system

Abstract

We provide the system. [Solution] A generative artificial intelligence that supports the work of educators, comprising an authentication means for verifying the identification information of educators, A processing method for selecting the type of educational work, A generation means that acquires relevant information based on selected educational tasks and generates a learning plan or assessment questions, A supply means for supplying a generated learning plan or assessment question to an information processing device of an educator, A means of making adjustments necessary for business support, A control means that provides instruction to an autonomous educational machine based on a generated learning plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The operations in educational institutions have diversified, and educators need to efficiently perform a wide range of operations such as lesson preparation, creation of examination questions, administrative work, and dealing with parents. However, these operations require time and effort, which are factors causing excessive workload for educators. As a result, there are problems such as a decline in the quality of education and an inability to secure sufficient time for communication with students.

Means for Solving the Problems

[0005] By introducing generative artificial intelligence (AI) for educators, we aim to improve the efficiency of their work. This AI authenticates educators' account information and allows them to select educational tasks such as lesson preparation and test question creation. Based on the selected tasks, it also has the function to automatically generate lesson plans or test questions by acquiring relevant data. The generated materials are provided to the educators' terminals and can be edited as needed. Furthermore, it is designed so that educators can configure the necessary customizations to support their work. As a result, educators can improve the efficiency of their work, enhance the quality of education, and allocate more time to communicating with students.

[0006] "Educational professionals" refers to people who are in a teaching position at educational institutions, including teachers and professors.

[0007] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically create new information and content based on input data.

[0008] "Authentication methods" refer to procedures and technologies used to verify that a user is a legitimate user, and are usually performed using a combination of username and password.

[0009] "Means of selection" refers to a function or device that allows one to choose a specific thing from several options.

[0010] "Generation means" refers to devices or functions for creating new information or content based on specific conditions or data.

[0011] "Means of delivery" refers to the methods and devices used to deliver generated information or content to users.

[0012] "Configuration means" refers to functions or devices used to adjust the operation of the system according to the user's requests and conditions. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a generative artificial intelligence system that streamlines the work of educators. The system supports a series of processes, starting with educator authentication, task selection, information generation, delivery, editing, and customization settings.

[0035] First, the educator logs into the system. The server receives the educator's account information and authenticates them by matching it with the database. If authentication is successful, the terminal displays the dashboard to the educator.

[0036] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device then sends the selection to the server.

[0037] The server retrieves relevant data based on the selected tasks and generates lesson plans or exam questions using an AI agent. In this process, the server utilizes historical data and curriculum information.

[0038] The generated lesson plans and exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The edited results are then sent back to the server.

[0039] Furthermore, the system allows users to configure their own settings to provide customized support for each educator. The server saves these settings and uses them to operate the system.

[0040] For example, if a user wants to prepare a math lesson for a second-year junior high school student, the system will provide relevant lesson materials. In this case, an AI agent will extract key points related to the unit and create a plan, saving educators the trouble of creating materials from scratch. Similarly, when creating periodic tests, the user can specify the range of the unit, and the server will generate appropriate test questions, which the user can then review, edit, and print.

[0041] In this way, the system supports the daily tasks of educators and streamlines them, creating an environment where they can dedicate more time to educational activities.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user accesses the education management system and enters their username and password on the login screen. The terminal stores the entered information and sends it to the server when the user presses the submit button.

[0045] Step 2:

[0046] The server verifies the received username and password against the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the device.

[0047] Step 3:

[0048] The device receives an authentication token and displays a dashboard screen for educators. From this screen, the user selects the type of task they wish to perform.

[0049] Step 4:

[0050] Instructions for tasks selected by the user (e.g., lesson planning or exam question creation) are sent from the terminal to the server.

[0051] Step 5:

[0052] The server retrieves data related to the selected task from the database. For example, if it's course structuring, it collects curriculum information; if it's exam question creation, it collects past exam question data.

[0053] Step 6:

[0054] The server uses the acquired data to generate lesson plans and exam questions using an AI agent. The generated content is tailored based on educational policies and historical data.

[0055] Step 7:

[0056] The server sends the generated lesson plan or exam questions to the terminal, which then displays a preview of them to the user.

[0057] Step 8:

[0058] The user reviews the preview and makes corrections on the spot if necessary. The device then sends the changes to the server.

[0059] Step 9:

[0060] The server saves the corrected information and generates the final version. This final version is then sent back to the terminal and provided in a downloadable and printable format.

[0061] Step 10:

[0062] If necessary, users can input their own customized settings from their terminals to facilitate educational activities. The server records these settings and applies them to future work support.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Educators spend a significant amount of time and effort creating lesson plans and exam questions, which limits the time available for improving the quality of education and providing individualized instruction to students. They also need to efficiently edit and manage these materials. Furthermore, there are limited means available for easily generating customized educational materials.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes authentication means for verifying the identification information of educators, selection means for selecting education-related tasks, and information generation means for extracting and generating sets of information. This enables educators to efficiently generate and edit educational materials, allowing them to dedicate more time to educational activities.

[0068] "Educational professional" refers to an individual or organization responsible for conducting educational activities.

[0069] An "information processing system" is a collection of devices and software that collect and process data and provide appropriate information to users.

[0070] "Identification information" refers to data used to identify and authenticate a user.

[0071] "Authentication methods" refer to mechanisms and technologies used to verify a user's identification information and confirm its legitimacy.

[0072] "Education-related work" refers to all tasks related to education, such as lesson planning and the creation of test evaluation materials.

[0073] A "selection method" refers to an interface or mechanism that allows a user to choose a specific item from among multiple options.

[0074] An "information set" is a collection of data that has been gathered and structured for a specific purpose.

[0075] "Information generation means" refers to technologies and processes for generating new information and materials based on collected data.

[0076] A "display device" is a device used to visually present information.

[0077] "Transmission means" refers to a method or device for transferring data from one point to another.

[0078] A "setting adjustment mechanism" is a system for customizing the operation of the system according to the user's requests.

[0079] "Editing means" refers to functions that allow users to manually or automatically correct or modify generated information or materials.

[0080] An "information management device" is a device or system for centrally managing data and updating information as needed.

[0081] "Artificial intelligence technology" refers to technologies that use machines to mimic human intellectual activity and automate or optimize tasks.

[0082] This invention is implemented as an information processing system to streamline the work of educators. The system operates in cooperation with a server, terminals, and users. Specifically, it has the following hardware and software configuration.

[0083] The server functions as a central processing unit and manages the identification information of educators using a database management system. The database stores educational materials and past curriculum information. A backend environment is built by combining server-side languages ​​such as Python and Node.js with this. In addition, a large-scale language model API is implemented as a generative AI model. This model is responsible for generating educational plans and assessment materials.

[0084] The terminal functions as an input and display device for educators to actually operate. Front-end technologies that allow for easy construction of user interfaces, such as ReactJS and Angular, are used here. The terminal communicates with the server and receives information based on the tasks selected by the educators.

[0085] Users log into the system, select individual educational tasks, and instruct the system to generate information. Specifically, when they select tasks such as creating lesson plans or generating exam questions, they send specific prompts to the server according to those requirements. Examples of prompts sent include: "Please create a lesson plan to teach quadratic equations to 8th grade math students. Please include key points and example problems."

[0086] As an example of the system, suppose a user wants to create a lesson plan for first-year junior high school English. In this case, the user uses a terminal to input a prompt such as, "Please generate a lesson plan to teach basic English grammar for first-year junior high school students." Based on this input, the server uses an AI model to generate relevant information and provides appropriate lesson materials on the terminal. The user can then conduct the lesson based on this. In this way, by utilizing information processing systems, educators can carry out their work more efficiently and improve the quality of education.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server receives the login information entered by the user and authenticates it by comparing it with the database. This input includes the user ID and password. Based on this, the server searches the database for matching account information and outputs whether the authentication was successful or not. If authentication is successful, the server generates a session ID and sends it to the terminal.

[0090] Step 2:

[0091] The terminal verifies the session ID received from the server and begins displaying the dashboard to the user. The displayed content includes options for education-related tasks. The input is session confirmation information from the server, and the terminal outputs the dashboard to the user based on this. Specifically, buttons such as "Create Lesson Plan" and "Create Exam Evaluation" are displayed on the screen.

[0092] Step 3:

[0093] The user selects their desired education-related task from the dashboard. This selection is sent to the server via the terminal. The server receives the type of task selected by the user as input and proceeds to the next data processing step. Specifically, this involves sending a request to the server when the user clicks on an option.

[0094] Step 4:

[0095] Based on the received selection information, the server retrieves relevant information from the database. The input in this step is the user's selection information, and based on this, the server extracts past educational materials and curriculum information, creates a prompt message, and sends it to the generating AI model. The output is an educational plan or test question generated by the AI ​​model. A concrete example of a prompt message is, "Generate a lesson plan to teach basic English grammar for first-year junior high school students."

[0096] Step 5:

[0097] The server receives information generated from the AI ​​model and sends it to the terminal. This transmission includes generated educational materials and exam questions. The terminal receives this information and displays it to the user. The input is the received data, and the output is a display of that data for the user to see. For example, a preview of the generated lesson plan might be displayed on the screen.

[0098] Step 6:

[0099] The user reviews the provided educational materials and edits them as needed. The edits are sent from the terminal to the server. The server receives the user's edits as input and updates the materials. The updated materials are saved to the database as output. Specific actions include correcting exam questions using a text editor.

[0100] Step 7:

[0101] The server updates system settings based on the received edits, providing customized support to educators in the future. Inputs include edit results and user settings. Based on this, the server updates the database and outputs support information that will be applied the next time the system is used. Specifically, settings such as prioritizing frequently used teaching materials are implemented.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] Currently, educators spend a great deal of time and effort preparing for lessons and exams, as well as supporting home learning. In particular, when using autonomous educational machines to support home learning, their operation and management are complex. Therefore, there is a need for a system that can easily and efficiently generate educational content and enable instruction by these autonomous machines. Solving this problem, reducing the burden on educators, and improving the effectiveness of home learning is the challenge.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes authentication means for verifying the identification information of the educator, processing means for selecting the type of educational task, and generation means for acquiring relevant information based on the selected educational task and generating a learning plan or assessment questions. This makes it possible for educators to easily prepare learning content, and for an autonomous educational machine to effectively support home learning based on that content.

[0107] An "educator" refers to a professional responsible for transmitting knowledge and skills.

[0108] "Identification information" refers to data used to identify an individual.

[0109] "Authentication means" refers to a method for verifying an individual's identification information and confirming its legitimacy.

[0110] "Educational work" refers to various activities related to the implementation of education, such as preparing lessons and creating exams.

[0111] "Processing means" refers to a method for performing a specific selection or operation.

[0112] A "generation method" is a method for creating new information or content based on specific conditions.

[0113] An "information processing device" is an electronic device that receives digital data and manipulates, processes, and outputs that data.

[0114] "Adjustment settings" are settings that allow you to change the system's operation to suit individual needs and conditions.

[0115] An "autonomous educational machine" refers to a device that automatically provides learning guidance based on a program.

[0116] "Control means" refers to methods for instructing and managing the operation of machines and systems.

[0117] To implement this invention, we will take a generative artificial intelligence system that supports the work of educators as an example. The system mainly consists of a server, an information processing device (terminal) for educators, and an autonomous machine for education.

[0118] When an educator accesses the server, it authenticates them using their identification information. After authentication, the server displays a task selection screen on the terminal and retrieves the necessary data to generate lesson plans and assessment questions based on the educator's selection. The data is analyzed using a generation AI model to generate content optimized for the selected task.

[0119] The generated learning plans and assessment questions are sent from the server to the terminal, where educators can review and modify them as needed. Modifications from the terminal are sent to the server and updated as the latest information. Furthermore, control instructions based on the generated learning plans are sent to the autonomous educational machine, which then provides learning guidance to students in accordance with the educator's intentions.

[0120] As a concrete example, consider a case where a middle school math teacher uses a system to create a geometry lesson plan. In this case, the teacher can use a "generative AI model" to input prompts that are in line with the school's curriculum and instruct the system to generate appropriate content.

[0121] An example of a prompt message would be, "Generate a lesson plan for 2nd-year junior high school geometry." The AI ​​can then automatically suggest relevant teaching materials and content. Another feature of this system is that educators can freely edit and customize the plan's content. In this way, educators can efficiently provide high-quality education.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user logs into the system using a terminal. The user enters their identification information and sends it to the server. The server compares this entered identification information with the database and performs authentication. If authentication is successful, the user is shown a dashboard screen. The input is the user's identification information, and the output is the authentication result. In this step, the server performs a database search and returns the authentication result.

[0125] Step 2:

[0126] The user selects the type of training activity they wish to perform on the dashboard. The user's selection is sent to the server via the terminal. The server retrieves information based on the user's selection and prepares the necessary training resources. The input is the selected training activity, and the output is the completion of retrieving the relevant data. In this step, the server retrieves resources from the database based on the user's selection.

