system

Abstract

We provide the system. [Solution] A means for the teacher to input the target grade level, subject, and question format, A means by which a generative artificial intelligence generates test questions or learning materials based on input information, A means of sending and displaying the generated content on the teacher's terminal, 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, including 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] In recent years, the workload of teachers in the educational field has been increasing. In particular, tasks such as creating teaching materials and tests, grading compositions and book reports, and planning guidance methods for club activities are weighing down on teachers. Therefore, it is urgent to create an environment in which teachers can more effectively concentrate on educational activities. In addition, support for improving students' ability to learn on their own and act autonomously is also required. Therefore, there is a demand for the provision of an efficient and effective system to solve these problems.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create club activity practice plans. The system includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; and means for transmitting and displaying the generated content on the teacher's terminal. It also includes means for teachers to upload students' essays or book reports; means for the generative artificial intelligence to correct the grammar and expression of the uploaded documents; and means for transmitting and displaying the corrected documents on the teacher's terminal. Furthermore, it includes means for students to input the type of club activity and practice content; means for the generative artificial intelligence to generate practice plans based on the input information; and means for transmitting and displaying the generated practice plans on the student's terminal. This reduces the workload of teachers and enables efficient support for fostering student autonomy.

[0006] A "teacher terminal" is a computer or electronic device operated by a teacher to input educational information or review generated materials.

[0007] "Generative artificial intelligence" refers to an algorithm or system that generates test questions, learning materials, essay corrections, practice plans, etc., based on input conditions.

[0008] "Learning materials" refer to documents or teaching materials, whether in paper or electronic format, created to convey specific educational content.

[0009] "Grammar and expression correction" refers to the process of correcting typographical errors, grammatical mistakes, and inappropriate expressions in a text, in order to clarify its meaning.

[0010] A "practice plan" is a schedule and details of practice sessions planned within a club activity to improve specific skills or abilities.

[0011] "Target grade level" refers to a specific group of students in a particular grade level to whom educational materials or tests are directed.

[0012] "Question format" refers to the specific format of questions in a test or examination (e.g., multiple choice, written response, etc.).

[0013] "Uploading" refers to the act of transferring electronic files or information from a local device to a server or another device.

[0014] "Display" refers to the visual presentation of information or data on the screen of a computer or other device.

[0015] "Input information" refers to the information and data that the user provides to the system, and the generative artificial intelligence processes this information. [Brief explanation of the drawing]

[0016] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for club activities. A specific example of this system, based on the following program processing, is shown.

[0038] Tests and document creation

[0039] overview

[0040] This system allows teachers to input the target grade level, subject, and question format, and then a generative artificial intelligence system generates appropriate test questions and learning materials based on that input, which are then sent to and displayed on the teacher's terminal.

[0041] Processing details

[0042] 1. User (Teacher)

[0043] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0044] 2. Terminal

[0045] Generate a request to send the teacher's input information to the server.

[0046] 3. Server

[0047] The server receives the input information and sends a request to the generative artificial intelligence to generate test questions.

[0048] 4. Generative Artificial Intelligence

[0049] Based on the specified conditions, generate test questions of appropriate difficulty and send them to the server.

[0050] 5. Server

[0051] The generated content is sent to the teacher's terminal and displayed.

[0052] 6. User (Teacher)

[0053] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0054] Specific example:

[0055] When a teacher creates a multiplication test for third graders, they input "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), which generates the test questions and sends them back. The teacher then reviews and corrects the generated test before distributing it to the students.

[0056] Creating materials for parents and proofreading essays and book reports.

[0057] overview

[0058] This system allows teachers to upload students' essays and book reports, which are then processed by a generative artificial intelligence system that corrects their grammar and expression, and then sent and displayed on the teacher's terminal.

[0059] Processing details

[0060] 1. User (Teacher)

[0061] Teachers upload student essays and reflection papers from the administration screen.

[0062] 2. Terminal

[0063] Generate a request to send the uploaded file to the server.

[0064] 3. Server

[0065] The server receives the file and sends a request for document modification to the generative artificial intelligence.

[0066] 4. Generative Artificial Intelligence

[0067] The system analyzes uploaded documents, corrects grammar and expression, and sends them to the server.

[0068] 5. Server

[0069] Send the revised document to the teacher's terminal and display it.

[0070] 6. User (Teacher)

[0071] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0072] Specific example:

[0073] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), which corrects grammar and expression. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[0074] Generating practice methods and coaching methods for club activities

[0075] overview

[0076] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence then generates a practice plan based on that input, which is then sent to and displayed on the student's device.

[0077] Processing details

[0078] 1. User (Student)

[0079] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0080] 2. Terminal

[0081] Generate a request to send the student's input information to the server.

[0082] 3. Server

[0083] The server receives the information and sends a request to the generative artificial intelligence to generate a practice plan.

[0084] 4. Generative Artificial Intelligence

[0085] Based on the specified conditions, a practice plan is generated and sent to the server.

[0086] 5. Server

[0087] The generated plan is sent to the student's device for display.

[0088] 6. User (Student)

[0089] Students review the generated practice plan and put it into practice.

