System

Abstract

To provide a system where a work of a prompt creator is appropriately evaluated.SOLUTION: A system includes means for acquiring information of a plurality of prompts from a database, means for evaluating each of the plurality of acquired prompts using a generative AI model, and means for buying the prompts meeting the prescribed criteria by evaluation online.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There are limited opportunities for prompt creators to have their work properly recognized and rewarded, and there is a lack of educational institutions and materials to help them improve their prompt-writing skills. [Means for solving the problem]

[0005] We provide a system for creating and selling prompts. This system purchases excellent prompts from their creators and appropriately evaluates them. We also headhunt and train prompt creators, and plan and manage prompt learning classes. We also sell related books to help improve prompt creation skills. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] This embodiment of the present invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing outstanding prompts from their creators. Specifically, the system automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[0029] "Example 2"

[0030] Furthermore, this embodiment of the present invention has the function of headhunting and training prompt creators. Specifically, the system analyzes the quality of prompts and the creators' activity history to identify creators who frequently create excellent prompts. The system then makes headhunting suggestions to identified creators. It also plans and manages online and group prompt learning classes to help improve prompt creation skills.

[0031] "Example 3"

[0032] Furthermore, this embodiment of the present invention has a function for selling related books. Specifically, books on prompt creation techniques and theories are sold on the website, allowing users to further improve their prompt creation skills.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: User visits the website and uploads their own prompt.

[0036] Step 2: The system automatically evaluates the quality of the uploaded prompts.

[0037] Step 3: The system purchases prompts that meet certain criteria from the creator.

[0038] "Example 2"

[0039] Step 1: The system analyzes the prompt quality and the creator's activity history.

[0040] Step 2: The system identifies creators who frequently create good prompts.

[0041] Step 3: The system will make headhunting suggestions to the identified creators. Step 4: Plan and manage online and group prompt learning classes to help improve prompt creation skills.

[0042] "Example 3"

[0043] Step 1: Sell books on prompt writing techniques and theory on your website.

[0044] Step 2: The user purchases the book and further develops their prompt-writing skills.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] In conventional prompt creation and sales systems, the quality of prompts created by users is evaluated manually, which takes time and results in inconsistent evaluation criteria. Furthermore, there was a lack of a system for efficiently discovering and purchasing excellent prompts from their creators, making it difficult to maximize the market value of prompts. Furthermore, there was no system that comprehensively provided prompt-related services, such as training and headhunting prompt creators and the sale of related books.

[0048] 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.

[0049] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for users to upload prompts, means for other users to purchase prompts, means for evaluating the quality of prompts using a generative AI model, and means for purchasing prompts based on the evaluation results. This enables prompt quality evaluation to be automated and performed quickly and consistently. Furthermore, by efficiently discovering excellent prompts and purchasing them from creators, the market value of prompts can be maximized. Furthermore, it becomes possible to comprehensively provide prompt-related services, such as training and headhunting prompt creators and selling related books.

[0050] A "prompt" is an input sentence that prompts a generative AI model to generate a specific output.

[0051] A "generative AI model" is a type of artificial intelligence that performs natural language processing and is a model that generates appropriate output for specific input.

[0052] "User" means any person or entity that uses the System to create, upload, or purchase prompts.

[0053] A "server" is a computer system that runs the entire system and processes requests from users.

[0054] A "database" is a system for storing and managing data such as prompts and user information.

[0055] A "prompt creation and sales means" is a part of the system that provides the functionality for users to create prompts and sell them to other users.

[0056] The "means of purchasing excellent prompts from producers" is a function that enables the system to automatically evaluate the quality of prompts and purchase prompts that meet certain standards from producers.

[0057] "Means for headhunting and training prompt creators" is a function for discovering and training excellent prompt creators.

[0058] "Means for planning and managing prompt learning classrooms" refers to the function for planning and managing classrooms for learning prompt creation techniques.

[0059] "Means for selling related books" is a function for selling books and materials related to prompt creation.

[0060] The "means for users to upload prompts" is a function that allows users to upload prompts that they have created themselves to the system.

[0061] "Means for other users to purchase prompts" refers to a function that allows users to purchase prompts that are published on the system.

[0062] "Means for evaluating the quality of prompts using a generative AI model" refers to a function for automatically evaluating the quality of uploaded prompts using a generative AI model.

[0063] "Means for purchasing prompts based on evaluation results" is a function for purchasing prompts that meet certain criteria from their creators based on the evaluation results of the generative AI model.

[0064] This invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing excellent prompts from their creators. Specifically, the server automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[0065] Hardware and software used

[0066] Server: Runs the entire website and processes user requests. Stores and manages data using a database management system (e.g., MySQL®).

[0067] Generative AI model: Used to evaluate the quality of prompts. Specifically, we use a generative AI model that performs natural language processing (e.g., GPT-3®).

[0068] User Device: The device used by the User to access the Website and create, upload, and purchase prompts. Use a common web browser (e.g., Google Chrome (registered trademark), Mozilla Firefox).

[0069] Program processing

[0070] The server performs the following process.

[0071] 1. User Registration and Login

[0072] A user visits a website and enters their email address and password into a registration form, which the server stores in a database and creates a user account.

[0073] When a user enters their email address and password into the login form, the server checks it against the database and allows them to log in if it matches.

[0074] 2. Upload the prompt

[0075] The user accesses the prompt creation page and enters a prompt sentence, which the server saves in the database.

[0076] Example: A user enters a prompt to make the AI ​​model generate a cat image and clicks the "Upload" button. The server receives this and saves the new prompt information in the database.

[0077] 3. Prompt Sales

[0078] Other users can browse a list of prompts published on the website and select the prompt they want to purchase. The server processes the purchase and provides the prompt to the purchaser.

[0079] Example: User B selects the "Cat Picture Generator Prompt" and clicks the "Purchase" button. The server processes the payment and displays the prompt to the buyer.

[0080] 4. Prompt Quality Assessment

[0081] The server evaluates the uploaded prompts using a generative AI model (e.g., GPT-3), and the evaluation results are stored in a database.

[0082] Example: The server inputs a "cat image generation prompt" into GPT-3 and obtains an evaluation score. The server stores this score in a database.

[0083] 5. Prompt Buyout

[0084] The server purchases high-quality prompts that meet certain criteria from the producers. The purchase price is determined based on the evaluation results and notified to the producers.

[0085] Example: The server checks the rating score of the "Cat Image Generation Prompt" and sends a purchase offer of 1,000 yen to the creator if it meets the criteria. If User A accepts the offer, the server processes the payment.

[0086] Prompt Sentence Examples

[0087] "Create a prompt that generates an image of a cat. Include details like the type of cat, the background, and the color."

[0088] The above is an embodiment of the present invention. This system automates the quality evaluation of prompts and enables it to be performed quickly and consistently. It also makes it possible to maximize the market value of prompts by efficiently discovering excellent prompts and purchasing them from their creators. It also makes it possible to provide comprehensive prompt-related services, such as training and headhunting prompt creators and selling related books.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] User Registration and Login

[0092] Input: The user enters their email address and password.

[0093] Specific behavior: The user enters "user@example.com" and "password123" and clicks the "Register" button.

[0094] Data processing: The server saves the entered information in a database and creates a new user account.

[0095] Output: The user account is created and the user can log in.

[0096] Input: The user enters their email address and password.

[0097] Specific action: The user enters "user@example.com" and "password123" and clicks the "Login" button.

[0098] Data calculation: The server checks the entered information against the database and allows login if there is a match.

[0099] Output: The user is redirected to the dashboard.

[0100] Step 2:

[0101] Uploading a Prompt

[0102] Input: The user enters the prompt text.

[0103] Specific operation: The user enters a prompt to have the AI ​​model generate an image of a cat and clicks the "Upload" button.

[0104] Data processing: The server saves the entered prompt sentence in the database.

[0105] Output: The prompt is saved in the database and can be viewed by other users.

[0106] Step 3:

[0107] Prompt Sales

[0108] Input: Select the prompt the user wants to purchase.

[0109] Specific behavior: User B selects the "Cat Image Generation Prompt" and clicks the "Purchase" button.

[0110] Data Calculation: The server processes the payment and provides prompts to the buyer.

[0111] Output: The buyer gets the prompt text.

[0112] Step 4:

[0113] Prompt quality assessment

[0114] Input: The uploaded prompt text.

[0115] Specific operation: The server inputs the "cat image generation prompt" into a generative AI model (e.g., GPT-3) and obtains an evaluation score.

[0116] Data Computing: Using a generative AI model to evaluate the quality of the prompts and store the evaluation results in a database.

[0117] Output: The prompt evaluation score is saved to the database.

[0118] Step 5:

[0119] Prompto Buyout

[0120] Input: The prompt's quality score based on the evaluation results.

[0121] Specific operation: The server checks the rating score of the "Cat Image Generator Prompt" and sends a purchase offer to the creator if it meets the criteria.

[0122] Data calculation: The server determines the purchase price based on the evaluation results and notifies the producer.

[0123] Output: If the producer accepts the offer, the server processes the payment.

[0124] The above are the specific processing steps of the program of this system.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1 of Embodiment 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."

[0127] Conventional prompt creation and sales systems lacked sufficient prompt quality evaluation, ranking display, and purchasing methods, making it difficult for users to efficiently find high-quality prompts. Furthermore, there was a lack of means to objectively evaluate prompt quality, meaning that creators of excellent prompts had few opportunities to be properly recognized.

[0128] 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.

[0129] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts. This allows users to efficiently find and purchase high-quality prompts. Furthermore, objective evaluation of prompt quality provides an opportunity for creators of excellent prompts to be properly recognized.

[0130] A "prompt" is an instruction to be input into a generative AI model.

[0131] "Creating a prompt" is the act of creating instructions that a user will input to a generative AI model.

[0132] "Selling prompts" refers to the act of a user offering prompts they have created to other users for a fee.

[0133] A "means for evaluating the quality of prompts" is a method or system for objectively evaluating the usefulness and effectiveness of the prompts created.

[0134] A "means for displaying a ranking of prompts" is a method or system for ranking rated prompts based on quality and popularity and displaying the rankings to the user.

[0135] A "means for purchasing prompts" is a method or system that allows a user to obtain prompts created by other users for a fee.

[0136] A "means for purchasing excellent prompts from creators" is a method or system by which the system directly purchases prompts that meet certain standards from creators.

[0137] A "means for headhunting and developing prompt writers" is a method or system for identifying individuals with the ability to write good prompts and further developing their abilities.

[0138] "Means for planning and managing a prompt learning classroom" refers to a method or system for planning and managing an educational program to learn the skills and knowledge of creating prompts.

[0139] "Means for selling related books" refers to methods or systems for selling books and materials related to prompt creation and generative AI models to users.

[0140] A system for carrying out this invention includes a server, a user terminal, and a generative AI model. The server has means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts.

[0141] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for users to create, upload, and purchase prompts. The generative AI model is an artificial intelligence model that receives user-created prompts as input and performs the specified task.

[0142] Program processing explanation

[0143] Hardware used

[0144] Smartphone

[0145] server

[0146] Software used

[0147] Flask (a Python micro web framework)

[0148] requests (a Python library for sending HTTP requests)

[0149] Data processing and calculation

[0150] The server receives prompts entered by users and evaluates their quality. The evaluation is performed using an algorithm to objectively measure the usefulness and effectiveness of the prompts. The evaluated prompts are ranked based on their quality scores and displayed to users. Users can purchase prompts by referring to the rankings.

[0151] For example, a user can create a prompt on the theme of "AI poetry generation" and upload it to the app. The server evaluates the quality of the prompt and displays it in a ranking. Other users can purchase this prompt and input it into the generative AI model to generate poetry.

[0152] Prompt Sentence Examples

[0153] "AI Poetry Generation" prompt:

[0154] "You are a poet. Create a poem on the theme of the arrival of spring."

[0155] In this way, users can leverage generative AI models to create high-quality content that can be shared and sold with other users.

[0156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0157] Step 1:

[0158] The user creates a prompt using a device. The user inputs instructions to be input to the generative AI model and sends them to the server through an application on the device. The input data is the prompt created by the user. The output data is the prompt sent to the server.

[0159] Step 2:

[0160] The server saves the prompt text it receives. The server stores the prompt text in a database and prepares it for evaluating the quality of the prompt. The input data is the prompt text sent by the user. The output data is the prompt text saved in the database.

[0161] Step 3:

[0162] The server evaluates the quality of the prompt. The server analyzes the prompt sentence and calculates a quality score using an evaluation algorithm. The input data is the prompt sentence stored in the database. The output data is the quality score.

[0163] Step 4:

[0164] The server generates a ranking of prompts. The server compares the quality scores of all prompts and creates a ranking. The input data is the quality score of each prompt. The output data is a ranking list.

[0165] Step 5:

[0166] The server displays the rankings on the user terminal. The user browses the rankings through the terminal and selects the prompts that interest them. The input data is the ranking list. The output data is the rankings displayed on the user terminal.

[0167] Step 6:

[0168] A user purchases a prompt. The user uses a terminal to purchase a prompt selected from the rankings. The input data is the prompt selected by the user. The output data is a notification of purchase completion.

[0169] Step 7:

[0170] The server provides the purchased prompt to the user. The server completes the purchase and sends the prompt to the user. The input data is the purchased prompt. The output data is the prompt provided to the user.

[0171] Step 8:

[0172] The user inputs the purchased prompt into the generative AI model. The user uses the device to input the purchased prompt into the generative AI model to perform the specified task. The input data is the purchased prompt. The output data is the content generated by the generative AI model.

[0173] Example 2

[0174] Next, a description will be given of Example 2 of Form 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."

[0175] Conventional prompt creation systems lacked functionality for evaluating prompt quality and improving prompt creator skills, making it difficult to identify and train excellent prompt creators. Furthermore, there was a lack of efficient methods for headhunting and training prompt creators, making it difficult to improve the quality of generative AI models.

[0176] 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.

[0177] In this invention, the server includes means for collecting log data of the generative AI model, means for analyzing the collected data, means for identifying excellent prompt creators, means for proposing headhunting to the identified prompt creators, and means for supporting the improvement of prompt creation skills using an online learning platform. This allows for efficient evaluation of prompt quality and improvement of creator skills, making it possible to identify and develop excellent prompt creators.

[0178] A "prompt" is text that is input as an instruction or question to a generative AI model.

[0179] A "generative AI model" is an algorithm that uses artificial intelligence to generate text and answers.

[0180] "Log data" is data that records the operation of the system and the operation history of the user.

[0181] "Data analysis" is the process of analyzing collected data using statistical methods and machine learning to extract useful information.

[0182] A "prompt creator" is a user who creates and inputs prompts to a generative AI model.

[0183] "Headhunting" is the act of poaching people with specific skills and abilities from other organizations.

[0184] An "online learning platform" is a system that provides educational content via the Internet and supports learning.

[0185] A "prompting classroom" is an educational program or workshop designed to improve prompting skills.

[0186] "Quality assessment" is the process of evaluating the usefulness and accuracy of the generated prompts.

[0187] A "database" is a system for efficiently storing, managing, and retrieving data.

[0188] MODE FOR CARRYING OUT THE INVENTION

[0189] The present invention relates to a prompt creation system using a generative AI model. A specific embodiment of this system will be described below.

[0190] Hardware and software used

[0191] Hardware: Servers, terminals

[0192] Software: generative AI models, data analysis tools (e.g., Python pandas, scikit-learn), online learning platforms (e.g., Moodle)

[0193] System Overview

[0194] This system has the functions of creating and selling prompts, purchasing excellent prompts, headhunting and training prompt creators, planning and managing prompt learning classes, and selling related books. It also collects and analyzes log data from the generative AI model to identify excellent prompt creators and make headhunting suggestions. It also supports the improvement of prompt creation skills using an online learning platform.

[0195] Data collection

[0196] The server collects log data from the generative AI model. Specifically, it stores information such as the evaluation score of each prompt, the creator's ID, and the creation date and time in a database. For example, the server obtains the log data through the generative AI model's API and stores it in a MySQL database.

[0197] Data analysis

[0198] The server analyzes the collected data using data analysis tools. Specifically, it creates a data frame using Python's pandas and trains a machine learning model to evaluate the quality of prompts using scikit-learn. For example, the server runs a Python script to read the data using pandas.read_sql("SELECT FROM prompts", connection) and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[0199] Identifying good prompt writers

[0200] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it creates a list of users who have created prompts with a certain evaluation score or higher a certain number of times. For example, the server uses pandas to extract users who meet the criteria using code such as df[df['score'] > threshold].groupby('user_id').count().

[0201] Headhunting proposals

[0202] The server makes headhunting proposals to the identified producers. Specifically, it sends emails and notifications to present offers. For example, the server uses an SMTP server to send emails using the smtplib library. For example, it executes the code server.sendmail(from_addr, to_addrs, msg).

[0203] Help improve prompt writing skills

[0204] The server plans and manages online and group-based prompt learning classrooms. Specifically, it creates courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills. For example, the server creates a new course using the Moodle API and sends a notification to users encouraging them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[0205] Examples of concrete examples and prompts

[0206] Examples:

[0207] If user A creates 10 highly rated prompts using the generative AI model, the server identifies user A as a good prompt creator.

[0208] The server sends User A an email saying, "We really appreciate your prompt creation skills. Would you like to join our team?"

[0209] Example prompt sentence:

[0210] "Use a generative AI model to create a prompt like: 'Suggest a marketing strategy for a new product.'"

[0211] The system aims to improve the quality of generative AI models by evaluating the skills of prompt creators and identifying and developing outstanding talent.

[0212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0213] Step 1:

[0214] Data collection

[0215] The server collects log data from the generative AI model, specifically, information such as the evaluation score of each prompt, the creator's ID, and the creation date and time, and stores it in a database.

[0216] Input: Generative AI model log data

[0217] Output: Prompt information stored in the database

[0218] Specific operation: The server retrieves the log data through the API of the generative AI model and stores it in a MySQL database, for example, by executing the SQL query INSERT INTO prompts (user_id, prompt_text, score, created_at) VALUES.

[0219] Step 2:

[0220] Data analysis

[0221] The server analyzes the collected data using data analysis tools, specifically, creating a data frame using Python's pandas and training a machine learning model using scikit-learn to evaluate the quality of prompts.

[0222] Input: Prompt information stored in the database

[0223] Output: Prompt quality assessment model

[0224] Specific operation: The server runs a Python script, reads data with pandas.read_sql("SELECT FROM prompts", connection), and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[0225] Step 3:

[0226] Identifying good prompt writers

[0227] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it lists users who have created prompts with a certain evaluation score or higher a certain number of times.

[0228] Input: prompt quality assessment model, prompt information stored in the database

[0229] Output: A list of great prompt creators

[0230] Specific operation: The server uses pandas to extract users who meet the criteria using code like df[df['score'] > threshold].groupby('user_id').count().

[0231] Step 4:

[0232] Headhunting proposals

[0233] The server then proposes headhunting to the identified creators by sending emails and notifications and presenting offers.

[0234] Input: A list of great prompt creators

[0235] Output: Headhunting proposal email or notification

[0236] Specific operation: The server uses the SMTP server to send the email using the smtplib library. For example, execute the code server.sendmail(from_addr, to_addrs, msg).

[0237] Step 5:

[0238] Help improve prompt writing skills

[0239] The server plans and manages online and group prompt learning classrooms. Specifically, it provides courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills.

[0240] Input: List of prompt creators, learning content

[0241] Output: Prompt learning classroom course, participation notification

[0242] Specific operation: The server creates a new course using the Moodle API and sends a notification to the user inviting them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[0243] (Application example 2)

[0244] Next, a description will be given of Application Example 2 of Form 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."

[0245] Conventional prompt creation systems did not adequately evaluate the quality of prompts or train prompt creators, making it difficult to identify and develop excellent prompt creators. Furthermore, it was not possible to efficiently plan and manage learning classes to help improve prompt creation skills. This resulted in a lack of high-quality prompts to provide optimal content to users in content delivery services that utilize generative AI models.

[0246] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0247] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.This enables the efficient evaluation of prompt quality and the identification and training of prompt creators, making it possible to supply high-quality prompts.

[0248] A "prompt" is text data input to a generative AI model that contains instructions for the model to output an appropriate response or product.

[0249] "Creating a prompt" is the act of generating text data to provide appropriate instructions to a generative AI model.

[0250] "Selling prompts" refers to the act of providing created prompts to other users or companies in exchange for payment.

[0251] "Prompt quality evaluation" is the act of evaluating the effectiveness and appropriateness of input prompts in order to improve the quality of the responses and products output by a generative AI model.

[0252] A "prompt creator" is a person or entity that creates the prompts that are input to a generative AI model.

[0253] "Headhunting" is the act of recruiting individuals with specific skills or abilities to other companies or organizations.

[0254] "Development" refers to providing education and training to individuals with specific skills and abilities to further improve their abilities.

[0255] A "prompt learning classroom" is a place that provides education and training to improve prompt creation skills.

[0256] "Planning and management" means planning, executing and managing a specific activity or project.

[0257] "Related books" are books and materials related to prompt creation and generative AI models.

[0258] "Activity history" refers to the records and track record of prompt creation that a prompt creator has performed in the past.

[0259] "Identification" means finding something based on specific conditions or criteria.

[0260] A "proposal" is a proposal that suggests a particular course of action or option to others and encourages their acceptance.

[0261] A "generative AI model" is a type of artificial intelligence that outputs appropriate responses or products based on input text data.

[0262] A system for implementing this invention includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.

[0263] The server evaluates the quality of the prompt using a generative AI model. Specifically, it receives the prompt created by the user as input and evaluates its quality using the generative AI model. The evaluation results are stored in a database for later analysis.

[0264] The terminal provides an interface for users to create prompts and send them to the server. Users can use the terminal to input prompts and check the quality evaluation results. The terminal also displays the activity history of prompt creators and provides information to identify excellent prompt creators.

[0265] The server analyzes the activity history of prompt creators to identify outstanding prompt creators. It then sends notifications to identified prompt creators to suggest headhunting. It also plans and runs learning classes to improve prompt creation skills and encourages users to participate.

[0266] For example, if a user creates a prompt such as "Enter a question about AI," the prompt is sent to a server, where the generative AI model evaluates its quality. The evaluation results are stored in a database and later analyzed. If a good prompt creator is identified, the user is offered a headhunting opportunity. Additionally, learning classes are organized to improve prompt creation skills, and users are encouraged to participate.

[0267] The hardware used includes smartphones and personal computers, and the software used includes Python, SQLite, and the OpenAI (registered trademark) API. This enables efficient prompt quality evaluation and prompt creator identification and training, enabling the supply of high-quality prompts.

[0268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0269] Step 1:

[0270] The user creates a prompt using the terminal.