[0127] Step 3:

[0128] The server uses a generation AI model to automatically generate lesson plans and assessment questions from acquired data. During the generation process, the server utilizes historical data and curriculum information, performing data processing and calculations based on prompts. The input is the acquired data, and the output is the generated educational content. This step includes operations that utilize the generation AI model for automatic creation.

[0129] Step 4:

[0130] The generated lesson plans and problems are provided from the server to the user's terminal. The user reviews the content and edits it as needed. The edited content is sent from the terminal to the server, where the server updates the information. The input is the generated educational content, and the output is the content edited by the user. In this step, the review and updating of edits are performed between the server and the terminal.

[0131] Step 5:

[0132] The server sends control instructions to the autonomous educational machine based on the generated and edited educational content. The autonomous educational machine carries out learning instruction according to the received instructions. The input is the edited educational content, and the output is the start of instruction by the autonomous educational machine. In this step, an action occurs in which control instructions are given to the machine to carry out the education.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a generative artificial intelligence system that streamlines the work of educators and provides support that takes their psychological state into consideration. The system covers a series of processes, starting with educator authentication, task selection, information generation, emotion recognition, delivery, editing, and customization settings.

[0135] First, the educator logs into the system. The server receives the educator's account information, compares it with the database, and performs authentication. If authentication is successful, the terminal displays a dashboard to the educator. At this point, the emotion engine recognizes and analyzes the educator's emotions in real time from their facial expressions and voice.

[0136] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device sends the selection to the server, and simultaneously sends the results of the emotion engine's analysis to the server as well.

[0137] The server considers the selected tasks and the analysis results from the emotion engine, and retrieves relevant data from the database. When creating lesson plans or exam questions, it is possible to select simpler materials than usual to make suggestions that are less burdensome, based on the emotional state of the educator.

[0138] The generated lesson plan or exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The terminal then sends the changes back to the server as data.

[0139] Furthermore, if the emotional engine determines that an educator is experiencing high stress levels, the system has a function to provide relaxing content. This content, which includes music and videos for mood enhancement, is delivered to the educator through their device.

[0140] In this way, the system supports the daily work of educators, not only improving work efficiency but also creating a more comfortable working environment by reducing psychological burden. For example, when a teacher is busy just before an exam, the emotional engine detects stress, suggests creating simplified exam questions, and plays relaxing music to further reduce stress.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user accesses the education management system and enters their username and password on the login screen. The terminal temporarily holds the entered information and sends it to the server when the submit button is pressed.

[0144] Step 2:

[0145] The server compares the received authentication information with the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the terminal.

[0146] Step 3:

[0147] The device receives an authentication token and displays a dashboard for educators. At this time, the device has an emotion engine built in that analyzes the user's facial expressions and voice to acquire emotion data in real time.

[0148] Step 4:

[0149] Users select tasks such as "course structure" or "exam question creation" on the dashboard. The selection results and sentiment data are sent from the device to the server.

[0150] Step 5:

[0151] The server retrieves relevant information from the database based on emotional data and selected tasks. For example, if a user is stressed, the server selects data that takes into account criteria for choosing simpler learning materials.

[0152] Step 6:

[0153] The server uses an AI agent to generate appropriate lesson plans and exam questions from the acquired data. During this generation process, the content is adjusted according to the selected emotional state.

[0154] Step 7:

[0155] The generated document is sent from the server to the terminal, which then displays a preview of it to the user.

[0156] Step 8:

[0157] Users can preview the document and edit or modify it if necessary. The edits are sent from the device to the server and updated.

[0158] Step 9:

[0159] The server saves the updated documents and generates the final version. The generated result is sent to the terminal and provided to the user in a format that can be downloaded or printed.

[0160] Step 10:

[0161] If the emotion engine detects a high-stress state, the device will provide the educator with relaxing content (e.g., music or video). Depending on the user's state, the system will take actions intended to reduce stress.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] Educators are required to perform their duties efficiently amidst a busy workload. However, conventional systems often fail to consider the emotional state of users, leading to increased psychological burden. Furthermore, the fixed selection of tasks and provision of materials, which cannot be adapted to the specific work content and circumstances of educators, resulted in insufficient and efficient support.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes authentication means for authenticating the personal information of educators, selection means for selecting the type of educational activity, and generation means for acquiring relevant information based on the selected educational activity and generating educational materials. This enables educators to receive optimal support tailored to their work content and emotional state.

[0167] An "information processing device" refers to an entire system that has the capability to input, process, and output data, and is used to improve the efficiency of business operations.

[0168] "Authentication means" refers to a mechanism that verifies the legitimacy of a person when they access a system, and typically includes verification using passwords or biometric information.

[0169] "Selection mechanism" refers to an interface or mechanism that allows a user to choose a specific operation or function within a system.

[0170] "Generative means" refers to the processes and mechanisms for creating relevant materials and information in accordance with selected educational activities.

[0171] "Means of delivery" refers to a method or device for presenting generated materials or information to the user, and typically includes output via screen or audio.

[0172] "Analysis method" refers to a mechanism that uses an emotion engine to analyze the user's facial expressions and voice to determine their psychological state.

[0173] "Means of providing responses" refers to the process of providing users with measures and suggestions to alleviate their psychological burden, based on the user's emotional state obtained through analytical means.

[0174] "Configuration means" refers to a mechanism for adjusting and modifying the operation of a system in order to improve the convenience and efficiency of business support.

[0175] This invention provides an educational support system centered on an information processing device. The user, an educational professional, logs into the system using the authentication means of the information processing device. This authentication means includes commonly used ID and password-based authentication technologies. After authentication, the user selects the type of educational activity via the terminal's interface. This selection is often done via a touchscreen or keyboard input.

[0176] The server utilizes generation methods based on selected educational activities to retrieve appropriate information from the database. This information is then processed in the most optimal way using a generative AI model. For example, the AI ​​can automatically construct lesson plans and create test questions tailored to the user's needs.

[0177] The server then sends the generated data to the user's terminal via a delivery method. The user reviews the received data on their terminal and makes corrections if necessary. These corrections can be easily made using a standard text editor. The corrected data is then sent back to the server, and the information is updated in the database.

[0178] Furthermore, emotional state analysis is also performed. The camera and microphone connected to the device are used as analysis tools to capture the user's facial expressions and voice, and perform emotional analysis. If this analysis detects the user's psychological burden, the server provides relaxation music or videos through a response mechanism.

[0179] For example, when a teacher spends time preparing for an exam, the system checks their emotional state and, if it determines that their stress level is high, suggests simplified exam questions. Additionally, calming music can be played in the background to help the teacher concentrate.

[0180] An example of a prompt message is, "Detect the teacher's stress level, suggest a simple test question, and play relaxing music." In this way, a system is built that supports the user's efficient work performance and psychological support.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The user logs into the system using a terminal. The ID and password entered by the user are processed by the authentication method and sent to the server. The server refers to the database and verifies the matching account information. If authentication is successful, the user is logged in, and a dashboard for selecting the type of educational activity is displayed on the terminal. The input is the login information, and the output is the dashboard display.

[0184] Step 2:

[0185] The user selects the type of educational activity from the options displayed on the terminal's dashboard. The selected information is sent to the server via the selection mechanism. The server receives this input and prepares to retrieve relevant educational materials from its database. The input is the selected educational activity information, and the output is the preparation of data based on the selected activity.

[0186] Step 3:

[0187] The server generates relevant materials based on the selected educational activity. In this process, it uses a generation AI model to process data and generate specific educational materials and test questions. Filtering and processing are performed based on references to materials in the database, resulting in optimized output. The input is the user's selection information, and the output is the generated educational materials.

[0188] Step 4:

[0189] The generated educational materials are sent from the server to the terminal via a delivery system. The user reviews these materials on the terminal and modifies them as needed. The terminal is equipped with a text editor, allowing the user to easily edit the materials. The input is the generated material, and the output is the modified educational material.

[0190] Step 5:

[0191] Once editing is complete on the terminal, the revised document is sent back to the server and saved to the database. The server updates the database based on the received information, keeping it up-to-date. The input is the revised document information, and the output is the updated database.

[0192] Step 6:

[0193] Simultaneously, the device analyzes the user's emotional state using analytical tools. Facial and voice data acquired from the camera and microphone are processed via an emotion engine. The judgment results are sent to a server and used to make decisions regarding the provision of appropriate responses. The input is real-time emotional data, and the output is the judgment result of the emotional state.

[0194] Step 7:

[0195] If the user's emotional state is determined to be high-stress, the server selects relaxing content as a means of providing a response and delivers music or videos through the device. This content is customized to reduce the user's psychological burden. The input is the result of the emotional state assessment, and the output is the provision of relaxing content.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] Traditional educational support systems have only provided educators with functions focused on improving work efficiency, and have limitations in providing individualized support based on the psychological state of educators. Furthermore, it is known that stress in the educational setting affects teachers' performance, and there is a need for means to adequately mitigate this.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes authentication means for authenticating the account information of educators, selection means for selecting the type of educational work, and analysis means for analyzing the emotional state of educators from their facial expressions and voice. This enables not only improved work efficiency for educators but also appropriate support and stress reduction tailored to their individual psychological states.

[0201] "Educational professionals" are people who engage in educational activities, such as preparing lessons and exam questions, and performing administrative tasks.

[0202] "Generative artificial intelligence" refers to an artificial intelligence system that provides customized educational support to educators through authentication and emotion analysis.

[0203] "Authentication methods" refer to a system that verifies the account information of educators and confirms that they are legitimate users.

[0204] "Selection method" refers to the function of specifying the types of educational tasks that educators perform.

[0205] "Generation means" refers to a function that creates lesson plans and exam questions from data related to selected educational tasks.

[0206] "Delivery method" refers to the function of distributing generated lesson plans and test questions to the display devices of educators.

[0207] The "analysis method" is a system that analyzes the facial expressions and voice of educators to determine their emotional state.

[0208] The "adjustment mechanism" is a function that selects and provides relaxation content based on the analyzed emotional state.

[0209] "Configuration method" refers to a function that customizes the content of work support to meet the needs of educators.

[0210] An "information processing device" is a device that receives input information from educators and updates data based on that information.

[0211] The system of this invention is configured to integrate various functions in order to support educators. The main components include authentication means, selection means, generation means, provision means, analysis means, adjustment means, and setting means.

[0212] The authentication method verifies the legitimacy of system access by checking the account information of the educator when the user logs into the system. This establishes a foundation for providing appropriate services.

[0213] The selection method allows users to choose the educational tasks they want to perform (e.g., lesson planning or exam question creation). This makes it easier for the system to acquire data that meets the user's needs. The generation method generates relevant data based on the selected tasks and creates lesson plans or exam questions.

[0214] The delivery method involves transmitting the generated content to the user's display device, allowing educators to review its contents. Educators can freely edit the provided content. The edited content is received by the information processing device and reflected on the server.

[0215] The analysis method identifies the user's emotional state using facial expressions and voice data. Machine learning technology using TENSORFLOW® is employed to diagnose the user's stress level and psychological burden in real time.

[0216] The adjustment mechanism selects and provides users with content that promotes relaxation according to their emotional state (e.g., calming music or relaxing videos). This helps to reduce the psychological burden on educators.

[0217] The configuration method allows users to customize various support functions to suit the preferences of educators, thereby achieving more effective support.

[0218] For example, if emotion analysis detects that a teacher is experiencing stress during class, the system can provide the teacher with a short relaxing video and calming background music. Another example of a prompt message is, "Please suggest what kind of support would be effective when a teacher is experiencing high stress levels in an educational setting."

[0219] By integrating these functions, it becomes possible to streamline the work of educators and provide a comfortable learning environment.

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The user logs into the system using a terminal. The terminal sends the educator's account information as input to the server. The server compares this information with the database, authenticates the user based on the input, and outputs the result to the terminal.

[0223] Step 2:

[0224] The user selects the task they want to perform (e.g., lesson planning, exam question creation) from a dashboard displayed on their device. The selected task type is sent from the device to the server. The server receives this information and selects data to prepare for the next step.

[0225] Step 3:

[0226] The device's camera and microphone collect user facial expressions and voice data as input. The device analyzes this data to identify the user's emotional state. TensorFlow-based facial expression recognition and voice emotion analysis models are used for the analysis, and the analysis results are sent to the server.

[0227] Step 4:

[0228] The server considers both the selected task type and the sentiment analysis results, and uses a generative AI model to generate relevant data. It retrieves lesson plans and exam question materials from the database, and simplifies them or recommends relaxing content as needed. The generated content is then provided to the terminal.

[0229] Step 5:

[0230] Users review the content provided on their devices and edit it as needed. The edits are sent from the device to the server, where the information is updated. This process generates and provides optimized educational materials and test questions specifically for educators.

[0231] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0247] This invention is a generative artificial intelligence system that streamlines the work of educators. The system supports a series of processes, starting with educator authentication, task selection, information generation, delivery, editing, and customization settings.