[0090] Specific example:

[0091] A student in the soccer club enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence (AI), which generates a specific practice plan and sends it back. The student then reviews the generated practice plan and uses it to improve their training.

[0092] ---

[0093] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[0094] The following describes the processing flow.

[0095] Tests and document creation

[0096] Processing steps

[0097] Step 1:

[0098] User (Teacher)

[0099] The teacher logs into the administration panel and selects the "Create Test" option.

[0100] Step 2:

[0101] User (Teacher)

[0102] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0103] Step 3:

[0104] terminal

[0105] The device generates a request to send the teacher's input information to the server.

[0106] Step 4:

[0107] server

[0108] The server receives the teacher's input information and sends a request to the generative artificial intelligence to generate test questions.

[0109] Step 5:

[0110] Generative artificial intelligence

[0111] A generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and sends the results back to the server.

[0112] Step 6:

[0113] server

[0114] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[0115] Step 7:

[0116] terminal

[0117] The device displays the received test questions, allowing the teacher to review and correct them.

[0118] Step 8:

[0119] User (Teacher)

[0120] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0121] Creating materials for parents and proofreading essays and book reports.

[0122] Processing steps

[0123] Step 1:

[0124] User (Teacher)

[0125] Teachers upload student essays and reflection papers from the administration screen.

[0126] Step 2:

[0127] terminal

[0128] The device generates a request to send the uploaded file to the server.

[0129] Step 3:

[0130] server

[0131] The server receives the uploaded file and sends a request for document modification to the generative artificial intelligence.

[0132] Step 4:

[0133] Generative artificial intelligence

[0134] The generative artificial intelligence analyzes the uploaded document, corrects its grammar and expression, and returns the results to the server.

[0135] Step 5:

[0136] server

[0137] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[0138] Step 6:

[0139] terminal

[0140] The device displays the received corrections, allowing the teacher to review and make final corrections.

[0141] Step 7:

[0142] User (Teacher)

[0143] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0144] Generating practice methods and coaching methods for club activities

[0145] Processing steps

[0146] Step 1:

[0147] User (student)

[0148] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0149] Step 2:

[0150] terminal

[0151] The terminal generates a request to send the student's input information to the server.

[0152] Step 3:

[0153] server

[0154] The server receives the student's input information and sends a request to the generative artificial intelligence to generate a practice plan.

[0155] Step 4:

[0156] Generative artificial intelligence

[0157] The generative artificial intelligence generates a practice plan based on specified conditions and sends the result back to the server.

[0158] Step 5:

[0159] server

[0160] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[0161] Step 6:

[0162] terminal

[0163] The device displays the received practice plan, allowing students to review and practice it.

[0164] Step 7:

[0165] User (student)

[0166] Students review the generated practice plans and use them to improve their actual practice.

[0167] (Example 1)

[0168] 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."

[0169] Traditional methods for creating teaching materials, correcting essays, and developing practice plans for extracurricular activities required teachers and students to do it manually, which was time-consuming and laborious. Furthermore, qualitative evaluation and planning were challenging. In particular, the increased workload on teachers and the resulting limitations on students' opportunities for independent learning made it difficult to achieve efficient educational activities.

[0170] 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.

[0171] In this invention, the server includes means for receiving information input from a user terminal and requesting processing from a generative artificial intelligence; means for returning the generation results received from the generative artificial intelligence to the user terminal; and means for specifying conditions for the generation process to the generative artificial intelligence. This enables the user to efficiently create teaching materials, correct essays, and generate practice plans.

[0172] A "user terminal" is a device operated by a user and is used for interacting with the system.

[0173] A "server" is a computing system that receives requests from user terminals, requests processing from generative artificial intelligence, and sends the results back to the user terminal.

[0174] "Generative artificial intelligence" refers to artificial intelligence models that generate test questions, document revisions, practice plans, and other similar materials based on information input by the user.

[0175] "Test questions or learning materials" refer to educational content created by generative artificial intelligence based on conditions specified by the user (teacher).

[0176] "Document correction" refers to the process by which generative artificial intelligence corrects the grammar and expressions within a document.

[0177] A "practice plan" is a set of specific practice method guidelines created by a generative artificial intelligence system based on the type of club activity, practice content, and goals entered by the student.

[0178] "Input method" refers to the method or interface by which a user provides information to a system.

[0179] "Transmission means" refers to the function that allows a user terminal or server to send information to other devices.

[0180] "Display means" refers to the function that provides the user with information received by the user terminal in a visual manner.

[0181] "Uploading" refers to the process of a user transferring files or data from their local device to a server.

[0182] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for their club activities. This system is realized through the cooperation of user terminals, a server, and generative artificial intelligence.

[0183] First, the teacher logs in using their user terminal and selects the "Create Test" option from the administration screen. Next, they enter the target grade level, subject, and question format, and the program sends this information to the server. The user terminals used are typical personal computers and tablet devices. An example of input would be "3rd Grade," "Mathematics," and "Multiple Choice."

