System

Abstract

To provide a system.SOLUTION: A system includes: means for providing a generative AI platform for education; means for collecting emotion data of a user; means for analyzing the collected emotion data of the user; means for generating, based on a result of analyzing the emotion data of the user, educational activities to be provided to the user, using the generative AI platform; and means for providing the generated educational activities to the user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, the burden on school teachers is increasing, and in particular, a lot of time is spent on lesson preparation and creating teaching materials. However, these tasks do not utilize teachers' expertise, and there is a need to reduce teachers' burden and improve the quality of education. [Means for solving the problem]

[0005] The present invention uses generative AI to create prompts and support their use. Furthermore, it provides a dedicated generative AI platform for education, and uses and develops AI. This efficiently supports teachers' educational activities and reduces their burden. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 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. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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 wide area network (WAN) and / or a local area network (LAN).

[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 and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing 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] One embodiment of the present invention is a prompt creation system that uses generative AI. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts. For example, when a teacher prepares a "math lesson for junior high school students," the generative AI generates "math problems that are easy for junior high school students to understand." This allows teachers to significantly reduce the time it takes to prepare lessons.

[0029] "Example 2"

[0030] Another embodiment of the present invention is a dedicated generative AI platform for education. This platform allows teachers to train and use AI to suit their own lessons. For example, teachers can have AI learn "teaching methods to improve students' understanding" on the platform. This allows teachers to receive support from AI optimized for their own lessons.

[0031] "Example 3"

[0032] Furthermore, another embodiment of the present invention is an AI teacher platform. This platform enables AI to play the role of a teacher. For example, AI can provide appropriate answers to students' questions and adjust the pace of a lesson according to the student's level of understanding. This allows teachers to improve the quality of education for each student.

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

[0034] "Example 1"

[0035] Step 1: The teacher inputs the lesson topic and goals into the system. For example, "Mathematics lesson for junior high school students."

[0036] Step 2: The generative AI generates prompts based on teacher input. These prompts correspond to teacher requests, such as "math problems that are easy for middle school students to understand."

[0037] Step 3: The generated prompts are presented to teachers to use in lesson preparation.

[0038] "Example 2"

[0039] Step 1: Teachers access a dedicated generative AI platform for education.

[0040] Step 2: Teachers input what they want the AI ​​to learn on the platform, such as "teaching methods to improve students' understanding."

[0041] Step 3: The AI ​​learns based on teacher input and provides optimized support for teachers' lessons.

[0042] "Example 3"

[0043] Step 1: The AI ​​teacher platform is launched and begins interacting with students.

[0044] Step 2: The AI ​​provides appropriate answers to the student's questions. For example, if a student asks a question about a math problem, the AI ​​provides an explanation for the problem.

[0045] Step 3: The AI ​​adjusts the pace of the lesson according to the student's level of understanding. For example, if a student is struggling to understand, the AI ​​will slow down the pace of the lesson.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of the embodiment 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".

[0048] In the past, teachers had to create lesson content from scratch, which required a lot of time and effort. In addition, there was a lack of support systems to efficiently create high-quality lesson materials. This increased the burden on teachers and could lead to a decline in the quality of education.

[0049] 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.

[0050] In this invention, the server includes a means for a user to access the system, a means for a user to input lesson contents, a means for the server to receive input information, a means for the server to call a generative AI model, a means for the generative AI model to generate a prompt, a means for the server to receive the generated prompt, a means for the server to send a prompt sentence to a terminal, a means for a user to confirm the prompt sentence, a means for providing a generative AI platform dedicated to education, and a means for utilizing and developing AI. This enables teachers to efficiently create high-quality lesson materials and significantly shortens the time required for lesson preparation.

[0051] "Means for a user to access the system" refers to the method by which a user uses a terminal to log in to the system's website or application and begin using it.

[0052] "Means for users to input lesson content" refers to an interface or form that allows users to input information such as the lesson theme and target grade into the system.

[0053] The "means for the server to receive input information" refers to a method by which the server receives information on lesson contents sent by a user and temporarily stores it.

[0054] "Means for the server to call the generative AI model" refers to the method by which the server calls the API of the generative AI model and passes the information entered by the user as a prompt.

[0055] "Means by which the generative AI model generates a prompt" refers to a method by which the generative AI model generates an appropriate prompt sentence based on information passed from the server.

[0056] "Means for the server to receive the generated prompt" refers to a method for the server to receive and re-store the prompt text returned from the generative AI model.

[0057] The "means for the server to send a prompt text to the terminal" refers to a method by which the server sends a saved prompt text to the user's terminal and displays it on the screen.

[0058] The "means for the user to confirm the prompt text" refers to a method by which the user can confirm the prompt text generated at the terminal and make corrections or additions as necessary.

[0059] "Means for providing a dedicated generative AI platform for education" means a method for providing a dedicated generative AI platform to support educational activities.

[0060] "Means for using and developing AI" refers to methods for using generative AI models and training and adjusting them to improve their performance.

[0061] This invention relates to a prompt creation system that uses generative AI. The purpose of this system is to drastically reduce the time it takes for a teacher to prepare for a lesson by having a generative AI automatically generate prompts.

[0062] Hardware and Software Used

[0063] The system uses the following hardware and software:

[0064] Terminal: A device such as a PC or tablet used by a teacher.

[0065] Server: A server for managing the entire system and calling generative AI models.

[0066] Generative AI models: For example, generative AI models such as GPT-4® from OpenAI®.

[0067] Data processing and calculation

[0068] 1. A user accesses the system

[0069] Users (teachers) access the prompt creation system website using a device such as a PC or tablet. The user enters their ID and password on the login screen to log in to the system.

[0070] 2. The user inputs the lesson content

[0071] After logging in, the user is presented with a form to enter information such as the topic of the lesson and the target grade. For example, the user might enter "Math lessons for junior high school students." Once the information is complete, the user clicks the "Submit" button.

[0072] 3. The server receives the input information

[0073] The server receives the lesson content information sent by the user and temporarily stores it in a database.

[0074] 4. The server calls the generative AI model

[0075] The server retrieves the saved lesson content information and calls a generative AI model (e.g., OpenAI's GPT-4). The server passes the information entered by the user as a prompt to the generative AI model.

[0076] 5. Generative AI model generates prompts

[0077] The generative AI model generates appropriate prompts based on the information passed from the server. For example, it generates "mathematics problems that are easy for junior high school students to understand."

[0078] 6. The server receives the generated prompt

[0079] The server receives the prompt text returned from the generative AI model and stores it again in the database.

[0080] 7. The server sends a prompt to the terminal.

[0081] The server sends the saved prompt text to the user's terminal, where it is displayed on the screen.

[0082] 8. The user confirms the prompt

[0083] The user can check the generated prompt sentences on the terminal and make corrections or additions as necessary. Finally, the user can use the generated prompt sentences in lesson preparation.

[0084] Examples

[0085] Example input

[0086] A teacher enters "math lessons for middle school students" into the system.

[0087] Example prompts for generative AI models

[0088] "Generate math problems that are easy for middle school students to understand."

[0089] Example of a generated prompt statement

[0090] "Solve the following equation: 2x + 3 = 7. Include your answer and explanation."

[0091] This system allows teachers to efficiently create high-quality lesson materials. By utilizing generative AI models, it is possible to significantly reduce lesson preparation time.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] A user accesses the system

[0095] Input: The user (teacher) accesses the system's website using a device such as a PC or tablet.

[0096] Specific operation: The user opens a browser and enters the system URL to access the system. The login screen is displayed and the user enters their ID and password to log in.

[0097] Output: The user is logged into the system and the main screen is displayed.

[0098] Step 2:

[0099] The user inputs the lesson content

[0100] Input: The user inputs information such as the topic of the lesson and the grade level it is aimed at. For example, the user inputs "Math lesson for middle school students."

[0101] Specific actions: The user enters the lesson details into the displayed form and clicks the "Submit" button.

[0102] Output: The entered lesson content information is sent to the server.

[0103] Step 3:

[0104] The server receives the input information

[0105] Input: Course content information submitted by the user.

[0106] Specific operation: The server receives the information sent by the user and temporarily stores it in a database.

[0107] Output: Course content information stored in a database.

[0108] Step 4:

[0109] The server calls the generative AI model

[0110] Input: Course content information stored in the database.

[0111] Specific operation: The server retrieves the stored information and calls the API of the generative AI model. The server passes the information entered by the user as a prompt to the generative AI model.

[0112] Output: The prompt information passed to the generative AI model.

[0113] Step 5:

[0114] A generative AI model generates prompts

[0115] Input: The prompt information passed by the server.

[0116] How it works: The generative AI model generates appropriate prompts based on the information passed to it. For example, it generates "math problems that are easy for junior high school students to understand."

[0117] Output: The generated prompt statement.

[0118] Step 6:

[0119] Receives server-generated prompts

[0120] Input: The prompt sentence returned by the generative AI model.

[0121] Specific operation: The server receives the prompt text returned from the generative AI model and saves it back in the database.

[0122] Output: The prompt statement saved in the database.

[0123] Step 7:

[0124] The server sends a prompt to the terminal.

[0125] Input: The prompt statement stored in the database.

[0126] Specific operation: The server retrieves the saved prompt text and sends it to the user's terminal.

[0127] Output: The prompt text sent to the user's terminal.

[0128] Step 8:

[0129] The user confirms the prompt.

[0130] Input: The prompt text sent to the user's terminal.

[0131] Specific action: The user checks the prompt generated in the terminal and makes corrections or additions as necessary.

[0132] Output: The final prompt sentence with any corrections or additions.

[0133] (Application example 1)

[0134] 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 device 14 will be referred to as a "terminal."

[0135] In conventional educational support systems, teachers had to spend a lot of time preparing lessons and creating teaching materials, making it difficult to conduct efficient educational activities. In addition, there was a problem that the burden on teachers increased because it was not easy to customize the generated teaching materials and prompts. Furthermore, there was a lack of support for educational content creators to efficiently create high-quality teaching materials.

[0136] 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.

[0137] In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing an education support application and enabling teachers and educational content creators to generate prompts for lessons and teaching materials, and a means for customizing the generated prompts. This enables teachers and educational content creators to efficiently prepare lessons and teaching materials and easily customize the generated prompts.

[0138] "Generative AI" is artificial intelligence that automatically generates text and content based on user input.

[0139] A "prompt" is an input sentence that asks a generative AI a specific task or question.

[0140] "Usage support" is a function that supports users to use the system effectively.

[0141] A "dedicated generative AI platform for education" is a platform that provides specialized generative AI to support educational activities.

[0142] "AI utilization and development" refers to the effective use of artificial intelligence and the training and adjustment required to improve its performance.

[0143] An "educational support application" is software designed to support educational activities.

[0144] A "teacher" is a professional who teaches students at an educational institution.

[0145] An "educational content creator" is a professional who creates educational materials and content.

[0146] A "class" is an educational activity conducted by a teacher for students at an educational institution.

[0147] "Teaching materials" refers to learning materials and teaching tools used in educational activities.

[0148] "Customization" means adapting or modifying a system or content to meet the needs and requirements of users.

[0149] The present invention provides an educational support application that allows teachers and educational content creators to efficiently generate and customize prompts for lessons and teaching materials. Specific embodiments of the system will be described below.

[0150] System Configuration

[0151] The system consists of the following main components:

[0152] 1. Server: Hosts the generative AI and provides prompt generation and customization functions.

[0153] 2. Devices: Smartphones and tablets used by teachers and educational content creators.

[0154] 3. Users: Teachers and educational content creators.

[0155] Hardware and Software Used

[0156] Hardware: Smartphones, tablets, servers

[0157] Software: OpenAI API, Python (registered trademark), educational support application

[0158] Data processing and calculation

[0159] Data Entry

[0160] A user uses a terminal to access an educational support application and inputs a lesson topic and a target grade. For example, the user inputs "math lessons for junior high school students."

[0161] Prompt Generation

[0162] The server generates prompts using the OpenAI API based on the input data. Specifically, the server sends the prompts to a generative AI model (e.g., GPT-3 (registered trademark)) and generates appropriate questions and explanations.

[0163] Results display

[0164] The generated prompts are displayed to the user through the educational support application on the terminal, and the user can customize the generated prompts.

[0165] Examples

[0166] Example 1: Teacher preparing for a lesson

[0167] When a teacher types in "Please generate linear equation problems for junior high school students," the server uses generative AI to generate appropriate math problems and displays them on the device. Teachers can customize the generated problems and use them in their classes.

[0168] Example 2: Educational content creator creating teaching materials

[0169] When an educational content creator inputs "The Basics of Trigonometric Functions," the server uses generative AI to generate basic questions about trigonometric functions and their explanations, which are then displayed on the device. Creators can customize the generated content and provide it as teaching materials.

[0170] Examples of prompt statements

[0171] "Generate linear equation problems for middle school students."

[0172] "Generate problems that illustrate the fundamentals of trigonometry."

[0173] In this way, the system enables teachers and educational content creators to efficiently prepare lessons and materials and easily customize the generated prompts.

[0174] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] A user uses a terminal to access an educational support application and inputs the lesson topic and the target grade.

[0177] Input: Lesson topic (e.g. "Math lessons for junior high school students"), target grade

[0178] Output: The input data is sent to the server.

[0179] Specific operations: The user enters the lesson topic and target grade into the application's input form and presses the "Generate" button.

[0180] Step 2:

[0181] The server receives the input data and sends a request to the generative AI model to generate a prompt.

[0182] Input: Lesson topic and target grade sent by the user

[0183] Output: A prompt generation request sent to the generative AI model

[0184] Specific operation: The server calls the OpenAI API and creates a prompt generation request based on the input data.

[0185] Step 3:

[0186] The generative AI model receives the prompt generation request and generates an appropriate prompt.

[0187] Input: Prompt generation request

[0188] Output: Generated prompt

[0189] What happens: A generative AI model (e.g. GPT-3) processes the request and generates an appropriate math problem and explanation.

[0190] Step 4:

[0191] The server receives the generated prompt and sends it to the terminal.

[0192] Input: Generated prompt

[0193] Output: Prompt data sent to the terminal

[0194] Specific operation: The server receives the generated prompt and converts it into a data format for transmission to the user's terminal.

[0195] Step 5:

[0196] The terminal displays the received prompt to the user.

[0197] Input: Prompt data sent from the server

[0198] Output: The prompt that is displayed to the user

[0199] Specific operation: The educational support application of the terminal receives the prompt data and displays it on the user interface.

[0200] Step 6:

[0201] Customize user-generated prompts.

[0202] Input: the prompt shown

[0203] Output: Customized prompt

[0204] What happens: The user edits the displayed prompt and makes the necessary changes.

[0205] Step 7:

[0206] Customized prompts can be saved and used in lessons or as teaching materials.

[0207] Input:Customized prompt

[0208] Output: Saved prompt data

[0209] Specific behavior: When the user completes customization and presses the save button, the prompt is saved to the database.

[0210] Example 2

[0211] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0212] In conventional education systems, it is difficult for teachers to provide teaching methods that correspond to the level of understanding of each individual student, and there is a lack of real-time feedback and support. This makes it difficult to effectively improve students' understanding.

[0213] 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.

[0214] In this invention, the server includes a means for teachers to input data using a terminal and the server to store the data, a means for the server to preprocess and analyze the stored data, and a means for the server to train a generative AI model using the analyzed data. This enables teachers to provide teaching methods that correspond to the level of understanding of each student, and allows them to receive feedback and support in real time.

[0215] "Generative AI" refers to artificial intelligence that generates information based on prompts entered by the user.

[0216] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[0217] A "dedicated generative AI platform for education" is a dedicated system that allows teachers to develop and use AI to suit their own lessons.

[0218] A "device" is a device, such as a computer or tablet, that a teacher uses to enter data or receive feedback.

[0219] "Server" refers to a central processing unit that stores data, pre-processes, analyzes, trains generative AI models, and provides feedback.

[0220] "Data preprocessing" refers to the process of converting stored data into a format that is easier to analyze, including data cleaning and normalization.

[0221] A "generative AI model" is a machine learning model that generates information based on input data.

[0222] "Feedback" refers to information that a generative AI model provides to teachers about the results of its learning.

[0223] "Real-time support" refers to analyzing students' level of understanding in real time during class and suggesting the most appropriate teaching methods to teachers.

[0224] This invention provides a system that provides a dedicated generative AI platform for education, enabling teachers to develop and use AI for their own lessons. A specific embodiment of this system will be described below.

[0225] System Overview

[0226] In this system, teachers input data using a terminal, and the server stores, preprocesses, and analyzes the data, and trains the generative AI model, proposing optimal teaching methods to teachers. The system is composed of a generative AI, a dedicated generative AI platform for education, terminals, and a server.

[0227] Hardware and Software Used

[0228] Terminal: A device such as a computer or tablet for teachers to enter data.

[0229] Server: A central processing unit that stores data, preprocesses, analyzes, trains generative AI models, and provides feedback. Specifically, it uses relational databases such as MySQL (registered trademark) and PostgreSQL, Python libraries such as pandas and numpy, and machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch.

[0230] Generative AI models: For example, advanced generative AI models such as OpenAI's GPT-4.

[0231] Entering and Saving Data

[0232] Users (teachers) use their devices to log in to the educational generative AI platform and input data on lesson content and students' understanding. The devices then send the input data to the server, which then stores the data in a database.

[0233] Data preprocessing and analysis

[0234] The server preprocesses the stored data, which includes cleaning and normalizing the data, and then analyzes the preprocessed data using Python libraries such as pandas and numpy.

[0235] Training generative AI models

[0236] The server uses the parsed data to train a generative AI model, and the training process is managed using a machine learning framework such as TensorFlow or PyTorch to optimize the model's parameters.

[0237] Providing feedback

[0238] The server feeds back the learning results to the user. The feedback includes the model-generated teaching method suggestions and the analysis results of the students' understanding. The terminal displays the feedback to the user, who then plans lessons based on it.

[0239] Real-time assistance

[0240] During lessons, the devices will provide real-time AI support, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[0241] Examples

[0242] For example, if a teacher wants to teach a generative AI model how to improve students' understanding in mathematics class, they can input a prompt like the following into the model:

[0243] Examples of prompt statements

[0244] "Please tell me some effective teaching methods to improve students' understanding in mathematics classes. Please provide specific examples and activities."

[0245] Given this prompt, the generative AI model would provide the following answer:

[0246] Example answers from generative AI models

[0247] The following teaching methods are effective in enhancing students' understanding:

[0248] 1. Use real-life examples: Connecting math concepts to real-life examples helps students understand them better.

[0249] 2. Group Discussion: By solving problems among students, they have more opportunities to teach each other.

[0250] 3. Interactive activities: Quizzes and games can help keep students engaged.”

[0251] In this way, teachers can receive support from AI while teaching.

[0252] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0253] Step 1:

[0254] Faculty login and data entry

[0255] The user (teacher) uses a terminal to log in to the educational generative AI platform by entering a user ID and password.

[0256] Input: User ID, Password

[0257] Output: Login success or failure message

[0258] Specific operation: The terminal sends the user ID and password to the server, and the server performs authentication. If the authentication is successful, the user can access the platform.

[0259] Step 2:

[0260] Input of lesson contents and student data

[0261] The user inputs data related to the content of the lesson and the students' level of understanding, such as the lesson topic, the teaching materials used, and the students' test results.

[0262] Input: Lesson topic, teaching material information, student test results

[0263] Output: Confirmation message for input data

[0264] Specific operation: The terminal provides an input form and transmits data entered by the user to the server.

[0265] Step 3:

[0266] Data transmission and storage

[0267] The terminal transmits the input data to the server.

[0268] The server stores the received data in a database.

[0269] Input: Lesson topic, teaching material information, student test results

[0270] Output: Data save confirmation message

[0271] Specific operation: The server stores data in a database (e.g., MySQL or PostgreSQL).

[0272] Step 4:

[0273] Data preprocessing and analysis

[0274] The server pre-processes the stored data, which includes cleaning and normalizing the data.

[0275] Input: Saved data

[0276] Output: Preprocessed data

[0277] Specific operation: The server uses Python libraries such as pandas and numpy to fill in missing values ​​in the data and remove outliers.

[0278] Step 5:

[0279] Training generative AI models

[0280] The server uses the preprocessed data to train a generative AI model.

[0281] Input: Preprocessed data

[0282] Output: A trained generative AI model

[0283] How it works: The server optimizes the model parameters using machine learning frameworks such as TensorFlow or PyTorch.

[0284] Step 6:

[0285] Feedback on learning results

[0286] The server then provides the user with feedback on the learning results, including model-generated teaching method suggestions and analysis of the students' understanding.

[0287] Input: A trained generative AI model

[0288] Output: Feedback information (suggested teaching methods, analysis of students' understanding)

[0289] Specific operation: the server sends the generated feedback information to the terminal, and the terminal displays it to the user.

[0290] Step 7:

[0291] Real-time assistance

[0292] The device will provide real-time AI support during lessons, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[0293] Input: Real-time student data (responses, test results)

[0294] Output: Real-time support information (suggestions for teaching methods)

[0295] Specific operation: The terminal transmits the data collected in real time to the server, which analyzes it and provides feedback.

[0296] (Application example 2)

[0297] 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."

[0298] Conventional educational generative AI platforms are specialized in providing information to support teachers' educational activities, but they do not provide sufficient support for improving the work learning and customer service skills of store staff. As a result, improving the work efficiency and customer service quality of store staff is an issue.

[0299] 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. In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for training, a means for using and training AI, a means for supporting store staff in learning the contents of their work, a means for performing a simulation to improve customer service skills, and a means for providing feedback in real time. This enables store staff to efficiently learn their work and improve their customer service skills.

[0300] "Generative AI" is an artificial intelligence technology that creates prompts and assists users.

[0301] The "dedicated generative AI platform for education" is a specialized platform that allows teachers to develop and use AI for their own lessons.

[0302] "Means of using and developing AI" refers to methods and technologies for effectively utilizing artificial intelligence and improving its performance.

[0303] The "means for supporting store staff in learning about their work" refers to support methods and techniques provided to store staff in order to efficiently learn about their work.

[0304] The "means for performing simulations to improve customer service skills" refers to simulation techniques used by store staff to improve their customer service skills.

[0305] "Means for providing feedback in real time" refers to technology that provides instant feedback to store staff while they are performing their duties.