[0248] First, the educator logs into the system. The server receives the educator's account information and authenticates them by matching it with the database. If authentication is successful, the terminal displays the dashboard to the educator.

[0249] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device then sends the selection to the server.

[0250] The server retrieves relevant data based on the selected tasks and generates lesson plans or exam questions using an AI agent. In this process, the server utilizes historical data and curriculum information.

[0251] The generated lesson plans and exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The edited results are then sent back to the server.

[0252] Furthermore, the system allows users to configure their own settings to provide customized support for each educator. The server saves these settings and uses them to operate the system.

[0253] For example, if a user wants to prepare a math lesson for a second-year junior high school student, the system will provide relevant lesson materials. In this case, an AI agent will extract key points related to the unit and create a plan, saving educators the trouble of creating materials from scratch. Similarly, when creating periodic tests, the user can specify the range of the unit, and the server will generate appropriate test questions, which the user can then review, edit, and print.

[0254] In this way, the system supports the daily tasks of educators and streamlines them, creating an environment where they can dedicate more time to educational activities.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The user accesses the education management system and enters their username and password on the login screen. The terminal stores the entered information and sends it to the server when the user presses the submit button.

[0258] Step 2:

[0259] The server verifies the received username and password against the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the device.

[0260] Step 3:

[0261] The device receives an authentication token and displays a dashboard screen for educators. From this screen, the user selects the type of task they wish to perform.

[0262] Step 4:

[0263] Instructions for tasks selected by the user (e.g., lesson planning or exam question creation) are sent from the terminal to the server.

[0264] Step 5:

[0265] The server retrieves data related to the selected task from the database. For example, if it's course structuring, it collects curriculum information; if it's exam question creation, it collects past exam question data.

[0266] Step 6:

[0267] The server uses the acquired data to generate lesson plans and exam questions using an AI agent. The generated content is tailored based on educational policies and historical data.

[0268] Step 7:

[0269] The server sends the generated lesson plan or exam questions to the terminal, which then displays a preview of them to the user.

[0270] Step 8:

[0271] The user reviews the preview and makes corrections on the spot if necessary. The device then sends the changes to the server.

[0272] Step 9:

[0273] The server saves the corrected information and generates the final version. This final version is then sent back to the terminal and provided in a downloadable and printable format.

[0274] Step 10:

[0275] If necessary, users can input their own customized settings from their terminals to facilitate educational activities. The server records these settings and applies them to future work support.

[0276] (Example 1)

[0277] Next, we will describe Example 1. 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."

[0278] Educators spend a significant amount of time and effort creating lesson plans and exam questions, which limits the time available for improving the quality of education and providing individualized instruction to students. They also need to efficiently edit and manage these materials. Furthermore, there are limited means available for easily generating customized educational materials.

[0279] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes an authentication means for verifying the identification information of education practitioners, a selection means for selecting education-related tasks, and an information generation means for extracting and generating an information set. As a result, education practitioners can efficiently generate and edit educational materials and spend more time on educational activities.

[0281] An "education practitioner" is an individual or organization responsible for conducting educational activities.

[0282] An "information processing system" is a collection of a series of devices and software that collect, process data, and supply appropriate information to users.

[0283] "Identification information" is data used to identify and authenticate a user.

[0284] "Authentication means" is a mechanism or technology for verifying a user's identification information and confirming its legitimacy.

[0285] "Education-related tasks" refer to all operations related to education, such as lesson planning and the creation of test evaluation materials.

[0286] "Selection means" is an interface or mechanism for allowing a user to select a specific item from a plurality of options.

[0287] An "information set" is a structured group of data collected according to a specific purpose.

[0288] "Information generation means" is a technology or process for generating new information and materials based on the collected data.

[0289] A "display device" is a device for visually presenting information.

[0290] "Transmission means" is a method or device for transferring data from one location to another.

[0291] A "setting adjustment mechanism" is a system for customizing the operation of the system according to the user's requests.

[0292] "Editing means" refers to functions that allow users to manually or automatically correct or modify generated information or materials.

[0293] An "information management device" is a device or system for centrally managing data and updating information as needed.

[0294] "Artificial intelligence technology" refers to technologies that use machines to mimic human intellectual activity and automate or optimize tasks.

[0295] This invention is implemented as an information processing system to streamline the work of educators. The system operates in cooperation with a server, terminals, and users. Specifically, it has the following hardware and software configuration.

[0296] The server functions as a central processing unit and manages the identification information of educators using a database management system. The database stores educational materials and past curriculum information. A backend environment is built by combining server-side languages ​​such as Python and Node.js with this. In addition, a large-scale language model API is implemented as a generative AI model. This model is responsible for generating educational plans and assessment materials.

[0297] The terminal functions as an input and display device for educators to actually operate. Front-end technologies that allow for easy construction of user interfaces, such as ReactJS and Angular, are used here. The terminal communicates with the server and receives information based on the tasks selected by the educators.

[0298] Users log into the system, select individual educational tasks, and instruct the system to generate information. Specifically, when they select tasks such as creating lesson plans or generating exam questions, they send specific prompts to the server according to those requirements. Examples of prompts sent include: "Please create a lesson plan to teach quadratic equations to 8th grade math students. Please include key points and example problems."

[0299] As an example of the system, suppose a user wants to create a lesson plan for first-year junior high school English. In this case, the user uses a terminal to input a prompt such as, "Please generate a lesson plan to teach basic English grammar for first-year junior high school students." Based on this input, the server uses an AI model to generate relevant information and provides appropriate lesson materials on the terminal. The user can then conduct the lesson based on this. In this way, by utilizing information processing systems, educators can carry out their work more efficiently and improve the quality of education.

[0300] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0301] Step 1:

[0302] The server receives the login information entered by the user and authenticates it by comparing it with the database. This input includes the user ID and password. Based on this, the server searches the database for matching account information and outputs whether the authentication was successful or not. If authentication is successful, the server generates a session ID and sends it to the terminal.

[0303] Step 2:

[0304] The terminal checks the session ID received from the server and starts the dashboard display for the user. The displayed content includes options for education-related tasks. The input is the session confirmation information from the server, and based on this, the terminal outputs the dashboard to the user. As specific operations, buttons such as "Create Lesson Plan" and "Create Test Evaluation" are displayed on the screen.

[0305] Step 3:

[0306] The user selects the desired education-related task from the dashboard. This selection is sent to the server through the terminal. The input is the type of task selected by the user, and the server receives this and proceeds to the next data processing step. Specifically, it includes the operation of sending a request to the server by clicking on the option.

[0307] Step 4:

[0308] Based on the received selection information, the server retrieves the relevant information set from the database. The input at this step is the user's selection information, and based on this, the server extracts past educational materials and curriculum information, creates a prompt text for the generative AI model, and sends it. As output, educational plans and test questions generated by the AI model are obtained. Specific examples of the prompt text include "Please generate a lesson plan for teaching basic English grammar to first-year junior high school students."

[0309] Step 5:

[0310] The server receives the information generated by the AI model and sends it to the terminal. This transmission includes the generated educational materials and test questions. The terminal receives this and displays it to the user. The input is the received data, and the output is the display in a form that shows it to the user. For example, there is an operation where a preview of the generated lesson plan is displayed on the screen.

[0311] Step 6:

[0312] The user reviews the provided educational materials and edits them as needed. The edits are sent from the terminal to the server. The server receives the user's edits as input and updates the materials. The updated materials are saved to the database as output. Specific actions include correcting exam questions using a text editor.

[0313] Step 7:

[0314] The server updates system settings based on the received edits, providing customized support to educators in the future. Inputs include edit results and user settings. Based on this, the server updates the database and outputs support information that will be applied the next time the system is used. Specifically, settings such as prioritizing frequently used teaching materials are implemented.

[0315] (Application Example 1)

[0316] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0317] Currently, educators spend a great deal of time and effort preparing for lessons and exams, as well as supporting home learning. In particular, when using autonomous educational machines to support home learning, their operation and management are complex. Therefore, there is a need for a system that can easily and efficiently generate educational content and enable instruction by these autonomous machines. Solving this problem, reducing the burden on educators, and improving the effectiveness of home learning is the challenge.

[0318] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0319] In this invention, the server includes authentication means for verifying the identification information of the educator, processing means for selecting the type of educational task, and generation means for acquiring relevant information based on the selected educational task and generating a learning plan or assessment questions. This makes it possible for educators to easily prepare learning content, and for an autonomous educational machine to effectively support home learning based on that content.

[0320] An "educator" refers to a professional responsible for transmitting knowledge and skills.

[0321] "Identification information" refers to data used to identify an individual.

[0322] "Authentication means" refers to a method for verifying an individual's identification information and confirming its legitimacy.

[0323] "Educational work" refers to various activities related to the implementation of education, such as preparing lessons and creating exams.

[0324] "Processing means" refers to a method for performing a specific selection or operation.

[0325] A "generation method" is a method for creating new information or content based on specific conditions.

[0326] An "information processing device" is an electronic device that receives digital data and manipulates, processes, and outputs that data.

[0327] "Adjustment settings" are settings that allow you to change the system's operation to suit individual needs and conditions.

[0328] An "autonomous educational machine" refers to a device that automatically provides learning guidance based on a program.

[0329] "Control means" refers to methods for instructing and managing the operation of machines and systems.

[0330] To implement this invention, we will take a generative artificial intelligence system that supports the work of educators as an example. The system mainly consists of a server, an information processing device (terminal) for educators, and an autonomous machine for education.

[0331] When an educator accesses the server, it authenticates them using their identification information. After authentication, the server displays a task selection screen on the terminal and retrieves the necessary data to generate lesson plans and assessment questions based on the educator's selection. The data is analyzed using a generation AI model to generate content optimized for the selected task.

[0332] The generated learning plans and assessment questions are sent from the server to the terminal, where educators can review and modify them as needed. Modifications from the terminal are sent to the server and updated as the latest information. Furthermore, control instructions based on the generated learning plans are sent to the autonomous educational machine, which then provides learning guidance to students in accordance with the educator's intentions.

[0333] As a concrete example, consider a case where a middle school math teacher uses a system to create a geometry lesson plan. In this case, the teacher can use a "generative AI model" to input prompts that are in line with the school's curriculum and instruct the system to generate appropriate content.

[0334] An example of a prompt message would be, "Generate a lesson plan for 2nd-year junior high school geometry." The AI ​​can then automatically suggest relevant teaching materials and content. Another feature of this system is that educators can freely edit and customize the plan's content. In this way, educators can efficiently provide high-quality education.

[0335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0336] Step 1:

[0337] The user logs into the system using a terminal. The user enters their identification information and sends it to the server. The server compares this entered identification information with the database and performs authentication. If authentication is successful, the user is shown a dashboard screen. The input is the user's identification information, and the output is the authentication result. In this step, the server performs a database search and returns the authentication result.

[0338] Step 2:

[0339] The user selects the type of training activity they wish to perform on the dashboard. The user's selection is sent to the server via the terminal. The server retrieves information based on the user's selection and prepares the necessary training resources. The input is the selected training activity, and the output is the completion of retrieving the relevant data. In this step, the server retrieves resources from the database based on the user's selection.

[0340] Step 3:

[0341] The server uses a generation AI model to automatically generate lesson plans and assessment questions from acquired data. During the generation process, the server utilizes historical data and curriculum information, performing data processing and calculations based on prompts. The input is the acquired data, and the output is the generated educational content. This step includes operations that utilize the generation AI model for automatic creation.

[0342] Step 4:

[0343] The generated lesson plans and problems are provided from the server to the user's terminal. The user reviews the content and edits it as needed. The edited content is sent from the terminal to the server, where the server updates the information. The input is the generated educational content, and the output is the content edited by the user. In this step, the review and updating of edits are performed between the server and the terminal.

[0344] Step 5:

[0345] The server sends control instructions to the autonomous educational machine based on the generated and edited educational content. The autonomous educational machine carries out learning instruction according to the received instructions. The input is the edited educational content, and the output is the start of instruction by the autonomous educational machine. In this step, an action occurs in which control instructions are given to the machine to carry out the education.

[0346] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0347] This invention is a generative artificial intelligence system that streamlines the work of educators and provides support that takes their psychological state into consideration. The system covers a series of processes, starting with educator authentication, task selection, information generation, emotion recognition, delivery, editing, and customization settings.

[0348] First, the educator logs into the system. The server receives the educator's account information, compares it with the database, and performs authentication. If authentication is successful, the terminal displays a dashboard to the educator. At this point, the emotion engine recognizes and analyzes the educator's emotions in real time from their facial expressions and voice.

[0349] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device sends the selection to the server, and simultaneously sends the results of the emotion engine's analysis to the server as well.

[0350] The server considers the selected tasks and the analysis results from the emotion engine, and retrieves relevant data from the database. When creating lesson plans or exam questions, it is possible to select simpler materials than usual to make suggestions that are less burdensome, based on the emotional state of the educator.