[0271] Input: The prompt the user enters (e.g., "Please enter your AI question.")

[0272] Output: The generated prompt

[0273] Specific operation: The user inputs a prompt sentence through the terminal interface and presses the send button.

[0274] Step 2:

[0275] The terminal sends the generated prompt to the server.

[0276] Input: A prompt text created by the user

[0277] Output: The prompt sent to the server

[0278] Specific operation: The terminal generates and sends an API request to send the prompt text to the server.

[0279] Step 3:

[0280] The server inputs the received prompt sentences into a generative AI model and evaluates their quality.

[0281] Input: The prompt sent to the server

[0282] Output: Quality assessment results (scores and feedback)

[0283] Specific operation: The server calls the API of the generative AI model and passes the prompt sentence as input. The generative AI model analyzes the prompt sentence and returns a quality evaluation result.

[0284] Step 4:

[0285] The server stores the quality evaluation results in a database.

[0286] Input: Quality evaluation results

[0287] Output: Evaluation results stored in a database

[0288] Specific operation: The server generates and executes SQL queries to store the quality assessment results in an SQLite database.

[0289] Step 5:

[0290] The server analyzes the prompt creators' activity history and identifies good prompt creators.

[0291] Input: Evaluation results and activity history stored in the database

[0292] Output: A list of identified good prompt writers

[0293] Specific operation: The server retrieves activity history and evaluation results from the database and uses statistical methods and algorithms to identify excellent prompt creators.

[0294] Step 6:

[0295] The server makes a headhunting proposal to the identified prompt creator.

[0296] Input: A list of identified good prompt writers

[0297] Output: Headhunting proposal notification

[0298] Specific Actions: The server generates and sends a notification to the identified prompt creator containing the headhunting offer.

[0299] Step 7:

[0300] The server plans and runs learning classes to improve prompt creation skills.

[0301] Input: A list of prompt creators and skills development materials and curriculum

[0302] Output: Class schedule and participant list

[0303] Specific operations: The server creates a schedule for learning classes, sends notifications to prompt creators to invite them to participate, prepares teaching materials and curricula, and manages online or group learning classes.

[0304] Example 3

[0305] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0306] Conventional prompt creation and sales systems make it difficult for users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, efficient online operations are required for training and headhunting prompt creators, as well as for planning and managing prompt learning classrooms. A comprehensive system is needed to solve these issues.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0308] In this invention, the server includes: means for users to access a website and search, select, purchase, and download books; means for the server to receive user requests, display a web page, provide search results and detailed book information, process payments, and generate download links; and means for the terminal to render the web page, display search results and detailed book information, and allow users who click on the download link to download the book file. This allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators and planning and management of prompt learning classes can also be efficiently carried out online.

[0309] A "prompt" refers to text or instructions input to a generative AI model.

[0310] "Sales" refers to the act of providing a product or service to a customer and receiving payment in return.

[0311] "Producer" refers to the person or entity that creates the prompt.

[0312] "Headhunting" refers to the act of poaching people with specific skills or knowledge from other organizations.

[0313] "Development" refers to the act of educating and developing personnel with specific skills and knowledge.

[0314] A "learning classroom" refers to an educational setting for learning specific knowledge or skills.

[0315] "Related books" refer to books related to a particular theme or field.

[0316] "Website" means a location on the Internet that provides publicly available information and services.

[0317] "Searching" refers to the act of researching a database or the Internet to find specific information.

[0318] "Selection" refers to the act of choosing one option from multiple options.

[0319] "Purchase" refers to the act of acquiring goods or services by paying money.

[0320] "Downloading" refers to the act of saving data from the Internet to your device.

[0321] A "server" refers to a computer that provides services to other computers on a network.

[0322] "Request" means a request for particular information or services.

[0323] "Web Page" refers to an individual page that makes up a website.

[0324] "Display" refers to the act of visually presenting information to a user.

[0325] "Payment processing" refers to the process of paying for goods or services.

[0326] "Link" refers to a hypertext element that connects web pages together.

[0327] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[0328] "Rendering" refers to the act of a computer processing data to produce a visual display.

[0329] This invention relates to a system for creating and selling prompts, and in particular to a system that allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation.

[0330] Hardware and software used

[0331] Server: Web server (e.g., Apache, Nginx)

[0332] Software: Web application frameworks (e.g., Django, Ruby on Rails), databases (e.g., MySQL, PostgreSQL)

[0333] Program processing

[0334] 1. A user visits a website

[0335] The user opens a browser (e.g., Google® Chrome, Firefox) and enters the URL of a website.

[0336] The server receives the user's request and sends the HTML, CSS, and JavaScript files of the web page to the user's terminal.

[0337] The terminal renders the received file and displays the web page to the user.

[0338] 2. User searches for a book

[0339] A user enters a keyword such as "create prompt" into the search bar on a web page and clicks the search button.

[0340] The server receives user input and retrieves relevant book information from a database (e.g., MySQL, PostgreSQL).

[0341] The terminal displays the search results received from the server to the user.

[0342] 3. The user selects a book

[0343] The user clicks on the book of interest from the search results.

[0344] The server retrieves detailed information about the selected book from the database and sends it to the user's terminal.

[0345] The terminal displays detailed information about the received book to the user.

[0346] 4. The user purchases the book

[0347] The user clicks the "Purchase" button on the book's details page to proceed with the purchase, and then enters payment information such as credit card information.

[0348] The server receives the user's purchase information, processes the payment through a payment gateway (e.g., Stripe, PayPal), and stores the purchase information in a database.

[0349] The terminal displays a notice to the user that the payment has been completed.

[0350] 5. The user downloads the book

[0351] Once the purchase is complete, the user clicks on a link to download the book.

[0352] The server generates a download link and sends it to the user's device.

[0353] The terminal allows the user who clicks on the download link to download the book file.

[0354] Specific examples

[0355] Example prompt: "Find books on prompt writing techniques."

[0356] When the user enters this prompt sentence, they type "create prompt" in the search bar and click the search button.

[0357] The server searches the database for relevant books and displays the results to the user.

[0358] The terminal displays the search results to the user.

[0359] Example of a book search result:

[0360] Book 1: "The Basics of Prompt Creation"

[0361] Book 2: "Advanced Prompting Techniques"

[0362] Book 3: "Application of Prompt Theory"

[0363] In this way, users can easily find, purchase, and download books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators, as well as planning and management of prompt learning classes, can also be efficiently carried out online. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0364] Step 1:

[0365] A user visits a website.

[0366] Input: A user types a website URL into their browser.

[0367] What happens: The user opens a browser and enters the URL of a website.

[0368] Data processing / calculation: The server receives the user's request and generates the HTML, CSS, and JavaScript files for the web page.

[0369] Output: The server sends the generated file to the user's terminal.

[0370] Step 2:

[0371] The device displays the web page.

[0372] Input: HTML, CSS, and JavaScript files sent by the server.

[0373] What happens: The device renders the received file and displays the web page to the user.

[0374] Data processing / computation: The device parses HTML, CSS, and JavaScript files to generate visual web pages.

[0375] Output: The web page that is displayed to the user.

[0376] Step 3:

[0377] A user searches for a book.

[0378] Input: A user enters a keyword, such as "create prompt," into the search bar and clicks the search button.

[0379] What happens: A user enters keywords into the search bar on a web page and clicks the search button.

[0380] Data processing / calculation: The server receives user input and searches for relevant book information from a database (e.g., MySQL, PostgreSQL).

[0381] Output: The server generates search results and sends them to the user's device.

[0382] Step 4:

[0383] Your device will display the search results.

[0384] Input: Search results sent from the server.

[0385] Specific operation: The terminal displays the search results received from the server to the user.

[0386] Data processing / calculation: The device analyzes the search results and generates data for visual display.

[0387] Output: The search results that are displayed to the user.

[0388] Step 5:

[0389] The user selects a book.

[0390] Input: User clicks on a book they are interested in from the search results.

[0391] Specific behavior: The user clicks on a book of interest from the search results.

[0392] Data processing / calculation: The server retrieves detailed information of the selected book from the database.

[0393] Output: The server generates the book details and sends them to the user's device.

[0394] Step 6:

[0395] The device will display detailed information about the book.

[0396] Input: Book details sent from the server.

[0397] Specific operation: The device displays detailed information about the received book to the user.

[0398] Data processing / calculation: The device analyzes the detailed information of the book and generates data for visual display.

[0399] Output: Book details displayed to the user.

[0400] Step 7:

[0401] A user purchases a book.

[0402] Enter: A user clicks the "Buy" button on a book detail page and enters their payment information.

[0403] Specific behavior: The user clicks the "Purchase" button on the book's detail page and enters payment information, including credit card information.

[0404] Data processing / calculation: The server receives the user's purchase information, processes the payment through a payment gateway (e.g., Stripe, PayPal), and saves the purchase information in a database.

[0405] Output: The server generates a notification of payment completion and sends it to the user's device.

[0406] Step 8:

[0407] The terminal will display a notification that the payment has been completed.

[0408] Input: Payment completion notification sent from the server.

[0409] Specific operation: The terminal displays a notification to the user that the payment has been completed.

[0410] Data processing / calculation: The terminal analyzes the payment completion notification and generates data for visual display.

[0411] Output: A notification to the user that payment has been completed.

[0412] Step 9:

[0413] A user downloads a book.

[0414] Input: User clicks on the download link.

[0415] What happens: The user clicks on a link to download the book.

[0416] Data processing / calculation: The server generates a download link and sends it to the user's device.

[0417] Output: The download link sent by the server.

[0418] Step 10:

[0419] The device downloads the book file.

[0420] Input: The download link sent by the server.

[0421] Specific operation: The device allows users who click on the download link to download the book file.

[0422] Data processing / calculation: The device analyzes the download link and generates a request to download the book file.

[0423] Output: Book file saved on user's device.

[0424] (Application example 3)

[0425] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0426] In conventional prompt creation systems, it was difficult for users to find appropriate books to improve their prompt creation skills. In addition, there was a lack of means to evaluate prompt sentences, so users could not objectively evaluate their own skills. This resulted in the problem of inefficient improvement of prompt creation skills.

[0427] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0428] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating prompt creation, and means for evaluating a user's prompt creation skills and recommending appropriate books, thereby enabling users to efficiently improve their prompt creation skills.

[0429] "Means for creating and selling prompts" refers to a function that allows users to create prompt sentences to input into generative AI models and sell them on an online platform.

[0430] "Means for purchasing excellent prompts from creators" is a function for purchasing prompt sentences from users who have created high-quality prompt sentences.

[0431] "Means for headhunting and developing prompt creators" refers to the function of identifying excellent prompt creators and providing programs and support to develop them.

[0432] "Means for planning and managing prompt learning classrooms" refers to the function of planning and running educational programs and workshops to improve prompt creation skills.

[0433] "Means for selling related books" is a function for selling books on prompt creation techniques and theories on an online platform.

[0434] The "means for evaluating prompt creation" is a function for evaluating prompt sentences created by users and objectively judging their quality.

[0435] The "means for evaluating a user's prompt creation skills and recommending appropriate books" is a function for evaluating a user's prompt creation skills and recommending appropriate books to the user based on the evaluation results.

[0436] A system for implementing this invention comprises a server and a user terminal. The server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating prompt creation, and means for evaluating a user's prompt creation skills and recommending appropriate books.

[0437] The server is built using a web framework such as Flask, and user devices include smartphones, PCs, etc. The server receives requests from users, accesses a database to retrieve the necessary information, and provides it to the user.

[0438] Specifically, when a user uses the application to improve their prompt creation skills, the process is carried out in the following steps:

[0439] 1. A user accesses an application using a smartphone or computer.

[0440] 2. Users can search for and purchase books on prompt writing.

[0441] 3. The user sends a prompt they created to the server, which evaluates the prompt using a generative AI model.

[0442] 4. The server evaluates the user's prompt creation skills based on the evaluation results and recommends appropriate books.

[0443] For example, if a user creates a prompt such as, "Please create a prompt to allow the generative AI model to evaluate the prompt entered by the user," the server will evaluate this prompt and provide the evaluation results to the user.

[0444] This system allows users to efficiently improve their prompt creation skills, and the server recommends appropriate books according to the user's skill level, allowing users to find the best learning resources for themselves.

[0445] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0446] Step 1:

[0447] Users access the application using a smartphone or computer.

[0448] Input: User's access request

[0449] Output: Application home screen

[0450] Specific operation: The user opens a browser or app and accesses the application URL. The server receives the request and sends the HTML data for the home screen to the user's device.

[0451] Step 2:

[0452] A user searches for and purchases a book on prompt creation.

[0453] Input: User search query, purchase request

[0454] Output: Search results display, purchase confirmation screen

[0455] Specific operation: The user enters keywords in the search bar and clicks the search button. The server retrieves related book information from the database and displays the search results. When the user clicks the purchase button, the server processes the purchase and displays a purchase confirmation screen.

[0456] Step 3:

[0457] Sends a user-created prompt to the server.

[0458] Input: A prompt text created by the user

[0459] Output: Evaluated prompt statement

[0460] Specific operation: The user enters a prompt and clicks the send button. The server receives the prompt, inputs it into the generative AI model, and evaluates it. The evaluation result is returned to the user.

[0461] Step 4:

[0462] The server evaluates the prompt creation skills of the user based on the evaluation results and recommends appropriate books.

[0463] Input: Evaluated result of prompt statement

[0464] Output: User skill evaluation results, recommended book list

[0465] Specific operation: The server analyzes the evaluation results and determines the user's skill level. It searches the database for appropriate books and generates a list of recommended books. It displays the skill evaluation results and the list of recommended books to the user.

[0466] Step 5:

[0467] The user checks the recommended books and purchases them if necessary.

[0468] Input: User's book review request, purchase request

[0469] Output: Book details screen, purchase confirmation screen

[0470] Specific operation: The user selects a book from the recommended book list and the details screen is displayed. The server displays detailed information about the book, and when the user clicks the purchase button, the server processes the purchase and displays a purchase confirmation screen.

[0471] 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.

[0472] "Example 1"

[0473] One embodiment of the present invention is a prompt creation and sales system incorporating an emotion engine. In this system, the emotion engine recognizes the emotion a user feels when creating a prompt and evaluates the quality of the prompt based on that emotion. Specifically, if a user feels joy or excitement when creating a prompt, the prompt is evaluated as high quality. Conversely, if the user feels discomfort or stress, the prompt is evaluated as low quality.

[0474] "Example 2"

[0475] The emotion engine is also used for headhunting and training prompt creators. Specifically, if a user frequently displays positive emotions when creating prompts, they are deemed to be excellent prompt creators and are targeted for headhunting. On the other hand, if a user frequently displays negative emotions when creating prompts, they are deemed to need to improve their prompt creation skills and are targeted for training programs.

[0476] "Example 3"

[0477] Furthermore, the emotion engine is also used to manage prompt learning classrooms. Specifically, the emotion engine recognizes the emotions of users when they participate in a learning classroom, and adjusts the content and progress of the learning classroom based on those emotions. For example, if a user feels excitement or anticipation when participating in a learning classroom, the learning classroom is evaluated as attractive to the user, and the content is continued. Conversely, if a user feels anxiety or fear when participating in a learning classroom, the learning classroom is evaluated as inappropriate for the user, and the content is improved.

[0478] The processing flow of each embodiment will be described below.

[0479] "Example 1"

[0480] Step 1: The user accesses the prompt creation screen.

[0481] Step 2: The user creates a prompt, and the emotion engine recognizes the user's emotions in real time.

[0482] Step 3: The emotion engine evaluates the quality of the prompt based on the user's emotion, and the evaluation result is fed back to the system and displayed as a prompt quality score.

[0483] "Example 2"

[0484] Step 1: The user accesses the prompt creation screen and creates a prompt. At this time, the user's emotions are recognized in real time by the emotion engine.

[0485] Step 2: The emotion engine evaluates the prompt creator based on the user's emotions, and the evaluation results are fed back to the system and reflected in the user's profile.

[0486] Step 3: The system determines targets for headhunting and training programs based on the evaluation results.

[0487] "Example 3"

[0488] Step 1: A user participates in a learning class, during which the user's emotions are recognized in real time by the emotion engine.

[0489] Step 2: The emotion engine evaluates the learning class based on the user's emotions. The evaluation results are fed back to the system, and the content and progress of the learning class are adjusted.

[0490] Step 3: The system improves the learning classroom based on the evaluation results.

[0491] Example 1

[0492] Next, a description will be given of Example 1 of Form 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."

[0493] In conventional prompt creation and sales systems, prompt quality evaluation was subjective and did not take into account the user's emotional state. This resulted in inconsistent prompt quality, making them unreliable for buyers. Furthermore, there was no system for automatically selecting and purchasing excellent prompts, which led to a decline in motivation for creators. Furthermore, prompt sales prices were not set appropriately, resulting in poor sales efficiency.

[0494] 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.

[0495] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for evaluating the quality of prompts by analyzing the user's emotions, means for setting a selling price for the evaluated prompts, and means for automatically selecting and purchasing prompts that meet certain criteria. This enables objective evaluation of prompt quality that takes the user's emotional state into account, making it possible to provide highly reliable prompts. Furthermore, automatically selecting and purchasing excellent prompts can improve creator motivation. Furthermore, appropriate selling price setting can improve sales efficiency.

[0496] "A means for creating and selling prompts" is a feature that provides an online platform for users to create prompts and sell them to other users.

[0497] "Means for purchasing excellent prompts from producers" is a function in which the system automatically selects high-quality prompts that meet certain standards and purchases them from producers.

[0498] "Means for headhunting and training prompt creators" refers to a function that identifies excellent prompt creators and recruits them to join the system, as well as providing educational programs to help them improve their prompt creation skills.

[0499] "Means for planning and managing prompt learning classes" refers to the function of planning classes to learn prompt creation techniques and running them online or in a group setting.

[0500] "Means for selling related books" is a function that provides an online store for selling books and materials related to prompt creation.

[0501] The "means for analyzing the user's emotions and evaluating the quality of the prompt" is a function that analyzes the emotional state of the user when creating a prompt and evaluates the quality of the prompt based on that emotion.

[0502] The "means for setting a selling price for a rated prompt" is a function for automatically setting a selling price for the prompt based on the quality of the prompt evaluated by sentiment analysis.

[0503] "Means for automatically selecting and purchasing prompts that meet certain criteria" is a function that automatically selects high-quality prompts that meet the criteria set by the system and purchases those prompts from the creators.

[0504] This invention relates to a prompt creation and sales system. The system provides an online platform for users to create prompts and sell them to other users. The system also has the ability to purchase outstanding prompts from creators.

[0505] In implementing the system, the server uses the following hardware and software: The server incorporates an emotion engine to analyze the emotions of users when creating prompts. This emotion engine uses, for example, an emotion analysis API of a general cloud service. Specifically, the Emotion API of Microsoft® Azure® Cognitive Services can be used.

[0506] When a user creates a prompt, the device collects the user's facial expressions and tone of voice through a camera and microphone. This data is sent to a server and analyzed by an emotion engine. If the user feels joy or excitement, the emotion engine rates the prompt as high quality. Conversely, if the user feels discomfort or stress, the prompt is rated as low quality.

[0507] The server evaluates the quality of the prompt based on the emotion data received from the emotion engine. Based on the evaluation result, the server sets a sales price for the prompt. The prompt is added to the sales list on the website along with the set price. The user is notified that their prompt has been added to the sales list.

[0508] Other users can select and purchase prompts they are interested in from a sales list on the website. The server processes the purchase and provides the prompt to the buyer. Once the purchase is complete, a notification is sent to the creator.

[0509] Furthermore, the server automatically selects high-quality prompts that meet certain criteria and purchases them from creators. For selected prompts, the server pays the creators a fee. Creators are notified that their prompts have been purchased.

[0510] As a concrete example, consider the case where a user creates a prompt on the theme of "How to train a new AI model." The user accesses a website and enters "How to train a new AI model" into the prompt creation form. The user enables the camera and microphone, and the emotion engine collects the user's facial expressions and voice. The emotion engine analyzes the user's smile and excited voice and determines that the prompt is "joyful." In this case, the server evaluates the prompt as high quality and sets a higher selling price.

[0511] Example prompt sentence:

[0512] "Please create a prompt on how to train a new AI model. Please also provide details on the specific steps and datasets used."

[0513] The system allows users to rate the quality of prompts based on their own feelings, motivating them to create better prompts, and allows other users to purchase high-quality prompts.

[0514] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0515] Step 1:

[0516] A user visits a website.

[0517] A user accesses the prompt creation and sales system website using a browser. The terminal accepts the user's access and displays the website's homepage. The input is the user's access information, and the output is the website's homepage.

[0518] Step 2:

[0519] The user creates the prompt.

[0520] The user begins inputting information into the prompt creation form on the website. The device receives and displays the user's input in real time. The emotion engine begins operation when the user enables the camera and microphone. The input is the user's prompt content and camera / microphone data, and the output is the prompt content displayed in real time.

[0521] Step 3:

[0522] The emotion engine analyzes the user's emotions.

[0523] The emotion engine collects the user's facial expressions and tone of voice through the device's camera and microphone. The server sends the collected data to the emotion analysis API and analyzes the user's emotional state. For example, if the user is smiling while speaking, the emotion engine will determine that the user is "happy." The input is data from the camera and microphone, and the output is analyzed emotional data.

[0524] Step 4:

[0525] The server evaluates the quality of the prompt.

[0526] The server evaluates the quality of the prompt based on the emotional data received from the emotion engine. For example, if the user feels "joy" or "excitement," the prompt is evaluated as high quality. Conversely, if the user feels "discomfort" or "stress," the prompt is evaluated as low quality. The input is emotional data, and the output is the quality evaluation result.

[0527] Step 5:

[0528] The server adds the prompt to the sales list.

[0529] The server sets a sales price for the prompt based on the evaluation results. The server adds the prompt to a sales list on the website along with the set price. The user is notified that their prompt has been added to the sales list. The input is the quality evaluation results and the prompt content, and the output is the prompt added to the sales list.

[0530] Step 6:

[0531] Other users purchase prompts.

[0532] Other users select prompts they are interested in from a list of products on the website and purchase them. The server processes the purchase and provides the prompts to the buyer. Once the purchase is complete, a notification is sent to the producer. The input is the buyer's selection information, and the output is a notification that the purchase has been completed.

[0533] Step 7:

[0534] The server purchases prompts that meet certain criteria from producers.

[0535] The server automatically selects high-quality prompts that meet certain criteria from the prompts in the sales list. The server purchases the selected prompts from the creator and pays them a fee. The creator is notified that the prompt has been purchased. The input is the sales list and the quality evaluation results, and the output is a purchase completion notification.

