The server also generates a revenue report based on the purchase data and profit distribution details, and displays it on the user's dashboard.

[1320] As a concrete example, consider the case where a user creates a "music recommendation AI model." The user inputs their favorite music list and listening history, and trains the AI ​​model based on that data. The completed model recommends new music that matches the user's tastes. An example of a prompt sentence would be, "The user inputs their favorite music list and trains the AI ​​model based on that data. The completed model recommends new music that is close to the user's tastes."

[1321] The hardware used is a server (e.g., Amazon Web Services, Google Cloud Platform), and the software used is a web framework (e.g., Flask), a database (e.g., MySQL, PostgreSQL), and a library for training AI models (e.g., TensorFlow, PyTorch).

[1322] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1323] Step 1:

[1324] A user accesses the system via a web browser, enters information such as name, email address, and password into the user registration form, and sends it to the server. The server receives this information and stores it in a database. In this process, the input is the user's personal information, and the output is the registration information stored in the database.

[1325] Step 2:

[1326] A user enters their email address and password on the login page and clicks the login button. The server checks this information against the database and, if authentication is successful, displays the user's dashboard. The input is an email address and password, and the output is the authentication result and the display of the dashboard.

[1327] Step 3:

[1328] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data that reflects their thoughts and preferences (e.g., music preference list, listening history, etc.). The input is the user's data, and the output is the transmission of the data to the server.

[1329] Step 4:

[1330] When the user clicks the "Start Training" button, the server starts training the AI ​​model based on the received data. Libraries such as TensorFlow and PyTorch are used for training. In this process, the input is the user data, and the output is the generated AI model.

[1331] Step 5:

[1332] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation uses model accuracy and performance indicators. The input is the generated AI model, and the output is a notification of the evaluation results.

[1333] Step 6:

[1334] The user inputs data to modify the model based on the evaluation results and performs retraining. The server receives the modified data and performs retraining. In this process, the input is the modified data and the output is an improved AI model.

[1335] Step 7:

[1336] The generated AI model is used to recommend content (e.g., music, videos, articles, etc.) that matches the user's preferences. The server uses a recommendation algorithm to extract content and suggest it to the user. The input is the generated AI model, and the output is the recommended content.

[1337] Step 8:

[1338] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description. The server receives this information and automatically generates and publishes a sales page. The input is the sales information, and the output is the published sales page.

[1339] Step 9:

[1340] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once the payment is completed, the server calculates the profit, deducts the fee, and distributes the remainder to the user. The input is the purchase information, and the output is the payment processing result and the distribution of the profit.

[1341] Step 10:

[1342] The server generates a revenue report based on the purchase data and profit sharing information and displays it on the user's dashboard. The input is the purchase data and profit sharing information, and the output is the revenue report.

[1343] 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.

[1344] This invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thoughts and earn revenue by selling those models, and includes the following components:

[1345] System configuration

[1346] 1. User registration method

[1347] 2. Login method

[1348] 3. Tool Provision Method

[1349] 4. Evaluation Methods

[1350] 5. Corrective and retraining measures

[1351] 6. Sales information input method

[1352] 7. Sales Page Generation Method

[1353] 8. Profit distribution means

[1354] 9. Report Generation Methods

[1355] 10. Emotion Engine

[1356] Detailed system description

[1357] User registration method

[1358] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1359] Login method

[1360] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1361] Tool provision method

[1362] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics their thoughts. After inputting the data, the user clicks the "Save" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[1363] Emotion Engine

[1364] The server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine uses natural language processing technology to analyze the user's emotions and adjusts the training data for the AI ​​model based on the analysis results. For example, if the data entered by the user contains a lot of positive emotions, the AI ​​model will be trained to emphasize positive data based on that information.

[1365] Evaluation methods

[1366] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[1367] Corrective and retraining measures

[1368] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[1369] Sales information input method

[1370] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[1371] Sales page generation method

[1372] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[1373] profit distribution means

[1374] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[1375] Report Generation Method

[1376] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1377] Specific examples

[1378] As a specific example of use, consider a scenario in which a user uses the emotion engine to create an AI model that reflects their own expertise. For example, suppose a user creates an AI model based on customer service emotion analysis. The user inputs customer text data through the system, and the emotion engine analyzes the data to recognize positive or negative emotions. The AI ​​model is trained based on this emotion information, enabling it to respond quickly to changes in customer emotion. The completed AI model can be sold on the platform, and users can earn revenue when other users purchase it.

[1379] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[1380] The processing flow will be explained below.

[1381] User registration and login process

[1382] Step 1:

[1383] The user accesses the system via a web browser and clicks the "Register" button.

[1384] Step 2:

[1385] The terminal displays a user registration form.

[1386] Step 3:

[1387] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[1388] Step 4:

[1389] The terminal transmits the entered registration information to the server.

[1390] Step 5:

[1391] The server stores the received registration information in a database and automatically sends a confirmation email.

[1392] Step 6:

[1393] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[1394] Step 7:

[1395] The user enters their email address and password on the login page and clicks the "Login" button.

[1396] Step 8:

[1397] The terminal sends the login information to the server.

[1398] Step 9:

[1399] The server checks the database to see if the entered email address and password match.

[1400] Step 10:

[1401] If the authentication is successful, the server displays the user's dashboard on the terminal.

[1402] Processing related to the provision of AI creation tools

[1403] Step 11:

[1404] The user clicks the "Create AI" button on the dashboard.

[1405] Step 12:

[1406] The device displays the interface of the AI ​​creation tool.

[1407] Step 13:

[1408] The user inputs text data based on their own thoughts and clicks the "Save" button.

[1409] Step 14:

[1410] The device temporarily stores the entered data and provides a "Start Training" button.

[1411] Processing for training AI models

[1412] Step 15:

[1413] The user clicks the "Start Training" button.

[1414] Step 16:

[1415] The terminal transmits the input data to the server.

[1416] Step 17:

[1417] The server begins training the AI ​​model based on the received data.

[1418] Step 18:

[1419] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[1420] Emotion engine processing

[1421] Step 19:

[1422] The server passes the data entered by the user to the emotion engine, which recognizes the emotion.

[1423] Step 20:

[1424] The emotion engine uses natural language processing technology to analyze the user's emotions from the input data.

[1425] Step 21:

[1426] The emotion engine passes the analysis results to a server, which generates instructions for adjusting the training data for the AI ​​model.

[1427] AI model evaluation and correction process

[1428] Step 22:

[1429] The server notifies the user based on the evaluation results and emotion engine information.

[1430] Step 23:

[1431] The terminal displays the evaluation results to the user.

[1432] Step 24:

[1433] The user modifies the model based on the evaluation results and clicks the "Retrain" button.

[1434] Step 25:

[1435] The terminal transmits the corrected data to the server.

[1436] Step 26:

[1437] The server retrains the modified AI model and calculates the evaluation results again.

[1438] Step 27:

[1439] The server sends the new evaluation results to the terminal and notifies the user.

[1440] Processing of AI model sales

[1441] Step 28:

[1442] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[1443] Step 29:

[1444] The terminal transmits the sales information to the server.

[1445] Step 30:

[1446] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[1447] Step 31:

[1448] The server notifies the terminal that the sales page has been published and displays it to the user.

[1449] Profit sharing and reporting process

[1450] Step 32:

[1451] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[1452] Step 33:

[1453] The server processes the payment through a payment system.

[1454] Step 34:

[1455] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[1456] Step 35:

[1457] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1458] This completes a series of steps that allows users to use the emotion engine to create and sell AI models that mimic their own thoughts and earn revenue.

[1459] Example 2

[1460] 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."