[0306] In order to implement the present invention, the following system configuration and program are required.

[0307] System Configuration

[0308] 1. Hardware:

[0309] Smartphone or tablet (iOS or ANDROID (registered trademark) device)

[0310] Server (Cloud server or on-premise server)

[0311] 2. Software:

[0312] Python 3.x

[0313] OpenAI API

[0314] Program processing explanation

[0315] The server uses generative AI to create prompts and assist users in using the system. Specifically, when teachers or store staff input the content they want to learn, the generative AI generates the optimal prompt based on that content. The prompts are generated using the OpenAI API.

[0316] The generated prompts are provided through a dedicated generative AI platform for education. This platform supports teachers and store staff in efficiently progressing with their learning. For example, when a store staff member is learning "how to display new products," the following prompts are generated:

[0317] Examples of prompt statements

[0318] What is the best way for store staff to learn how to display new products?

[0319] Based on these prompts, the generative AI will suggest optimal learning methods, which store staff can review via smartphone or tablet and apply to their work.

[0320] In addition, the server will provide a means to assist store staff in learning about their work. For example, the AI ​​will provide specific procedures and points to note for tasks such as cash register operation and inventory management.

[0321] It also includes a means to conduct simulations to improve customer service skills. Through simulations provided by AI, store staff can improve their skills in handling complaints and explaining products.

[0322] Finally, it also includes a means to provide real-time feedback, where AI can provide real-time feedback to store staff as they carry out their duties, advising them on appropriate language and attitudes.

[0323] This will enable store staff to learn their jobs efficiently and improve their customer service skills.

[0324] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0325] Step 1:

[0326] A user (a teacher or store staff member) uses a terminal (a smartphone or tablet) to input the content they want to learn. The input content is sent to the server. The input data is the specific learning content, such as "how to display new products."

[0327] Step 2:

[0328] The server has the generative AI create a prompt based on the received input data. The generative AI analyzes the input data and generates the optimal prompt sentence. For example, the generated prompt sentence might be, "What is the best way for store staff to learn how to display new products?"

[0329] Step 3:

[0330] The server sends the generated prompt sentence to the OpenAI API and receives a response from the AI ​​model. The OpenAI API suggests the optimal learning method based on the prompt sentence. The obtained response is a specific instruction such as "Please follow the steps below to display the new product."

[0331] Step 4:

[0332] The server provides the acquired responses to the user through a dedicated generation AI platform for education. The user can check the responses on their device and proceed with their learning. The responses include specific procedures and points to note.

[0333] Step 5:

[0334] As the user progresses with the learning process, the server provides feedback in real time. It monitors the user's operations and behavior and generates appropriate feedback. For example, it provides real-time advice on points to note and areas for improvement when the user displays products.

[0335] Step 6:

[0336] The server evaluates the user's learning progress and skill improvement, and provides additional learning content and simulations as necessary. For example, the server runs a simulation to improve customer service skills and provides feedback to the user.

[0337] Step 7:

[0338] Users improve their work skills based on the feedback and additional learning content provided. The server accumulates the user's learning data and uses it to support future learning sessions. This improves the user's work efficiency and the quality of customer service.

[0339] Example 3

[0340] 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 smart device 14 will be referred to as a "terminal."

[0341] In the conventional education system, it was difficult for teachers to adjust the pace of lessons according to each student's level of understanding, and it was also difficult to provide quick and appropriate answers to students' questions. This resulted in a decrease in students' learning efficiency and inconsistent quality of education.

[0342] 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.

[0343] In this invention, the server includes a means for accepting a user's question, a means for using a generative AI model that analyzes the question and generates an answer, a means for providing the generated answer, a means for evaluating the user's level of understanding, and a means for adjusting the speed of the lesson based on the evaluation result. This makes it possible to proceed with the lesson according to each student's level of understanding, and to improve learning efficiency by providing quick and appropriate answers.

[0344] The "means for accepting a question from a user" is a function for transmitting a question input by a user using a terminal to a server.

[0345] "Means of utilizing a generative AI model that analyzes questions and generates answers" refers to a function that inputs questions received by the server into a generative AI model and generates an appropriate answer.

[0346] "Means for providing a generated answer" refers to a function for sending the answer generated by the generative AI model to the user's terminal and displaying it.

[0347] The "means for evaluating the user's level of understanding" is a function for evaluating the user's level of understanding based on the user's reaction and additional questions, and storing the results in a database.

[0348] The "means for adjusting the pace of the lesson based on the evaluation results" is a function for adjusting the pace of the lesson based on the comprehension information stored in the database and sending the adjusted lesson plan to the user's terminal.

[0349] A "dedicated generative AI platform for education" is a platform that uses generative AI models designed to support teachers' educational activities.

[0350] "Means of using and developing AI" refers to functions that use generative AI models to support educational activities, and furthermore, to continuously improve and develop those AI models.

[0351] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0352] This invention is a system that supports educational activities using an AI teacher platform. This system accepts questions from users, generates appropriate answers using a generative AI model, and provides them to the users. It also has a function to evaluate the user's level of understanding and adjust the pace of the lesson based on the evaluation results.

[0353] Hardware and Software Used

[0354] Hardware: Servers, terminals (PCs, tablets, smartphones)

[0355] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), web servers (e.g. Apache)

[0356] Specific operation of the system

[0357] 1. Accepting user questions

[0358] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[0359] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[0360] 2. Question Analysis and Answer Generation

[0361] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[0362] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[0363] Server: Receives the generated answer and proceeds to the next step.

[0364] 3. Providing answers

[0365] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[0366] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[0367] 4. Evaluate comprehension

[0368] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[0369] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[0370] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[0371] Server: Stores the evaluation results in a database. Specifically, it inserts the evaluation data into the database using a SQL query.

[0372] 5. Adjusting the pace of lessons

[0373] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[0374] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[0375] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[0376] Examples

[0377] Accepting user questions

[0378] A user types into a terminal, "Please tell me how to solve a quadratic equation."

[0379] The device sends this question to the server in an HTTP request.

[0380] Question analysis and answer generation

[0381] The server sends the question to the API of the generative AI model (GPT-4).

[0382] GPT-4 generates the answer: "To solve a quadratic equation, first transform the equation into a standard form, and then use the solution formula to find the solution."

[0383] The server receives the generated answer and sends it to the terminal in an HTTP response.

[0384] Providing answers

[0385] The terminal will display to the user, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[0386] Assessment of understanding

[0387] The user responds, "Okay."

[0388] The terminal sends this response to the server in an HTTP request.

[0389] The server rates the response "OK" as "high" and stores it in the database using an SQL query.

[0390] Adjusting the pace of lessons

[0391] The server adjusts the speed at which the next lesson content is advanced based on the comprehension level information in the database.

[0392] The server sends the adjusted lesson plan to the device in an HTTP response.

[0393] The terminal will display "Continue to next topic" to the user.

[0394] Examples of prompt statements

[0395] "Please teach me how to solve quadratic equations"

[0396] "Teach me more about World War II in history class."

[0397] "I have a question about English grammar. Can you tell me how to use the present perfect tense?"

[0398] The above is a specific embodiment for carrying out the invention of the AI ​​teacher platform. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0399] Step 1:

[0400] Accepting user questions

[0401] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[0402] Input: The question text entered by the user.

[0403] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[0404] Output: The question data sent to the server.

[0405] Step 2:

[0406] Question analysis and answer generation

[0407] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[0408] Input: The question data sent to the server.

[0409] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[0410] Output: The generated response data.

[0411] Step 3:

[0412] Providing answers

[0413] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[0414] Input: The generated answer data.

[0415] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[0416] Output: The answer displayed to the user.

[0417] Step 4:

[0418] Assessment of understanding

[0419] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[0420] Input: The user's response text.

[0421] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[0422] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[0423] Output: Evaluated comprehension data.

[0424] Step 5:

[0425] Adjusting the pace of lessons

[0426] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[0427] Input: Comprehension information stored in the database.

[0428] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[0429] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[0430] Output: The lesson plan displayed to the user.

[0431] (Application example 3)

[0432] 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."

[0433] In the conventional education system, it was difficult for teachers to respond to each student individually, which led to the problem of inconsistent quality of education. In addition, even in physical stores, customers often did not receive appropriate answers when they asked questions about products, which led to a lack of improvement in the purchasing experience. To solve these problems, it is necessary to use AI to provide individual responses and improve the quality of education and purchasing experiences.

[0434] 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.

[0435] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing appropriate answers to questions from customers, and a means for making product suggestions based on the customer's purchase history. This makes it possible to improve the quality of education and the customer's purchase experience in physical stores.

[0436] "Generative AI" is artificial intelligence that generates text and information based on user input.

[0437] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[0438] "Usage support" is a function that supports users to use the system effectively.

[0439] A "dedicated generative AI platform for education" is a system that provides generative AI specialized to support educational activities.

[0440] "AI utilization and development" refers to activities aimed at effectively utilizing artificial intelligence and improving its performance.

[0441] "Means of providing appropriate answers to questions from customers" refers to a function that uses generative AI to provide appropriate answers when customers ask questions about products or services.

[0442] "Means for making product suggestions based on a customer's purchasing history" refers to a function that analyzes a customer's past purchasing data and suggests new products based on that data.

[0443] A system for implementing this invention includes means for creating prompts using generative AI and providing usage support, means for providing a dedicated generative AI platform for educational purposes, means for using and developing AI, means for providing appropriate answers to questions from customers, and means for making product suggestions based on the customer's purchasing history.

[0444] The server uses generative AI to create prompts and generate appropriate answers to user questions. Specifically, it uses OpenAI's API to receive questions from users and generate answers to those questions. For example, if a user asks, "What is the battery life of this smartphone?", the server uses generative AI to respond, "The battery life of this smartphone is approximately 24 hours."

[0445] The server also analyzes the customer's purchase history and makes product suggestions based on that. For example, if the customer's purchase history is "smartphone, wireless earphones, smartwatch," the server will use generative AI to suggest, "We recommend a new smartphone case or a high-performance charger for you."

[0446] To realize this system, the following hardware and software are used.

[0447] Hardware: Smartphones, servers

[0448] Software: OpenAI API, Python

[0449] The server receives questions from users in text format and uses OpenAI's generative AI model to generate appropriate answers. The generated answers are sent to the user's smartphone and displayed. The server also receives customer purchase history in text format and uses the same generative AI model to generate product suggestions. The generated product suggestions are also sent to the user's smartphone and displayed.

[0450] As an example, use the following prompt:

[0451] "Customer Question: What is the battery life of this smartphone? Please provide more information about the product."

[0452] "Customer purchase history: Smartphone, Wireless earphones, Smartwatch. Please suggest products that you would recommend to this customer."

[0453] This will improve the quality of education and enhance the customer's purchasing experience in physical stores.

[0454] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0455] Step 1:

[0456] The user enters a question using a smartphone.

[0457] Input: A user question (e.g., "What is the battery life of this smartphone?")

[0458] Output: User question text data

[0459] Step 2:

[0460] The terminal transmits the user's question to the server.

[0461] Input: User question text data

[0462] Output: The question data sent to the server.

[0463] Step 3:

[0464] The server converts the received question data into prompt text to be input into the generative AI model.

[0465] Input: User question data

[0466] Output: Prompt sentence for the generative AI model (e.g., "Customer question: What is the battery life of this smartphone? Please provide more information about the product.")

[0467] Step 4:

[0468] The server uses a generative AI model to generate an answer based on the prompt.

[0469] Input: A prompt for the generative AI model

[0470] Output: The generated answer text (e.g. "This smartphone's battery life is approximately 24 hours.")

[0471] Step 5:

[0472] The server transmits the generated answer text to the terminal.

[0473] Input: Generated answer text

[0474] Output: Answer data sent to the terminal

[0475] Step 6:

[0476] The terminal displays the received answer data to the user.

[0477] Input: Answer data sent to the terminal

[0478] Output: The answer shown to the user (e.g., "This smartphone's battery life is approximately 24 hours.")

[0479] Step 7:

[0480] A user enters a purchase history using a smartphone.

[0481] Input: User's purchase history (e.g. "smartphone, wireless earphones, smartwatch")

[0482] Output: User purchase history text data

[0483] Step 8:

[0484] The terminal transmits the user's purchase history to the server.

[0485] Input: User purchase history text data

[0486] Output: Purchase history data sent to the server

[0487] Step 9:

[0488] The server converts the received purchase history data into prompt text to be input into the generative AI model.

[0489] Input: User purchase history data

[0490] Output: Prompt sentence for the generative AI model (e.g., "Customer purchase history: Smartphone, wireless earphones, smartwatch. Please suggest products that you would recommend to this customer.")

[0491] Step 10:

[0492] The server uses the generative AI model to generate product suggestions based on the prompt sentence.

[0493] Input: A prompt for the generative AI model

[0494] Output: Generated product recommendation text (e.g. "We recommend a new smartphone case and a high-performance charger for you.")

[0495] Step 11:

[0496] The server transmits the generated product suggestion text to the terminal.

[0497] Input: Generated product suggestion text

[0498] Output: Product recommendation data sent to the terminal

[0499] Step 12:

[0500] The terminal displays the received product suggestion data to the user.

[0501] Input: Product recommendation data sent to the terminal

[0502] Output: Product recommendations displayed to the user (e.g., "We recommend a new phone case or a high-performance charger for you.")

[0503] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0504] "Example 1"

[0505] In the form of the invention that combines an emotion engine, the generative AI recognizes the user's emotions and generates prompts according to those emotions. Specifically, the emotion engine analyzes the user's emotions from the user's tone of voice, facial expressions, choice of words, etc., and conveys the results to the generative AI. Based on that information, the generative AI generates appropriate prompts when the user is feeling joy, excitement, confusion, anxiety, etc. For example, when the user is confused, it generates a prompt that provides hints and explanations to solve the problem.

[0506] "Example 2"

[0507] The combination of an emotion engine with a dedicated generative AI platform for education takes into account the user's emotions when providing educational support information. Specifically, the emotion engine analyzes the user's emotions and transmits the results to the dedicated generative AI platform for education. Based on that information, the platform provides appropriate educational support information when the user is feeling joy, excitement, confusion, anxiety, or other emotions. For example, when the user is confused, it provides hints and explanations to solve the problem.

[0508] "Example 3"

[0509] The form in which AI uses an emotion engine to carry out educational activities is to adjust educational activities according to the user's emotions. Specifically, the emotion engine analyzes the user's emotions and conveys the results to the AI. Based on that information, the AI ​​will carry out appropriate educational activities when the user is feeling happiness, excitement, confusion, anxiety, etc. For example, when the user is confused, it will provide hints and explanations to solve the problem. Also, when the user is feeling happiness or excitement, it will carry out educational activities that will maintain those emotions.

[0510] The process flow of each embodiment will be described below.

[0511] "Example 1"

[0512] Step 1: The emotion engine analyzes the user's emotions from their tone of voice, facial expressions, choice of words, etc.

[0513] Step 2: The emotion engine communicates the analysis results to the generative AI.

[0514] Step 3: The generative AI uses the information from the emotion engine to generate prompts that correspond to the emotions the user is feeling.

[0515] "Example 2"

[0516] Step 1: The emotion engine analyzes the user's emotions.

[0517] Step 2: The emotion engine communicates the analysis results to a dedicated generative AI platform for education.

[0518] Step 3: The dedicated generative AI platform for education uses information from the emotion engine to provide educational support information that corresponds to the emotions the user is feeling.

[0519] "Example 3"

[0520] Step 1: The emotion engine analyzes the user's emotions.

[0521] Step 2: The emotion engine communicates the analysis results to the AI.

[0522] Step 3: Based on the information from the emotion engine, the AI ​​carries out educational activities according to the emotions the user is feeling.

[0523] Example 1

[0524] Next, a description will be given of Example 1 of the embodiment 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".

[0525] In conventional educational support systems, teachers had to spend a lot of time preparing for lessons, making it difficult to prepare lessons efficiently. In addition, it was not possible to provide appropriate support according to the teacher's emotions and situation, which increased the burden on teachers.

[0526] 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. In this invention, the server includes a means for creating a prompt using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for generating a prompt according to the emotion using an emotion engine that analyzes the emotion of the user, and a means for displaying the generated prompt on the user's terminal. This allows teachers to significantly reduce the time it takes to prepare for lessons and further provides appropriate support according to the emotion and situation of the teacher.

[0527] "Generative AI" refers to artificial intelligence that automatically generates text and information based on user input.

[0528] A "prompt" is an instruction, question, or piece of information that a generative AI provides to a user.

[0529] "Usage support" refers to functions and services that assist users in using the system effectively.

[0530] A "dedicated generative AI platform for education" is a base system for providing specialized generative AI to support educational activities.

[0531] "Use and development of AI" refers to utilizing artificial intelligence and training and adjusting it to improve its performance and functions.

[0532] An "emotion engine" is a technology or system that analyzes emotions from a user's tone of voice, facial expressions, choice of words, etc.

[0533] A "terminal" is a device, such as a computer, tablet, or smartphone, that a user uses to access the system.

[0534] This invention is a system that uses generative AI to create prompts and support educational activities. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts, and furthermore, an emotion engine is used to provide prompts according to the user's emotions.

[0535] Hardware and Software Used

[0536] Hardware: Servers, terminals (PCs, tablets, smartphones, etc.)

[0537] Software: Generative AI models (e.g. GPT-4), emotion engines

[0538] Specific operation of the system

[0539] 1. User Input

[0540] A user (teacher) uses a terminal to input information necessary for preparing a lesson. For example, the user might input "I will teach how to solve linear equations in a math class for junior high school students."

[0541] 2. Data Receipt and Processing by the Server

[0542] The server receives the data sent by the user and converts it into an appropriate format, performing data formatting and pre-processing.

[0543] 3. Prompt generation by generative AI

[0544] The server feeds the formatted data into a generative AI model (e.g., GPT-4) to generate appropriate prompts, such as "problems that explain how to solve linear equations step-by-step, in a way that is easy for middle school students to understand."

[0545] 4. Emotion Analysis Using an Emotion Engine

[0546] The server uses an emotion engine to analyze the user's tone of voice, facial expressions, choice of words, etc., and determines the user's emotions.

[0547] 5. Emotion-based prompt generation

[0548] The server conveys the emotion information obtained from the emotion engine to the generative AI, which then uses that information to generate prompts that correspond to the user's emotions. For example, when a user is confused, the generative AI generates prompts that provide hints and explanations to solve the problem.

[0549] 6. Displaying the results on the terminal

[0550] The server sends the generated prompt to the terminal to display to the user, and the teacher's terminal is shown a "step-by-step problem on how to solve a linear equation," with further explanations provided if the teacher is confused.

[0551] Examples

[0552] Example: A teacher types, "I teach middle school math how to solve linear equations."

[0553] Example of a generated prompt: A generative AI generates a problem that explains how to solve a linear equation step-by-step, in a way that is easy for junior high school students to understand.

[0554] Emotion analysis example: If a teacher has a confused expression, the emotion engine will analyze that expression and determine that the expression is "confused."

[0555] Example of an emotion-sensitive prompt: A generative AI generates a prompt to provide further explanation to a struggling teacher on how to solve a linear equation.

[0556] Examples of prompt statements

[0557] Input prompt for generative AI model: "Generate step-by-step problems for solving linear equations for middle school students."

[0558] Input prompt for emotion engine: "Analyze the user's tone of voice and facial expressions to determine their emotions, and communicate the results to the generative AI."

[0559] This system will enable teachers to significantly reduce the time they spend preparing lessons and also provide appropriate support based on their emotions and situations.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] A user uses a terminal to input information necessary for preparing a lesson. For example, the user inputs "I will teach how to solve linear equations in a mathematics class for junior high school students." The input information is sent from the terminal to the server.

[0563] Step 2:

[0564] The server receives the data sent by the user and converts it into an appropriate format. Specifically, it converts the text data into JSON format and formats it in a format that is easy for the generative AI model to understand. The input is the user's text data, and the output is formatted JSON data.

[0565] Step 3:

[0566] The server inputs the formatted data into a generative AI model (e.g., GPT-4) to generate an appropriate prompt. The generative AI model generates text based on the input data and outputs a prompt. For example, it generates a problem that provides a step-by-step explanation of how to solve a linear equation so that it is easy for junior high school students to understand.

[0567] Step 4:

[0568] The server uses an emotion engine to analyze the user's tone of voice, facial expressions, choice of words, etc. The emotion engine receives the user's voice data and video data as input and performs emotion analysis. The output is data that indicates the user's emotional state.

[0569] Step 5:

[0570] The server conveys the emotion information obtained from the emotion engine to the generative AI. Based on that information, the generative AI generates a prompt that corresponds to the user's emotion. For example, when the user is confused, it generates a prompt that provides hints and explanations to solve the problem. The input is emotion information, and the output is a prompt that corresponds to the emotion.

[0571] Step 6:

[0572] The server sends the generated prompt to the terminal to display it to the user. The terminal displays the prompt it receives from the server on its screen. For example, a teacher's terminal might display a "step-by-step problem on how to solve a linear equation," with further explanations if the teacher is confused. The input is the generated prompt, and the output is what is displayed on the terminal screen.

[0573] (Application example 1)

[0574] 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 device 14 will be referred to as a "terminal."

[0575] Conventional prompt creation systems using generative AI generate uniform prompts without considering the user's emotions, which means that they cannot provide appropriate support according to the user's situation or emotions. In addition, they are limited to the education field, making them difficult to apply to other fields.

[0576] 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. In this invention, the server includes a means for creating a prompt using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for analyzing the user's emotion using an emotion recognition engine and generating a prompt according to the emotion, and a means for displaying a prompt on a smart device. This allows the generation of an appropriate prompt according to the user's emotion, making it possible to apply the system in various fields such as brick-and-mortar stores.

[0577] "Generative AI" is artificial intelligence that automatically generates new information and content based on data.

[0578] A "prompt" is a phrase or message that presents instructions or questions to the user.

[0579] "Usage assistance" refers to supporting users to perform specific tasks or activities efficiently.

[0580] A "dedicated generative AI platform for education" is a basic system for utilizing generative AI designed specifically for the field of education.

[0581] "AI use and development" refers to applying artificial intelligence to actual business operations and activities, and providing training to improve its performance and functions.

