System

Abstract

A system is provided.SOLUTION: A system for constructing generation AI environments that operate in a closed network, the system including network settings accessible only from specific client terminals, the system comprising: authenticating means for restricting access from the client terminals; restricting means for permitting access only during class hours; and log managing means for recording usage logs of the client terminals.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current public generative AI tools have not been widely adopted in educational institutions due to concerns about information leaks and the difficulty of account management. Furthermore, educational activities using generative AI lack an appropriate environment for students to safely and effectively learn educational content. Furthermore, customization based on each school's curriculum is required, and manually doing this increases the burden on teachers. This invention solves these issues and provides a system for safely and efficiently using generative AI in educational institutions. [Means for solving the problem]

[0005] The present invention provides the following means. A system is provided that includes network settings that allow access only from specific user terminals, for building a generative AI environment that operates within a closed network. Specifically, the system includes authentication means for restricting access from user terminals, restriction means for allowing access only during class hours, and log management means for recording user terminal usage logs. The system also includes customization means for generating prompt templates according to a curriculum, means for providing the generated prompt templates to user terminals, and means for collecting and analyzing usage results of the generative AI tool, in order to provide a generative AI tool customized for educational institutions. The system also includes means for creating educational content that covers everything from basic knowledge of generative AI to applied techniques, means for setting access rights to the educational content, and means for monitoring the usage of the educational content, in order to provide educational content. In this way, the system solves issues of information leakage and account management, enables the use of generative AI according to the curriculum, and effectively introduces generative AI into educational institutions.

[0006] A "closed network" is a network that can only be used within a specific organization or group, and strengthens security by restricting access from outside.

[0007] "Generative AI" is a specific branch of artificial intelligence that refers to algorithms that autonomously generate content, including text, images, and audio.

[0008] "Network configuration" refers to the process or result of specifying the various parameters and setting information required to build and operate a computer network.

[0009] "Authentication measures" refers to technologies and methods used to verify that a user or device has legitimate authority when accessing a network or system.

[0010] "Restriction Measures" refers to technologies or methods for restricting the use of a system or application based on specific times or conditions.

[0011] "Log management measures" refer to technologies and methods for recording the usage history of systems and applications and for later analysis and monitoring.

[0012] "Educational content" refers to a collection of materials and information created for educational purposes, and may take a variety of forms, including text, video, and quizzes.

[0013] A "prompt template" is an input sentence format that instructs the generation AI on a specific output, and serves as a guideline for obtaining the desired generation result.

[0014] "Customization methods" refers to techniques and methods for adapting systems and tools based on specific requirements and conditions.

[0015] "Access rights" refers to rights that grant specific users or devices the right to use information resources or system functions.

[0016] "Monitoring means" refers to technologies and methods for monitoring the operation and usage status of systems and applications and collecting necessary information. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] 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.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for safely and effectively using generative AI in educational institutions. Specifically, this system is configured as follows.

[0039] 1. Setting up and managing a closed network

[0040] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[0041] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[0042] 2. Account Management

[0043] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[0044] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[0045] 3. Customizing the generated AI

[0046] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[0047] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[0048] 4. Providing educational content

[0049] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[0050] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[0051] 5. Provision of cloud services

[0052] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0053] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0054] The processing flow will be explained below.

[0055] Setting up and managing a closed network

[0056] Step 1: Network Configuration

[0057] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[0058] The server will set up firewall rules to block access from outside the specified IP ranges.

[0059] Step 2: VPN Settings

[0060] The server configures the VPN server and generates authentication information for remote access.

[0061] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[0062] Step 3: Check access control

[0063] The server performs a test access to check whether access to the private network is being controlled properly.

[0064] The terminal attempts a test access and confirms that it is within the closed network.

[0065] Account Management

[0066] Step 1: Upload your account information

[0067] The teacher's terminal will prepare a CSV file containing student account information.

[0068] The teacher's terminal uploads this CSV file to the server.

[0069] Step 2: Create an account

[0070] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[0071] The server sets parental controls for each account, allowing access only during school hours.

[0072] Step 3: Record usage logs

[0073] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[0074] Customizing the generated AI

[0075] Step 1: Enter curriculum data

[0076] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[0077] Step 2: Prompt generation

[0078] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[0079] Step 3: Apply customization

[0080] After logging in, the student terminal receives a customized prompt template from the server.

[0081] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[0082] Providing educational content

[0083] Step 1: Create educational content

[0084] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[0085] Step 2: Content Delivery

[0086] The server sets and provides access rights to educational content to teachers and students.

[0087] Step 3: Content Usage

[0088] The teacher's terminal displays educational content during class and explains the learning content.

[0089] Students use their devices to study independently using materials provided at home or during class.

[0090] Cloud service provision

[0091] Step 1: Subdomain Setup

[0092] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[0093] Step 2: Cloud Access

[0094] The student terminal accesses the individual subdomain provided by the browser.

[0095] Student devices enter login credentials and use the generative AI tool.

[0096] Step 3: Resource Management

[0097] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[0098] By repeatedly performing these steps, a safe and effective environment for utilizing generative AI can be achieved.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] The use of conventional generative AI tools has posed risks of information leaks and unauthorized access at educational institutions. It has also been difficult to customize generative AI tools to effectively incorporate them into lessons, and to properly manage accounts for them. Furthermore, there has been a lack of a systematic way to provide educational content covering everything from the basics to applications of generative AI. These issues have been major obstacles to providing a safe and effective environment for using generative AI.

[0102] 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.

[0103] In this invention, the server includes a means for setting up a closed network through a network gateway and creating an environment accessible only from a specific IP address range, a means for preventing unauthorized access from outside using a firewall and VPN, a means for setting parental control that allows access only during specific time periods, a means for automatically generating accounts from data files containing student information, a means for generating and providing prompt templates according to lesson content, and a means for creating and providing educational content for generative AI. This makes it possible to provide generative AI tools in a safe environment with strict access control, and further enables the effective use of generative AI according to the curriculum of educational institutions and the improvement of student learning outcomes.

[0104] A "network gateway" is a device or software that manages and controls communication between an internal network and an external network.

[0105] A "closed network" is a limited network environment that can only be accessed by specific users or devices.

[0106] An "IP address range" refers to a specific range of IP addresses, and access rights can be set only for addresses within that range.

[0107] A "firewall" is a hardware or software security system that protects a network from unauthorized access.

[0108] "VPN" stands for Virtual Private Network, a technology for establishing secure communications over the Internet.

[0109] "Parental control" is a function that restricts access rights to specific time periods or content.

[0110] A "data file" is a file format for storing specific information on a computer.

[0111] "Creating an account" refers to setting a user's identification information and authentication information to create a new account.

[0112] A "prompt template" is a predefined document format that provides specific instructions or questions to the generative AI.

[0113] "Educational content for generative AI" refers to teaching materials for systematically learning everything from basic knowledge to applied techniques of generative AI.

[0114] The present invention is a system for the safe and effective use of generative AI in educational institutions. The system consists of the following components:

[0115] Setting up and managing a closed network

[0116] The server is set up as a closed network through the institution's network gateway. Specifically, firewall software (e.g., pfSense) is used to specify the IP range "192.168.0.0 / 24" so that only devices within this range can access it. VPN server software (e.g., OpenVPN) is also set up to allow secure access from outside. This prevents unauthorized access and reduces the risk of information leaks.

[0117] Account Management

[0118] To centrally manage student account information, teachers' devices create a CSV file (e.g., students.csv) and upload it to the server. The server analyzes this file and automatically creates student accounts. At this time, a parental control function is also added to allow access only during class hours. For example, the server might set class hours to "weekdays 9:00-17:00" and restrict access outside of these hours.

[0119] Customizing the generated AI

[0120] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server. The server then generates prompts based on this and provides them to the user's terminal. For example, the server may prepare templates for five subjects (Japanese, mathematics, science, social studies, and English) in advance and customize them as needed. An example of a specific prompt sentence used in a mathematics class is "Please explain how to solve a cubic equation."

[0121] Providing educational content

[0122] The server creates educational content covering everything from the basics to applications of generative AI and provides it to teachers and students. This educational content consists of videos, PDFs, and quizzes. The teacher's device projects the content during class, and the students' devices use the content for self-study. Specific examples include a video called "Fundamentals of Generative AI" and a PDF of the "Ethics Education Handbook" provided by the server.

[0123] Cloud service provision

[0124] This system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students can use the generation AI tool by accessing this URL from their browser. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0125] As a specific example, a user (student) accesses "schoolname.generateai.com" and logs in using the provided credentials. After that, they select "Please explain how to solve a cubic equation" as a math prompt template, and the Generative AI returns an answer based on that.

[0126] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] Setting up and managing a closed network

[0130] The server sets up a closed network through the network gateway. Specifically, the server uses firewall software (e.g., pfSense) to specify the IP range "192.168.0.0 / 24" to allow access only from devices within this range. In addition, VPN server software (e.g., OpenVPN) is configured to allow secure access from outside.

[0131] Input: Network setting information (IP range, VPN settings)

[0132] Data processing / calculation: Firewall rule settings, VPN connection settings

[0133] Output: Closed network setting completion notification

[0134] Specific actions: The server reads the configuration file, applies network rules, and starts the VPN server.

[0135] Step 2:

[0136] Account Management

[0137] To centrally manage student account information, teachers create a CSV file (e.g., students.csv) on their devices and upload it to the server. The server then analyzes this file and automatically creates student accounts. A parental control feature is also added to allow access only during class hours.

[0138] Input: CSV file (student list)

[0139] Data processing / calculation: CSV file analysis, account information generation, parental control settings

[0140] Output: Generated account information

[0141] Specific operation: The server parses the CSV file, registers the new account in the database, and applies access restrictions outside of class hours.

[0142] Step 3:

[0143] Customizing the generated AI

[0144] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server, which then generates prompts based on the template and provides them to the user's terminal.

[0145] Input: Prompt template content

[0146] Data processing / calculation: Creating and saving templates

[0147] Output: Customized prompt template

[0148] Specific operation: The server receives template data from the teacher's terminal, stores it in a database, and generates prompts based on it.

[0149] Step 4:

[0150] Providing educational content

[0151] The server creates educational content covering everything from the basics to applications of generative AI, and provides it to teachers and students' devices. The educational content includes videos, PDFs, and quizzes.

[0152] Input: Educational content information (videos, PDFs, quizzes)

[0153] Data processing / calculation: content format conversion and storage

[0154] Output: Served educational content

[0155] Specific operation: The server creates educational content, converts it into the specified format, and uploads it to the online portal.

[0156] Step 5:

[0157] Cloud service provision

[0158] The server sets up a separate subdomain for each educational institution, allowing students to access the generative AI tool by accessing this URL from their browser.

[0159] Input: Educational institution registration information

[0160] Data processing / calculation: Subdomain settings, authentication information generation

[0161] Output: Access URL and authentication information

[0162] Specific operations: The server receives the new educational institution information, sets up a subdomain, and prepares the login screen.

[0163] These are the processing steps of this system, which enables the safe and effective use of generative AI in educational institutions.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] This invention is based on a generative AI system for educational institutions, and aims to improve the efficiency of store management and the skills and knowledge required for virtual store operation. Conventional generative AI systems for educational institutions contribute to improving the quality of education, but they have the problem of not being able to address the knowledge required for virtual store operation and the efficiency of management work. Furthermore, as virtual store operations are increasing today, the lack of support systems specialized for virtual store operation is becoming a serious problem.

[0167] 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.

[0168] In this invention, the server is a system that includes network settings that allow access only from specific user terminals to build a generative AI environment that operates within a closed network, and includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, means for providing generative AI tools for virtual store operation, and customization means for streamlining various management tasks. This makes it possible to learn the knowledge and skills necessary for virtual store operation and to streamline management tasks.

[0169] A "closed network" is a limited network environment that is accessible only to a select few users, and is used to ensure security and privacy.

[0170] A "generative AI environment" is an environment that provides a system that uses artificial intelligence to make predictions and generate results based on user input.

[0171] "User terminal" means a user's device used to access the system, including smartphones, tablets, PCs, etc.

[0172] "Network settings" refers to the process of specifying IP addresses and setting authentication protocols to allow access only from specific devices.

[0173] "Authentication means" refers to a mechanism for verifying the validity of a user terminal when accessing a system. For example, it includes authentication of a user name and password.

[0174] "Restriction measures" are functions that restrict the use of the system depending on time and conditions, and include restrictions such as only allowing access during certain hours.

[0175] "Log management means" refers to a mechanism for recording and managing system usage, and records access logs and operation history.

[0176] A "virtual store" is an online store that sells products and services over the Internet and does not have a physical store.

[0177] "Generative AI tools" are tools that use generative AI models to automate various tasks, including text generation from prompts and data analysis.

[0178] "Customization" refers to the functionality and processes that allow users to adapt and modify systems and tools to meet their specific needs.

[0179] A "prompt template" is a template of instructions that a generative AI uses to generate a response to a specific task or question.

[0180] "Educational content" is a general term for teaching materials and resources provided to learners, including videos, slides, PDFs, etc.

[0181] "Account Management" refers to the process and system for registering and managing user account information.

[0182] The present invention provides a generative AI system for supporting virtual store operations, and its main functions are as follows:

[0183] 1. Private network configuration and management:

[0184] The server creates a closed network that can only be accessed from specific user terminals and configures the network. Specifically, AWS VPC is used to set up a specific IP address range and provide secure access using a VPN connection. This closed network enables the safe operation of the virtual store.

[0185] 2. Account Management:

[0186] The server centrally manages the accounts of virtual store managers and staff. Account information is imported from a CSV file and saved in the database. This allows for role-based access permissions, enabling efficient account management. For example, managers can register products, while staff can only check inventory.

[0187] 3. Customizing generative AI tools:

[0188] The server provides a generative AI tool specialized for virtual store operations. This tool performs various data analysis and predictions using generative AI models based on prompt templates, such as optimizing inventory management, automating customer service, and predicting sales trends.

[0189] Example prompt: "Generate a sales forecast for next month."

[0190] 4. Providing educational content:

[0191] The server provides educational content for learning the skills necessary for running a virtual store. It allows users to access videos, slides, PDF documents, etc. through a smartphone app using React Native. It also provides a function to monitor the usage of this content.

[0192] Example prompt: "Please show me a video presentation that explains how to manage inventory efficiently."

[0193] 5. Provision of Cloud Services:

[0194] The server sets up a subdomain for each virtual store, allowing it to be accessed from a browser. This allows users to log in and access various management tools and educational content. Using cloud services reduces implementation costs and enables smooth operation.

[0195] To achieve these functions, the server utilizes hardware and software such as AWS EC2 instances, AWS VPC, OpenVPN, Node.js, and React Native. On the user side, smartphones and head-mounted displays can be used to efficiently learn about and manage virtual store operations.

[0196] Specifically, the server uses AWS EC2 to build a closed network and manages accounts using a database, while data analysis using generative AI models and the provision of educational content can be used to streamline virtual store operations.

[0197] For example, if a virtual store manager needs a sales forecast, they can use a generative AI tool to enter the following prompt:

[0198] "Generate a sales forecast for next month."

[0199] This allows the generative AI model to automatically analyze data and provide predictions, while also providing managers with prompts to train new staff:

[0200] "Show me video materials that explain efficient inventory management methods."

[0201] This allows educational content suitable for smartphone devices to be provided.

[0202] In this way, the present invention provides an advanced support system for virtual store management, and is equipped with features for realizing efficient and effective management.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The server creates a closed network using AWS VPC. It sets a specific IP address range (e.g., 192.168.0.0 / 24) as input and provides a secure network environment as output. The server then configures OpenVPN to enable secure remote access.

[0206] Step 2:

[0207] The user uploads a CSV file to the server using a teacher's terminal and imports the account information of the virtual store manager and staff. The CSV file containing the account information is prepared as input, and the account data stored in the database is obtained as output. Specifically, the server parses the CSV file and inserts each account information into the database.

[0208] Step 3:

[0209] The server provides tools using generative AI models, and users (administrators) input prompts related to virtual store operations. Prompts such as "Please generate sales forecasts for next month" are used as input, and sales forecast data is generated as output. The server analyzes the prompts and executes the generative AI model to calculate the forecast results.

[0210] Step 4:

[0211] The server provides the educational content required for running a virtual store through a React Native app. It receives a request from the administrator as input (e.g., "Please display a video document explaining efficient inventory management methods.") and delivers the appropriate educational content to the user's device as output. Specifically, the server retrieves the relevant educational content from the database and sends it to the user's smartphone.

[0212] Step 5:

[0213] Users (managers and staff) use the delivered educational content to learn knowledge that will be useful in running a virtual store. They select content on the smartphone app as input, and obtain the learned knowledge as output. Users acquire practical skills by watching videos and viewing slides.

[0214] Step 6:

[0215] As a cloud service, the server automatically sets up a subdomain so that users can access it from their browser. It takes the information for a new virtual store as input and provides a dedicated subdomain (e.g., shopname.example.com) as output. This setup allows users to easily access the system and start operating.

[0216] These steps seamlessly provide the various functions necessary for virtual store operation, enabling efficient and effective operation.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] This invention is a system for safely and effectively using generative AI in educational institutions, which is combined with an emotion engine that recognizes user emotions. The system is configured as follows.