[1461] Conventional AI model creation and sales systems have had the problem that it is difficult for users to create models that take into account their own thoughts and emotions. It is also difficult for users to evaluate the emotional balance of the AI ​​models they have created. Furthermore, the process of selling the created models and earning revenue is cumbersome. There was a need to solve these issues and provide a more user-friendly and efficient system.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1463] In this invention, the server includes a user registration means accessible by a user via a web browser, a login means for authenticating registered user information accessible by the user, a tool provision means for constructing an AI model based on data provided by the user, an evaluation means for evaluating the constructed AI model, a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results, a sales information input means for selling the generated AI model on the platform, a sales page generation means for generating and publishing a sales page based on the sales information, a profit distribution means for distributing profits earned from the sold AI model to users, a report generation means for generating a profit report, and an emotion engine for analyzing emotions using natural language processing. This enables users to create, evaluate, sell, and distribute profits from AI models that take emotions into account.

[1464] The "user registration means" is a function that allows a user to access the system via a web browser, enter their own information, and register with the system.

[1465] The "login means" is a function that allows a user to access the system based on registered user information.

[1466] "Tool provision means" is a function that provides interfaces and tools for building AI models based on data provided by the user.

[1467] "Evaluation means" is a function for evaluating the performance and quality of the constructed AI model.

[1468] "Modification and retraining means" is a function for modifying the AI ​​model based on the evaluation results and retraining it.

[1469] The "sales information input means" is a function for inputting information for selling the generated AI model.

[1470] The "sales page generation means" is a function for automatically generating and publishing a sales page based on input sales information.

[1471] "Profit distribution means" is a function for distributing profits earned from sold AI models to users.

[1472] The "report generation means" is a function for generating a profit income report and providing it to the user.

[1473] The "emotion engine" is a function that uses natural language processing to analyze the emotions in input data and optimize the training data for AI models.

[1474] The present invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. Specific embodiments are described below.

[1475] System Components

[1476] This system mainly consists of the following components:

[1477] 1. User registration method

[1478] 2. Login method

[1479] 3. Tool Provision Method

[1480] 4. Evaluation Methods

[1481] 5. Corrective and retraining measures

[1482] 6. Sales information input method

[1483] 7. Sales Page Generation Method

[1484] 8. Profit distribution means

[1485] 9. Report Generation Methods

[1486] 10. Emotion Engine

[1487] User registration method

[1488] The user accesses the system via a web browser (e.g., Google Chrome, Mozilla Firefox) and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as their name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database (e.g., MySQL, PostgreSQL), automatically sends a confirmation email, and registration is complete.

[1489] Login method

[1490] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1491] Tool provision method

[1492] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data that mimics their thoughts (e.g., text data such as "The weather is very nice today, and I'm feeling very good."). After inputting the data, the user clicks the "Save" button to send the data to the server. The server uses the received data to begin training an AI model using natural language processing tools (e.g., NLTK, spaCy) and machine learning tools (e.g., TensorFlow, PyTorch), and stores the generated model in a database.

[1493] Emotion Engine

[1494] A distinctive feature of this system is that the server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine analyzes the user's emotions using natural language processing techniques (e.g., BERT, GPT-3), and adjusts the training data for the AI ​​model based on the analysis results.

[1495] Evaluation methods

[1496] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[1497] Corrective and retraining measures

[1498] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[1499] Sales information input method

[1500] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[1501] Sales page generation method

[1502] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[1503] profit distribution means

[1504] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe). Once payment is completed, the server calculates the profit and distributes the remainder to the user after deducting a certain fee.

[1505] Report Generation Method

[1506] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1507] Specific examples

[1508] Consider a scenario where a user is creating an AI model based on customer service sentiment analysis. The user inputs customer text data (e.g., "The customer is satisfied with the service") through the system, and the sentiment engine analyzes the data to recognize positive or negative sentiment. The AI ​​model is trained based on this emotional information and can quickly respond to changes in customer sentiment. The completed AI model can be sold on the platform, and other users can purchase it, generating revenue for the user.

[1509] Prompt Sentence Examples

[1510] "Please enter text data. Example: 'The weather is very nice today and I'm feeling great.'"

[1511] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[1512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1513] Processing Steps

[1514] Step 1: User Registration

[1515] The user opens a web browser to the system's home page and clicks the "Register" button.

[1516] The terminal displays a user registration form, where the user enters information such as name, email address, and password, and clicks the "Submit" button.

[1517] The server receives the entered information, stores it in a database, and automatically sends a confirmation email to notify the user that their registration is complete.

[1518] Input: User's name, email address, and password

[1519] Data processing: Save input information to a database

[1520] Output: Sending a confirmation email and notifying the completion of registration

[1521] Step 2: Log in

[1522] The user enters their email address and password on the login page and clicks the "Login" button.

[1523] The terminal sends the entered information to the server, which then refers to a database and performs authentication.

[1524] If authentication is successful, the server displays the user's dashboard.

[1525] Input: Email address, Password

[1526] Data calculation: Authentication in the database based on input information

[1527] Output: Display dashboard

[1528] Step 3: Creating an AI model

[1529] The user clicks the "Create AI" button on the dashboard.

[1530] The device displays an interface for creating AI, and the user inputs data that mimics their thoughts. For example, they input text data such as, "The weather is very nice today, and I'm in a great mood."

[1531] The user clicks the "Save" button to send the data to the server.

[1532] The server uses natural language processing tools to analyze the text based on the received data and begins training the AI ​​model. The generated model is then stored in a database.

[1533] Input: User's text data

[1534] Data processing: Text analysis using natural language processing

[1535] Data Computing: Generating training data and training AI models

[1536] Output: Save the trained AI model

[1537] Step 4: Analysis by the Emotion Engine

[1538] The server inputs the training data into the emotion engine and performs emotion analysis.

[1539] The sentiment engine uses natural language processing techniques to identify sentiment within the data and calculate the percentage of positive and negative sentiment.

[1540] Based on the results of the sentiment analysis, the server optimizes the training data for the AI ​​model.

[1541] Input: Training data

[1542] Data Computation: Extracting Emotional Information through Sentiment Analysis

[1543] Output: Optimized training data

[1544] Step 5: Evaluate the model

[1545] The server performs evaluation tests on the generated AI model.

[1546] The evaluation results are displayed on the terminal, allowing the user to check the evaluation results.

[1547] Emotional balance is also evaluated using an emotion engine.

[1548] Input: AI model

[1549] Data calculation: Evaluation test administration and emotional balance measurement

[1550] Output: Display of evaluation results

[1551] Step 6: Modify and retrain the model

[1552] If the user is not satisfied with the evaluation results, he or she can re-enter the corrected data and click the "Retrain" button.

[1553] The terminal transmits the corrected data to the server.

[1554] The server retrains the AI ​​model based on the new data and generates a new model.

[1555] The evaluation results for the new model will be displayed on the device again.

[1556] Input: Correction data

[1557] Data Calculation: Retraining

[1558] Output: Re-evaluated AI model and results display

[1559] Step 7: Enter sales information

[1560] To sell a completed AI model, users click the "Sell" button and enter sales information such as price and description.

[1561] This information is sent to a server and recorded in a database.

[1562] Input: Sales information such as price, description, etc.

[1563] Data processing: Sales information stored in a database

[1564] Output: Sales information stored in a database

[1565] Step 8: Generate a sales page

[1566] The server automatically generates a sales page based on the entered sales information.

[1567] Once the sales page is published, the server notifies the user.

[1568] Input: Sales information

[1569] Data processing: Sales page generation

[1570] Output: Published sales page and notification

[1571] Step 9: Profit sharing

[1572] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe).

[1573] Once payment is completed, the server deducts the fee, calculates the profit, and distributes the remainder to the user.

[1574] Input: Purchase Information

[1575] Data calculation: settlement processing and profit calculation

[1576] Output: Profit sharing

[1577] Step 10: Generate reports

[1578] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1579] Input: Purchase data, profit sharing information

[1580] Data Processing: Report Generation

[1581] Output: View the revenue report

[1582] (Application example 2)

[1583] 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."