[0351] The generated lesson plan or exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The terminal then sends the changes back to the server as data.

[0352] Furthermore, if the emotional engine determines that an educator is experiencing high stress levels, the system has a function to provide relaxing content. This content, which includes music and videos for mood enhancement, is delivered to the educator through their device.

[0353] In this way, the system supports the daily work of educators, not only improving work efficiency but also creating a more comfortable working environment by reducing psychological burden. For example, when a teacher is busy just before an exam, the emotional engine detects stress, suggests creating simplified exam questions, and plays relaxing music to further reduce stress.

[0354] The following describes the processing flow.

[0355] Step 1:

[0356] The user accesses the education management system and enters their username and password on the login screen. The terminal temporarily holds the entered information and sends it to the server when the submit button is pressed.

[0357] Step 2:

[0358] The server compares the received authentication information with the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the terminal.

[0359] Step 3:

[0360] The device receives an authentication token and displays a dashboard for educators. At this time, the device has an emotion engine built in that analyzes the user's facial expressions and voice to acquire emotion data in real time.

[0361] Step 4:

[0362] Users select tasks such as "course structure" or "exam question creation" on the dashboard. The selection results and sentiment data are sent from the device to the server.

[0363] Step 5:

[0364] The server retrieves relevant information from the database based on emotional data and selected tasks. For example, if a user is stressed, the server selects data that takes into account criteria for choosing simpler learning materials.

[0365] Step 6:

[0366] The server uses an AI agent to generate appropriate lesson plans and exam questions from the acquired data. During this generation process, the content is adjusted according to the selected emotional state.

[0367] Step 7:

[0368] The generated document is sent from the server to the terminal, which then displays a preview of it to the user.

[0369] Step 8:

[0370] Users can preview the document and edit or modify it if necessary. The edits are sent from the device to the server and updated.

[0371] Step 9:

[0372] The server saves the updated documents and generates the final version. The generated result is sent to the terminal and provided to the user in a format that can be downloaded or printed.

[0373] Step 10:

[0374] If the emotion engine detects a high-stress state, the device will provide the educator with relaxing content (e.g., music or video). Depending on the user's state, the system will take actions intended to reduce stress.

[0375] (Example 2)

[0376] Next, we will describe Example 2. 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".

[0377] Educators are required to perform their duties efficiently amidst a busy workload. However, conventional systems often fail to consider the emotional state of users, leading to increased psychological burden. Furthermore, the fixed selection of tasks and provision of materials, which cannot be adapted to the specific work content and circumstances of educators, resulted in insufficient and efficient support.

[0378] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0379] In this invention, the server includes authentication means for authenticating the personal information of educators, selection means for selecting the type of educational activity, and generation means for acquiring relevant information based on the selected educational activity and generating educational materials. This enables educators to receive optimal support tailored to their work content and emotional state.

[0380] An "information processing device" refers to an entire system that has the capability to input, process, and output data, and is used to improve the efficiency of business operations.

[0381] "Authentication means" refers to a mechanism that verifies the legitimacy of a person when they access a system, and typically includes verification using passwords or biometric information.

[0382] "Selection mechanism" refers to an interface or mechanism that allows a user to choose a specific operation or function within a system.

[0383] "Generative means" refers to the processes and mechanisms for creating relevant materials and information in accordance with selected educational activities.

[0384] "Means of delivery" refers to a method or device for presenting generated materials or information to the user, and typically includes output via screen or audio.

[0385] "Analysis method" refers to a mechanism that uses an emotion engine to analyze the user's facial expressions and voice to determine their psychological state.

[0386] "Means of providing responses" refers to the process of providing users with measures and suggestions to alleviate their psychological burden, based on the user's emotional state obtained through analytical means.

[0387] "Configuration means" refers to a mechanism for adjusting and modifying the operation of a system in order to improve the convenience and efficiency of business support.

[0388] This invention provides an educational support system centered on an information processing device. The user, an educational professional, logs into the system using the authentication means of the information processing device. This authentication means includes commonly used ID and password-based authentication technologies. After authentication, the user selects the type of educational activity via the terminal's interface. This selection is often done via a touchscreen or keyboard input.

[0389] The server utilizes generation methods based on selected educational activities to retrieve appropriate information from the database. This information is then processed in the most optimal way using a generative AI model. For example, the AI ​​can automatically construct lesson plans and create test questions tailored to the user's needs.

[0390] The server then sends the generated data to the user's terminal via a delivery method. The user reviews the received data on their terminal and makes corrections if necessary. These corrections can be easily made using a standard text editor. The corrected data is then sent back to the server, and the information is updated in the database.

[0391] Furthermore, emotional state analysis is also performed. The camera and microphone connected to the device are used as analysis tools to capture the user's facial expressions and voice, and perform emotional analysis. If this analysis detects the user's psychological burden, the server provides relaxation music or videos through a response mechanism.

[0392] For example, when a teacher spends time preparing for an exam, the system checks their emotional state and, if it determines that their stress level is high, suggests simplified exam questions. Additionally, calming music can be played in the background to help the teacher concentrate.

[0393] An example of a prompt message is, "Detect the teacher's stress level, suggest a simple test question, and play relaxing music." In this way, a system is built that supports the user's efficient work performance and psychological support.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The user logs into the system using a terminal. The ID and password entered by the user are processed by the authentication method and sent to the server. The server refers to the database and verifies the matching account information. If authentication is successful, the user is logged in, and a dashboard for selecting the type of educational activity is displayed on the terminal. The input is the login information, and the output is the dashboard display.

[0397] Step 2:

[0398] The user selects the type of educational activity from the options displayed on the terminal's dashboard. The selected information is sent to the server via the selection mechanism. The server receives this input and prepares to retrieve relevant educational materials from its database. The input is the selected educational activity information, and the output is the preparation of data based on the selected activity.

[0399] Step 3:

[0400] The server generates relevant materials based on the selected educational activity. In this process, it uses a generation AI model to process data and generate specific educational materials and test questions. Filtering and processing are performed based on references to materials in the database, resulting in optimized output. The input is the user's selection information, and the output is the generated educational materials.

[0401] Step 4:

[0402] The generated educational materials are sent from the server to the terminal via a delivery system. The user reviews these materials on the terminal and modifies them as needed. The terminal is equipped with a text editor, allowing the user to easily edit the materials. The input is the generated material, and the output is the modified educational material.

[0403] Step 5:

[0404] Once editing is complete on the terminal, the revised document is sent back to the server and saved to the database. The server updates the database based on the received information, keeping it up-to-date. The input is the revised document information, and the output is the updated database.

[0405] Step 6:

[0406] Simultaneously, the device analyzes the user's emotional state using analytical tools. Facial and voice data acquired from the camera and microphone are processed via an emotion engine. The judgment results are sent to a server and used to make decisions regarding the provision of appropriate responses. The input is real-time emotional data, and the output is the judgment result of the emotional state.

[0407] Step 7:

[0408] If the user's emotional state is determined to be high-stress, the server selects relaxing content as a means of providing a response and delivers music or videos through the device. This content is customized to reduce the user's psychological burden. The input is the result of the emotional state assessment, and the output is the provision of relaxing content.

[0409] (Application Example 2)

[0410] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0411] Traditional educational support systems have only provided educators with functions focused on improving work efficiency, and have limitations in providing individualized support based on the psychological state of educators. Furthermore, it is known that stress in the educational setting affects teachers' performance, and there is a need for means to adequately mitigate this.

[0412] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0413] In this invention, the server includes authentication means for authenticating the account information of educators, selection means for selecting the type of educational work, and analysis means for analyzing the emotional state of educators from their facial expressions and voice. This enables not only improved work efficiency for educators but also appropriate support and stress reduction tailored to their individual psychological states.

[0414] "Educational professionals" are people who engage in educational activities, such as preparing lessons and exam questions, and performing administrative tasks.

[0415] "Generative artificial intelligence" refers to an artificial intelligence system that provides customized educational support to educators through authentication and emotion analysis.

[0416] "Authentication methods" refer to a system that verifies the account information of educators and confirms that they are legitimate users.

[0417] "Selection method" refers to the function of specifying the types of educational tasks that educators perform.

[0418] "Generation means" refers to a function that creates lesson plans and exam questions from data related to selected educational tasks.

[0419] "Delivery method" refers to the function of distributing generated lesson plans and test questions to the display devices of educators.

[0420] The "analysis method" is a system that analyzes the facial expressions and voice of educators to determine their emotional state.

[0421] The "adjustment mechanism" is a function that selects and provides relaxation content based on the analyzed emotional state.

[0422] "Configuration method" refers to a function that customizes the content of work support to meet the needs of educators.

[0423] An "information processing device" is a device that receives input information from educators and updates data based on that information.

[0424] The system of this invention is configured to integrate various functions in order to support educators. The main components include authentication means, selection means, generation means, provision means, analysis means, adjustment means, and setting means.

[0425] The authentication method verifies the legitimacy of system access by checking the account information of the educator when the user logs into the system. This establishes a foundation for providing appropriate services.

[0426] The selection method allows users to choose the educational tasks they want to perform (e.g., lesson planning or exam question creation). This makes it easier for the system to acquire data that meets the user's needs. The generation method generates relevant data based on the selected tasks and creates lesson plans or exam questions.

[0427] The delivery method involves transmitting the generated content to the user's display device, allowing educators to review its contents. Educators can freely edit the provided content. The edited content is received by the information processing device and reflected on the server.

[0428] The analysis method identifies the user's emotional state using facial expressions and voice data. Machine learning techniques using TensorFlow are employed to diagnose the user's stress level and psychological burden in real time.

[0429] The adjustment mechanism selects and provides users with content that promotes relaxation according to their emotional state (e.g., calming music or relaxing videos). This helps to reduce the psychological burden on educators.

[0430] The configuration method allows users to customize various support functions to suit the preferences of educators, thereby achieving more effective support.

[0431] For example, if emotion analysis detects that a teacher is experiencing stress during class, the system can provide the teacher with a short relaxing video and calming background music. Another example of a prompt message is, "Please suggest what kind of support would be effective when a teacher is experiencing high stress levels in an educational setting."

[0432] By integrating these functions, it becomes possible to streamline the work of educators and provide a comfortable learning environment.

[0433] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0434] Step 1:

[0435] The user logs into the system using a terminal. The terminal sends the educator's account information as input to the server. The server compares this information with the database, authenticates the user based on the input, and outputs the result to the terminal.

[0436] Step 2:

[0437] The user selects the task they want to perform (e.g., lesson planning, exam question creation) from a dashboard displayed on their device. The selected task type is sent from the device to the server. The server receives this information and selects data to prepare for the next step.

[0438] Step 3:

[0439] The device's camera and microphone collect user facial expressions and voice data as input. The device analyzes this data to identify the user's emotional state. TensorFlow-based facial expression recognition and voice emotion analysis models are used for the analysis, and the analysis results are sent to the server.

[0440] Step 4:

[0441] The server considers both the selected task type and the sentiment analysis results, and uses a generative AI model to generate relevant data. It retrieves lesson plans and exam question materials from the database, and simplifies them or recommends relaxing content as needed. The generated content is then provided to the terminal.

[0442] Step 5:

[0443] Users review the content provided on their devices and edit it as needed. The edits are sent from the device to the server, where the information is updated. This process generates and provides optimized educational materials and test questions specifically for educators.

[0444] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0445] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0450] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0452] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0454] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0455] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0456] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0457] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0459] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0460] This invention is a generative artificial intelligence system that streamlines the work of educators. The system supports a series of processes, starting with educator authentication, task selection, information generation, delivery, editing, and customization settings.

[0461] First, the educator logs into the system. The server receives the educator's account information and authenticates them by matching it with the database. If authentication is successful, the terminal displays the dashboard to the educator.

[0462] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device then sends the selection to the server.

[0463] The server retrieves relevant data based on the selected tasks and generates lesson plans or exam questions using an AI agent. In this process, the server utilizes historical data and curriculum information.

[0464] The generated lesson plans and exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The edited results are then sent back to the server.

[0465] Furthermore, the system allows users to configure their own settings to provide customized support for each educator. The server saves these settings and uses them to operate the system.

[0466] For example, if a user wants to prepare a math lesson for a second-year junior high school student, the system will provide relevant lesson materials. In this case, an AI agent will extract key points related to the unit and create a plan, saving educators the trouble of creating materials from scratch. Similarly, when creating periodic tests, the user can specify the range of the unit, and the server will generate appropriate test questions, which the user can then review, edit, and print.

[0467] In this way, the system supports the daily tasks of educators and streamlines them, creating an environment where they can dedicate more time to educational activities.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user accesses the education management system and enters their username and password on the login screen. The terminal stores the entered information and sends it to the server when the user presses the submit button.

[0471] Step 2:

[0472] The server verifies the received username and password against the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the device.

[0473] Step 3:

[0474] The device receives an authentication token and displays a dashboard screen for educators. From this screen, the user selects the type of task they wish to perform.