[0536] (Application example 1)

[0537] Next, a description will be given of Application Example 1 of Embodiment 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."

[0538] Conventional prompt creation and sales systems lacked a means to objectively evaluate the quality of prompts, making it difficult to accurately determine the value of prompts created by users. Furthermore, because quality evaluation did not take into account the prompt creator's feelings, users' creativity and motivation could not be maximized. Furthermore, there was an inadequate system for featuring or purchasing high-quality prompts, which resulted in excellent prompts being buried.

[0539] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for recognizing emotions at the time of prompt creation using an emotion engine and evaluating the quality of the prompts based on those emotions, and means for featuring or purchasing prompts evaluated as high quality. This makes it possible to objectively evaluate the quality of prompts created by users and to feature or purchase high-quality prompts.

[0540] A "prompt" is an instruction to be input into a generative AI model.

[0541] A "venue for creating and selling prompts" is an online platform that allows users to create prompts and sell them to other users.

[0542] "Means for purchasing excellent prompts from creators" is a mechanism by which the system automatically evaluates the quality of prompts and purchases prompts that meet certain standards from creators.

[0543] "Means for headhunting and developing prompt creators" refers to a system for finding excellent prompt creators and providing support to help them improve their skills.

[0544] "Means for planning and managing prompt learning classrooms" refers to a system for planning and managing educational programs to learn the techniques of creating prompts.

[0545] A "means for selling related books" is an online store for selling books and materials related to prompt creation.

[0546] An "emotion engine" is a technology that recognizes the emotions a user feels when creating a prompt and evaluates the quality of the prompt based on those emotions.

[0547] "Means of featuring or purchasing prompts rated as high quality" refers to a mechanism in which prompts rated as high quality by the system are featured on a featured page, or the operator purchases the prompts.

[0548] A system for implementing this invention includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for recognizing emotions at the time of prompt creation using an emotion engine and evaluating the quality of the prompts based on those emotions, and means for featuring or purchasing prompts evaluated as being of high quality.

[0549] System configuration

[0550] The system uses the following hardware and software:

[0551] Hardware: Server, user device (smartphone, PC)

[0552] Software: Python, TextBlob (for emotion analysis), EmotionRecognizer (for emotion recognition)

[0553] Program processing

[0554] The server receives the text when the user creates a prompt. The prompt sent from the user's device is analyzed by the emotion engine on the server. The emotion engine uses TextBlob and EmotionRecognizer to recognize the emotion the user is expressing when creating the prompt.

[0555] Based on the recognized emotion, the server evaluates the quality of the prompt: if the user feels joy or excitement, the prompt is evaluated as high quality; conversely, if the user feels discomfort or stress, the prompt is evaluated as low quality.

[0556] Rated prompts are classified based on their quality score. Prompts that are rated as high quality can be featured on a featured page or purchased by the publisher. This allows users to know how their prompts are rated, and motivates them to create even higher quality prompts.

[0557] Specific examples

[0558] For example, if a user creates a prompt saying "Create a story about a brave knight," the emotion engine can recognize the user's emotion from this text. If the user felt joy or excitement while creating this prompt, the prompt will be rated as high quality. Prompts that are rated high quality can be featured on a special page or purchased by the publisher.

[0559] This system allows users to know how their prompts are evaluated, motivating them to create even higher quality prompts. It also allows excellent prompts to be shared widely with other users, rather than being buried.

[0560] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0561] Step 1:

[0562] The user creates a prompt sentence and sends it from the terminal to the server. The input is the prompt sentence created by the user, and the output is the prompt sentence sent to the server. In concrete terms, the user enters the prompt sentence into the input form on the terminal and presses the "send" button.

[0563] Step 2:

[0564] The server passes the received prompt sentence to the emotion engine to recognize the emotion. The input is the prompt sentence sent to the server, and the output is the recognized emotion. Specifically, the server passes the prompt sentence to TextBlob and EmotionRecognizer for emotion analysis.

[0565] Step 3:

[0566] The server evaluates the quality of the prompt based on the recognized emotion. The input is the recognized emotion, and the output is a quality score. Specifically, if the emotion is "joy" or "excitement," the server evaluates it as high quality and sets a high quality score. Conversely, if the emotion is "discomfort" or "stress," the server evaluates it as low quality and sets a low quality score.

[0567] Step 4:

[0568] The server categorizes prompts based on their quality scores and stores them in a database. The input is the quality score, and the output is storing the classified prompts in the database. Specifically, the server categorizes prompts with high quality scores into a "high quality" category and prompts with low quality scores into a "low quality" category, and stores them in the database according to their respective categories.

[0569] Step 5:

[0570] The server displays prompts that are rated as high quality on a featured page, or the operator purchases them. The input is a prompt classified as "high quality," and the output is a notification of display on a featured page or purchase. Specifically, the server displays prompts classified as "high quality" on a featured page and sends a notification of purchase to the operator.

[0571] Step 6:

[0572] A user browses the featured page and purchases a prompt. The input is the prompt displayed on the featured page, and the output is the purchased prompt. Specifically, the user browses the featured page, selects a prompt they like, and presses the purchase button.

[0573] Example 2

[0574] Next, a description will be given of Example 2 of Form 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."

[0575] Conventional prompt creation systems do not adequately evaluate the quality of prompts or provide feedback on skill improvement for prompt creators, making it difficult to discover and develop excellent prompt creators. Furthermore, because evaluation does not utilize emotional data, appropriate feedback based on the user's emotional state is not provided. This means that prompt creators' motivation and skills are not sufficiently improved.

[0576] 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.

[0577] In this invention, the server includes means for evaluating the quality of prompts using a generative AI model, means for performing emotion analysis to analyze the emotion data of the prompt creator, and means for providing feedback based on the evaluation results. This automates the evaluation of prompt quality and provides appropriate feedback based on the emotion data, making it possible to discover and develop excellent prompt creators.

[0578] A "prompt" is a statement of instruction or question that is input to a generative AI model.

[0579] A "generative AI model" is an advanced algorithm for natural language processing that generates appropriate responses to input prompts.

[0580] "Quality evaluation" is the process of judging prompts based on factors such as clarity, specificity, and creativity.

[0581] "Emotion analysis" is a technology that analyzes a user's emotional state based on data such as facial expressions and tone of voice.

[0582] "Feedback" refers to evaluation and suggestion information provided to the user based on the prompt quality evaluation and sentiment analysis results.

[0583] "Headhunting" is the process of identifying talented prompt writers and making proposals to lure them away.

[0584] "Development" refers to the activity of providing education and training to help prompt creators improve their skills.

[0585] A "prompt learning classroom" is a place that provides educational programs to improve prompt creation skills.

[0586] "Online" refers to the provision of services and information via the Internet.

[0587] "Group-based" refers to a form of education or training that takes place in a physical location.

[0588] This invention is a system for creating, evaluating, and providing feedback on prompts, and it uses generative AI models and emotion analysis technology to support the skill improvement and headhunting of prompt creators. A specific embodiment of this system is described below.

[0589] Hardware and software used

[0590] Server: A high-performance database server (e.g., MySQL, PostgreSQL)

[0591] Generative AI models: Advanced natural language processing models such as GPT-4®

[0592] Emotion engine: Emotion analysis software (e.g., IBM Watson® Tone Analyzer)

[0593] Terminal: The device that provides the user interface (e.g., PC, tablet, smartphone)

[0594] Sensors: Cameras and microphones to collect emotional data

[0595] Specific operation of the system

[0596] Server Processing

[0597] The server uses a generative AI model to analyze the quality of prompts and the creator's activity history. Specifically, the server retrieves the prompt and creator history data stored in a database and inputs it into the generative AI model. The generative AI model evaluates the quality of the prompts and identifies creators who frequently create excellent prompts. In this process, the server also uses an emotion engine to analyze the emotional data displayed by users when creating prompts. Users who frequently display positive emotions are determined to be excellent prompt creators, while users who frequently display negative emotions are determined to need to improve their skills.

[0598] Terminal handling

[0599] The device provides an interface for users to create prompts. When the user enters a prompt, the device sends the data to the server. The device is also equipped with sensors and cameras to collect the user's emotional data, and sends this data to the server. The device also receives feedback from the server and displays it to the user. For example, it may display headhunting suggestions or information about a training program.

[0600] User Action

[0601] Users create prompts using their devices. Emotional data is collected when creating prompts, so users are encouraged to work in a natural state. If a user creates a good prompt, the server will send them a headhunting proposal. If it is determined that their skills need improvement, they will be shown information about a training program.

[0602] Specific examples

[0603] For example, consider the case where a user inputs the following prompt sentence into a generative AI model:

[0604] Example prompt sentence:

[0605] Please suggest a new marketing strategy.

[0606] When a user enters a prompt, the server uses a generative AI model to evaluate its quality and analyzes the user's emotional data. If the user displays a high percentage of positive emotions, the user is deemed a good prompt creator and is offered a headhunting opportunity. Conversely, if the user displays a high percentage of negative emotions, the user is considered for a training program.

[0607] This system will enable prompt creators to improve their skills and efficiently headhunt suitable talent.

[0608] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0609] Step 1:

[0610] The user enters a prompt statement.

[0611] The user uses the device interface to input a prompt for the generative AI model, for example, "Please suggest a new marketing strategy." The input prompt is saved on the device.

[0612] Step 2:

[0613] The device collects prompts and emotion data.

[0614] The device records the prompt text entered by the user. At the same time, it uses the device's built-in camera and microphone to collect emotional data such as the user's facial expressions and tone of voice. This records the user's emotional state when creating the prompt text. The collected data is temporarily stored on the device.

[0615] Step 3:

[0616] The terminal transmits the collected data to the server.

[0617] The device sends the collected prompts and emotion data to the server, which then analyzes and evaluates them. Specifically, the prompts and emotion data are stored in the server's database.

[0618] Step 4:

[0619] The server evaluates the quality of the prompt sentence.

[0620] The server inputs the received prompt into a generative AI model (e.g., GPT-4) to evaluate its quality. The generative AI model calculates a score based on factors such as the clarity, specificity, and creativity of the prompt. The evaluation results are stored in the server's database.

[0621] Step 5:

[0622] The server analyzes the emotion data.

[0623] The server uses emotion analysis software (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion data. If a lot of positive emotion is displayed, the user is judged to be an excellent prompt creator. Conversely, if a lot of negative emotion is displayed, the user is judged to need to improve their skills. The analysis results are stored in the server's database.

[0624] Step 6:

[0625] The server sends feedback to the user based on the evaluation results.

[0626] The server generates feedback for users based on the quality evaluation of the prompt sentences and the analysis of the emotional data. For example, a headhunting suggestion is sent to a good prompt creator, and information about a training program is sent to a user who needs to improve their skills. The feedback is then sent to the terminal.

[0627] Step 7:

[0628] The user receives feedback.

[0629] The user receives feedback from the server via their device, including the prompt evaluation results, sentiment analysis results, and specific suggestions and guidance. Based on this, the user decides on their next action.

[0630] (Application example 2)

[0631] Next, a description will be given of Application Example 2 of Form 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."

[0632] Conventional prompt creation systems lack sufficient prompt quality evaluation, creator sentiment analysis, and activity history tracking, making it difficult to identify and train excellent prompt creators. There was also a lack of effective training programs to help improve prompt creation skills. This resulted in a decline in the quality of prompts for generative AI models, which in turn led to a loss of quality in content delivery services.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0634] In this invention, the server includes a means for evaluating the quality of prompts, a means for analyzing emotions at the time of prompt creation, and a means for tracking the activity history of prompt creators, which makes it possible to identify and headhunt excellent prompt creators, and to provide a training program to help improve prompt creation skills.

[0635] A "prompt" is a piece of text or instruction that is input into a generative AI model.

[0636] "Creating a prompt" is the act of generating appropriate text or instructions for a generative AI model.

[0637] "Selling prompts" is the act of offering created prompts to other users or companies.

[0638] "Prompt Creator" means a person or entity that creates prompts for a generative AI model.

[0639] "Headhunting" is the act of identifying talented prompt writers and offering them special offers and rewards.

[0640] "Development" is the act of providing education and training to improve prompt creators' skills.

[0641] A "prompting classroom" is an educational program or workshop designed to teach prompting skills.

[0642] "Related books" refers to books and materials related to prompt creation and generative AI models.

[0643] "Means for assessing prompt quality" refers to methods or tools for evaluating the effectiveness and appropriateness of the prompts created.

[0644] "Means for analyzing emotions when creating prompts" refers to methods or tools that allow prompt creators to detect and analyze their emotions when creating prompts.

[0645] "Means for tracking prompt creator activity history" refers to methods and tools that prompt creators use to record and analyze prompts they have created in the past and their outcomes.

[0646] A "development program" is an educational curriculum or training sessions designed to improve prompting skills.

[0647] A system for implementing the present invention includes means for evaluating the quality of a prompt, means for analyzing emotions at the time of creating a prompt, and means for tracking the activity history of a prompt creator. Specific embodiments are described below.

[0648] System Configuration

[0649] The system includes a server, a user device, and a generative AI model. The server runs software for prompt quality evaluation, sentiment analysis, and activity history tracking. The user device is a device such as a smartphone or head-mounted display that users use to create and input prompts.

[0650] Hardware and software used

[0651] Hardware: Smartphone, head-mounted display

[0652] Software: Python, sentiment analysis library (e.g., sentiment_analysis), prompt quality evaluation library (e.g., prompt_quality_evaluator), user activity tracking library (e.g., user_activity_tracker)

[0653] Processing flow

[0654] 1. Enter the prompt:

[0655] The user inputs a prompt sentence using a smartphone or a head-mounted display, such as "Please give me an example of a prompt sentence to input to the generative AI model" or "Please tell me some tips for creating prompts."

[0656] 2. Prompt quality assessment:

[0657] The server evaluates the quality of the input prompt sentence using the prompt quality evaluation library, and the evaluation result is returned as a score and fed back to the user.

[0658] 3. Sentiment analysis:

[0659] The server uses a sentiment analysis library to analyze the user's sentiment at the time of creating the prompt, determining whether the sentiment was positive or negative, and recording the result.

[0660] 4. Activity Tracking:

[0661] The server uses a user activity tracking library to record the user's prompt creation history, along with the prompt quality score and sentiment analysis results.

[0662] 5. Headhunting proposal:

[0663] The server will then offer headhunting suggestions to users who show positive emotions, for example, sending a message like, "You're a great prompt creator. We'll give you a special offer."

[0664] 6. Providing training programs:

[0665] The server provides training programs to users who show negative emotions, such as sending a message like, "Please join an online learning class to improve your prompt writing skills."

[0666] Specific examples

[0667] User A uses a smartphone to input a prompt such as, "Please give me an example of a prompt sentence to input into the generative AI model." The server evaluates the quality of this prompt sentence and performs sentiment analysis. If the evaluation result is high and indicates a positive sentiment, a headhunting proposal is made to User A. Conversely, if the evaluation result is low and indicates a negative sentiment, a training program is offered to User A.

[0668] In this way, the system can improve prompt writers' skills, identify excellent prompt writers, and offer headhunting and development programs.

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1:

[0671] The user inputs a prompt using a device (such as a smartphone or head-mounted display). The input prompt is then sent to the server. For example, a prompt such as "Please give us an example of a prompt to input to the generative AI model" is input.

[0672] Step 2:

[0673] The server uses a prompt quality evaluation library to evaluate the quality of the input prompt sentence. Specifically, it analyzes the structure and content of the prompt sentence and scores its effectiveness against the generative AI model. The input is the prompt sentence, and the output is a quality score.

[0674] Step 3:

[0675] The server uses a sentiment analysis library to analyze the user's sentiment when creating the prompt. Specifically, it analyzes the linguistic features of the prompt and the user's behavioral data when entering the prompt to determine whether the sentiment is positive or negative. The input is the prompt and the user's behavioral data, and the output is the sentiment analysis results.

[0676] Step 4:

[0677] The server uses a user activity tracking library to record the user's prompt creation history. Specifically, it stores the prompt sentence, quality score, and sentiment analysis results in a database. The input is the prompt sentence, quality score, and sentiment analysis results, and the output is the updated activity history data.

[0678] Step 5:

[0679] The server makes headhunting suggestions to users who show positive emotions. Specifically, it generates a message proposing special offers and rewards to the user and sends it to the terminal. The input is the emotion analysis result, and the output is the headhunting suggestion message.

[0680] Step 6:

[0681] The server provides a training program to users who show negative emotions. Specifically, it generates information about online learning classes and workshops to improve prompt creation skills and sends it to the terminal. The input is the emotion analysis results, and the output is a training program information message.

[0682] In this way, the system can improve prompt writers' skills, identify excellent prompt writers, and offer headhunting and development programs.

[0683] Example 3

[0684] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0685] With conventional prompt creation and sales systems, it was difficult to operate a learning classroom that took into account the user's emotional state, and it was not possible to maximize the user's learning effectiveness. Furthermore, when selling related books, there was a lack of means to provide books that met the user's needs. This led to problems such as a decrease in user satisfaction and learning effectiveness.

[0686] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0687] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for analyzing a user's emotional state using an emotion engine and adjusting the content and progress of the learning class, means for selling books on a website, and means for transmitting a user's facial expression and voice data to the emotion engine in real time. This enables the operation of a learning class based on the user's emotional state, maximizing the user's learning effectiveness. Furthermore, when selling related books, it becomes possible to provide books that meet the user's needs.

[0688] A "prompt" refers to text or instructions input to a generative AI model.

[0689] "Means for creating and selling prompts" refers to the functionality that allows users to create and sell prompts to be used with generative AI models.

[0690] "Means to purchase high-quality prompts from creators" refers to a function for purchasing high-quality prompts from prompt creators.

[0691] "Means for headhunting and training prompt creators" refers to a function for finding, hiring, or training excellent prompt creators.

[0692] "Means for planning and managing prompt learning classrooms" refers to the function of planning and running classrooms for learning prompt creation techniques.

[0693] "Means for selling related books" refers to a function for selling books related to prompt creation.

[0694] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0695] "Means for analyzing the user's emotional state and adjusting the content and progress of the learning class" refers to a function for analyzing the user's emotions using an emotion engine and changing the content and progress of the learning class based on the results.

[0696] "Means for selling books on the website" refers to the functionality for selling books via the Internet.

[0697] "Means for transmitting the user's facial expression and voice data to the emotion engine in real time" refers to a function for transmitting facial expression and voice data from the user's terminal to the emotion engine in real time.

[0698] This invention is a system that includes the creation and sale of prompts, the purchase of excellent prompts, the headhunting and training of prompt creators, the planning and management of prompt learning classes, the sale of related books, the adjustment of the content and progress of learning classes using an emotion engine, the sale of books on a website, and the real-time transmission of user facial expressions and voice data.

[0699] Book sales function

[0700] The server hosts a website and makes it accessible to users. The user visits the website and searches for books about prompt writing. The server displays a list of related books based on the user's search query. The user selects the book they want to purchase and checks out. The server processes the user's purchase information and provides a download link for the book.

[0701] For example, if a user searches for the keyword "prompting techniques," the server displays a book called "Prompting Theory and Practice." If the user purchases the book, the server provides a download link.

[0702] Prompt learning classroom using emotion engine

[0703] A user logs in to a website to participate in a prompt learning classroom. The server authenticates the user's login information and redirects them to the learning classroom page. When a user participates in a learning classroom, the device (e.g., a PC or smartphone) sends the user's facial expressions and voice to the emotion engine. The emotion engine analyzes the user's facial expressions and voice data and recognizes the user's emotional state. The server receives feedback from the emotion engine and adjusts the content and progress of the learning classroom.

[0704] For example, a user can participate in a learning class and send data to the emotion engine via a camera and microphone. The emotion engine recognizes the user's excitement and the server continues the content.

[0705] Hardware and software used

[0706] Server: Hosts the website and processes data.

[0707] Terminal: A device (PC, smartphone, etc.) that transmits the user's facial expressions and voice data to the emotion engine.

[0708] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize their emotional state.

[0709] Prompt Sentence Examples

[0710] "Find books on prompt writing techniques."

[0711] "Recognize the emotions of your classroom participants and adjust your content based on their feedback."

[0712] This system allows users to easily purchase books to improve their prompt creation skills and to learn effectively in learning classes that utilize the emotion engine.The flow of the specific processing in Example 3 will be explained using Figure 21.

[0713] Book sales function

[0714] Step 1:

[0715] The user opens a web browser and enters the system's website URL to access it.

[0716] Input: The user types a URL into the browser's address bar.

[0717] Output: The website home page is displayed.

[0718] Specific behavior: The user types "https: / / example.com" into the browser address bar and presses Enter.

[0719] Step 2:

[0720] The server sends the website's home page to the user's terminal for display.

[0721] Input: The user's access request.

[0722] Output: Home page HTML, CSS and JavaScript files.

[0723] What happens: The server sends HTML, CSS, and JavaScript files to the user's device, and the browser renders them.

[0724] Step 3:

[0725] A user types "prompt writing techniques" into the search bar and clicks the search button.

[0726] Input: The keyword the user entered into the search bar.

[0727] Output: Display of search results.

[0728] What happens: A user enters keywords into the search bar and clicks the search icon.

[0729] Step 4:

[0730] The server searches the database and sends a list of relevant books to the user's terminal.

[0731] Input: The user's search query.

[0732] Output: A list of related books.

[0733] What happens: The server executes the SQL query and sends the results in HTML format to the user's browser.

[0734] Step 5:

[0735] The user selects "Prompt Writing Theory and Practice" from the displayed book list and clicks the purchase button.

[0736] Input: User's book selection and purchase request.

[0737] Output: Display of checkout page.

[0738] What happens: A user navigates to a book's detail page and clicks the buy button.

[0739] Step 6:

[0740] The server processes the user's purchase information and provides the user with a download link for the book after payment is completed.

[0741] Input: User's purchase and payment information.

[0742] Output: Provide a download link.

[0743] What happens: The server communicates with the payment gateway, and after the payment is confirmed, it generates a download link and displays it to the user.

[0744] Prompt learning classroom using emotion engine

[0745] Step 1:

[0746] A user visits a website and navigates to the login page.

[0747] Input: The user's access request.

[0748] Output: Login page displayed.

[0749] Specific behavior: The user enters the URL of the login page and presses the Enter key.

[0750] Step 2:

[0751] The server checks the login information (user name and password) entered by the user against the database and performs authentication.

[0752] Enter your username and password.

[0753] Output: Authentication result.

[0754] Specific operation: The server compares the username and password with the information in the database, and if they match, the authentication is successful.

[0755] Step 3:

[0756] A user accesses a class page and clicks the join button.

[0757] Input: User's join request.

[0758] Output: Display of classroom page.

[0759] Specific behavior: The user clicks on the link for the learning class and presses the join button.