[1584] In modern e-commerce, it is difficult for users to select products that best suit their preferences and needs from the wide variety of products available. This is particularly true when review information is scattered and often lacks reliability. Furthermore, when users make purchasing decisions based on product reviews, it is difficult to accurately reflect the impact of the review content on the user's emotions. Therefore, it is important to improve the user experience and stimulate purchasing motivation by suggesting products that are appropriate for each user based on sentiment analysis.

[1585] The identification processing by the identification 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: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; a report generation means for generating a profit report; a sentiment analysis means for analyzing the sentiment of product reviews when the user views them; and a product suggestion means for making customized product suggestions to the user based on the sentiment analysis results. This allows users to receive product suggestions that match their preferences and needs through the sentiment analysis of reviews, thereby improving their purchasing experience.

[1586] The "user registration means" is a means by which a user accesses the system via a web browser and registers by entering personal information such as name, email address, and password.

[1587] "Login means" refers to the means by which a user accesses the system using a registered email address and password and is authenticated.

[1588] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[1589] "Evaluation means" refers to a means for evaluating the constructed AI model and providing the evaluation results to the user.

[1590] "Modification and retraining means" refers to a means of modifying an AI model based on the evaluation results and retraining it.

[1591] The "sales information input means" is a means for inputting information for selling the generated AI model on the platform.

[1592] The "sales page generation means" is a means for automatically generating and publishing a sales page based on input sales information.

[1593] A "profit distribution method" is a method for distributing profits earned from sold AI models to users after deducting a certain fee.

[1594] The "report generating means" is a means for generating a profit income report and providing it to the user.

[1595] The "sentiment analysis means" is a means for analyzing the sentiment of a product review when the user views the review and determining whether the sentiment is positive or negative.

[1596] The "product suggestion means" is a means for making customized product suggestions to the user based on the emotion analysis results.

[1597] The present invention combines an emotion engine with a system that allows users to create AI models that mimic their own thoughts and sell those models. Specific embodiments of the present invention will be described below.

[1598] 1. System Overview

[1599] This system includes means for user registration, login, tool provision, evaluation, correction and retraining, sales information input, sales page generation, profit distribution, report generation, sentiment analysis, and product suggestion. By using this system, users can easily create and sell AI models that reflect their own emotions.

[1600] 2. Hardware and Software Used

[1601] Hardware: Smartphone (iOS, Android)

[1602] software:

[1603] Frontend: React Native

[1604] Backend: Node.js, Express.js

[1605] Database: MongoDB

[1606] Emotion Engine: Google Cloud Natural Language API

[1607] Payment system: Stripe API

[1608] 3. Processing Flow

[1609] User Registration

[1610] Users access the system via a web browser and enter the required information (name, email address, password) using the user registration method. The entered information is stored in a MongoDB database via a Node.js server.

[1611] Log in

[1612] The user enters their email address and password on the login page, and is authenticated again via the Node.js server. If authentication is successful, the dashboard is displayed.

[1613] Creating an AI model

[1614] Users use the provided tools to input data based on their own thoughts and build an AI model, which is then sent to the server and analyzed through the emotion engine, after which the AI ​​model is trained.

[1615] Sentiment analysis and product recommendations

[1616] When a user browses a product review, the review's text data undergoes sentiment analysis using the Google Cloud Natural Language API. Based on the results of this analysis, the product suggestion tool creates a customized list of products to suggest to the user.

[1617] Sales and Revenue Sharing

[1618] Completed AI models are sold on the platform through the sales information input means. The sales page generation means automatically generates and publishes a sales page. Profits from the sold AI models are distributed to users after deducting fees using the Stripe API, and a profit revenue report is generated.

[1619] 4. Examples and prompts

[1620] As a concrete example, let's consider a case where a user values ​​positive reviews. For example, if the user enters the following prompt, the AI ​​will suggest suitable products for the user.

[1621] Example of input prompt:

[1622] "Generate the following product suggestions based on recent purchase history and sentiment analysis of reviews. Users prefer positive reviews, so list products that match that."

[1623] Purchase History:

[1624] Product A (positive evaluation)

[1625] Product B (negative rating)

[1626] Review sentiment analysis results:

[1627] Product C: 80% of reviews contain positive sentiment

[1628] Product D: 50% of reviews contain negative sentiment

[1629] Using this example prompt sentence, the system can suggest an appropriate product (e.g., product C) to the user.

[1630] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1631] Step 1:

[1632] User Registration:

[1633] A user accesses the registration page via a web browser and enters their name, email address, and password. The input information is sent from the terminal to the Node.js server. The server stores the received information in a MongoDB database. Once the storage is complete, a confirmation email is sent. Input: User information. Output: Data saved in MongoDB, confirmation email sent.

[1634] Step 2:

[1635] Login:

[1636] The user enters their email address and password on the login page and sends them again from their terminal to the Node.js server. The server performs authentication by referencing the MongoDB database. If authentication is successful, the user's dashboard is displayed. Input: Email address, password. Output: Dashboard displayed when authentication is successful.

[1637] Step 3:

[1638] Creating an AI model:

[1639] Users input data on the dashboard and create an AI model using the tools provided. The input data is sent from the device to the server and analyzed using the emotion engine (Google Cloud Natural Language API). The analysis results are used to train the AI ​​model on the server. Input: User data (text, etc.). Output: Trained AI model.

[1640] Step 4:

[1641] Assessment and Remediation / Retraining:

[1642] The server evaluates the trained AI model and notifies the device of the evaluation results. The user modifies the model based on the evaluation results and retrains it. The modified data is sent back to the server, and retraining is performed. Input: Modified data. Output: Retrained AI model, notification of evaluation results.

[1643] Step 5:

[1644] Sentiment analysis and product recommendations:

[1645] When a user browses a product review, the review's text data is sent from the device to the server. The server performs sentiment analysis using the Google Cloud Natural Language API. The analysis results are stored in a database, and customized product suggestions are generated based on the results. Input: Review text data. Output: Sentiment analysis results, customized product suggestions.

[1646] Step 6:

[1647] Sales and Revenue Sharing:

[1648] The user enters sales information for the completed AI model and sends it from the terminal to the server. The server automatically generates and publishes a sales page, and the AI ​​model is sold. The sales data is processed by the Stripe API, and profits are calculated after deducting fees. This profit is distributed to the user, and a revenue report is generated. Input: Sales information, payment information. Output: Automatically generated sales page, revenue report.

[1649] Step 7:

[1650] Notifications and History Management:

[1651] The evaluation results and revenue information of the AI ​​model generated by the user are notified to the device and can be viewed by the user. In addition, past purchase history and recommendation history are stored in a MongoDB database and can be viewed by the user on their personal page. Input: Evaluation results, revenue information. Output: Notification, display of history information.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] [Fourth embodiment]

[1656] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1657] 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.

[1658] 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).

[1659] 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.

[1660] 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.

[1661] 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).

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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."

[1669] This invention describes a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling the models. This system includes the following components:

[1670] System configuration

[1671] 1. User registration method

[1672] 2. Login method

[1673] 3. Tool Provision Method

[1674] 4. Evaluation Methods

[1675] 5. Corrective and retraining measures

[1676] 6. Sales information input method

[1677] 7. Sales Page Generation Method

[1678] 8. Profit distribution means

[1679] 9. Report Generation Methods

[1680] Detailed system description

[1681] User registration method

[1682] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1683] Login method

[1684] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1685] Tool provision method

[1686] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. After inputting the data, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[1687] Evaluation methods

[1688] The server evaluates the generated AI model and notifies the user of the results, which are then displayed on the device for the user to review.

[1689] Corrective and retraining measures

[1690] The user inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The device sends the modified data to the server, which then performs retraining. The generated new model is then reevaluated, and the results are notified to the user.