[0219] 1. Setting up and managing a closed network

[0220] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[0221] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[0222] 2. Account Management

[0223] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[0224] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[0225] 3. Customizing the generated AI

[0226] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[0227] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[0228] 4. Providing educational content

[0229] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[0230] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[0231] 5. Provision of cloud services

[0232] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0233] 6. Introducing the Emotion Engine

[0234] An emotion engine that recognizes user emotions has been added to the system to improve teaching effectiveness. The server collects emotion data in real time when using the generative AI tool and adjusts prompt content and AI responses accordingly. In addition, teacher devices can refer to the emotion data to understand students' learning status.

[0235] For example, the emotion engine combines facial expression data, voice tone, and text sentiment analysis to identify a user's emotions. This allows the generative AI to respond according to the user's level of stress or interest, improving educational effectiveness. The emotion engine can also identify when a student is facing a difficult problem and provide additional hints or support.

[0236] In this way, the present invention provides a system that combines generative AI and emotion engines in educational institutions, contributing to improving the quality of education while reducing the risk of information leakage.

[0237] The processing flow will be explained below.

[0238] Setting up and managing a closed network

[0239] Step 1: Network Configuration

[0240] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[0241] The server sets up firewall rules to block access from outside the specified IP ranges.

[0242] Step 2: VPN Settings

[0243] The server configures the VPN server and generates authentication information for remote access.

[0244] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[0245] Step 3: Check access control

[0246] The server performs a test access to check whether access to the private network is being controlled properly.

[0247] The terminal attempts a test access and confirms that it is within the closed network.

[0248] Account Management

[0249] Step 1: Upload your account information

[0250] The teacher's terminal will prepare a CSV file containing student account information.

[0251] The teacher's terminal uploads this CSV file to the server.

[0252] Step 2: Create an account

[0253] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[0254] The server sets parental controls for each account, allowing access only during school hours.

[0255] Step 3: Record usage logs

[0256] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[0257] Customizing the generated AI

[0258] Step 1: Enter curriculum data

[0259] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[0260] Step 2: Prompt generation

[0261] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[0262] Step 3: Apply customization

[0263] After logging in, the student terminal receives a customized prompt template from the server.

[0264] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[0265] Providing educational content

[0266] Step 1: Create educational content

[0267] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[0268] Step 2: Content Delivery

[0269] The server sets and provides access rights to educational content to teachers and students.

[0270] Step 3: Content Usage

[0271] The teacher's terminal displays educational content during class and explains the learning content.

[0272] Students use their devices to study independently using materials provided at home or during class.

[0273] Cloud service provision

[0274] Step 1: Subdomain Setup

[0275] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[0276] Step 2: Cloud Access

[0277] The student terminal accesses the individual subdomain provided by the browser.

[0278] Student devices enter login credentials and use the generative AI tool.

[0279] Step 3: Resource Management

[0280] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[0281] Introducing the Emotion Engine

[0282] Step 1: Collecting emotion data

[0283] The student terminals use cameras and microphones to collect the user's facial expressions, voice tone, gestures, etc. in real time.

[0284] The server receives this data and analyzes the user's emotional state.

[0285] Step 2: Feedback of emotional data

[0286] The server adjusts the prompts and responses of the generating AI based on the analyzed emotional data.

[0287] The student terminal receives feedback according to the user's status.

[0288] Step 3: Analyze the sentiment data

[0289] The server integrates the collected emotional data with the usage logs of the generative AI to evaluate the effectiveness of the education.

[0290] Based on the analysis results, the teacher's device will be able to grasp students' learning situation and emotional state, which will be used to improve the content of lessons.

[0291] These steps will enable a safe and effective educational system that utilizes generative AI and emotion engines.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] Conventional generative AI environments in educational institutions have a high risk of unauthorized access and information leaks from outside, and there are many restrictions on the use of generative AI tools. It is also difficult to monitor students' emotional states in real time and provide appropriate teaching methods. Therefore, a new system that can balance information security and educational quality is needed.

[0295] 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.

[0296] In this invention, the server

[0297] an authentication means for restricting access from a user terminal;

[0298] Restrictions that allow access only during school hours;

[0299] a log management means for recording a usage log of the user terminal;

[0300] A customization means for generating curriculum-specific prompt templates;

[0301] means for providing the generated prompt template to a user terminal;

[0302] A means of collecting and analyzing the results of using generative AI tools;

[0303] a means of collecting emotion data in real time;

[0304] a means for adjusting the response of the generative AI based on the emotion data; and

[0305] a means of monitoring the use of educational content;

[0306] This will enable the safe and effective use of generative AI, reducing the risk of information leakage while providing appropriate education tailored to students' emotional states.

[0307] "Authentication methods" are methods for ensuring that only specific users can access a system, and include IDs, passwords, and two-factor authentication.

[0308] "Restriction measures" are methods of allowing access to the system only under certain conditions, such as during school hours, and include time restrictions and access control lists.

[0309] "Log management means" refers to a method for recording and saving the access and operation history of a user's terminal, which can be used for later verification or to detect unauthorized access.

[0310] "Customization methods" refer to methods for generating and adjusting prompt templates for generative AI tools according to the curriculum of educational institutions, and include dedicated configuration tools and template management functions.

[0311] "Providing means" refers to a method for providing the generated prompt templates and educational content to a user terminal, and includes a data distribution system and an access link.

[0312] "Collection and analysis means" refers to methods for collecting and analyzing the results of using the generative AI tool and user operation information, and includes database systems and analysis algorithms.

[0313] "Emotion data" is data that indicates the user's emotional state, and includes facial expression data, voice tone, text analysis results, and the like.

[0314] "Means for collecting data in real time" refers to methods for acquiring user emotional data in real time, including sensors and analysis software.

[0315] "Means for adjusting the response of the generative AI" refers to a method for appropriately adjusting the response of the generative AI based on the user's emotional data, and includes dialogue systems and response generation algorithms.

[0316] "Monitoring means" refers to methods for monitoring the usage of educational content and collecting data, including log collection tools and analysis dashboards.

[0317] In this invention, the following system configuration and method are used to safely and effectively operate a generative AI environment in an educational institution. The system mainly consists of a server, a teacher terminal, and a student terminal.

[0318] Setting up and managing a closed network

[0319] The server will be set up as a closed network through the network gateway within the educational institution. By defining a specific IP address range (e.g., 192.168.0.0 / 24) and setting up a firewall and VPN, external access will be blocked, creating a secure communication environment.

[0320] Specifically, the server accesses the network gateway settings screen and specifies an IP range. Next, firewall rules are set to allow communication only from devices within this IP range. Finally, a VPN is set up to ensure secure remote access.

[0321] Account Management

[0322] The teacher's terminal compiles student account information into a CSV file and uploads it to the server. The server analyzes this CSV file and automatically creates accounts for each student, adding a parental control function that allows use only during class time.

[0323] When a teacher uploads the CSV file to the server, the server analyzes the file and automatically creates an account for each student. Parental controls are then set to restrict access outside of class hours.

[0324] Customizing the generated AI

[0325] The teacher's terminal creates prompt templates for the generative AI tool according to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[0326] As a specific example, the server prepares prompt templates for Japanese, mathematics, science, social studies, and English. For example, in a mathematics class, a prompt might be set to "Please explain how to solve a cubic equation." This is set by the teacher's device and sent to the server, where it is reflected in the generation AI.

[0327] Providing educational content

[0328] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for self-study on their devices.

[0329] Specifically, a server creates educational content in the form of videos, PDFs, quizzes, etc. and provides it online. Teachers' devices display this content on a projector during class, and students' devices view the educational content at home.

[0330] Cloud service provision

[0331] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL from their browsers to use the generation AI tool. By logging in, users can select and use prompt templates for each subject.

[0332] Introducing the Emotion Engine

[0333] The server collects real-time emotional data from users when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can access students' emotional data and understand their learning status.

[0334] The emotion engine combines facial expression data with voice tone and text emotion analysis to identify the user's emotions. Based on this, the generative AI can respond according to the user's level of stress and interest, improving teaching effectiveness. In addition, the teacher's device uses a dashboard to monitor students' emotional data in real time.

[0335] This system enables the safe and effective use of generative AI, reducing the risk of information leaks while providing appropriate education tailored to students' emotional states.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Setting up and managing a closed network

[0339] The server accesses the institution's network gateway and sets a specific IP address range (e.g., 192.168.0.0 / 24). The server specifies the IP range in the network gateway settings screen and sets firewall rules to allow communication only from devices within this IP range. The server also sets up a VPN to allow secure remote access.

[0340] Specific behavior:

[0341] Input: Specific IP address range and VPN configuration information

[0342] Data processing: Apply IP address ranges, set firewall rules, configure VPN

[0343] Output: A secure network environment that can only be accessed by user terminals within the closed network

[0344] Step 2:

[0345] Account Management

[0346] The teacher's terminal creates student account information as a CSV file and uploads it to the server. The server analyzes the CSV file and automatically creates accounts for each student. At the same time, the server sets up parental control functions to restrict access outside of class hours.

[0347] Specific behavior:

[0348] Input: CSV file containing a list of students

[0349] Data processing: CSV file analysis, automatic creation of student accounts, setting parental controls

[0350] Output: Generated student accounts and access restriction functions

[0351] Step 3:

[0352] Customizing the generated AI

[0353] The teacher's terminal creates prompt templates tailored to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[0354] Specific behavior:

[0355] Input: Prompt template sent from the teacher's terminal

[0356] Data processing: Saving and generating prompt templates, and reflecting them in the generation AI

[0357] Output: Provides the generated prompt to the user's terminal

[0358] Step 4:

[0359] Providing educational content

[0360] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for home study.

[0361] Specific behavior:

[0362] Input: Educational content materials (videos, PDFs, quizzes, etc.)

[0363] Data processing: Creation of educational content and preparation for online distribution

[0364] Output: Provide the completed educational content to teacher and student devices

[0365] Step 5:

[0366] Cloud service provision

[0367] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL to use the generation AI tool. Users log in and select prompt templates for each subject.

[0368] Specific behavior:

[0369] Input: Subdomain information of each educational institution and URL of the AI ​​tool

[0370] Data processing: Subdomain settings, access rights settings

[0371] Output: A subdomain dedicated to each educational institution and the access URL for the generated AI tool

[0372] Step 6:

[0373] Introducing the Emotion Engine

[0374] The server collects users' emotional data in real time when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can refer to students' emotional data and understand their learning status.

[0375] Specific behavior:

[0376] Input: facial expression data, voice tone, text emotion data

[0377] Data processing: Real-time analysis of emotional data and feedback to generative AI

[0378] Output: Response of the generative AI based on emotion data, emotion data monitoring results displayed on the teacher's terminal

[0379] (Application example 2)

[0380] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] There is a need for a method to safely and effectively use generative AI in environments such as educational institutions and brick-and-mortar stores, while providing appropriate responses based on user emotions. However, existing systems do not adequately ensure security, analyze emotional data in real time, or adjust responses. Furthermore, it is difficult to restrict access from specific user devices and manage usage time, making it difficult to maximize the effectiveness of generative AI. This poses a challenge in achieving sufficient educational effectiveness and customer satisfaction.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, emotion recognition means for collecting and analyzing emotion data in real time, prompt generation means for adjusting the response of the generation AI based on the emotion data, and security means for safely managing user information. This allows the generation AI to provide responses that correspond to the user's emotional state, improving educational effectiveness and customer satisfaction.

[0383] A "closed network" is a network environment that can only be accessed from specific authorized user terminals and prevents unauthorized access from outside.

[0384] A "generative AI environment" refers to the entire system that uses artificial intelligence technology to generate content such as text and images.

[0385] "Authentication means" is a function that provides an authentication process to confirm that a user terminal has legitimate access rights.

[0386] "Restriction measures" are functions that restrict access to the generated AI environment based on specific time periods or conditions.

[0387] "Log management means" is a function that records and manages the history of users' access to the system.

[0388] "Emotion recognition means" is a function that collects and analyzes emotional data from the user's facial expressions, voice tone, and text content.

[0389] The "prompt generation means" is an artificial intelligence function that generates an appropriate response based on emotional data and the user's situation.

[0390] "Security measures" are functions for protecting user information and system data from external threats.

[0391] "Customization" refers to the ability to create prompt templates for the generative AI tool to suit specific curriculum and requirements.

[0392] "Means for collecting and analyzing usage results" refers to the function of collecting data associated with the use of generative AI tools and evaluating and analyzing their effectiveness.

[0393] "Educational content" refers to the teaching materials and resources provided to learners, and includes everything from basic knowledge of generative AI to applied techniques.

[0394] The "means for setting access rights" is a function for granting access rights to educational content to specific users.

[0395] "Means for monitoring" refers to the function of monitoring the usage of educational content and collecting that data.

[0396] "Means for analyzing educational effectiveness" is a function that evaluates and analyzes the learning effectiveness of educational content based on emotion recognition.

[0397] The present invention relates to a generative AI system for use in educational institutions and brick-and-mortar stores, and in particular to an AI system that automatically adjusts responses based on user emotion recognition. Hereinafter, a detailed description of an embodiment of the present invention will be given.

[0398] 1. System Configuration

[0399] This system consists of a server, a user terminal, and multiple devices including smart glasses. These devices are securely connected via a closed network and can only be accessed from specific user terminals.

[0400] 2. Closed Networks and Access Restrictions

[0401] The server uses a firewall and VPN to create a closed network to prevent unauthorized access from outside. Access from user devices is managed by authentication methods and is limited to class hours and business hours. This allows the generative AI system to be used in a secure environment.

[0402] 3. Emotion Recognition and Prompt Generation

[0403] Using smart glasses or a camera-equipped PC, emotion data is collected by analyzing the user's facial expressions and tone of voice in real time using OpenCV and Transformers libraries. The emotion recognition results are sent to a server, where generative AI creates an optimal response based on the emotion.

[0404] For example, the following prompt is set:

[0405] "Please explain how to solve a cubic equation."

[0406] "Suggest a product that's on promotion"

[0407] "Do you have any particular favorite products?"

[0408] 4. Data Management and Security

[0409] User access history is recorded by a log management system. Emotional data and response data are also strictly managed, and measures are taken to prevent information leaks, including data encryption and access control.

[0410] 5. Providing customized and educational content

[0411] The server customizes prompt templates for the generative AI tool according to the needs of each educational institution or brick-and-mortar store. The educational content is provided as teaching materials covering everything from basic knowledge of generative AI to applied techniques. Users can use this for self-study.

[0412] As described above, the present invention provides a response adjustment system for generative AI that utilizes emotion recognition data, thereby realizing safe and effective learning and customer service support.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The server creates a closed network and prepares an environment that can only be accessed from specific user terminals. To achieve this, a firewall and VPN are set up. The input is the IP address and network setting information of the user terminal, and the output is a notification that the closed network has been successfully created. This step establishes a secure network environment.

[0416] Step 2:

[0417] The terminal authenticates the user's access using authentication means. The input is the user's authentication information (ID and password, etc.), and the output is a notification of authentication success or failure. After successful authentication, the user is allowed access to the system.

[0418] Step 3:

[0419] The server uses a log management tool to record access logs from user terminals. The input is access information such as the access time, user ID, and connection IP address, and the output is the recorded log data. This data is later used for analysis and auditing.

[0420] Step 4:

[0421] The device uses smart glasses and a camera to analyze the user's facial expressions and tone of voice in real time. The input is camera footage and audio data, and the output is analyzed emotion data. Specifically, it extracts facial features using OpenCV and recognizes emotions using the Transformers library.

[0422] Step 5:

[0423] The server receives the emotion data and uses a generative AI model to generate an optimal prompt based on that data. The input is the emotion data and a pre-defined prompt template, and the output is the generated prompt. For example, if the emotion is "happy," the generated prompt is "Please suggest a product that is currently on promotion."

[0424] Step 6:

[0425] The terminal displays prompts received from the server to the user. The input is the generated prompt, and the output is text displayed on a display or smart glasses, allowing the user to take appropriate action.

[0426] Step 7:

[0427] The server then records the user's responses and actions using a log management tool. The input is the user's response data and action records, and the output is updated log data. This information is later used to analyze the effectiveness and develop improvement plans.

[0428] Step 8:

[0429] The server uses security measures to safely store all data, encrypting and controlling access as necessary. The input is confidential information such as log data and emotion data, and the output is securely stored data. This reduces the risk of information leakage.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0431] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0433] [Second embodiment]

[0434] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0435] 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.

[0436] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] 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.

[0438] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0440] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0441] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0446] The present invention is a system for safely and effectively using generative AI in educational institutions. Specifically, this system is configured as follows.

[0447] 1. Setting up and managing a closed network

[0448] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[0449] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[0450] 2. Account Management

[0451] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[0452] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[0453] 3. Customizing the generated AI

[0454] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[0455] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[0456] 4. Providing educational content

[0457] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[0458] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[0459] 5. Provision of cloud services

[0460] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0461] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0462] The processing flow will be explained below.

[0463] Setting up and managing a closed network

[0464] Step 1: Network Configuration

[0465] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[0466] The server will set up firewall rules to block access from outside the specified IP ranges.

[0467] Step 2: VPN Settings

[0468] The server configures the VPN server and generates authentication information for remote access.

[0469] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[0470] Step 3: Check access control

[0471] The server performs a test access to check whether access to the private network is being controlled properly.