[0760] Step 4:

[0761] The device uses the user's camera and microphone to transmit facial and voice data to the emotion engine in real time.

[0762] Input: User's facial and voice data.

[0763] Output: Sending data to the emotion engine.

[0764] Specific operation: The device activates the camera and microphone and sends the data to the emotion engine API.

[0765] Step 5:

[0766] The emotion engine analyzes the received data and recognizes the user's emotional state (excitement, anticipation, anxiety, fear, etc.).

[0767] Input: facial expression and speech data.

[0768] Output: Emotional state recognition results.

[0769] What it does: The emotion engine uses machine learning models to analyze facial and voice data and classify emotions.

[0770] Step 6:

[0771] The server receives feedback from the emotion engine and adjusts the content and progress of the learning classroom.

[0772] Input: Emotion engine feedback.

[0773] Output: Adjusted learning classroom content.

[0774] Specific operation: Based on the results of the emotion engine, the server changes the content of the slides and videos in the classroom and adjusts the speed of the videos.

[0775] (Application example 3)

[0776] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0777] In conventional prompt learning classrooms, it was difficult to provide an optimal learning experience for each individual participant because the learning content could not be adjusted based on the participants' emotions. In addition, related books had to be sold separately, resulting in insufficient coordination between the learning experience and book purchases.

[0778] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for emotion recognition, means for adjusting learning content based on emotions, and means for displaying related information using a smart device. This makes it possible to adjust learning content in real time based on participants' emotions and provide an optimal learning experience. In addition, related books can also be sold seamlessly through the smart device.

[0779] "Means for creating and selling prompts" refers to the functionality that allows users to create prompts to input into generative AI models and sell them.

[0780] "Mechanism for purchasing excellent prompts from creators" is a function for purchasing prompts from creators who have created high-quality prompts.

[0781] "Means for headhunting and training prompt creators" is a function for finding, hiring, and training excellent prompt creators.

[0782] "Means for planning and managing prompt learning classrooms" refers to the function for planning and managing classrooms for learning prompt creation techniques.

[0783] The "means for selling related books" is a function for selling books related to the creation of a prompt.

[0784] The "means for emotion recognition" is a function for analyzing the user's facial expressions and tone of voice and recognizing their emotions.

[0785] The "means for adjusting learning content based on emotions" is a function for adjusting learning content and progress in real time based on the recognized emotions of the user.

[0786] The "means for displaying related information using a smart device" is a function for displaying related information to a user using a device such as smart glasses or a smartphone.

[0787] This invention is a system that integrates the creation and sale of prompts, the operation of prompt learning classrooms, the adjustment of learning content using emotion recognition, and the sale of related books. Specific embodiments for implementing this system are described below.

[0788] System Configuration

[0789] The system consists of the following main components:

[0790] 1. Server: Manages the creation and sale of prompts, operation of prompt learning classrooms, emotion recognition, adjustment of learning content, and sales of related books.

[0791] 2. Smart Device: The device used by the user, such as smart glasses or a smartphone.

[0792] 3. Emotion recognition engine: Software that analyzes the user's facial expressions and tone of voice to recognize emotions.

[0793] 4. Generative AI Model: The artificial intelligence model used to create the prompt.

[0794] Program processing

[0795] The server does the following:

[0796] 1. Creating and selling prompts:

[0797] The server uses the generative AI model to create prompts and sells them on an online platform.

[0798] Users can purchase and download prompts through their smart devices.

[0799] 2. Prompt Learning Classroom Management:

[0800] The server plans and manages prompt learning classrooms online or in groups.

[0801] Users use their smart devices to participate in classes and receive learning content.

[0802] 3. Emotion recognition and learning adjustment:

[0803] The camera and microphone on the smart device are used to capture the user's facial expressions and tone of voice.

[0804] The emotion recognition engine analyzes this data and recognizes the user's emotions.

[0805] The server adjusts the learning content in real time based on the perceived emotion, for example, providing more advanced content if the user is excited, or less advanced content if the user is anxious.

[0806] 4. Related Book Sales:

[0807] The server manages book information related to prompt creation and displays it on the smart device display.

[0808] Users can purchase books through their smart devices.

[0809] Hardware and software used

[0810] Hardware: smart glasses, smartphone, camera, microphone

[0811] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition), BookStore (book sales system)

[0812] Specific examples

[0813] For example, consider a scenario in which a user is participating in a prompting class. If the user is excited, the smart glasses display could show information about a book called "Advanced Prompting Techniques" and provide a purchase link. The system also includes the ability to adjust learning content in real time based on the user's emotions.

[0814] Example prompts to input to a generative AI model:

[0815] Consider a scenario where a user is participating in a prompt learning class. If the user is excited, the smart glasses display will show information about the book "Advanced Prompting Techniques" and provide a link to purchase it, including the ability to adjust learning content in real time based on the user's emotions.

[0816] In this way, the system can provide an optimal learning experience based on the user's emotions and seamlessly sell related books.

[0817] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0818] Step 1:

[0819] The server creates prompts using a generative AI model. As input, it receives themes and keywords provided by the user, and the generative AI model generates prompt sentences based on these. As output, it obtains the generated prompt sentences.

[0820] Step 2:

[0821] The server uploads the generated prompt text to the online platform and starts selling. As input, it receives the generated prompt text and its sales information (price, description, etc.) and stores them in a database. As output, the prompt text is displayed on the online platform.

[0822] Step 3:

[0823] A user accesses the online platform using a smart device to purchase a prompt. The server receives the user's purchase request and payment information as input, processes them, and provides the user with a notification of purchase completion and a download link for the prompt.

[0824] Step 4:

[0825] The server plans prompt learning classes and manages them online or in a group setting. As input, it sets the schedule and content of the learning class and manages the participant list. As output, it notifies participants of the detailed information of the learning class.

[0826] Step 5:

[0827] A user participates in a prompt learning classroom using a smart device. As input, the server receives the user's participation request and the classroom's access information, and processes them. As output, the learning classroom content is provided to the user.

[0828] Step 6:

[0829] The camera and microphone on the smart device capture the user's facial expressions and tone of voice. As input, real-time video and audio data are acquired and sent to an emotion recognition engine. As output, the user's emotional data is obtained.

[0830] Step 7:

[0831] The emotion recognition engine analyzes the captured video and audio data to recognize the user's emotions. As input, it receives the video and audio data and applies the emotion recognition algorithm. As output, the recognized emotion data is sent to the server.

[0832] Step 8:

[0833] The server adjusts the learning content in real time based on the recognized emotion data. As input, it receives the emotion data and the current learning content and makes appropriate adjustments. As output, the adjusted learning content is provided to the user.

[0834] Step 9:

[0835] The server manages the information of related books and displays it on the smart device display. As input, it retrieves the information of related books (title, author, price, etc.) from the database and sends it to the smart device. As output, the information of related books is displayed on the smart device.

[0836] Step 10:

[0837] The user purchases the relevant book through their smart device. The server receives the user's purchase request and payment information as input, processes them, and provides the user with a purchase completion notification and a download link for the book as output.

[0838] 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.

[0839] 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> ) 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.

[0840] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0841] 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.

[0842] [Second embodiment]

[0843] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0844] 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.

[0845] 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).

[0846] 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.

[0847] 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.

[0848] 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).

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0855] "Example 1"

[0856] This embodiment of the present invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing outstanding prompts from their creators. Specifically, the system automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[0857] "Example 2"

[0858] Furthermore, this embodiment of the present invention has the function of headhunting and training prompt creators. Specifically, the system analyzes the quality of prompts and the creators' activity history to identify creators who frequently create excellent prompts. The system then makes headhunting suggestions to identified creators. It also plans and manages online and group prompt learning classes to help improve prompt creation skills.

[0859] "Example 3"

[0860] Furthermore, this embodiment of the present invention has a function for selling related books. Specifically, books on prompt creation techniques and theories are sold on the website, allowing users to further improve their prompt creation skills.

[0861] The processing flow of each embodiment will be described below.

[0862] "Example 1"

[0863] Step 1: User visits the website and uploads their own prompt.

[0864] Step 2: The system automatically evaluates the quality of the uploaded prompts.

[0865] Step 3: The system purchases prompts that meet certain criteria from the creator.

[0866] "Example 2"

[0867] Step 1: The system analyzes the prompt quality and the creator's activity history.

[0868] Step 2: The system identifies creators who frequently create good prompts.

[0869] Step 3: The system will make headhunting suggestions to the identified creators. Step 4: Plan and manage online and group prompt learning classes to help improve prompt creation skills.

[0870] "Example 3"

[0871] Step 1: Sell books on prompt writing techniques and theory on your website.

[0872] Step 2: The user purchases the book and further develops their prompt-writing skills.

[0873] Example 1

[0874] Next, a description will be given of Example 1 of Form 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."

[0875] In conventional prompt creation and sales systems, the quality of prompts created by users is evaluated manually, which takes time and results in inconsistent evaluation criteria. Furthermore, there was a lack of a system for efficiently discovering and purchasing excellent prompts from their creators, making it difficult to maximize the market value of prompts. Furthermore, there was no system that comprehensively provided prompt-related services, such as training and headhunting prompt creators and the sale of related books.

[0876] 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.

[0877] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for users to upload prompts, means for other users to purchase prompts, means for evaluating the quality of prompts using a generative AI model, and means for purchasing prompts based on the evaluation results. This enables prompt quality evaluation to be automated and performed quickly and consistently. Furthermore, by efficiently discovering excellent prompts and purchasing them from creators, the market value of prompts can be maximized. Furthermore, it becomes possible to comprehensively provide prompt-related services, such as training and headhunting prompt creators and selling related books.

[0878] A "prompt" is an input sentence that prompts a generative AI model to generate a specific output.

[0879] A "generative AI model" is a type of artificial intelligence that performs natural language processing and is a model that generates appropriate output for specific input.

[0880] "User" means any person or entity that uses the System to create, upload, or purchase prompts.

[0881] A "server" is a computer system that runs the entire system and processes requests from users.

[0882] A "database" is a system for storing and managing data such as prompts and user information.

[0883] A "prompt creation and sales means" is a part of the system that provides the functionality for users to create prompts and sell them to other users.

[0884] The "means of purchasing excellent prompts from producers" is a function that enables the system to automatically evaluate the quality of prompts and purchase prompts that meet certain standards from producers.

[0885] "Means for headhunting and training prompt creators" is a function for discovering and training excellent prompt creators.

[0886] "Means for planning and managing prompt learning classrooms" refers to the function for planning and managing classrooms for learning prompt creation techniques.

[0887] "Means for selling related books" is a function for selling books and materials related to prompt creation.

[0888] The "means for users to upload prompts" is a function that allows users to upload prompts that they have created themselves to the system.

[0889] "Means for other users to purchase prompts" refers to a function that allows users to purchase prompts that are published on the system.

[0890] "Means for evaluating the quality of prompts using a generative AI model" refers to a function for automatically evaluating the quality of uploaded prompts using a generative AI model.

[0891] "Means for purchasing prompts based on evaluation results" is a function for purchasing prompts that meet certain criteria from their creators based on the evaluation results of the generative AI model.

[0892] This invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing excellent prompts from their creators. Specifically, the server automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[0893] Hardware and software used

[0894] Server: runs the entire website, processes user requests, and stores and manages data using a database management system (e.g., MySQL).

[0895] Generative AI models: Used to evaluate the quality of prompts, specifically using generative AI models that perform natural language processing (e.g., GPT-3).

[0896] User Device: The device used by the user to access the website and create, upload, and purchase prompts. Use a common web browser (e.g., Google Chrome, Mozilla Firefox).

[0897] Program processing

[0898] The server performs the following process.

[0899] 1. User Registration and Login

[0900] A user visits a website and enters their email address and password into a registration form, which the server stores in a database and creates a user account.

[0901] When a user enters their email address and password into the login form, the server checks it against the database and allows them to log in if it matches.

[0902] 2. Upload the prompt

[0903] The user accesses the prompt creation page and enters a prompt sentence, which the server saves in the database.

[0904] Example: A user enters a prompt to make the AI ​​model generate a cat image and clicks the "Upload" button. The server receives this and saves the new prompt information in the database.

[0905] 3. Prompt Sales

[0906] Other users can browse a list of prompts published on the website and select the prompt they want to purchase. The server processes the purchase and provides the prompt to the purchaser.

[0907] Example: User B selects the "Cat Picture Generator Prompt" and clicks the "Purchase" button. The server processes the payment and displays the prompt to the buyer.

[0908] 4. Prompt Quality Assessment

[0909] The server evaluates the uploaded prompts using a generative AI model (e.g., GPT-3), and the evaluation results are stored in a database.

[0910] Example: The server inputs a "cat image generation prompt" into GPT-3 and obtains an evaluation score. The server stores this score in a database.

[0911] 5. Prompt Buyout

[0912] The server purchases high-quality prompts that meet certain criteria from the producers. The purchase price is determined based on the evaluation results and notified to the producers.

[0913] Example: The server checks the rating score of the "Cat Image Generation Prompt" and sends a purchase offer of 1,000 yen to the creator if it meets the criteria. If User A accepts the offer, the server processes the payment.

[0914] Prompt Sentence Examples

[0915] "Create a prompt that generates an image of a cat. Include details like the type of cat, the background, and the color."

[0916] The above is an embodiment of the present invention. This system automates the quality evaluation of prompts and enables it to be performed quickly and consistently. It also makes it possible to maximize the market value of prompts by efficiently discovering excellent prompts and purchasing them from their creators. It also makes it possible to provide comprehensive prompt-related services, such as training and headhunting prompt creators and selling related books.

[0917] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0918] Step 1:

[0919] User Registration and Login

[0920] Input: The user enters their email address and password.

[0921] Specific behavior: The user enters "user@example.com" and "password123" and clicks the "Register" button.

[0922] Data processing: The server saves the entered information in a database and creates a new user account.

[0923] Output: The user account is created and the user can log in.

[0924] Input: The user enters their email address and password.

[0925] Specific action: The user enters "user@example.com" and "password123" and clicks the "Login" button.

[0926] Data calculation: The server checks the entered information against the database and allows login if there is a match.

[0927] Output: The user is redirected to the dashboard.

[0928] Step 2:

[0929] Uploading a Prompt

[0930] Input: The user enters the prompt text.

[0931] Specific operation: The user enters a prompt to have the AI ​​model generate an image of a cat and clicks the "Upload" button.

[0932] Data processing: The server saves the entered prompt sentence in the database.

[0933] Output: The prompt is saved in the database and can be viewed by other users.

[0934] Step 3:

[0935] Prompt Sales

[0936] Input: Select the prompt the user wants to purchase.

[0937] Specific behavior: User B selects the "Cat Image Generation Prompt" and clicks the "Purchase" button.

[0938] Data Calculation: The server processes the payment and provides prompts to the buyer.

[0939] Output: The buyer gets the prompt text.

[0940] Step 4:

[0941] Prompt quality assessment

[0942] Input: The uploaded prompt text.

[0943] Specific operation: The server inputs the "cat image generation prompt" into a generative AI model (e.g., GPT-3) and obtains an evaluation score.

[0944] Data Computing: Using a generative AI model to evaluate the quality of the prompts and store the evaluation results in a database.

[0945] Output: The prompt evaluation score is saved to the database.

[0946] Step 5:

[0947] Prompto Buyout

[0948] Input: The prompt's quality score based on the evaluation results.

[0949] Specific operation: The server checks the rating score of the "Cat Image Generator Prompt" and sends a purchase offer to the creator if it meets the criteria.

[0950] Data calculation: The server determines the purchase price based on the evaluation results and notifies the producer.

[0951] Output: If the producer accepts the offer, the server processes the payment.

[0952] The above are the specific processing steps of the program of this system.

[0953] (Application example 1)

[0954] Next, a description will be given of Application Example 1 of Form 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."

[0955] Conventional prompt creation and sales systems lacked sufficient prompt quality evaluation, ranking display, and purchasing methods, making it difficult for users to efficiently find high-quality prompts. Furthermore, there was a lack of means to objectively evaluate prompt quality, meaning that creators of excellent prompts had few opportunities to be properly recognized.

[0956] 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.

[0957] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts. This allows users to efficiently find and purchase high-quality prompts. Furthermore, objective evaluation of prompt quality provides an opportunity for creators of excellent prompts to be properly recognized.

[0958] A "prompt" is an instruction to be input into a generative AI model.

[0959] "Creating a prompt" is the act of creating instructions that a user will input to a generative AI model.

[0960] "Selling prompts" refers to the act of a user offering prompts they have created to other users for a fee.

[0961] A "means for evaluating the quality of prompts" is a method or system for objectively evaluating the usefulness and effectiveness of the prompts created.

[0962] A "means for displaying a ranking of prompts" is a method or system for ranking rated prompts based on quality and popularity and displaying the rankings to the user.

[0963] A "means for purchasing prompts" is a method or system that allows a user to obtain prompts created by other users for a fee.

[0964] A "means for purchasing excellent prompts from creators" is a method or system by which the system directly purchases prompts that meet certain standards from creators.

[0965] A "means for headhunting and developing prompt writers" is a method or system for identifying individuals with the ability to write good prompts and further developing their abilities.

[0966] "Means for planning and managing a prompt learning classroom" refers to a method or system for planning and managing an educational program to learn the skills and knowledge of creating prompts.

[0967] "Means for selling related books" refers to methods or systems for selling books and materials related to prompt creation and generative AI models to users.

[0968] A system for carrying out this invention includes a server, a user terminal, and a generative AI model. The server has means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts.

[0969] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for users to create, upload, and purchase prompts. The generative AI model is an artificial intelligence model that receives user-created prompts as input and performs the specified task.

[0970] Program processing explanation

[0971] Hardware used

[0972] Smartphone

[0973] server

[0974] Software used

[0975] Flask (a Python micro web framework)

[0976] requests (a Python library for sending HTTP requests)

[0977] Data processing and calculation

[0978] The server receives prompts entered by users and evaluates their quality. The evaluation is performed using an algorithm to objectively measure the usefulness and effectiveness of the prompts. The evaluated prompts are ranked based on their quality scores and displayed to users. Users can purchase prompts by referring to the rankings.

[0979] For example, a user can create a prompt on the theme of "AI poetry generation" and upload it to the app. The server evaluates the quality of the prompt and displays it in a ranking. Other users can purchase this prompt and input it into the generative AI model to generate poetry.

[0980] Prompt Sentence Examples

[0981] "AI Poetry Generation" prompt:

[0982] "You are a poet. Create a poem on the theme of the arrival of spring."

[0983] In this way, users can leverage generative AI models to create high-quality content that can be shared and sold with other users.

[0984] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0985] Step 1:

[0986] The user creates a prompt using a device. The user inputs instructions to be input to the generative AI model and sends them to the server through an application on the device. The input data is the prompt created by the user. The output data is the prompt sent to the server.

[0987] Step 2:

[0988] The server saves the prompt text it receives. The server stores the prompt text in a database and prepares it for evaluating the quality of the prompt. The input data is the prompt text sent by the user. The output data is the prompt text saved in the database.

[0989] Step 3:

[0990] The server evaluates the quality of the prompt. The server analyzes the prompt sentence and calculates a quality score using an evaluation algorithm. The input data is the prompt sentence stored in the database. The output data is the quality score.

[0991] Step 4:

[0992] The server generates a ranking of prompts. The server compares the quality scores of all prompts and creates a ranking. The input data is the quality score of each prompt. The output data is a ranking list.

[0993] Step 5:

[0994] The server displays the rankings on the user terminal. The user browses the rankings through the terminal and selects the prompts that interest them. The input data is the ranking list. The output data is the rankings displayed on the user terminal.

[0995] Step 6:

[0996] A user purchases a prompt. The user uses a terminal to purchase a prompt selected from the rankings. The input data is the prompt selected by the user. The output data is a notification of purchase completion.

[0997] Step 7:

[0998] The server provides the purchased prompt to the user. The server completes the purchase and sends the prompt to the user. The input data is the purchased prompt. The output data is the prompt provided to the user.

[0999] Step 8:

[1000] The user inputs the purchased prompt into the generative AI model. The user uses the device to input the purchased prompt into the generative AI model to perform the specified task. The input data is the purchased prompt. The output data is the content generated by the generative AI model.

[1001] Example 2

[1002] Next, a description will be given of Example 2 of Form 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."

[1003] Conventional prompt creation systems lacked functionality for evaluating prompt quality and improving prompt creator skills, making it difficult to identify and train excellent prompt creators. Furthermore, there was a lack of efficient methods for headhunting and training prompt creators, making it difficult to improve the quality of generative AI models.

[1004] 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.

[1005] In this invention, the server includes means for collecting log data of the generative AI model, means for analyzing the collected data, means for identifying excellent prompt creators, means for proposing headhunting to the identified prompt creators, and means for supporting the improvement of prompt creation skills using an online learning platform. This allows for efficient evaluation of prompt quality and improvement of creator skills, making it possible to identify and develop excellent prompt creators.

[1006] A "prompt" is text that is input as an instruction or question to a generative AI model.

[1007] A "generative AI model" is an algorithm that uses artificial intelligence to generate text and answers.

[1008] "Log data" is data that records the operation of the system and the operation history of the user.

[1009] "Data analysis" is the process of analyzing collected data using statistical methods and machine learning to extract useful information.

[1010] A "prompt creator" is a user who creates and inputs prompts to a generative AI model.

[1011] "Headhunting" is the act of poaching people with specific skills and abilities from other organizations.

[1012] An "online learning platform" is a system that provides educational content via the Internet and supports learning.

[1013] A "prompting classroom" is an educational program or workshop designed to improve prompting skills.

[1014] "Quality assessment" is the process of evaluating the usefulness and accuracy of the generated prompts.

[1015] A "database" is a system for efficiently storing, managing, and retrieving data.

[1016] MODE FOR CARRYING OUT THE INVENTION

[1017] The present invention relates to a prompt creation system using a generative AI model. A specific embodiment of this system will be described below.

[1018] Hardware and software used

[1019] Hardware: Servers, terminals

[1020] Software: generative AI models, data analysis tools (e.g., Python pandas, scikit-learn), online learning platforms (e.g., Moodle)

[1021] System Overview

[1022] This system has the functions of creating and selling prompts, purchasing excellent prompts, headhunting and training prompt creators, planning and managing prompt learning classes, and selling related books. It also collects and analyzes log data from the generative AI model to identify excellent prompt creators and make headhunting suggestions. It also supports the improvement of prompt creation skills using an online learning platform.

[1023] Data collection

[1024] The server collects log data from the generative AI model. Specifically, it stores information such as the evaluation score of each prompt, the creator's ID, and the creation date and time in a database. For example, the server obtains the log data through the generative AI model's API and stores it in a MySQL database.