[1691] Sales information input method

[1692] To sell a completed AI model, the user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[1693] Sales page generation method

[1694] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[1695] profit distribution means

[1696] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[1697] Report Generation Method

[1698] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1699] Specific examples

[1700] As a specific example of use, consider a scenario in which a user creates and sells an AI model that reflects their own expertise. For example, suppose a user uses their knowledge of finance to create an AI model for stock price prediction. The user inputs data through the system to generate the AI ​​model. The generated model is evaluated and modified as necessary. The final completed AI model is sold on the platform, and users can earn revenue by purchasing it from other users.

[1701] As described above, the present invention is an excellent system that allows users to easily create AI models and earn revenue through a series of processes including evaluation, modification, retraining, and sales.

[1702] The processing flow will be explained below.

[1703] User Registration and Login

[1704] Step 1:

[1705] The user accesses the system via a web browser and clicks the "Register" button.

[1706] Step 2:

[1707] The terminal displays a user registration form.

[1708] Step 3:

[1709] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[1710] Step 4:

[1711] The terminal transmits the entered registration information to the server.

[1712] Step 5:

[1713] The server stores the received registration information in a database and automatically sends a confirmation email.

[1714] Step 6:

[1715] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[1716] Step 7:

[1717] The user enters their email address and password on the login page and clicks the "Login" button.

[1718] Step 8:

[1719] The terminal sends the login information to the server.

[1720] Step 9:

[1721] The server checks the database to see if the entered email address and password match.

[1722] Step 10:

[1723] If the authentication is successful, the server displays the user's dashboard on the terminal.

[1724] Providing AI creation tools

[1725] Step 11:

[1726] The user clicks the "Create AI" button on the dashboard.

[1727] Step 12:

[1728] The device displays the interface of the AI ​​creation tool.

[1729] Step 13:

[1730] The user inputs data based on their own thoughts (e.g., text data) and clicks the "Save" button.

[1731] Step 14:

[1732] The device temporarily stores the entered data and provides a "Start Training" button.

[1733] Training an AI model

[1734] Step 15:

[1735] The user clicks the "Start Training" button.

[1736] Step 16:

[1737] The terminal transmits the input data to the server.

[1738] Step 17:

[1739] The server begins training the AI ​​model based on the received data.

[1740] Step 18:

[1741] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[1742] Evaluating and correcting AI models

[1743] Step 19:

[1744] The server transmits the evaluation results to the terminal and notifies the user.

[1745] Step 20:

[1746] The terminal displays the evaluation results to the user.

[1747] Step 21:

[1748] The user inputs data to modify the model based on the evaluation results and clicks the "Retrain" button.

[1749] Step 22:

[1750] The terminal transmits the corrected data to the server.

[1751] Step 23:

[1752] The server retrains the modified model and recalculates the evaluation results.

[1753] Step 24:

[1754] The server sends the new evaluation results to the terminal and notifies the user.

[1755] Selling AI models

[1756] Step 25:

[1757] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[1758] Step 26:

[1759] The terminal transmits the sales information to the server.

[1760] Step 27:

[1761] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[1762] Step 28:

[1763] The server notifies the terminal that the sales page has been published and displays it to the user.

[1764] Profit sharing and reporting

[1765] Step 29:

[1766] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[1767] Step 30:

[1768] The server processes the payment through a payment system.

[1769] Step 31:

[1770] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[1771] Step 32:

[1772] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1773] This completes a series of steps that allows users to create and sell AI models that mimic their own thinking and earn revenue.

[1774] Example 1

[1775] 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."

[1776] In conventional AI model generation systems, the process required for individuals to create AI models that reflect their own thinking and sell them to generate revenue was complex and often required specialized knowledge. Furthermore, the process of evaluating, correcting, retraining, and selling the models was not efficient, placing a heavy burden on users and making it difficult to monetize.

[1777] 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.

[1778] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing a generative AI model based on data provided by the user; an evaluation means for evaluating the constructed generative AI model; a correction and retraining means for correcting and retraining the generative AI model based on the evaluation results; a sales information input means for selling the generated generative AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold generative AI model to users; and a report generation means for generating a profit report of the profits. This enables individuals to easily create, modify, and sell AI models and earn profits thereby.

[1779] The "user registration means" is a means by which a user accesses the system via a web browser and registers with the system by entering information such as name, email address, and password.

[1780] "Login means" refers to the means by which a registered user logs into the system by entering an email address and password.

[1781] "Tool provision means" refers to a means of providing an interface and functionality for building a generative AI model based on data provided by the user.

[1782] The "evaluation means" is a means for evaluating the constructed generative AI model and notifying the user of the results.

[1783] "Modification and retraining means" refers to a means for modifying the generative AI model based on the evaluation results and retraining it.

[1784] The "sales information input means" is a means for a user to input a price and description for selling a generated AI model.

[1785] The "sales page generation means" is a means for automatically generating a sales page based on sales information and publishing it on the platform.

[1786] "Profit sharing means" means a means for calculating and distributing profits earned from the sold generative AI models to users.

[1787] The "report generation means" is a means for generating profit revenue reports and displaying them on the user's dashboard.

[1788] "Payment processing means" refers to a means for processing payments from potential purchasers regarding the sale of AI models.

[1789] The "notification means" is a means for notifying the user's device of the evaluation results of the AI ​​model generated by the user.

[1790] The present invention is a system that allows users to create AI models that mimic their own thinking and earn revenue by selling those models. This system is implemented using the following hardware and software.

[1791] Hardware and software used

[1792] Hardware

[1793] Server: Includes database, AI training and evaluation system, and payment processing system

[1794] Terminal (user device): PC, smartphone, tablet, etc.

[1795] software

[1796] Web browser

[1797] Database management system (e.g. MySQL)

[1798] AI training platforms (e.g. TensorFlow, PyTorch)

[1799] Program processing

[1800] User registration method

[1801] The user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters information such as name, email address, and password into the form and submits the registration information. The server saves the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1802] Login method

[1803] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then authenticates the user by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1804] Tool provision method

[1805] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics the user's thoughts. The user then clicks the "Start Training" button to send the data to the server. The server uses the received data to start training the AI ​​model and saves the generated model.

[1806] Evaluation methods

[1807] The server evaluates the generated AI model and notifies the user of the evaluation results, which are then displayed on the device for the user to review.

[1808] Corrective and retraining measures

[1809] The user modifies the model based on the evaluation results and retrains it by inputting new data. The device then sends the modified data to the server, which then retrains it. The generated new model is then reevaluated, and the results are notified to the user.

[1810] Sales information input method

[1811] The user clicks the "Sell" button and enters the price and description of the AI ​​model, and the device sends this information to the server.

[1812] Sales page generation method

[1813] The server automatically generates a sales page based on the received sales information and publishes it on the platform, and a publication notification is sent to the user.

[1814] profit distribution means

[1815] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system, calculates the profit, and distributes it to the user after deducting a commission fee.

[1816] Report Generation Method

[1817] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1818] Specific examples

[1819] Example of user registration procedure

[1820] The user accesses the system's URL and clicks the "Register" button on the page that appears. The terminal displays a form for entering name, email address, and password. The user enters the information and clicks the "Submit" button. The server receives the entered information and stores it in a database. The server automatically sends a confirmation email and displays a message to the user saying "Registration completed."

[1821] Specific examples of AI model creation

[1822] The user clicks the "Create AI" button on the dashboard. The terminal displays the AI ​​creation interface, allowing the user to enter text data. The user enters text data related to asset management into the system and clicks the "Start Training" button. The server receives the entered data and begins training the AI ​​model. The server saves the trained model and notifies the user of the generated results.

[1823] Prompt Sentence Examples

[1824] "Use your financial knowledge to create and sell an AI model for predicting stock prices. Please provide a detailed description of the steps from user registration to sales."

[1825] As described above, the present invention is a system that enables individuals to easily create, modify, and sell AI models, thereby earning revenue.