[0472] The terminal attempts a test access and confirms that it is within the closed network.

[0473] Account Management

[0474] Step 1: Upload your account information

[0475] The teacher's terminal will prepare a CSV file containing student account information.

[0476] The teacher's terminal uploads this CSV file to the server.

[0477] Step 2: Create an account

[0478] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[0479] The server sets parental controls for each account, allowing access only during school hours.

[0480] Step 3: Record usage logs

[0481] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[0482] Customizing the generated AI

[0483] Step 1: Enter curriculum data

[0484] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[0485] Step 2: Prompt generation

[0486] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[0487] Step 3: Apply customization

[0488] After logging in, the student terminal receives a customized prompt template from the server.

[0489] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[0490] Providing educational content

[0491] Step 1: Create educational content

[0492] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[0493] Step 2: Content Delivery

[0494] The server sets and provides access rights to educational content to teachers and students.

[0495] Step 3: Content Usage

[0496] The teacher's terminal displays educational content during class and explains the learning content.

[0497] Students use their devices to study independently using materials provided at home or during class.

[0498] Cloud service provision

[0499] Step 1: Subdomain Setup

[0500] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[0501] Step 2: Cloud Access

[0502] The student terminal accesses the individual subdomain provided by the browser.

[0503] Student devices enter login credentials and use the generative AI tool.

[0504] Step 3: Resource Management

[0505] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[0506] By repeatedly performing these steps, a safe and effective environment for utilizing generative AI can be achieved.

[0507] Example 1

[0508] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0509] The use of conventional generative AI tools has posed risks of information leaks and unauthorized access at educational institutions. It has also been difficult to customize generative AI tools to effectively incorporate them into lessons, and to properly manage accounts for them. Furthermore, there has been a lack of a systematic way to provide educational content covering everything from the basics to applications of generative AI. These issues have been major obstacles to providing a safe and effective environment for using generative AI.

[0510] 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.

[0511] In this invention, the server includes a means for setting up a closed network through a network gateway and creating an environment accessible only from a specific IP address range, a means for preventing unauthorized access from outside using a firewall and VPN, a means for setting parental control that allows access only during specific time periods, a means for automatically generating accounts from data files containing student information, a means for generating and providing prompt templates according to lesson content, and a means for creating and providing educational content for generative AI. This makes it possible to provide generative AI tools in a safe environment with strict access control, and further enables the effective use of generative AI according to the curriculum of educational institutions and the improvement of student learning outcomes.

[0512] A "network gateway" is a device or software that manages and controls communication between an internal network and an external network.

[0513] A "closed network" is a limited network environment that can only be accessed by specific users or devices.

[0514] An "IP address range" refers to a specific range of IP addresses, and access rights can be set only for addresses within that range.

[0515] A "firewall" is a hardware or software security system that protects a network from unauthorized access.

[0516] "VPN" stands for Virtual Private Network, a technology for establishing secure communications over the Internet.

[0517] "Parental control" is a function that restricts access rights to specific time periods or content.

[0518] A "data file" is a file format for storing specific information on a computer.

[0519] "Creating an account" refers to setting a user's identification information and authentication information to create a new account.

[0520] A "prompt template" is a predefined document format that provides specific instructions or questions to the generative AI.

[0521] "Educational content for generative AI" refers to teaching materials for systematically learning everything from basic knowledge to applied techniques of generative AI.

[0522] The present invention is a system for the safe and effective use of generative AI in educational institutions. The system consists of the following components:

[0523] Setting up and managing a closed network

[0524] The server is set up as a closed network through the institution's network gateway. Specifically, firewall software (e.g., pfSense) is used to specify the IP range "192.168.0.0 / 24" so that only devices within this range can access it. VPN server software (e.g., OpenVPN) is also set up to allow secure access from outside. This prevents unauthorized access and reduces the risk of information leaks.

[0525] Account Management

[0526] To centrally manage student account information, teachers' devices create a CSV file (e.g., students.csv) and upload it to the server. The server analyzes this file and automatically creates student accounts. At this time, a parental control function is also added to allow access only during class hours. For example, the server might set class hours to "weekdays 9:00-17:00" and restrict access outside of these hours.

[0527] Customizing the generated AI

[0528] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server. The server then generates prompts based on this and provides them to the user's terminal. For example, the server may prepare templates for five subjects (Japanese, mathematics, science, social studies, and English) in advance and customize them as needed. An example of a specific prompt sentence used in a mathematics class is "Please explain how to solve a cubic equation."

[0529] Providing educational content

[0530] The server creates educational content covering everything from the basics to applications of generative AI and provides it to teachers and students. This educational content consists of videos, PDFs, and quizzes. The teacher's device projects the content during class, and the students' devices use the content for self-study. Specific examples include a video called "Fundamentals of Generative AI" and a PDF of the "Ethics Education Handbook" provided by the server.

[0531] Cloud service provision

[0532] This system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students can use the generation AI tool by accessing this URL from their browser. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0533] As a specific example, a user (student) accesses "schoolname.generateai.com" and logs in using the provided credentials. After that, they select "Please explain how to solve a cubic equation" as a math prompt template, and the Generative AI returns an answer based on that.

[0534] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] Setting up and managing a closed network

[0538] The server sets up a closed network through the network gateway. Specifically, the server uses firewall software (e.g., pfSense) to specify the IP range "192.168.0.0 / 24" to allow access only from devices within this range. In addition, VPN server software (e.g., OpenVPN) is configured to allow secure access from outside.

[0539] Input: Network setting information (IP range, VPN settings)

[0540] Data processing / calculation: Firewall rule settings, VPN connection settings

[0541] Output: Closed network setting completion notification

[0542] Specific actions: The server reads the configuration file, applies network rules, and starts the VPN server.

[0543] Step 2:

[0544] Account Management

[0545] To centrally manage student account information, teachers create a CSV file (e.g., students.csv) on their devices and upload it to the server. The server then analyzes this file and automatically creates student accounts. A parental control feature is also added to allow access only during class hours.

[0546] Input: CSV file (student list)

[0547] Data processing / calculation: CSV file analysis, account information generation, parental control settings

[0548] Output: Generated account information

[0549] Specific operation: The server parses the CSV file, registers the new account in the database, and applies access restrictions outside of class hours.

[0550] Step 3:

[0551] Customizing the generated AI

[0552] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server, which then generates prompts based on the template and provides them to the user's terminal.

[0553] Input: Prompt template content

[0554] Data processing / calculation: Creating and saving templates

[0555] Output: Customized prompt template

[0556] Specific operation: The server receives template data from the teacher's terminal, stores it in a database, and generates prompts based on it.

[0557] Step 4:

[0558] Providing educational content

[0559] The server creates educational content covering everything from the basics to applications of generative AI, and provides it to teachers and students' devices. The educational content includes videos, PDFs, and quizzes.

[0560] Input: Educational content information (videos, PDFs, quizzes)

[0561] Data processing / calculation: content format conversion and storage

[0562] Output: Served educational content

[0563] Specific operation: The server creates educational content, converts it into the specified format, and uploads it to the online portal.

[0564] Step 5:

[0565] Cloud service provision

[0566] The server sets up a separate subdomain for each educational institution, allowing students to access the generative AI tool by accessing this URL from their browser.

[0567] Input: Educational institution registration information

[0568] Data processing / calculation: Subdomain settings, authentication information generation

[0569] Output: Access URL and authentication information

[0570] Specific operations: The server receives the new educational institution information, sets up a subdomain, and prepares the login screen.

[0571] These are the processing steps of this system, which enables the safe and effective use of generative AI in educational institutions.

[0572] (Application example 1)

[0573] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0574] This invention is based on a generative AI system for educational institutions, and aims to improve the efficiency of store management and the skills and knowledge required for virtual store operation. Conventional generative AI systems for educational institutions contribute to improving the quality of education, but they have the problem of not being able to address the knowledge required for virtual store operation and the efficiency of management work. Furthermore, as virtual store operations are increasing today, the lack of support systems specialized for virtual store operation is becoming a serious problem.

[0575] 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.

[0576] In this invention, the server is a system that includes network settings that allow access only from specific user terminals to build a generative AI environment that operates within a closed network, and includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, means for providing generative AI tools for virtual store operation, and customization means for streamlining various management tasks. This makes it possible to learn the knowledge and skills necessary for virtual store operation and to streamline management tasks.

[0577] A "closed network" is a limited network environment that is accessible only to a select few users, and is used to ensure security and privacy.

[0578] A "generative AI environment" is an environment that provides a system that uses artificial intelligence to make predictions and generate results based on user input.

[0579] "User terminal" means a user's device used to access the system, including smartphones, tablets, PCs, etc.

[0580] "Network settings" refers to the process of specifying IP addresses and setting authentication protocols to allow access only from specific devices.

[0581] "Authentication means" refers to a mechanism for verifying the validity of a user terminal when accessing a system. For example, it includes authentication of a user name and password.

[0582] "Restriction measures" are functions that restrict the use of the system depending on time and conditions, and include restrictions such as only allowing access during certain hours.

[0583] "Log management means" refers to a mechanism for recording and managing system usage, and records access logs and operation history.

[0584] A "virtual store" is an online store that sells products and services over the Internet and does not have a physical store.

[0585] "Generative AI tools" are tools that use generative AI models to automate various tasks, including text generation from prompts and data analysis.

[0586] "Customization" refers to the functionality and processes that allow users to adapt and modify systems and tools to meet their specific needs.

[0587] A "prompt template" is a template of instructions that a generative AI uses to generate a response to a specific task or question.

[0588] "Educational content" is a general term for teaching materials and resources provided to learners, including videos, slides, PDFs, etc.

[0589] "Account Management" refers to the process and system for registering and managing user account information.

[0590] The present invention provides a generative AI system for supporting virtual store operations, and its main functions are as follows:

[0591] 1. Private network configuration and management:

[0592] The server creates a closed network that can only be accessed from specific user terminals and configures the network. Specifically, AWS VPC is used to set up a specific IP address range and provide secure access using a VPN connection. This closed network enables the safe operation of the virtual store.

[0593] 2. Account Management:

[0594] The server centrally manages the accounts of virtual store managers and staff. Account information is imported from a CSV file and saved in the database. This allows for role-based access permissions, enabling efficient account management. For example, managers can register products, while staff can only check inventory.

[0595] 3. Customizing generative AI tools:

[0596] The server provides a generative AI tool specialized for virtual store operations. This tool performs various data analysis and predictions using generative AI models based on prompt templates, such as optimizing inventory management, automating customer service, and predicting sales trends.

[0597] Example prompt: "Generate a sales forecast for next month."

[0598] 4. Providing educational content:

[0599] The server provides educational content for learning the skills necessary for running a virtual store. It allows users to access videos, slides, PDF documents, etc. through a smartphone app using React Native. It also provides a function to monitor the usage of this content.

[0600] Example prompt: "Please show me a video presentation that explains how to manage inventory efficiently."

[0601] 5. Provision of Cloud Services:

[0602] The server sets up a subdomain for each virtual store, allowing it to be accessed from a browser. This allows users to log in and access various management tools and educational content. Using cloud services reduces implementation costs and enables smooth operation.

[0603] To achieve these functions, the server utilizes hardware and software such as AWS EC2 instances, AWS VPC, OpenVPN, Node.js, and React Native. On the user side, smartphones and head-mounted displays can be used to efficiently learn about and manage virtual store operations.

[0604] Specifically, the server uses AWS EC2 to build a closed network and manages accounts using a database, while data analysis using generative AI models and the provision of educational content can be used to streamline virtual store operations.

[0605] For example, if a virtual store manager needs a sales forecast, they can use a generative AI tool to enter the following prompt:

[0606] "Generate a sales forecast for next month."

[0607] This allows the generative AI model to automatically analyze data and provide predictions, while also providing managers with prompts to train new staff:

[0608] "Show me video materials that explain efficient inventory management methods."

[0609] This allows educational content suitable for smartphone devices to be provided.

[0610] In this way, the present invention provides an advanced support system for virtual store management, and is equipped with features for realizing efficient and effective management.

[0611] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0612] Step 1:

[0613] The server creates a closed network using AWS VPC. It sets a specific IP address range (e.g., 192.168.0.0 / 24) as input and provides a secure network environment as output. The server then configures OpenVPN to enable secure remote access.

[0614] Step 2:

[0615] The user uploads a CSV file to the server using a teacher's terminal and imports the account information of the virtual store manager and staff. The CSV file containing the account information is prepared as input, and the account data stored in the database is obtained as output. Specifically, the server parses the CSV file and inserts each account information into the database.

[0616] Step 3:

[0617] The server provides tools using generative AI models, and users (administrators) input prompts related to virtual store operations. Prompts such as "Please generate sales forecasts for next month" are used as input, and sales forecast data is generated as output. The server analyzes the prompts and executes the generative AI model to calculate the forecast results.

[0618] Step 4:

[0619] The server provides the educational content required for running a virtual store through a React Native app. It receives a request from the administrator as input (e.g., "Please display a video document explaining efficient inventory management methods.") and delivers the appropriate educational content to the user's device as output. Specifically, the server retrieves the relevant educational content from the database and sends it to the user's smartphone.

[0620] Step 5:

[0621] Users (managers and staff) use the delivered educational content to learn knowledge that will be useful in running a virtual store. They select content on the smartphone app as input, and obtain the learned knowledge as output. Users acquire practical skills by watching videos and viewing slides.

[0622] Step 6:

[0623] As a cloud service, the server automatically sets up a subdomain so that users can access it from their browser. It takes the information for a new virtual store as input and provides a dedicated subdomain (e.g., shopname.example.com) as output. This setup allows users to easily access the system and start operating.

[0624] These steps seamlessly provide the various functions necessary for virtual store operation, enabling efficient and effective operation.

[0625] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0626] This invention is a system for safely and effectively using generative AI in educational institutions, which is combined with an emotion engine that recognizes user emotions. The system is configured as follows.

[0627] 1. Setting up and managing a closed network

[0628] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[0629] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[0630] 2. Account Management

[0631] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[0632] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[0633] 3. Customizing the generated AI

[0634] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[0635] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[0636] 4. Providing educational content

[0637] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[0638] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[0639] 5. Provision of cloud services

[0640] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0641] 6. Introducing the Emotion Engine

[0642] An emotion engine that recognizes user emotions has been added to the system to improve teaching effectiveness. The server collects emotion data in real time when using the generative AI tool and adjusts prompt content and AI responses accordingly. In addition, teacher devices can refer to the emotion data to understand students' learning status.

[0643] For example, the emotion engine combines facial expression data, voice tone, and text sentiment analysis to identify a user's emotions. This allows the generative AI to respond according to the user's level of stress or interest, improving educational effectiveness. The emotion engine can also identify when a student is facing a difficult problem and provide additional hints or support.

[0644] In this way, the present invention provides a system that combines generative AI and emotion engines in educational institutions, contributing to improving the quality of education while reducing the risk of information leakage.

[0645] The processing flow will be explained below.

[0646] Setting up and managing a closed network

[0647] Step 1: Network Configuration

[0648] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[0649] The server sets up firewall rules to block access from outside the specified IP ranges.

[0650] Step 2: VPN Settings

[0651] The server configures the VPN server and generates authentication information for remote access.

[0652] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[0653] Step 3: Check access control

[0654] The server performs a test access to check whether access to the private network is being controlled properly.

[0655] The terminal attempts a test access and confirms that it is within the closed network.

[0656] Account Management

[0657] Step 1: Upload your account information

[0658] The teacher's terminal will prepare a CSV file containing student account information.

[0659] The teacher's terminal uploads this CSV file to the server.

[0660] Step 2: Create an account

[0661] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[0662] The server sets parental controls for each account, allowing access only during school hours.

[0663] Step 3: Record usage logs

[0664] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[0665] Customizing the generated AI

[0666] Step 1: Enter curriculum data

[0667] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[0668] Step 2: Prompt generation

[0669] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[0670] Step 3: Apply customization

[0671] After logging in, the student terminal receives a customized prompt template from the server.

[0672] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[0673] Providing educational content

[0674] Step 1: Create educational content

[0675] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[0676] Step 2: Content Delivery

[0677] The server sets and provides access rights to educational content to teachers and students.

[0678] Step 3: Content Usage

[0679] The teacher's terminal displays educational content during class and explains the learning content.

[0680] Students use their devices to study independently using materials provided at home or during class.

[0681] Cloud service provision

[0682] Step 1: Subdomain Setup

[0683] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[0684] Step 2: Cloud Access

[0685] The student terminal accesses the individual subdomain provided by the browser.

[0686] Student devices enter login credentials and use the generative AI tool.

[0687] Step 3: Resource Management

[0688] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[0689] Introducing the Emotion Engine

[0690] Step 1: Collecting emotion data

[0691] The student terminals use cameras and microphones to collect the user's facial expressions, voice tone, gestures, etc. in real time.

[0692] The server receives this data and analyzes the user's emotional state.

[0693] Step 2: Feedback of emotional data

[0694] The server adjusts the prompts and responses of the generating AI based on the analyzed emotional data.

[0695] The student terminal receives feedback according to the user's status.

[0696] Step 3: Analyze the sentiment data

[0697] The server integrates the collected emotional data with the usage logs of the generative AI to evaluate the effectiveness of the education.