[0582] An "emotion recognition engine" is a technology that analyzes emotions from a user's tone of voice, facial expressions, choice of words, etc.

[0583] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities, including smartphones and smart glasses.

[0584] A "user emotion" is a psychological state that a user has in a particular situation, such as happiness, excitement, confusion, anxiety, etc.

[0585] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase goods or services.

[0586] The system for implementing this invention is configured by combining a generative AI, an emotion recognition engine, and a smart device. Specific embodiments are described below.

[0587] System Configuration

[0588] 1. Generative AI: This is artificial intelligence that automatically generates new information and content based on data. This generative AI is used to generate prompts based on the user's emotions.

[0589] 2. Emotion recognition engine: This technology analyzes emotions from the user's tone of voice, facial expressions, choice of words, etc. This makes it possible to recognize the user's current emotions in real time.

[0590] 3. Smart Devices: Electronic devices with Internet connectivity and advanced computing capabilities, such as smartphones and smart glasses. These devices are used to display the generated prompts to the user.

[0591] Program processing explanation

[0592] Hardware and Software

[0593] Hardware: Smart glasses (e.g. Google® Glass®), camera (built into smart glasses)

[0594] Software: OpenCV (image processing library), emotion recognition engine, prompt generation engine

[0595] Data processing and calculation

[0596] 1. Acquiring camera input: Acquiring video in real time from the camera built into the smart glasses, allowing us to capture the user's face and facial expressions.

[0597] 2. Emotion Recognition: Detect faces from captured video and use an emotion recognition engine to analyze the user's emotions, for example determining whether the user is confused or excited.

[0598] 3. Prompt Generation: Based on the recognized emotion, the prompt generation engine generates an appropriate prompt, for example, if the user is confused, it generates a prompt that provides hints or explanations to solve the problem.

[0599] 4. Prompt display: The generated prompts are displayed on the smart glasses display, allowing the user to receive appropriate assistance in real time.

[0600] Examples

[0601] If the customer is stumped: "May I help you with what you're looking for?"

[0602] If the customer is excited: "We have new items in stock! Want to take a look?"

[0603] In this way, the system can generate appropriate prompts based on the user's emotions, making it possible to apply the system in a variety of fields, such as brick-and-mortar stores.

[0604] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0605] Step 1:

[0606] The server acquires video in real time from the camera built into the smart glasses. The input is the camera video, and the output is video data. This video data is used to capture the user's face and facial expressions.

[0607] Step 2:

[0608] The server uses OpenCV to detect faces from the captured video data. The input is the video data, and the output is the coordinate data of the face area. Based on this coordinate data, the position of the face is identified.

[0609] Step 3:

[0610] The server uses an emotion recognition engine to analyze the user's emotions based on the coordinate data of the face region. The input is the coordinate data of the face region, and the output is emotion data (e.g., joy, excitement, confusion, anxiety). Based on this emotion data, the server identifies the user's current emotional state.

[0611] Step 4:

[0612] The server uses a prompt generation engine to generate an appropriate prompt based on the emotion data. The input is emotion data, and the output is a prompt sentence. For example, if the user is confused, the server generates the prompt sentence "May I help you with the product you are looking for?"

[0613] Step 5:

[0614] The server displays the generated prompt sentence on the display of the smart glasses. The input is the prompt sentence, and the output is the text to be displayed on the display. This allows the user to receive appropriate assistance in real time.

[0615] Step 6:

[0616] The user checks the prompt sentence displayed on the display of the smart glasses and takes the next action if necessary. The input is the prompt sentence displayed on the display, and the output is the user's behavior. For example, if the user is confused, he or she can ask staff for help.

[0617] Example 2

[0618] Next, a second embodiment of the second embodiment will be described. 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."

[0619] With conventional educational support systems, it was difficult for teachers to provide appropriate support according to the level of understanding and emotional state of each individual student. In addition, there were limited means for teachers to develop and use AI to optimize their own teaching methods. This resulted in issues such as uneven quality of education and not maximizing the learning effect of students.

[0620] 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.

[0621] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and cultivating AI, a means for analyzing the user's emotions using an emotion analysis engine, and a means for providing appropriate educational support information based on the analysis results. This allows teachers to optimize their own teaching methods and provide appropriate support according to the students' levels of understanding and emotional states.

[0622] "Generative AI" is an artificial intelligence technique for generating appropriate prompts and information based on user input.

[0623] A "prompt" is an input sentence that instructs an artificial intelligence on a specific task or question.

[0624] The "dedicated generative artificial intelligence platform for education" is an artificial intelligence platform designed to help teachers optimize their teaching methods and support educational activities.

[0625] "Artificial intelligence application and development" refers to the process of applying artificial intelligence to specific tasks and training it to improve its performance.

[0626] An "emotion analysis engine" is a technology that analyzes a user's emotional state and takes appropriate action based on the results.

[0627] "Educational support information" is information and advice provided to support the educational activities of teachers and students.

[0628] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0629] This invention is a system that uses a dedicated generative AI platform for education to enable teachers to optimize their own teaching methods and provide appropriate support according to students' levels of understanding and emotional states. A specific embodiment of this system is shown below.

[0630] Generating a Program

[0631] The server generates programs for a dedicated generative AI platform for education, which allows teachers to develop and use AI for their own classes.

[0632] Program Processing

[0633] The server executes the generated program and performs the following processes.

[0634] 1. Cultivating AI:

[0635] Teachers use the devices to access a dedicated generative AI platform for education.

[0636] On the platform, teachers can train the AI ​​to learn "teaching methods that will improve students' understanding."

[0637] The platform trains the AI ​​using data and feedback provided by teachers, using machine learning libraries such as TensorFlow and PyTorch.

[0638] 2. Emotion engine combination:

[0639] Users (students and teachers) access the platform using a terminal.

[0640] The emotion engine analyzes the user's emotions using OpenAI's GPT-4 and IBM Watson (registered trademark) emotion analysis API.

[0641] The emotion engine communicates the analysis results to a dedicated generative AI platform for education.

[0642] 3. Providing educational support information:

[0643] Based on the information obtained from the emotion engine, the platform provides educational support information that corresponds to the emotions the user is feeling.

[0644] For example, if a user is confused, the platform will provide assistance such as "re-explaining the basic concepts of calculus" or "providing concrete examples." In this case, OpenAI's GPT-4 is used for natural language generation.

[0645] Examples

[0646] When a teacher teaches mathematics, the platform learns how to teach calculus in a way that is easy for students to understand.

[0647] If a student is confused while using the platform to learn, the emotion engine will analyze that emotion and communicate it to the platform.

[0648] The platform provides support information such as "re-explaining the basic concepts of differential and integral calculus" and "providing concrete examples."

[0649] Examples of prompt statements

[0650] "Use concrete examples to help students understand the basic concepts of calculus."

[0651] “If a student is stumped, how do you provide them with hints to solve the problem?”

[0652] In this way, the dedicated generative AI platform for education provides optimal teaching support tailored to the needs of teachers and students.

[0653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0654] Step 1:

[0655] Teachers access the platform.

[0656] Teachers use their terminals to log in to a dedicated generative AI platform for education. The server checks the teacher's authentication information and grants access. The input is the teacher's authentication information, and the output is the permission to access the platform.

[0657] Step 2:

[0658] Teachers teach AI how to teach.

[0659] Teachers use their devices to input "teaching methods to improve students' understanding" on the platform. The server receives the data entered by the teacher and trains the AI ​​model. Machine learning libraries such as TensorFlow and PyTorch are used for this. The input is the teaching method data provided by the teacher, and the output is the trained AI model.

[0660] Step 3:

[0661] A user accesses the platform.

[0662] Users (students and teachers) use their terminals to access a dedicated generative AI platform for education. The server checks the user's authentication information and grants access. The input is the user's authentication information, and the output is the permission to access the platform.

[0663] Step 4:

[0664] The emotion engine analyzes the user's emotions.

[0665] While the user is using the platform, the emotion engine analyzes the user's facial expressions and inputs. The emotion engine uses OpenAI's GPT-4 and IBM Watson's emotion analysis API to identify the user's emotions. The input is the user's facial expression data and inputs, and the output is the analyzed emotion data.

[0666] Step 5:

[0667] The platform provides appropriate educational support information.

[0668] Based on the emotion data received from the emotion engine, the server generates educational support information according to the emotion the user is feeling. For example, if the user is confused, the server generates support information such as "explain the basic concepts of differential and integral calculus again" or "provide concrete examples." The input is the analyzed emotion data, and the output is the generated educational support information. The server sends the generated support information to the user's terminal and displays it to the user.

[0669] (Application example 2)

[0670] 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."

[0671] In conventional educational support systems, it was difficult for teachers to grasp students' emotions in real time and provide appropriate educational support accordingly. In addition, there was no system in real stores that could suggest customer service methods in real time according to customers' emotions, so improving customer satisfaction was an issue.

[0672] 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.

[0673] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and training AI, a means for analyzing the user's emotions using an emotion analysis engine, a means for providing appropriate support information based on the analysis results, and a means for displaying the emotion analysis results in real time. This allows teachers to grasp the emotions of their students in real time and provide appropriate educational support accordingly. In addition, in physical stores, it is possible to propose customer service methods according to the customer's emotions in real time, which improves customer satisfaction.

[0674] "Generative AI" is an artificial intelligence technology that creates prompts and provides usage assistance.

[0675] The "dedicated generative AI platform for education" is a platform that allows teachers to develop and use AI optimized for their own lessons.

[0676] "AI utilization and development" refers to the process of applying artificial intelligence to actual educational activities and other uses and improving its performance.

[0677] An "emotion analysis engine" is a technology for analyzing a user's emotions and identifying emotions from facial expressions, tone of voice, etc.

[0678] "Providing appropriate support information based on the analysis results" means using the results of the emotion analysis engine to provide optimal information and support according to the user's condition.

[0679] "Displaying emotion analysis results in real time" means instantly outputting the results generated by the emotion analysis engine to a display device.

[0680] As an embodiment of the present invention, a customer service support system for a brick-and-mortar store will be described as an example. This system is composed of smart glasses, a server, an emotion analysis engine, a generative AI, and a dedicated generative AI platform for training.

[0681] System Configuration

[0682] 1. Smart glasses:

[0683] It has a built-in camera and microphone that captures the customer's facial expressions and tone of voice in real time.

[0684] Analysis results and customer service suggestions are shown to store staff on the display.

[0685] 2. Server:

[0686] An image processing library (OpenCV) is used to process the image data transmitted from the smart glasses.

[0687] An emotion analysis engine (emotion recognition engine) is used to analyze customer emotions from video and audio data.

[0688] Using a dedicated generative AI platform for educational purposes, customer service suggestions are generated based on the results of emotion analysis.

[0689] Data processing and calculation

[0690] 1. Video and audio capture:

[0691] Video and audio data is collected in real time from the smart glasses' camera and microphone.

[0692] The acquired data is sent to the server.

[0693] 2. Emotion analysis:

[0694] The server processes the video data using an image processing library (OpenCV) and identifies the customer's emotions using an emotion analysis engine (emotion recognition engine).

[0695] The analysis results indicate whether the customer is happy, confused, excited, etc.

[0696] 3. Generate customer service suggestions:

[0697] Based on the results of emotion analysis, a dedicated generative AI platform for educational purposes generates appropriate customer service methods.

[0698] The generated service suggestions are displayed on the smart glasses' display.

[0699] Examples

[0700] For example, if a customer is confused, the sentiment analysis engine will identify that state, and a dedicated generative AI platform for training will generate customer service suggestions such as:

[0701] Example prompt:

[0702] "If a customer seems confused, suggest the best way to serve them."

[0703] The suggestion appears on the smart glasses' display, saying: "Sounds like you're having trouble. Is there anything we can help you with?"

[0704] In this way, store clerks can provide optimal customer service in real time according to the customer's emotions, and this system is expected to improve customer satisfaction.

[0705] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0706] Step 1:

[0707] The camera and microphone of the smart glasses capture the customer's facial expressions and tone of voice in real time. The input is the customer's video and audio data, and the output is to send these data to the server.

[0708] Step 2:

[0709] The server processes the video data sent from the smart glasses using a video processing library (OpenCV). The input is the video data sent from the smart glasses, and the output is the pre-processed video data to be passed to the emotion analysis engine.

[0710] Step 3:

[0711] The server uses an emotion analysis engine (emotion recognition engine) to analyze the customer's emotion from the preprocessed video data and audio data. The input is the preprocessed video data and audio data, and the output is the customer's emotional state (e.g., joy, confusion, excitement, etc.).

[0712] Step 4:

[0713] The server sends the emotion analysis results to a dedicated generative AI platform for training, which generates appropriate customer service methods. The input is the emotion analysis results, and the output is the generated customer service suggestions.

[0714] Step 5:

[0715] The server displays the generated service suggestions on the display of the smart glasses. The input is the generated service suggestions, and the output is the service suggestions displayed on the display of the smart glasses.

[0716] Step 6:

[0717] The salesperson provides appropriate service to the customer based on the service suggestions displayed on the display of the smart glasses. The input is the service suggestions displayed on the display of the smart glasses, and the output is the actual service behavior toward the customer.

[0718] Example 3

[0719] 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 smart device 14 will be referred to as a "terminal."

[0720] In the conventional education system, it was difficult for teachers to conduct educational activities according to the emotions and level of understanding of each student, resulting in uneven quality of education. It was also very difficult for teachers to grasp students' emotions in real time and respond appropriately accordingly. This could lead to a decline in students' motivation to learn and their level of understanding.

[0721] 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.

[0722] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for collecting user emotion data, a means for analyzing the emotion data, a means for generating educational activities based on the analysis results, and a means for providing the generated educational activities to the user. This makes it possible to provide appropriate educational activities in real time according to the user's emotions.

[0723] "Generative AI" refers to artificial intelligence that generates appropriate prompts and information based on user input and the situation.

[0724] A "prompt" is a phrase or message that instructs or asks a question to the user.

[0725] A "dedicated generative AI platform for education" is a base system for providing specialized generative AI to support educational activities.

[0726] "Use and development of AI" refers to utilizing artificial intelligence and training and adjusting it to improve its performance and functions.

[0727] "User emotion data" refers to information about emotions obtained from the user's facial expressions, voice, etc.

[0728] "Means for collecting emotion data" refers to a method for obtaining data regarding a user's emotions using devices such as a camera or microphone.

[0729] "Means for analyzing emotion data" refers to techniques or algorithms used to analyze the collected emotion data and identify the user's emotional state.

[0730] "Means for generating educational activities based on analysis results" refers to methods and techniques for generating optimal educational activities for a user based on the analysis results of emotion data.

[0731] The "means for providing the generated educational activity to the user" refers to a method or technology for communicating the generated educational activity to the user and executing the same.

[0732] This invention is a system that uses generative AI to create prompts and provide usage support. This system provides a dedicated generative AI platform for education, and uses and develops AI. It also collects and analyzes the user's emotional data, generates educational activities based on the results, and provides them to the user.

[0733] The server uses a high-performance server (e.g., a server equipped with a GPU) to run an emotion analysis engine (e.g., emotion analysis software) and a generative AI model (e.g., generative AI software).The terminal uses a device equipped with a camera and microphone (e.g., a PC or tablet) to collect user emotion data.

[0734] Specifically, the device captures the user's facial expressions and voice in real time and sends them to the server. The server then uses an emotion analysis engine to analyze the user's emotion data. For example, if the user is confused, the emotion analysis engine will output a result of "confusion level 80%."

[0735] The server then sends the analysis results to the generative AI model, which generates appropriate educational activities based on the analysis results. For example, if the user is confused, the generative AI model generates a prompt sentence such as, "I'll give you a hint to solve this problem. First, compare the left and right sides of the equation."

[0736] Finally, the server sends the generated prompt sentence to the terminal, which displays it to the user, so that the user can receive the appropriate educational activity.

[0737] As a concrete example, consider a scenario in which a user is working on a mathematics problem.

[0738] User Sentiment: Confused

[0739] Sentiment analysis engine output: 80% confusion

[0740] Output of generative AI model: “Here’s a hint to help you solve this problem. First, compare the left and right sides of the equation.”

[0741] In this way, a system is realized in which the server, the terminal, and the user work together to provide appropriate educational activities according to the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0742] Step 1:

[0743] The terminal collects the user's emotion data. Specifically, it uses a camera and microphone mounted on the terminal to capture the user's facial expressions and voice in real time. The input is the user's facial expression and voice data, and the output is to transmit these data to the server.

[0744] Step 2:

[0745] The server receives emotion data sent from the device. The input is facial expression and voice data sent from the device, and the output is to pass this data to the emotion analysis engine.

[0746] Step 3:

[0747] The server uses an emotion analysis engine to analyze the emotion data. The input is facial expression and voice data, and the output is the user's emotional state (e.g., 80% confusion). In concrete terms, the emotion analysis engine analyzes the user's facial expression and voice to identify emotions such as joy, excitement, confusion, and anxiety.

[0748] Step 4:

[0749] The server sends the analysis results obtained from the emotion analysis engine to the generative AI model. The input is the emotion analysis result, and the output is the data passed to the generative AI model. In concrete terms, the server sends the emotion analysis result to the generative AI model.

[0750] Step 5:

[0751] The generative AI model generates appropriate educational activities based on the results of sentiment analysis. The input is the result of sentiment analysis, and the output is the generated prompt sentence. In concrete terms, the generative AI model generates a prompt sentence based on the result of sentiment analysis. For example, if the user is confused, the model generates a prompt sentence such as, "We will provide you with a hint to solve this problem. First, let's compare the left and right sides of the equation."

[0752] Step 6:

[0753] The server sends the prompt sentence obtained from the generative AI model to the terminal. The input is the generated prompt sentence, and the output is the data to be sent to the terminal. In concrete terms, the server sends the generated prompt sentence to the terminal.

[0754] Step 7:

[0755] The terminal displays the generated prompt to the user. The input is the prompt sent from the server, and the output is the message displayed to the user. Specifically, the terminal displays the message "Here is a hint to help you solve this problem. First, compare the left and right sides of the equation."

[0756] In this way, a system is realized in which the server, terminal, and user work together to provide appropriate educational activities according to the user's emotions.

[0757] (Application example 3)

[0758] 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."

[0759] In the conventional education system, it was difficult to respond to each student's level of understanding and emotions, resulting in uneven quality of education. In addition, in customer service work in brick-and-mortar stores, it was difficult to respond to customers' emotions, making it difficult to improve customer satisfaction. To solve these problems, individual responses using AI are required.

[0760] 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.

[0761] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for analyzing the user's emotions using an emotion recognition engine and adjusting educational activities based on the results, and a means for displaying on a smart device. This allows for individualized responses according to the level of understanding and emotions of each student, improving the quality of education. In addition, in customer service operations in physical stores, it is possible to respond according to the customer's emotions, which is expected to improve customer satisfaction.

[0762] "Generative AI" is artificial intelligence that generates appropriate prompts and information based on user input.

[0763] A "prompt" is a form of instruction or question that a generative AI provides to a user.

[0764] "Usage support" is a function that allows generative AI to support user activities.

[0765] A "dedicated generative AI platform for education" is a system that provides specialized generative AI to support educational activities.

[0766] "AI utilization and development" refers to the activity of utilizing artificial intelligence and training and adjusting it to improve its performance.

[0767] An "emotion recognition engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[0768] A "smart device" is a portable electronic device with Internet connectivity and advanced computing capabilities.

[0769] An "educational activity" is a set of actions or processes for imparting knowledge or skills to learners.

[0770] "Individualized response" means providing special responses according to the characteristics and circumstances of each user.

[0771] "Customer satisfaction" is the degree of satisfaction a customer feels with the services or products provided.

[0772] This invention is a system that uses generative AI to create prompts and support their use. It provides a dedicated generative AI platform for education, and uses and develops AI. It also uses an emotion recognition engine to analyze the user's emotions and adjusts educational activities based on the results. It also includes a function to display the results on a smart device.

[0773] System Programming

[0774] This system is realized using the following hardware and software.

[0775] Hardware: smart glasses, camera, server

[0776] Software: OpenCV (face detection), emotion recognition engine (emotion recognition), AI teacher platform

[0777] Processing Description

[0778] The server captures images from the camera in real time and processes each frame. First, it detects faces and recognizes emotions from the detected faces. The emotion recognition engine is a technology that analyzes emotions from the user's facial expressions and voice, and thereby grasps the user's emotional state.

[0779] Then, based on the recognized emotions, the AI ​​teacher platform will determine the appropriate teaching activities and customer service methods. For example, if the user is confused, it will provide hints and explanations to solve the problem, and if the user is happy, it will provide teaching activities that maintain that emotion.

[0780] The educational activities and customer service methods that have been decided upon are displayed on smart glasses, which are portable electronic devices with Internet connectivity and advanced computing capabilities that allow store clerks and teachers to receive information in real time when worn by them.

[0781] Examples

[0782] Confused customer: If a customer is confused, the smart glasses will display, "Please explain this product in more detail."

[0783] Happy Customer: If a customer is happy, the smart glasses will display a message saying, "Keep up the good work and recommend other products to us."

[0784] Examples of prompt statements

[0785] If a customer is confused, have the smart glasses display, "Please tell me more about this product."

[0786] If a customer is happy, the smart glasses should display a message saying, "Keep up the good work and recommend other products to us."

[0787] In this way, the server can respond individually to the user's emotions, improving the quality of education and customer satisfaction.

[0788] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0789] Step 1:

[0790] The server acquires video from the camera in real time. The input is the camera video, and the output is image data for each frame. Specifically, the server captures the video stream from the camera and saves each frame as image data.

[0791] Step 2:

[0792] The server detects faces from the captured image data for each frame. The input is the image data for each frame, and the output is the position information of the detected face. Specifically, the server converts the image data to grayscale using OpenCV and applies a face detection algorithm.

[0793] Step 3:

[0794] The server recognizes emotions based on the detected facial position information. The input is facial position information, and the output is recognized emotion data. Specifically, the server uses an emotion recognition engine to extract facial features, which are then input into an emotion classification model to determine the emotion.

[0795] Step 4:

[0796] The server determines appropriate educational activities and customer service methods based on the recognized emotion data. The input is emotion data, and the output is instructions for educational activities and customer service methods. Specifically, the server uses the AI ​​teacher platform to analyze the emotion data and generate appropriate prompts.