[1025] Data analysis

[1026] The server analyzes the collected data using data analysis tools. Specifically, it creates a data frame using Python's pandas and trains a machine learning model to evaluate the quality of prompts using scikit-learn. For example, the server runs a Python script to read the data using pandas.read_sql("SELECT FROM prompts", connection) and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[1027] Identifying good prompt writers

[1028] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it creates a list of users who have created prompts with a certain evaluation score or higher a certain number of times. For example, the server uses pandas to extract users who meet the criteria using code such as df[df['score'] > threshold].groupby('user_id').count().

[1029] Headhunting proposals

[1030] The server makes headhunting proposals to the identified producers. Specifically, it sends emails and notifications to present offers. For example, the server uses an SMTP server to send emails using the smtplib library. For example, it executes the code server.sendmail(from_addr, to_addrs, msg).

[1031] Help improve prompt writing skills

[1032] The server plans and manages online and group-based prompt learning classrooms. Specifically, it creates courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills. For example, the server creates a new course using the Moodle API and sends a notification to users encouraging them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[1033] Examples of concrete examples and prompts

[1034] Examples:

[1035] If user A creates 10 highly rated prompts using the generative AI model, the server identifies user A as a good prompt creator.

[1036] The server sends User A an email saying, "We really appreciate your prompt creation skills. Would you like to join our team?"

[1037] Example prompt sentence:

[1038] "Use a generative AI model to create a prompt like: 'Suggest a marketing strategy for a new product.'"

[1039] The system aims to improve the quality of generative AI models by evaluating the skills of prompt creators and identifying and developing outstanding talent.

[1040] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] Data collection

[1043] The server collects log data from the generative AI model, specifically, information such as the evaluation score of each prompt, the creator's ID, and the creation date and time, and stores it in a database.

[1044] Input: Generative AI model log data

[1045] Output: Prompt information stored in the database

[1046] Specific operation: The server retrieves the log data through the API of the generative AI model and stores it in a MySQL database, for example, by executing the SQL query INSERT INTO prompts (user_id, prompt_text, score, created_at) VALUES.

[1047] Step 2:

[1048] Data analysis

[1049] The server analyzes the collected data using data analysis tools, specifically, creating a data frame using Python's pandas and training a machine learning model using scikit-learn to evaluate the quality of prompts.

[1050] Input: Prompt information stored in the database

[1051] Output: Prompt quality assessment model

[1052] Specific operation: The server runs a Python script, reads data with pandas.read_sql("SELECT FROM prompts", connection), and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[1053] Step 3:

[1054] Identifying good prompt writers

[1055] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it lists users who have created prompts with a certain evaluation score or higher a certain number of times.

[1056] Input: prompt quality assessment model, prompt information stored in the database

[1057] Output: A list of great prompt creators

[1058] Specific operation: The server uses pandas to extract users who meet the criteria using code like df[df['score'] > threshold].groupby('user_id').count().

[1059] Step 4:

[1060] Headhunting proposals

[1061] The server then proposes headhunting to the identified creators by sending emails and notifications and presenting offers.

[1062] Input: A list of great prompt creators

[1063] Output: Headhunting proposal email or notification

[1064] Specific operation: The server uses the SMTP server to send the email using the smtplib library. For example, execute the code server.sendmail(from_addr, to_addrs, msg).

[1065] Step 5:

[1066] Help improve prompt writing skills

[1067] The server plans and manages online and group prompt learning classrooms. Specifically, it provides courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills.

[1068] Input: List of prompt creators, learning content

[1069] Output: Prompt learning classroom course, participation notification

[1070] Specific operation: The server creates a new course using the Moodle API and sends a notification to the user inviting them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[1071] (Application example 2)

[1072] Next, a description will be given of Application Example 2 of Form 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."

[1073] Conventional prompt creation systems did not adequately evaluate the quality of prompts or train prompt creators, making it difficult to identify and develop excellent prompt creators. Furthermore, it was not possible to efficiently plan and manage learning classes to help improve prompt creation skills. This resulted in a lack of high-quality prompts to provide optimal content to users in content delivery services that utilize generative AI models.

[1074] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1075] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.This enables the efficient evaluation of prompt quality and the identification and training of prompt creators, making it possible to supply high-quality prompts.

[1076] A "prompt" is text data input to a generative AI model that contains instructions for the model to output an appropriate response or product.

[1077] "Creating a prompt" is the act of generating text data to provide appropriate instructions to a generative AI model.

[1078] "Selling prompts" refers to the act of providing created prompts to other users or companies in exchange for payment.

[1079] "Prompt quality evaluation" is the act of evaluating the effectiveness and appropriateness of input prompts in order to improve the quality of the responses and products output by a generative AI model.

[1080] A "prompt creator" is a person or entity that creates the prompts that are input to a generative AI model.

[1081] "Headhunting" is the act of recruiting individuals with specific skills or abilities to other companies or organizations.

[1082] "Development" refers to providing education and training to individuals with specific skills and abilities to further improve their abilities.

[1083] A "prompt learning classroom" is a place that provides education and training to improve prompt creation skills.

[1084] "Planning and management" means planning, executing and managing a specific activity or project.

[1085] "Related books" are books and materials related to prompt creation and generative AI models.

[1086] "Activity history" refers to the records and track record of prompt creation that a prompt creator has performed in the past.

[1087] "Identification" means finding something based on specific conditions or criteria.

[1088] A "proposal" is a proposal that suggests a particular course of action or option to others and encourages their acceptance.

[1089] A "generative AI model" is a type of artificial intelligence that outputs appropriate responses or products based on input text data.

[1090] A system for implementing this invention includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.

[1091] The server evaluates the quality of the prompt using a generative AI model. Specifically, it receives the prompt created by the user as input and evaluates its quality using the generative AI model. The evaluation results are stored in a database for later analysis.

[1092] The terminal provides an interface for users to create prompts and send them to the server. Users can use the terminal to input prompts and check the quality evaluation results. The terminal also displays the activity history of prompt creators and provides information to identify excellent prompt creators.

[1093] The server analyzes the activity history of prompt creators to identify outstanding prompt creators. It then sends notifications to identified prompt creators to suggest headhunting. It also plans and runs learning classes to improve prompt creation skills and encourages users to participate.

[1094] For example, if a user creates a prompt such as "Enter a question about AI," the prompt is sent to a server, where the generative AI model evaluates its quality. The evaluation results are stored in a database and later analyzed. If a good prompt creator is identified, the user is offered a headhunting opportunity. Additionally, learning classes are organized to improve prompt creation skills, and users are encouraged to participate.

[1095] The hardware used includes smartphones and personal computers, and the software used includes Python, SQLite, and the OpenAI API. This enables efficient prompt quality evaluation and prompt creator identification and training, enabling the supply of high-quality prompts.

[1096] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1097] Step 1:

[1098] The user creates a prompt using the terminal.

[1099] Input: The prompt the user enters (e.g., "Please enter your AI question.")

[1100] Output: The generated prompt

[1101] Specific operation: The user inputs a prompt sentence through the terminal interface and presses the send button.

[1102] Step 2:

[1103] The terminal sends the generated prompt to the server.

[1104] Input: A prompt text created by the user

[1105] Output: The prompt sent to the server

[1106] Specific operation: The terminal generates and sends an API request to send the prompt text to the server.

[1107] Step 3:

[1108] The server inputs the received prompt sentences into a generative AI model and evaluates their quality.

[1109] Input: The prompt sent to the server

[1110] Output: Quality assessment results (scores and feedback)

[1111] Specific operation: The server calls the API of the generative AI model and passes the prompt sentence as input. The generative AI model analyzes the prompt sentence and returns a quality evaluation result.

[1112] Step 4:

[1113] The server stores the quality evaluation results in a database.

[1114] Input: Quality evaluation results

[1115] Output: Evaluation results stored in a database

[1116] Specific operation: The server generates and executes SQL queries to store the quality assessment results in an SQLite database.

[1117] Step 5:

[1118] The server analyzes the prompt creators' activity history and identifies good prompt creators.

[1119] Input: Evaluation results and activity history stored in the database

[1120] Output: A list of identified good prompt writers

[1121] Specific operation: The server retrieves activity history and evaluation results from the database and uses statistical methods and algorithms to identify excellent prompt creators.

[1122] Step 6:

[1123] The server makes a headhunting proposal to the identified prompt creator.

[1124] Input: A list of identified good prompt writers

[1125] Output: Headhunting proposal notification

[1126] Specific Actions: The server generates and sends a notification to the identified prompt creator containing the headhunting offer.

[1127] Step 7:

[1128] The server plans and runs learning classes to improve prompt creation skills.

[1129] Input: A list of prompt creators and skills development materials and curriculum

[1130] Output: Class schedule and participant list

[1131] Specific operations: The server creates a schedule for learning classes, sends notifications to prompt creators to invite them to participate, prepares teaching materials and curricula, and manages online or group learning classes.

[1132] Example 3

[1133] Next, a description will be given of Example 3 of Form Example 3. 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."

[1134] Conventional prompt creation and sales systems make it difficult for users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, efficient online operations are required for training and headhunting prompt creators, as well as for planning and managing prompt learning classrooms. A comprehensive system is needed to solve these issues.

[1135] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1136] In this invention, the server includes: means for users to access a website and search, select, purchase, and download books; means for the server to receive user requests, display a web page, provide search results and detailed book information, process payments, and generate download links; and means for the terminal to render the web page, display search results and detailed book information, and allow users who click on the download link to download the book file. This allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators and planning and management of prompt learning classes can also be efficiently carried out online.

[1137] A "prompt" refers to text or instructions input to a generative AI model.

[1138] "Sales" refers to the act of providing a product or service to a customer and receiving payment in return.

[1139] "Producer" refers to the person or entity that creates the prompt.

[1140] "Headhunting" refers to the act of poaching people with specific skills or knowledge from other organizations.

[1141] "Development" refers to the act of educating and developing personnel with specific skills and knowledge.

[1142] A "learning classroom" refers to an educational setting for learning specific knowledge or skills.

[1143] "Related books" refer to books related to a particular theme or field.

[1144] "Website" means a location on the Internet that provides publicly available information and services.

[1145] "Searching" refers to the act of researching a database or the Internet to find specific information.

[1146] "Selection" refers to the act of choosing one option from multiple options.

[1147] "Purchase" refers to the act of acquiring goods or services by paying money.

[1148] "Downloading" refers to the act of saving data from the Internet to your device.

[1149] A "server" refers to a computer that provides services to other computers on a network.

[1150] "Request" means a request for particular information or services.

[1151] "Web Page" refers to an individual page that makes up a website.

[1152] "Display" refers to the act of visually presenting information to a user.

[1153] "Payment processing" refers to the process of paying for goods or services.

[1154] "Link" refers to a hypertext element that connects web pages together.

[1155] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[1156] "Rendering" refers to the act of a computer processing data to produce a visual display.

[1157] This invention relates to a system for creating and selling prompts, and in particular to a system that allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation.

[1158] Hardware and software used

[1159] Server: Web server (e.g. Apache, Nginx)

[1160] Software: Web application frameworks (e.g., Django, Ruby on Rails), databases (e.g., MySQL, PostgreSQL)

[1161] Program processing

[1162] 1. A user visits a website

[1163] A user opens a browser and enters the URL of a website.

[1164] The server receives the user's request and sends the HTML, CSS, and JavaScript files for the web page to the user's device.

[1165] The terminal renders the received file and displays the web page to the user.

[1166] 2. User searches for a book

[1167] A user enters a keyword such as "create prompt" into the search bar on a web page and clicks the search button.

[1168] The server receives user input and retrieves relevant book information from a database (e.g., MySQL, PostgreSQL).

[1169] The terminal displays the search results received from the server to the user.

[1170] 3. The user selects a book

[1171] The user clicks on the book of interest from the search results.

[1172] The server retrieves detailed information about the selected book from the database and sends it to the user's terminal.

[1173] The terminal displays detailed information about the received book to the user.

[1174] 4. The user purchases the book

[1175] The user clicks the "Purchase" button on the book's details page to proceed with the purchase, and then enters payment information such as credit card information.

[1176] The server receives the user's purchase information, processes the payment through a payment gateway (e.g., Stripe, PayPal), and stores the purchase information in a database.

[1177] The terminal displays a notice to the user that the payment has been completed.

[1178] 5. The user downloads the book

[1179] Once the purchase is complete, the user clicks on a link to download the book.

[1180] The server generates a download link and sends it to the user's device.

[1181] The terminal allows the user who clicks on the download link to download the book file.

[1182] Specific examples

[1183] Example prompt: "Find books on prompt writing techniques."

[1184] When the user enters this prompt sentence, they type "create prompt" in the search bar and click the search button.

[1185] The server searches the database for relevant books and displays the results to the user.

[1186] The terminal displays the search results to the user.

[1187] Example of a book search result:

[1188] Book 1: "The Basics of Prompt Creation"

[1189] Book 2: "Advanced Prompting Techniques"

[1190] Book 3: "Application of Prompt Theory"

[1191] In this way, users can easily find, purchase, and download books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators, as well as planning and management of prompt learning classes, can also be efficiently carried out online. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1192] Step 1:

[1193] A user visits a website.

[1194] Input: A user types a website URL into their browser.

[1195] What happens: The user opens a browser and enters the URL of a website.

[1196] Data processing / calculation: The server receives the user's request and generates the HTML, CSS, and JavaScript files for the web page.

[1197] Output: The server sends the generated file to the user's terminal.

[1198] Step 2:

[1199] The device displays the web page.

[1200] Input: HTML, CSS, and JavaScript files sent by the server.

[1201] What happens: The device renders the received file and displays the web page to the user.

[1202] Data processing / computation: The device parses HTML, CSS, and JavaScript files to generate visual web pages.

[1203] Output: The web page that is displayed to the user.

[1204] Step 3:

[1205] A user searches for a book.

[1206] Input: A user enters a keyword, such as "create prompt," into the search bar and clicks the search button.

[1207] What happens: A user enters keywords into the search bar on a web page and clicks the search button.

[1208] Data processing / calculation: The server receives user input and searches for relevant book information from a database (e.g., MySQL, PostgreSQL).

[1209] Output: The server generates search results and sends them to the user's device.

[1210] Step 4:

[1211] Your device will display the search results.

[1212] Input: Search results sent from the server.

[1213] Specific operation: The terminal displays the search results received from the server to the user.

[1214] Data processing / calculation: The device analyzes the search results and generates data for visual display.

[1215] Output: The search results that are displayed to the user.

[1216] Step 5:

[1217] The user selects a book.

[1218] Input: User clicks on a book they are interested in from the search results.

[1219] Specific behavior: The user clicks on a book of interest from the search results.

[1220] Data processing / calculation: The server retrieves detailed information of the selected book from the database.

[1221] Output: The server generates the book details and sends them to the user's device.

[1222] Step 6:

[1223] The device will display detailed information about the book.

[1224] Input: Book details sent from the server.

[1225] Specific operation: The device displays detailed information about the received book to the user.

[1226] Data processing / calculation: The device analyzes the detailed information of the book and generates data for visual display.

[1227] Output: Book details displayed to the user.

[1228] Step 7:

[1229] A user purchases a book.

[1230] Enter: A user clicks the "Buy" button on a book detail page and enters their payment information.

[1231] Specific behavior: The user clicks the "Purchase" button on the book's detail page and enters payment information, including credit card information.

[1232] Data processing / calculation: The server receives the user's purchase information, processes the payment through a payment gateway (e.g., Stripe, PayPal), and saves the purchase information in a database.

[1233] Output: The server generates a notification of payment completion and sends it to the user's device.

[1234] Step 8:

[1235] The terminal will display a notification that the payment has been completed.

[1236] Input: Payment completion notification sent from the server.

[1237] Specific operation: The terminal displays a notification to the user that the payment has been completed.

[1238] Data processing / calculation: The terminal analyzes the payment completion notification and generates data for visual display.

[1239] Output: A notification to the user that payment has been completed.

[1240] Step 9:

[1241] A user downloads a book.

[1242] Input: User clicks on the download link.

[1243] What happens: The user clicks on a link to download the book.

[1244] Data processing / calculation: The server generates a download link and sends it to the user's device.

[1245] Output: The download link sent by the server.

[1246] Step 10:

[1247] The device downloads the book file.

[1248] Input: The download link sent by the server.

[1249] Specific operation: The device allows users who click on the download link to download the book file.

[1250] Data processing / calculation: The device analyzes the download link and generates a request to download the book file.

[1251] Output: Book file saved on user's device.

[1252] (Application example 3)

[1253] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1254] In conventional prompt creation systems, it was difficult for users to find appropriate books to improve their prompt creation skills. In addition, there was a lack of means to evaluate prompt sentences, so users could not objectively evaluate their own skills. This resulted in the problem of inefficient improvement of prompt creation skills.

[1255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1256] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating prompt creation, and means for evaluating a user's prompt creation skills and recommending appropriate books, thereby enabling users to efficiently improve their prompt creation skills.

[1257] "Means for creating and selling prompts" refers to a function that allows users to create prompt sentences to input into generative AI models and sell them on an online platform.

[1258] "Means for purchasing excellent prompts from creators" is a function for purchasing prompt sentences from users who have created high-quality prompt sentences.

[1259] "Means for headhunting and developing prompt creators" refers to the function of identifying excellent prompt creators and providing programs and support to develop them.

[1260] "Means for planning and managing prompt learning classrooms" refers to the function of planning and running educational programs and workshops to improve prompt creation skills.

[1261] "Means for selling related books" is a function for selling books on prompt creation techniques and theories on an online platform.

[1262] The "means for evaluating prompt creation" is a function for evaluating prompt sentences created by users and objectively judging their quality.

[1263] The "means for evaluating a user's prompt creation skills and recommending appropriate books" is a function for evaluating a user's prompt creation skills and recommending appropriate books to the user based on the evaluation results.

[1264] A system for implementing this invention comprises a server and a user terminal. The server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating prompt creation, and means for evaluating a user's prompt creation skills and recommending appropriate books.

[1265] The server is built using a web framework such as Flask, and user devices include smartphones, PCs, etc. The server receives requests from users, accesses a database to retrieve the necessary information, and provides it to the user.

[1266] Specifically, when a user uses the application to improve their prompt creation skills, the process is carried out in the following steps:

[1267] 1. A user accesses an application using a smartphone or computer.

[1268] 2. Users can search for and purchase books on prompt writing.

[1269] 3. The user sends a prompt they created to the server, which evaluates the prompt using a generative AI model.

[1270] 4. The server evaluates the user's prompt creation skills based on the evaluation results and recommends appropriate books.

[1271] For example, if a user creates a prompt such as, "Please create a prompt to allow the generative AI model to evaluate the prompt entered by the user," the server will evaluate this prompt and provide the evaluation results to the user.

[1272] This system allows users to efficiently improve their prompt creation skills, and the server recommends appropriate books according to the user's skill level, allowing users to find the best learning resources for themselves.

[1273] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1274] Step 1:

[1275] Users access the application using a smartphone or computer.

[1276] Input: User's access request

[1277] Output: Application home screen

[1278] Specific operation: The user opens a browser or app and accesses the application URL. The server receives the request and sends the HTML data for the home screen to the user's device.

[1279] Step 2:

[1280] A user searches for and purchases a book on prompt creation.

[1281] Input: User search query, purchase request

[1282] Output: Search results display, purchase confirmation screen

[1283] Specific operation: The user enters keywords in the search bar and clicks the search button. The server retrieves related book information from the database and displays the search results. When the user clicks the purchase button, the server processes the purchase and displays a purchase confirmation screen.

[1284] Step 3:

[1285] Sends a user-created prompt to the server.

[1286] Input: A prompt text created by the user

[1287] Output: Evaluated prompt statement

[1288] Specific operation: The user enters a prompt and clicks the send button. The server receives the prompt, inputs it into the generative AI model, and evaluates it. The evaluation result is returned to the user.

[1289] Step 4:

[1290] The server evaluates the prompt creation skills of the user based on the evaluation results and recommends appropriate books.

[1291] Input: Evaluated result of prompt statement

[1292] Output: User skill evaluation results, recommended book list

[1293] Specific operation: The server analyzes the evaluation results and determines the user's skill level. It searches the database for appropriate books and generates a list of recommended books. It displays the skill evaluation results and the list of recommended books to the user.

[1294] Step 5:

[1295] The user checks the recommended books and purchases them if necessary.

[1296] Input: User's book review request, purchase request

[1297] Output: Book details screen, purchase confirmation screen

[1298] Specific operation: The user selects a book from the recommended book list and the details screen is displayed. The server displays detailed information about the book, and when the user clicks the purchase button, the server processes the purchase and displays a purchase confirmation screen.

[1299] 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.

[1300] "Example 1"

[1301] One embodiment of the present invention is a prompt creation and sales system incorporating an emotion engine. In this system, the emotion engine recognizes the emotion a user feels when creating a prompt and evaluates the quality of the prompt based on that emotion. Specifically, if a user feels joy or excitement when creating a prompt, the prompt is evaluated as high quality. Conversely, if the user feels discomfort or stress, the prompt is evaluated as low quality.

[1302] "Example 2"

[1303] The emotion engine is also used for headhunting and training prompt creators. Specifically, if a user frequently displays positive emotions when creating prompts, they are deemed to be excellent prompt creators and are targeted for headhunting. On the other hand, if a user frequently displays negative emotions when creating prompts, they are deemed to need to improve their prompt creation skills and are targeted for training programs.

[1304] "Example 3"

[1305] Furthermore, the emotion engine is also used to manage prompt learning classrooms. Specifically, the emotion engine recognizes the emotions of users when they participate in a learning classroom, and adjusts the content and progress of the learning classroom based on those emotions. For example, if a user feels excitement or anticipation when participating in a learning classroom, the learning classroom is evaluated as attractive to the user, and the content is continued. Conversely, if a user feels anxiety or fear when participating in a learning classroom, the learning classroom is evaluated as inappropriate for the user, and the content is improved.

[1306] The processing flow of each embodiment will be described below.

[1307] "Example 1"

[1308] Step 1: The user accesses the prompt creation screen.

[1309] Step 2: The user creates a prompt, and the emotion engine recognizes the user's emotions in real time.

[1310] Step 3: The emotion engine evaluates the quality of the prompt based on the user's emotion, and the evaluation result is fed back to the system and displayed as a prompt quality score.

[1311] "Example 2"

[1312] Step 1: The user accesses the prompt creation screen and creates a prompt. At this time, the user's emotions are recognized in real time by the emotion engine.

[1313] Step 2: The emotion engine evaluates the prompt creator based on the user's emotions, and the evaluation results are fed back to the system and reflected in the user's profile.

[1314] Step 3: The system determines targets for headhunting and training programs based on the evaluation results.