[0184] The server analyzes the received information and sends the data to a generative artificial intelligence (e.g., OpenAI® GPT-3®). The generative AI generates test questions based on the provided prompt, "Generate multiple-choice math test questions for third-year students." The generated results are returned to the server in JSON format.

[0185] The server sends the generated test questions to the user's terminal and displays them for the teacher to review. The teacher reviews the generated test questions and makes corrections as needed. They then print the final test questions for distribution to students or share them online.

[0186] Furthermore, teachers upload document files from the management screen to efficiently correct students' essays and book reports. The program sends the uploaded files to the server, which then sends a document correction request to the generative artificial intelligence. The generative AI corrects the grammar and expressions within the document and returns the corrected document to the server. The server sends the results to the user's terminal and displays them for the teacher to review. The teacher reviews the corrections and provides final feedback to the student or their parents.

[0187] Furthermore, students access the administration screen to create practice plans for their club activities, entering the type of club activity, practice content, and goals. The program generates and sends a request to the server. The server sends a practice plan generation request to a generative artificial intelligence. The generative artificial intelligence generates a practice plan based on the specified conditions and sends the result back to the server. The server sends the generated practice plan to the user's terminal and displays it so that the student can actually check it. For example, if "soccer," "dribbling practice," and "physical fitness improvement" are entered, a specific practice plan will be generated.

[0188] These concrete examples enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[0189] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0190] Tests and document creation

[0191] Step 1:

[0192] The user (teacher) logs into the administration panel and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0193] Input: Login information (username, password), grade level, subject, question format

[0194] Output: Input information is displayed and confirmed on the management screen.

[0195] Step 2:

[0196] The terminal generates a request to send the entered information to the server.

[0197] Input: Information entered by the user (teacher)

[0198] Output: The generated HTTP request is sent to the server.

[0199] Step 3:

[0200] The server receives an HTTP request and parses the information contained in the request body.

[0201] Input: HTTP request sent from the terminal

[0202] Output: The analyzed information is sent to the generative artificial intelligence.

[0203] Step 4:

[0204] Generative artificial intelligence generates test questions based on specified conditions.

[0205] Input: Test creation conditions sent from the server

[0206] Output: Generated test questions (JSON format)

[0207] Step 5:

[0208] The server adjusts the data format as needed based on the generation results received from the generative artificial intelligence and sends them to the user terminal as an HTTP response.

[0209] Input: Generated test questions (JSON format)

[0210] Output: Adjusted test question data

[0211] Step 6:

[0212] The user (teacher) reviews the generated test questions and makes corrections as needed.

[0213] Input: Test question data sent from the server

[0214] Output: Corrected test questions

[0215] Correction of essays and book reports

[0216] Step 1:

[0217] Users (teachers) upload student essays and reflection papers from the administration screen.

[0218] Input: Essay or reflection paper file

[0219] Output: Uploaded files are displayed on the admin screen.

[0220] Step 2:

[0221] The terminal generates a request to send the uploaded file to the server.

[0222] Input: Uploaded document file

[0223] Output: The generated HTTP request is sent to the server.

[0224] Step 3:

[0225] The server receives the file, analyzes its contents and metadata, and sends a document modification request to the generative artificial intelligence.

[0226] Input: Document file sent from the terminal

[0227] Output: The analyzed document data is sent to the generative artificial intelligence.

[0228] Step 4:

[0229] Generative artificial intelligence analyzes uploaded documents and corrects their grammar and expression.

[0230] Input: Document data sent from the server

[0231] Output: Modified document (JSON format)

[0232] Step 5:

[0233] The server receives the modified document and generates a response to display it on the user's terminal.

[0234] Input: Modified document (JSON format)

[0235] Output: Generated response data

[0236] Step 6:

[0237] The user (teacher) reviews the revised document and makes further revisions as needed.

[0238] Input: Modified document sent from the server

[0239] Output: Final revised document

[0240] Creating practice plans for club activities

[0241] Step 1:

[0242] The user (student) accesses the administration screen and selects the "Create Practice Plan" option. They then enter the type of club activity (e.g., soccer), the content of the practice (e.g., dribbling practice), and the goal (e.g., improving physical fitness).

[0243] Input: Type of club activity, practice content, goals

[0244] Output: Input information is displayed and confirmed on the management screen.

[0245] Step 2:

[0246] The terminal generates a request to send the entered information to the server.

[0247] Input: Information entered by the user (student)

[0248] Output: The generated HTTP request is sent to the server.

[0249] Step 3:

[0250] The server receives an HTTP request and parses the information contained in the request body.

[0251] Input: HTTP request sent from the terminal

[0252] Output: The analyzed information is sent to the generative artificial intelligence.

[0253] Step 4:

[0254] Generative artificial intelligence generates a practice plan based on specified conditions.

[0255] Input: Practice plan creation conditions sent from the server

[0256] Output: Generated practice plan (JSON format)

[0257] Step 5:

[0258] The server generates a response to send the generation results received from the generative artificial intelligence to the student terminals.

[0259] Input: Generated practice plan (JSON format)

[0260] Output: Generated response data

[0261] Step 6:

[0262] The user (student) reviews the generated practice plan and puts it into practice.