[0797] Step 5:

[0798] The server sends the determined instruction for the educational activity or customer service method to the smart device. The input is the instruction for the educational activity or customer service method, and the output is text information to be displayed on the smart device. Specifically, the server converts the generated prompt into a data format for display on the smart glasses and transmits it over the network.

[0799] Step 6:

[0800] The terminal (smart glasses) displays the instructions sent from the server. The input is the text information sent from the server, and the output is the instructions to be displayed on the display of the smart glasses. As a specific operation, the terminal performs processing to display the received text information on the display.

[0801] Step 7:

[0802] The user (a store clerk or teacher) acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is the user's specific actions. As a specific action, the user responds to customers or students based on the displayed instructions.

[0803] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0804] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0805] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0806] In the above embodiment, a specific process is performed by the data processing device 12. However, the technology of the present disclosure is not limited to this. The specific process may be performed by the smart device 14.

[0807] This may be done.

[0808] [Second embodiment]

[0809] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0810] 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.

[0811] 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 wide area network (WAN) and / or a local area network (LAN).

[0812] 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.

[0813] 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 the voice according to instructions from the processor 46.

[0814] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0815] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0816] Fig. 4 shows an example of 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0821] "Example 1"

[0822] One embodiment of the present invention is a prompt creation system that uses generative AI. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts. For example, when a teacher prepares a "math lesson for junior high school students," the generative AI generates "math problems that are easy for junior high school students to understand." This allows teachers to significantly reduce the time it takes to prepare lessons.

[0823] "Example 2"

[0824] Another embodiment of the present invention is a dedicated generative AI platform for education. This platform allows teachers to train and use AI to suit their own lessons. For example, teachers can have AI learn "teaching methods to improve students' understanding" on the platform. This allows teachers to receive support from AI optimized for their own lessons.

[0825] "Example 3"

[0826] Furthermore, another embodiment of the present invention is an AI teacher platform. This platform enables AI to play the role of a teacher. For example, AI can provide appropriate answers to students' questions and adjust the pace of a lesson according to the student's level of understanding. This allows teachers to improve the quality of education for each student.

[0827] The process flow of each embodiment will be described below.

[0828] "Example 1"

[0829] Step 1: The teacher inputs the lesson topic and goals into the system. For example, "Mathematics lesson for junior high school students."

[0830] Step 2: The generative AI generates prompts based on teacher input. These prompts correspond to teacher requests, such as "math problems that are easy for middle school students to understand."

[0831] Step 3: The generated prompts are presented to teachers to use in lesson preparation.

[0832] "Example 2"

[0833] Step 1: Teachers access a dedicated generative AI platform for education.

[0834] Step 2: Teachers input what they want the AI ​​to learn on the platform, such as "teaching methods to improve students' understanding."

[0835] Step 3: The AI ​​learns based on teacher input and provides optimized support for teachers' lessons.

[0836] "Example 3"

[0837] Step 1: The AI ​​teacher platform is launched and begins interacting with students.

[0838] Step 2: The AI ​​provides appropriate answers to the student's questions. For example, if a student asks a question about a math problem, the AI ​​provides an explanation for the problem.

[0839] Step 3: The AI ​​adjusts the pace of the lesson according to the student's level of understanding. For example, if a student is struggling to understand, the AI ​​will slow down the pace of the lesson.

[0840] Example 1

[0841] 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".

[0842] In the past, teachers had to create lesson content from scratch, which required a lot of time and effort. In addition, there was a lack of support systems to efficiently create high-quality lesson materials. This increased the burden on teachers and could lead to a decline in the quality of education.

[0843] 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.

[0844] In this invention, the server includes a means for a user to access the system, a means for a user to input lesson contents, a means for the server to receive input information, a means for the server to call a generative AI model, a means for the generative AI model to generate a prompt, a means for the server to receive the generated prompt, a means for the server to send a prompt sentence to a terminal, a means for a user to confirm the prompt sentence, a means for providing a generative AI platform dedicated to education, and a means for utilizing and developing AI. This enables teachers to efficiently create high-quality lesson materials and significantly shortens the time required for lesson preparation.

[0845] "Means for a user to access the system" refers to the method by which a user uses a terminal to log in to the system's website or application and begin using it.

[0846] "Means for users to input lesson content" refers to an interface or form that allows users to input information such as the lesson theme and target grade into the system.

[0847] The "means for the server to receive input information" refers to a method by which the server receives information on lesson contents sent by a user and temporarily stores it.

[0848] "Means for the server to call the generative AI model" refers to the method by which the server calls the API of the generative AI model and passes the information entered by the user as a prompt.

[0849] "Means by which the generative AI model generates a prompt" refers to a method by which the generative AI model generates an appropriate prompt sentence based on information passed from the server.

[0850] "Means for the server to receive the generated prompt" refers to a method for the server to receive and re-store the prompt text returned from the generative AI model.

[0851] The "means for the server to send a prompt text to the terminal" refers to a method by which the server sends a saved prompt text to the user's terminal and displays it on the screen.

[0852] The "means for the user to confirm the prompt text" refers to a method by which the user can confirm the prompt text generated at the terminal and make corrections or additions as necessary.

[0853] "Means for providing a dedicated generative AI platform for education" means a method for providing a dedicated generative AI platform to support educational activities.

[0854] "Means for using and developing AI" refers to methods for using generative AI models and training and adjusting them to improve their performance.

[0855] This invention relates to a prompt creation system that uses generative AI. The purpose of this system is to drastically reduce the time it takes for a teacher to prepare for a lesson by having a generative AI automatically generate prompts.

[0856] Hardware and Software Used

[0857] The system uses the following hardware and software:

[0858] Terminal: A device such as a PC or tablet used by a teacher.

[0859] Server: A server for managing the entire system and calling generative AI models.

[0860] Generative AI models: For example, generative AI models such as OpenAI's GPT-4.

[0861] Data processing and calculation

[0862] 1. A user accesses the system

[0863] Users (teachers) access the prompt creation system website using a device such as a PC or tablet. The user enters their ID and password on the login screen to log in to the system.

[0864] 2. The user inputs the lesson content

[0865] After logging in, the user is presented with a form to enter information such as the topic of the lesson and the target grade. For example, the user might enter "Math lessons for junior high school students." Once the information is complete, the user clicks the "Submit" button.

[0866] 3. The server receives the input information

[0867] The server receives the lesson content information sent by the user and temporarily stores it in a database.

[0868] 4. The server calls the generative AI model

[0869] The server retrieves the saved lesson content information and calls a generative AI model (e.g., OpenAI's GPT-4). The server passes the information entered by the user as a prompt to the generative AI model.

[0870] 5. Generative AI model generates prompts

[0871] The generative AI model generates appropriate prompts based on the information passed from the server. For example, it generates "mathematics problems that are easy for junior high school students to understand."

[0872] 6. The server receives the generated prompt

[0873] The server receives the prompt text returned from the generative AI model and stores it again in the database.

[0874] 7. The server sends a prompt to the terminal.

[0875] The server sends the saved prompt text to the user's terminal, where it is displayed on the screen.

[0876] 8. The user confirms the prompt

[0877] The user can check the generated prompt sentences on the terminal and make corrections or additions as necessary. Finally, the user can use the generated prompt sentences in lesson preparation.

[0878] Examples

[0879] Example input

[0880] A teacher enters "math lessons for middle school students" into the system.

[0881] Example prompts for generative AI models

[0882] "Generate math problems that are easy for middle school students to understand."

[0883] Example of a generated prompt statement

[0884] "Solve the following equation: 2x + 3 = 7. Include your answer and explanation."

[0885] This system allows teachers to efficiently create high-quality lesson materials. By utilizing generative AI models, it is possible to significantly reduce lesson preparation time.

[0886] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0887] Step 1:

[0888] A user accesses the system

[0889] Input: The user (teacher) accesses the system's website using a device such as a PC or tablet.

[0890] Specific operation: The user opens a browser and enters the system URL to access the system. The login screen is displayed and the user enters their ID and password to log in.

[0891] Output: The user is logged into the system and the main screen is displayed.

[0892] Step 2:

[0893] The user inputs the lesson content

[0894] Input: The user inputs information such as the topic of the lesson and the grade level it is aimed at. For example, the user inputs "Math lesson for middle school students."

[0895] Specific actions: The user enters the lesson details into the displayed form and clicks the "Submit" button.

[0896] Output: The entered lesson content information is sent to the server.

[0897] Step 3:

[0898] The server receives the input information

[0899] Input: Course content information submitted by the user.

[0900] Specific operation: The server receives the information sent by the user and temporarily stores it in a database.

[0901] Output: Course content information stored in a database.

[0902] Step 4:

[0903] The server calls the generative AI model

[0904] Input: Course content information stored in the database.

[0905] Specific operation: The server retrieves the stored information and calls the API of the generative AI model. The server passes the information entered by the user as a prompt to the generative AI model.

[0906] Output: The prompt information passed to the generative AI model.

[0907] Step 5:

[0908] A generative AI model generates prompts

[0909] Input: The prompt information passed by the server.

[0910] How it works: The generative AI model generates appropriate prompts based on the information passed to it. For example, it generates "math problems that are easy for junior high school students to understand."

[0911] Output: The generated prompt statement.

[0912] Step 6:

[0913] Receives server-generated prompts

[0914] Input: The prompt sentence returned by the generative AI model.

[0915] Specific operation: The server receives the prompt text returned from the generative AI model and saves it back in the database.

[0916] Output: The prompt statement saved in the database.

[0917] Step 7:

[0918] The server sends a prompt to the terminal.

[0919] Input: The prompt statement stored in the database.

[0920] Specific operation: The server retrieves the saved prompt text and sends it to the user's terminal.

[0921] Output: The prompt text sent to the user's terminal.

[0922] Step 8:

[0923] The user confirms the prompt.

[0924] Input: The prompt text sent to the user's terminal.

[0925] Specific action: The user checks the prompt generated in the terminal and makes corrections or additions as necessary.

[0926] Output: The final prompt sentence with any corrections or additions.

[0927] (Application example 1)

[0928] 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".

[0929] In conventional educational support systems, teachers had to spend a lot of time preparing lessons and creating teaching materials, making it difficult to conduct efficient educational activities. In addition, there was a problem that the burden on teachers increased because it was not easy to customize the generated teaching materials and prompts. Furthermore, there was a lack of support for educational content creators to efficiently create high-quality teaching materials.

[0930] 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.

[0931] In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing an education support application and enabling teachers and educational content creators to generate prompts for lessons and teaching materials, and a means for customizing the generated prompts. This enables teachers and educational content creators to efficiently prepare lessons and teaching materials and easily customize the generated prompts.

[0932] "Generative AI" is artificial intelligence that automatically generates text and content based on user input.

[0933] A "prompt" is an input sentence that asks a generative AI a specific task or question.

[0934] "Usage support" is a function that supports users to use the system effectively.

[0935] A "dedicated generative AI platform for education" is a platform that provides specialized generative AI to support educational activities.

[0936] "AI utilization and development" refers to the effective use of artificial intelligence and the training and adjustment required to improve its performance.

[0937] An "educational support application" is software designed to support educational activities.

[0938] A "teacher" is a professional who teaches students at an educational institution.

[0939] An "educational content creator" is a professional who creates educational materials and content.

[0940] A "class" is an educational activity conducted by a teacher for students at an educational institution.

[0941] "Teaching materials" refers to learning materials and teaching tools used in educational activities.

[0942] "Customization" means adapting or modifying a system or content to meet the needs and requirements of users.

[0943] The present invention provides an educational support application that allows teachers and educational content creators to efficiently generate and customize prompts for lessons and teaching materials. Specific embodiments of the system will be described below.

[0944] System Configuration

[0945] The system consists of the following main components:

[0946] 1. Server: Hosts the generative AI and provides prompt generation and customization functions.

[0947] 2. Devices: Smartphones and tablets used by teachers and educational content creators.

[0948] 3. Users: Teachers and educational content creators.

[0949] Hardware and Software Used

[0950] Hardware: Smartphones, tablets, servers

[0951] Software: OpenAI API, Python, educational support application

[0952] Data processing and calculation

[0953] Data Entry

[0954] A user uses a terminal to access an educational support application and inputs a lesson topic and a target grade. For example, the user inputs "math lessons for junior high school students."

[0955] Prompt Generation

[0956] The server uses the OpenAI API to generate prompts based on the input data. Specifically, the server sends the prompts to a generative AI model (e.g., GPT-3) to generate appropriate questions and explanations.

[0957] Results display

[0958] The generated prompts are displayed to the user through the educational support application on the terminal, and the user can customize the generated prompts.

[0959] Examples

[0960] Example 1: Teacher preparing for a lesson

[0961] When a teacher types in "Please generate linear equation problems for junior high school students," the server uses generative AI to generate appropriate math problems and displays them on the device. Teachers can customize the generated problems and use them in their classes.

[0962] Example 2: Educational content creator creating teaching materials

[0963] When an educational content creator inputs "The Basics of Trigonometric Functions," the server uses generative AI to generate basic questions about trigonometric functions and their explanations, which are then displayed on the device. Creators can customize the generated content and provide it as teaching materials.

[0964] Examples of prompt statements

[0965] "Generate linear equation problems for middle school students."

[0966] "Generate problems that illustrate the fundamentals of trigonometry."

[0967] In this way, the system enables teachers and educational content creators to efficiently prepare lessons and materials and easily customize the generated prompts.

[0968] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0969] Step 1:

[0970] A user uses a terminal to access an educational support application and inputs the lesson topic and the target grade.

[0971] Input: Lesson topic (e.g. "Math lessons for junior high school students"), target grade

[0972] Output: The input data is sent to the server.

[0973] Specific operations: The user enters the lesson topic and target grade into the application's input form and presses the "Generate" button.

[0974] Step 2:

[0975] The server receives the input data and sends a request to the generative AI model to generate a prompt.

[0976] Input: Lesson topic and target grade sent by the user

[0977] Output: A prompt generation request sent to the generative AI model

[0978] Specific operation: The server calls the OpenAI API and creates a prompt generation request based on the input data.

[0979] Step 3:

[0980] The generative AI model receives the prompt generation request and generates an appropriate prompt.

[0981] Input: Prompt generation request

[0982] Output: Generated prompt

[0983] What happens: A generative AI model (e.g. GPT-3) processes the request and generates an appropriate math problem and explanation.

[0984] Step 4:

[0985] The server receives the generated prompt and sends it to the terminal.

[0986] Input: Generated prompt

[0987] Output: Prompt data sent to the terminal

[0988] Specific operation: The server receives the generated prompt and converts it into a data format for transmission to the user's terminal.

[0989] Step 5:

[0990] The terminal displays the received prompt to the user.

[0991] Input: Prompt data sent from the server

[0992] Output: The prompt that is displayed to the user

[0993] Specific operation: The educational support application of the terminal receives the prompt data and displays it on the user interface.

[0994] Step 6:

[0995] Customize user-generated prompts.

[0996] Input: the prompt shown

[0997] Output: Customized prompt

[0998] What happens: The user edits the displayed prompt and makes the necessary changes.

[0999] Step 7:

[1000] Customized prompts can be saved and used in lessons or as teaching materials.

[1001] Input:Customized prompt

[1002] Output: Saved prompt data

[1003] Specific behavior: When the user completes customization and presses the save button, the prompt is saved to the database.

[1004] Example 2

[1005] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1006] In conventional education systems, it is difficult for teachers to provide teaching methods that correspond to the level of understanding of each individual student, and there is a lack of real-time feedback and support. This makes it difficult to effectively improve students' understanding.

[1007] 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.

[1008] In this invention, the server includes a means for teachers to input data using a terminal and the server to store the data, a means for the server to preprocess and analyze the stored data, and a means for the server to train a generative AI model using the analyzed data. This enables teachers to provide teaching methods that correspond to the level of understanding of each student, and allows them to receive feedback and support in real time.

[1009] "Generative AI" refers to artificial intelligence that generates information based on prompts entered by the user.

[1010] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[1011] A "dedicated generative AI platform for education" is a dedicated system that allows teachers to develop and use AI to suit their own lessons.

[1012] A "device" is a device, such as a computer or tablet, that a teacher uses to enter data or receive feedback.

[1013] "Server" refers to a central processing unit that stores data, pre-processes, analyzes, trains generative AI models, and provides feedback.

[1014] "Data preprocessing" refers to the process of converting stored data into a format that is easier to analyze, including data cleaning and normalization.

[1015] A "generative AI model" is a machine learning model that generates information based on input data.

[1016] "Feedback" refers to information that a generative AI model provides to teachers about the results of its learning.

[1017] "Real-time support" refers to analyzing students' level of understanding in real time during class and suggesting the most appropriate teaching methods to teachers.

[1018] This invention provides a system that provides a dedicated generative AI platform for education, enabling teachers to develop and use AI for their own lessons. A specific embodiment of this system will be described below.

[1019] System Overview

[1020] In this system, teachers input data using a terminal, and the server stores, preprocesses, and analyzes the data, and trains the generative AI model, proposing optimal teaching methods to teachers. The system is composed of a generative AI, a dedicated generative AI platform for education, terminals, and a server.

[1021] Hardware and Software Used

[1022] Terminal: A device such as a computer or tablet for teachers to enter data.

[1023] Server: A central processing unit that stores data, pre-processes it, analyzes it, trains generative AI models, and provides feedback, using relational databases such as MySQL or PostgreSQL, Python libraries such as pandas or numpy, and machine learning frameworks such as TensorFlow or PyTorch.

[1024] Generative AI models: For example, advanced generative AI models such as OpenAI's GPT-4.

[1025] Entering and Saving Data

[1026] Users (teachers) use their devices to log in to the educational generative AI platform and input data on lesson content and students' understanding. The devices then send the input data to the server, which then stores the data in a database.

[1027] Data preprocessing and analysis

[1028] The server preprocesses the stored data, which includes cleaning and normalizing the data, and then analyzes the preprocessed data using Python libraries such as pandas and numpy.

[1029] Training generative AI models

[1030] The server uses the parsed data to train a generative AI model, and the training process is managed using a machine learning framework such as TensorFlow or PyTorch to optimize the model's parameters.

[1031] Providing feedback

[1032] The server feeds back the learning results to the user. The feedback includes the model-generated teaching method suggestions and the analysis results of the students' understanding. The terminal displays the feedback to the user, who then plans lessons based on it.

[1033] Real-time assistance

[1034] During lessons, the devices will provide real-time AI support, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[1035] Examples

[1036] For example, if a teacher wants to teach a generative AI model how to improve students' understanding in mathematics class, they can input a prompt like the following into the model:

[1037] Examples of prompt statements

[1038] "Please tell me some effective teaching methods to improve students' understanding in mathematics classes. Please provide specific examples and activities."

[1039] Given this prompt, the generative AI model would provide the following answer:

[1040] Example answers from generative AI models

[1041] The following teaching methods are effective in enhancing students' understanding:

[1042] 1. Use real-life examples: Connecting math concepts to real-life examples helps students understand them better.

[1043] 2. Group Discussion: By solving problems among students, they have more opportunities to teach each other.

[1044] 3. Interactive activities: Quizzes and games can help keep students engaged.”

[1045] In this way, teachers can receive support from AI while teaching.

[1046] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1047] Step 1:

[1048] Faculty login and data entry

[1049] The user (teacher) uses a terminal to log in to the educational generative AI platform by entering a user ID and password.

[1050] Input: User ID, Password

[1051] Output: Login success or failure message

[1052] Specific operation: The terminal sends the user ID and password to the server, and the server performs authentication. If the authentication is successful, the user can access the platform.

[1053] Step 2:

[1054] Input of lesson contents and student data

[1055] The user inputs data related to the content of the lesson and the students' level of understanding, such as the lesson topic, the teaching materials used, and the students' test results.

[1056] Input: Lesson topic, teaching material information, student test results

[1057] Output: Confirmation message for input data

[1058] Specific operation: The terminal provides an input form and transmits data entered by the user to the server.

[1059] Step 3:

[1060] Data transmission and storage

[1061] The terminal transmits the input data to the server.

[1062] The server stores the received data in a database.

[1063] Input: Lesson topic, teaching material information, student test results

[1064] Output: Data save confirmation message

[1065] Specific operation: The server stores data in a database (e.g., MySQL or PostgreSQL).

[1066] Step 4:

[1067] Data preprocessing and analysis

[1068] The server pre-processes the stored data, which includes cleaning and normalizing the data.

[1069] Input: Saved data

[1070] Output: Preprocessed data

[1071] Specific operation: The server uses Python libraries such as pandas and numpy to fill in missing values ​​in the data and remove outliers.

[1072] Step 5:

[1073] Training generative AI models

[1074] The server uses the preprocessed data to train a generative AI model.

[1075] Input: Preprocessed data

[1076] Output: A trained generative AI model

[1077] How it works: The server optimizes the model parameters using machine learning frameworks such as TensorFlow or PyTorch.

[1078] Step 6:

[1079] Feedback on learning results

[1080] The server then provides the user with feedback on the learning results, including model-generated teaching method suggestions and analysis of the students' understanding.

[1081] Input: A trained generative AI model

[1082] Output: Feedback information (suggested teaching methods, analysis of students' understanding)

[1083] Specific operation: the server sends the generated feedback information to the terminal, and the terminal displays it to the user.

[1084] Step 7:

[1085] Real-time assistance

[1086] The device will provide real-time AI support during lessons, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[1087] Input: Real-time student data (responses, test results)

[1088] Output: Real-time support information (suggestions for teaching methods)

[1089] Specific operation: The terminal transmits the data collected in real time to the server, which analyzes it and provides feedback.

[1090] (Application example 2)

[1091] 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".

[1092] Conventional educational generative AI platforms are specialized in providing information to support teachers' educational activities, but they do not provide sufficient support for improving the work learning and customer service skills of store staff. As a result, improving the work efficiency and customer service quality of store staff is an issue.

[1093] 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. In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for training, a means for using and training AI, a means for supporting store staff in learning the contents of their work, a means for performing a simulation to improve customer service skills, and a means for providing feedback in real time. This enables store staff to efficiently learn their work and improve their customer service skills.

[1094] "Generative AI" is an artificial intelligence technology that creates prompts and assists users.