[0698] Based on the analysis results, the teacher's device will be able to grasp students' learning situation and emotional state, which will be used to improve the content of lessons.

[0699] These steps will enable a safe and effective educational system that utilizes generative AI and emotion engines.

[0700] Example 2

[0701] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] Conventional generative AI environments in educational institutions have a high risk of unauthorized access and information leaks from outside, and there are many restrictions on the use of generative AI tools. It is also difficult to monitor students' emotional states in real time and provide appropriate teaching methods. Therefore, a new system that can balance information security and educational quality is needed.

[0703] 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.

[0704] In this invention, the server

[0705] an authentication means for restricting access from a user terminal;

[0706] Restrictions that allow access only during school hours;

[0707] a log management means for recording a usage log of the user terminal;

[0708] A customization means for generating curriculum-specific prompt templates;

[0709] means for providing the generated prompt template to a user terminal;

[0710] A means of collecting and analyzing the results of using generative AI tools;

[0711] a means of collecting emotion data in real time;

[0712] a means for adjusting the response of the generative AI based on the emotion data; and

[0713] a means of monitoring the use of educational content;

[0714] This will enable the safe and effective use of generative AI, reducing the risk of information leakage while providing appropriate education tailored to students' emotional states.

[0715] "Authentication methods" are methods for ensuring that only specific users can access a system, and include IDs, passwords, and two-factor authentication.

[0716] "Restriction measures" are methods of allowing access to the system only under certain conditions, such as during school hours, and include time restrictions and access control lists.

[0717] "Log management means" refers to a method for recording and saving the access and operation history of a user's terminal, which can be used for later verification or to detect unauthorized access.

[0718] "Customization methods" refer to methods for generating and adjusting prompt templates for generative AI tools according to the curriculum of educational institutions, and include dedicated configuration tools and template management functions.

[0719] "Providing means" refers to a method for providing the generated prompt templates and educational content to a user terminal, and includes a data distribution system and an access link.

[0720] "Collection and analysis means" refers to methods for collecting and analyzing the results of using the generative AI tool and user operation information, and includes database systems and analysis algorithms.

[0721] "Emotion data" is data that indicates the user's emotional state, and includes facial expression data, voice tone, text analysis results, and the like.

[0722] "Means for collecting data in real time" refers to methods for acquiring user emotional data in real time, including sensors and analysis software.

[0723] "Means for adjusting the response of the generative AI" refers to a method for appropriately adjusting the response of the generative AI based on the user's emotional data, and includes dialogue systems and response generation algorithms.

[0724] "Monitoring means" refers to methods for monitoring the usage of educational content and collecting data, including log collection tools and analysis dashboards.

[0725] In this invention, the following system configuration and method are used to safely and effectively operate a generative AI environment in an educational institution. The system mainly consists of a server, a teacher terminal, and a student terminal.

[0726] Setting up and managing a closed network

[0727] The server will be set up as a closed network through the network gateway within the educational institution. By defining a specific IP address range (e.g., 192.168.0.0 / 24) and setting up a firewall and VPN, external access will be blocked, creating a secure communication environment.

[0728] Specifically, the server accesses the network gateway settings screen and specifies an IP range. Next, firewall rules are set to allow communication only from devices within this IP range. Finally, a VPN is set up to ensure secure remote access.

[0729] Account Management

[0730] The teacher's terminal compiles student account information into a CSV file and uploads it to the server. The server analyzes this CSV file and automatically creates accounts for each student, adding a parental control function that allows use only during class time.

[0731] When a teacher uploads the CSV file to the server, the server analyzes the file and automatically creates an account for each student. Parental controls are then set to restrict access outside of class hours.

[0732] Customizing the generated AI

[0733] The teacher's terminal creates prompt templates for the generative AI tool according to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[0734] As a specific example, the server prepares prompt templates for Japanese, mathematics, science, social studies, and English. For example, in a mathematics class, a prompt might be set to "Please explain how to solve a cubic equation." This is set by the teacher's device and sent to the server, where it is reflected in the generation AI.

[0735] Providing educational content

[0736] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for self-study on their devices.

[0737] Specifically, a server creates educational content in the form of videos, PDFs, quizzes, etc. and provides it online. Teachers' devices display this content on a projector during class, and students' devices view the educational content at home.

[0738] Cloud service provision

[0739] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL from their browsers to use the generation AI tool. By logging in, users can select and use prompt templates for each subject.

[0740] Introducing the Emotion Engine

[0741] The server collects real-time emotional data from users when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can access students' emotional data and understand their learning status.

[0742] The emotion engine combines facial expression data with voice tone and text emotion analysis to identify the user's emotions. Based on this, the generative AI can respond according to the user's level of stress and interest, improving teaching effectiveness. In addition, the teacher's device uses a dashboard to monitor students' emotional data in real time.

[0743] This system enables the safe and effective use of generative AI, reducing the risk of information leaks while providing appropriate education tailored to students' emotional states.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1:

[0746] Setting up and managing a closed network

[0747] The server accesses the institution's network gateway and sets a specific IP address range (e.g., 192.168.0.0 / 24). The server specifies the IP range in the network gateway settings screen and sets firewall rules to allow communication only from devices within this IP range. The server also sets up a VPN to allow secure remote access.

[0748] Specific behavior:

[0749] Input: Specific IP address range and VPN configuration information

[0750] Data processing: Apply IP address ranges, set firewall rules, configure VPN

[0751] Output: A secure network environment that can only be accessed by user terminals within the closed network

[0752] Step 2:

[0753] Account Management

[0754] The teacher's terminal creates student account information as a CSV file and uploads it to the server. The server analyzes the CSV file and automatically creates accounts for each student. At the same time, the server sets up parental control functions to restrict access outside of class hours.

[0755] Specific behavior:

[0756] Input: CSV file containing a list of students

[0757] Data processing: CSV file analysis, automatic creation of student accounts, setting parental controls

[0758] Output: Generated student accounts and access restriction functions

[0759] Step 3:

[0760] Customizing the generated AI

[0761] The teacher's terminal creates prompt templates tailored to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[0762] Specific behavior:

[0763] Input: Prompt template sent from the teacher's terminal

[0764] Data processing: Saving and generating prompt templates, and reflecting them in the generation AI

[0765] Output: Provides the generated prompt to the user's terminal

[0766] Step 4:

[0767] Providing educational content

[0768] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for home study.

[0769] Specific behavior:

[0770] Input: Educational content materials (videos, PDFs, quizzes, etc.)

[0771] Data processing: Creation of educational content and preparation for online distribution

[0772] Output: Provide the completed educational content to teacher and student devices

[0773] Step 5:

[0774] Cloud service provision

[0775] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL to use the generation AI tool. Users log in and select prompt templates for each subject.

[0776] Specific behavior:

[0777] Input: Subdomain information of each educational institution and URL of the AI ​​tool

[0778] Data processing: Subdomain settings, access rights settings

[0779] Output: A subdomain dedicated to each educational institution and the access URL for the generated AI tool

[0780] Step 6:

[0781] Introducing the Emotion Engine

[0782] The server collects users' emotional data in real time when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can refer to students' emotional data and understand their learning status.

[0783] Specific behavior:

[0784] Input: facial expression data, voice tone, text emotion data

[0785] Data processing: Real-time analysis of emotional data and feedback to generative AI

[0786] Output: Response of the generative AI based on emotion data, emotion data monitoring results displayed on the teacher's terminal

[0787] (Application example 2)

[0788] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0789] There is a need for a method to safely and effectively use generative AI in environments such as educational institutions and brick-and-mortar stores, while providing appropriate responses based on user emotions. However, existing systems do not adequately ensure security, analyze emotional data in real time, or adjust responses. Furthermore, it is difficult to restrict access from specific user devices and manage usage time, making it difficult to maximize the effectiveness of generative AI. This poses a challenge in achieving sufficient educational effectiveness and customer satisfaction.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, emotion recognition means for collecting and analyzing emotion data in real time, prompt generation means for adjusting the response of the generation AI based on the emotion data, and security means for safely managing user information. This allows the generation AI to provide responses that correspond to the user's emotional state, improving educational effectiveness and customer satisfaction.

[0791] A "closed network" is a network environment that can only be accessed from specific authorized user terminals and prevents unauthorized access from outside.

[0792] A "generative AI environment" refers to the entire system that uses artificial intelligence technology to generate content such as text and images.

[0793] "Authentication means" is a function that provides an authentication process to confirm that a user terminal has legitimate access rights.

[0794] "Restriction measures" are functions that restrict access to the generated AI environment based on specific time periods or conditions.

[0795] "Log management means" is a function that records and manages the history of users' access to the system.

[0796] "Emotion recognition means" is a function that collects and analyzes emotional data from the user's facial expressions, voice tone, and text content.

[0797] The "prompt generation means" is an artificial intelligence function that generates an appropriate response based on emotional data and the user's situation.

[0798] "Security measures" are functions for protecting user information and system data from external threats.

[0799] "Customization" refers to the ability to create prompt templates for the generative AI tool to suit specific curriculum and requirements.

[0800] "Means for collecting and analyzing usage results" refers to the function of collecting data associated with the use of generative AI tools and evaluating and analyzing their effectiveness.

[0801] "Educational content" refers to the teaching materials and resources provided to learners, and includes everything from basic knowledge of generative AI to applied techniques.

[0802] The "means for setting access rights" is a function for granting access rights to educational content to specific users.

[0803] "Means for monitoring" refers to the function of monitoring the usage of educational content and collecting that data.

[0804] "Means for analyzing educational effectiveness" is a function that evaluates and analyzes the learning effectiveness of educational content based on emotion recognition.

[0805] The present invention relates to a generative AI system for use in educational institutions and brick-and-mortar stores, and in particular to an AI system that automatically adjusts responses based on user emotion recognition. Hereinafter, a detailed description of an embodiment of the present invention will be given.

[0806] 1. System Configuration

[0807] This system consists of a server, a user terminal, and multiple devices including smart glasses. These devices are securely connected via a closed network and can only be accessed from specific user terminals.

[0808] 2. Closed Networks and Access Restrictions

[0809] The server uses a firewall and VPN to create a closed network to prevent unauthorized access from outside. Access from user devices is managed by authentication methods and is limited to class hours and business hours. This allows the generative AI system to be used in a secure environment.

[0810] 3. Emotion Recognition and Prompt Generation

[0811] Using smart glasses or a camera-equipped PC, emotion data is collected by analyzing the user's facial expressions and tone of voice in real time using OpenCV and Transformers libraries. The emotion recognition results are sent to a server, where generative AI creates an optimal response based on the emotion.

[0812] For example, the following prompt is set:

[0813] "Please explain how to solve a cubic equation."

[0814] "Suggest a product that's on promotion"

[0815] "Do you have any particular favorite products?"

[0816] 4. Data Management and Security

[0817] User access history is recorded by a log management system. Emotional data and response data are also strictly managed, and measures are taken to prevent information leaks, including data encryption and access control.

[0818] 5. Providing customized and educational content

[0819] The server customizes prompt templates for the generative AI tool according to the needs of each educational institution or brick-and-mortar store. The educational content is provided as teaching materials covering everything from basic knowledge of generative AI to applied techniques. Users can use this for self-study.

[0820] As described above, the present invention provides a response adjustment system for generative AI that utilizes emotion recognition data, thereby realizing safe and effective learning and customer service support.

[0821] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0822] Step 1:

[0823] The server creates a closed network and prepares an environment that can only be accessed from specific user terminals. To achieve this, a firewall and VPN are set up. The input is the IP address and network setting information of the user terminal, and the output is a notification that the closed network has been successfully created. This step establishes a secure network environment.

[0824] Step 2:

[0825] The terminal authenticates the user's access using authentication means. The input is the user's authentication information (ID and password, etc.), and the output is a notification of authentication success or failure. After successful authentication, the user is allowed access to the system.

[0826] Step 3:

[0827] The server uses a log management tool to record access logs from user terminals. The input is access information such as the access time, user ID, and connection IP address, and the output is the recorded log data. This data is later used for analysis and auditing.

[0828] Step 4:

[0829] The device uses smart glasses and a camera to analyze the user's facial expressions and tone of voice in real time. The input is camera footage and audio data, and the output is analyzed emotion data. Specifically, it extracts facial features using OpenCV and recognizes emotions using the Transformers library.

[0830] Step 5:

[0831] The server receives the emotion data and uses a generative AI model to generate an optimal prompt based on that data. The input is the emotion data and a pre-defined prompt template, and the output is the generated prompt. For example, if the emotion is "happy," the generated prompt is "Please suggest a product that is currently on promotion."

[0832] Step 6:

[0833] The terminal displays prompts received from the server to the user. The input is the generated prompt, and the output is text displayed on a display or smart glasses, allowing the user to take appropriate action.

[0834] Step 7:

[0835] The server then records the user's responses and actions using a log management tool. The input is the user's response data and action records, and the output is updated log data. This information is later used to analyze the effectiveness and develop improvement plans.

[0836] Step 8:

[0837] The server uses security measures to safely store all data, encrypting and controlling access as necessary. The input is confidential information such as log data and emotion data, and the output is securely stored data. This reduces the risk of information leakage.

[0838] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0839] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0840] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0841] [Third embodiment]

[0842] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0843] 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.

[0844] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0845] 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.

[0846] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0847] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0848] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0849] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0850] 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.

[0851] 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.

[0852] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0853] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0854] The present invention is a system for safely and effectively using generative AI in educational institutions. Specifically, this system is configured as follows.

[0855] 1. Setting up and managing a closed network

[0856] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[0857] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[0858] 2. Account Management

[0859] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[0860] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[0861] 3. Customizing the generated AI

[0862] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[0863] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[0864] 4. Providing educational content

[0865] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[0866] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[0867] 5. Provision of cloud services

[0868] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0869] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0870] The processing flow will be explained below.

[0871] Setting up and managing a closed network

[0872] Step 1: Network Configuration

[0873] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[0874] The server will set up firewall rules to block access from outside the specified IP ranges.

[0875] Step 2: VPN Settings

[0876] The server configures the VPN server and generates authentication information for remote access.

[0877] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[0878] Step 3: Check access control

[0879] The server performs a test access to check whether access to the private network is being controlled properly.

[0880] The terminal attempts a test access and confirms that it is within the closed network.

[0881] Account Management

[0882] Step 1: Upload your account information

[0883] The teacher's terminal will prepare a CSV file containing student account information.

[0884] The teacher's terminal uploads this CSV file to the server.

[0885] Step 2: Create an account

[0886] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[0887] The server sets parental controls for each account, allowing access only during school hours.

[0888] Step 3: Record usage logs

[0889] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[0890] Customizing the generated AI

[0891] Step 1: Enter curriculum data

[0892] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[0893] Step 2: Prompt generation

[0894] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[0895] Step 3: Apply customization

[0896] After logging in, the student terminal receives a customized prompt template from the server.

[0897] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[0898] Providing educational content

[0899] Step 1: Create educational content

[0900] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[0901] Step 2: Content Delivery

[0902] The server sets and provides access rights to educational content to teachers and students.

[0903] Step 3: Content Usage

[0904] The teacher's terminal displays educational content during class and explains the learning content.

[0905] Students use their devices to study independently using materials provided at home or during class.

[0906] Cloud service provision

[0907] Step 1: Subdomain Setup

[0908] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[0909] Step 2: Cloud Access

[0910] The student terminal accesses the individual subdomain provided by the browser.

[0911] Student devices enter login credentials and use the generative AI tool.

[0912] Step 3: Resource Management

[0913] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[0914] By repeatedly performing these steps, a safe and effective environment for utilizing generative AI can be achieved.

[0915] Example 1

[0916] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0917] The use of conventional generative AI tools has posed risks of information leaks and unauthorized access at educational institutions. It has also been difficult to customize generative AI tools to effectively incorporate them into lessons, and to properly manage accounts for them. Furthermore, there has been a lack of a systematic way to provide educational content covering everything from the basics to applications of generative AI. These issues have been major obstacles to providing a safe and effective environment for using generative AI.

[0918] 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.

[0919] In this invention, the server includes a means for setting up a closed network through a network gateway and creating an environment accessible only from a specific IP address range, a means for preventing unauthorized access from outside using a firewall and VPN, a means for setting parental control that allows access only during specific time periods, a means for automatically generating accounts from data files containing student information, a means for generating and providing prompt templates according to lesson content, and a means for creating and providing educational content for generative AI. This makes it possible to provide generative AI tools in a safe environment with strict access control, and further enables the effective use of generative AI according to the curriculum of educational institutions and the improvement of student learning outcomes.

[0920] A "network gateway" is a device or software that manages and controls communication between an internal network and an external network.

[0921] A "closed network" is a limited network environment that can only be accessed by specific users or devices.

[0922] An "IP address range" refers to a specific range of IP addresses, and access rights can be set only for addresses within that range.

[0923] A "firewall" is a hardware or software security system that protects a network from unauthorized access.

[0924] "VPN" stands for Virtual Private Network, a technology for establishing secure communications over the Internet.

[0925] "Parental control" is a function that restricts access rights to specific time periods or content.

[0926] A "data file" is a file format for storing specific information on a computer.

[0927] "Creating an account" refers to setting a user's identification information and authentication information to create a new account.

[0928] A "prompt template" is a predefined document format that provides specific instructions or questions to the generative AI.