[0263] Input: Practice plan data sent from the server

[0264] Output: Implemented practice plan

[0265] (Application Example 1)

[0266] 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."

[0267] In today's educational environment, teachers spend a great deal of time and effort on lesson preparation and student evaluation, particularly creating teaching materials, correcting essays, and planning extracurricular activity practices. Furthermore, when providing educational services in physical stores, there is a lack of means to quickly and effectively prepare and distribute teaching materials. This increases the workload on teachers and reduces opportunities for students' independent learning and growth.

[0268] 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.

[0269] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for transmitting and displaying the generated content on a teacher's terminal; and means for creating and distributing teaching materials at physical stores. This makes it possible for teachers to quickly create teaching materials and to effectively provide educational services even at physical stores.

[0270] The system also includes means for teachers to upload students' essays or book reports, means for a generative artificial intelligence to correct the grammar and expression of the uploaded documents, means for sending and displaying the corrected documents on the teacher's terminal, and means for providing real-time corrections to essays and book reports. This streamlines the process of correcting essays and book reports and reduces the burden on teachers.

[0271] Furthermore, the system includes means for students to input the type of club activity and practice content, means for a generative artificial intelligence to generate a practice plan based on the input information, means for transmitting and displaying the generated practice plan on the student's terminal, and means for providing practice plans related to club activities and hobbies. This enables students to spontaneously plan and implement practice plans for their club activities.

[0272] A "teacher terminal" is an electronic device operated by a teacher, used for tasks such as generating test questions and learning materials, correcting essays, and reviewing practice plans.

[0273] "Generative artificial intelligence" refers to a system that includes algorithms and machine learning models for automatically generating test questions, learning materials, practice plans, and document revisions based on input information.

[0274] A "physical store" is a physical location that serves as a place for educational services or learning.

[0275] "Educational materials" refer to materials and tools used for educational purposes, including test questions, worksheets, and drills.

[0276] "Means of creation" refers to methods or devices used to generate information for a specific purpose.

[0277] "Transmission means" refers to functions or devices for transmitting generated information or data to other devices or systems.

[0278] "Display means" refers to devices or software that allow users to visually confirm transmitted information.

[0279] "Essays and book reviews" are written pieces in which students express their own thoughts and feelings, and are evaluated as part of their education.

[0280] "Grammar and expression correction" refers to the process of correcting grammatical errors and improving expressions within a text.

[0281] The "Practice Plan for Club Activities" refers to what shows the practice content and plan that students carry out in club activities.

[0282] This invention is a system that enables teachers to efficiently create teaching materials and tests, grade students' compositions and reading reflections, and allows students to independently formulate practice plans for club activities. This system utilizes generative artificial intelligence.

[0283] Hardware and Software to be Used

[0284] The hardware to be used includes teacher terminals, student terminals, and display devices within physical stores. Specifically, smartphones, tablets, personal computers, smart glasses, etc. are applicable. The software includes a Python library implementing the algorithm of generative artificial intelligence and an API server for data transmission and reception. Specifically, Python, the requests library, the API of the generative AI model, etc. are used.

[0285] Generation of Teaching Materials and Tests

[0286] The server transmits this information to the generative artificial intelligence by the teacher inputting the target school year, subject, and question type, and generates appropriate test questions and learning materials. The generated content is transmitted to and displayed on the teacher terminal.

[0287] Specific Example:

[0288] When a teacher creates a multiplication test for third - grade elementary school students, the teacher inputs the information of "third grade", "mathematics", and "multiple - choice" from the teacher terminal. The server receives this and transmits it to the generative artificial intelligence. The AI generates test questions based on this and transmits and displays them on the teacher terminal via the server.

[0289] Example of Prompt Sentence:

[0290] grade = 3

[0291] subject = 'mathematics'

[0292] question_type = 'multiple_choice'

[0293] Correction of essays and book reviews

[0294] Teachers upload students' essays and book reports, and a generative artificial intelligence system corrects their grammar and expression. The corrected documents are sent to the teacher's terminal and displayed.

[0295] Specific example:

[0296] When a teacher uploads a student's book report and requests correction from AI, the teacher uploads the report file from their terminal. The server receives it and sends it to the generative artificial intelligence. The AI ​​corrects grammar and expression, and then sends it back to the teacher's terminal via the server for display.

[0297] Example of a prompt:

[0298] essay_text = 'My name is Tanaka.'

[0299] Generating practice plans for club activities

[0300] Students input the type of club activity and practice content, and a generative artificial intelligence generates a personalized practice plan. The generated practice plan is sent to the student's device and displayed.

[0301] Specific example:

[0302] A student in the soccer club inputs "soccer," "dribbling practice," and "physical fitness improvement" to brainstorm new dribbling drills. The server receives this input and sends it to a generative artificial intelligence (AI). The AI ​​generates a specific practice plan, which is then sent to the student's terminal via the server for display.