[1095] The "dedicated generative AI platform for education" is a specialized platform that allows teachers to develop and use AI for their own lessons.

[1096] "Means of using and developing AI" refers to methods and technologies for effectively utilizing artificial intelligence and improving its performance.

[1097] The "means for supporting store staff in learning about their work" refers to support methods and techniques provided to store staff in order to efficiently learn about their work.

[1098] The "means for performing simulations to improve customer service skills" refers to simulation techniques used by store staff to improve their customer service skills.

[1099] "Means for providing feedback in real time" refers to technology that provides instant feedback to store staff while they are performing their duties.

[1100] In order to implement the present invention, the following system configuration and program are required.

[1101] System Configuration

[1102] 1. Hardware:

[1103] A smartphone or tablet (iOS or Android device)

[1104] Server (Cloud server or on-premise server)

[1105] 2. Software:

[1106] Python 3.x

[1107] OpenAI API

[1108] Program processing explanation

[1109] The server uses generative AI to create prompts and assist users in using the system. Specifically, when teachers or store staff input the content they want to learn, the generative AI generates the optimal prompt based on that content. The prompts are generated using the OpenAI API.

[1110] The generated prompts are provided through a dedicated generative AI platform for education. This platform supports teachers and store staff in efficiently progressing with their learning. For example, when a store staff member is learning "how to display new products," the following prompts are generated:

[1111] Examples of prompt statements

[1112] What is the best way for store staff to learn how to display new products?

[1113] Based on these prompts, the generative AI will suggest optimal learning methods, which store staff can review via smartphone or tablet and apply to their work.

[1114] In addition, the server will provide a means to assist store staff in learning about their work. For example, the AI ​​will provide specific procedures and points to note for tasks such as cash register operation and inventory management.

[1115] It also includes a means to conduct simulations to improve customer service skills. Through simulations provided by AI, store staff can improve their skills in handling complaints and explaining products.

[1116] Finally, it also includes a means to provide real-time feedback, where AI can provide real-time feedback to store staff as they carry out their duties, advising them on appropriate language and attitudes.

[1117] This will enable store staff to learn their jobs efficiently and improve their customer service skills.

[1118] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1119] Step 1:

[1120] A user (a teacher or store staff member) uses a terminal (a smartphone or tablet) to input the content they want to learn. The input content is sent to the server. The input data is the specific learning content, such as "how to display new products."

[1121] Step 2:

[1122] The server has the generative AI create a prompt based on the received input data. The generative AI analyzes the input data and generates the optimal prompt sentence. For example, the generated prompt sentence might be, "What is the best way for store staff to learn how to display new products?"

[1123] Step 3:

[1124] The server sends the generated prompt sentence to the OpenAI API and receives a response from the AI ​​model. The OpenAI API suggests the optimal learning method based on the prompt sentence. The obtained response is a specific instruction such as "Please follow the steps below to display the new product."

[1125] Step 4:

[1126] The server provides the acquired responses to the user through a dedicated generation AI platform for education. The user can check the responses on their device and proceed with their learning. The responses include specific procedures and points to note.

[1127] Step 5:

[1128] As the user progresses with the learning process, the server provides feedback in real time. It monitors the user's operations and behavior and generates appropriate feedback. For example, it provides real-time advice on points to note and areas for improvement when the user displays products.

[1129] Step 6:

[1130] The server evaluates the user's learning progress and skill improvement, and provides additional learning content and simulations as necessary. For example, the server runs a simulation to improve customer service skills and provides feedback to the user.

[1131] Step 7:

[1132] Users improve their work skills based on the feedback and additional learning content provided. The server accumulates the user's learning data and uses it to support future learning sessions. This improves the user's work efficiency and the quality of customer service.

[1133] Example 3

[1134] 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 smart glasses 214 will be referred to as a "terminal".

[1135] In the conventional education system, it was difficult for teachers to adjust the pace of lessons according to each student's level of understanding, and it was also difficult to provide quick and appropriate answers to students' questions. This resulted in a decrease in students' learning efficiency and inconsistent quality of education.

[1136] 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.

[1137] In this invention, the server includes a means for accepting a user's question, a means for using a generative AI model that analyzes the question and generates an answer, a means for providing the generated answer, a means for evaluating the user's level of understanding, and a means for adjusting the speed of the lesson based on the evaluation result. This makes it possible to proceed with the lesson according to each student's level of understanding, and to improve learning efficiency by providing quick and appropriate answers.

[1138] The "means for accepting a question from a user" is a function for transmitting a question input by a user using a terminal to a server.

[1139] "Means of utilizing a generative AI model that analyzes questions and generates answers" refers to a function that inputs questions received by the server into a generative AI model and generates an appropriate answer.

[1140] "Means for providing a generated answer" refers to a function for sending the answer generated by the generative AI model to the user's terminal and displaying it.

[1141] The "means for evaluating the user's level of understanding" is a function for evaluating the user's level of understanding based on the user's reaction and additional questions, and storing the results in a database.

[1142] The "means for adjusting the pace of the lesson based on the evaluation results" is a function for adjusting the pace of the lesson based on the comprehension information stored in the database and sending the adjusted lesson plan to the user's terminal.

[1143] A "dedicated generative AI platform for education" is a platform that uses generative AI models designed to support teachers' educational activities.

[1144] "Means of using and developing AI" refers to functions that use generative AI models to support educational activities, and furthermore, to continuously improve and develop those AI models.

[1145] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1146] This invention is a system that supports educational activities using an AI teacher platform. This system accepts questions from users, generates appropriate answers using a generative AI model, and provides them to the users. It also has a function to evaluate the user's level of understanding and adjust the pace of the lesson based on the evaluation results.

[1147] Hardware and Software Used

[1148] Hardware: Servers, terminals (PCs, tablets, smartphones)

[1149] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), web servers (e.g. Apache)

[1150] Specific operation of the system

[1151] 1. Accepting user questions

[1152] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[1153] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[1154] 2. Question Analysis and Answer Generation

[1155] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[1156] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1157] Server: Receives the generated answer and proceeds to the next step.

[1158] 3. Providing answers

[1159] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[1160] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1161] 4. Evaluate comprehension

[1162] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[1163] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[1164] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[1165] Server: Stores the evaluation results in a database. Specifically, it inserts the evaluation data into the database using a SQL query.

[1166] 5. Adjusting the pace of lessons

[1167] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[1168] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[1169] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[1170] Examples

[1171] Accepting user questions

[1172] A user types into a terminal, "Please tell me how to solve a quadratic equation."

[1173] The device sends this question to the server in an HTTP request.

[1174] Question analysis and answer generation

[1175] The server sends the question to the API of the generative AI model (GPT-4).

[1176] GPT-4 generates the answer: "To solve a quadratic equation, first transform the equation into a standard form, and then use the solution formula to find the solution."

[1177] The server receives the generated answer and sends it to the terminal in an HTTP response.

[1178] Providing answers

[1179] The terminal will display to the user, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1180] Assessment of understanding

[1181] The user responds, "Okay."

[1182] The terminal sends this response to the server in an HTTP request.

[1183] The server rates the response "OK" as "high" and stores it in the database using an SQL query.

[1184] Adjusting the pace of lessons

[1185] The server adjusts the speed at which the next lesson content is advanced based on the comprehension level information in the database.

[1186] The server sends the adjusted lesson plan to the device in an HTTP response.

[1187] The terminal will display "Continue to next topic" to the user.

[1188] Examples of prompt statements

[1189] "Please teach me how to solve quadratic equations"

[1190] "Teach me more about World War II in history class."

[1191] "I have a question about English grammar. Can you tell me how to use the present perfect tense?"

[1192] The above is a specific embodiment for carrying out the invention of the AI ​​teacher platform. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1193] Step 1:

[1194] Accepting user questions

[1195] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[1196] Input: The question text entered by the user.

[1197] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[1198] Output: The question data sent to the server.

[1199] Step 2:

[1200] Question analysis and answer generation

[1201] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[1202] Input: The question data sent to the server.

[1203] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1204] Output: The generated response data.

[1205] Step 3:

[1206] Providing answers

[1207] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[1208] Input: The generated answer data.

[1209] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1210] Output: The answer displayed to the user.

[1211] Step 4:

[1212] Assessment of understanding

[1213] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[1214] Input: The user's response text.

[1215] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[1216] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[1217] Output: Evaluated comprehension data.

[1218] Step 5:

[1219] Adjusting the pace of lessons

[1220] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[1221] Input: Comprehension information stored in the database.

[1222] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[1223] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[1224] Output: The lesson plan displayed to the user.

[1225] (Application example 3)

[1226] 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".

[1227] In the conventional education system, it was difficult for teachers to respond to each student individually, which led to the problem of inconsistent quality of education. In addition, even in physical stores, customers often did not receive appropriate answers when they asked questions about products, which led to a lack of improvement in the purchasing experience. To solve these problems, it is necessary to use AI to provide individual responses and improve the quality of education and purchasing experiences.

[1228] 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.

[1229] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing appropriate answers to questions from customers, and a means for making product suggestions based on the customer's purchase history. This makes it possible to improve the quality of education and the customer's purchase experience in physical stores.

[1230] "Generative AI" is artificial intelligence that generates text and information based on user input.

[1231] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[1232] "Usage support" is a function that supports users to use the system effectively.

[1233] A "dedicated generative AI platform for education" is a system that provides generative AI specialized to support educational activities.

[1234] "AI utilization and development" refers to activities aimed at effectively utilizing artificial intelligence and improving its performance.

[1235] "Means of providing appropriate answers to questions from customers" refers to a function that uses generative AI to provide appropriate answers when customers ask questions about products or services.

[1236] "Means for making product suggestions based on a customer's purchasing history" refers to a function that analyzes a customer's past purchasing data and suggests new products based on that data.

[1237] A system for implementing this invention includes means for creating prompts using generative AI and providing usage support, means for providing a dedicated generative AI platform for educational purposes, means for using and developing AI, means for providing appropriate answers to questions from customers, and means for making product suggestions based on the customer's purchasing history.

[1238] The server uses generative AI to create prompts and generate appropriate answers to user questions. Specifically, it uses OpenAI's API to receive questions from users and generate answers to those questions. For example, if a user asks, "What is the battery life of this smartphone?", the server uses generative AI to respond, "The battery life of this smartphone is approximately 24 hours."

[1239] The server also analyzes the customer's purchase history and makes product suggestions based on that. For example, if the customer's purchase history is "smartphone, wireless earphones, smartwatch," the server will use generative AI to suggest, "We recommend a new smartphone case or a high-performance charger for you."

[1240] To realize this system, the following hardware and software are used.

[1241] Hardware: Smartphones, servers

[1242] Software: OpenAI API, Python

[1243] The server receives questions from users in text format and uses OpenAI's generative AI model to generate appropriate answers. The generated answers are sent to the user's smartphone and displayed. The server also receives customer purchase history in text format and uses the same generative AI model to generate product suggestions. The generated product suggestions are also sent to the user's smartphone and displayed.

[1244] As an example, use the following prompt:

[1245] "Customer Question: What is the battery life of this smartphone? Please provide more information about the product."

[1246] "Customer purchase history: Smartphone, Wireless earphones, Smartwatch. Please suggest products that you would recommend to this customer."

[1247] This will improve the quality of education and enhance the customer's purchasing experience in physical stores.

[1248] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1249] Step 1:

[1250] The user enters a question using a smartphone.

[1251] Input: A user question (e.g., "What is the battery life of this smartphone?")

[1252] Output: User question text data

[1253] Step 2:

[1254] The terminal transmits the user's question to the server.

[1255] Input: User question text data

[1256] Output: The question data sent to the server.

[1257] Step 3:

[1258] The server converts the received question data into prompt text to be input into the generative AI model.

[1259] Input: User question data

[1260] Output: Prompt sentence for the generative AI model (e.g., "Customer question: What is the battery life of this smartphone? Please provide more information about the product.")

[1261] Step 4:

[1262] The server uses a generative AI model to generate an answer based on the prompt.

[1263] Input: A prompt for the generative AI model

[1264] Output: The generated answer text (e.g. "This smartphone's battery life is approximately 24 hours.")

[1265] Step 5:

[1266] The server transmits the generated answer text to the terminal.

[1267] Input: Generated answer text

[1268] Output: Answer data sent to the terminal

[1269] Step 6:

[1270] The terminal displays the received answer data to the user.

[1271] Input: Answer data sent to the terminal

[1272] Output: The answer shown to the user (e.g., "This smartphone's battery life is approximately 24 hours.")

[1273] Step 7:

[1274] A user enters a purchase history using a smartphone.

[1275] Input: User's purchase history (e.g. "smartphone, wireless earphones, smartwatch")

[1276] Output: User purchase history text data

[1277] Step 8:

[1278] The terminal transmits the user's purchase history to the server.

[1279] Input: User purchase history text data

[1280] Output: Purchase history data sent to the server

[1281] Step 9:

[1282] The server converts the received purchase history data into prompt text to be input into the generative AI model.

[1283] Input: User purchase history data

[1284] Output: Prompt sentence for the generative AI model (e.g., "Customer purchase history: Smartphone, wireless earphones, smartwatch. Please suggest products that you would recommend to this customer.")

[1285] Step 10:

[1286] The server uses the generative AI model to generate product suggestions based on the prompt sentence.

[1287] Input: A prompt for the generative AI model

[1288] Output: Generated product recommendation text (e.g. "We recommend a new smartphone case and a high-performance charger for you.")

[1289] Step 11:

[1290] The server transmits the generated product suggestion text to the terminal.

[1291] Input: Generated product suggestion text

[1292] Output: Product recommendation data sent to the terminal

[1293] Step 12:

[1294] The terminal displays the received product suggestion data to the user.

[1295] Input: Product recommendation data sent to the terminal

[1296] Output: Product recommendations displayed to the user (e.g., "We recommend a new phone case or a high-performance charger for you.")

[1297] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[1298] "Example 1"

[1299] In the form of the invention that combines an emotion engine, the generative AI recognizes the user's emotions and generates prompts according to those emotions. Specifically, the emotion engine analyzes the user's emotions from the user's tone of voice, facial expressions, choice of words, etc., and conveys the results to the generative AI. Based on that information, the generative AI generates appropriate prompts when the user is feeling joy, excitement, confusion, anxiety, etc. For example, when the user is confused, it generates a prompt that provides hints and explanations to solve the problem.

[1300] "Example 2"

[1301] The combination of an emotion engine with a dedicated generative AI platform for education takes into account the user's emotions when providing educational support information. Specifically, the emotion engine analyzes the user's emotions and transmits the results to the dedicated generative AI platform for education. Based on that information, the platform provides appropriate educational support information when the user is feeling joy, excitement, confusion, anxiety, or other emotions. For example, when the user is confused, it provides hints and explanations to solve the problem.

[1302] "Example 3"

[1303] The form in which AI uses an emotion engine to carry out educational activities is to adjust educational activities according to the user's emotions. Specifically, the emotion engine analyzes the user's emotions and conveys the results to the AI. Based on that information, the AI ​​will carry out appropriate educational activities when the user is feeling happiness, excitement, confusion, anxiety, etc. For example, when the user is confused, it will provide hints and explanations to solve the problem. Also, when the user is feeling happiness or excitement, it will carry out educational activities that will maintain those emotions.

[1304] The process flow of each embodiment will be described below.

[1305] "Example 1"

[1306] Step 1: The emotion engine analyzes the user's emotions from their tone of voice, facial expressions, choice of words, etc.

[1307] Step 2: The emotion engine communicates the analysis results to the generative AI.

[1308] Step 3: The generative AI uses the information from the emotion engine to generate prompts that correspond to the emotions the user is feeling.

[1309] "Example 2"

[1310] Step 1: The emotion engine analyzes the user's emotions.

[1311] Step 2: The emotion engine communicates the analysis results to a dedicated generative AI platform for education.

[1312] Step 3: The dedicated generative AI platform for education uses information from the emotion engine to provide educational support information that corresponds to the emotions the user is feeling.

[1313] "Example 3"

[1314] Step 1: The emotion engine analyzes the user's emotions.

[1315] Step 2: The emotion engine communicates the analysis results to the AI.

[1316] Step 3: Based on the information from the emotion engine, the AI ​​carries out educational activities according to the emotions the user is feeling.

[1317] Example 1

[1318] 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".

[1319] In conventional educational support systems, teachers had to spend a lot of time preparing for lessons, making it difficult to prepare lessons efficiently. In addition, it was not possible to provide appropriate support according to the teacher's emotions and situation, which increased the burden on teachers.

[1320] 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. In this invention, the server includes a means for creating a prompt using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for generating a prompt according to the emotion using an emotion engine that analyzes the emotion of the user, and a means for displaying the generated prompt on the user's terminal. This allows teachers to significantly reduce the time it takes to prepare for lessons and further provides appropriate support according to the emotion and situation of the teacher.

[1321] "Generative AI" refers to artificial intelligence that automatically generates text and information based on user input.

[1322] A "prompt" is an instruction, question, or piece of information that a generative AI provides to a user.

[1323] "Usage support" refers to functions and services that assist users in using the system effectively.

[1324] A "dedicated generative AI platform for education" is a base system for providing specialized generative AI to support educational activities.

[1325] "Use and development of AI" refers to utilizing artificial intelligence and training and adjusting it to improve its performance and functions.

[1326] An "emotion engine" is a technology or system that analyzes emotions from a user's tone of voice, facial expressions, choice of words, etc.

[1327] A "terminal" is a device, such as a computer, tablet, or smartphone, that a user uses to access the system.

[1328] This invention is a system that uses generative AI to create prompts and support educational activities. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts, and furthermore, an emotion engine is used to provide prompts according to the user's emotions.

[1329] Hardware and Software Used

[1330] Hardware: Servers, terminals (PCs, tablets, smartphones, etc.)

[1331] Software: Generative AI models (e.g. GPT-4), emotion engines

[1332] Specific operation of the system

[1333] 1. User Input

[1334] A user (teacher) uses a terminal to input information necessary for preparing a lesson. For example, the user might input "I will teach how to solve linear equations in a math class for junior high school students."

[1335] 2. Data Receipt and Processing by the Server

[1336] The server receives the data sent by the user and converts it into an appropriate format, performing data formatting and pre-processing.

[1337] 3. Prompt generation by generative AI

[1338] The server feeds the formatted data into a generative AI model (e.g., GPT-4) to generate appropriate prompts, such as "problems that explain how to solve linear equations step-by-step, in a way that is easy for middle school students to understand."

[1339] 4. Emotion Analysis Using an Emotion Engine

[1340] The server uses an emotion engine to analyze the user's tone of voice, facial expressions, choice of words, etc., and determines the user's emotions.

[1341] 5. Emotion-based prompt generation

[1342] The server conveys the emotion information obtained from the emotion engine to the generative AI, which then uses that information to generate prompts that correspond to the user's emotions. For example, when a user is confused, the generative AI generates prompts that provide hints and explanations to solve the problem.

[1343] 6. Displaying the results on the terminal

[1344] The server sends the generated prompt to the terminal to display to the user, and the teacher's terminal is shown a "step-by-step problem on how to solve a linear equation," with further explanations provided if the teacher is confused.

[1345] Examples

[1346] Example: A teacher types, "I teach middle school math how to solve linear equations."

[1347] Example of a generated prompt: A generative AI generates a problem that explains how to solve a linear equation step-by-step, in a way that is easy for junior high school students to understand.

[1348] Emotion analysis example: If a teacher has a confused expression, the emotion engine will analyze that expression and determine that the expression is "confused."

[1349] Example of an emotion-sensitive prompt: A generative AI generates a prompt to provide further explanation to a struggling teacher on how to solve a linear equation.

[1350] Examples of prompt statements

[1351] Input prompt for generative AI model: "Generate step-by-step problems for solving linear equations for middle school students."

[1352] Input prompt for emotion engine: "Analyze the user's tone of voice and facial expressions to determine their emotions, and communicate the results to the generative AI."

[1353] This system will enable teachers to significantly reduce the time they spend preparing lessons and also provide appropriate support based on their emotions and situations.

[1354] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1355] Step 1:

[1356] A user uses a terminal to input information necessary for preparing a lesson. For example, the user inputs "I will teach how to solve linear equations in a mathematics class for junior high school students." The input information is sent from the terminal to the server.

[1357] Step 2:

[1358] The server receives the data sent by the user and converts it into an appropriate format. Specifically, it converts the text data into JSON format and formats it in a format that is easy for the generative AI model to understand. The input is the user's text data, and the output is formatted JSON data.

[1359] Step 3:

[1360] The server inputs the formatted data into a generative AI model (e.g., GPT-4) to generate an appropriate prompt. The generative AI model generates text based on the input data and outputs a prompt. For example, it generates a problem that provides a step-by-step explanation of how to solve a linear equation so that it is easy for junior high school students to understand.

[1361] Step 4:

[1362] The server uses an emotion engine to analyze the user's tone of voice, facial expressions, choice of words, etc. The emotion engine receives the user's voice data and video data as input and performs emotion analysis. The output is data that indicates the user's emotional state.

[1363] Step 5:

[1364] The server conveys the emotion information obtained from the emotion engine to the generative AI. Based on that information, the generative AI generates a prompt that corresponds to the user's emotion. For example, when the user is confused, it generates a prompt that provides hints and explanations to solve the problem. The input is emotion information, and the output is a prompt that corresponds to the emotion.

[1365] Step 6:

[1366] The server sends the generated prompt to the terminal to display it to the user. The terminal displays the prompt it receives from the server on its screen. For example, a teacher's terminal might display a "step-by-step problem on how to solve a linear equation," with further explanations if the teacher is confused. The input is the generated prompt, and the output is what is displayed on the terminal screen.

[1367] (Application example 1)

[1368] 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".

[1369] Conventional prompt creation systems using generative AI generate uniform prompts without considering the user's emotions, which means that they cannot provide appropriate support according to the user's situation or emotions. In addition, they are limited to the education field, making them difficult to apply to other fields.

[1370] 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. In this invention, the server includes a means for creating a prompt using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for analyzing the user's emotion using an emotion recognition engine and generating a prompt according to the emotion, and a means for displaying a prompt on a smart device. This allows the generation of an appropriate prompt according to the user's emotion, making it possible to apply the system in various fields such as brick-and-mortar stores.

[1371] "Generative AI" is artificial intelligence that automatically generates new information and content based on data.