[0929] "Educational content for generative AI" refers to teaching materials for systematically learning everything from basic knowledge to applied techniques of generative AI.

[0930] The present invention is a system for the safe and effective use of generative AI in educational institutions. The system consists of the following components:

[0931] Setting up and managing a closed network

[0932] The server is set up as a closed network through the institution's network gateway. Specifically, firewall software (e.g., pfSense) is used to specify the IP range "192.168.0.0 / 24" so that only devices within this range can access it. VPN server software (e.g., OpenVPN) is also set up to allow secure access from outside. This prevents unauthorized access and reduces the risk of information leaks.

[0933] Account Management

[0934] To centrally manage student account information, teachers' devices create a CSV file (e.g., students.csv) and upload it to the server. The server analyzes this file and automatically creates student accounts. At this time, a parental control function is also added to allow access only during class hours. For example, the server might set class hours to "weekdays 9:00-17:00" and restrict access outside of these hours.

[0935] Customizing the generated AI

[0936] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server. The server then generates prompts based on this and provides them to the user's terminal. For example, the server may prepare templates for five subjects (Japanese, mathematics, science, social studies, and English) in advance and customize them as needed. An example of a specific prompt sentence used in a mathematics class is "Please explain how to solve a cubic equation."

[0937] Providing educational content

[0938] The server creates educational content covering everything from the basics to applications of generative AI and provides it to teachers and students. This educational content consists of videos, PDFs, and quizzes. The teacher's device projects the content during class, and the students' devices use the content for self-study. Specific examples include a video called "Fundamentals of Generative AI" and a PDF of the "Ethics Education Handbook" provided by the server.

[0939] Cloud service provision

[0940] This system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students can use the generation AI tool by accessing this URL from their browser. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[0941] As a specific example, a user (student) accesses "schoolname.generateai.com" and logs in using the provided credentials. After that, they select "Please explain how to solve a cubic equation" as a math prompt template, and the Generative AI returns an answer based on that.

[0942] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[0943] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0944] Step 1:

[0945] Setting up and managing a closed network

[0946] The server sets up a closed network through the network gateway. Specifically, the server uses firewall software (e.g., pfSense) to specify the IP range "192.168.0.0 / 24" to allow access only from devices within this range. In addition, VPN server software (e.g., OpenVPN) is configured to allow secure access from outside.

[0947] Input: Network setting information (IP range, VPN settings)

[0948] Data processing / calculation: Firewall rule settings, VPN connection settings

[0949] Output: Closed network setting completion notification

[0950] Specific actions: The server reads the configuration file, applies network rules, and starts the VPN server.

[0951] Step 2:

[0952] Account Management

[0953] To centrally manage student account information, teachers create a CSV file (e.g., students.csv) on their devices and upload it to the server. The server then analyzes this file and automatically creates student accounts. A parental control feature is also added to allow access only during class hours.

[0954] Input: CSV file (student list)

[0955] Data processing / calculation: CSV file analysis, account information generation, parental control settings

[0956] Output: Generated account information

[0957] Specific operation: The server parses the CSV file, registers the new account in the database, and applies access restrictions outside of class hours.

[0958] Step 3:

[0959] Customizing the generated AI

[0960] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server, which then generates prompts based on the template and provides them to the user's terminal.

[0961] Input: Prompt template content

[0962] Data processing / calculation: Creating and saving templates

[0963] Output: Customized prompt template

[0964] Specific operation: The server receives template data from the teacher's terminal, stores it in a database, and generates prompts based on it.

[0965] Step 4:

[0966] Providing educational content

[0967] The server creates educational content covering everything from the basics to applications of generative AI, and provides it to teachers and students' devices. The educational content includes videos, PDFs, and quizzes.

[0968] Input: Educational content information (videos, PDFs, quizzes)

[0969] Data processing / calculation: content format conversion and storage

[0970] Output: Served educational content

[0971] Specific operation: The server creates educational content, converts it into the specified format, and uploads it to the online portal.

[0972] Step 5:

[0973] Cloud service provision

[0974] The server sets up a separate subdomain for each educational institution, allowing students to access the generative AI tool by accessing this URL from their browser.

[0975] Input: Educational institution registration information

[0976] Data processing / calculation: Subdomain settings, authentication information generation

[0977] Output: Access URL and authentication information

[0978] Specific operations: The server receives the new educational institution information, sets up a subdomain, and prepares the login screen.

[0979] These are the processing steps of this system, which enables the safe and effective use of generative AI in educational institutions.

[0980] (Application example 1)

[0981] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0982] This invention is based on a generative AI system for educational institutions, and aims to improve the efficiency of store management and the skills and knowledge required for virtual store operation. Conventional generative AI systems for educational institutions contribute to improving the quality of education, but they have the problem of not being able to address the knowledge required for virtual store operation and the efficiency of management work. Furthermore, as virtual store operations are increasing today, the lack of support systems specialized for virtual store operation is becoming a serious problem.

[0983] 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.

[0984] In this invention, the server is a system that includes network settings that allow access only from specific user terminals to build a generative AI environment that operates within a closed network, and includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, means for providing generative AI tools for virtual store operation, and customization means for streamlining various management tasks. This makes it possible to learn the knowledge and skills necessary for virtual store operation and to streamline management tasks.

[0985] A "closed network" is a limited network environment that is accessible only to a select few users, and is used to ensure security and privacy.

[0986] A "generative AI environment" is an environment that provides a system that uses artificial intelligence to make predictions and generate results based on user input.

[0987] "User terminal" means a user's device used to access the system, including smartphones, tablets, PCs, etc.

[0988] "Network settings" refers to the process of specifying IP addresses and setting authentication protocols to allow access only from specific devices.

[0989] "Authentication means" refers to a mechanism for verifying the validity of a user terminal when accessing a system. For example, it includes authentication of a user name and password.

[0990] "Restriction measures" are functions that restrict the use of the system depending on time and conditions, and include restrictions such as only allowing access during certain hours.

[0991] "Log management means" refers to a mechanism for recording and managing system usage, and records access logs and operation history.

[0992] A "virtual store" is an online store that sells products and services over the Internet and does not have a physical store.

[0993] "Generative AI tools" are tools that use generative AI models to automate various tasks, including text generation from prompts and data analysis.

[0994] "Customization" refers to the functionality and processes that allow users to adapt and modify systems and tools to meet their specific needs.

[0995] A "prompt template" is a template of instructions that a generative AI uses to generate a response to a specific task or question.

[0996] "Educational content" is a general term for teaching materials and resources provided to learners, including videos, slides, PDFs, etc.

[0997] "Account Management" refers to the process and system for registering and managing user account information.

[0998] The present invention provides a generative AI system for supporting virtual store operations, and its main functions are as follows:

[0999] 1. Private network configuration and management:

[1000] The server creates a closed network that can only be accessed from specific user terminals and configures the network. Specifically, AWS VPC is used to set up a specific IP address range and provide secure access using a VPN connection. This closed network enables the safe operation of the virtual store.

[1001] 2. Account Management:

[1002] The server centrally manages the accounts of virtual store managers and staff. Account information is imported from a CSV file and saved in the database. This allows for role-based access permissions, enabling efficient account management. For example, managers can register products, while staff can only check inventory.

[1003] 3. Customizing generative AI tools:

[1004] The server provides a generative AI tool specialized for virtual store operations. This tool performs various data analysis and predictions using generative AI models based on prompt templates, such as optimizing inventory management, automating customer service, and predicting sales trends.

[1005] Example prompt: "Generate a sales forecast for next month."

[1006] 4. Providing educational content:

[1007] The server provides educational content for learning the skills necessary for running a virtual store. It allows users to access videos, slides, PDF documents, etc. through a smartphone app using React Native. It also provides a function to monitor the usage of this content.

[1008] Example prompt: "Please show me a video presentation that explains how to manage inventory efficiently."

[1009] 5. Provision of Cloud Services:

[1010] The server sets up a subdomain for each virtual store, allowing it to be accessed from a browser. This allows users to log in and access various management tools and educational content. Using cloud services reduces implementation costs and enables smooth operation.

[1011] To achieve these functions, the server utilizes hardware and software such as AWS EC2 instances, AWS VPC, OpenVPN, Node.js, and React Native. On the user side, smartphones and head-mounted displays can be used to efficiently learn about and manage virtual store operations.

[1012] Specifically, the server uses AWS EC2 to build a closed network and manages accounts using a database, while data analysis using generative AI models and the provision of educational content can be used to streamline virtual store operations.

[1013] For example, if a virtual store manager needs a sales forecast, they can use a generative AI tool to enter the following prompt:

[1014] "Generate a sales forecast for next month."

[1015] This allows the generative AI model to automatically analyze data and provide predictions, while also providing managers with prompts to train new staff:

[1016] "Show me video materials that explain efficient inventory management methods."

[1017] This allows educational content suitable for smartphone devices to be provided.

[1018] In this way, the present invention provides an advanced support system for virtual store management, and is equipped with features for realizing efficient and effective management.

[1019] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1020] Step 1:

[1021] The server creates a closed network using AWS VPC. It sets a specific IP address range (e.g., 192.168.0.0 / 24) as input and provides a secure network environment as output. The server then configures OpenVPN to enable secure remote access.

[1022] Step 2:

[1023] The user uploads a CSV file to the server using a teacher's terminal and imports the account information of the virtual store manager and staff. The CSV file containing the account information is prepared as input, and the account data stored in the database is obtained as output. Specifically, the server parses the CSV file and inserts each account information into the database.

[1024] Step 3:

[1025] The server provides tools using generative AI models, and users (administrators) input prompts related to virtual store operations. Prompts such as "Please generate sales forecasts for next month" are used as input, and sales forecast data is generated as output. The server analyzes the prompts and executes the generative AI model to calculate the forecast results.

[1026] Step 4:

[1027] The server provides the educational content required for running a virtual store through a React Native app. It receives a request from the administrator as input (e.g., "Please display a video document explaining efficient inventory management methods.") and delivers the appropriate educational content to the user's device as output. Specifically, the server retrieves the relevant educational content from the database and sends it to the user's smartphone.

[1028] Step 5:

[1029] Users (managers and staff) use the delivered educational content to learn knowledge that will be useful in running a virtual store. They select content on the smartphone app as input, and obtain the learned knowledge as output. Users acquire practical skills by watching videos and viewing slides.

[1030] Step 6:

[1031] As a cloud service, the server automatically sets up a subdomain so that users can access it from their browser. It takes the information for a new virtual store as input and provides a dedicated subdomain (e.g., shopname.example.com) as output. This setup allows users to easily access the system and start operating.

[1032] These steps seamlessly provide the various functions necessary for virtual store operation, enabling efficient and effective operation.

[1033] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1034] This invention is a system for safely and effectively using generative AI in educational institutions, which is combined with an emotion engine that recognizes user emotions. The system is configured as follows.

[1035] 1. Setting up and managing a closed network

[1036] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[1037] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[1038] 2. Account Management

[1039] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[1040] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[1041] 3. Customizing the generated AI

[1042] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[1043] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[1044] 4. Providing educational content

[1045] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[1046] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[1047] 5. Provision of cloud services

[1048] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[1049] 6. Introducing the Emotion Engine

[1050] An emotion engine that recognizes user emotions has been added to the system to improve teaching effectiveness. The server collects emotion data in real time when using the generative AI tool and adjusts prompt content and AI responses accordingly. In addition, teacher devices can refer to the emotion data to understand students' learning status.

[1051] For example, the emotion engine combines facial expression data, voice tone, and text sentiment analysis to identify a user's emotions. This allows the generative AI to respond according to the user's level of stress or interest, improving educational effectiveness. The emotion engine can also identify when a student is facing a difficult problem and provide additional hints or support.

[1052] In this way, the present invention provides a system that combines generative AI and emotion engines in educational institutions, contributing to improving the quality of education while reducing the risk of information leakage.

[1053] The processing flow will be explained below.

[1054] Setting up and managing a closed network

[1055] Step 1: Network Configuration

[1056] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[1057] The server sets up firewall rules to block access from outside the specified IP ranges.

[1058] Step 2: VPN Settings

[1059] The server configures the VPN server and generates authentication information for remote access.

[1060] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[1061] Step 3: Check access control

[1062] The server performs a test access to check whether access to the private network is being controlled properly.

[1063] The terminal attempts a test access and confirms that it is within the closed network.

[1064] Account Management

[1065] Step 1: Upload your account information

[1066] The teacher's terminal will prepare a CSV file containing student account information.

[1067] The teacher's terminal uploads this CSV file to the server.

[1068] Step 2: Create an account

[1069] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[1070] The server sets parental controls for each account, allowing access only during school hours.

[1071] Step 3: Record usage logs

[1072] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[1073] Customizing the generated AI

[1074] Step 1: Enter curriculum data

[1075] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[1076] Step 2: Prompt generation

[1077] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[1078] Step 3: Apply customization

[1079] After logging in, the student terminal receives a customized prompt template from the server.

[1080] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[1081] Providing educational content

[1082] Step 1: Create educational content

[1083] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[1084] Step 2: Content Delivery

[1085] The server sets and provides access rights to educational content to teachers and students.

[1086] Step 3: Content Usage

[1087] The teacher's terminal displays educational content during class and explains the learning content.

[1088] Students use their devices to study independently using materials provided at home or during class.

[1089] Cloud service provision

[1090] Step 1: Subdomain Setup

[1091] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[1092] Step 2: Cloud Access

[1093] The student terminal accesses the individual subdomain provided by the browser.

[1094] Student devices enter login credentials and use the generative AI tool.

[1095] Step 3: Resource Management

[1096] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[1097] Introducing the Emotion Engine

[1098] Step 1: Collecting emotion data

[1099] The student terminals use cameras and microphones to collect the user's facial expressions, voice tone, gestures, etc. in real time.

[1100] The server receives this data and analyzes the user's emotional state.

[1101] Step 2: Feedback of emotional data

[1102] The server adjusts the prompts and responses of the generating AI based on the analyzed emotional data.

[1103] The student terminal receives feedback according to the user's status.

[1104] Step 3: Analyze the sentiment data

[1105] The server integrates the collected emotional data with the usage logs of the generative AI to evaluate the effectiveness of the education.

[1106] Based on the analysis results, the teacher's device will be able to grasp students' learning situation and emotional state, which will be used to improve the content of lessons.

[1107] These steps will enable a safe and effective educational system that utilizes generative AI and emotion engines.

[1108] Example 2

[1109] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1110] Conventional generative AI environments in educational institutions have a high risk of unauthorized access and information leaks from outside, and there are many restrictions on the use of generative AI tools. It is also difficult to monitor students' emotional states in real time and provide appropriate teaching methods. Therefore, a new system that can balance information security and educational quality is needed.

[1111] 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.

[1112] In this invention, the server

[1113] an authentication means for restricting access from a user terminal;

[1114] Restrictions that allow access only during school hours;

[1115] a log management means for recording a usage log of the user terminal;

[1116] A customization means for generating curriculum-specific prompt templates;

[1117] means for providing the generated prompt template to a user terminal;

[1118] A means of collecting and analyzing the results of using generative AI tools;

[1119] a means of collecting emotion data in real time;

[1120] a means for adjusting the response of the generative AI based on the emotion data; and

[1121] a means of monitoring the use of educational content;

[1122] This will enable the safe and effective use of generative AI, reducing the risk of information leakage while providing appropriate education tailored to students' emotional states.

[1123] "Authentication methods" are methods for ensuring that only specific users can access a system, and include IDs, passwords, and two-factor authentication.

[1124] "Restriction measures" are methods of allowing access to the system only under certain conditions, such as during school hours, and include time restrictions and access control lists.

[1125] "Log management means" refers to a method for recording and saving the access and operation history of a user's terminal, which can be used for later verification or to detect unauthorized access.

[1126] "Customization methods" refer to methods for generating and adjusting prompt templates for generative AI tools according to the curriculum of educational institutions, and include dedicated configuration tools and template management functions.

[1127] "Providing means" refers to a method for providing the generated prompt templates and educational content to a user terminal, and includes a data distribution system and an access link.

[1128] "Collection and analysis means" refers to methods for collecting and analyzing the results of using the generative AI tool and user operation information, and includes database systems and analysis algorithms.

[1129] "Emotion data" is data that indicates the user's emotional state, and includes facial expression data, voice tone, text analysis results, and the like.

[1130] "Means for collecting data in real time" refers to methods for acquiring user emotional data in real time, including sensors and analysis software.

[1131] "Means for adjusting the response of the generative AI" refers to a method for appropriately adjusting the response of the generative AI based on the user's emotional data, and includes dialogue systems and response generation algorithms.

[1132] "Monitoring means" refers to methods for monitoring the usage of educational content and collecting data, including log collection tools and analysis dashboards.

[1133] In this invention, the following system configuration and method are used to safely and effectively operate a generative AI environment in an educational institution. The system mainly consists of a server, a teacher terminal, and a student terminal.

[1134] Setting up and managing a closed network

[1135] The server will be set up as a closed network through the network gateway within the educational institution. By defining a specific IP address range (e.g., 192.168.0.0 / 24) and setting up a firewall and VPN, external access will be blocked, creating a secure communication environment.

[1136] Specifically, the server accesses the network gateway settings screen and specifies an IP range. Next, firewall rules are set to allow communication only from devices within this IP range. Finally, a VPN is set up to ensure secure remote access.