[0303] Example of prompt text:

[0304] activity = 'soccer'

[0305] goal = 'improve stamina'

[0306] practice_type = 'dribbling'

[0307] With these functions, teachers can quickly prepare teaching materials, streamline the correction of compositions, and students can independently formulate practice plans for club activities. As a result, educational activities can be carried out efficiently and effectively, reducing the workload of teachers and promoting the independent learning and growth of students.

[0308] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The user (teacher) logs in to the dedicated management screen and selects the "Test Creation" option. Enter the target school year (e.g., third grade), subject (e.g., mathematics), and question type (e.g., multiple-choice). The input information here forms the basis of the data used in subsequent processing steps. The output is an input confirmation screen, and the teacher checks the entered content.

[0311] Step 2:

[0312] The terminal collects the teacher's input information and generates a request to send to the server. Specifically, it converts the input data into JSON format and prepares for making an HTTP request to the appropriate endpoint. As output, appropriately configured request data is generated.

[0313] Step 3:

[0314] The server receives request data sent from the terminal. The server analyzes this data and sends a request to the generative artificial intelligence (AI) to generate test questions. This process involves creating a prompt statement to generate the most suitable test questions based on the input conditions and sending a request to the AI's API. The output is the response data from the AI, which includes the generated test questions.

[0315] Step 4:

[0316] The generative artificial intelligence generates test questions of a specified difficulty level and format based on the received prompt. A pre-trained model is used in this process. The output is the generated test questions, which are then sent back to the server.

[0317] Step 5:

[0318] The server prepares to send the test question data received from the generative artificial intelligence to the terminal. Specifically, it reconstructs the test question data into a format that is easy for the teacher to understand and sends it back to the terminal as an HTTP response. The output is the reconstructed test question data.

[0319] Step 6:

[0320] The terminal displays test question data received from the server to the teacher. The teacher reviews this data, makes corrections as needed, and generates the final test questions. User interaction is emphasized during this correction process. Finally, the corrected test questions are generated and ready to be distributed to students.

[0321] Step 7:

[0322] Users (teachers) upload student essays and reflection papers from the administration screen. These files are analyzed by the system and used in the next step. The output is the uploaded file.

[0323] Step 8:

[0324] The terminal generates a request to send the uploaded essay or review file to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[0325] Step 9:

[0326] The server sends the received essay and reflection files to a generative artificial intelligence system and requests corrections to their grammar and expression. This process involves data analysis and the generation of appropriate prompts. The output is the analyzed data that was sent.

[0327] Step 10:

[0328] The generative artificial intelligence analyzes uploaded essay and review files, correcting grammar and expression. The corrected document data is generated and sent back to the server. The output is the corrected document.

[0329] Step 11:

[0330] The server reconstructs the modified document received from the generative artificial intelligence and prepares it for transmission to the teacher terminal. The output is the reconstructed modified document.

[0331] Step 12:

[0332] The terminal displays the revised document data received from the server to the teacher. The teacher reviews it, makes any necessary final revisions, and returns it to the student. The output is the final revised document.

[0333] Step 13:

[0334] Users (students) access the administration screen and input the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The output is the club activity information entered.

[0335] Step 14:

[0336] The terminal generates a request to send student input information to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[0337] Step 15:

[0338] The server analyzes the received club activity information and sends a request to a generative artificial intelligence system to generate a practice plan. The data is then analyzed, and appropriate prompts are generated. The output is the analyzed data that was sent.

[0339] Step 16:

[0340] The generative artificial intelligence generates a practice plan based on specified conditions and sends it to the server. The generated plan is returned as a data format. The output is the generated practice plan.

[0341] Step 17:

[0342] The server prepares to send the practice plan data received from the generative artificial intelligence to the student's terminal. The reconstructed data is sent to the terminal. The output is the reconstructed practice plan data.

[0343] Step 18:

[0344] The terminal displays practice plan data received from the server to the student. The student reviews and then practices it. The output is the displayed practice plan.

[0345] 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.

[0346] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create club activity practice plans. Furthermore, by incorporating an emotion engine, it adds a function that adjusts the content of tools and materials based on the emotions of the users (teachers and students). This makes it possible to maximize the effectiveness of educational activities. As a specific example of this system, an embodiment based on the following program processing is shown.

[0347] Tests and document creation

[0348] overview

[0349] This system allows teachers to input the target grade level, subject, and question format, and a generative artificial intelligence then generates appropriate test questions and learning materials based on that input. An emotion engine recognizes the user's emotions, adjusts the content accordingly, and then transmits and displays the materials on the teacher's terminal.

[0350] Processing details

[0351] 1. User (Teacher)

[0352] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0353] 2. Terminal

[0354] Generate a request to send the teacher's input information to the server.

[0355] 3. Server

[0356] The server receives the input information and sends a request to the emotion engine to recognize the user's emotions.

[0357] 4. Emotional Engine

[0358] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[0359] 5. Server

[0360] The server sends a request to the generative artificial intelligence to generate test questions, along with the results from the emotion engine.

[0361] 6. Generative Artificial Intelligence

[0362] Based on the specified conditions and the results of the emotion engine, test questions of appropriate difficulty are generated and sent back to the server.