[1315] "Example 3"

[1316] Step 1: A user participates in a learning class, during which the user's emotions are recognized in real time by the emotion engine.

[1317] Step 2: The emotion engine evaluates the learning class based on the user's emotions. The evaluation results are fed back to the system, and the content and progress of the learning class are adjusted.

[1318] Step 3: The system improves the learning classroom based on the evaluation results.

[1319] Example 1

[1320] Next, a description will be given of Example 1 of Form 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."

[1321] In conventional prompt creation and sales systems, prompt quality evaluation was subjective and did not take into account the user's emotional state. This resulted in inconsistent prompt quality, making them unreliable for buyers. Furthermore, there was no system for automatically selecting and purchasing excellent prompts, which led to a decline in motivation for creators. Furthermore, prompt sales prices were not set appropriately, resulting in poor sales efficiency.

[1322] 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.

[1323] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for evaluating the quality of prompts by analyzing the user's emotions, means for setting a selling price for the evaluated prompts, and means for automatically selecting and purchasing prompts that meet certain criteria. This enables objective evaluation of prompt quality that takes the user's emotional state into account, making it possible to provide highly reliable prompts. Furthermore, automatically selecting and purchasing excellent prompts can improve creator motivation. Furthermore, appropriate selling price setting can improve sales efficiency.

[1324] "A means for creating and selling prompts" is a feature that provides an online platform for users to create prompts and sell them to other users.

[1325] "Means for purchasing excellent prompts from producers" is a function in which the system automatically selects high-quality prompts that meet certain standards and purchases them from producers.

[1326] "Means for headhunting and training prompt creators" refers to a function that identifies excellent prompt creators and recruits them to join the system, as well as providing educational programs to help them improve their prompt creation skills.

[1327] "Means for planning and managing prompt learning classes" refers to the function of planning classes to learn prompt creation techniques and running them online or in a group setting.

[1328] "Means for selling related books" is a function that provides an online store for selling books and materials related to prompt creation.

[1329] The "means for analyzing the user's emotions and evaluating the quality of the prompt" is a function that analyzes the emotional state of the user when creating a prompt and evaluates the quality of the prompt based on that emotion.

[1330] The "means for setting a selling price for a rated prompt" is a function for automatically setting a selling price for the prompt based on the quality of the prompt evaluated by sentiment analysis.

[1331] "Means for automatically selecting and purchasing prompts that meet certain criteria" is a function that automatically selects high-quality prompts that meet the criteria set by the system and purchases those prompts from the creators.

[1332] This invention relates to a prompt creation and sales system. The system provides an online platform for users to create prompts and sell them to other users. The system also has the ability to purchase outstanding prompts from creators.

[1333] To implement the system, the server uses the following hardware and software: The server incorporates an emotion engine to analyze the emotions of users when creating prompts. This emotion engine uses, for example, an emotion analysis API of a general cloud service. Specifically, the Emotion API of Microsoft's Azure Cognitive Services can be used.

[1334] When a user creates a prompt, the device collects the user's facial expressions and tone of voice through a camera and microphone. This data is sent to a server and analyzed by an emotion engine. If the user feels joy or excitement, the emotion engine rates the prompt as high quality. Conversely, if the user feels discomfort or stress, the prompt is rated as low quality.

[1335] The server evaluates the quality of the prompt based on the emotion data received from the emotion engine. Based on the evaluation result, the server sets a sales price for the prompt. The prompt is added to the sales list on the website along with the set price. The user is notified that their prompt has been added to the sales list.

[1336] Other users can select and purchase prompts they are interested in from a sales list on the website. The server processes the purchase and provides the prompt to the buyer. Once the purchase is complete, a notification is sent to the creator.

[1337] Furthermore, the server automatically selects high-quality prompts that meet certain criteria and purchases them from creators. For selected prompts, the server pays the creators a fee. Creators are notified that their prompts have been purchased.

[1338] As a concrete example, consider the case where a user creates a prompt on the theme of "How to train a new AI model." The user accesses a website and enters "How to train a new AI model" into the prompt creation form. The user enables the camera and microphone, and the emotion engine collects the user's facial expressions and voice. The emotion engine analyzes the user's smile and excited voice and determines that the prompt is "joyful." In this case, the server evaluates the prompt as high quality and sets a higher selling price.

[1339] Example prompt sentence:

[1340] "Please create a prompt on how to train a new AI model. Please also provide details on the specific steps and datasets used."

[1341] The system allows users to rate the quality of prompts based on their own feelings, motivating them to create better prompts, and allows other users to purchase high-quality prompts.

[1342] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1343] Step 1:

[1344] A user visits a website.

[1345] A user accesses the prompt creation and sales system website using a browser. The terminal accepts the user's access and displays the website's homepage. The input is the user's access information, and the output is the website's homepage.

[1346] Step 2:

[1347] The user creates the prompt.

[1348] The user begins inputting information into the prompt creation form on the website. The device receives and displays the user's input in real time. The emotion engine begins operation when the user enables the camera and microphone. The input is the user's prompt content and camera / microphone data, and the output is the prompt content displayed in real time.

[1349] Step 3:

[1350] The emotion engine analyzes the user's emotions.

[1351] The emotion engine collects the user's facial expressions and tone of voice through the device's camera and microphone. The server sends the collected data to the emotion analysis API and analyzes the user's emotional state. For example, if the user is smiling while speaking, the emotion engine will determine that the user is "happy." The input is data from the camera and microphone, and the output is analyzed emotional data.

[1352] Step 4:

[1353] The server evaluates the quality of the prompt.

[1354] The server evaluates the quality of the prompt based on the emotional data received from the emotion engine. For example, if the user feels "joy" or "excitement," the prompt is evaluated as high quality. Conversely, if the user feels "discomfort" or "stress," the prompt is evaluated as low quality. The input is emotional data, and the output is the quality evaluation result.

[1355] Step 5:

[1356] The server adds the prompt to the sales list.

[1357] The server sets a sales price for the prompt based on the evaluation results. The server adds the prompt to a sales list on the website along with the set price. The user is notified that their prompt has been added to the sales list. The input is the quality evaluation results and the prompt content, and the output is the prompt added to the sales list.

[1358] Step 6:

[1359] Other users purchase prompts.

[1360] Other users select prompts they are interested in from a list of products on the website and purchase them. The server processes the purchase and provides the prompts to the buyer. Once the purchase is complete, a notification is sent to the producer. The input is the buyer's selection information, and the output is a notification that the purchase has been completed.

[1361] Step 7:

[1362] The server purchases prompts that meet certain criteria from producers.

[1363] The server automatically selects high-quality prompts that meet certain criteria from the prompts in the sales list. The server purchases the selected prompts from the creator and pays them a fee. The creator is notified that the prompt has been purchased. The input is the sales list and the quality evaluation results, and the output is a purchase completion notification.

[1364] (Application example 1)

[1365] Next, a description will be given of Application Example 1 of Form 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."

[1366] Conventional prompt creation and sales systems lacked a means to objectively evaluate the quality of prompts, making it difficult to accurately determine the value of prompts created by users. Furthermore, because quality evaluation did not take into account the prompt creator's feelings, users' creativity and motivation could not be maximized. Furthermore, there was an inadequate system for featuring or purchasing high-quality prompts, which resulted in excellent prompts being buried.

[1367] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for recognizing emotions at the time of prompt creation using an emotion engine and evaluating the quality of the prompts based on those emotions, and means for featuring or purchasing prompts evaluated as high quality. This makes it possible to objectively evaluate the quality of prompts created by users and to feature or purchase high-quality prompts.

[1368] A "prompt" is an instruction to be input into a generative AI model.

[1369] A "venue for creating and selling prompts" is an online platform that allows users to create prompts and sell them to other users.

[1370] "Means for purchasing excellent prompts from creators" is a mechanism by which the system automatically evaluates the quality of prompts and purchases prompts that meet certain standards from creators.

[1371] "Means for headhunting and developing prompt creators" refers to a system for finding excellent prompt creators and providing support to help them improve their skills.

[1372] "Means for planning and managing prompt learning classrooms" refers to a system for planning and managing educational programs to learn the techniques of creating prompts.

[1373] A "means for selling related books" is an online store for selling books and materials related to prompt creation.

[1374] An "emotion engine" is a technology that recognizes the emotions a user feels when creating a prompt and evaluates the quality of the prompt based on those emotions.

[1375] "Means of featuring or purchasing prompts rated as high quality" refers to a mechanism in which prompts rated as high quality by the system are featured on a featured page, or the operator purchases the prompts.

[1376] A system for implementing this invention includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for recognizing emotions at the time of prompt creation using an emotion engine and evaluating the quality of the prompts based on those emotions, and means for featuring or purchasing prompts evaluated as being of high quality.

[1377] System configuration

[1378] The system uses the following hardware and software:

[1379] Hardware: Server, user device (smartphone, PC)

[1380] Software: Python, TextBlob (for emotion analysis), EmotionRecognizer (for emotion recognition)

[1381] Program processing

[1382] The server receives the text when the user creates a prompt. The prompt sent from the user's device is analyzed by the emotion engine on the server. The emotion engine uses TextBlob and EmotionRecognizer to recognize the emotion the user is expressing when creating the prompt.

[1383] Based on the recognized emotion, the server evaluates the quality of the prompt: if the user feels joy or excitement, the prompt is evaluated as high quality; conversely, if the user feels discomfort or stress, the prompt is evaluated as low quality.

[1384] Rated prompts are classified based on their quality score. Prompts that are rated as high quality can be featured on a featured page or purchased by the publisher. This allows users to know how their prompts are rated, and motivates them to create even higher quality prompts.

[1385] Specific examples

[1386] For example, if a user creates a prompt saying "Create a story about a brave knight," the emotion engine can recognize the user's emotion from this text. If the user felt joy or excitement while creating this prompt, the prompt will be rated as high quality. Prompts that are rated high quality can be featured on a special page or purchased by the publisher.

[1387] This system allows users to know how their prompts are evaluated, motivating them to create even higher quality prompts. It also allows excellent prompts to be shared widely with other users, rather than being buried.

[1388] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1389] Step 1:

[1390] The user creates a prompt sentence and sends it from the terminal to the server. The input is the prompt sentence created by the user, and the output is the prompt sentence sent to the server. In concrete terms, the user enters the prompt sentence into the input form on the terminal and presses the "send" button.

[1391] Step 2:

[1392] The server passes the received prompt sentence to the emotion engine to recognize the emotion. The input is the prompt sentence sent to the server, and the output is the recognized emotion. Specifically, the server passes the prompt sentence to TextBlob and EmotionRecognizer for emotion analysis.

[1393] Step 3:

[1394] The server evaluates the quality of the prompt based on the recognized emotion. The input is the recognized emotion, and the output is a quality score. Specifically, if the emotion is "joy" or "excitement," the server evaluates it as high quality and sets a high quality score. Conversely, if the emotion is "discomfort" or "stress," the server evaluates it as low quality and sets a low quality score.

[1395] Step 4:

[1396] The server categorizes prompts based on their quality scores and stores them in a database. The input is the quality score, and the output is storing the classified prompts in the database. Specifically, the server categorizes prompts with high quality scores into a "high quality" category and prompts with low quality scores into a "low quality" category, and stores them in the database according to their respective categories.

[1397] Step 5:

[1398] The server displays prompts that are rated as high quality on a featured page, or the operator purchases them. The input is a prompt classified as "high quality," and the output is a notification of display on a featured page or purchase. Specifically, the server displays prompts classified as "high quality" on a featured page and sends a notification of purchase to the operator.

[1399] Step 6:

[1400] A user browses the featured page and purchases a prompt. The input is the prompt displayed on the featured page, and the output is the purchased prompt. Specifically, the user browses the featured page, selects a prompt they like, and presses the purchase button.

[1401] Example 2

[1402] Next, a description will be given of Example 2 of Form 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."

[1403] Conventional prompt creation systems do not adequately evaluate the quality of prompts or provide feedback on skill improvement for prompt creators, making it difficult to discover and develop excellent prompt creators. Furthermore, because evaluation does not utilize emotional data, appropriate feedback based on the user's emotional state is not provided. This means that prompt creators' motivation and skills are not sufficiently improved.

[1404] 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.

[1405] In this invention, the server includes means for evaluating the quality of prompts using a generative AI model, means for performing emotion analysis to analyze the emotion data of the prompt creator, and means for providing feedback based on the evaluation results. This automates the evaluation of prompt quality and provides appropriate feedback based on the emotion data, making it possible to discover and develop excellent prompt creators.

[1406] A "prompt" is a statement of instruction or question that is input to a generative AI model.

[1407] A "generative AI model" is an advanced algorithm for natural language processing that generates appropriate responses to input prompts.

[1408] "Quality evaluation" is the process of judging prompts based on factors such as clarity, specificity, and creativity.

[1409] "Emotion analysis" is a technology that analyzes a user's emotional state based on data such as facial expressions and tone of voice.

[1410] "Feedback" refers to evaluation and suggestion information provided to the user based on the prompt quality evaluation and sentiment analysis results.

[1411] "Headhunting" is the process of identifying talented prompt writers and making proposals to lure them away.

[1412] "Development" refers to the activity of providing education and training to help prompt creators improve their skills.

[1413] A "prompt learning classroom" is a place that provides educational programs to improve prompt creation skills.

[1414] "Online" refers to the provision of services and information via the Internet.

[1415] "Group-based" refers to a form of education or training that takes place in a physical location.

[1416] This invention is a system for creating, evaluating, and providing feedback on prompts, and it uses generative AI models and emotion analysis technology to support the skill improvement and headhunting of prompt creators. A specific embodiment of this system is described below.

[1417] Hardware and software used

[1418] Server: A high-performance database server (e.g., MySQL, PostgreSQL)

[1419] Generative AI models: Advanced natural language processing models such as GPT-4

[1420] Emotion engine: Emotion analysis software (e.g. IBM Watson Tone Analyzer)

[1421] Terminal: The device that provides the user interface (e.g., PC, tablet, smartphone)

[1422] Sensors: Cameras and microphones to collect emotional data

[1423] Specific operation of the system

[1424] Server Processing

[1425] The server uses a generative AI model to analyze the quality of prompts and the creator's activity history. Specifically, the server retrieves the prompt and creator history data stored in a database and inputs it into the generative AI model. The generative AI model evaluates the quality of the prompts and identifies creators who frequently create excellent prompts. In this process, the server also uses an emotion engine to analyze the emotional data displayed by users when creating prompts. Users who frequently display positive emotions are determined to be excellent prompt creators, while users who frequently display negative emotions are determined to need to improve their skills.

[1426] Terminal handling

[1427] The device provides an interface for users to create prompts. When the user enters a prompt, the device sends the data to the server. The device is also equipped with sensors and cameras to collect the user's emotional data, and sends this data to the server. The device also receives feedback from the server and displays it to the user. For example, it may display headhunting suggestions or information about a training program.

[1428] User Action

[1429] Users create prompts using their devices. Emotional data is collected when creating prompts, so users are encouraged to work in a natural state. If a user creates a good prompt, the server will send them a headhunting proposal. If it is determined that their skills need improvement, they will be shown information about a training program.

[1430] Specific examples

[1431] For example, consider the case where a user inputs the following prompt sentence into a generative AI model:

[1432] Example prompt sentence:

[1433] Please suggest a new marketing strategy.

[1434] When a user enters a prompt, the server uses a generative AI model to evaluate its quality and analyzes the user's emotional data. If the user displays a high percentage of positive emotions, the user is deemed a good prompt creator and is offered a headhunting opportunity. Conversely, if the user displays a high percentage of negative emotions, the user is considered for a training program.

[1435] This system will enable prompt creators to improve their skills and efficiently headhunt suitable talent.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Step 1:

[1438] The user enters a prompt statement.

[1439] The user uses the device interface to input a prompt for the generative AI model, for example, "Please suggest a new marketing strategy." The input prompt is saved on the device.

[1440] Step 2:

[1441] The device collects prompts and emotion data.

[1442] The device records the prompt text entered by the user. At the same time, it uses the device's built-in camera and microphone to collect emotional data such as the user's facial expressions and tone of voice. This records the user's emotional state when creating the prompt text. The collected data is temporarily stored on the device.

[1443] Step 3:

[1444] The terminal transmits the collected data to the server.

[1445] The device sends the collected prompts and emotion data to the server, which then analyzes and evaluates them. Specifically, the prompts and emotion data are stored in the server's database.

[1446] Step 4:

[1447] The server evaluates the quality of the prompt sentence.

[1448] The server inputs the received prompt into a generative AI model (e.g., GPT-4) to evaluate its quality. The generative AI model calculates a score based on factors such as the clarity, specificity, and creativity of the prompt. The evaluation results are stored in the server's database.

[1449] Step 5:

[1450] The server analyzes the emotion data.

[1451] The server uses emotion analysis software (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion data. If a lot of positive emotion is displayed, the user is judged to be an excellent prompt creator. Conversely, if a lot of negative emotion is displayed, the user is judged to need to improve their skills. The analysis results are stored in the server's database.

[1452] Step 6:

[1453] The server sends feedback to the user based on the evaluation results.

[1454] The server generates feedback for users based on the quality evaluation of the prompt sentences and the analysis of the emotional data. For example, a headhunting suggestion is sent to a good prompt creator, and information about a training program is sent to a user who needs to improve their skills. The feedback is then sent to the terminal.

[1455] Step 7:

[1456] The user receives feedback.

[1457] The user receives feedback from the server via their device, including the prompt evaluation results, sentiment analysis results, and specific suggestions and guidance. Based on this, the user decides on their next action.

[1458] (Application example 2)

[1459] Next, a description will be given of Application Example 2 of Form 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."

[1460] Conventional prompt creation systems lack sufficient prompt quality evaluation, creator sentiment analysis, and activity history tracking, making it difficult to identify and train excellent prompt creators. There was also a lack of effective training programs to help improve prompt creation skills. This resulted in a decline in the quality of prompts for generative AI models, which in turn led to a loss of quality in content delivery services.

[1461] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1462] In this invention, the server includes a means for evaluating the quality of prompts, a means for analyzing emotions at the time of prompt creation, and a means for tracking the activity history of prompt creators, which makes it possible to identify and headhunt excellent prompt creators, and to provide a training program to help improve prompt creation skills.

[1463] A "prompt" is a piece of text or instruction that is input into a generative AI model.

[1464] "Creating a prompt" is the act of generating appropriate text or instructions for a generative AI model.

[1465] "Selling prompts" is the act of offering created prompts to other users or companies.

[1466] "Prompt Creator" means a person or entity that creates prompts for a generative AI model.

[1467] "Headhunting" is the act of identifying talented prompt writers and offering them special offers and rewards.

[1468] "Development" is the act of providing education and training to improve prompt creators' skills.

[1469] A "prompting classroom" is an educational program or workshop designed to teach prompting skills.

[1470] "Related books" refers to books and materials related to prompt creation and generative AI models.

[1471] "Means for assessing prompt quality" refers to methods or tools for evaluating the effectiveness and appropriateness of the prompts created.

[1472] "Means for analyzing emotions when creating prompts" refers to methods or tools that allow prompt creators to detect and analyze their emotions when creating prompts.

[1473] "Means for tracking prompt creator activity history" refers to methods and tools that prompt creators use to record and analyze prompts they have created in the past and their outcomes.

[1474] A "development program" is an educational curriculum or training sessions designed to improve prompting skills.

[1475] A system for implementing the present invention includes means for evaluating the quality of a prompt, means for analyzing emotions at the time of creating a prompt, and means for tracking the activity history of a prompt creator. Specific embodiments are described below.

[1476] System Configuration

[1477] The system includes a server, a user device, and a generative AI model. The server runs software for prompt quality evaluation, sentiment analysis, and activity history tracking. The user device is a device such as a smartphone or head-mounted display that users use to create and input prompts.

[1478] Hardware and software used

[1479] Hardware: Smartphone, head-mounted display

[1480] Software: Python, sentiment analysis library (e.g., sentiment_analysis), prompt quality evaluation library (e.g., prompt_quality_evaluator), user activity tracking library (e.g., user_activity_tracker)

[1481] Processing flow

[1482] 1. Enter the prompt:

[1483] The user inputs a prompt sentence using a smartphone or a head-mounted display, such as "Please give me an example of a prompt sentence to input to the generative AI model" or "Please tell me some tips for creating prompts."

[1484] 2. Prompt quality assessment:

[1485] The server evaluates the quality of the input prompt sentence using the prompt quality evaluation library, and the evaluation result is returned as a score and fed back to the user.

[1486] 3. Sentiment analysis:

[1487] The server uses a sentiment analysis library to analyze the user's sentiment at the time of creating the prompt, determining whether the sentiment was positive or negative, and recording the result.

[1488] 4. Activity Tracking:

[1489] The server uses a user activity tracking library to record the user's prompt creation history, along with the prompt quality score and sentiment analysis results.

[1490] 5. Headhunting proposal:

[1491] The server will then offer headhunting suggestions to users who show positive emotions, for example, sending a message like, "You're a great prompt creator. We'll give you a special offer."

[1492] 6. Providing training programs:

[1493] The server provides training programs to users who show negative emotions, such as sending a message like, "Please join an online learning class to improve your prompt writing skills."

[1494] Specific examples

[1495] User A uses a smartphone to input a prompt such as, "Please give me an example of a prompt sentence to input into the generative AI model." The server evaluates the quality of this prompt sentence and performs sentiment analysis. If the evaluation result is high and indicates a positive sentiment, a headhunting proposal is made to User A. Conversely, if the evaluation result is low and indicates a negative sentiment, a training program is offered to User A.

[1496] In this way, the system can improve prompt writers' skills, identify excellent prompt writers, and offer headhunting and development programs.

[1497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1498] Step 1:

[1499] The user inputs a prompt using a device (such as a smartphone or head-mounted display). The input prompt is then sent to the server. For example, a prompt such as "Please give us an example of a prompt to input to the generative AI model" is input.

[1500] Step 2:

[1501] The server uses a prompt quality evaluation library to evaluate the quality of the input prompt sentence. Specifically, it analyzes the structure and content of the prompt sentence and scores its effectiveness against the generative AI model. The input is the prompt sentence, and the output is a quality score.

[1502] Step 3:

[1503] The server uses a sentiment analysis library to analyze the user's sentiment when creating the prompt. Specifically, it analyzes the linguistic features of the prompt and the user's behavioral data when entering the prompt to determine whether the sentiment is positive or negative. The input is the prompt and the user's behavioral data, and the output is the sentiment analysis results.

[1504] Step 4:

[1505] The server uses a user activity tracking library to record the user's prompt creation history. Specifically, it stores the prompt sentence, quality score, and sentiment analysis results in a database. The input is the prompt sentence, quality score, and sentiment analysis results, and the output is the updated activity history data.