[0475] Step 4:

[0476] Instructions for tasks selected by the user (e.g., lesson planning or exam question creation) are sent from the terminal to the server.

[0477] Step 5:

[0478] The server retrieves data related to the selected task from the database. For example, if it's course structuring, it collects curriculum information; if it's exam question creation, it collects past exam question data.

[0479] Step 6:

[0480] The server uses the acquired data to generate lesson plans and exam questions using an AI agent. The generated content is tailored based on educational policies and historical data.

[0481] Step 7:

[0482] The server sends the generated lesson plan or exam questions to the terminal, which then displays a preview of them to the user.

[0483] Step 8:

[0484] The user reviews the preview and makes corrections on the spot if necessary. The device then sends the changes to the server.

[0485] Step 9:

[0486] The server saves the corrected information and generates the final version. This final version is then sent back to the terminal and provided in a downloadable and printable format.

[0487] Step 10:

[0488] If necessary, users can input their own customized settings from their terminals to facilitate educational activities. The server records these settings and applies them to future work support.

[0489] (Example 1)

[0490] Next, we will describe Example 1. 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."

[0491] Educators spend a significant amount of time and effort creating lesson plans and exam questions, which limits the time available for improving the quality of education and providing individualized instruction to students. They also need to efficiently edit and manage these materials. Furthermore, there are limited means available for easily generating customized educational materials.

[0492] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0493] In this invention, the server includes authentication means for verifying the identification information of educators, selection means for selecting education-related tasks, and information generation means for extracting and generating sets of information. This enables educators to efficiently generate and edit educational materials, allowing them to dedicate more time to educational activities.

[0494] "Educational professional" refers to an individual or organization responsible for conducting educational activities.

[0495] An "information processing system" is a collection of devices and software that collect and process data and provide appropriate information to users.

[0496] "Identification information" refers to data used to identify and authenticate a user.

[0497] "Authentication methods" refer to mechanisms and technologies used to verify a user's identification information and confirm its legitimacy.

[0498] "Education-related work" refers to all tasks related to education, such as lesson planning and the creation of test evaluation materials.

[0499] A "selection method" refers to an interface or mechanism that allows a user to choose a specific item from among multiple options.

[0500] An "information set" is a collection of data that has been gathered and structured for a specific purpose.

[0501] "Information generation means" refers to technologies and processes for generating new information and materials based on collected data.

[0502] A "display device" is a device used to visually present information.

[0503] "Transmission means" refers to a method or device for transferring data from one point to another.

[0504] A "setting adjustment mechanism" is a system for customizing the operation of the system according to the user's requests.

[0505] "Editing means" refers to functions that allow users to manually or automatically correct or modify generated information or materials.

[0506] An "information management device" is a device or system for centrally managing data and updating information as needed.

[0507] "Artificial intelligence technology" refers to technologies that use machines to mimic human intellectual activity and automate or optimize tasks.

[0508] This invention is implemented as an information processing system to streamline the work of educators. The system operates in cooperation with a server, terminals, and users. Specifically, it has the following hardware and software configuration.

[0509] The server functions as a central processing unit and manages the identification information of educators using a database management system. The database stores educational materials and past curriculum information. A backend environment is built by combining server-side languages ​​such as Python and Node.js with this. In addition, a large-scale language model API is implemented as a generative AI model. This model is responsible for generating educational plans and assessment materials.

[0510] The terminal functions as an input and display device for educators to actually operate. Front-end technologies that allow for easy construction of user interfaces, such as ReactJS and Angular, are used here. The terminal communicates with the server and receives information based on the tasks selected by the educators.

[0511] Users log into the system, select individual educational tasks, and instruct the system to generate information. Specifically, when they select tasks such as creating lesson plans or generating exam questions, they send specific prompts to the server according to those requirements. Examples of prompts sent include: "Please create a lesson plan to teach quadratic equations to 8th grade math students. Please include key points and example problems."

[0512] As an example of the system, suppose a user wants to create a lesson plan for first-year junior high school English. In this case, the user uses a terminal to input a prompt such as, "Please generate a lesson plan to teach basic English grammar for first-year junior high school students." Based on this input, the server uses an AI model to generate relevant information and provides appropriate lesson materials on the terminal. The user can then conduct the lesson based on this. In this way, by utilizing information processing systems, educators can carry out their work more efficiently and improve the quality of education.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The server receives the login information entered by the user and authenticates it by comparing it with the database. This input includes the user ID and password. Based on this, the server searches the database for matching account information and outputs whether the authentication was successful or not. If authentication is successful, the server generates a session ID and sends it to the terminal.

[0516] Step 2:

[0517] The terminal verifies the session ID received from the server and begins displaying the dashboard to the user. The displayed content includes options for education-related tasks. The input is session confirmation information from the server, and the terminal outputs the dashboard to the user based on this. Specifically, buttons such as "Create Lesson Plan" and "Create Exam Evaluation" are displayed on the screen.

[0518] Step 3:

[0519] The user selects their desired education-related task from the dashboard. This selection is sent to the server via the terminal. The server receives the type of task selected by the user as input and proceeds to the next data processing step. Specifically, this involves sending a request to the server when the user clicks on an option.

[0520] Step 4:

[0521] Based on the received selection information, the server retrieves relevant information from the database. The input in this step is the user's selection information, and based on this, the server extracts past educational materials and curriculum information, creates a prompt message, and sends it to the generating AI model. The output is an educational plan or test question generated by the AI ​​model. A concrete example of a prompt message is, "Generate a lesson plan to teach basic English grammar for first-year junior high school students."

[0522] Step 5:

[0523] The server receives information generated from the AI ​​model and sends it to the terminal. This transmission includes generated educational materials and exam questions. The terminal receives this information and displays it to the user. The input is the received data, and the output is a display of that data for the user to see. For example, a preview of the generated lesson plan might be displayed on the screen.

[0524] Step 6:

[0525] The user reviews the provided educational materials and edits them as needed. The edits are sent from the terminal to the server. The server receives the user's edits as input and updates the materials. The updated materials are saved to the database as output. Specific actions include correcting exam questions using a text editor.

[0526] Step 7:

[0527] The server updates system settings based on the received edits, providing customized support to educators in the future. Inputs include edit results and user settings. Based on this, the server updates the database and outputs support information that will be applied the next time the system is used. Specifically, settings such as prioritizing frequently used teaching materials are implemented.

[0528] (Application Example 1)

[0529] Next, we will explain Application Example 1. In the following explanation, 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."

[0530] Currently, educators spend a great deal of time and effort preparing for lessons and exams, as well as supporting home learning. In particular, when using autonomous educational machines to support home learning, their operation and management are complex. Therefore, there is a need for a system that can easily and efficiently generate educational content and enable instruction by these autonomous machines. Solving this problem, reducing the burden on educators, and improving the effectiveness of home learning is the challenge.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0532] In this invention, the server includes authentication means for verifying the identification information of the educator, processing means for selecting the type of educational task, and generation means for acquiring relevant information based on the selected educational task and generating a learning plan or assessment questions. This makes it possible for educators to easily prepare learning content, and for an autonomous educational machine to effectively support home learning based on that content.

[0533] An "educator" refers to a professional responsible for transmitting knowledge and skills.

[0534] "Identification information" refers to data used to identify an individual.

[0535] "Authentication means" refers to a method for verifying an individual's identification information and confirming its legitimacy.

[0536] "Educational work" refers to various activities related to the implementation of education, such as preparing lessons and creating exams.

[0537] "Processing means" refers to a method for performing a specific selection or operation.

[0538] A "generation method" is a method for creating new information or content based on specific conditions.

[0539] An "information processing device" is an electronic device that receives digital data and manipulates, processes, and outputs that data.

[0540] "Adjustment settings" are settings that allow you to change the system's operation to suit individual needs and conditions.

[0541] An "autonomous educational machine" refers to a device that automatically provides learning guidance based on a program.

[0542] "Control means" refers to methods for instructing and managing the operation of machines and systems.

[0543] To implement this invention, we will take a generative artificial intelligence system that supports the work of educators as an example. The system mainly consists of a server, an information processing device (terminal) for educators, and an autonomous machine for education.

[0544] When an educator accesses the server, it authenticates them using their identification information. After authentication, the server displays a task selection screen on the terminal and retrieves the necessary data to generate lesson plans and assessment questions based on the educator's selection. The data is analyzed using a generation AI model to generate content optimized for the selected task.

[0545] The generated learning plans and assessment questions are sent from the server to the terminal, where educators can review and modify them as needed. Modifications from the terminal are sent to the server and updated as the latest information. Furthermore, control instructions based on the generated learning plans are sent to the autonomous educational machine, which then provides learning guidance to students in accordance with the educator's intentions.

[0546] As a concrete example, consider a case where a middle school math teacher uses a system to create a geometry lesson plan. In this case, the teacher can use a "generative AI model" to input prompts that are in line with the school's curriculum and instruct the system to generate appropriate content.

[0547] An example of a prompt message would be, "Generate a lesson plan for 2nd-year junior high school geometry." The AI ​​can then automatically suggest relevant teaching materials and content. Another feature of this system is that educators can freely edit and customize the plan's content. In this way, educators can efficiently provide high-quality education.

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The user logs into the system using a terminal. The user enters their identification information and sends it to the server. The server compares this entered identification information with the database and performs authentication. If authentication is successful, the user is shown a dashboard screen. The input is the user's identification information, and the output is the authentication result. In this step, the server performs a database search and returns the authentication result.

[0551] Step 2:

[0552] The user selects the type of training activity they wish to perform on the dashboard. The user's selection is sent to the server via the terminal. The server retrieves information based on the user's selection and prepares the necessary training resources. The input is the selected training activity, and the output is the completion of retrieving the relevant data. In this step, the server retrieves resources from the database based on the user's selection.

[0553] Step 3:

[0554] The server uses a generation AI model to automatically generate lesson plans and assessment questions from acquired data. During the generation process, the server utilizes historical data and curriculum information, performing data processing and calculations based on prompts. The input is the acquired data, and the output is the generated educational content. This step includes operations that utilize the generation AI model for automatic creation.

[0555] Step 4:

[0556] The generated lesson plans and problems are provided from the server to the user's terminal. The user reviews the content and edits it as needed. The edited content is sent from the terminal to the server, where the server updates the information. The input is the generated educational content, and the output is the content edited by the user. In this step, the review and updating of edits are performed between the server and the terminal.

[0557] Step 5:

[0558] The server sends control instructions to the autonomous educational machine based on the generated and edited educational content. The autonomous educational machine carries out learning instruction according to the received instructions. The input is the edited educational content, and the output is the start of instruction by the autonomous educational machine. In this step, an action occurs in which control instructions are given to the machine to carry out the education.

[0559] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0560] This invention is a generative artificial intelligence system that streamlines the work of educators and provides support that takes their psychological state into consideration. The system covers a series of processes, starting with educator authentication, task selection, information generation, emotion recognition, delivery, editing, and customization settings.

[0561] First, the educator logs into the system. The server receives the educator's account information, compares it with the database, and performs authentication. If authentication is successful, the terminal displays a dashboard to the educator. At this point, the emotion engine recognizes and analyzes the educator's emotions in real time from their facial expressions and voice.

[0562] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device sends the selection to the server, and simultaneously sends the results of the emotion engine's analysis to the server as well.

[0563] The server considers the selected tasks and the analysis results from the emotion engine, and retrieves relevant data from the database. When creating lesson plans or exam questions, it is possible to select simpler materials than usual to make suggestions that are less burdensome, based on the emotional state of the educator.

[0564] The generated lesson plan or exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The terminal then sends the changes back to the server as data.

[0565] Furthermore, if the emotional engine determines that an educator is experiencing high stress levels, the system has a function to provide relaxing content. This content, which includes music and videos for mood enhancement, is delivered to the educator through their device.

[0566] In this way, the system supports the daily work of educators, not only improving work efficiency but also creating a more comfortable working environment by reducing psychological burden. For example, when a teacher is busy just before an exam, the emotional engine detects stress, suggests creating simplified exam questions, and plays relaxing music to further reduce stress.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The user accesses the education management system and enters their username and password on the login screen. The terminal temporarily holds the entered information and sends it to the server when the submit button is pressed.

[0570] Step 2:

[0571] The server compares the received authentication information with the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the terminal.

[0572] Step 3:

[0573] The device receives an authentication token and displays a dashboard for educators. At this time, the device has an emotion engine built in that analyzes the user's facial expressions and voice to acquire emotion data in real time.

[0574] Step 4:

[0575] Users select tasks such as "course structure" or "exam question creation" on the dashboard. The selection results and sentiment data are sent from the device to the server.

[0576] Step 5:

[0577] The server retrieves relevant information from the database based on emotional data and selected tasks. For example, if a user is stressed, the server selects data that takes into account criteria for choosing simpler learning materials.

[0578] Step 6:

[0579] The server uses an AI agent to generate appropriate lesson plans and exam questions from the acquired data. During this generation process, the content is adjusted according to the selected emotional state.