[1372] A "prompt" is a phrase or message that presents instructions or questions to the user.

[1373] "Usage assistance" refers to supporting users to perform specific tasks or activities efficiently.

[1374] A "dedicated generative AI platform for education" is a basic system for utilizing generative AI designed specifically for the field of education.

[1375] "AI use and development" refers to applying artificial intelligence to actual business operations and activities, and providing training to improve its performance and functions.

[1376] An "emotion recognition engine" is a technology that analyzes emotions from a user's tone of voice, facial expressions, choice of words, etc.

[1377] A "smart device" is an electronic device with Internet connectivity and advanced computing capabilities, including smartphones and smart glasses.

[1378] A "user emotion" is a psychological state that a user has in a particular situation, such as happiness, excitement, confusion, anxiety, etc.

[1379] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase goods or services.

[1380] The system for implementing this invention is configured by combining a generative AI, an emotion recognition engine, and a smart device. Specific embodiments are described below.

[1381] System Configuration

[1382] 1. Generative AI: This is artificial intelligence that automatically generates new information and content based on data. This generative AI is used to generate prompts based on the user's emotions.

[1383] 2. Emotion recognition engine: This technology analyzes emotions from the user's tone of voice, facial expressions, choice of words, etc. This makes it possible to recognize the user's current emotions in real time.

[1384] 3. Smart Devices: Electronic devices with Internet connectivity and advanced computing capabilities, such as smartphones and smart glasses. These devices are used to display the generated prompts to the user.

[1385] Program processing explanation

[1386] Hardware and Software

[1387] Hardware: Smart glasses (e.g. Google Glass), camera (built into smart glasses)

[1388] Software: OpenCV (image processing library), emotion recognition engine, prompt generation engine

[1389] Data processing and calculation

[1390] 1. Acquiring camera input: Acquiring video in real time from the camera built into the smart glasses, allowing us to capture the user's face and facial expressions.

[1391] 2. Emotion Recognition: Detect faces from captured video and use an emotion recognition engine to analyze the user's emotions, for example determining whether the user is confused or excited.

[1392] 3. Prompt Generation: Based on the recognized emotion, the prompt generation engine generates an appropriate prompt, for example, if the user is confused, it generates a prompt that provides hints or explanations to solve the problem.

[1393] 4. Prompt display: The generated prompts are displayed on the smart glasses display, allowing the user to receive appropriate assistance in real time.

[1394] Examples

[1395] If the customer is stumped: "May I help you with what you're looking for?"

[1396] If the customer is excited: "We have new items in stock! Want to take a look?"

[1397] In this way, the system can generate appropriate prompts based on the user's emotions, making it possible to apply the system in a variety of fields, such as brick-and-mortar stores.

[1398] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1399] Step 1:

[1400] The server acquires video in real time from the camera built into the smart glasses. The input is the camera video, and the output is video data. This video data is used to capture the user's face and facial expressions.

[1401] Step 2:

[1402] The server uses OpenCV to detect faces from the captured video data. The input is the video data, and the output is the coordinate data of the face area. Based on this coordinate data, the position of the face is identified.

[1403] Step 3:

[1404] The server uses an emotion recognition engine to analyze the user's emotions based on the coordinate data of the face region. The input is the coordinate data of the face region, and the output is emotion data (e.g., joy, excitement, confusion, anxiety). Based on this emotion data, the server identifies the user's current emotional state.

[1405] Step 4:

[1406] The server uses a prompt generation engine to generate an appropriate prompt based on the emotion data. The input is emotion data, and the output is a prompt sentence. For example, if the user is confused, the server generates the prompt sentence "May I help you with the product you are looking for?"

[1407] Step 5:

[1408] The server displays the generated prompt sentence on the display of the smart glasses. The input is the prompt sentence, and the output is the text to be displayed on the display. This allows the user to receive appropriate assistance in real time.

[1409] Step 6:

[1410] The user checks the prompt sentence displayed on the display of the smart glasses and takes the next action if necessary. The input is the prompt sentence displayed on the display, and the output is the user's behavior. For example, if the user is confused, he or she can ask staff for help.

[1411] Example 2

[1412] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1413] With conventional educational support systems, it was difficult for teachers to provide appropriate support according to the level of understanding and emotional state of each individual student. In addition, there were limited means for teachers to develop and use AI to optimize their own teaching methods. This resulted in issues such as uneven quality of education and not maximizing the learning effect of students.

[1414] 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.

[1415] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and cultivating AI, a means for analyzing the user's emotions using an emotion analysis engine, and a means for providing appropriate educational support information based on the analysis results. This allows teachers to optimize their own teaching methods and provide appropriate support according to the students' levels of understanding and emotional states.

[1416] "Generative AI" is an artificial intelligence technique for generating appropriate prompts and information based on user input.

[1417] A "prompt" is an input sentence that instructs an artificial intelligence on a specific task or question.

[1418] The "dedicated generative artificial intelligence platform for education" is an artificial intelligence platform designed to help teachers optimize their teaching methods and support educational activities.

[1419] "Artificial intelligence application and development" refers to the process of applying artificial intelligence to specific tasks and training it to improve its performance.

[1420] An "emotion analysis engine" is a technology that analyzes a user's emotional state and takes appropriate action based on the results.

[1421] "Educational support information" is information and advice provided to support the educational activities of teachers and students.

[1422] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1423] This invention is a system that uses a dedicated generative AI platform for education to enable teachers to optimize their own teaching methods and provide appropriate support according to students' levels of understanding and emotional states. A specific embodiment of this system is shown below.

[1424] Generating a Program

[1425] The server generates programs for a dedicated generative AI platform for education, which allows teachers to develop and use AI for their own classes.

[1426] Program Processing

[1427] The server executes the generated program and performs the following processes.

[1428] 1. Cultivating AI:

[1429] Teachers use the devices to access a dedicated generative AI platform for education.

[1430] On the platform, teachers can train the AI ​​to learn "teaching methods that will improve students' understanding."

[1431] The platform trains the AI ​​using data and feedback provided by teachers, using machine learning libraries such as TensorFlow and PyTorch.

[1432] 2. Emotion engine combination:

[1433] Users (students and teachers) access the platform using a terminal.

[1434] The emotion engine analyzes the user's emotions using OpenAI's GPT-4 and IBM Watson's emotion analysis API.

[1435] The emotion engine communicates the analysis results to a dedicated generative AI platform for education.

[1436] 3. Providing educational support information:

[1437] Based on the information obtained from the emotion engine, the platform provides educational support information that corresponds to the emotions the user is feeling.

[1438] For example, if a user is confused, the platform will provide assistance such as "re-explaining the basic concepts of calculus" or "providing concrete examples." In this case, OpenAI's GPT-4 is used for natural language generation.

[1439] Examples

[1440] When a teacher teaches mathematics, the platform learns how to teach calculus in a way that is easy for students to understand.

[1441] If a student is confused while using the platform to learn, the emotion engine will analyze that emotion and communicate it to the platform.

[1442] The platform provides support information such as "re-explaining the basic concepts of differential and integral calculus" and "providing concrete examples."

[1443] Examples of prompt statements

[1444] "Use concrete examples to help students understand the basic concepts of calculus."

[1445] “If a student is stumped, how do you provide them with hints to solve the problem?”

[1446] In this way, the dedicated generative AI platform for education provides optimal teaching support tailored to the needs of teachers and students.

[1447] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1448] Step 1:

[1449] Teachers access the platform.

[1450] Teachers use their terminals to log in to a dedicated generative AI platform for education. The server checks the teacher's authentication information and grants access. The input is the teacher's authentication information, and the output is the permission to access the platform.

[1451] Step 2:

[1452] Teachers teach AI how to teach.

[1453] Teachers use their devices to input "teaching methods to improve students' understanding" on the platform. The server receives the data entered by the teacher and trains the AI ​​model. Machine learning libraries such as TensorFlow and PyTorch are used for this. The input is the teaching method data provided by the teacher, and the output is the trained AI model.

[1454] Step 3:

[1455] A user accesses the platform.

[1456] Users (students and teachers) use their terminals to access a dedicated generative AI platform for education. The server checks the user's authentication information and grants access. The input is the user's authentication information, and the output is the permission to access the platform.

[1457] Step 4:

[1458] The emotion engine analyzes the user's emotions.

[1459] While the user is using the platform, the emotion engine analyzes the user's facial expressions and inputs. The emotion engine uses OpenAI's GPT-4 and IBM Watson's emotion analysis API to identify the user's emotions. The input is the user's facial expression data and inputs, and the output is the analyzed emotion data.

[1460] Step 5:

[1461] The platform provides appropriate educational support information.

[1462] Based on the emotion data received from the emotion engine, the server generates educational support information according to the emotion the user is feeling. For example, if the user is confused, the server generates support information such as "explain the basic concepts of differential and integral calculus again" or "provide concrete examples." The input is the analyzed emotion data, and the output is the generated educational support information. The server sends the generated support information to the user's terminal and displays it to the user.

[1463] (Application example 2)

[1464] 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".

[1465] In conventional educational support systems, it was difficult for teachers to grasp students' emotions in real time and provide appropriate educational support accordingly. In addition, there was no system in real stores that could suggest customer service methods in real time according to customers' emotions, so improving customer satisfaction was an issue.

[1466] 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.

[1467] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and training AI, a means for analyzing the user's emotions using an emotion analysis engine, a means for providing appropriate support information based on the analysis results, and a means for displaying the emotion analysis results in real time. This allows teachers to grasp the emotions of their students in real time and provide appropriate educational support accordingly. In addition, in physical stores, it is possible to propose customer service methods according to the customer's emotions in real time, which improves customer satisfaction.

[1468] "Generative AI" is an artificial intelligence technology that creates prompts and provides usage assistance.

[1469] The "dedicated generative AI platform for education" is a platform that allows teachers to develop and use AI optimized for their own lessons.

[1470] "AI utilization and development" refers to the process of applying artificial intelligence to actual educational activities and other uses and improving its performance.

[1471] An "emotion analysis engine" is a technology for analyzing a user's emotions and identifying emotions from facial expressions, tone of voice, etc.

[1472] "Providing appropriate support information based on the analysis results" means using the results of the emotion analysis engine to provide optimal information and support according to the user's condition.

[1473] "Displaying emotion analysis results in real time" means instantly outputting the results generated by the emotion analysis engine to a display device.

[1474] As an embodiment of the present invention, a customer service support system for a brick-and-mortar store will be described as an example. This system is composed of smart glasses, a server, an emotion analysis engine, a generative AI, and a dedicated generative AI platform for training.

[1475] System Configuration

[1476] 1. Smart glasses:

[1477] It has a built-in camera and microphone that captures the customer's facial expressions and tone of voice in real time.

[1478] Analysis results and customer service suggestions are shown to store staff on the display.

[1479] 2. Server:

[1480] An image processing library (OpenCV) is used to process the image data transmitted from the smart glasses.

[1481] An emotion analysis engine (emotion recognition engine) is used to analyze customer emotions from video and audio data.

[1482] Using a dedicated generative AI platform for educational purposes, customer service suggestions are generated based on the results of emotion analysis.

[1483] Data processing and calculation

[1484] 1. Video and audio capture:

[1485] Video and audio data is collected in real time from the smart glasses' camera and microphone.

[1486] The acquired data is sent to the server.

[1487] 2. Emotion analysis:

[1488] The server processes the video data using an image processing library (OpenCV) and identifies the customer's emotions using an emotion analysis engine (emotion recognition engine).

[1489] The analysis results indicate whether the customer is happy, confused, excited, etc.

[1490] 3. Generate customer service suggestions:

[1491] Based on the results of emotion analysis, a dedicated generative AI platform for educational purposes generates appropriate customer service methods.

[1492] The generated service suggestions are displayed on the smart glasses' display.

[1493] Examples

[1494] For example, if a customer is confused, the sentiment analysis engine will identify that state, and a dedicated generative AI platform for training will generate customer service suggestions such as:

[1495] Example prompt:

[1496] "If a customer seems confused, suggest the best way to serve them."

[1497] The suggestion appears on the smart glasses' display, saying: "Sounds like you're having trouble. Is there anything we can help you with?"

[1498] In this way, store clerks can provide optimal customer service in real time according to the customer's emotions, and this system is expected to improve customer satisfaction.

[1499] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1500] Step 1:

[1501] The camera and microphone of the smart glasses capture the customer's facial expressions and tone of voice in real time. The input is the customer's video and audio data, and the output is to send these data to the server.

[1502] Step 2:

[1503] The server processes the video data sent from the smart glasses using a video processing library (OpenCV). The input is the video data sent from the smart glasses, and the output is the pre-processed video data to be passed to the emotion analysis engine.

[1504] Step 3:

[1505] The server uses an emotion analysis engine (emotion recognition engine) to analyze the customer's emotion from the preprocessed video data and audio data. The input is the preprocessed video data and audio data, and the output is the customer's emotional state (e.g., joy, confusion, excitement, etc.).

[1506] Step 4:

[1507] The server sends the emotion analysis results to a dedicated generative AI platform for training, which generates appropriate customer service methods. The input is the emotion analysis results, and the output is the generated customer service suggestions.

[1508] Step 5:

[1509] The server displays the generated service suggestions on the display of the smart glasses. The input is the generated service suggestions, and the output is the service suggestions displayed on the display of the smart glasses.

[1510] Step 6:

[1511] The salesperson provides appropriate service to the customer based on the service suggestions displayed on the display of the smart glasses. The input is the service suggestions displayed on the display of the smart glasses, and the output is the actual service behavior toward the customer.

[1512] Example 3

[1513] 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 smart glasses 214 will be referred to as a "terminal".

[1514] In the conventional education system, it was difficult for teachers to conduct educational activities according to the emotions and level of understanding of each student, resulting in uneven quality of education. It was also very difficult for teachers to grasp students' emotions in real time and respond appropriately accordingly. This could lead to a decline in students' motivation to learn and their level of understanding.

[1515] 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.

[1516] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for collecting user emotion data, a means for analyzing the emotion data, a means for generating educational activities based on the analysis results, and a means for providing the generated educational activities to the user. This makes it possible to provide appropriate educational activities in real time according to the user's emotions.

[1517] "Generative AI" refers to artificial intelligence that generates appropriate prompts and information based on user input and the situation.

[1518] A "prompt" is a phrase or message that instructs or asks a question to the user.

[1519] A "dedicated generative AI platform for education" is a base system for providing specialized generative AI to support educational activities.

[1520] "Use and development of AI" refers to utilizing artificial intelligence and training and adjusting it to improve its performance and functions.

[1521] "User emotion data" refers to information about emotions obtained from the user's facial expressions, voice, etc.

[1522] "Means for collecting emotion data" refers to a method for obtaining data regarding a user's emotions using devices such as a camera or microphone.

[1523] "Means for analyzing emotion data" refers to techniques or algorithms used to analyze the collected emotion data and identify the user's emotional state.

[1524] "Means for generating educational activities based on analysis results" refers to methods and techniques for generating optimal educational activities for a user based on the analysis results of emotion data.

[1525] The "means for providing the generated educational activity to the user" refers to a method or technology for communicating the generated educational activity to the user and executing the same.

[1526] This invention is a system that uses generative AI to create prompts and provide usage support. This system provides a dedicated generative AI platform for education, and uses and develops AI. It also collects and analyzes the user's emotional data, generates educational activities based on the results, and provides them to the user.

[1527] The server uses a high-performance server (e.g., a server equipped with a GPU) to run an emotion analysis engine (e.g., emotion analysis software) and a generative AI model (e.g., generative AI software).The terminal uses a device equipped with a camera and microphone (e.g., a PC or tablet) to collect user emotion data.

[1528] Specifically, the device captures the user's facial expressions and voice in real time and sends them to the server. The server then uses an emotion analysis engine to analyze the user's emotion data. For example, if the user is confused, the emotion analysis engine will output a result of "confusion level 80%."

[1529] The server then sends the analysis results to the generative AI model, which generates appropriate educational activities based on the analysis results. For example, if the user is confused, the generative AI model generates a prompt sentence such as, "I'll give you a hint to solve this problem. First, compare the left and right sides of the equation."

[1530] Finally, the server sends the generated prompt sentence to the terminal, which displays it to the user, so that the user can receive the appropriate educational activity.

[1531] As a concrete example, consider a scenario in which a user is working on a mathematics problem.

[1532] User Sentiment: Confused

[1533] Sentiment analysis engine output: 80% confusion

[1534] Output of generative AI model: “Here’s a hint to help you solve this problem. First, compare the left and right sides of the equation.”

[1535] In this way, a system is realized in which the server, the terminal, and the user work together to provide appropriate educational activities according to the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1536] Step 1:

[1537] The terminal collects the user's emotion data. Specifically, it uses a camera and microphone mounted on the terminal to capture the user's facial expressions and voice in real time. The input is the user's facial expression and voice data, and the output is to transmit these data to the server.

[1538] Step 2:

[1539] The server receives emotion data sent from the device. The input is facial expression and voice data sent from the device, and the output is to pass this data to the emotion analysis engine.

[1540] Step 3:

[1541] The server uses an emotion analysis engine to analyze the emotion data. The input is facial expression and voice data, and the output is the user's emotional state (e.g., 80% confusion). In concrete terms, the emotion analysis engine analyzes the user's facial expression and voice to identify emotions such as joy, excitement, confusion, and anxiety.

[1542] Step 4:

[1543] The server sends the analysis results obtained from the emotion analysis engine to the generative AI model. The input is the emotion analysis result, and the output is the data passed to the generative AI model. In concrete terms, the server sends the emotion analysis result to the generative AI model.

[1544] Step 5:

[1545] The generative AI model generates appropriate educational activities based on the results of sentiment analysis. The input is the result of sentiment analysis, and the output is the generated prompt sentence. In concrete terms, the generative AI model generates a prompt sentence based on the result of sentiment analysis. For example, if the user is confused, the model generates a prompt sentence such as, "We will provide you with a hint to solve this problem. First, let's compare the left and right sides of the equation."

[1546] Step 6:

[1547] The server sends the prompt sentence obtained from the generative AI model to the terminal. The input is the generated prompt sentence, and the output is the data to be sent to the terminal. In concrete terms, the server sends the generated prompt sentence to the terminal.

[1548] Step 7:

[1549] The terminal displays the generated prompt to the user. The input is the prompt sent from the server, and the output is the message displayed to the user. Specifically, the terminal displays the message "Here is a hint to help you solve this problem. First, compare the left and right sides of the equation."

[1550] In this way, a system is realized in which the server, terminal, and user work together to provide appropriate educational activities according to the user's emotions.

[1551] (Application example 3)

[1552] 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".

[1553] In the conventional education system, it was difficult to respond to each student's level of understanding and emotions, resulting in uneven quality of education. In addition, in customer service work in brick-and-mortar stores, it was difficult to respond to customers' emotions, making it difficult to improve customer satisfaction. To solve these problems, individual responses using AI are required.

[1554] 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.

[1555] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for analyzing the user's emotions using an emotion recognition engine and adjusting educational activities based on the results, and a means for displaying on a smart device. This allows for individualized responses according to the level of understanding and emotions of each student, improving the quality of education. In addition, in customer service operations in physical stores, it is possible to respond according to the customer's emotions, which is expected to improve customer satisfaction.

[1556] "Generative AI" is artificial intelligence that generates appropriate prompts and information based on user input.

[1557] A "prompt" is a form of instruction or question that a generative AI provides to a user.

[1558] "Usage support" is a function that allows generative AI to support user activities.

[1559] A "dedicated generative AI platform for education" is a system that provides specialized generative AI to support educational activities.

[1560] "AI utilization and development" refers to the activity of utilizing artificial intelligence and training and adjusting it to improve its performance.

[1561] An "emotion recognition engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[1562] A "smart device" is a portable electronic device with Internet connectivity and advanced computing capabilities.

[1563] An "educational activity" is a set of actions or processes for imparting knowledge or skills to learners.

[1564] "Individualized response" means providing special responses according to the characteristics and circumstances of each user.

[1565] "Customer satisfaction" is the degree of satisfaction a customer feels with the services or products provided.

[1566] This invention is a system that uses generative AI to create prompts and support their use. It provides a dedicated generative AI platform for education, and uses and develops AI. It also uses an emotion recognition engine to analyze the user's emotions and adjusts educational activities based on the results. It also includes a function to display the results on a smart device.

[1567] System Programming

[1568] This system is realized using the following hardware and software.

[1569] Hardware: smart glasses, camera, server

[1570] Software: OpenCV (face detection), emotion recognition engine (emotion recognition), AI teacher platform

[1571] Processing Description

[1572] The server captures images from the camera in real time and processes each frame. First, it detects faces and recognizes emotions from the detected faces. The emotion recognition engine is a technology that analyzes emotions from the user's facial expressions and voice, and thereby grasps the user's emotional state.

[1573] Then, based on the recognized emotions, the AI ​​teacher platform will determine the appropriate teaching activities and customer service methods. For example, if the user is confused, it will provide hints and explanations to solve the problem, and if the user is happy, it will provide teaching activities that maintain that emotion.

[1574] The educational activities and customer service methods that have been decided upon are displayed on smart glasses, which are portable electronic devices with Internet connectivity and advanced computing capabilities that allow store clerks and teachers to receive information in real time when worn by them.

[1575] Examples

[1576] Confused customer: If a customer is confused, the smart glasses will display, "Please explain this product in more detail."

[1577] Happy Customer: If a customer is happy, the smart glasses will display a message saying, "Keep up the good work and recommend other products to us."

[1578] Examples of prompt statements

[1579] If a customer is confused, have the smart glasses display, "Please tell me more about this product."

[1580] If a customer is happy, the smart glasses should display a message saying, "Keep up the good work and recommend other products to us."

[1581] In this way, the server can respond individually to the user's emotions, improving the quality of education and customer satisfaction.

[1582] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1583] Step 1:

[1584] The server acquires video from the camera in real time. The input is the camera video, and the output is image data for each frame. Specifically, the server captures the video stream from the camera and saves each frame as image data.

[1585] Step 2:

[1586] The server detects faces from the captured image data for each frame. The input is the image data for each frame, and the output is the position information of the detected face. Specifically, the server converts the image data to grayscale using OpenCV and applies a face detection algorithm.