[1506] Step 5:

[1507] The server makes headhunting suggestions to users who show positive emotions. Specifically, it generates a message proposing special offers and rewards to the user and sends it to the terminal. The input is the emotion analysis result, and the output is the headhunting suggestion message.

[1508] Step 6:

[1509] The server provides a training program to users who show negative emotions. Specifically, it generates information about online learning classes and workshops to improve prompt creation skills and sends it to the terminal. The input is the emotion analysis results, and the output is a training program information message.

[1510] In this way, the system can improve prompt writers' skills, identify excellent prompt writers, and offer headhunting and development programs.

[1511] Example 3

[1512] Next, a description will be given of Example 3 of Form Example 3. 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."

[1513] With conventional prompt creation and sales systems, it was difficult to operate a learning classroom that took into account the user's emotional state, and it was not possible to maximize the user's learning effectiveness. Furthermore, when selling related books, there was a lack of means to provide books that met the user's needs. This led to problems such as a decrease in user satisfaction and learning effectiveness.

[1514] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1515] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for analyzing a user's emotional state using an emotion engine and adjusting the content and progress of the learning class, means for selling books on a website, and means for transmitting a user's facial expression and voice data to the emotion engine in real time. This enables the operation of a learning class based on the user's emotional state, maximizing the user's learning effectiveness. Furthermore, when selling related books, it becomes possible to provide books that meet the user's needs.

[1516] A "prompt" refers to text or instructions input to a generative AI model.

[1517] "Means for creating and selling prompts" refers to the functionality that allows users to create and sell prompts to be used with generative AI models.

[1518] "Means to purchase high-quality prompts from creators" refers to a function for purchasing high-quality prompts from prompt creators.

[1519] "Means for headhunting and training prompt creators" refers to a function for finding, hiring, or training excellent prompt creators.

[1520] "Means for planning and managing prompt learning classrooms" refers to the function of planning and running classrooms for learning prompt creation techniques.

[1521] "Means for selling related books" refers to a function for selling books related to prompt creation.

[1522] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1523] "Means for analyzing the user's emotional state and adjusting the content and progress of the learning class" refers to a function for analyzing the user's emotions using an emotion engine and changing the content and progress of the learning class based on the results.

[1524] "Means for selling books on the website" refers to the functionality for selling books via the Internet.

[1525] "Means for transmitting the user's facial expression and voice data to the emotion engine in real time" refers to a function for transmitting facial expression and voice data from the user's terminal to the emotion engine in real time.

[1526] This invention is a system that includes the creation and sale of prompts, the purchase of excellent prompts, the headhunting and training of prompt creators, the planning and management of prompt learning classes, the sale of related books, the adjustment of the content and progress of learning classes using an emotion engine, the sale of books on a website, and the real-time transmission of user facial expressions and voice data.

[1527] Book sales function

[1528] The server hosts a website and makes it accessible to users. The user visits the website and searches for books about prompt writing. The server displays a list of related books based on the user's search query. The user selects the book they want to purchase and checks out. The server processes the user's purchase information and provides a download link for the book.

[1529] For example, if a user searches for the keyword "prompting techniques," the server displays a book called "Prompting Theory and Practice." If the user purchases the book, the server provides a download link.

[1530] Prompt learning classroom using emotion engine

[1531] A user logs in to a website to participate in a prompt learning classroom. The server authenticates the user's login information and redirects them to the learning classroom page. When a user participates in a learning classroom, the device (e.g., a PC or smartphone) sends the user's facial expressions and voice to the emotion engine. The emotion engine analyzes the user's facial expressions and voice data and recognizes the user's emotional state. The server receives feedback from the emotion engine and adjusts the content and progress of the learning classroom.

[1532] For example, a user can participate in a learning class and send data to the emotion engine via a camera and microphone. The emotion engine recognizes the user's excitement and the server continues the content.

[1533] Hardware and software used

[1534] Server: Hosts the website and processes data.

[1535] Terminal: A device (PC, smartphone, etc.) that transmits the user's facial expressions and voice data to the emotion engine.

[1536] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize their emotional state.

[1537] Prompt Sentence Examples

[1538] "Find books on prompt writing techniques."

[1539] "Recognize the emotions of your classroom participants and adjust your content based on their feedback."

[1540] This system allows users to easily purchase books to improve their prompt creation skills and to learn effectively in learning classes that utilize the emotion engine.The flow of the specific processing in Example 3 will be explained using Figure 21.

[1541] Book sales function

[1542] Step 1:

[1543] The user opens a web browser and enters the system's website URL to access it.

[1544] Input: The user types a URL into the browser's address bar.

[1545] Output: The website home page is displayed.

[1546] Specific behavior: The user types "https: / / example.com" into the browser address bar and presses Enter.

[1547] Step 2:

[1548] The server sends the website's home page to the user's terminal for display.

[1549] Input: The user's access request.

[1550] Output: Home page HTML, CSS and JavaScript files.

[1551] What happens: The server sends HTML, CSS, and JavaScript files to the user's device, and the browser renders them.

[1552] Step 3:

[1553] A user types "prompt writing techniques" into the search bar and clicks the search button.

[1554] Input: The keyword the user entered into the search bar.

[1555] Output: Display of search results.

[1556] What happens: A user enters keywords into the search bar and clicks the search icon.

[1557] Step 4:

[1558] The server searches the database and sends a list of relevant books to the user's terminal.

[1559] Input: The user's search query.

[1560] Output: A list of related books.

[1561] What happens: The server executes the SQL query and sends the results in HTML format to the user's browser.

[1562] Step 5:

[1563] The user selects "Prompt Writing Theory and Practice" from the displayed book list and clicks the purchase button.

[1564] Input: User's book selection and purchase request.

[1565] Output: Display of checkout page.

[1566] What happens: A user navigates to a book's detail page and clicks the buy button.

[1567] Step 6:

[1568] The server processes the user's purchase information and provides the user with a download link for the book after payment is completed.

[1569] Input: User's purchase and payment information.

[1570] Output: Provide a download link.

[1571] What happens: The server communicates with the payment gateway, and after the payment is confirmed, it generates a download link and displays it to the user.

[1572] Prompt learning classroom using emotion engine

[1573] Step 1:

[1574] A user visits a website and navigates to the login page.

[1575] Input: The user's access request.

[1576] Output: Login page displayed.

[1577] Specific behavior: The user enters the URL of the login page and presses the Enter key.

[1578] Step 2:

[1579] The server checks the login information (user name and password) entered by the user against the database and performs authentication.

[1580] Enter your username and password.

[1581] Output: Authentication result.

[1582] Specific operation: The server compares the username and password with the information in the database, and if they match, the authentication is successful.

[1583] Step 3:

[1584] A user accesses a class page and clicks the join button.

[1585] Input: User's join request.

[1586] Output: Display of classroom page.

[1587] Specific behavior: The user clicks on the link for the learning class and presses the join button.

[1588] Step 4:

[1589] The device uses the user's camera and microphone to transmit facial and voice data to the emotion engine in real time.

[1590] Input: User's facial and voice data.

[1591] Output: Sending data to the emotion engine.

[1592] Specific operation: The device activates the camera and microphone and sends the data to the emotion engine API.

[1593] Step 5:

[1594] The emotion engine analyzes the received data and recognizes the user's emotional state (excitement, anticipation, anxiety, fear, etc.).

[1595] Input: facial expression and speech data.

[1596] Output: Emotional state recognition results.

[1597] What it does: The emotion engine uses machine learning models to analyze facial and voice data and classify emotions.

[1598] Step 6:

[1599] The server receives feedback from the emotion engine and adjusts the content and progress of the learning classroom.

[1600] Input: Emotion engine feedback.

[1601] Output: Adjusted learning classroom content.

[1602] Specific operation: Based on the results of the emotion engine, the server changes the content of the slides and videos in the classroom and adjusts the speed of the videos.

[1603] (Application example 3)

[1604] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1605] In conventional prompt learning classrooms, it was difficult to provide an optimal learning experience for each individual participant because the learning content could not be adjusted based on the participants' emotions. In addition, related books had to be sold separately, resulting in insufficient coordination between the learning experience and book purchases.

[1606] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for emotion recognition, means for adjusting learning content based on emotions, and means for displaying related information using a smart device. This makes it possible to adjust learning content in real time based on participants' emotions and provide an optimal learning experience. In addition, related books can also be sold seamlessly through the smart device.

[1607] "Means for creating and selling prompts" refers to the functionality that allows users to create prompts to input into generative AI models and sell them.

[1608] "Mechanism for purchasing excellent prompts from creators" is a function for purchasing prompts from creators who have created high-quality prompts.

[1609] "Means for headhunting and training prompt creators" is a function for finding, hiring, and training excellent prompt creators.

[1610] "Means for planning and managing prompt learning classrooms" refers to the function for planning and managing classrooms for learning prompt creation techniques.

[1611] The "means for selling related books" is a function for selling books related to the creation of a prompt.

[1612] The "means for emotion recognition" is a function for analyzing the user's facial expressions and tone of voice and recognizing their emotions.

[1613] The "means for adjusting learning content based on emotions" is a function for adjusting learning content and progress in real time based on the recognized emotions of the user.

[1614] The "means for displaying related information using a smart device" is a function for displaying related information to a user using a device such as smart glasses or a smartphone.

[1615] This invention is a system that integrates the creation and sale of prompts, the operation of prompt learning classrooms, the adjustment of learning content using emotion recognition, and the sale of related books. Specific embodiments for implementing this system are described below.

[1616] System Configuration

[1617] The system consists of the following main components:

[1618] 1. Server: Manages the creation and sale of prompts, operation of prompt learning classrooms, emotion recognition, adjustment of learning content, and sales of related books.

[1619] 2. Smart Device: The device used by the user, such as smart glasses or a smartphone.

[1620] 3. Emotion recognition engine: Software that analyzes the user's facial expressions and tone of voice to recognize emotions.

[1621] 4. Generative AI Model: The artificial intelligence model used to create the prompt.

[1622] Program processing

[1623] The server does the following:

[1624] 1. Creating and selling prompts:

[1625] The server uses the generative AI model to create prompts and sells them on an online platform.

[1626] Users can purchase and download prompts through their smart devices.

[1627] 2. Prompt Learning Classroom Management:

[1628] The server plans and manages prompt learning classrooms online or in groups.

[1629] Users use their smart devices to participate in classes and receive learning content.

[1630] 3. Emotion recognition and learning adjustment:

[1631] The camera and microphone on the smart device are used to capture the user's facial expressions and tone of voice.

[1632] The emotion recognition engine analyzes this data and recognizes the user's emotions.

[1633] The server adjusts the learning content in real time based on the perceived emotion, for example, providing more advanced content if the user is excited, or less advanced content if the user is anxious.

[1634] 4. Related Book Sales:

[1635] The server manages book information related to prompt creation and displays it on the smart device display.

[1636] Users can purchase books through their smart devices.

[1637] Hardware and software used

[1638] Hardware: smart glasses, smartphone, camera, microphone

[1639] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition), BookStore (book sales system)

[1640] Specific examples

[1641] For example, consider a scenario in which a user is participating in a prompting class. If the user is excited, the smart glasses display could show information about a book called "Advanced Prompting Techniques" and provide a purchase link. The system also includes the ability to adjust learning content in real time based on the user's emotions.

[1642] Example prompts to input to a generative AI model:

[1643] Consider a scenario where a user is participating in a prompt learning class. If the user is excited, the smart glasses display will show information about the book "Advanced Prompting Techniques" and provide a link to purchase it, including the ability to adjust learning content in real time based on the user's emotions.

[1644] In this way, the system can provide an optimal learning experience based on the user's emotions and seamlessly sell related books.

[1645] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1646] Step 1:

[1647] The server creates prompts using a generative AI model. As input, it receives themes and keywords provided by the user, and the generative AI model generates prompt sentences based on these. As output, it obtains the generated prompt sentences.

[1648] Step 2:

[1649] The server uploads the generated prompt text to the online platform and starts selling. As input, it receives the generated prompt text and its sales information (price, description, etc.) and stores them in a database. As output, the prompt text is displayed on the online platform.

[1650] Step 3:

[1651] A user accesses the online platform using a smart device to purchase a prompt. The server receives the user's purchase request and payment information as input, processes them, and provides the user with a notification of purchase completion and a download link for the prompt.

[1652] Step 4:

[1653] The server plans prompt learning classes and manages them online or in a group setting. As input, it sets the schedule and content of the learning class and manages the participant list. As output, it notifies participants of the detailed information of the learning class.

[1654] Step 5:

[1655] A user participates in a prompt learning classroom using a smart device. As input, the server receives the user's participation request and the classroom's access information, and processes them. As output, the learning classroom content is provided to the user.

[1656] Step 6:

[1657] The camera and microphone on the smart device capture the user's facial expressions and tone of voice. As input, real-time video and audio data are acquired and sent to an emotion recognition engine. As output, the user's emotional data is obtained.

[1658] Step 7:

[1659] The emotion recognition engine analyzes the captured video and audio data to recognize the user's emotions. As input, it receives the video and audio data and applies the emotion recognition algorithm. As output, the recognized emotion data is sent to the server.

[1660] Step 8:

[1661] The server adjusts the learning content in real time based on the recognized emotion data. As input, it receives the emotion data and the current learning content and makes appropriate adjustments. As output, the adjusted learning content is provided to the user.

[1662] Step 9:

[1663] The server manages the information of related books and displays it on the smart device display. As input, it retrieves the information of related books (title, author, price, etc.) from the database and sends it to the smart device. As output, the information of related books is displayed on the smart device.

[1664] Step 10:

[1665] The user purchases the relevant book through their smart device. The server receives the user's purchase request and payment information as input, processes them, and provides the user with a purchase completion notification and a download link for the book as output.

[1666] 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.

[1667] 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> ) 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.

[1668] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1669] 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.

[1670] [Third embodiment]

[1671] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1672] 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.

[1673] 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).

[1674] 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.

[1675] 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.

[1676] 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).

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1683] "Example 1"

[1684] This embodiment of the present invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing outstanding prompts from their creators. Specifically, the system automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[1685] "Example 2"

[1686] Furthermore, this embodiment of the present invention has the function of headhunting and training prompt creators. Specifically, the system analyzes the quality of prompts and the creators' activity history to identify creators who frequently create excellent prompts. The system then makes headhunting suggestions to identified creators. It also plans and manages online and group prompt learning classes to help improve prompt creation skills.

[1687] "Example 3"

[1688] Furthermore, this embodiment of the present invention has a function for selling related books. Specifically, books on prompt creation techniques and theories are sold on the website, allowing users to further improve their prompt creation skills.

[1689] The processing flow of each embodiment will be described below.

[1690] "Example 1"

[1691] Step 1: User visits the website and uploads their own prompt.

[1692] Step 2: The system automatically evaluates the quality of the uploaded prompts.

[1693] Step 3: The system purchases prompts that meet certain criteria from the creator.

[1694] "Example 2"

[1695] Step 1: The system analyzes the prompt quality and the creator's activity history.

[1696] Step 2: The system identifies creators who frequently create good prompts.

[1697] Step 3: The system will make headhunting suggestions to the identified creators. Step 4: Plan and manage online and group prompt learning classes to help improve prompt creation skills.

[1698] "Example 3"

[1699] Step 1: Sell books on prompt writing techniques and theory on your website.

[1700] Step 2: The user purchases the book and further develops their prompt-writing skills.

[1701] Example 1

[1702] Next, a description will be given of Example 1 of Form 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."

[1703] In conventional prompt creation and sales systems, the quality of prompts created by users is evaluated manually, which takes time and results in inconsistent evaluation criteria. Furthermore, there was a lack of a system for efficiently discovering and purchasing excellent prompts from their creators, making it difficult to maximize the market value of prompts. Furthermore, there was no system that comprehensively provided prompt-related services, such as training and headhunting prompt creators and the sale of related books.

[1704] 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.

[1705] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for users to upload prompts, means for other users to purchase prompts, means for evaluating the quality of prompts using a generative AI model, and means for purchasing prompts based on the evaluation results. This enables prompt quality evaluation to be automated and performed quickly and consistently. Furthermore, by efficiently discovering excellent prompts and purchasing them from creators, the market value of prompts can be maximized. Furthermore, it becomes possible to comprehensively provide prompt-related services, such as training and headhunting prompt creators and selling related books.

[1706] A "prompt" is an input sentence that prompts a generative AI model to generate a specific output.

[1707] A "generative AI model" is a type of artificial intelligence that performs natural language processing and is a model that generates appropriate output for specific input.

[1708] "User" means any person or entity that uses the System to create, upload, or purchase prompts.

[1709] A "server" is a computer system that runs the entire system and processes requests from users.

[1710] A "database" is a system for storing and managing data such as prompts and user information.

[1711] A "prompt creation and sales means" is a part of the system that provides the functionality for users to create prompts and sell them to other users.

[1712] The "means of purchasing excellent prompts from producers" is a function that enables the system to automatically evaluate the quality of prompts and purchase prompts that meet certain standards from producers.

[1713] "Means for headhunting and training prompt creators" is a function for discovering and training excellent prompt creators.

[1714] "Means for planning and managing prompt learning classrooms" refers to the function for planning and managing classrooms for learning prompt creation techniques.

[1715] "Means for selling related books" is a function for selling books and materials related to prompt creation.

[1716] The "means for users to upload prompts" is a function that allows users to upload prompts that they have created themselves to the system.

[1717] "Means for other users to purchase prompts" refers to a function that allows users to purchase prompts that are published on the system.

[1718] "Means for evaluating the quality of prompts using a generative AI model" refers to a function for automatically evaluating the quality of uploaded prompts using a generative AI model.

[1719] "Means for purchasing prompts based on evaluation results" is a function for purchasing prompts that meet certain criteria from their creators based on the evaluation results of the generative AI model.

[1720] This invention is implemented through a website for creating and selling prompts. On this website, users can upload their own prompts and other users can purchase them. The website also has a function for purchasing excellent prompts from their creators. Specifically, the server automatically evaluates the quality of prompts, and prompts that meet certain standards are purchased from the creators.

[1721] Hardware and software used

[1722] Server: runs the entire website, processes user requests, and stores and manages data using a database management system (e.g., MySQL).

[1723] Generative AI models: Used to evaluate the quality of prompts, specifically using generative AI models that perform natural language processing (e.g., GPT-3).

[1724] User Device: The device used by the user to access the website and create, upload, and purchase prompts. Use a common web browser (e.g., Google Chrome, Mozilla Firefox).

[1725] Program processing

[1726] The server performs the following process.

[1727] 1. User Registration and Login

[1728] A user visits a website and enters their email address and password into a registration form, which the server stores in a database and creates a user account.

[1729] When a user enters their email address and password into the login form, the server checks it against the database and allows them to log in if it matches.

[1730] 2. Upload the prompt

[1731] The user accesses the prompt creation page and enters a prompt sentence, which the server saves in the database.

[1732] Example: A user enters a prompt to make the AI ​​model generate a cat image and clicks the "Upload" button. The server receives this and saves the new prompt information in the database.

[1733] 3. Prompt Sales

[1734] Other users can browse a list of prompts published on the website and select the prompt they want to purchase. The server processes the purchase and provides the prompt to the purchaser.

[1735] Example: User B selects the "Cat Picture Generator Prompt" and clicks the "Purchase" button. The server processes the payment and displays the prompt to the buyer.

[1736] 4. Prompt Quality Assessment

[1737] The server evaluates the uploaded prompts using a generative AI model (e.g., GPT-3), and the evaluation results are stored in a database.

[1738] Example: The server inputs a "cat image generation prompt" into GPT-3 and obtains an evaluation score. The server stores this score in a database.

[1739] 5. Prompt Buyout

[1740] The server purchases high-quality prompts that meet certain criteria from the producers. The purchase price is determined based on the evaluation results and notified to the producers.

[1741] Example: The server checks the rating score of the "Cat Image Generation Prompt" and sends a purchase offer of 1,000 yen to the creator if it meets the criteria. If User A accepts the offer, the server processes the payment.

[1742] Prompt Sentence Examples

[1743] "Create a prompt that generates an image of a cat. Include details like the type of cat, the background, and the color."

[1744] The above is an embodiment of the present invention. This system automates the quality evaluation of prompts and enables it to be performed quickly and consistently. It also makes it possible to maximize the market value of prompts by efficiently discovering excellent prompts and purchasing them from their creators. It also makes it possible to provide comprehensive prompt-related services, such as training and headhunting prompt creators and selling related books.

[1745] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1746] Step 1:

[1747] User Registration and Login

[1748] Input: The user enters their email address and password.

[1749] Specific behavior: The user enters "user@example.com" and "password123" and clicks the "Register" button.

[1750] Data processing: The server saves the entered information in a database and creates a new user account.

[1751] Output: The user account is created and the user can log in.

[1752] Input: The user enters their email address and password.

[1753] Specific action: The user enters "user@example.com" and "password123" and clicks the "Login" button.

[1754] Data calculation: The server checks the entered information against the database and allows login if there is a match.

[1755] Output: The user is redirected to the dashboard.

[1756] Step 2:

[1757] Uploading a Prompt

[1758] Input: The user enters the prompt text.

[1759] Specific operation: The user enters a prompt to have the AI ​​model generate an image of a cat and clicks the "Upload" button.

[1760] Data processing: The server saves the entered prompt sentence in the database.

[1761] Output: The prompt is saved in the database and can be viewed by other users.

[1762] Step 3:

[1763] Prompt Sales

[1764] Input: Select the prompt the user wants to purchase.

[1765] Specific behavior: User B selects the "Cat Image Generation Prompt" and clicks the "Purchase" button.

[1766] Data Calculation: The server processes the payment and provides prompts to the buyer.

[1767] Output: The buyer gets the prompt text.

[1768] Step 4:

[1769] Prompt quality assessment

[1770] Input: The uploaded prompt text.

[1771] Specific operation: The server inputs the "cat image generation prompt" into a generative AI model (e.g., GPT-3) and obtains an evaluation score.

[1772] Data Computing: Using a generative AI model to evaluate the quality of the prompts and store the evaluation results in a database.

[1773] Output: The prompt evaluation score is saved to the database.

[1774] Step 5:

[1775] Prompto Buyout

[1776] Input: The prompt's quality score based on the evaluation results.

[1777] Specific operation: The server checks the rating score of the "Cat Image Generator Prompt" and sends a purchase offer to the creator if it meets the criteria.

[1778] Data calculation: The server determines the purchase price based on the evaluation results and notifies the producer.

[1779] Output: If the producer accepts the offer, the server processes the payment.

[1780] The above are the specific processing steps of the program of this system.

[1781] (Application example 1)

[1782] Next, a description will be given of Application Example 1 of Form 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."