[1137] Account Management

[1138] The teacher's terminal compiles student account information into a CSV file and uploads it to the server. The server analyzes this CSV file and automatically creates accounts for each student, adding a parental control function that allows use only during class time.

[1139] When a teacher uploads the CSV file to the server, the server analyzes the file and automatically creates an account for each student. Parental controls are then set to restrict access outside of class hours.

[1140] Customizing the generated AI

[1141] The teacher's terminal creates prompt templates for the generative AI tool according to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[1142] As a specific example, the server prepares prompt templates for Japanese, mathematics, science, social studies, and English. For example, in a mathematics class, a prompt might be set to "Please explain how to solve a cubic equation." This is set by the teacher's device and sent to the server, where it is reflected in the generation AI.

[1143] Providing educational content

[1144] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for self-study on their devices.

[1145] Specifically, a server creates educational content in the form of videos, PDFs, quizzes, etc. and provides it online. Teachers' devices display this content on a projector during class, and students' devices view the educational content at home.

[1146] Cloud service provision

[1147] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL from their browsers to use the generation AI tool. By logging in, users can select and use prompt templates for each subject.

[1148] Introducing the Emotion Engine

[1149] The server collects real-time emotional data from users when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can access students' emotional data and understand their learning status.

[1150] The emotion engine combines facial expression data with voice tone and text emotion analysis to identify the user's emotions. Based on this, the generative AI can respond according to the user's level of stress and interest, improving teaching effectiveness. In addition, the teacher's device uses a dashboard to monitor students' emotional data in real time.

[1151] This system enables the safe and effective use of generative AI, reducing the risk of information leaks while providing appropriate education tailored to students' emotional states.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Step 1:

[1154] Setting up and managing a closed network

[1155] The server accesses the institution's network gateway and sets a specific IP address range (e.g., 192.168.0.0 / 24). The server specifies the IP range in the network gateway settings screen and sets firewall rules to allow communication only from devices within this IP range. The server also sets up a VPN to allow secure remote access.

[1156] Specific behavior:

[1157] Input: Specific IP address range and VPN configuration information

[1158] Data processing: Apply IP address ranges, set firewall rules, configure VPN

[1159] Output: A secure network environment that can only be accessed by user terminals within the closed network

[1160] Step 2:

[1161] Account Management

[1162] The teacher's terminal creates student account information as a CSV file and uploads it to the server. The server analyzes the CSV file and automatically creates accounts for each student. At the same time, the server sets up parental control functions to restrict access outside of class hours.

[1163] Specific behavior:

[1164] Input: CSV file containing a list of students

[1165] Data processing: CSV file analysis, automatic creation of student accounts, setting parental controls

[1166] Output: Generated student accounts and access restriction functions

[1167] Step 3:

[1168] Customizing the generated AI

[1169] The teacher's terminal creates prompt templates tailored to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[1170] Specific behavior:

[1171] Input: Prompt template sent from the teacher's terminal

[1172] Data processing: Saving and generating prompt templates, and reflecting them in the generation AI

[1173] Output: Provides the generated prompt to the user's terminal

[1174] Step 4:

[1175] Providing educational content

[1176] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for home study.

[1177] Specific behavior:

[1178] Input: Educational content materials (videos, PDFs, quizzes, etc.)

[1179] Data processing: Creation of educational content and preparation for online distribution

[1180] Output: Provide the completed educational content to teacher and student devices

[1181] Step 5:

[1182] Cloud service provision

[1183] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL to use the generation AI tool. Users log in and select prompt templates for each subject.

[1184] Specific behavior:

[1185] Input: Subdomain information of each educational institution and URL of the AI ​​tool

[1186] Data processing: Subdomain settings, access rights settings

[1187] Output: A subdomain dedicated to each educational institution and the access URL for the generated AI tool

[1188] Step 6:

[1189] Introducing the Emotion Engine

[1190] The server collects users' emotional data in real time when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can refer to students' emotional data and understand their learning status.

[1191] Specific behavior:

[1192] Input: facial expression data, voice tone, text emotion data

[1193] Data processing: Real-time analysis of emotional data and feedback to generative AI

[1194] Output: Response of the generative AI based on emotion data, emotion data monitoring results displayed on the teacher's terminal

[1195] (Application example 2)

[1196] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1197] There is a need for a method to safely and effectively use generative AI in environments such as educational institutions and brick-and-mortar stores, while providing appropriate responses based on user emotions. However, existing systems do not adequately ensure security, analyze emotional data in real time, or adjust responses. Furthermore, it is difficult to restrict access from specific user devices and manage usage time, making it difficult to maximize the effectiveness of generative AI. This poses a challenge in achieving sufficient educational effectiveness and customer satisfaction.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, emotion recognition means for collecting and analyzing emotion data in real time, prompt generation means for adjusting the response of the generation AI based on the emotion data, and security means for safely managing user information. This allows the generation AI to provide responses that correspond to the user's emotional state, improving educational effectiveness and customer satisfaction.

[1199] A "closed network" is a network environment that can only be accessed from specific authorized user terminals and prevents unauthorized access from outside.

[1200] A "generative AI environment" refers to the entire system that uses artificial intelligence technology to generate content such as text and images.

[1201] "Authentication means" is a function that provides an authentication process to confirm that a user terminal has legitimate access rights.

[1202] "Restriction measures" are functions that restrict access to the generated AI environment based on specific time periods or conditions.

[1203] "Log management means" is a function that records and manages the history of users' access to the system.

[1204] "Emotion recognition means" is a function that collects and analyzes emotional data from the user's facial expressions, voice tone, and text content.

[1205] The "prompt generation means" is an artificial intelligence function that generates an appropriate response based on emotional data and the user's situation.

[1206] "Security measures" are functions for protecting user information and system data from external threats.

[1207] "Customization" refers to the ability to create prompt templates for the generative AI tool to suit specific curriculum and requirements.

[1208] "Means for collecting and analyzing usage results" refers to the function of collecting data associated with the use of generative AI tools and evaluating and analyzing their effectiveness.

[1209] "Educational content" refers to the teaching materials and resources provided to learners, and includes everything from basic knowledge of generative AI to applied techniques.

[1210] The "means for setting access rights" is a function for granting access rights to educational content to specific users.

[1211] "Means for monitoring" refers to the function of monitoring the usage of educational content and collecting that data.

[1212] "Means for analyzing educational effectiveness" is a function that evaluates and analyzes the learning effectiveness of educational content based on emotion recognition.

[1213] The present invention relates to a generative AI system for use in educational institutions and brick-and-mortar stores, and in particular to an AI system that automatically adjusts responses based on user emotion recognition. Hereinafter, a detailed description of an embodiment of the present invention will be given.

[1214] 1. System Configuration

[1215] This system consists of a server, a user terminal, and multiple devices including smart glasses. These devices are securely connected via a closed network and can only be accessed from specific user terminals.

[1216] 2. Closed Networks and Access Restrictions

[1217] The server uses a firewall and VPN to create a closed network to prevent unauthorized access from outside. Access from user devices is managed by authentication methods and is limited to class hours and business hours. This allows the generative AI system to be used in a secure environment.

[1218] 3. Emotion Recognition and Prompt Generation

[1219] Using smart glasses or a camera-equipped PC, emotion data is collected by analyzing the user's facial expressions and tone of voice in real time using OpenCV and Transformers libraries. The emotion recognition results are sent to a server, where generative AI creates an optimal response based on the emotion.

[1220] For example, the following prompt is set:

[1221] "Please explain how to solve a cubic equation."

[1222] "Suggest a product that's on promotion"

[1223] "Do you have any particular favorite products?"

[1224] 4. Data Management and Security

[1225] User access history is recorded by a log management system. Emotional data and response data are also strictly managed, and measures are taken to prevent information leaks, including data encryption and access control.

[1226] 5. Providing customized and educational content

[1227] The server customizes prompt templates for the generative AI tool according to the needs of each educational institution or brick-and-mortar store. The educational content is provided as teaching materials covering everything from basic knowledge of generative AI to applied techniques. Users can use this for self-study.

[1228] As described above, the present invention provides a response adjustment system for generative AI that utilizes emotion recognition data, thereby realizing safe and effective learning and customer service support.

[1229] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1230] Step 1:

[1231] The server creates a closed network and prepares an environment that can only be accessed from specific user terminals. To achieve this, a firewall and VPN are set up. The input is the IP address and network setting information of the user terminal, and the output is a notification that the closed network has been successfully created. This step establishes a secure network environment.

[1232] Step 2:

[1233] The terminal authenticates the user's access using authentication means. The input is the user's authentication information (ID and password, etc.), and the output is a notification of authentication success or failure. After successful authentication, the user is allowed access to the system.

[1234] Step 3:

[1235] The server uses a log management tool to record access logs from user terminals. The input is access information such as the access time, user ID, and connection IP address, and the output is the recorded log data. This data is later used for analysis and auditing.

[1236] Step 4:

[1237] The device uses smart glasses and a camera to analyze the user's facial expressions and tone of voice in real time. The input is camera footage and audio data, and the output is analyzed emotion data. Specifically, it extracts facial features using OpenCV and recognizes emotions using the Transformers library.

[1238] Step 5:

[1239] The server receives the emotion data and uses a generative AI model to generate an optimal prompt based on that data. The input is the emotion data and a pre-defined prompt template, and the output is the generated prompt. For example, if the emotion is "happy," the generated prompt is "Please suggest a product that is currently on promotion."

[1240] Step 6:

[1241] The terminal displays prompts received from the server to the user. The input is the generated prompt, and the output is text displayed on a display or smart glasses, allowing the user to take appropriate action.

[1242] Step 7:

[1243] The server then records the user's responses and actions using a log management tool. The input is the user's response data and action records, and the output is updated log data. This information is later used to analyze the effectiveness and develop improvement plans.

[1244] Step 8:

[1245] The server uses security measures to safely store all data, encrypting and controlling access as necessary. The input is confidential information such as log data and emotion data, and the output is securely stored data. This reduces the risk of information leakage.

[1246] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1247] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1248] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1249] [Fourth embodiment]

[1250] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1251] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1252] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1253] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1254] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1255] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1256] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1257] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1258] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1259] 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.

[1260] 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.

[1261] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1262] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1263] The present invention is a system for safely and effectively using generative AI in educational institutions. Specifically, this system is configured as follows.

[1264] 1. Setting up and managing a closed network

[1265] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[1266] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[1267] 2. Account Management

[1268] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[1269] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[1270] 3. Customizing the generated AI

[1271] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[1272] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[1273] 4. Providing educational content

[1274] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[1275] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[1276] 5. Provision of cloud services

[1277] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[1278] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[1279] The processing flow will be explained below.

[1280] Setting up and managing a closed network

[1281] Step 1: Network Configuration

[1282] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[1283] The server will set up firewall rules to block access from outside the specified IP ranges.

[1284] Step 2: VPN Settings

[1285] The server configures the VPN server and generates authentication information for remote access.

[1286] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[1287] Step 3: Check access control

[1288] The server performs a test access to check whether access to the private network is being controlled properly.

[1289] The terminal attempts a test access and confirms that it is within the closed network.

[1290] Account Management

[1291] Step 1: Upload your account information

[1292] The teacher's terminal will prepare a CSV file containing student account information.

[1293] The teacher's terminal uploads this CSV file to the server.

[1294] Step 2: Create an account

[1295] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[1296] The server sets parental controls for each account, allowing access only during school hours.

[1297] Step 3: Record usage logs

[1298] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[1299] Customizing the generated AI

[1300] Step 1: Enter curriculum data

[1301] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[1302] Step 2: Prompt generation

[1303] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[1304] Step 3: Apply customization

[1305] After logging in, the student terminal receives a customized prompt template from the server.

[1306] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[1307] Providing educational content

[1308] Step 1: Create educational content

[1309] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[1310] Step 2: Content Delivery

[1311] The server sets and provides access rights to educational content to teachers and students.

[1312] Step 3: Content Usage

[1313] The teacher's terminal displays educational content during class and explains the learning content.

[1314] Students use their devices to study independently using materials provided at home or during class.

[1315] Cloud service provision

[1316] Step 1: Subdomain Setup

[1317] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[1318] Step 2: Cloud Access

[1319] The student terminal accesses the individual subdomain provided by the browser.

[1320] Student devices enter login credentials and use the generative AI tool.

[1321] Step 3: Resource Management

[1322] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[1323] By repeatedly performing these steps, a safe and effective environment for utilizing generative AI can be achieved.

[1324] Example 1

[1325] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1326] The use of conventional generative AI tools has posed risks of information leaks and unauthorized access at educational institutions. It has also been difficult to customize generative AI tools to effectively incorporate them into lessons, and to properly manage accounts for them. Furthermore, there has been a lack of a systematic way to provide educational content covering everything from the basics to applications of generative AI. These issues have been major obstacles to providing a safe and effective environment for using generative AI.

[1327] 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.

[1328] In this invention, the server includes a means for setting up a closed network through a network gateway and creating an environment accessible only from a specific IP address range, a means for preventing unauthorized access from outside using a firewall and VPN, a means for setting parental control that allows access only during specific time periods, a means for automatically generating accounts from data files containing student information, a means for generating and providing prompt templates according to lesson content, and a means for creating and providing educational content for generative AI. This makes it possible to provide generative AI tools in a safe environment with strict access control, and further enables the effective use of generative AI according to the curriculum of educational institutions and the improvement of student learning outcomes.

[1329] A "network gateway" is a device or software that manages and controls communication between an internal network and an external network.

[1330] A "closed network" is a limited network environment that can only be accessed by specific users or devices.

[1331] An "IP address range" refers to a specific range of IP addresses, and access rights can be set only for addresses within that range.

[1332] A "firewall" is a hardware or software security system that protects a network from unauthorized access.

[1333] "VPN" stands for Virtual Private Network, a technology for establishing secure communications over the Internet.

[1334] "Parental control" is a function that restricts access rights to specific time periods or content.

[1335] A "data file" is a file format for storing specific information on a computer.

[1336] "Creating an account" refers to setting a user's identification information and authentication information to create a new account.

[1337] A "prompt template" is a predefined document format that provides specific instructions or questions to the generative AI.

[1338] "Educational content for generative AI" refers to teaching materials for systematically learning everything from basic knowledge to applied techniques of generative AI.

[1339] The present invention is a system for the safe and effective use of generative AI in educational institutions. The system consists of the following components:

[1340] Setting up and managing a closed network

[1341] The server is set up as a closed network through the institution's network gateway. Specifically, firewall software (e.g., pfSense) is used to specify the IP range "192.168.0.0 / 24" so that only devices within this range can access it. VPN server software (e.g., OpenVPN) is also set up to allow secure access from outside. This prevents unauthorized access and reduces the risk of information leaks.

[1342] Account Management

[1343] To centrally manage student account information, teachers' devices create a CSV file (e.g., students.csv) and upload it to the server. The server analyzes this file and automatically creates student accounts. At this time, a parental control function is also added to allow access only during class hours. For example, the server might set class hours to "weekdays 9:00-17:00" and restrict access outside of these hours.

[1344] Customizing the generated AI

[1345] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server. The server then generates prompts based on this and provides them to the user's terminal. For example, the server may prepare templates for five subjects (Japanese, mathematics, science, social studies, and English) in advance and customize them as needed. An example of a specific prompt sentence used in a mathematics class is "Please explain how to solve a cubic equation."

[1346] Providing educational content

[1347] The server creates educational content covering everything from the basics to applications of generative AI and provides it to teachers and students. This educational content consists of videos, PDFs, and quizzes. The teacher's device projects the content during class, and the students' devices use the content for self-study. Specific examples include a video called "Fundamentals of Generative AI" and a PDF of the "Ethics Education Handbook" provided by the server.

[1348] Cloud service provision

[1349] This system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students can use the generation AI tool by accessing this URL from their browser. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[1350] As a specific example, a user (student) accesses "schoolname.generateai.com" and logs in using the provided credentials. After that, they select "Please explain how to solve a cubic equation" as a math prompt template, and the Generative AI returns an answer based on that.

[1351] In this way, the present invention provides an environment in which generative AI can be used safely and effectively in educational institutions, reducing the risk of information leakage and contributing to improving the quality of education.

[1352] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1353] Step 1:

[1354] Setting up and managing a closed network

[1355] The server sets up a closed network through the network gateway. Specifically, the server uses firewall software (e.g., pfSense) to specify the IP range "192.168.0.0 / 24" to allow access only from devices within this range. In addition, VPN server software (e.g., OpenVPN) is configured to allow secure access from outside.

[1356] Input: Network setting information (IP range, VPN settings)

[1357] Data processing / calculation: Firewall rule settings, VPN connection settings

[1358] Output: Closed network setting completion notification

[1359] Specific actions: The server reads the configuration file, applies network rules, and starts the VPN server.

[1360] Step 2:

[1361] Account Management

[1362] To centrally manage student account information, teachers create a CSV file (e.g., students.csv) on their devices and upload it to the server. The server then analyzes this file and automatically creates student accounts. A parental control feature is also added to allow access only during class hours.

[1363] Input: CSV file (student list)

[1364] Data processing / calculation: CSV file analysis, account information generation, parental control settings

[1365] Output: Generated account information

[1366] Specific operation: The server parses the CSV file, registers the new account in the database, and applies access restrictions outside of class hours.