[0580] Step 7:

[0581] The generated document is sent from the server to the terminal, which then displays a preview of it to the user.

[0582] Step 8:

[0583] Users can preview the document and edit or modify it if necessary. The edits are sent from the device to the server and updated.

[0584] Step 9:

[0585] The server saves the updated documents and generates the final version. The generated result is sent to the terminal and provided to the user in a format that can be downloaded or printed.

[0586] Step 10:

[0587] If the emotion engine detects a high-stress state, the device will provide the educator with relaxing content (e.g., music or video). Depending on the user's state, the system will take actions intended to reduce stress.

[0588] (Example 2)

[0589] Next, we will describe Example 2. 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."

[0590] Educators are required to perform their duties efficiently amidst a busy workload. However, conventional systems often fail to consider the emotional state of users, leading to increased psychological burden. Furthermore, the fixed selection of tasks and provision of materials, which cannot be adapted to the specific work content and circumstances of educators, resulted in insufficient and efficient support.

[0591] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0592] In this invention, the server includes authentication means for authenticating the personal information of educators, selection means for selecting the type of educational activity, and generation means for acquiring relevant information based on the selected educational activity and generating educational materials. This enables educators to receive optimal support tailored to their work content and emotional state.

[0593] An "information processing device" refers to an entire system that has the capability to input, process, and output data, and is used to improve the efficiency of business operations.

[0594] "Authentication means" refers to a mechanism that verifies the legitimacy of a person when they access a system, and typically includes verification using passwords or biometric information.

[0595] "Selection mechanism" refers to an interface or mechanism that allows a user to choose a specific operation or function within a system.

[0596] "Generative means" refers to the processes and mechanisms for creating relevant materials and information in accordance with selected educational activities.

[0597] "Means of delivery" refers to a method or device for presenting generated materials or information to the user, and typically includes output via screen or audio.

[0598] "Analysis method" refers to a mechanism that uses an emotion engine to analyze the user's facial expressions and voice to determine their psychological state.

[0599] "Means of providing responses" refers to the process of providing users with measures and suggestions to alleviate their psychological burden, based on the user's emotional state obtained through analytical means.

[0600] "Configuration means" refers to a mechanism for adjusting and modifying the operation of a system in order to improve the convenience and efficiency of business support.

[0601] This invention provides an educational support system centered on an information processing device. The user, an educational professional, logs into the system using the authentication means of the information processing device. This authentication means includes commonly used ID and password-based authentication technologies. After authentication, the user selects the type of educational activity via the terminal's interface. This selection is often done via a touchscreen or keyboard input.

[0602] The server utilizes generation methods based on selected educational activities to retrieve appropriate information from the database. This information is then processed in the most optimal way using a generative AI model. For example, the AI ​​can automatically construct lesson plans and create test questions tailored to the user's needs.

[0603] The server then sends the generated data to the user's terminal via a delivery method. The user reviews the received data on their terminal and makes corrections if necessary. These corrections can be easily made using a standard text editor. The corrected data is then sent back to the server, and the information is updated in the database.

[0604] Furthermore, emotional state analysis is also performed. The camera and microphone connected to the device are used as analysis tools to capture the user's facial expressions and voice, and perform emotional analysis. If this analysis detects the user's psychological burden, the server provides relaxation music or videos through a response mechanism.

[0605] For example, when a teacher spends time preparing for an exam, the system checks their emotional state and, if it determines that their stress level is high, suggests simplified exam questions. Additionally, calming music can be played in the background to help the teacher concentrate.

[0606] An example of a prompt message is, "Detect the teacher's stress level, suggest a simple test question, and play relaxing music." In this way, a system is built that supports the user's efficient work performance and psychological support.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] The user logs into the system using a terminal. The ID and password entered by the user are processed by the authentication method and sent to the server. The server refers to the database and verifies the matching account information. If authentication is successful, the user is logged in, and a dashboard for selecting the type of educational activity is displayed on the terminal. The input is the login information, and the output is the dashboard display.

[0610] Step 2:

[0611] The user selects the type of educational activity from the options displayed on the terminal's dashboard. The selected information is sent to the server via the selection mechanism. The server receives this input and prepares to retrieve relevant educational materials from its database. The input is the selected educational activity information, and the output is the preparation of data based on the selected activity.

[0612] Step 3:

[0613] The server generates relevant materials based on the selected educational activity. In this process, it uses a generation AI model to process data and generate specific educational materials and test questions. Filtering and processing are performed based on references to materials in the database, resulting in optimized output. The input is the user's selection information, and the output is the generated educational materials.

[0614] Step 4:

[0615] The generated educational materials are sent from the server to the terminal via a delivery system. The user reviews these materials on the terminal and modifies them as needed. The terminal is equipped with a text editor, allowing the user to easily edit the materials. The input is the generated material, and the output is the modified educational material.

[0616] Step 5:

[0617] Once editing is complete on the terminal, the revised document is sent back to the server and saved to the database. The server updates the database based on the received information, keeping it up-to-date. The input is the revised document information, and the output is the updated database.

[0618] Step 6:

[0619] Simultaneously, the device analyzes the user's emotional state using analytical tools. Facial and voice data acquired from the camera and microphone are processed via an emotion engine. The judgment results are sent to a server and used to make decisions regarding the provision of appropriate responses. The input is real-time emotional data, and the output is the judgment result of the emotional state.

[0620] Step 7:

[0621] If the user's emotional state is determined to be high-stress, the server selects relaxing content as a means of providing a response and delivers music or videos through the device. This content is customized to reduce the user's psychological burden. The input is the result of the emotional state assessment, and the output is the provision of relaxing content.

[0622] (Application Example 2)

[0623] Next, we will explain application example 2. In the following explanation, 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."

[0624] Traditional educational support systems have only provided educators with functions focused on improving work efficiency, and have limitations in providing individualized support based on the psychological state of educators. Furthermore, it is known that stress in the educational setting affects teachers' performance, and there is a need for means to adequately mitigate this.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0626] In this invention, the server includes authentication means for authenticating the account information of educators, selection means for selecting the type of educational work, and analysis means for analyzing the emotional state of educators from their facial expressions and voice. This enables not only improved work efficiency for educators but also appropriate support and stress reduction tailored to their individual psychological states.

[0627] "Educational professionals" are people who engage in educational activities, such as preparing lessons and exam questions, and performing administrative tasks.

[0628] "Generative artificial intelligence" refers to an artificial intelligence system that provides customized educational support to educators through authentication and emotion analysis.

[0629] "Authentication methods" refer to a system that verifies the account information of educators and confirms that they are legitimate users.

[0630] "Selection method" refers to the function of specifying the types of educational tasks that educators perform.

[0631] "Generation means" refers to a function that creates lesson plans and exam questions from data related to selected educational tasks.

[0632] "Delivery method" refers to the function of distributing generated lesson plans and test questions to the display devices of educators.

[0633] The "analysis method" is a system that analyzes the facial expressions and voice of educators to determine their emotional state.

[0634] The "adjustment mechanism" is a function that selects and provides relaxation content based on the analyzed emotional state.

[0635] "Configuration method" refers to a function that customizes the content of work support to meet the needs of educators.

[0636] An "information processing device" is a device that receives input information from educators and updates data based on that information.

[0637] The system of this invention is configured to integrate various functions in order to support educators. The main components include authentication means, selection means, generation means, provision means, analysis means, adjustment means, and setting means.

[0638] The authentication method verifies the legitimacy of system access by checking the account information of the educator when the user logs into the system. This establishes a foundation for providing appropriate services.

[0639] The selection method allows users to choose the educational tasks they want to perform (e.g., lesson planning or exam question creation). This makes it easier for the system to acquire data that meets the user's needs. The generation method generates relevant data based on the selected tasks and creates lesson plans or exam questions.

[0640] The delivery method involves transmitting the generated content to the user's display device, allowing educators to review its contents. Educators can freely edit the provided content. The edited content is received by the information processing device and reflected on the server.

[0641] The analysis method identifies the user's emotional state using facial expressions and voice data. Machine learning techniques using TensorFlow are employed to diagnose the user's stress level and psychological burden in real time.

[0642] The adjustment mechanism selects and provides users with content that promotes relaxation according to their emotional state (e.g., calming music or relaxing videos). This helps to reduce the psychological burden on educators.

[0643] The configuration method allows users to customize various support functions to suit the preferences of educators, thereby achieving more effective support.

[0644] For example, if emotion analysis detects that a teacher is experiencing stress during class, the system can provide the teacher with a short relaxing video and calming background music. Another example of a prompt message is, "Please suggest what kind of support would be effective when a teacher is experiencing high stress levels in an educational setting."

[0645] By integrating these functions, it becomes possible to streamline the work of educators and provide a comfortable learning environment.

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The user logs into the system using a terminal. The terminal sends the educator's account information as input to the server. The server compares this information with the database, authenticates the user based on the input, and outputs the result to the terminal.

[0649] Step 2:

[0650] The user selects the task they want to perform (e.g., lesson planning, exam question creation) from a dashboard displayed on their device. The selected task type is sent from the device to the server. The server receives this information and selects data to prepare for the next step.

[0651] Step 3:

[0652] The device's camera and microphone collect user facial expressions and voice data as input. The device analyzes this data to identify the user's emotional state. TensorFlow-based facial expression recognition and voice emotion analysis models are used for the analysis, and the analysis results are sent to the server.

[0653] Step 4:

[0654] The server considers both the selected task type and the sentiment analysis results, and uses a generative AI model to generate relevant data. It retrieves lesson plans and exam question materials from the database, and simplifies them or recommends relaxing content as needed. The generated content is then provided to the terminal.

[0655] Step 5:

[0656] Users review the content provided on their devices and edit it as needed. The edits are sent from the device to the server, where the information is updated. This process generates and provides optimized educational materials and test questions specifically for educators.

[0657] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] As shown in Figure 7, the 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.

[0663] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0665] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0667] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0668] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0669] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0670] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0671] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0673] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] This invention is a generative artificial intelligence system that streamlines the work of educators. The system supports a series of processes, starting with educator authentication, task selection, information generation, delivery, editing, and customization settings.

[0675] First, the educator logs into the system. The server receives the educator's account information and authenticates them by matching it with the database. If authentication is successful, the terminal displays the dashboard to the educator.

[0676] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device then sends the selection to the server.

[0677] The server retrieves relevant data based on the selected tasks and generates lesson plans or exam questions using an AI agent. In this process, the server utilizes historical data and curriculum information.

[0678] The generated lesson plans and exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The edited results are then sent back to the server.

[0679] Furthermore, the system allows users to configure their own settings to provide customized support for each educator. The server saves these settings and uses them to operate the system.

[0680] For example, if a user wants to prepare a math lesson for a second-year junior high school student, the system will provide relevant lesson materials. In this case, an AI agent will extract key points related to the unit and create a plan, saving educators the trouble of creating materials from scratch. Similarly, when creating periodic tests, the user can specify the range of the unit, and the server will generate appropriate test questions, which the user can then review, edit, and print.

[0681] In this way, the system supports the daily tasks of educators and streamlines them, creating an environment where they can dedicate more time to educational activities.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user accesses the education management system and enters their username and password on the login screen. The terminal stores the entered information and sends it to the server when the user presses the submit button.

[0685] Step 2:

[0686] The server verifies the received username and password against the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the device.

[0687] Step 3:

[0688] The device receives an authentication token and displays a dashboard screen for educators. From this screen, the user selects the type of task they wish to perform.

[0689] Step 4:

[0690] Instructions for tasks selected by the user (e.g., lesson planning or exam question creation) are sent from the terminal to the server.

[0691] Step 5:

[0692] The server retrieves data related to the selected task from the database. For example, if it's course structuring, it collects curriculum information; if it's exam question creation, it collects past exam question data.

[0693] Step 6:

[0694] The server uses the acquired data to generate lesson plans and exam questions using an AI agent. The generated content is tailored based on educational policies and historical data.

[0695] Step 7:

[0696] The server sends the generated lesson plan or exam questions to the terminal, which then displays a preview of them to the user.

[0697] Step 8:

[0698] The user reviews the preview and makes corrections on the spot if necessary. The device then sends the changes to the server.

[0699] Step 9:

[0700] The server saves the corrected information and generates the final version. This final version is then sent back to the terminal and provided in a downloadable and printable format.

[0701] Step 10:

[0702] If necessary, users can input their own customized settings from their terminals to facilitate educational activities. The server records these settings and applies them to future work support.

[0703] (Example 1)

[0704] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] Educators spend a significant amount of time and effort creating lesson plans and exam questions, which limits the time available for improving the quality of education and providing individualized instruction to students. They also need to efficiently edit and manage these materials. Furthermore, there are limited means available for easily generating customized educational materials.

[0706] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0707] In this invention, the server includes authentication means for verifying the identification information of educators, selection means for selecting education-related tasks, and information generation means for extracting and generating sets of information. This enables educators to efficiently generate and edit educational materials, allowing them to dedicate more time to educational activities.