[1783] Conventional prompt creation and sales systems lacked sufficient prompt quality evaluation, ranking display, and purchasing methods, making it difficult for users to efficiently find high-quality prompts. Furthermore, there was a lack of means to objectively evaluate prompt quality, meaning that creators of excellent prompts had few opportunities to be properly recognized.

[1784] 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.

[1785] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts. This allows users to efficiently find and purchase high-quality prompts. Furthermore, objective evaluation of prompt quality provides an opportunity for creators of excellent prompts to be properly recognized.

[1786] A "prompt" is an instruction to be input into a generative AI model.

[1787] "Creating a prompt" is the act of creating instructions that a user will input to a generative AI model.

[1788] "Selling prompts" refers to the act of a user offering prompts they have created to other users for a fee.

[1789] A "means for evaluating the quality of prompts" is a method or system for objectively evaluating the usefulness and effectiveness of the prompts created.

[1790] A "means for displaying a ranking of prompts" is a method or system for ranking rated prompts based on quality and popularity and displaying the rankings to the user.

[1791] A "means for purchasing prompts" is a method or system that allows a user to obtain prompts created by other users for a fee.

[1792] A "means for purchasing excellent prompts from creators" is a method or system by which the system directly purchases prompts that meet certain standards from creators.

[1793] A "means for headhunting and developing prompt writers" is a method or system for identifying individuals with the ability to write good prompts and further developing their abilities.

[1794] "Means for planning and managing a prompt learning classroom" refers to a method or system for planning and managing an educational program to learn the skills and knowledge of creating prompts.

[1795] "Means for selling related books" refers to methods or systems for selling books and materials related to prompt creation and generative AI models to users.

[1796] A system for carrying out this invention includes a server, a user terminal, and a generative AI model. The server has means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for displaying prompt rankings, and means for purchasing prompts.

[1797] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for users to create, upload, and purchase prompts. The generative AI model is an artificial intelligence model that receives user-created prompts as input and performs the specified task.

[1798] Program processing explanation

[1799] Hardware used

[1800] Smartphone

[1801] server

[1802] Software used

[1803] Flask (a Python micro web framework)

[1804] requests (a Python library for sending HTTP requests)

[1805] Data processing and calculation

[1806] The server receives prompts entered by users and evaluates their quality. The evaluation is performed using an algorithm to objectively measure the usefulness and effectiveness of the prompts. The evaluated prompts are ranked based on their quality scores and displayed to users. Users can purchase prompts by referring to the rankings.

[1807] For example, a user can create a prompt on the theme of "AI poetry generation" and upload it to the app. The server evaluates the quality of the prompt and displays it in a ranking. Other users can purchase this prompt and input it into the generative AI model to generate poetry.

[1808] Prompt Sentence Examples

[1809] "AI Poetry Generation" prompt:

[1810] "You are a poet. Create a poem on the theme of the arrival of spring."

[1811] In this way, users can leverage generative AI models to create high-quality content that can be shared and sold with other users.

[1812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1813] Step 1:

[1814] The user creates a prompt using a device. The user inputs instructions to be input to the generative AI model and sends them to the server through an application on the device. The input data is the prompt created by the user. The output data is the prompt sent to the server.

[1815] Step 2:

[1816] The server saves the prompt text it receives. The server stores the prompt text in a database and prepares it for evaluating the quality of the prompt. The input data is the prompt text sent by the user. The output data is the prompt text saved in the database.

[1817] Step 3:

[1818] The server evaluates the quality of the prompt. The server analyzes the prompt sentence and calculates a quality score using an evaluation algorithm. The input data is the prompt sentence stored in the database. The output data is the quality score.

[1819] Step 4:

[1820] The server generates a ranking of prompts. The server compares the quality scores of all prompts and creates a ranking. The input data is the quality score of each prompt. The output data is a ranking list.

[1821] Step 5:

[1822] The server displays the rankings on the user terminal. The user browses the rankings through the terminal and selects the prompts that interest them. The input data is the ranking list. The output data is the rankings displayed on the user terminal.

[1823] Step 6:

[1824] A user purchases a prompt. The user uses a terminal to purchase a prompt selected from the rankings. The input data is the prompt selected by the user. The output data is a notification of purchase completion.

[1825] Step 7:

[1826] The server provides the purchased prompt to the user. The server completes the purchase and sends the prompt to the user. The input data is the purchased prompt. The output data is the prompt provided to the user.

[1827] Step 8:

[1828] The user inputs the purchased prompt into the generative AI model. The user uses the device to input the purchased prompt into the generative AI model to perform the specified task. The input data is the purchased prompt. The output data is the content generated by the generative AI model.

[1829] Example 2

[1830] Next, a description will be given of Example 2 of Form 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."

[1831] Conventional prompt creation systems lacked functionality for evaluating prompt quality and improving prompt creator skills, making it difficult to identify and train excellent prompt creators. Furthermore, there was a lack of efficient methods for headhunting and training prompt creators, making it difficult to improve the quality of generative AI models.

[1832] 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.

[1833] In this invention, the server includes means for collecting log data of the generative AI model, means for analyzing the collected data, means for identifying excellent prompt creators, means for proposing headhunting to the identified prompt creators, and means for supporting the improvement of prompt creation skills using an online learning platform. This allows for efficient evaluation of prompt quality and improvement of creator skills, making it possible to identify and develop excellent prompt creators.

[1834] A "prompt" is text that is input as an instruction or question to a generative AI model.

[1835] A "generative AI model" is an algorithm that uses artificial intelligence to generate text and answers.

[1836] "Log data" is data that records the operation of the system and the operation history of the user.

[1837] "Data analysis" is the process of analyzing collected data using statistical methods and machine learning to extract useful information.

[1838] A "prompt creator" is a user who creates and inputs prompts to a generative AI model.

[1839] "Headhunting" is the act of poaching people with specific skills and abilities from other organizations.

[1840] An "online learning platform" is a system that provides educational content via the Internet and supports learning.

[1841] A "prompting classroom" is an educational program or workshop designed to improve prompting skills.

[1842] "Quality assessment" is the process of evaluating the usefulness and accuracy of the generated prompts.

[1843] A "database" is a system for efficiently storing, managing, and retrieving data.

[1844] MODE FOR CARRYING OUT THE INVENTION

[1845] The present invention relates to a prompt creation system using a generative AI model. A specific embodiment of this system will be described below.

[1846] Hardware and software used

[1847] Hardware: Servers, terminals

[1848] Software: generative AI models, data analysis tools (e.g., Python pandas, scikit-learn), online learning platforms (e.g., Moodle)

[1849] System Overview

[1850] This system has the functions of creating and selling prompts, purchasing excellent prompts, headhunting and training prompt creators, planning and managing prompt learning classes, and selling related books. It also collects and analyzes log data from the generative AI model to identify excellent prompt creators and make headhunting suggestions. It also supports the improvement of prompt creation skills using an online learning platform.

[1851] Data collection

[1852] The server collects log data from the generative AI model. Specifically, it stores information such as the evaluation score of each prompt, the creator's ID, and the creation date and time in a database. For example, the server obtains the log data through the generative AI model's API and stores it in a MySQL database.

[1853] Data analysis

[1854] The server analyzes the collected data using data analysis tools. Specifically, it creates a data frame using Python's pandas and trains a machine learning model to evaluate the quality of prompts using scikit-learn. For example, the server runs a Python script to read the data using pandas.read_sql("SELECT FROM prompts", connection) and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[1855] Identifying good prompt writers

[1856] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it creates a list of users who have created prompts with a certain evaluation score or higher a certain number of times. For example, the server uses pandas to extract users who meet the criteria using code such as df[df['score'] > threshold].groupby('user_id').count().

[1857] Headhunting proposals

[1858] The server makes headhunting proposals to the identified producers. Specifically, it sends emails and notifications to present offers. For example, the server uses an SMTP server to send emails using the smtplib library. For example, it executes the code server.sendmail(from_addr, to_addrs, msg).

[1859] Help improve prompt writing skills

[1860] The server plans and manages online and group-based prompt learning classrooms. Specifically, it creates courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills. For example, the server creates a new course using the Moodle API and sends a notification to users encouraging them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[1861] Examples of concrete examples and prompts

[1862] Examples:

[1863] If user A creates 10 highly rated prompts using the generative AI model, the server identifies user A as a good prompt creator.

[1864] The server sends User A an email saying, "We really appreciate your prompt creation skills. Would you like to join our team?"

[1865] Example prompt sentence:

[1866] "Use a generative AI model to create a prompt like: 'Suggest a marketing strategy for a new product.'"

[1867] The system aims to improve the quality of generative AI models by evaluating the skills of prompt creators and identifying and developing outstanding talent.

[1868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1869] Step 1:

[1870] Data collection

[1871] The server collects log data from the generative AI model, specifically, information such as the evaluation score of each prompt, the creator's ID, and the creation date and time, and stores it in a database.

[1872] Input: Generative AI model log data

[1873] Output: Prompt information stored in the database

[1874] Specific operation: The server retrieves the log data through the API of the generative AI model and stores it in a MySQL database, for example, by executing the SQL query INSERT INTO prompts (user_id, prompt_text, score, created_at) VALUES.

[1875] Step 2:

[1876] Data analysis

[1877] The server analyzes the collected data using data analysis tools, specifically, creating a data frame using Python's pandas and training a machine learning model using scikit-learn to evaluate the quality of prompts.

[1878] Input: Prompt information stored in the database

[1879] Output: Prompt quality assessment model

[1880] Specific operation: The server runs a Python script, reads data with pandas.read_sql("SELECT FROM prompts", connection), and trains a prompt evaluation model using scikit-learn's RandomForestClassifier.

[1881] Step 3:

[1882] Identifying good prompt writers

[1883] Based on the analysis results, the server identifies creators who frequently create excellent prompts. Specifically, it lists users who have created prompts with a certain evaluation score or higher a certain number of times.

[1884] Input: prompt quality assessment model, prompt information stored in the database

[1885] Output: A list of great prompt creators

[1886] Specific operation: The server uses pandas to extract users who meet the criteria using code like df[df['score'] > threshold].groupby('user_id').count().

[1887] Step 4:

[1888] Headhunting proposals

[1889] The server then proposes headhunting to the identified creators by sending emails and notifications and presenting offers.

[1890] Input: A list of great prompt creators

[1891] Output: Headhunting proposal email or notification

[1892] Specific operation: The server uses the SMTP server to send the email using the smtplib library. For example, execute the code server.sendmail(from_addr, to_addrs, msg).

[1893] Step 5:

[1894] Help improve prompt writing skills

[1895] The server plans and manages online and group prompt learning classrooms. Specifically, it provides courses using an online learning platform (e.g., Moodle) and provides an environment where users can learn prompt creation skills.

[1896] Input: List of prompt creators, learning content

[1897] Output: Prompt learning classroom course, participation notification

[1898] Specific operation: The server creates a new course using the Moodle API and sends a notification to the user inviting them to join the course. For example, it makes an API call such as moodle.create_course(course_name, description).

[1899] (Application example 2)

[1900] Next, a description will be given of Application Example 2 of Form 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."

[1901] Conventional prompt creation systems did not adequately evaluate the quality of prompts or train prompt creators, making it difficult to identify and develop excellent prompt creators. Furthermore, it was not possible to efficiently plan and manage learning classes to help improve prompt creation skills. This resulted in a lack of high-quality prompts to provide optimal content to users in content delivery services that utilize generative AI models.

[1902] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1903] In this invention, the server includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.This enables the efficient evaluation of prompt quality and the identification and training of prompt creators, making it possible to supply high-quality prompts.

[1904] A "prompt" is text data input to a generative AI model that contains instructions for the model to output an appropriate response or product.

[1905] "Creating a prompt" is the act of generating text data to provide appropriate instructions to a generative AI model.

[1906] "Selling prompts" refers to the act of providing created prompts to other users or companies in exchange for payment.

[1907] "Prompt quality evaluation" is the act of evaluating the effectiveness and appropriateness of input prompts in order to improve the quality of the responses and products output by a generative AI model.

[1908] A "prompt creator" is a person or entity that creates the prompts that are input to a generative AI model.

[1909] "Headhunting" is the act of recruiting individuals with specific skills or abilities to other companies or organizations.

[1910] "Development" refers to providing education and training to individuals with specific skills and abilities to further improve their abilities.

[1911] A "prompt learning classroom" is a place that provides education and training to improve prompt creation skills.

[1912] "Planning and management" means planning, executing and managing a specific activity or project.

[1913] "Related books" are books and materials related to prompt creation and generative AI models.

[1914] "Activity history" refers to the records and track record of prompt creation that a prompt creator has performed in the past.

[1915] "Identification" means finding something based on specific conditions or criteria.

[1916] A "proposal" is a proposal that suggests a particular course of action or option to others and encourages their acceptance.

[1917] A "generative AI model" is a type of artificial intelligence that outputs appropriate responses or products based on input text data.

[1918] A system for implementing this invention includes means for creating and selling prompts, means for purchasing excellent prompts from creators, means for headhunting and training prompt creators, means for planning and managing prompt learning classes, means for selling related books, means for evaluating the quality of prompts, means for analyzing the activity history of prompt creators, means for identifying excellent prompt creators, means for proposing headhunting to identified prompt creators, and means for planning and managing learning classes to improve prompt creation skills.

[1919] The server evaluates the quality of the prompt using a generative AI model. Specifically, it receives the prompt created by the user as input and evaluates its quality using the generative AI model. The evaluation results are stored in a database for later analysis.

[1920] The terminal provides an interface for users to create prompts and send them to the server. Users can use the terminal to input prompts and check the quality evaluation results. The terminal also displays the activity history of prompt creators and provides information to identify excellent prompt creators.

[1921] The server analyzes the activity history of prompt creators to identify outstanding prompt creators. It then sends notifications to identified prompt creators to suggest headhunting. It also plans and runs learning classes to improve prompt creation skills and encourages users to participate.

[1922] For example, if a user creates a prompt such as "Enter a question about AI," the prompt is sent to a server, where the generative AI model evaluates its quality. The evaluation results are stored in a database and later analyzed. If a good prompt creator is identified, the user is offered a headhunting opportunity. Additionally, learning classes are organized to improve prompt creation skills, and users are encouraged to participate.

[1923] The hardware used includes smartphones and personal computers, and the software used includes Python, SQLite, and the OpenAI API. This enables efficient prompt quality evaluation and prompt creator identification and training, enabling the supply of high-quality prompts.

[1924] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1925] Step 1:

[1926] The user creates a prompt using the terminal.

[1927] Input: The prompt the user enters (e.g., "Please enter your AI question.")

[1928] Output: The generated prompt

[1929] Specific operation: The user inputs a prompt sentence through the terminal interface and presses the send button.

[1930] Step 2:

[1931] The terminal sends the generated prompt to the server.

[1932] Input: A prompt text created by the user

[1933] Output: The prompt sent to the server

[1934] Specific operation: The terminal generates and sends an API request to send the prompt text to the server.

[1935] Step 3:

[1936] The server inputs the received prompt sentences into a generative AI model and evaluates their quality.

[1937] Input: The prompt sent to the server

[1938] Output: Quality assessment results (scores and feedback)

[1939] Specific operation: The server calls the API of the generative AI model and passes the prompt sentence as input. The generative AI model analyzes the prompt sentence and returns a quality evaluation result.

[1940] Step 4:

[1941] The server stores the quality evaluation results in a database.

[1942] Input: Quality evaluation results

[1943] Output: Evaluation results stored in a database

[1944] Specific operation: The server generates and executes SQL queries to store the quality assessment results in an SQLite database.

[1945] Step 5:

[1946] The server analyzes the prompt creators' activity history and identifies good prompt creators.

[1947] Input: Evaluation results and activity history stored in the database

[1948] Output: A list of identified good prompt writers

[1949] Specific operation: The server retrieves activity history and evaluation results from the database and uses statistical methods and algorithms to identify excellent prompt creators.

[1950] Step 6:

[1951] The server makes a headhunting proposal to the identified prompt creator.

[1952] Input: A list of identified good prompt writers

[1953] Output: Headhunting proposal notification

[1954] Specific Actions: The server generates and sends a notification to the identified prompt creator containing the headhunting offer.

[1955] Step 7:

[1956] The server plans and runs learning classes to improve prompt creation skills.

[1957] Input: A list of prompt creators and skills development materials and curriculum

[1958] Output: Class schedule and participant list

[1959] Specific operations: The server creates a schedule for learning classes, sends notifications to prompt creators to invite them to participate, prepares teaching materials and curricula, and manages online or group learning classes.

[1960] Example 3

[1961] Next, a third embodiment of the third embodiment will be described. 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."

[1962] Conventional prompt creation and sales systems make it difficult for users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, efficient online operations are required for training and headhunting prompt creators, as well as for planning and managing prompt learning classrooms. A comprehensive system is needed to solve these issues.

[1963] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1964] In this invention, the server includes: means for users to access a website and search, select, purchase, and download books; means for the server to receive user requests, display a web page, provide search results and detailed book information, process payments, and generate download links; and means for the terminal to render the web page, display search results and detailed book information, and allow users who click on the download link to download the book file. This allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators and planning and management of prompt learning classes can also be efficiently carried out online.

[1965] A "prompt" refers to text or instructions input to a generative AI model.

[1966] "Sales" refers to the act of providing a product or service to a customer and receiving payment in return.

[1967] "Producer" refers to the person or entity that creates the prompt.

[1968] "Headhunting" refers to the act of poaching people with specific skills or knowledge from other organizations.

[1969] "Development" refers to the act of educating and developing personnel with specific skills and knowledge.

[1970] A "learning classroom" refers to an educational setting for learning specific knowledge or skills.

[1971] "Related books" refer to books related to a particular theme or field.

[1972] "Website" means a location on the Internet that provides publicly available information and services.

[1973] "Searching" refers to the act of researching a database or the Internet to find specific information.

[1974] "Selection" refers to the act of choosing one option from multiple options.

[1975] "Purchase" refers to the act of acquiring goods or services by paying money.

[1976] "Downloading" refers to the act of saving data from the Internet to your device.

[1977] A "server" refers to a computer that provides services to other computers on a network.

[1978] "Request" means a request for particular information or services.

[1979] "Web Page" refers to an individual page that makes up a website.

[1980] "Display" refers to the act of visually presenting information to a user.

[1981] "Payment processing" refers to the process of paying for goods or services.

[1982] "Link" refers to a hypertext element that connects web pages together.

[1983] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[1984] "Rendering" refers to the act of a computer processing data to produce a visual display.

[1985] This invention relates to a system for creating and selling prompts, and in particular to a system that allows users to efficiently search, select, purchase, and download related books to deepen their knowledge of prompt creation.

[1986] Hardware and software used

[1987] Server: Web server (e.g. Apache, Nginx)

[1988] Software: Web application frameworks (e.g., Django, Ruby on Rails), databases (e.g., MySQL, PostgreSQL)

[1989] Program processing

[1990] 1. A user visits a website

[1991] A user opens a browser and enters the URL of a website.

[1992] The server receives the user's request and sends the HTML, CSS, and JavaScript files for the web page to the user's device.

[1993] The terminal renders the received file and displays the web page to the user.

[1994] 2. User searches for a book

[1995] A user enters a keyword such as "create prompt" into the search bar on a web page and clicks the search button.

[1996] The server receives user input and retrieves relevant book information from a database (e.g., MySQL, PostgreSQL).

[1997] The terminal displays the search results received from the server to the user.

[1998] 3. The user selects a book

[1999] The user clicks on the book of interest from the search results.

[2000] The server retrieves detailed information about the selected book from the database and sends it to the user's terminal.

[2001] The terminal displays detailed information about the received book to the user.

[2002] 4. The user purchases the book

[2003] The user clicks the "Purchase" button on the book's details page to proceed with the purchase, and then enters payment information such as credit card information.

[2004] The server receives the user's purchase information, processes the payment through a payment gateway (e.g., Stripe, PayPal), and stores the purchase information in a database.

[2005] The terminal displays a notice to the user that the payment has been completed.

[2006] 5. The user downloads the book

[2007] Once the purchase is complete, the user clicks on a link to download the book.

[2008] The server generates a download link and sends it to the user's device.

[2009] The terminal allows the user who clicks on the download link to download the book file.

[2010] Specific examples

[2011] Example prompt: "Find books on prompt writing techniques."

[2012] When the user enters this prompt sentence, they type "create prompt" in the search bar and click the search button.

[2013] The server searches the database for relevant books and displays the results to the user.

[2014] The terminal displays the search results to the user.

[2015] Example of a book search result:

[2016] Book 1: "The Basics of Prompt Creation"

[2017] Book 2: "Advanced Prompting Techniques"

[2018] Book 3: "Application of Prompt Theory"

[2019] In this way, users can easily find, purchase, and download books to deepen their knowledge of prompt creation. Furthermore, training and headhunting of prompt creators, as well as planning and management of prompt learning classes, can also be efficiently carried out online. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[2020] Step 1:

[2021] A user visits a website.

[2022] Input: A user types a website URL into their browser.

[2023] What happens: The user opens a browser and enters the URL of a website.

[2024] Data processing / calculation: The server receives the user's request and generates the HTML, CSS, and JavaScript files for the web page.

[2025] Output: The server sends the generated file to the user's terminal.

[2026] Step 2:

[2027] The device displays the web page.

[2028] Input: HTML, CSS, and JavaScript files sent by the server.

[2029] What happens: The device renders the received file and displays the web page to the user.

[2030] Data processing / computation: The device parses HTML, CSS, and JavaScript files to generate visual web pages.

[2031] Output: The web page that is displayed to the user.

[2032] Step 3:

[2033] A user searches for a book.

[2034] Input: A user enters a keyword, such as "create prompt," into the search bar and clicks the search button.

[2035] What happens: A user enters keywords into the search bar on a web page and clicks the search button.

[2036] Data processing / calculation: The server receives user input and searches for relevant book information from a database (e.g., MySQL, PostgreSQL).

[2037] Outpu...

Claims

1. A means of retrieving information for multiple prompts from a database; A means for evaluating each of the plurality of acquired prompts using a generative AI model; means for purchasing said prompts online that meet predetermined criteria through evaluation; A system including:

2. A means of retrieving information for multiple prompts from a database; A means for evaluating each of the plurality of acquired prompts using a generative AI model; means for identifying creators of said prompts that meet predetermined criteria through evaluation; A means for sending an email to the creator of the identified prompt with a proposal for support in improving skills using an online learning platform and a proposal for headhunting; A system including:

3. means for analyzing an emotional state of a user using an emotion engine; means for recognizing a user's emotion based on a textual sentence of the prompt; a means for increasing an evaluation of the prompt when the user has at least one of joy and excitement at the time of creating the prompt based on the recognized user's emotions; The system of claim 1 , comprising:

Images