[1587] Step 3:

[1588] The server recognizes emotions based on the detected facial position information. The input is facial position information, and the output is recognized emotion data. Specifically, the server uses an emotion recognition engine to extract facial features, which are then input into an emotion classification model to determine the emotion.

[1589] Step 4:

[1590] The server determines appropriate educational activities and customer service methods based on the recognized emotion data. The input is emotion data, and the output is instructions for educational activities and customer service methods. Specifically, the server uses the AI ​​teacher platform to analyze the emotion data and generate appropriate prompts.

[1591] Step 5:

[1592] The server sends the determined instruction for the educational activity or customer service method to the smart device. The input is the instruction for the educational activity or customer service method, and the output is text information to be displayed on the smart device. Specifically, the server converts the generated prompt into a data format for display on the smart glasses and transmits it over the network.

[1593] Step 6:

[1594] The terminal (smart glasses) displays the instructions sent from the server. The input is the text information sent from the server, and the output is the instructions to be displayed on the display of the smart glasses. As a specific operation, the terminal performs processing to display the received text information on the display.

[1595] Step 7:

[1596] The user (a store clerk or teacher) acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is the user's specific actions. As a specific action, the user responds to customers or students based on the displayed instructions.

[1597] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1598] 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 making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1599] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1600] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1601] [Third embodiment]

[1602] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1603] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1604] 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 wide area network (WAN) and / or a local area network (LAN).

[1605] 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.

[1606] 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 the voice according to instructions from the processor 46.

[1607] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1608] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1609] Fig. 6 shows an example of 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.

[1610] 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.

[1611] 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.

[1612] In the headset type terminal 314, 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.

[1613] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1614] "Example 1"

[1615] One embodiment of the present invention is a prompt creation system that uses generative AI. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts. For example, when a teacher prepares a "math lesson for junior high school students," the generative AI generates "math problems that are easy for junior high school students to understand." This allows teachers to significantly reduce the time it takes to prepare lessons.

[1616] "Example 2"

[1617] Another embodiment of the present invention is a dedicated generative AI platform for education. This platform allows teachers to train and use AI to suit their own lessons. For example, teachers can have AI learn "teaching methods to improve students' understanding" on the platform. This allows teachers to receive support from AI optimized for their own lessons.

[1618] "Example 3"

[1619] Furthermore, another embodiment of the present invention is an AI teacher platform. This platform enables AI to play the role of a teacher. For example, AI can provide appropriate answers to students' questions and adjust the pace of a lesson according to the student's level of understanding. This allows teachers to improve the quality of education for each student.

[1620] The process flow of each embodiment will be described below.

[1621] "Example 1"

[1622] Step 1: The teacher inputs the lesson topic and goals into the system. For example, "Mathematics lesson for junior high school students."

[1623] Step 2: The generative AI generates prompts based on teacher input. These prompts correspond to teacher requests, such as "math problems that are easy for middle school students to understand."

[1624] Step 3: The generated prompts are presented to teachers to use in lesson preparation.

[1625] "Example 2"

[1626] Step 1: Teachers access a dedicated generative AI platform for education.

[1627] Step 2: Teachers input what they want the AI ​​to learn on the platform, such as "teaching methods to improve students' understanding."

[1628] Step 3: The AI ​​learns based on teacher input and provides optimized support for teachers' lessons.

[1629] "Example 3"

[1630] Step 1: The AI ​​teacher platform is launched and begins interacting with students.

[1631] Step 2: The AI ​​provides appropriate answers to the student's questions. For example, if a student asks a question about a math problem, the AI ​​provides an explanation for the problem.

[1632] Step 3: The AI ​​adjusts the pace of the lesson according to the student's level of understanding. For example, if a student is struggling to understand, the AI ​​will slow down the pace of the lesson.

[1633] Example 1

[1634] Next, a description will be given of Example 1 of the embodiment 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".

[1635] In the past, teachers had to create lesson content from scratch, which required a lot of time and effort. In addition, there was a lack of support systems to efficiently create high-quality lesson materials. This increased the burden on teachers and could lead to a decline in the quality of education.

[1636] 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.

[1637] In this invention, the server includes a means for a user to access the system, a means for a user to input lesson contents, a means for the server to receive input information, a means for the server to call a generative AI model, a means for the generative AI model to generate a prompt, a means for the server to receive the generated prompt, a means for the server to send a prompt sentence to a terminal, a means for a user to confirm the prompt sentence, a means for providing a generative AI platform dedicated to education, and a means for utilizing and developing AI. This enables teachers to efficiently create high-quality lesson materials and significantly shortens the time required for lesson preparation.

[1638] "Means for a user to access the system" refers to the method by which a user uses a terminal to log in to the system's website or application and begin using it.

[1639] "Means for users to input lesson content" refers to an interface or form that allows users to input information such as the lesson theme and target grade into the system.

[1640] The "means for the server to receive input information" refers to a method by which the server receives information on lesson contents sent by a user and temporarily stores it.

[1641] "Means for the server to call the generative AI model" refers to the method by which the server calls the API of the generative AI model and passes the information entered by the user as a prompt.

[1642] "Means by which the generative AI model generates a prompt" refers to a method by which the generative AI model generates an appropriate prompt sentence based on information passed from the server.

[1643] "Means for the server to receive the generated prompt" refers to a method for the server to receive and re-store the prompt text returned from the generative AI model.

[1644] The "means for the server to send a prompt text to the terminal" refers to a method by which the server sends a saved prompt text to the user's terminal and displays it on the screen.

[1645] The "means for the user to confirm the prompt text" refers to a method by which the user can confirm the prompt text generated at the terminal and make corrections or additions as necessary.

[1646] "Means for providing a dedicated generative AI platform for education" means a method for providing a dedicated generative AI platform to support educational activities.

[1647] "Means for using and developing AI" refers to methods for using generative AI models and training and adjusting them to improve their performance.

[1648] This invention relates to a prompt creation system that uses generative AI. The purpose of this system is to drastically reduce the time it takes for a teacher to prepare for a lesson by having a generative AI automatically generate prompts.

[1649] Hardware and Software Used

[1650] The system uses the following hardware and software:

[1651] Terminal: A device such as a PC or tablet used by a teacher.

[1652] Server: A server for managing the entire system and calling generative AI models.

[1653] Generative AI models: For example, generative AI models such as OpenAI's GPT-4.

[1654] Data processing and calculation

[1655] 1. A user accesses the system

[1656] Users (teachers) access the prompt creation system website using a device such as a PC or tablet. The user enters their ID and password on the login screen to log in to the system.

[1657] 2. The user inputs the lesson content

[1658] After logging in, the user is presented with a form to enter information such as the topic of the lesson and the target grade. For example, the user might enter "Math lessons for junior high school students." Once the information is complete, the user clicks the "Submit" button.

[1659] 3. The server receives the input information

[1660] The server receives the lesson content information sent by the user and temporarily stores it in a database.

[1661] 4. The server calls the generative AI model

[1662] The server retrieves the saved lesson content information and calls a generative AI model (e.g., OpenAI's GPT-4). The server passes the information entered by the user as a prompt to the generative AI model.

[1663] 5. Generative AI model generates prompts

[1664] The generative AI model generates appropriate prompts based on the information passed from the server. For example, it generates "mathematics problems that are easy for junior high school students to understand."

[1665] 6. The server receives the generated prompt

[1666] The server receives the prompt text returned from the generative AI model and stores it again in the database.

[1667] 7. The server sends a prompt to the terminal.

[1668] The server sends the saved prompt text to the user's terminal, where it is displayed on the screen.

[1669] 8. The user confirms the prompt

[1670] The user can check the generated prompt sentences on the terminal and make corrections or additions as necessary. Finally, the user can use the generated prompt sentences in lesson preparation.

[1671] Examples

[1672] Example input

[1673] A teacher enters "math lessons for middle school students" into the system.

[1674] Example prompts for generative AI models

[1675] "Generate math problems that are easy for middle school students to understand."

[1676] Example of a generated prompt statement

[1677] "Solve the following equation: 2x + 3 = 7. Include your answer and explanation."

[1678] This system allows teachers to efficiently create high-quality lesson materials. By utilizing generative AI models, it is possible to significantly reduce lesson preparation time.

[1679] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1680] Step 1:

[1681] A user accesses the system

[1682] Input: The user (teacher) accesses the system's website using a device such as a PC or tablet.

[1683] Specific operation: The user opens a browser and enters the system URL to access the system. The login screen is displayed and the user enters their ID and password to log in.

[1684] Output: The user is logged into the system and the main screen is displayed.

[1685] Step 2:

[1686] The user inputs the lesson content

[1687] Input: The user inputs information such as the topic of the lesson and the grade level it is aimed at. For example, the user inputs "Math lesson for middle school students."

[1688] Specific actions: The user enters the lesson details into the displayed form and clicks the "Submit" button.

[1689] Output: The entered lesson content information is sent to the server.

[1690] Step 3:

[1691] The server receives the input information

[1692] Input: Course content information submitted by the user.

[1693] Specific operation: The server receives the information sent by the user and temporarily stores it in a database.

[1694] Output: Course content information stored in a database.

[1695] Step 4:

[1696] The server calls the generative AI model

[1697] Input: Course content information stored in the database.

[1698] Specific operation: The server retrieves the stored information and calls the API of the generative AI model. The server passes the information entered by the user as a prompt to the generative AI model.

[1699] Output: The prompt information passed to the generative AI model.

[1700] Step 5:

[1701] A generative AI model generates prompts

[1702] Input: The prompt information passed by the server.

[1703] How it works: The generative AI model generates appropriate prompts based on the information passed to it. For example, it generates "math problems that are easy for junior high school students to understand."

[1704] Output: The generated prompt statement.

[1705] Step 6:

[1706] Receives server-generated prompts

[1707] Input: The prompt sentence returned by the generative AI model.

[1708] Specific operation: The server receives the prompt text returned from the generative AI model and saves it back in the database.

[1709] Output: The prompt statement saved in the database.

[1710] Step 7:

[1711] The server sends a prompt to the terminal.

[1712] Input: The prompt statement stored in the database.

[1713] Specific operation: The server retrieves the saved prompt text and sends it to the user's terminal.

[1714] Output: The prompt text sent to the user's terminal.

[1715] Step 8:

[1716] The user confirms the prompt.

[1717] Input: The prompt text sent to the user's terminal.

[1718] Specific action: The user checks the prompt generated in the terminal and makes corrections or additions as necessary.

[1719] Output: The final prompt sentence with any corrections or additions.

[1720] (Application example 1)

[1721] 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".

[1722] In conventional educational support systems, teachers had to spend a lot of time preparing lessons and creating teaching materials, making it difficult to conduct efficient educational activities. In addition, there was a problem that the burden on teachers increased because it was not easy to customize the generated teaching materials and prompts. Furthermore, there was a lack of support for educational content creators to efficiently create high-quality teaching materials.

[1723] 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.

[1724] In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing an education support application and enabling teachers and educational content creators to generate prompts for lessons and teaching materials, and a means for customizing the generated prompts. This enables teachers and educational content creators to efficiently prepare lessons and teaching materials and easily customize the generated prompts.

[1725] "Generative AI" is artificial intelligence that automatically generates text and content based on user input.

[1726] A "prompt" is an input sentence that asks a generative AI a specific task or question.

[1727] "Usage support" is a function that supports users to use the system effectively.

[1728] A "dedicated generative AI platform for education" is a platform that provides specialized generative AI to support educational activities.

[1729] "AI utilization and development" refers to the effective use of artificial intelligence and the training and adjustment required to improve its performance.

[1730] An "educational support application" is software designed to support educational activities.

[1731] A "teacher" is a professional who teaches students at an educational institution.

[1732] An "educational content creator" is a professional who creates educational materials and content.

[1733] A "class" is an educational activity conducted by a teacher for students at an educational institution.

[1734] "Teaching materials" refers to learning materials and teaching tools used in educational activities.

[1735] "Customization" means adapting or modifying a system or content to meet the needs and requirements of users.

[1736] The present invention provides an educational support application that allows teachers and educational content creators to efficiently generate and customize prompts for lessons and teaching materials. Specific embodiments of the system will be described below.

[1737] System Configuration

[1738] The system consists of the following main components:

[1739] 1. Server: Hosts the generative AI and provides prompt generation and customization functions.

[1740] 2. Devices: Smartphones and tablets used by teachers and educational content creators.

[1741] 3. Users: Teachers and educational content creators.

[1742] Hardware and Software Used

[1743] Hardware: Smartphones, tablets, servers

[1744] Software: OpenAI API, Python, educational support application

[1745] Data processing and calculation

[1746] Data Entry

[1747] A user uses a terminal to access an educational support application and inputs a lesson topic and a target grade. For example, the user inputs "math lessons for junior high school students."

[1748] Prompt Generation

[1749] The server uses the OpenAI API to generate prompts based on the input data. Specifically, the server sends the prompts to a generative AI model (e.g., GPT-3) to generate appropriate questions and explanations.

[1750] Results display

[1751] The generated prompts are displayed to the user through the educational support application on the terminal, and the user can customize the generated prompts.

[1752] Examples

[1753] Example 1: Teacher preparing for a lesson

[1754] When a teacher types in "Please generate linear equation problems for junior high school students," the server uses generative AI to generate appropriate math problems and displays them on the device. Teachers can customize the generated problems and use them in their classes.

[1755] Example 2: Educational content creator creating teaching materials

[1756] When an educational content creator inputs "The Basics of Trigonometric Functions," the server uses generative AI to generate basic questions about trigonometric functions and their explanations, which are then displayed on the device. Creators can customize the generated content and provide it as teaching materials.

[1757] Examples of prompt statements

[1758] "Generate linear equation problems for middle school students."

[1759] "Generate problems that illustrate the fundamentals of trigonometry."

[1760] In this way, the system enables teachers and educational content creators to efficiently prepare lessons and materials and easily customize the generated prompts.

[1761] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1762] Step 1:

[1763] A user uses a terminal to access an educational support application and inputs the lesson topic and the target grade.

[1764] Input: Lesson topic (e.g. "Math lessons for junior high school students"), target grade

[1765] Output: The input data is sent to the server.

[1766] Specific operations: The user enters the lesson topic and target grade into the application's input form and presses the "Generate" button.

[1767] Step 2:

[1768] The server receives the input data and sends a request to the generative AI model to generate a prompt.

[1769] Input: Lesson topic and target grade sent by the user

[1770] Output: A prompt generation request sent to the generative AI model

[1771] Specific operation: The server calls the OpenAI API and creates a prompt generation request based on the input data.

[1772] Step 3:

[1773] The generative AI model receives the prompt generation request and generates an appropriate prompt.

[1774] Input: Prompt generation request

[1775] Output: Generated prompt

[1776] What happens: A generative AI model (e.g. GPT-3) processes the request and generates an appropriate math problem and explanation.

[1777] Step 4:

[1778] The server receives the generated prompt and sends it to the terminal.

[1779] Input: Generated prompt

[1780] Output: Prompt data sent to the terminal

[1781] Specific operation: The server receives the generated prompt and converts it into a data format for transmission to the user's terminal.

[1782] Step 5:

[1783] The terminal displays the received prompt to the user.

[1784] Input: Prompt data sent from the server

[1785] Output: The prompt that is displayed to the user

[1786] Specific operation: The educational support application of the terminal receives the prompt data and displays it on the user interface.

[1787] Step 6:

[1788] Customize user-generated prompts.

[1789] Input: the prompt shown

[1790] Output: Customized prompt

[1791] What happens: The user edits the displayed prompt and makes the necessary changes.

[1792] Step 7:

[1793] Customized prompts can be saved and used in lessons or as teaching materials.

[1794] Input:Customized prompt

[1795] Output: Saved prompt data

[1796] Specific behavior: When the user completes customization and presses the save button, the prompt is saved to the database.

[1797] Example 2

[1798] Next, a second embodiment of the second form 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".

[1799] In conventional education systems, it is difficult for teachers to provide teaching methods that correspond to the level of understanding of each individual student, and there is a lack of real-time feedback and support. This makes it difficult to effectively improve students' understanding.

[1800] 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.

[1801] In this invention, the server includes a means for teachers to input data using a terminal and the server to store the data, a means for the server to preprocess and analyze the stored data, and a means for the server to train a generative AI model using the analyzed data. This enables teachers to provide teaching methods that correspond to the level of understanding of each student, and allows them to receive feedback and support in real time.

[1802] "Generative AI" refers to artificial intelligence that generates information based on prompts entered by the user.

[1803] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[1804] A "dedicated generative AI platform for education" is a dedicated system that allows teachers to develop and use AI to suit their own lessons.

[1805] A "device" is a device, such as a computer or tablet, that a teacher uses to enter data or receive feedback.

[1806] "Server" refers to a central processing unit that stores data, pre-processes, analyzes, trains generative AI models, and provides feedback.

[1807] "Data preprocessing" refers to the process of converting stored data into a format that is easier to analyze, including data cleaning and normalization.

[1808] A "generative AI model" is a machine learning model that generates information based on input data.

[1809] "Feedback" refers to information that a generative AI model provides to teachers about the results of its learning.

[1810] "Real-time support" refers to analyzing students' level of understanding in real time during class and suggesting the most appropriate teaching methods to teachers.

[1811] This invention provides a system that provides a dedicated generative AI platform for education, enabling teachers to develop and use AI for their own lessons. A specific embodiment of this system will be described below.

[1812] System Overview

[1813] In this system, teachers input data using a terminal, and the server stores, preprocesses, and analyzes the data, and trains the generative AI model, proposing optimal teaching methods to teachers. The system is composed of a generative AI, a dedicated generative AI platform for education, terminals, and a server.

[1814] Hardware and Software Used

[1815] Terminal: A device such as a computer or tablet for teachers to enter data.

[1816] Server: A central processing unit that stores data, pre-processes it, analyzes it, trains generative AI models, and provides feedback, using relational databases such as MySQL or PostgreSQL, Python libraries such as pandas or numpy, and machine learning frameworks such as TensorFlow or PyTorch.

[1817] Generative AI models: For example, advanced generative AI models such as OpenAI's GPT-4.

[1818] Entering and Saving Data

[1819] Users (teachers) use their devices to log in to the educational generative AI platform and input data on lesson content and students' understanding. The devices then send the input data to the server, which then stores the data in a database.

[1820] Data preprocessing and analysis

[1821] The server preprocesses the stored data, which includes cleaning and normalizing the data, and then analyzes the preprocessed data using Python libraries such as pandas and numpy.

[1822] Training generative AI models

[1823] The server uses the parsed data to train a generative AI model, and the training process is managed using a machine learning framework such as TensorFlow or PyTorch to optimize the model's parameters.

[1824] Providing feedback

[1825] The server feeds back the learning results to the user. The feedback includes the model-generated teaching method suggestions and the analysis results of the students' understanding. The terminal displays the feedback to the user, who then plans lessons based on it.

[1826] Real-time assistance

[1827] During lessons, the devices will provide real-time AI support, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[1828] Examples

[1829] For example, if a teacher wants to teach a generative AI model how to improve students' understanding in mathematics class, they can input a prompt like the following into the model:

[1830] Examples of prompt statements

[1831] "Please tell me some effective teaching methods to improve students' understanding in mathematics classes. Please provide specific examples and activities."

[1832] Given this prompt, the generative AI model would provide the following answer:

[1833] Example answers from generative AI models

[1834] The following teaching methods are effective in enhancing students' understanding:

[1835] 1. Use real-life examples: Connecting math concepts to real-life examples helps students understand them better.

[1836] 2. Group Discussion: By solving problems among students, they have more opportunities to teach each other.

[1837] 3. Interactive activities: Quizzes and games can help keep students engaged.”

[1838] In this way, teachers can receive support from AI while teaching.

[1839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1840] Step 1:

[1841] Faculty login and data entry

[1842] The user (teacher) uses a terminal to log in to the educational generative AI platform by entering a user ID and password.

[1843] Input: User ID, Password

[1844] Output: Login success or failure message

[1845] Specific operation: The terminal sends the user ID and password to the server, and the server performs authentication. If the authentication is successful, the user can access the platform.

[1846] Step 2:

[1847] Input of lesson contents and student data

[1848] The user inputs data related to the content of the lesson and the students' level of understanding, such as the lesson topic, the teaching materials used, and the students' test results.

[1849] Input: Lesson topic, teaching material information, student test results

[1850] Output: Confirmation message for input data

[1851] Specific operation: The terminal provides an input form and transmits data entered by the user to the server.

[1852] Step 3:

[1853] Data transmission and storage

[1854] The terminal transmits the input data to the server.

[1855] The server stores the received data in a database.

[1856] Input: Lesson topic, teaching material information, student test results

[1857] Output: Data save confirmation message

[1858] Specific operation: The server stores data in a database (e.g., MySQL or PostgreSQL).

[1859] Step 4:

[1860] Data preprocessing and analysis

[1861] The server pre-processes the stored data, which includes cleaning and normalizing the data.

[1862] Input: Saved data

[1863] Output: Preprocessed data

[1864] Specific operation: The server uses Python libraries such as pandas and numpy to fill in missing values ​​in the data and remove outliers.

[1865] Step 5:

[1866] Training generative AI models

[1867] The server uses the preprocessed data to train a generative AI model.

[1868] Input: Preprocessed data

[1869] Output: A trained generative AI model

[1870] How it works: The server optimizes the model parameters using machine learning frameworks such as TensorFlow or PyTorch.

[1871] Step 6:

[1872] Feedback on learning results

[1873] The server then provides the user with feedback on the learning results, including model-generated teaching method suggestions and analysis of the students' understanding.

[1874] Input: A trained generative AI model

[1875] Output: Feedback information (suggested teaching methods, analysis of students' understanding)

[1876] Specific operation: the server sends the generated feedback information to the terminal, and the terminal displays it to the user.

[1877] Step 7:

[1878] Real-time assistance

[1879] The device will provide real-time AI support during lessons, for example by analyzing students' responses and test results in real time and suggesting appropriate actions to teachers.

[1880] Input: Real-time student data (responses, test results)

[1881] Output: Real-time support information (suggestions for teaching methods)

[1882] Specific operation: The terminal transmits the data collected in real time to the server, which analyzes it and provides feedback.

[1883] (Application example 2)

[1884] 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".