[1826] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1827] Step 1:

[1828] A user accesses the system via a web browser and clicks the "Register" button. The terminal displays a user registration form, and the user enters their name, email address, and password. The entered information is sent to the server, which stores the received information in a database and automatically sends a confirmation email to complete the registration. The input is user information (name, email address, password), and the output is the user information stored in the database and a confirmation email.

[1829] Step 2:

[1830] The user enters their email address and password on the login page and clicks the "Login" button. The device sends the login information to the server, which then references the database to authenticate the user. If authentication is successful, the server displays the user's dashboard. The input is login information (email address, password), and the output is the authentication result and the dashboard display.

[1831] Step 3:

[1832] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, and the user inputs data (e.g., text data) that mimics their thoughts. Once the input is complete, the user clicks the "Start Training" button to send the data to the server. The server begins training the AI ​​model using the received data and saves the generated model. The input is the training data (text data), and the output is the generated AI model.

[1833] Step 4:

[1834] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal so that the user can check them. The input is the generated AI model, and the output is the evaluation results.

[1835] Step 5:

[1836] The user modifies the model based on the evaluation results and inputs new data. When the user clicks the "Start Training" button, the device sends the modified data to the server. The server then retrains, reevaluates the generated new model, and notifies the user of the results. The input is the modified data, and the output is the reevaluated AI model and its evaluation results.

[1837] Step 6:

[1838] The user clicks the "Sell" button and enters the price and description of the AI ​​model. The device sends this information to the server. The server automatically generates a sales page based on the received sales information and publishes it on the platform. A publication notification is sent to the user. The input is sales information (price, description), and the output is the published sales page.

[1839] Step 7:

[1840] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. The server calculates profits and distributes them to the user after deducting a fee. The input is the purchase transaction, and the output is payment processing and profit distribution.

[1841] Step 8:

[1842] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard. The input is the purchase data and profit sharing data, and the output is the revenue report.

[1843] (Application example 1)

[1844] 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."

[1845] Conventional AI model creation systems lacked the functionality to allow users to easily create models that reflect their own thoughts and preferences, and to sell those models to other users. Furthermore, they lacked the functionality to utilize the created AI models for content recommendations, which prevented them from improving user convenience. This made it difficult to provide users with a personalized experience.

[1846] 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.

[1847] In this invention, the server includes: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a recommendation means for using the generated AI model to recommend content; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; and a report generation means for generating a profit report. This enables users to easily create AI models that reflect their own thoughts and preferences, use the models to recommend personalized content, and sell them to other users.

[1848] The "user registration means" is a means by which a user accesses the system via a web browser, enters information such as name, email address, and password, and registers.

[1849] A "login means" is a means for a user to enter an email address and password for authentication when accessing a system.

[1850] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[1851] "Evaluation means" refers to a means for evaluating the constructed AI model and notifying the user of the evaluation results.

[1852] "Modification and retraining measures" are measures to modify the AI ​​model based on the evaluation results and retrain it using new data.

[1853] A "recommendation method" is a method of using the generated AI model to recommend content that matches the user's preferences.

[1854] The "sales information input means" is a means for inputting information such as price and description in order to sell the generated AI model on the platform.

[1855] The "sales page generation means" is a means for automatically generating and publishing a sales page based on sales information.

[1856] A "profit distribution method" is a method for calculating profits earned from sold AI models and distributing them to users after deducting fees.

[1857] The "report generating means" is a means for generating a profit income report and providing it to the user.

[1858] This invention provides a system that allows users to create an AI model that reflects their own thoughts and preferences, and use that model for content recommendation and sales. Specific embodiments of this system are described below.

[1859] The system's components are mainly related to the server, terminals, and users. Specifically, it consists of a user registration means, a login means, a tool provision means, an evaluation means, a correction and retraining means, a recommendation means, a sales information input means, a sales page generation means, a profit distribution means, and a report generation means.

[1860] A user accesses the system via a web browser, enters their name, email address, password, etc. into the user registration form, and submits it to the server. The server saves the registration information in a database and automatically sends a confirmation email to complete the user registration. Next, the user enters their email address and password on the login page, and the server authenticates the information by referencing it in the database. If authentication is successful, the user's dashboard is displayed.

[1861] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data reflecting their thoughts and preferences (e.g., music preference list, viewing history, etc.). After inputting the data, the user clicks the "Start Training" button, and the server begins training the AI ​​model based on the received data and saves the generated model.

[1862] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation results are displayed on the terminal and can be viewed by the user. The user then inputs data to modify the model based on the evaluation results and clicks the "Start Training" button again. The server retrains the AI ​​model based on the modified data, reevaluates the generated new model, and notifies the user of the results.

[1863] The generated AI model is used to recommend content (music, videos, articles, etc.) that matches the user's preferences. The recommendation tool allows users to receive personalized content recommendations. To sell the completed AI model, users click the "Sell" button and enter sales information such as price and description. This information is sent to the server, and a sales page is automatically generated and published.

[1864] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit and distributes the remainder to the user after deducting a certain fee. The server also generates a revenue report based on the purchase data and profit distribution details, and displays it on the user's dashboard.

[1865] As a concrete example, consider the case where a user creates a "music recommendation AI model." The user inputs their favorite music list and listening history, and trains the AI ​​model based on that data. The completed model recommends new music that matches the user's tastes. An example of a prompt sentence would be, "The user inputs their favorite music list and trains the AI ​​model based on that data. The completed model recommends new music that is close to the user's tastes."

[1866] The hardware used is a server (e.g., Amazon Web Services, Google Cloud Platform), and the software used is a web framework (e.g., Flask), a database (e.g., MySQL, PostgreSQL), and a library for training AI models (e.g., TensorFlow, PyTorch).

[1867] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1868] Step 1:

[1869] A user accesses the system via a web browser, enters information such as name, email address, and password into the user registration form, and sends it to the server. The server receives this information and stores it in a database. In this process, the input is the user's personal information, and the output is the registration information stored in the database.

[1870] Step 2:

[1871] A user enters their email address and password on the login page and clicks the login button. The server checks this information against the database and, if authentication is successful, displays the user's dashboard. The input is an email address and password, and the output is the authentication result and the display of the dashboard.

[1872] Step 3:

[1873] The user clicks the "Create AI" button on the dashboard, and the device displays an interface for creating AI. The user inputs data that reflects their thoughts and preferences (e.g., music preference list, listening history, etc.). The input is the user's data, and the output is the transmission of the data to the server.

[1874] Step 4:

[1875] When the user clicks the "Start Training" button, the server starts training the AI ​​model based on the received data. Libraries such as TensorFlow and PyTorch are used for training. In this process, the input is the user data, and the output is the generated AI model.

[1876] Step 5:

[1877] The server evaluates the generated AI model and notifies the user of the evaluation results. The evaluation uses model accuracy and performance indicators. The input is the generated AI model, and the output is a notification of the evaluation results.

[1878] Step 6:

[1879] The user inputs data to modify the model based on the evaluation results and performs retraining. The server receives the modified data and performs retraining. In this process, the input is the modified data and the output is an improved AI model.

[1880] Step 7:

[1881] The generated AI model is used to recommend content (e.g., music, videos, articles, etc.) that matches the user's preferences. The server uses a recommendation algorithm to extract content and suggest it to the user. The input is the generated AI model, and the output is the recommended content.

[1882] Step 8:

[1883] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description. The server receives this information and automatically generates and publishes a sales page. The input is the sales information, and the output is the published sales page.

[1884] Step 9:

[1885] When other users purchase an AI model from the sales page, the server processes the payment through the payment system. Once the payment is completed, the server calculates the profit, deducts the fee, and distributes the remainder to the user. The input is the purchase information, and the output is the payment processing result and the distribution of the profit.

[1886] Step 10:

[1887] The server generates a revenue report based on the purchase data and profit sharing information and displays it on the user's dashboard. The input is the purchase data and profit sharing information, and the output is the revenue report.