[0363] 7. Server

[0364] The generated content is sent to the teacher's terminal and displayed.

[0365] 8. User (Teacher)

[0366] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0367] Specific example:

[0368] When a teacher creates a multiplication test for third graders, they enter "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), and before the AI ​​generates and returns the test questions, an emotion engine analyzes the teacher's emotions. If the teacher is nervous, the difficulty level of the test questions is adjusted to be lower. The teacher then reviews and corrects the generated test before distributing it to the students.

[0369] Creating materials for parents and proofreading essays and book reports.

[0370] overview

[0371] This system allows teachers to upload students' essays and book reports, whereupon a generative artificial intelligence corrects the grammar and expression, an emotion engine recognizes the user's emotions and adjusts the corrections accordingly, and then sends and displays the corrected work on the teacher's terminal.

[0372] Processing details

[0373] 1. User (Teacher)

[0374] Teachers upload student essays and reflection papers from the administration screen.

[0375] 2. Terminal

[0376] Generate a request to send the uploaded file to the server.

[0377] 3. Server

[0378] The server receives the file and sends a request for document revision instructions to the sentiment engine.

[0379] 4. Emotional Engine

[0380] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence.

[0381] 5. Generative Artificial Intelligence

[0382] The uploaded document is analyzed, grammatical and phrasing corrections are made based on the sentiment engine's results, and the results are sent back to the server.

[0383] 6. Server

[0384] Send the revised document to the teacher's terminal and display it.

[0385] 7. User (Teacher)

[0386] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0387] Specific example:

[0388] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), and before the AI ​​corrects grammar and expression, an emotion engine analyzes the teacher's emotions. If the teacher is tired, the correction suggestions are softer; if they are stressed, the approach is more cautious. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[0389] Generating practice methods and coaching methods for club activities

[0390] overview

[0391] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence generates a practice plan based on that input. An emotion engine then recognizes the user's emotions, adjusts the plan accordingly, and sends and displays it on the student's device.

[0392] Processing details

[0393] 1. User (Student)

[0394] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0395] 2. Terminal

[0396] Generate a request to send the student's input information to the server.

[0397] 3. Server

[0398] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[0399] 4. Emotional Engine

[0400] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results to the generative artificial intelligence system.

[0401] 5. Generative Artificial Intelligence

[0402] Based on the specified conditions and the results from the emotion engine, a practice plan is generated and the results are sent back to the server.

[0403] 6. Server

[0404] The generated plan is sent to the student's device for display.

[0405] 7. User (Student)

[0406] Students review the generated practice plan and use it to improve their actual practice.

[0407] Specific example:

[0408] A soccer club student enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence, and before the AI ​​generates a practice plan, an emotion engine analyzes the student's emotions. If the student is tired, it suggests a plan that includes rest; if highly motivated, it presents a challenging plan. The student reviews the generated practice plan and uses it to improve their training.

[0409] ---

[0410] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth. Furthermore, the introduction of an emotion engine allows for flexible responses based on the user's emotions, improving the quality of the educational environment.

[0411] The following describes the processing flow.

[0412] Tests and document creation

[0413] Processing steps

[0414] Step 1:

[0415] User (Teacher)

[0416] The teacher logs into a dedicated administration panel and selects the "Create Test" option.

[0417] Step 2:

[0418] User (Teacher)

[0419] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0420] Step 3:

[0421] terminal

[0422] The device generates a request to send the teacher's input information to the server.

[0423] Step 4:

[0424] server

[0425] The server receives the input information and sends a request to the emotion engine to recognize the user's (teacher's) emotions.

[0426] Step 5:

[0427] Emotional Engine

[0428] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[0429] Step 6:

[0430] server

[0431] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate test questions, incorporating these results.

[0432] Step 7:

[0433] Generative artificial intelligence

[0434] The generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and the teacher's emotional information, and sends the results back to the server.

[0435] Step 8:

[0436] server

[0437] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[0438] Step 9:

[0439] terminal

[0440] The device displays the received test questions, allowing the teacher to review and correct them.

[0441] Step 10:

[0442] User (Teacher)

[0443] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0444] Creating materials for parents and proofreading essays and book reports.

[0445] Processing steps

[0446] Step 1:

[0447] User (Teacher)

[0448] Teachers upload student essays and reflection papers from the administration screen.

[0449] Step 2:

[0450] terminal

[0451] The device generates a request to send the uploaded file to the server.

[0452] Step 3:

[0453] server

[0454] The server receives the uploaded file and sends a request for document revision instructions to the emotion engine.

[0455] Step 4:

[0456] Emotional Engine

[0457] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[0458] Step 5:

[0459] server

[0460] The server receives the results from the emotion engine and sends a document revision request to the generative artificial intelligence, incorporating these results.

[0461] Step 6:

[0462] Generative artificial intelligence

[0463] The generative artificial intelligence corrects the grammar and expression of a given document based on the teacher's emotional information, and returns the results to the server.

[0464] Step 7:

[0465] server

[0466] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[0467] Step 8:

[0468] terminal

[0469] The device displays the received corrections, allowing the teacher to review and make final corrections.