[1367] Step 3:

[1368] Customizing the generated AI

[1369] The teacher's terminal creates a prompt template according to the content of the lesson and sends it to the server, which then generates a prompt based on the template and provides it to the user's terminal.

[1370] Input: Prompt template content

[1371] Data processing / calculation: Creating and saving templates

[1372] Output: Customized prompt template

[1373] Specific operation: The server receives template data from the teacher's terminal, stores it in a database, and generates prompts based on it.

[1374] Step 4:

[1375] Providing educational content

[1376] The server creates educational content covering everything from the basics to applications of generative AI, and provides it to teachers and students' devices. The educational content includes videos, PDFs, and quizzes.

[1377] Input: Educational content information (videos, PDFs, quizzes)

[1378] Data processing / calculation: content format conversion and storage

[1379] Output: Served educational content

[1380] Specific operation: The server creates educational content, converts it into the specified format, and uploads it to the online portal.

[1381] Step 5:

[1382] Cloud service provision

[1383] The server sets up a separate subdomain for each educational institution, allowing students to access the generative AI tool by accessing this URL from their browser.

[1384] Input: Educational institution registration information

[1385] Data processing / calculation: Subdomain settings, authentication information generation

[1386] Output: Access URL and authentication information

[1387] Specific operations: The server receives the new educational institution information, sets up a subdomain, and prepares the login screen.

[1388] These are the processing steps of this system, which enables the safe and effective use of generative AI in educational institutions.

[1389] (Application example 1)

[1390] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1391] This invention is based on a generative AI system for educational institutions, and aims to improve the efficiency of store management and the skills and knowledge required for virtual store operation. Conventional generative AI systems for educational institutions contribute to improving the quality of education, but they have the problem of not being able to address the knowledge required for virtual store operation and the efficiency of management work. Furthermore, as virtual store operations are increasing today, the lack of support systems specialized for virtual store operation is becoming a serious problem.

[1392] 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.

[1393] In this invention, the server is a system that includes network settings that allow access only from specific user terminals to build a generative AI environment that operates within a closed network, and includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, means for providing generative AI tools for virtual store operation, and customization means for streamlining various management tasks. This makes it possible to learn the knowledge and skills necessary for virtual store operation and to streamline management tasks.

[1394] A "closed network" is a limited network environment that is accessible only to a select few users, and is used to ensure security and privacy.

[1395] A "generative AI environment" is an environment that provides a system that uses artificial intelligence to make predictions and generate results based on user input.

[1396] "User terminal" means a user's device used to access the system, including smartphones, tablets, PCs, etc.

[1397] "Network settings" refers to the process of specifying IP addresses and setting authentication protocols to allow access only from specific devices.

[1398] "Authentication means" refers to a mechanism for verifying the validity of a user terminal when accessing a system. For example, it includes authentication of a user name and password.

[1399] "Restriction measures" are functions that restrict the use of the system depending on time and conditions, and include restrictions such as only allowing access during certain hours.

[1400] "Log management means" refers to a mechanism for recording and managing system usage, and records access logs and operation history.

[1401] A "virtual store" is a virtual store that sells products and services over the Internet, and refers to an online shop that does not have a physical store.

[1402] "Generative AI tools" are tools that use generative AI models to automate various tasks, including text generation from prompts and data analysis.

[1403] "Customization" refers to the functionality and processes that allow users to adapt and modify systems and tools to meet their specific needs.

[1404] A "prompt template" is a template of instructions that a generative AI uses to generate a response to a specific task or question.

[1405] "Educational content" is a general term for teaching materials and resources provided to learners, including videos, slides, PDFs, etc.

[1406] "Account Management" refers to the process and system for registering and managing user account information.

[1407] The present invention provides a generative AI system for supporting virtual store operations, and its main functions are as follows:

[1408] 1. Private network configuration and management:

[1409] The server creates a closed network that can only be accessed from specific user terminals and configures the network. Specifically, AWS VPC is used to set up a specific IP address range and provide secure access using a VPN connection. This closed network enables the safe operation of the virtual store.

[1410] 2. Account Management:

[1411] The server centrally manages the accounts of virtual store managers and staff. Account information is imported from a CSV file and saved in the database. This allows for role-based access permissions, enabling efficient account management. For example, managers can register products, while staff can only check inventory.

[1412] 3. Customizing generative AI tools:

[1413] The server provides a generative AI tool specialized for virtual store operations. This tool performs various data analysis and predictions using generative AI models based on prompt templates, such as optimizing inventory management, automating customer service, and predicting sales trends.

[1414] Example prompt: "Generate a sales forecast for next month."

[1415] 4. Providing educational content:

[1416] The server provides educational content for learning the skills necessary for running a virtual store. It allows users to access videos, slides, PDF documents, etc. through a smartphone app using React Native. It also provides a function to monitor the usage of this content.

[1417] Example prompt: "Please show me a video presentation that explains how to manage inventory efficiently."

[1418] 5. Provision of Cloud Services:

[1419] The server sets up a subdomain for each virtual store and makes it accessible from a browser. This allows users to log in and access various management tools and educational content. Using cloud services reduces implementation costs and enables smooth operation.

[1420] To achieve these functions, the server utilizes hardware and software such as AWS EC2 instances, AWS VPC, OpenVPN, Node.js, and React Native. On the user side, smartphones and head-mounted displays can be used to efficiently learn about and manage virtual store operations.

[1421] Specifically, the server uses AWS EC2 to build a closed network and manages accounts using a database, while data analysis using generative AI models and the provision of educational content can be used to streamline virtual store operations.

[1422] For example, if a virtual store manager needs a sales forecast, they can use a generative AI tool to enter the following prompt:

[1423] "Generate a sales forecast for next month."

[1424] This allows the generative AI model to automatically analyze data and provide predictions, while also providing managers with prompts to train new staff:

[1425] "Show me video materials that explain efficient inventory management methods."

[1426] This allows educational content suitable for smartphone devices to be provided.

[1427] In this way, the present invention provides an advanced support system for virtual store management, and is equipped with features for realizing efficient and effective management.

[1428] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1429] Step 1:

[1430] The server creates a closed network using AWS VPC. It sets a specific IP address range (e.g., 192.168.0.0 / 24) as input and provides a secure network environment as output. The server then configures OpenVPN to enable secure remote access.

[1431] Step 2:

[1432] The user uploads a CSV file to the server using a teacher's terminal and imports the account information of the virtual store manager and staff. The CSV file containing the account information is prepared as input, and the account data stored in the database is obtained as output. Specifically, the server parses the CSV file and inserts each account information into the database.

[1433] Step 3:

[1434] The server provides tools using generative AI models, and users (administrators) input prompts related to virtual store operations. Prompts such as "Please generate sales forecasts for next month" are used as input, and sales forecast data is generated as output. The server analyzes the prompts and executes the generative AI model to calculate the forecast results.

[1435] Step 4:

[1436] The server provides the educational content required for running a virtual store through a React Native app. It receives a request from the administrator as input (e.g., "Please display a video document explaining efficient inventory management methods.") and delivers the appropriate educational content to the user's device as output. Specifically, the server retrieves the relevant educational content from the database and sends it to the user's smartphone.

[1437] Step 5:

[1438] Users (managers and staff) use the delivered educational content to learn knowledge that will be useful in running a virtual store. They select content on the smartphone app as input, and obtain the learned knowledge as output. Users acquire practical skills by watching videos and viewing slides.

[1439] Step 6:

[1440] As a cloud service, the server automatically sets up a subdomain so that users can access it from their browser. It takes the information for a new virtual store as input and provides a dedicated subdomain (e.g., shopname.example.com) as output. This setup allows users to easily access the system and start operating.

[1441] These steps seamlessly provide the various functions necessary for virtual store operation, enabling efficient and effective operation.

[1442] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1443] This invention is a system for safely and effectively using generative AI in educational institutions, which is combined with an emotion engine that recognizes user emotions. The system is configured as follows.

[1444] 1. Setting up and managing a closed network

[1445] The server is set up in a closed network through the educational institution's network gateway, creating an environment that can only be accessed from specific IP addresses. This closed network uses a firewall and VPN to prevent unauthorized access from outside, reducing the risk of information leaks.

[1446] For example, the server will specify the IP range 192.168.0.0 / 24, and only user devices within this range will be able to access the generated AI environment. A VPN connection will also be used to enable secure access even from remote locations.

[1447] 2. Account Management

[1448] Teachers' devices can register and manage student account information all at once. The server automatically generates accounts from a student list provided by the teacher (e.g., a CSV file), and adds a parental control function to each account that allows use only during class time.

[1449] For example, a teacher's terminal uploads a CSV file containing a student list to a server, which then automatically generates student accounts based on this file. The server also restricts access to the AI ​​tool outside of class hours.

[1450] 3. Customizing the generated AI

[1451] The AI ​​generation tool's prompt templates are customized to fit each educational institution's curriculum. Teachers' devices input prompt templates according to the content of their lessons and send them to the server. The server generates prompts based on these templates and provides them to users' devices.

[1452] For example, the server prepares templates for five subjects (Japanese, mathematics, science, social studies, and English), and the teacher's terminal uses these to set a prompt appropriate for each lesson. For example, in a mathematics lesson, the prompt might be, "Please explain how to solve a cubic equation."

[1453] 4. Providing educational content

[1454] The system provides educational content covering everything from the basics to applications of generative AI. The server creates content including how to use generative AI, ethics education, and how to handle data, and provides it to teachers and students. This content is displayed on teacher devices during lessons, and students use it for self-study.

[1455] For example, the server provides educational content in the form of online teaching materials such as videos, PDFs, quizzes, etc. For example, a teacher's device projects video materials during class, while a student's device views slides at home.

[1456] 5. Provision of cloud services

[1457] The system is provided as a cloud service, reducing implementation costs and enabling smooth operation. The server sets up an individual subdomain (e.g., schoolname.generateai.com) for each educational institution, and students use the generation AI tool by accessing this URL from their browsers. When users log in, prompt templates for each subject are displayed, and they can select and use them.

[1458] 6. Introducing the Emotion Engine

[1459] An emotion engine that recognizes user emotions has been added to the system to improve teaching effectiveness. The server collects emotion data in real time when using the generative AI tool and adjusts prompt content and AI responses accordingly. In addition, teacher devices can refer to the emotion data to understand students' learning status.

[1460] For example, the emotion engine combines facial expression data, voice tone, and text sentiment analysis to identify a user's emotions. This allows the generative AI to respond according to the user's level of stress or interest, improving educational effectiveness. The emotion engine can also identify when a student is facing a difficult problem and provide additional hints or support.

[1461] In this way, the present invention provides a system that combines generative AI and emotion engines in educational institutions, contributing to improving the quality of education while reducing the risk of information leakage.

[1462] The processing flow will be explained below.

[1463] Setting up and managing a closed network

[1464] Step 1: Network Configuration

[1465] The server specifies the IP range 192.168.0.0 / 24 and configures the network so that only devices within this range can access it.

[1466] The server sets up firewall rules to block access from outside the specified IP ranges.

[1467] Step 2: VPN Settings

[1468] The server configures the VPN server and generates authentication information for remote access.

[1469] The user terminal sets up a VPN connection using the VPN setting information provided by the server.

[1470] Step 3: Check access control

[1471] The server performs a test access to check whether access to the private network is being controlled properly.

[1472] The terminal attempts a test access and confirms that it is within the closed network.

[1473] Account Management

[1474] Step 1: Upload your account information

[1475] The teacher's terminal will prepare a CSV file containing student account information.

[1476] The teacher's terminal uploads this CSV file to the server.

[1477] Step 2: Create an account

[1478] The server analyzes the uploaded CSV file and automatically generates an account for each student.

[1479] The server sets parental controls for each account, allowing access only during school hours.

[1480] Step 3: Record usage logs

[1481] The server records students' use of the AI ​​generation tool and periodically saves and analyzes the usage logs.

[1482] Customizing the generated AI

[1483] Step 1: Enter curriculum data

[1484] The teacher's terminal inputs a prompt template that matches the curriculum and transmits it to the server.

[1485] Step 2: Prompt generation

[1486] The server generates prompt templates appropriate for each subject based on the curriculum data sent.

[1487] Step 3: Apply customization

[1488] After logging in, the student terminal receives a customized prompt template from the server.

[1489] The student device selects the appropriate prompt template from the presented ones and uses the generation AI function.

[1490] Providing educational content

[1491] Step 1: Create educational content

[1492] The server creates educational content covering everything from the basics to applications of generative AI, and the content is provided in the form of videos, PDFs, and quizzes.

[1493] Step 2: Content Delivery

[1494] The server sets and provides access rights to educational content to teachers and students.

[1495] Step 3: Content Usage

[1496] The teacher's terminal displays educational content during class and explains the learning content.

[1497] Students use their devices to study independently using materials provided at home or during class.

[1498] Cloud service provision

[1499] Step 1: Subdomain Setup

[1500] The server generates a separate subdomain for each educational institution (e.g., schoolname.generateai.com).

[1501] Step 2: Cloud Access

[1502] The student terminal accesses the individual subdomain provided by the browser.

[1503] Student devices enter login credentials and use the generative AI tool.

[1504] Step 3: Resource Management

[1505] The server monitors the usage of each educational institution and adjusts resources (e.g., storage and computing power).

[1506] Introducing the Emotion Engine

[1507] Step 1: Collecting emotion data

[1508] The student terminals use cameras and microphones to collect the user's facial expressions, voice tone, gestures, etc. in real time.

[1509] The server receives this data and analyzes the user's emotional state.

[1510] Step 2: Feedback of emotional data

[1511] The server adjusts the prompts and responses of the generating AI based on the analyzed emotional data.

[1512] The student terminal receives feedback according to the user's status.

[1513] Step 3: Analyze the sentiment data

[1514] The server integrates the collected emotional data with the usage logs of the generative AI to evaluate the effectiveness of the education.

[1515] Based on the analysis results, the teacher's device will be able to grasp students' learning situation and emotional state, which will be used to improve the content of lessons.

[1516] These steps will enable a safe and effective educational system that utilizes generative AI and emotion engines.

[1517] Example 2

[1518] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1519] Conventional generative AI environments in educational institutions have a high risk of unauthorized access and information leaks from outside, and there are many restrictions on the use of generative AI tools. It is also difficult to monitor students' emotional states in real time and provide appropriate teaching methods. Therefore, a new system that can balance information security and educational quality is needed.

[1520] 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.

[1521] In this invention, the server

[1522] an authentication means for restricting access from a user terminal;

[1523] Restrictions that allow access only during school hours;

[1524] a log management means for recording a usage log of the user terminal;

[1525] A customization means for generating curriculum-specific prompt templates;

[1526] means for providing the generated prompt template to a user terminal;

[1527] A means of collecting and analyzing the results of using generative AI tools;

[1528] a means of collecting emotion data in real time;

[1529] a means for adjusting the response of the generative AI based on the emotion data; and

[1530] a means of monitoring the use of educational content;

[1531] This will enable the safe and effective use of generative AI, reducing the risk of information leakage while providing appropriate education tailored to students' emotional states.

[1532] "Authentication methods" are methods for ensuring that only specific users can access a system, and include IDs, passwords, and two-factor authentication.

[1533] "Restriction measures" are methods of allowing access to the system only under certain conditions, such as during school hours, and include time restrictions and access control lists.

[1534] "Log management means" refers to a method for recording and saving the access and operation history of a user's terminal, which can be used for later verification or to detect unauthorized access.

[1535] "Customization methods" refer to methods for generating and adjusting prompt templates for generative AI tools according to the curriculum of educational institutions, and include dedicated configuration tools and template management functions.

[1536] "Providing means" refers to a method for providing the generated prompt templates and educational content to the user terminal, and includes a data distribution system and an access link.

[1537] "Collection and analysis means" refers to methods for collecting and analyzing the results of using the generative AI tool and user operation information, and includes database systems and analysis algorithms.

[1538] "Emotion data" is data that indicates the user's emotional state, and includes facial expression data, voice tone, text analysis results, and the like.

[1539] "Means for collecting data in real time" refers to methods for acquiring user emotional data in real time, including sensors and analysis software.

[1540] "Means for adjusting the response of the generative AI" refers to a method for appropriately adjusting the response of the generative AI based on the user's emotional data, and includes dialogue systems and response generation algorithms.

[1541] "Monitoring means" refers to methods for monitoring the usage of educational content and collecting data, including log collection tools and analysis dashboards.

[1542] In this invention, the following system configuration and method are used to safely and effectively operate a generative AI environment in an educational institution. The system mainly consists of a server, a teacher terminal, and a student terminal.

[1543] Setting up and managing a closed network

[1544] The server will be set up as a closed network through the network gateway within the educational institution. By defining a specific IP address range (e.g., 192.168.0.0 / 24) and setting up a firewall and VPN, external access will be blocked, creating a secure communication environment.

[1545] Specifically, the server accesses the network gateway settings screen and specifies an IP range. Next, firewall rules are set to allow communication only from devices within this IP range. Finally, a VPN is set up to ensure secure remote access.

[1546] Account Management

[1547] The teacher's terminal compiles student account information into a CSV file and uploads it to the server. The server analyzes this CSV file and automatically creates accounts for each student, adding a parental control function that allows use only during class time.