[0708] "Educational professional" refers to an individual or organization responsible for conducting educational activities.

[0709] An "information processing system" is a collection of devices and software that collect and process data and provide appropriate information to users.

[0710] "Identification information" refers to data used to identify and authenticate a user.

[0711] "Authentication methods" refer to mechanisms and technologies used to verify a user's identification information and confirm its legitimacy.

[0712] "Education-related work" refers to all tasks related to education, such as lesson planning and the creation of test evaluation materials.

[0713] A "selection method" refers to an interface or mechanism that allows a user to choose a specific item from among multiple options.

[0714] An "information set" is a collection of data that has been gathered and structured for a specific purpose.

[0715] "Information generation means" refers to technologies and processes for generating new information and materials based on collected data.

[0716] A "display device" is a device used to visually present information.

[0717] "Transmission means" refers to a method or device for transferring data from one point to another.

[0718] A "setting adjustment mechanism" is a system for customizing the operation of the system according to the user's requests.

[0719] "Editing means" refers to functions that allow users to manually or automatically correct or modify generated information or materials.

[0720] An "information management device" is a device or system for centrally managing data and updating information as needed.

[0721] "Artificial intelligence technology" refers to technologies that use machines to mimic human intellectual activity and automate or optimize tasks.

[0722] This invention is implemented as an information processing system to streamline the work of educators. The system operates in cooperation with a server, terminals, and users. Specifically, it has the following hardware and software configuration.

[0723] The server functions as a central processing unit and manages the identification information of educators using a database management system. The database stores educational materials and past curriculum information. A backend environment is built by combining server-side languages ​​such as Python and Node.js with this. In addition, a large-scale language model API is implemented as a generative AI model. This model is responsible for generating educational plans and assessment materials.

[0724] The terminal functions as an input and display device for educators to actually operate. Front-end technologies that allow for easy construction of user interfaces, such as ReactJS and Angular, are used here. The terminal communicates with the server and receives information based on the tasks selected by the educators.

[0725] Users log into the system, select individual educational tasks, and instruct the system to generate information. Specifically, when they select tasks such as creating lesson plans or generating exam questions, they send specific prompts to the server according to those requirements. Examples of prompts sent include: "Please create a lesson plan to teach quadratic equations to 8th grade math students. Please include key points and example problems."

[0726] As an example of the system, suppose a user wants to create a lesson plan for first-year junior high school English. In this case, the user uses a terminal to input a prompt such as, "Please generate a lesson plan to teach basic English grammar for first-year junior high school students." Based on this input, the server uses an AI model to generate relevant information and provides appropriate lesson materials on the terminal. The user can then conduct the lesson based on this. In this way, by utilizing information processing systems, educators can carry out their work more efficiently and improve the quality of education.

[0727] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0728] Step 1:

[0729] The server receives the login information entered by the user and authenticates it by comparing it with the database. This input includes the user ID and password. Based on this, the server searches the database for matching account information and outputs whether the authentication was successful or not. If authentication is successful, the server generates a session ID and sends it to the terminal.

[0730] Step 2:

[0731] The terminal verifies the session ID received from the server and begins displaying the dashboard to the user. The displayed content includes options for education-related tasks. The input is session confirmation information from the server, and the terminal outputs the dashboard to the user based on this. Specifically, buttons such as "Create Lesson Plan" and "Create Exam Evaluation" are displayed on the screen.

[0732] Step 3:

[0733] The user selects their desired education-related task from the dashboard. This selection is sent to the server via the terminal. The server receives the type of task selected by the user as input and proceeds to the next data processing step. Specifically, this involves sending a request to the server when the user clicks on an option.

[0734] Step 4:

[0735] Based on the received selection information, the server retrieves relevant information from the database. The input in this step is the user's selection information, and based on this, the server extracts past educational materials and curriculum information, creates a prompt message, and sends it to the generating AI model. The output is an educational plan or test question generated by the AI ​​model. A concrete example of a prompt message is, "Generate a lesson plan to teach basic English grammar for first-year junior high school students."

[0736] Step 5:

[0737] The server receives information generated from the AI ​​model and sends it to the terminal. This transmission includes generated educational materials and exam questions. The terminal receives this information and displays it to the user. The input is the received data, and the output is a display of that data for the user to see. For example, a preview of the generated lesson plan might be displayed on the screen.

[0738] Step 6:

[0739] The user reviews the provided educational materials and edits them as needed. The edits are sent from the terminal to the server. The server receives the user's edits as input and updates the materials. The updated materials are saved to the database as output. Specific actions include correcting exam questions using a text editor.

[0740] Step 7:

[0741] The server updates system settings based on the received edits, providing customized support to educators in the future. Inputs include edit results and user settings. Based on this, the server updates the database and outputs support information that will be applied the next time the system is used. Specifically, settings such as prioritizing frequently used teaching materials are implemented.

[0742] (Application Example 1)

[0743] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] Currently, educators spend a great deal of time and effort preparing for lessons and exams, as well as supporting home learning. In particular, when using autonomous educational machines to support home learning, their operation and management are complex. Therefore, there is a need for a system that can easily and efficiently generate educational content and enable instruction by these autonomous machines. Solving this problem, reducing the burden on educators, and improving the effectiveness of home learning is the challenge.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0746] In this invention, the server includes authentication means for verifying the identification information of the educator, processing means for selecting the type of educational task, and generation means for acquiring relevant information based on the selected educational task and generating a learning plan or assessment questions. This makes it possible for educators to easily prepare learning content, and for an autonomous educational machine to effectively support home learning based on that content.

[0747] An "educator" refers to a professional responsible for transmitting knowledge and skills.

[0748] "Identification information" refers to data used to identify an individual.

[0749] "Authentication means" refers to a method for verifying an individual's identification information and confirming its legitimacy.

[0750] "Educational work" refers to various activities related to the implementation of education, such as preparing lessons and creating exams.

[0751] "Processing means" refers to a method for performing a specific selection or operation.

[0752] A "generation method" is a method for creating new information or content based on specific conditions.

[0753] An "information processing device" is an electronic device that receives digital data and manipulates, processes, and outputs that data.

[0754] "Adjustment settings" are settings that allow you to change the system's operation to suit individual needs and conditions.

[0755] An "autonomous educational machine" refers to a device that automatically provides learning guidance based on a program.

[0756] "Control means" refers to methods for instructing and managing the operation of machines and systems.

[0757] To implement this invention, we will take a generative artificial intelligence system that supports the work of educators as an example. The system mainly consists of a server, an information processing device (terminal) for educators, and an autonomous machine for education.

[0758] When an educator accesses the server, it authenticates them using their identification information. After authentication, the server displays a task selection screen on the terminal and retrieves the necessary data to generate lesson plans and assessment questions based on the educator's selection. The data is analyzed using a generation AI model to generate content optimized for the selected task.

[0759] The generated learning plans and assessment questions are sent from the server to the terminal, where educators can review and modify them as needed. Modifications from the terminal are sent to the server and updated as the latest information. Furthermore, control instructions based on the generated learning plans are sent to the autonomous educational machine, which then provides learning guidance to students in accordance with the educator's intentions.

[0760] As a concrete example, consider a case where a middle school math teacher uses a system to create a geometry lesson plan. In this case, the teacher can use a "generative AI model" to input prompts that are in line with the school's curriculum and instruct the system to generate appropriate content.

[0761] An example of a prompt message would be, "Generate a lesson plan for 2nd-year junior high school geometry." The AI ​​can then automatically suggest relevant teaching materials and content. Another feature of this system is that educators can freely edit and customize the plan's content. In this way, educators can efficiently provide high-quality education.

[0762] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0763] Step 1:

[0764] The user logs into the system using a terminal. The user enters their identification information and sends it to the server. The server compares this entered identification information with the database and performs authentication. If authentication is successful, the user is shown a dashboard screen. The input is the user's identification information, and the output is the authentication result. In this step, the server performs a database search and returns the authentication result.

[0765] Step 2:

[0766] The user selects the type of training activity they wish to perform on the dashboard. The user's selection is sent to the server via the terminal. The server retrieves information based on the user's selection and prepares the necessary training resources. The input is the selected training activity, and the output is the completion of retrieving the relevant data. In this step, the server retrieves resources from the database based on the user's selection.

[0767] Step 3:

[0768] The server uses a generation AI model to automatically generate lesson plans and assessment questions from acquired data. During the generation process, the server utilizes historical data and curriculum information, performing data processing and calculations based on prompts. The input is the acquired data, and the output is the generated educational content. This step includes operations that utilize the generation AI model for automatic creation.

[0769] Step 4:

[0770] The generated lesson plans and problems are provided from the server to the user's terminal. The user reviews the content and edits it as needed. The edited content is sent from the terminal to the server, where the server updates the information. The input is the generated educational content, and the output is the content edited by the user. In this step, the review and updating of edits are performed between the server and the terminal.

[0771] Step 5:

[0772] The server sends control instructions to the autonomous educational machine based on the generated and edited educational content. The autonomous educational machine carries out learning instruction according to the received instructions. The input is the edited educational content, and the output is the start of instruction by the autonomous educational machine. In this step, an action occurs in which control instructions are given to the machine to carry out the education.

[0773] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0774] This invention is a generative artificial intelligence system that streamlines the work of educators and provides support that takes their psychological state into consideration. The system covers a series of processes, starting with educator authentication, task selection, information generation, emotion recognition, delivery, editing, and customization settings.

[0775] First, the educator logs into the system. The server receives the educator's account information, compares it with the database, and performs authentication. If authentication is successful, the terminal displays a dashboard to the educator. At this point, the emotion engine recognizes and analyzes the educator's emotions in real time from their facial expressions and voice.

[0776] Next, the user selects the type of task on the dashboard. Options include lesson planning, exam question creation, administrative work, and parent communication. The device sends the selection to the server, and simultaneously sends the results of the emotion engine's analysis to the server as well.

[0777] The server considers the selected tasks and the analysis results from the emotion engine, and retrieves relevant data from the database. When creating lesson plans or exam questions, it is possible to select simpler materials than usual to make suggestions that are less burdensome, based on the emotional state of the educator.

[0778] The generated lesson plan or exam questions are sent from the server to the terminal and provided to the user. The user can review this content and edit it as needed. The terminal then sends the changes back to the server as data.

[0779] Furthermore, if the emotional engine determines that an educator is experiencing high stress levels, the system has a function to provide relaxing content. This content, which includes music and videos for mood enhancement, is delivered to the educator through their device.

[0780] In this way, the system supports the daily work of educators, not only improving work efficiency but also creating a more comfortable working environment by reducing psychological burden. For example, when a teacher is busy just before an exam, the emotional engine detects stress, suggests creating simplified exam questions, and plays relaxing music to further reduce stress.

[0781] The following describes the processing flow.

[0782] Step 1:

[0783] The user accesses the education management system and enters their username and password on the login screen. The terminal temporarily holds the entered information and sends it to the server when the submit button is pressed.

[0784] Step 2:

[0785] The server compares the received authentication information with the registered information in the database. If authentication is successful, the server starts a session, generates an authentication token, and sends it to the terminal.

[0786] Step 3:

[0787] The device receives an authentication token and displays a dashboard for educators. At this time, the device has an emotion engine built in that analyzes the user's facial expressions and voice to acquire emotion data in real time.

[0788] Step 4:

[0789] Users select tasks such as "course structure" or "exam question creation" on the dashboard. The selection results and sentiment data are sent from the device to the server.

[0790] Step 5:

[0791] The server retrieves relevant information from the database based on emotional data and selected tasks. For example, if a user is stressed, the server selects data that takes into account criteria for choosing simpler learning materials.

[0792] Step 6:

[0793] The server uses an AI agent to generate appropriate lesson plans and exam questions from the acquired data. During this generation process, the content is adjusted according to the selected emotional state.

[0794] Step 7:

[0795] The generated document is sent from the server to the terminal, which then displays a preview of it to the user.

[0796] Step 8:

[0797] Users can preview the document and edit or modify it if necessary. The edits are sent from the device to the server and updated.

[0798] Step 9:

[0799] The server saves the updated documents and generates the final version. The generated result is sent to the terminal and provided to the user in a format that can be downloaded or printed.

[0800] Step 10:

[0801] If the emotion engine detects a high-stress state, the device will provide the educator with relaxing content (e.g., music or video). Depending on the user's state, the system will take actions intended to reduce stress.

[0802] (Example 2)

[0803] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0804] Educators are required to perform their duties efficiently amidst a busy workload. However, conventional systems often fail to consider the emotional state of users, leading to increased psychological burden. Furthermore, the fixed selection of tasks and provision of materials, which cannot be adapted to the specific work content and circumstances of educators, resulted in insufficient and efficient support.

[0805] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0806] In this invention, the server includes authentication means for authenticating the personal information of educators, selection means for selecting the type of educational activity, and generation means for acquiring relevant information based on the selected educational activity and generating educational materials. This enables educators to receive optimal support tailored to their work content and emotional state.