[0470] Step 9:

[0471] User (Teacher)

[0472] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0473] Generating practice methods and coaching methods for club activities

[0474] Processing steps

[0475] Step 1:

[0476] User (student)

[0477] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0478] Step 2:

[0479] terminal

[0480] The device generates a request to send the student's input information to the server.

[0481] Step 3:

[0482] server

[0483] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[0484] Step 4:

[0485] Emotional Engine

[0486] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results back to the server.

[0487] Step 5:

[0488] server

[0489] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate a practice plan, incorporating these results.

[0490] Step 6:

[0491] Generative artificial intelligence

[0492] A generative artificial intelligence generates a practice plan based on specified conditions and student emotional information, and sends the result back to the server.

[0493] Step 7:

[0494] server

[0495] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[0496] Step 8:

[0497] terminal

[0498] The device displays the received practice plan, allowing students to review and practice it.

[0499] Step 9:

[0500] User (student)

[0501] Students review the generated practice plans and use them to improve their actual practice.

[0502] (Example 2)

[0503] 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".

[0504] In educational settings, teachers are required to quickly create teaching materials and tests, and to efficiently correct students' essays and book reports. Furthermore, support is needed when students independently create practice plans for extracurricular activities, but there is a lack of effective systems to consistently provide these services. In addition, improving the quality of education requires flexible responses based on the emotions of both teachers and students, but the current system is insufficient to address this.

[0505] 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.

[0506] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for an emotion analysis engine to analyze the teacher's emotions and provide the results to the generative artificial intelligence; means for the generative artificial intelligence to adjust the generated content based on the results of the emotion analysis engine; and means for transmitting and displaying the generated content on the teacher's terminal. This enables teachers to create teaching materials and tests efficiently and effectively, and to adjust educational content based on the user's emotions.

[0507] A "teacher" is a professional who provides education, primarily responsible for teaching subjects to students in educational institutions such as schools.

[0508] "Target grade level" refers to the grade level of students in the curriculum and is information used to indicate the learning stage of students targeted by specific educational content.

[0509] A "subject" refers to a specific academic field or subject taught in the curriculum, and examples include mathematics, Japanese language, and science.

[0510] "Question format" refers to the style of questions in test questions or learning materials, and includes formats such as multiple-choice, written response, and fill-in-the-blank.

[0511] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates problems and materials based on given input information.

[0512] An "emotion analysis engine" refers to a technology that analyzes a user's emotions based on their facial expressions, voice, etc., and provides the results to other systems.

[0513] "Users" refer to individuals who utilize this system, and primarily include educators such as teachers and students.

[0514] "Generated content" refers to content such as test questions, practice plans, and learning materials created by a generative artificial intelligence based on input information and sentiment analysis results.

[0515] A "teacher terminal" refers to a device such as a computer or tablet used by a teacher, which displays generated content and other information.

[0516] "Uploading" refers to the act of sending data stored on a local device to a server over a network.

[0517] "Document correction" refers to the process performed by generative artificial intelligence to improve the grammar and expression of uploaded documents.

[0518] A "student" is a person who receives education, and primarily refers to learners who attend educational institutions such as schools.

[0519] A "practice plan" is a detailed plan outlining the practice activities a student plans to undertake, including the type of practice, specific methods, and goals.

[0520] "Student terminals" refer to devices such as computers and tablets used by students, which display generated practice plans and other information.

[0521] This invention provides a system that enables teachers to quickly create teaching materials and tests in educational settings, efficiently correct students' essays and book reports, and allow students to independently create practice plans for extracurricular activities. By incorporating an emotion analysis engine, this system appropriately adjusts its content based on the emotions of the user, both the teacher and the student.

[0522] Tests and document creation

[0523] The user, a teacher, logs into a dedicated administration screen and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The terminal analyzes the teacher's input and generates a request to send it to the server.

[0524] The server sends a request to the emotion analysis engine along with the received input information to obtain the teacher's emotion analysis results. The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice and sends the results back to the server.

[0525] Subsequently, the server sends a request to the generative artificial intelligence to generate test questions. Specifically, information including the results of the sentiment analysis engine is sent. Based on this, the generative artificial intelligence adjusts the difficulty and content of the test questions and generates a series of questions. The generated content is sent to the teacher's terminal via the server, where the teacher reviews the generated materials and makes corrections as needed.

[0526] Specific example:

[0527] If a teacher is creating a multiplication test for third graders, they would enter "3rd grade," "math," and "multiple choice."

[0528] If the emotion analysis engine detects teacher anxiety, the generative artificial intelligence generates test questions with a lower difficulty level.

[0529] The teacher reviews this test and distributes it to the students.

[0530] Creating materials for parents and proofreading essays and book reports.

[0531] The user, a teacher, accesses the administration screen and uploads student essays and reflection papers. The terminal generates a request to send the uploaded files to the server. The server receives the files and sends a request for document revision instructions to the sentiment analysis engine. The sentiment analysis engine analyzes the teacher's emotions and provides the results to the generative artificial intelligence.