[1548] When a teacher uploads the CSV file to the server, the server analyzes the file and automatically creates an account for each student. Parental controls are then set to restrict access outside of class hours.

[1549] Customizing the generated AI

[1550] The teacher's terminal creates prompt templates for the generative AI tool according to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[1551] As a specific example, the server prepares prompt templates for Japanese, mathematics, science, social studies, and English. For example, in a mathematics class, a prompt might be set to "Please explain how to solve a cubic equation." This is set by the teacher's device and sent to the server, where it is reflected in the generation AI.

[1552] Providing educational content

[1553] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for self-study on their devices.

[1554] Specifically, a server creates educational content in the form of videos, PDFs, quizzes, etc. and provides it online. Teachers' devices display this content on a projector during class, and students' devices view the educational content at home.

[1555] Cloud service provision

[1556] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL from their browsers to use the generation AI tool. By logging in, users can select and use prompt templates for each subject.

[1557] Introducing the Emotion Engine

[1558] The server collects real-time emotional data from users when using the generative AI tool and adjusts prompts and AI responses. Teachers' devices can access students' emotional data and understand their learning status.

[1559] The emotion engine combines facial expression data with voice tone and text emotion analysis to identify the user's emotions. Based on this, the generative AI can respond according to the user's level of stress and interest, improving teaching effectiveness. In addition, the teacher's device uses a dashboard to monitor students' emotional data in real time.

[1560] This system enables the safe and effective use of generative AI, reducing the risk of information leaks while providing appropriate education tailored to students' emotional states.

[1561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1562] Step 1:

[1563] Setting up and managing a closed network

[1564] The server accesses the institution's network gateway and sets a specific IP address range (e.g., 192.168.0.0 / 24). The server specifies the IP range in the network gateway settings screen and sets firewall rules to allow communication only from devices within this IP range. The server also sets up a VPN to allow secure remote access.

[1565] Specific behavior:

[1566] Input: Specific IP address range and VPN configuration information

[1567] Data processing: Apply IP address ranges, set firewall rules, configure VPN

[1568] Output: A secure network environment that can only be accessed by user terminals within the closed network

[1569] Step 2:

[1570] Account Management

[1571] The teacher's terminal creates student account information as a CSV file and uploads it to the server. The server analyzes the CSV file and automatically creates accounts for each student. At the same time, the server sets up parental control functions to restrict access outside of class hours.

[1572] Specific behavior:

[1573] Input: CSV file containing a list of students

[1574] Data processing: CSV file analysis, automatic creation of student accounts, setting parental controls

[1575] Output: Generated student accounts and access restriction functions

[1576] Step 3:

[1577] Customizing the generated AI

[1578] The teacher's terminal creates prompt templates tailored to the curriculum and sends them to the server, which then generates prompts based on these templates and provides them to the student's terminal.

[1579] Specific behavior:

[1580] Input: Prompt template sent from the teacher's terminal

[1581] Data processing: Saving and generating prompt templates, and reflecting them in the generation AI

[1582] Output: Provides the generated prompt to the user's terminal

[1583] Step 4:

[1584] Providing educational content

[1585] The server creates educational content for teachers and students, including how to use generative AI, ethics education, and how to handle data. This content can be displayed on teacher devices during lessons, and students can use it for home study.

[1586] Specific behavior:

[1587] Input: Educational content materials (videos, PDFs, quizzes, etc.)

[1588] Data processing: Creation of educational content and preparation for online distribution

[1589] Output: Provide the completed educational content to teacher and student devices

[1590] Step 5:

[1591] Cloud service provision

[1592] The server sets up a separate subdomain (e.g., schoolname.generateai.com) for each educational institution. Student devices access this URL to use the generation AI tool. Users log in and select prompt templates for each subject.

[1593] Specific behavior:

[1594] Input: Subdomain information of each educational institution and URL of the AI ​​tool

[1595] Data processing: Subdomain settings, access rights settings

[1596] Output: A subdomain dedicated to each educational institution and the access URL for the generated AI tool

[1597] Step 6:

[1598] Introducing the Emotion Engine

[1599] The server collects users' emotional data in real time when using the generative AI tool and adjusts prompts and AI responses. The teacher's device can refer to students' emotional data and grasp their learning status.

[1600] Specific behavior:

[1601] Input: facial expression data, voice tone, text emotion data

[1602] Data processing: Real-time analysis of emotional data and feedback to generative AI

[1603] Output: Response of the generative AI based on emotion data, emotion data monitoring results displayed on the teacher's terminal

[1604] (Application example 2)

[1605] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1606] There is a need for a method to safely and effectively use generative AI in environments such as educational institutions and brick-and-mortar stores, while providing appropriate responses based on user emotions. However, existing systems do not adequately ensure security, analyze emotional data in real time, or adjust responses. Furthermore, it is difficult to restrict access from specific user devices and manage usage time, making it difficult to maximize the effectiveness of generative AI. This poses a challenge in achieving sufficient educational effectiveness and customer satisfaction.

[1607] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes authentication means for restricting access from user terminals, restriction means for permitting access only during class hours, log management means for recording usage logs of user terminals, emotion recognition means for collecting and analyzing emotion data in real time, prompt generation means for adjusting the response of the generation AI based on the emotion data, and security means for safely managing user information. This allows the generation AI to provide responses that correspond to the user's emotional state, improving educational effectiveness and customer satisfaction.

[1608] A "closed network" is a network environment that can only be accessed from specific authorized user terminals and prevents unauthorized access from outside.

[1609] A "generative AI environment" refers to the entire system that uses artificial intelligence technology to generate content such as text and images.

[1610] "Authentication means" is a function that provides an authentication process to confirm that a user terminal has legitimate access rights.

[1611] "Restriction measures" are functions that restrict access to the generated AI environment based on specific time periods or conditions.

[1612] "Log management means" is a function that records and manages the history of users' access to the system.

[1613] "Emotion recognition means" is a function that collects and analyzes emotional data from the user's facial expressions, voice tone, and text content.

[1614] The "prompt generation means" is an artificial intelligence function that generates an appropriate response based on emotional data and the user's situation.

[1615] "Security measures" are functions for protecting user information and system data from external threats.

[1616] "Customization" refers to the ability to create prompt templates for the generative AI tool to suit specific curriculum and requirements.

[1617] "Means for collecting and analyzing usage results" refers to the function of collecting data associated with the use of generative AI tools and evaluating and analyzing their effectiveness.

[1618] "Educational content" refers to the teaching materials and resources provided to learners, and includes everything from basic knowledge of generative AI to applied techniques.

[1619] The "means for setting access rights" is a function for granting access rights to educational content to specific users.

[1620] "Means for monitoring" refers to the function of monitoring the usage of educational content and collecting that data.

[1621] "Means for analyzing educational effectiveness" is a function that evaluates and analyzes the learning effectiveness of educational content based on emotion recognition.

[1622] The present invention relates to a generative AI system for use in educational institutions and brick-and-mortar stores, and in particular to an AI system that automatically adjusts responses based on user emotion recognition. Hereinafter, a detailed description of an embodiment of the present invention will be given.

[1623] 1. System Configuration

[1624] This system consists of a server, a user terminal, and multiple devices including smart glasses. These devices are securely connected via a closed network and can only be accessed from specific user terminals.

[1625] 2. Closed Networks and Access Restrictions

[1626] The server uses a firewall and VPN to create a closed network to prevent unauthorized access from outside. Access from user devices is managed by authentication methods and is limited to class hours and business hours. This allows the generative AI system to be used in a secure environment.

[1627] 3. Emotion Recognition and Prompt Generation

[1628] Using smart glasses or a camera-equipped PC, emotion data is collected by analyzing the user's facial expressions and tone of voice in real time using OpenCV and Transformers libraries. The emotion recognition results are sent to a server, where generative AI creates an optimal response based on the emotion.

[1629] For example, the following prompt is set:

[1630] "Please explain how to solve a cubic equation."

[1631] "Suggest a product that's on promotion"

[1632] "Do you have any particular favorite products?"

[1633] 4. Data Management and Security

[1634] User access history is recorded by a log management system. Emotional data and response data are also strictly managed, and measures are taken to prevent information leaks, including data encryption and access control.

[1635] 5. Providing customized and educational content

[1636] The server customizes prompt templates for the generative AI tool according to the needs of each educational institution or brick-and-mortar store. The educational content is provided as teaching materials covering everything from basic knowledge of generative AI to applied techniques. Users can use this for self-study.

[1637] As described above, the present invention provides a response adjustment system for generative AI that utilizes emotion recognition data, thereby realizing safe and effective learning and customer service support.

[1638] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1639] Step 1:

[1640] The server creates a closed network and prepares an environment that can only be accessed from specific user terminals. To achieve this, a firewall and VPN are set up. The input is the IP address and network setting information of the user terminal, and the output is a notification that the closed network has been successfully created. This step establishes a secure network environment.

[1641] Step 2:

[1642] The terminal authenticates the user's access using authentication means. The input is the user's authentication information (ID and password, etc.), and the output is a notification of authentication success or failure. After successful authentication, the user is allowed access to the system.

[1643] Step 3:

[1644] The server uses a log management tool to record access logs from user terminals. The input is access information such as the access time, user ID, and connection IP address, and the output is the recorded log data. This data is later used for analysis and auditing.

[1645] Step 4:

[1646] The device uses smart glasses and a camera to analyze the user's facial expressions and tone of voice in real time. The input is camera footage and audio data, and the output is analyzed emotion data. Specifically, it extracts facial features using OpenCV and recognizes emotions using the Transformers library.

[1647] Step 5:

[1648] The server receives the emotion data and uses a generative AI model to generate an optimal prompt based on that data. The input is the emotion data and a pre-defined prompt template, and the output is the generated prompt. For example, if the emotion is "happy," the generated prompt is "Please suggest a product that is currently on promotion."

[1649] Step 6:

[1650] The terminal displays prompts received from the server to the user. The input is the generated prompt, and the output is text displayed on a display or smart glasses, allowing the user to take appropriate action.

[1651] Step 7:

[1652] The server then records the user's responses and actions using a log management tool. The input is the user's response data and action records, and the output is updated log data. This information is later used to analyze the effectiveness and develop improvement plans.

[1653] Step 8:

[1654] The server uses security measures to safely store all data, encrypting and controlling access as necessary. The input is confidential information such as log data and emotion data, and the output is securely stored data. This reduces the risk of information leakage.

[1655] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1656] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1657] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1658] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1659] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1660] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1661] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1662] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1663] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1664] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1665] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1666] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1667] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1668] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1669] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1670] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1671] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1672] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1673] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1674] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1675] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1676] The following is further disclosed regarding the above embodiment.

[1677] (Claim 1)

[1678] A system that includes network settings that can only be accessed from specific user terminals to build a generative AI environment that operates within a closed network,

[1679] an authentication means for restricting access from a user terminal;

[1680] Restrictions that allow access only during school hours;

[1681] a log management means for recording a usage log of the user terminal;

[1682] A system including:

[1683] (Claim 2)

[1684] 1. A system for providing customized generative AI tools for educational institutions, comprising:

[1685] A customization means for generating curriculum-specific prompt templates;

[1686] means for providing the generated prompt template to a user terminal;

[1687] A means of collecting and analyzing the results of using generative AI tools;

[1688] A system including:

[1689] (Claim 3)

[1690] 1. A system for providing educational content, comprising:

[1691] A means to create educational content covering everything from basic knowledge to applied technology of generative AI,

[1692] a means for setting access rights to educational content;

[1693] a means of monitoring the use of educational content;

[1694] A system including:

[1695] "Example 1"

[1696] (Claim 1)

[1697] A method to set up a closed network through a network gateway and create an environment that can only be accessed from a specific IP address range.

[1698] Measures to prevent unauthorized access from outside using firewalls and VPNs,

[1699] A means for setting parental controls to allow access only during certain hours;

[1700] a means for automatically generating accounts from a data file containing student information;

[1701] A means for generating and providing prompt templates according to lesson content;

[1702] A means of creating and providing educational content for generative AI;

[1703] A system including:

[1704] (Claim 2)

[1705] The system of claim 1, comprising: a customization means for generating prompt templates according to a curriculum in order to provide a generative AI tool customized for an educational institution; a means for providing the generated prompt templates to a user terminal; and a means for collecting and analyzing the results of using the generative AI tool.

[1706] (Claim 3)

[1707] The system of claim 1, comprising: a means for creating educational content covering everything from basic knowledge of generative AI to applied techniques; a means for setting access rights to the educational content; and a means for monitoring the usage of the educational content.

[1708] "Application Example 1"

[1709] (Claim 1)

[1710] A system that includes network settings that can only be accessed from specific user terminals to build a generative AI environment that operates within a closed network,

[1711] an authentication means for restricting access from a user terminal;

[1712] Restrictions that allow access only during school hours;

[1713] a log management means for recording a usage log of the user terminal;

[1714] A means of providing generative AI tools for virtual store operations;

[1715] Customization methods to streamline various management tasks,

[1716] A system including:

[1717] (Claim 2)

[1718] 1. A system for providing customized generative AI tools for educational institutions, comprising:

[1719] A customization means for generating curriculum-specific prompt templates;

[1720] means for providing the generated prompt template to a user terminal;

[1721] A means of collecting and analyzing the results of using generative AI tools;

[1722] A means of providing educational content for learning virtual store management skills;

[1723] A system including:

[1724] (Claim 3)

[1725] 1. A system for providing educational content, comprising:

[1726] A means to create educational content covering everything from basic knowledge to applied technology of generative AI,

[1727] a means for setting access rights to educational content;

[1728] a means of monitoring the use of educational content;

[1729] A means to manage virtual store manager and staff accounts;

[1730] A system including:

[1731] "Example 2: Combining Emotion Engines"

[1732] (Claim 1)

[1733] A system that includes network settings that can only be accessed from specific user terminals to build a generative AI environment that operates within a closed network,

[1734] an authentication means for restricting access from a user terminal;

[1735] Restrictions that allow access only during school hours;

[1736] a log management means for recording a usage log of the user terminal;

[1737] A customization means for generating curriculum-specific prompt templates;

[1738] means for providing the generated prompt template to a user terminal;

[1739] A means of collecting and analyzing the results of using generative AI tools;

[1740] a means of collecting emotion data in real time;

[1741] a means for adjusting the response of the generative AI based on the emotion data; and

[1742] a means of monitoring the use of educational content;

[1743] A system including:

[1744] (Claim 2)

[1745] 1. A system for providing customized generative AI tools for educational institutions, comprising:

[1746] A customization means for generating curriculum-specific prompt templates;

[1747] means for providing the generated prompt template to a user terminal;

[1748] A means of collecting and analyzing the results of using generative AI tools;

[1749] a means of collecting emotion data in real time;

[1750] a means for adjusting the response of the generative AI based on the emotion data; and

[1751] 10. The system of claim 1, comprising:

[1752] (Claim 3)

[1753] 1. A system for providing educational content, comprising:

[1754] A means to create educational content covering everything from basic knowledge to applied technology of generative AI,

[1755] a means for setting access rights to educational content;

[1756] a means of monitoring the use of educational content;

[1757] a means of collecting emotion data in real time;

[1758] means for adjusting the display of educational content based on the emotion data;

[1759] 10. The system of claim 1, comprising:

[1760] "Application example 2 when combining emotion engines"

[1761] (Claim 1)

[1762] A system that includes network settings that can only be accessed from specific user terminals to build a generative AI environment that operates within a closed network,

[1763] an authentication means for restricting access from a user terminal;

[1764] Restrictions that allow access only during school hours;

[1765] a log management means for recording a usage log of the user terminal;

[1766] An emotion recognition means for collecting and analyzing emotion data in real time;

[1767] a prompt generation means for adjusting the response of the generation AI based on the emotion data;

[1768] Security measures to safely manage user information;

[1769] A system including:

[1770] (Claim 2)

[1771] 1. A system for providing customized generative AI tools for educational institutions, comprising:

[1772] A customization means for generating curriculum-specific prompt templates;

[1773] means for providing the generated prompt template to a user terminal;

[1774] A means of collecting and analyzing the results of using generative AI tools;

[1775] A means to adjust the response of the generative AI based on emotional data;

[1776] 10. The system of claim 1.

[1777] (Claim 3)

[1778] 1. A system for providing educational content, comprising:

[1779] A means to create educational content covering everything from basic knowledge to applied technology of generative AI,

[1780] a means for setting access rights to educational content;

[1781] a means of monitoring the use of educational content;

[1782] A means of analyzing educational effectiveness based on emotion recognition,

[1783] 10. The system of claim 1. [Explanation of symbols]

[1784] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system that includes network settings that can only be accessed from specific user terminals to build a generative AI environment that operates within a closed network, an authentication means for restricting access from a user terminal; Restrictions that allow access only during school hours; a log management means for recording a usage log of the user terminal; A system including:

2. 1. A system for providing customized generative AI tools for educational institutions, comprising: A customization means for generating curriculum-specific prompt templates; means for providing the generated prompt template to a user terminal; A means of collecting and analyzing the results of using generative AI tools; A system including:

3. 1. A system for providing educational content, comprising: A means to create educational content covering everything from basic knowledge to applied technology of generative AI, a means for setting access rights to educational content; a means of monitoring the use of educational content; A system including:

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