[1888] 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.

[1889] This invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thoughts and earn revenue by selling those models, and includes the following components:

[1890] System configuration

[1891] 1. User registration method

[1892] 2. Login method

[1893] 3. Tool Provision Method

[1894] 4. Evaluation Methods

[1895] 5. Corrective and retraining measures

[1896] 6. Sales information input method

[1897] 7. Sales Page Generation Method

[1898] 8. Profit distribution means

[1899] 9. Report Generation Methods

[1900] 10. Emotion Engine

[1901] Detailed system description

[1902] User registration method

[1903] The user accesses the system via a web browser and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database and automatically sends a confirmation email, completing the registration.

[1904] Login method

[1905] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[1906] Tool provision method

[1907] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data (e.g., text data) that mimics their thoughts. After inputting the data, the user clicks the "Save" button to send the data to the server. The server begins training the AI ​​model based on the received data and saves the generated model.

[1908] Emotion Engine

[1909] The server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine uses natural language processing technology to analyze the user's emotions and adjusts the training data for the AI ​​model based on the analysis results. For example, if the data entered by the user contains a lot of positive emotions, the AI ​​model will be trained to emphasize positive data based on that information.

[1910] Evaluation methods

[1911] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[1912] Corrective and retraining measures

[1913] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[1914] Sales information input method

[1915] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[1916] Sales page generation method

[1917] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[1918] profit distribution means

[1919] When a prospective buyer purchases an AI model from the sales page, the server processes the payment through the payment system. Once payment is complete, the server calculates the profit, deducts a certain fee, and distributes the remainder to the user.

[1920] Report Generation Method

[1921] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[1922] Specific examples

[1923] As a specific example of use, consider a scenario in which a user uses the emotion engine to create an AI model that reflects their own expertise. For example, suppose a user creates an AI model based on customer service emotion analysis. The user inputs customer text data through the system, and the emotion engine analyzes the data to recognize positive or negative emotions. The AI ​​model is trained based on this emotion information, enabling it to respond quickly to changes in customer emotion. The completed AI model can be sold on the platform, and users can earn revenue when other users purchase it.

[1924] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[1925] The processing flow will be explained below.

[1926] User registration and login process

[1927] Step 1:

[1928] The user accesses the system via a web browser and clicks the "Register" button.

[1929] Step 2:

[1930] The terminal displays a user registration form.

[1931] Step 3:

[1932] The user enters information such as their name, email address, and password into the form and clicks the "Submit" button.

[1933] Step 4:

[1934] The terminal transmits the entered registration information to the server.

[1935] Step 5:

[1936] The server stores the received registration information in a database and automatically sends a confirmation email.

[1937] Step 6:

[1938] The server notifies the terminal that registration has been completed, and displays a registration completion screen.

[1939] Step 7:

[1940] The user enters their email address and password on the login page and clicks the "Login" button.

[1941] Step 8:

[1942] The terminal sends the login information to the server.

[1943] Step 9:

[1944] The server checks the database to see if the entered email address and password match.

[1945] Step 10:

[1946] If the authentication is successful, the server displays the user's dashboard on the terminal.

[1947] Processing related to the provision of AI creation tools

[1948] Step 11:

[1949] The user clicks the "Create AI" button on the dashboard.

[1950] Step 12:

[1951] The device displays the interface of the AI ​​creation tool.

[1952] Step 13:

[1953] The user inputs text data based on their own thoughts and clicks the "Save" button.

[1954] Step 14:

[1955] The device temporarily stores the entered data and provides a "Start Training" button.

[1956] Processing for training AI models

[1957] Step 15:

[1958] The user clicks the "Start Training" button.

[1959] Step 16:

[1960] The terminal transmits the input data to the server.

[1961] Step 17:

[1962] The server begins training the AI ​​model based on the received data.

[1963] Step 18:

[1964] Once training is complete, the server stores the generated AI model in a database and calculates the evaluation results.

[1965] Emotion engine processing

[1966] Step 19:

[1967] The server passes the data entered by the user to the emotion engine, which recognizes the emotion.

[1968] Step 20:

[1969] The emotion engine uses natural language processing technology to analyze the user's emotions from the input data.

[1970] Step 21:

[1971] The emotion engine passes the analysis results to a server, which generates instructions for adjusting the training data for the AI ​​model.

[1972] AI model evaluation and correction process

[1973] Step 22:

[1974] The server notifies the user based on the evaluation results and emotion engine information.

[1975] Step 23:

[1976] The terminal displays the evaluation results to the user.

[1977] Step 24:

[1978] The user modifies the model based on the evaluation results and clicks the "Retrain" button.

[1979] Step 25:

[1980] The terminal transmits the corrected data to the server.

[1981] Step 26:

[1982] The server retrains the modified AI model and calculates the evaluation results again.

[1983] Step 27:

[1984] The server sends the new evaluation results to the terminal and notifies the user.

[1985] Processing of AI model sales

[1986] Step 28:

[1987] The user clicks the "Sell" button on the dashboard, enters sales information (price, description, etc.), and clicks the "Submit" button.

[1988] Step 29:

[1989] The terminal transmits the sales information to the server.

[1990] Step 30:

[1991] The server automatically generates a sales page based on the sales information and publishes it on the platform.

[1992] Step 31:

[1993] The server notifies the terminal that the sales page has been published and displays it to the user.

[1994] Profit sharing and reporting process

[1995] Step 32:

[1996] Potential buyers select an AI model on the sales page and click the "Purchase" button.

[1997] Step 33:

[1998] The server processes the payment through a payment system.

[1999] Step 34:

[2000] The server deducts a portion of the sales as a commission and transfers the remainder to the user's account.

[2001] Step 35:

[2002] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[2003] This completes a series of steps that allows users to use the emotion engine to create and sell AI models that mimic their own thoughts and earn revenue.

[2004] Example 2

[2005] 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."

[2006] Conventional AI model creation and sales systems have had the problem that it is difficult for users to create models that take into account their own thoughts and emotions. It is also difficult for users to evaluate the emotional balance of the AI ​​models they have created. Furthermore, the process of selling the created models and earning revenue is cumbersome. There was a need to solve these issues and provide a more user-friendly and efficient system.

[2007] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2008] In this invention, the server includes a user registration means accessible by a user via a web browser, a login means for authenticating registered user information accessible by the user, a tool provision means for constructing an AI model based on data provided by the user, an evaluation means for evaluating the constructed AI model, a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results, a sales information input means for selling the generated AI model on the platform, a sales page generation means for generating and publishing a sales page based on the sales information, a profit distribution means for distributing profits earned from the sold AI model to users, a report generation means for generating a profit report, and an emotion engine for analyzing emotions using natural language processing. This enables users to create, evaluate, sell, and distribute profits from AI models that take emotions into account.

[2009] The "user registration means" is a function that allows a user to access the system via a web browser, enter their own information, and register with the system.

[2010] The "login means" is a function that allows a user to access the system based on registered user information.

[2011] "Tool provision means" is a function that provides interfaces and tools for building AI models based on data provided by the user.

[2012] "Evaluation means" is a function for evaluating the performance and quality of the constructed AI model.

[2013] "Modification and retraining means" is a function for modifying the AI ​​model based on the evaluation results and retraining it.

[2014] The "sales information input means" is a function for inputting information for selling the generated AI model.

[2015] The "sales page generation means" is a function for automatically generating and publishing a sales page based on input sales information.

[2016] "Profit distribution means" is a function for distributing profits earned from sold AI models to users.

[2017] The "report generation means" is a function for generating a profit income report and providing it to the user.

[2018] The "emotion engine" is a function that uses natural language processing to analyze the emotions in input data and optimize the training data for AI models.

[2019] The present invention combines an emotion engine with a system that allows individuals to create AI models that mimic their own thinking and earn revenue by selling those models. Specific embodiments are described below.