[1885] Conventional educational generative AI platforms are specialized in providing information to support teachers' educational activities, but they do not provide sufficient support for improving the work learning and customer service skills of store staff. As a result, improving the work efficiency and customer service quality of store staff is an issue.

[1886] 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. In this invention, the server includes a means for creating prompts using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for training, a means for using and training AI, a means for supporting store staff in learning the contents of their work, a means for performing a simulation to improve customer service skills, and a means for providing feedback in real time. This enables store staff to efficiently learn their work and improve their customer service skills.

[1887] "Generative AI" is an artificial intelligence technology that creates prompts and assists users.

[1888] The "dedicated generative AI platform for education" is a specialized platform that allows teachers to develop and use AI for their own lessons.

[1889] "Means of using and developing AI" refers to methods and technologies for effectively utilizing artificial intelligence and improving its performance.

[1890] The "means for supporting store staff in learning about their work" refers to support methods and techniques provided to store staff in order to efficiently learn about their work.

[1891] The "means for performing simulations to improve customer service skills" refers to simulation techniques used by store staff to improve their customer service skills.

[1892] "Means for providing feedback in real time" refers to technology that provides instant feedback to store staff while they are performing their duties.

[1893] In order to implement the present invention, the following system configuration and program are required.

[1894] System Configuration

[1895] 1. Hardware:

[1896] A smartphone or tablet (iOS or Android device)

[1897] Server (Cloud server or on-premise server)

[1898] 2. Software:

[1899] Python 3.x

[1900] OpenAI API

[1901] Program processing explanation

[1902] The server uses generative AI to create prompts and assist users in using the system. Specifically, when teachers or store staff input the content they want to learn, the generative AI generates the optimal prompt based on that content. The prompts are generated using the OpenAI API.

[1903] The generated prompts are provided through a dedicated generative AI platform for education. This platform supports teachers and store staff in efficiently progressing with their learning. For example, when a store staff member is learning "how to display new products," the following prompts are generated:

[1904] Examples of prompt statements

[1905] What is the best way for store staff to learn how to display new products?

[1906] Based on these prompts, the generative AI will suggest optimal learning methods, which store staff can review via smartphone or tablet and apply to their work.

[1907] In addition, the server will provide a means to assist store staff in learning about their work. For example, the AI ​​will provide specific procedures and points to note for tasks such as cash register operation and inventory management.

[1908] It also includes a means to conduct simulations to improve customer service skills. Through simulations provided by AI, store staff can improve their skills in handling complaints and explaining products.

[1909] Finally, it also includes a means to provide real-time feedback, where AI can provide real-time feedback to store staff as they carry out their duties, advising them on appropriate language and attitudes.

[1910] This will enable store staff to learn their jobs efficiently and improve their customer service skills.

[1911] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1912] Step 1:

[1913] A user (a teacher or store staff member) uses a terminal (a smartphone or tablet) to input the content they want to learn. The input content is sent to the server. The input data is the specific learning content, such as "how to display new products."

[1914] Step 2:

[1915] The server has the generative AI create a prompt based on the received input data. The generative AI analyzes the input data and generates the optimal prompt sentence. For example, the generated prompt sentence might be, "What is the best way for store staff to learn how to display new products?"

[1916] Step 3:

[1917] The server sends the generated prompt sentence to the OpenAI API and receives a response from the AI ​​model. The OpenAI API suggests the optimal learning method based on the prompt sentence. The obtained response is a specific instruction such as "Please follow the steps below to display the new product."

[1918] Step 4:

[1919] The server provides the acquired responses to the user through a dedicated generation AI platform for education. The user can check the responses on their device and proceed with their learning. The responses include specific procedures and points to note.

[1920] Step 5:

[1921] As the user progresses with the learning process, the server provides feedback in real time. It monitors the user's operations and behavior and generates appropriate feedback. For example, it provides real-time advice on points to note and areas for improvement when the user displays products.

[1922] Step 6:

[1923] The server evaluates the user's learning progress and skill improvement, and provides additional learning content and simulations as necessary. For example, the server runs a simulation to improve customer service skills and provides feedback to the user.

[1924] Step 7:

[1925] Users improve their work skills based on the feedback and additional learning content provided. The server accumulates the user's learning data and uses it to support future learning sessions. This improves the user's work efficiency and the quality of customer service.

[1926] Example 3

[1927] 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".

[1928] In the conventional education system, it was difficult for teachers to adjust the pace of lessons according to each student's level of understanding, and it was also difficult to provide quick and appropriate answers to students' questions. This resulted in a decrease in students' learning efficiency and inconsistent quality of education.

[1929] 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.

[1930] In this invention, the server includes a means for accepting a user's question, a means for using a generative AI model that analyzes the question and generates an answer, a means for providing the generated answer, a means for evaluating the user's level of understanding, and a means for adjusting the speed of the lesson based on the evaluation result. This makes it possible to proceed with the lesson according to each student's level of understanding, and to improve learning efficiency by providing quick and appropriate answers.

[1931] The "means for accepting a question from a user" is a function for transmitting a question input by a user using a terminal to a server.

[1932] "Means of utilizing a generative AI model that analyzes questions and generates answers" refers to a function that inputs questions received by the server into a generative AI model and generates an appropriate answer.

[1933] "Means for providing a generated answer" refers to a function for sending the answer generated by the generative AI model to the user's terminal and displaying it.

[1934] The "means for evaluating the user's level of understanding" is a function for evaluating the user's level of understanding based on the user's reaction and additional questions, and storing the results in a database.

[1935] The "means for adjusting the pace of the lesson based on the evaluation results" is a function for adjusting the pace of the lesson based on the comprehension information stored in the database and sending the adjusted lesson plan to the user's terminal.

[1936] A "dedicated generative AI platform for education" is a platform that uses generative AI models designed to support teachers' educational activities.

[1937] "Means of using and developing AI" refers to functions that use generative AI models to support educational activities, and furthermore, to continuously improve and develop those AI models.

[1938] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1939] This invention is a system that supports educational activities using an AI teacher platform. This system accepts questions from users, generates appropriate answers using a generative AI model, and provides them to the users. It also has a function to evaluate the user's level of understanding and adjust the pace of the lesson based on the evaluation results.

[1940] Hardware and Software Used

[1941] Hardware: Servers, terminals (PCs, tablets, smartphones)

[1942] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), web servers (e.g. Apache)

[1943] Specific operation of the system

[1944] 1. Accepting user questions

[1945] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[1946] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[1947] 2. Question Analysis and Answer Generation

[1948] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[1949] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1950] Server: Receives the generated answer and proceeds to the next step.

[1951] 3. Providing answers

[1952] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[1953] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1954] 4. Evaluate comprehension

[1955] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[1956] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[1957] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[1958] Server: Stores the evaluation results in a database. Specifically, it inserts the evaluation data into the database using a SQL query.

[1959] 5. Adjusting the pace of lessons

[1960] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[1961] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[1962] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[1963] Examples

[1964] Accepting user questions

[1965] A user types into a terminal, "Please tell me how to solve a quadratic equation."

[1966] The device sends this question to the server in an HTTP request.

[1967] Question analysis and answer generation

[1968] The server sends the question to the API of the generative AI model (GPT-4).

[1969] GPT-4 generates the answer: "To solve a quadratic equation, first transform the equation into a standard form, and then use the solution formula to find the solution."

[1970] The server receives the generated answer and sends it to the terminal in an HTTP response.

[1971] Providing answers

[1972] The terminal will display to the user, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1973] Assessment of understanding

[1974] The user responds, "Okay."

[1975] The terminal sends this response to the server in an HTTP request.

[1976] The server rates the response "OK" as "high" and stores it in the database using an SQL query.

[1977] Adjusting the pace of lessons

[1978] The server adjusts the speed at which the next lesson content is advanced based on the comprehension level information in the database.

[1979] The server sends the adjusted lesson plan to the device in an HTTP response.

[1980] The terminal will display "Continue to next topic" to the user.

[1981] Examples of prompt statements

[1982] "Please teach me how to solve quadratic equations"

[1983] "Teach me more about World War II in history class."

[1984] "I have a question about English grammar. Can you tell me how to use the present perfect tense?"

[1985] The above is a specific embodiment for carrying out the invention of the AI ​​teacher platform. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1986] Step 1:

[1987] Accepting user questions

[1988] User: Uses the terminal to type a question, for example, "How do I solve a quadratic equation?"

[1989] Input: The question text entered by the user.

[1990] Terminal: Sends the entered question to the server. Specifically, it sends the question data to the server using an HTTP request.

[1991] Output: The question data sent to the server.

[1992] Step 2:

[1993] Question analysis and answer generation

[1994] Server: Inputs the received question into the generative AI model. Specifically, the question data is sent to the API of the generative AI model (e.g., GPT-4).

[1995] Input: The question data sent to the server.

[1996] Generative AI models analyze questions and generate appropriate answers, such as "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[1997] Output: The generated response data.

[1998] Step 3:

[1999] Providing answers

[2000] Server: Sends the generated answer to the terminal. Specifically, it sends the answer data to the terminal using an HTTP response.

[2001] Input: The generated answer data.

[2002] Terminal: Display the answer to the user, for example, "To solve a quadratic equation, first transform the equation into a standard form, then use the solution formula to find the solution."

[2003] Output: The answer displayed to the user.

[2004] Step 4:

[2005] Assessment of understanding

[2006] User: Checks the answer and enters a response based on their understanding, for example, "I understand."

[2007] Input: The user's response text.

[2008] Terminal: Sends the user's reaction to the server. Specifically, it sends the reaction data to the server using an HTTP request.

[2009] Server: Evaluate the user's level of understanding based on the response received. For example, the response "I understand" is evaluated as "high."

[2010] Output: Evaluated comprehension data.

[2011] Step 5:

[2012] Adjusting the pace of lessons

[2013] Server: Adjusts the pace of the lesson based on the level of understanding stored in the database. For example, if the level of understanding is high, the speed at which the lesson progresses will be increased.

[2014] Input: Comprehension information stored in the database.

[2015] Server: Sends the adjusted lesson plan to the device. Specifically, it sends the lesson plan data to the device using an HTTP response.

[2016] Terminal: Display the adjusted lesson plan to the user, for example, displaying "Continue to next topic."

[2017] Output: The lesson plan displayed to the user.

[2018] (Application example 3)

[2019] 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 headset type terminal 314 will be referred to as a "terminal".

[2020] In the conventional education system, it was difficult for teachers to respond to each student individually, which led to the problem of inconsistent quality of education. In addition, even in physical stores, customers often did not receive appropriate answers when they asked questions about products, which led to a lack of improvement in the purchasing experience. To solve these problems, it is necessary to use AI to provide individual responses and improve the quality of education and purchasing experiences.

[2021] 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.

[2022] In this invention, the server includes a means for creating prompts using generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for providing appropriate answers to questions from customers, and a means for making product suggestions based on the customer's purchase history. This makes it possible to improve the quality of education and the customer's purchase experience in physical stores.

[2023] "Generative AI" is artificial intelligence that generates text and information based on user input.

[2024] A "prompt" is an input sentence that causes a generative AI to generate specific information.

[2025] "Usage support" is a function that supports users to use the system effectively.

[2026] A "dedicated generative AI platform for education" is a system that provides generative AI specialized to support educational activities.

[2027] "AI utilization and development" refers to activities aimed at effectively utilizing artificial intelligence and improving its performance.

[2028] "Means of providing appropriate answers to questions from customers" refers to a function that uses generative AI to provide appropriate answers when customers ask questions about products or services.

[2029] "Means for making product suggestions based on a customer's purchasing history" refers to a function that analyzes a customer's past purchasing data and suggests new products based on that data.

[2030] A system for implementing this invention includes means for creating prompts using generative AI and providing usage support, means for providing a dedicated generative AI platform for educational purposes, means for using and developing AI, means for providing appropriate answers to questions from customers, and means for making product suggestions based on the customer's purchasing history.

[2031] The server uses generative AI to create prompts and generate appropriate answers to user questions. Specifically, it uses OpenAI's API to receive questions from users and generate answers to those questions. For example, if a user asks, "What is the battery life of this smartphone?", the server uses generative AI to respond, "The battery life of this smartphone is approximately 24 hours."

[2032] The server also analyzes the customer's purchase history and makes product suggestions based on that. For example, if the customer's purchase history is "smartphone, wireless earphones, smartwatch," the server will use generative AI to suggest, "We recommend a new smartphone case or a high-performance charger for you."

[2033] To realize this system, the following hardware and software are used.

[2034] Hardware: Smartphones, servers

[2035] Software: OpenAI API, Python

[2036] The server receives questions from users in text format and uses OpenAI's generative AI model to generate appropriate answers. The generated answers are sent to the user's smartphone and displayed. The server also receives customer purchase history in text format and uses the same generative AI model to generate product suggestions. The generated product suggestions are also sent to the user's smartphone and displayed.

[2037] As an example, use the following prompt:

[2038] "Customer Question: What is the battery life of this smartphone? Please provide more information about the product."

[2039] "Customer purchase history: Smartphone, Wireless earphones, Smartwatch. Please suggest products that you would recommend to this customer."

[2040] This will improve the quality of education and enhance the customer's purchasing experience in physical stores.

[2041] The flow of the specific process in the application example 3 will be described with reference to FIG.

[2042] Step 1:

[2043] The user enters a question using a smartphone.

[2044] Input: A user question (e.g., "What is the battery life of this smartphone?")

[2045] Output: User question text data

[2046] Step 2:

[2047] The terminal transmits the user's question to the server.

[2048] Input: User question text data

[2049] Output: The question data sent to the server.

[2050] Step 3:

[2051] The server converts the received question data into prompt text to be input into the generative AI model.

[2052] Input: User question data

[2053] Output: Prompt sentence for the generative AI model (e.g., "Customer question: What is the battery life of this smartphone? Please provide more information about the product.")

[2054] Step 4:

[2055] The server uses a generative AI model to generate an answer based on the prompt.

[2056] Input: A prompt for the generative AI model

[2057] Output: The generated answer text (e.g. "This smartphone's battery life is approximately 24 hours.")

[2058] Step 5:

[2059] The server transmits the generated answer text to the terminal.

[2060] Input: Generated answer text

[2061] Output: Answer data sent to the terminal

[2062] Step 6:

[2063] The terminal displays the received answer data to the user.

[2064] Input: Answer data sent to the terminal

[2065] Output: The answer shown to the user (e.g., "This smartphone's battery life is approximately 24 hours.")

[2066] Step 7:

[2067] A user enters a purchase history using a smartphone.

[2068] Input: User's purchase history (e.g. "smartphone, wireless earphones, smartwatch")

[2069] Output: User purchase history text data

[2070] Step 8:

[2071] The terminal transmits the user's purchase history to the server.

[2072] Input: User purchase history text data

[2073] Output: Purchase history data sent to the server

[2074] Step 9:

[2075] The server converts the received purchase history data into prompt text to be input into the generative AI model.

[2076] Input: User purchase history data

[2077] Output: Prompt sentence for the generative AI model (e.g., "Customer purchase history: Smartphone, wireless earphones, smartwatch. Please suggest products that you would recommend to this customer.")

[2078] Step 10:

[2079] The server uses the generative AI model to generate product suggestions based on the prompt sentence.

[2080] Input: A prompt for the generative AI model

[2081] Output: Generated product recommendation text (e.g. "We recommend a new smartphone case and a high-performance charger for you.")

[2082] Step 11:

[2083] The server transmits the generated product suggestion text to the terminal.

[2084] Input: Generated product suggestion text

[2085] Output: Product recommendation data sent to the terminal

[2086] Step 12:

[2087] The terminal displays the received product suggestion data to the user.

[2088] Input: Product recommendation data sent to the terminal

[2089] Output: Product recommendations displayed to the user (e.g., "We recommend a new phone case or a high-performance charger for you.")

[2090] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[2091] "Example 1"

[2092] In the form of the invention that combines an emotion engine, the generative AI recognizes the user's emotions and generates prompts according to those emotions. Specifically, the emotion engine analyzes the user's emotions from the user's tone of voice, facial expressions, choice of words, etc., and conveys the results to the generative AI. Based on that information, the generative AI generates appropriate prompts when the user is feeling joy, excitement, confusion, anxiety, etc. For example, when the user is confused, it generates a prompt that provides hints and explanations to solve the problem.

[2093] "Example 2"

[2094] The combination of an emotion engine with a dedicated generative AI platform for education takes into account the user's emotions when providing educational support information. Specifically, the emotion engine analyzes the user's emotions and transmits the results to the dedicated generative AI platform for education. Based on that information, the platform provides appropriate educational support information when the user is feeling joy, excitement, confusion, anxiety, or other emotions. For example, when the user is confused, it provides hints and explanations to solve the problem.

[2095] "Example 3"

[2096] The form in which AI uses an emotion engine to carry out educational activities is to adjust educational activities according to the user's emotions. Specifically, the emotion engine analyzes the user's emotions and conveys the results to the AI. Based on that information, the AI ​​will carry out appropriate educational activities when the user is feeling happiness, excitement, confusion, anxiety, etc. For example, when the user is confused, it will provide hints and explanations to solve the problem. Also, when the user is feeling happiness or excitement, it will carry out educational activities that will maintain those emotions.

[2097] The process flow of each embodiment will be described below.

[2098] "Example 1"

[2099] Step 1: The emotion engine analyzes the user's emotions from their tone of voice, facial expressions, choice of words, etc.

[2100] Step 2: The emotion engine communicates the analysis results to the generative AI.

[2101] Step 3: The generative AI uses the information from the emotion engine to generate prompts that correspond to the emotions the user is feeling.

[2102] "Example 2"

[2103] Step 1: The emotion engine analyzes the user's emotions.

[2104] Step 2: The emotion engine communicates the analysis results to a dedicated generative AI platform for education.

[2105] Step 3: The dedicated generative AI platform for education uses information from the emotion engine to provide educational support information that corresponds to the emotions the user is feeling.

[2106] "Example 3"

[2107] Step 1: The emotion engine analyzes the user's emotions.

[2108] Step 2: The emotion engine communicates the analysis results to the AI.

[2109] Step 3: Based on the information from the emotion engine, the AI ​​carries out educational activities according to the emotions the user is feeling.

[2110] Example 1

[2111] Next, a description will be given of Example 1 of the embodiment 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".

[2112] In conventional educational support systems, teachers had to spend a lot of time preparing for lessons, making it difficult to prepare lessons efficiently. In addition, it was not possible to provide appropriate support according to the teacher's emotions and situation, which increased the burden on teachers.

[2113] 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. In this invention, the server includes a means for creating a prompt using a generative AI and providing usage support, a means for providing a dedicated generative AI platform for education, a means for using and developing AI, a means for generating a prompt according to the emotion using an emotion engine that analyzes the emotion of the user, and a means for displaying the generated prompt on the user's terminal. This allows teachers to significantly reduce the time it takes to prepare for lessons and further provides appropriate support according to the emotion and situation of the teacher.

[2114] "Generative AI" refers to artificial intelligence that automatically generates text and information based on user input.

[2115] A "prompt" is an instruction, question, or piece of information that a generative AI provides to a user.

[2116] "Usage support" refers to functions and services that assist users in using the system effectively.

[2117] A "dedicated generative AI platform for education" is a base system for providing specialized generative AI to support educational activities.

[2118] "Use and development of AI" refers to utilizing artificial intelligence and training and adjusting it to improve its performance and functions.

[2119] An "emotion engine" is a technology or system that analyzes emotions from a user's tone of voice, facial expressions, choice of words, etc.

[2120] A "terminal" is a device, such as a computer, tablet, or smartphone, that a user uses to access the system.

[2121] This invention is a system that uses generative AI to create prompts and support educational activities. In this system, when a teacher prepares a lesson, the generative AI automatically generates prompts, and furthermore, an emotion engine is used to provide prompts according to the user's emotions.

[2122] Hardware and Software Used

[2123] Hardware: Servers, terminals (PCs, tablets, smartphones, etc.)

[2124] Software: Generative AI models (e.g. GPT-4), emotion engines

[2125] Specific operation of the system

[2126] 1. User Input

[2127] A user (teacher) uses a terminal to input information necessary for preparing a lesson. For example, the user might input "I will teach how to solve linear equations in a math class for junior high school students."

[2128] 2. Data Receipt and Processing by the Server

[2129] The server receives the data sent by the user and converts it into an appropriate format, performing data formatting and pre-processing.

[2130] 3. Prompt generation by generative AI

[2131] The server feeds the formatted data into a generative AI model (e.g., GPT-4) to generate appropriate prompts, such as "problems that explain how to solve linear equations step-by-step, in a way that is easy for middle school students to understand."

[2132] 4. Emotion Analysis Using an Emotion Engine

[2133] The server uses an emotion engine to analyze the user's tone of voice, facial expressions, choice of words, etc., and determines the user's emotions.

[2134] 5. Emotion-based prompt generation

[2135] The server conveys the emotion information obtained from the emotion engine to the generative AI, which then uses that information to generate prompts that correspond to the user's emotions. For example, when a user is confused, the generative AI generates prompts that provide hints and explanations to solve the problem.

[2136] 6. Displaying the results on the terminal

[2137] The server sends the generated prompt to the terminal to display to the user, and the teacher's terminal is shown a "step-by-step problem on how to solve a linear equation," with further explanations if the teacher is confused.

[2138] Examples

[2139] Example: A teacher types, "I teach middle school math how to solve linear equations."

[2140] Example of a generated prompt: A generative AI generates a problem that explains how to solve a linear equation step-by-step, in a way that is easy for junior high school students to understand.

[2141] Emotion analysis example: If a teacher has a confused expression, the emotion engine will analyze that expression and determine that the expression is "confused."

[2142] Example of an emotion-sensitive prompt: A generative AI generates a prompt to provide further explanation to a struggling teacher on how to solve a linear equation.

[2143] Examples of prompt statements ...

Claims

1. A means for providing an educational generative AI platform; A means for collecting user emotion data; A means for analyzing the collected emotion data of the user; A means for generating an educational activity to be provided to the user using the generation AI platform based on an analysis result of the emotion data of the user; means for providing the generated educational activity to the user; A system including:

2. The generating AI platform generates information to support the educational activities of the breeder. The system of claim 1 .

3. The providing means proposes an educational activity generated using a generative AI model based on a prompt sentence corresponding to the user's emotion. The system of claim 1 .

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