[0807] An "information processing device" refers to an entire system that has the capability to input, process, and output data, and is used to improve the efficiency of business operations.

[0808] "Authentication means" refers to a mechanism that verifies the legitimacy of a person when they access a system, and typically includes verification using passwords or biometric information.

[0809] "Selection mechanism" refers to an interface or mechanism that allows a user to choose a specific operation or function within a system.

[0810] "Generative means" refers to the processes and mechanisms for creating relevant materials and information in accordance with selected educational activities.

[0811] "Means of delivery" refers to a method or device for presenting generated materials or information to the user, and typically includes output via screen or audio.

[0812] "Analysis method" refers to a mechanism that uses an emotion engine to analyze the user's facial expressions and voice to determine their psychological state.

[0813] "Means of providing responses" refers to the process of providing users with measures and suggestions to alleviate their psychological burden, based on the user's emotional state obtained through analytical means.

[0814] "Configuration means" refers to a mechanism for adjusting and modifying the operation of a system in order to improve the convenience and efficiency of business support.

[0815] This invention provides an educational support system centered on an information processing device. The user, an educational professional, logs into the system using the authentication means of the information processing device. This authentication means includes commonly used ID and password-based authentication technologies. After authentication, the user selects the type of educational activity via the terminal's interface. This selection is often done via a touchscreen or keyboard input.

[0816] The server utilizes generation methods based on selected educational activities to retrieve appropriate information from the database. This information is then processed in the most optimal way using a generative AI model. For example, the AI ​​can automatically construct lesson plans and create test questions tailored to the user's needs.

[0817] The server then sends the generated data to the user's terminal via a delivery method. The user reviews the received data on their terminal and makes corrections if necessary. These corrections can be easily made using a standard text editor. The corrected data is then sent back to the server, and the information is updated in the database.

[0818] Furthermore, emotional state analysis is also performed. The camera and microphone connected to the device are used as analysis tools to capture the user's facial expressions and voice, and perform emotional analysis. If this analysis detects the user's psychological burden, the server provides relaxation music or videos through a response mechanism.

[0819] For example, when a teacher spends time preparing for an exam, the system checks their emotional state and, if it determines that their stress level is high, suggests simplified exam questions. Additionally, calming music can be played in the background to help the teacher concentrate.

[0820] An example of a prompt message is, "Detect the teacher's stress level, suggest a simple test question, and play relaxing music." In this way, a system is built that supports the user's efficient work performance and psychological support.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] The user logs into the system using a terminal. The ID and password entered by the user are processed by the authentication method and sent to the server. The server refers to the database and verifies the matching account information. If authentication is successful, the user is logged in, and a dashboard for selecting the type of educational activity is displayed on the terminal. The input is the login information, and the output is the dashboard display.

[0824] Step 2:

[0825] The user selects the type of educational activity from the options displayed on the terminal's dashboard. The selected information is sent to the server via the selection mechanism. The server receives this input and prepares to retrieve relevant educational materials from its database. The input is the selected educational activity information, and the output is the preparation of data based on the selected activity.

[0826] Step 3:

[0827] The server generates relevant materials based on the selected educational activity. In this process, it uses a generation AI model to process data and generate specific educational materials and test questions. Filtering and processing are performed based on references to materials in the database, resulting in optimized output. The input is the user's selection information, and the output is the generated educational materials.

[0828] Step 4:

[0829] The generated educational materials are sent from the server to the terminal via a delivery system. The user reviews these materials on the terminal and modifies them as needed. The terminal is equipped with a text editor, allowing the user to easily edit the materials. The input is the generated material, and the output is the modified educational material.

[0830] Step 5:

[0831] Once editing is complete on the terminal, the revised document is sent back to the server and saved to the database. The server updates the database based on the received information, keeping it up-to-date. The input is the revised document information, and the output is the updated database.

[0832] Step 6:

[0833] Simultaneously, the device analyzes the user's emotional state using analytical tools. Facial and voice data acquired from the camera and microphone are processed via an emotion engine. The judgment results are sent to a server and used to make decisions regarding the provision of appropriate responses. The input is real-time emotional data, and the output is the judgment result of the emotional state.

[0834] Step 7:

[0835] If the user's emotional state is determined to be high-stress, the server selects relaxing content as a means of providing a response and delivers music or videos through the device. This content is customized to reduce the user's psychological burden. The input is the result of the emotional state assessment, and the output is the provision of relaxing content.

[0836] (Application Example 2)

[0837] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0838] Traditional educational support systems have only provided educators with functions focused on improving work efficiency, and have limitations in providing individualized support based on the psychological state of educators. Furthermore, it is known that stress in the educational setting affects teachers' performance, and there is a need for means to adequately mitigate this.

[0839] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0840] In this invention, the server includes authentication means for authenticating the account information of educators, selection means for selecting the type of educational work, and analysis means for analyzing the emotional state of educators from their facial expressions and voice. This enables not only improved work efficiency for educators but also appropriate support and stress reduction tailored to their individual psychological states.

[0841] "Educational professionals" are people who engage in educational activities, such as preparing lessons and exam questions, and performing administrative tasks.

[0842] "Generative artificial intelligence" refers to an artificial intelligence system that provides customized educational support to educators through authentication and emotion analysis.

[0843] "Authentication methods" refer to a system that verifies the account information of educators and confirms that they are legitimate users.

[0844] "Selection method" refers to the function of specifying the types of educational tasks that educators perform.

[0845] "Generation means" refers to a function that creates lesson plans and exam questions from data related to selected educational tasks.

[0846] "Delivery method" refers to the function of distributing generated lesson plans and test questions to the display devices of educators.

[0847] The "analysis method" is a system that analyzes the facial expressions and voice of educators to determine their emotional state.

[0848] The "adjustment mechanism" is a function that selects and provides relaxation content based on the analyzed emotional state.

[0849] "Configuration method" refers to a function that customizes the content of work support to meet the needs of educators.

[0850] An "information processing device" is a device that receives input information from educators and updates data based on that information.

[0851] The system of this invention is configured to integrate various functions in order to support educators. The main components include authentication means, selection means, generation means, provision means, analysis means, adjustment means, and setting means.

[0852] The authentication method verifies the legitimacy of system access by checking the account information of the educator when the user logs into the system. This establishes a foundation for providing appropriate services.

[0853] The selection method allows users to choose the educational tasks they want to perform (e.g., lesson planning or exam question creation). This makes it easier for the system to acquire data that meets the user's needs. The generation method generates relevant data based on the selected tasks and creates lesson plans or exam questions.

[0854] The delivery method involves transmitting the generated content to the user's display device, allowing educators to review its contents. Educators can freely edit the provided content. The edited content is received by the information processing device and reflected on the server.

[0855] The analysis method identifies the user's emotional state using facial expressions and voice data. Machine learning techniques using TensorFlow are employed to diagnose the user's stress level and psychological burden in real time.

[0856] The adjustment mechanism selects and provides users with content that promotes relaxation according to their emotional state (e.g., calming music or relaxing videos). This helps to reduce the psychological burden on educators.

[0857] The configuration method allows users to customize various support functions to suit the preferences of educators, thereby achieving more effective support.

[0858] For example, if emotion analysis detects that a teacher is experiencing stress during class, the system can provide the teacher with a short relaxing video and calming background music. Another example of a prompt message is, "Please suggest what kind of support would be effective when a teacher is experiencing high stress levels in an educational setting."

[0859] By integrating these functions, it becomes possible to streamline the work of educators and provide a comfortable learning environment.

[0860] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0861] Step 1:

[0862] The user logs into the system using a terminal. The terminal sends the educator's account information as input to the server. The server compares this information with the database, authenticates the user based on the input, and outputs the result to the terminal.

[0863] Step 2:

[0864] The user selects the task they want to perform (e.g., lesson planning, exam question creation) from a dashboard displayed on their device. The selected task type is sent from the device to the server. The server receives this information and selects data to prepare for the next step.

[0865] Step 3:

[0866] The device's camera and microphone collect user facial expressions and voice data as input. The device analyzes this data to identify the user's emotional state. TensorFlow-based facial expression recognition and voice emotion analysis models are used for the analysis, and the analysis results are sent to the server.

[0867] Step 4:

[0868] The server considers both the selected task type and the sentiment analysis results, and uses a generative AI model to generate relevant data. It retrieves lesson plans and exam question materials from the database, and simplifies them or recommends relaxing content as needed. The generated content is then provided to the terminal.

[0869] Step 5:

[0870] Users review the content provided on their devices and edit it as needed. The edits are sent from the device to the server, where the information is updated. This process generates and provides optimized educational materials and test questions specifically for educators.

[0871] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0872] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0874] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0875] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0876] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0877] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0878] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0879] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0880] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0881] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0882] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0883] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0884] 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.

[0885] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0886] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0887] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0888] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0889] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0890] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0891] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0892] The following is further disclosed regarding the embodiments described above.

[0893] (Claim 1)

[0894] A generative artificial intelligence that supports the work of educators, comprising an authentication means for authenticating the account information of educators,

[0895] The means of selecting the type of educational work,

[0896] A generation means that acquires relevant data based on selected educational tasks and generates lesson plans or exam questions,

[0897] A means of providing the generated lesson plan or exam questions to the terminal of an educator,

[0898] A configuration method for performing custom settings necessary for business support,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which allows educators to edit the generated test questions.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for a server to receive edited content transmitted from an educator's terminal and update the information.

[0904] "Example 1"

[0905] (Claim 1)

[0906] An information processing system that supports the work of educational personnel, comprising an authentication means for verifying the identification information of educational personnel,

[0907] A means of selecting the type of education-related work,

[0908] Information generation means for extracting information sets based on selected education-related tasks and generating educational plans or evaluation materials,

[0909] A transmission means for transmitting the generated educational plan or evaluation materials to a display device of an educator,

[0910] A configuration tool for making necessary settings adjustments to support business operations,

[0911] An editing method that allows editing of the work results,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein an information management device receives the editing results transmitted from the display device of an educator and updates the information.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising means for optimizing the generated information using artificial intelligence technology.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A generative artificial intelligence that supports the work of educators, comprising an authentication means for verifying the identification information of educators,

[0920] A processing method for selecting the type of educational work,

[0921] A generation means that acquires relevant information based on selected educational tasks and generates a learning plan or assessment questions,

[0922] A supply means for supplying a generated learning plan or assessment question to an information processing device of an educator,

[0923] A means of making adjustments necessary for business support,

[0924] A control means that provides instruction to an autonomous educational machine based on a generated learning plan,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which allows educators to modify the generated assessment problems.

[0928] (Claim 3)

[0929] The system according to claim 1, comprising means for an information processing device to receive corrections transmitted from an information processing device of an educator and update the information.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] An information processing device that supports the work of educators,

[0933] Authentication methods for verifying the personal information of educators,

[0934] The means of selecting the type of educational activity,

[0935] A generation means that acquires relevant information based on selected educational activities and generates educational materials,

[0936] A means for providing generated educational materials to a display device for educators,

[0937] An analytical method that analyzes facial expressions and voice to recognize the emotional state of educators,

[0938] A means of providing responses to alleviate psychological burden based on emotional state,

[0939] A setting method for performing adjustments and settings necessary for business support,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, which allows educators to modify the generated educational materials.

[0943] (Claim 3)

[0944] The system according to claim 1, further comprising means for an information processing device to receive correction information transmitted from a display device of an educator and update the data.

[0945] "Application example 2 when combining with an emotional engine"

[0946] (Claim 1)

[0947] A generative artificial intelligence that supports the work of educators, comprising an authentication means for authenticating the account information of educators,

[0948] The means of selecting the type of educational work,

[0949] A generation means that acquires relevant data based on selected educational tasks and generates lesson plans or exam questions,

[0950] A means for providing the generated lesson plan or exam questions to a display device for educators,

[0951] An analytical means for analyzing the emotional state of educators from their facial expressions and voice,

[0952] A means of providing relaxation content based on the analyzed emotional state,

[0953] A configuration method for performing custom settings necessary for business support,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, which allows educators to edit the generated test questions.

[0957] (Claim 3)

[0958] The system according to claim 1, further comprising means for an information processing device to receive edited content transmitted from a display device of an educator and update the information. [Explanation of Symbols]

[0959] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A generative artificial intelligence that supports the work of educators, comprising an authentication means for verifying the identification information of educators, A processing method for selecting the type of educational work, A generation means that acquires relevant information based on selected educational tasks and generates a learning plan or assessment questions, A supply means for supplying a generated learning plan or assessment question to an information processing device of an educator, A means of making adjustments necessary for business support, A control means that provides instruction to an autonomous educational machine based on a generated learning plan, A system that includes this.

2. The system according to claim 1, which allows educators to modify the generated assessment problems.

3. The system according to claim 1, further comprising means for an information processing device to receive corrections transmitted from an information processing device of an educator and update the information.

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