[2020] System Components

[2021] This system mainly consists of the following components:

[2022] 1. User registration method

[2023] 2. Login method

[2024] 3. Tool Provision Method

[2025] 4. Evaluation Methods

[2026] 5. Corrective and retraining measures

[2027] 6. Sales information input method

[2028] 7. Sales Page Generation Method

[2029] 8. Profit distribution means

[2030] 9. Report Generation Methods

[2031] 10. Emotion Engine

[2032] User registration method

[2033] The user accesses the system via a web browser (e.g., Google Chrome, Mozilla Firefox) and clicks the "Register" button. The terminal then displays a user registration form. The user enters information such as their name, email address, and password into the form and sends it to the server. The server stores the received registration information in a database (e.g., MySQL, PostgreSQL), automatically sends a confirmation email, and registration is complete.

[2034] Login method

[2035] The user enters their email address and password on the login page and clicks the "Login" button. The device sends this information to the server, which then authenticates them by referencing the database. If authentication is successful, the server displays the user's dashboard.

[2036] Tool provision method

[2037] The user clicks the "Create AI" button on the dashboard. The device provides an interface for creating AI, allowing the user to input data that mimics their thoughts (e.g., text data such as "The weather is very nice today, and I'm feeling very good."). After inputting the data, the user clicks the "Save" button to send the data to the server. The server uses the received data to begin training an AI model using natural language processing tools (e.g., NLTK, spaCy) and machine learning tools (e.g., TensorFlow, PyTorch), and stores the generated model in a database.

[2038] Emotion Engine

[2039] A distinctive feature of this system is that the server is equipped with an emotion engine that recognizes emotions based on data entered by the user. The emotion engine analyzes the user's emotions using natural language processing techniques (e.g., BERT, GPT-3), and adjusts the training data for the AI ​​model based on the analysis results.

[2040] Evaluation methods

[2041] The server evaluates the generated AI model and notifies the user of the results. The evaluation results are displayed on the device so that the user can check them. The evaluation means also evaluates the model based on the emotional information recognized by the emotion engine, and can measure the emotional balance of the model.

[2042] Corrective and retraining measures

[2043] The user inputs data to correct the model based on the evaluation results and clicks the "Retrain" button. The device sends the corrected data to the server, which then performs retraining. The generated new model is then re-evaluated, and the results are notified to the user.

[2044] Sales information input method

[2045] To sell a completed AI model, a user clicks the "Sell" button and enters sales information such as price and description, which is then sent to the server.

[2046] Sales page generation method

[2047] The server automatically generates a sales page based on the submitted sales information and publishes it on the platform. Users are notified that the page has been published.

[2048] profit distribution means

[2049] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe). Once payment is completed, the server calculates the profit and distributes the remainder to the user after deducting a certain fee.

[2050] Report Generation Method

[2051] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[2052] Specific examples

[2053] Consider a scenario where a user is creating an AI model based on customer service sentiment analysis. The user inputs customer text data (e.g., "The customer is satisfied with the service") through the system, and the sentiment engine analyzes the data to recognize positive or negative sentiment. The AI ​​model is trained based on this emotional information and can quickly respond to changes in customer sentiment. The completed AI model can be sold on the platform, and other users can purchase it, generating revenue for the user.

[2054] Prompt Sentence Examples

[2055] "Please enter text data. Example: 'The weather is very nice today and I'm feeling great.'"

[2056] In this way, by combining an emotion engine, this invention is an excellent system that enables the creation and sale of AI models that take user emotions into account, thereby generating revenue.

[2057] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2058] Processing Steps

[2059] Step 1: User Registration

[2060] The user opens a web browser to the system's home page and clicks the "Register" button.

[2061] The terminal displays a user registration form, where the user enters information such as name, email address, and password, and clicks the "Submit" button.

[2062] The server receives the entered information, stores it in a database, and automatically sends a confirmation email to notify the user that their registration is complete.

[2063] Input: User's name, email address, and password

[2064] Data processing: Save input information to a database

[2065] Output: Sending a confirmation email and notifying the completion of registration

[2066] Step 2: Log in

[2067] The user enters their email address and password on the login page and clicks the "Login" button.

[2068] The terminal sends the entered information to the server, which then refers to a database and performs authentication.

[2069] If authentication is successful, the server displays the user's dashboard.

[2070] Input: Email address, Password

[2071] Data calculation: Authentication in the database based on input information

[2072] Output: Display dashboard

[2073] Step 3: Creating an AI model

[2074] The user clicks the "Create AI" button on the dashboard.

[2075] The device displays an interface for creating AI, and the user inputs data that mimics their thoughts. For example, they input text data such as, "The weather is very nice today, and I'm in a great mood."

[2076] The user clicks the "Save" button to send the data to the server.

[2077] The server uses natural language processing tools to analyze the text based on the received data and begins training the AI ​​model. The generated model is then stored in a database.

[2078] Input: User's text data

[2079] Data processing: Text analysis using natural language processing

[2080] Data Computing: Generating training data and training AI models

[2081] Output: Save the trained AI model

[2082] Step 4: Analysis by the Emotion Engine

[2083] The server inputs the training data into the emotion engine and performs emotion analysis.

[2084] The sentiment engine uses natural language processing techniques to identify sentiment within the data and calculate the percentage of positive and negative sentiment.

[2085] Based on the results of the sentiment analysis, the server optimizes the training data for the AI ​​model.

[2086] Input: Training data

[2087] Data Computation: Extracting Emotional Information through Sentiment Analysis

[2088] Output: Optimized training data

[2089] Step 5: Evaluate the model

[2090] The server performs evaluation tests on the generated AI model.

[2091] The evaluation results are displayed on the terminal, allowing the user to check the evaluation results.

[2092] Emotional balance is also evaluated using an emotion engine.

[2093] Input: AI model

[2094] Data calculation: Evaluation test administration and emotional balance measurement

[2095] Output: Display of evaluation results

[2096] Step 6: Modify and retrain the model

[2097] If the user is not satisfied with the evaluation results, he or she can re-enter the corrected data and click the "Retrain" button.

[2098] The terminal transmits the corrected data to the server.

[2099] The server retrains the AI ​​model based on the new data and generates a new model.

[2100] The evaluation results for the new model will be displayed on the device again.

[2101] Input: Correction data

[2102] Data Calculation: Retraining

[2103] Output: Re-evaluated AI model and results display

[2104] Step 7: Enter sales information

[2105] To sell a completed AI model, users click the "Sell" button and enter sales information such as price and description.

[2106] This information is sent to a server and recorded in a database.

[2107] Input: Sales information such as price, description, etc.

[2108] Data processing: Sales information stored in a database

[2109] Output: Sales information stored in a database

[2110] Step 8: Generate a sales page

[2111] The server automatically generates a sales page based on the entered sales information.

[2112] Once the sales page is published, the server notifies the user.

[2113] Input: Sales information

[2114] Data processing: Sales page generation

[2115] Output: Published sales page and notification

[2116] Step 9: Profit sharing

[2117] When a potential buyer purchases an AI model from the sales page, the server processes the payment through a payment system (e.g., PayPal, Stripe).

[2118] Once payment is completed, the server deducts the fee, calculates the profit, and distributes the remainder to the user.

[2119] Input: Purchase Information

[2120] Data calculation: settlement processing and profit calculation

[2121] Output: Profit sharing

[2122] Step 10: Generate reports

[2123] The server generates a revenue report based on the purchase data and profit sharing details and displays it on the user's dashboard.

[2124] Input: Purchase data, profit sharing information

[2125] Data Processing: Report Generation

[2126] Output: View the revenue report

[2127] (Application example 2)

[2128] 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."

[2129] In modern e-commerce, it is difficult for users to select products that best suit their preferences and needs from the wide variety of products available. This is particularly true when review information is scattered and often lacks reliability. Furthermore, when users make purchasing decisions based on product reviews, it is difficult to accurately reflect the impact of the review content on the user's emotions. Therefore, it is important to improve the user experience and stimulate purchasing motivation by suggesting products that are appropriate for each user based on sentiment analysis.

[2130] The identification processing by the identification 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: a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; a tool provision means for constructing an AI model based on data provided by the user; an evaluation means for evaluating the constructed AI model; a correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a sales information input means for selling the generated AI model on the platform; a sales page generation means for generating and publishing a sales page based on the sales information; a profit distribution means for distributing profits earned from the sold AI model to users; a report generation means for generating a profit report; a sentiment analysis means for analyzing the sentiment of product reviews when the user views them; and a product suggestion means for making customized product suggestions to the user based on the sentiment analysis results. This allows users to receive product suggestions that match their preferences and needs through the sentiment analysis of reviews, thereby improving their purchasing experience.

[2131] The "user registration means" is a means by which a user accesses the system via a web browser and registers by entering personal information such as name, email address, and password.

[2132] "Login means" refers to the means by which a user accesses the system using a registered email address and password and is authenticated.

[2133] "Tool provision means" refers to a means of providing interfaces and tools for building AI models based on data provided by users.

[2134] "Evaluation means" refers to a means for evaluating the constructed AI model and providing the evaluation results to the user.

[2135] "Modification and retraining means" refers to a means of modifying an AI model based on the evaluation results and retraining it.

[2136] The "sales information input means" is a means for inputting information for selling the generated AI model on the platform.

[2137] The "sales page generation means" is a means for automatically generating and publishing a sales page based on input sales information.

[2138] A "profit distribution method" is a method for distributing profits earned from sold AI models to users after deducting a certain fee.

[2139] The "report generating means" is a means for generating a profit income report and providing it to the user.

[2140] The "sentiment analysis means" is a means for analyzing the sentiment of a product review when the user views the review and determining whether the sentiment is positive or negative.

[2141] The "product suggestion means" is a means for making customized product suggestions to the user based on the emotion analysis results.

[2142] The present invention combines an emotion engine with a system that allows users to create AI models that mimic their own thoughts and sell those models. Specific embodiments of the present invention will be described below.

[2143] 1. System Overview

[2144] This system includes means for user registration, login, tool provision, evaluation, correction and retraining, sales information input, sales page generation, profit distribution, report generation, sentiment analysis, and product suggestion. By using this system, users can easily create and sell AI models that reflect their own emotions.

[2145] 2. Hardware and Software Used

[2146] Hardware: Smartphone (iOS, Android)

[2147] software:

[2148] Frontend: React Native

[2149] Backend: Node.js, Express.js

[2150] Database: MongoDB

[2151] Emotion Engine: Google Cloud Natural Language API

[2152] Payment system: Stripe API

[2153] 3. Processing Flow

[2154] User Registration

[2155] Users access the system via a web browser and enter the required information (name, email address, password) using the user registration method. The entered information is stored in a MongoDB database via a Node.js server.

[2156] Log in

[2157] The user enters their email address and password on the login page, and is authenticated again via the Node.js server. If authentication is successful, the dashboard is displayed.

[2158] Creating an AI model

[2159] Users use the provided tools to input data based on their own thoughts and build an AI model, which is then sent to the server and analyzed through the emotion engine, after which the AI ​​model is trained.

[2160] Sentiment analysis and product recommendations

[2161] When a user browses a product review, the review's text data undergoes sentiment analysis using the Google Cloud Natural Language API. Based on the results of this analysis, the product suggestion tool creates a customized list of products to suggest to the user.

[2162] Sales and Revenue Sharing

[2163] Completed AI models are sold on the platform through the sales information input means. The sales page generation means automatically generates and publishes a sales page. Profits from the sold AI models are distributed to users after deducting fees using the Stripe API, and a profit revenue report is generated.

[2164] 4. Examples and prompts

[2165] As a concrete example, let's consider a case where a user values ​​positive reviews. For example, if the user enters the following prompt, the AI ​​will suggest suitable products for the user.

[2166] Example of input prompt:

[2167] "Generate the following product suggestions based on recent purchase history and sentiment analysis of reviews. Users prefer positive reviews, so list products that match that."

[2168] Purchase History:

[2169] Product A (positive evaluation)

[2170] Product B (negative rating)

[2171] Review sentiment analysis results:

[2172] Product C: 80% of reviews contain positive sentiment

[2173] Product D: 50% of reviews contain negative sentiment

[2174] Using this example prompt sentence, the system can suggest an appropriate product (e.g., product C) to the user.

[2175] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2176] Step 1:

[2177] User Registration:

[2178] A user accesses the registration page via a web browser and enters their name, email address, and password. The input information is sent from the terminal to the Node.js server. The server stores the received information in a MongoDB database. Once the storage is complete, a confirmation email is sent. Input: User information. Output: Data saved in MongoDB, confirmation email sent.

[2179] Step 2:

[2180] Login:

[2181] The user enters their email address and password on the login page and sends them again from their terminal to the Node.js server. The server performs authentication by referencing the MongoDB database. If authentication is successful, the user's dashboard is displayed. Input: Email address, password. Output: Dashboard displayed when authentication is successful.

[2182] Step 3:

[2183] Creating an AI model:

[2184] Users input data on the dashboard and create an AI model using the tools provided. The input data is sent from the device to the server and analyzed using the emotion engine (Google Cloud Natural Language API). The analysis results are used to train the AI ​​model on the server. Input: User data (text, etc.). Output: Trained AI model.

[2185] Step 4:

[2186] Assessment and Remediation / Retraining:

[2187] The server evaluates the trained AI model and notifies the device of the evaluation results. The user modifies the model based on the evaluation results and retrains it. The modified data is sent back to the server, and retraining is performed. Input: Modified data. Output: Retrained AI model, notification of evaluation results.

[2188] Step 5:

[2189] Sentiment analysis and product recommendations:

[2190] When a user browses a product review, the review's text data is sent from the device to the server. The server performs sentiment analysis using the Google Cloud Natural Language API. The analysis results are stored in a database, and customized product suggestions are generated based on the results. Input: Review text data. Output: Sentiment analysis results, customized product suggestions.

[2191] Step 6:

[2192] Sales and Revenue Sharing:

[2193] The user enters sales information for the completed AI model and sends it from the terminal to the server. The server automatically generates and publishes a sales page, and the AI ​​model is sold. The sales data is processed by the Stripe API, and profits are calculated after deducting fees. This profit is distributed to the user, and a revenue report is generated. Input: Sales information, payment information. Output: Automatically generated sales page, revenue report.

[2194] Step 7:

[2195] Notifications and History Management:

[2196] The evaluation results and revenue information of the AI ​​model generated by the user are notified to the device and can be viewed by the user. In addition, past purchase history and recommendation history are stored in a MongoDB database and can be viewed by the user on their personal page. Input: Evaluation results, revenue information. Output: Notification, display of history information.

[2197] 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.

[2198] 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.

[2199] 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.

[2200] 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.

[2201] 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.

[2202] 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.

[2203] 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).

[2204] 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.

[2205] 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 ...

Claims

1. a user registration means accessible by a user via a web browser; a login means for authenticating user information registered in a state accessible by the user; A tool providing means to build AI models based on user-supplied data, An evaluation method for evaluating the constructed AI model; A correction and retraining means for correcting and retraining the AI ​​model based on the evaluation results; a sales information input means for selling the generated AI model on the platform; a sales page generating means for generating and publishing a sales page based on the sales information; A profit distribution method for distributing profits from the sold AI models to users; report generation means for generating a profit revenue report; A system including:

2. The system according to claim 1, further comprising a payment processing means for processing payments from prospective purchasers regarding the sale of an AI model.

3. The system according to claim 1, further comprising a notification means for notifying the user's terminal of the evaluation results of the AI ​​model generated by the user.

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