[0532] The generative artificial intelligence analyzes uploaded documents and corrects grammar and expression based on the results of the sentiment analysis engine. The corrected results are sent to the teacher's terminal via the server, where the teacher reviews the changes and makes additional corrections as needed. Feedback is then provided to students and parents.

[0533] Specific example:

[0534] A teacher uploads a file of a student's book report to have it corrected.

[0535] If the emotion analysis engine detects teacher fatigue, the generative artificial intelligence will offer correction suggestions in a gentle tone.

[0536] The teacher reviews the corrections and provides feedback to the students.

[0537] Generating practice methods and coaching methods for club activities

[0538] The student user accesses the management screen and enters the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The device generates a request to send the entered information to the server. The server, along with the received information, sends a request to the sentiment analysis engine to analyze the student's emotions.

[0539] The emotion analysis engine analyzes the student's emotions from their facial expressions and voice, and sends the results back to the server. The server sends a request to the generative artificial intelligence to generate a practice plan, including the results from the emotion analysis engine. The generative artificial intelligence generates a practice plan based on the specified conditions and the results of the emotion analysis, and sends the results back to the server. The server sends the generated practice plan to the student's terminal, where the student reviews it and uses it to help with their actual practice.

[0540] Specific example:

[0541] A student in the soccer club types "soccer," "dribbling practice," and "physical fitness improvement" into their input to come up with a new dribbling practice method.

[0542] If the emotion analysis engine detects a high level of student motivation, the generative artificial intelligence generates a challenging practice plan.

[0543] Students review the generated plan and begin practicing.

[0544] As a result, teachers and students can conduct educational activities efficiently and effectively, and the introduction of the emotion analysis engine enables flexible responses based on the user's emotions.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Process steps for creating tests and documents

[0547] Step 1:

[0548] User (Teacher)

[0549] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0550] Input: Target grade level, subject, question format

[0551] Output: Form input data

[0552] Step 2:

[0553] terminal

[0554] The terminal generates a request to send to the server based on the teacher's input information. Specifically, it constructs the input information as JSON data and sends it to the server as an HTTP request.

[0555] Input: Form input data

[0556] Output: Request to send to server

[0557] Step 3:

[0558] server

[0559] The server parses the incoming request and extracts the necessary data. Next, it sends an emotion recognition request to the emotion analysis engine. This request includes the teacher's facial image and voice data.

[0560] Input: Request to send to server

[0561] Output: Sentiment analysis request

[0562] Step 4:

[0563] Emotion analysis engine

[0564] The emotion analysis engine analyzes the teacher's emotions based on facial images and voice data, and sends the results back to the server. The analysis results include information such as tension, fatigue, and relaxation.

[0565] Input: Sentiment analysis request (facial image and audio data)

[0566] Output: Emotion analysis results (tension, fatigue, relaxation, etc.)

[0567] Step 5:

[0568] server

[0569] The server receives the emotion analysis results and, along with information on the target grade level, subject, and question format, sends a request to the generative artificial intelligence to generate test questions.

[0570] Input: Sentiment analysis results, target grade level, subject, question format

[0571] Output: Request to generative artificial intelligence

[0572] Step 6:

[0573] Generative artificial intelligence

[0574] The generative artificial intelligence generates test questions based on specified conditions and the results of sentiment analysis. The generated test questions are then sent back to the server.

[0575] Input: Request to a generative artificial intelligence (conditions and sentiment analysis results)

[0576] Output: Generated test questions

[0577] Step 7:

[0578] server

[0579] The server receives the generated test questions, formats them, and sends them to the teacher's terminal.

[0580] Input: Generated test questions

[0581] Output: Request to send to the teacher's terminal

[0582] Step 8:

[0583] User (Teacher)

[0584] The teacher reviews the generated test questions displayed on their teacher's terminal and makes corrections as needed. Finally, they distribute the test to the students.

[0585] Input: Generated test questions

[0586] Output: Confirmed and corrected test questions

[0587] Steps for creating materials for parents and proofreading essays and book reports

[0588] Step 1:

[0589] User (Teacher)

[0590] Teachers access the administration panel and upload student essays and reflection papers.

[0591] Input: Essay or reflection paper file

[0592] Output: Upload request from teacher's terminal

[0593] Step 2:

[0594] terminal

[0595] The terminal generates a request to send to the server based on the uploaded file information. Specifically, it creates an HTTP POST request containing binary data.

[0596] Input: Upload request from teacher's terminal

[0597] Output: Request to send to server

[0598] Step 3:

[0599] server

[0600] The server processes the received file data and sends a request for document revision instructions to the sentiment analysis engine. This request also includes the teacher's facial expressions and voice data.

[0601] Input: Request to send to the server (file data)

[0602] Output: Sentiment analysis request

[0603] Step 4:

[0604] Emotion analysis engine

[0605] The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence system.

[0606] Input: Emotion analysis request (facial expression and voice data)

[0607] Output: Emotion analysis results

[0608] Step 5:

[0609] Generative artificial intelligence

[0610] The generative artificial intelligence analyzes the uploaded document and corrects its grammar and expression based on the sentiment analysis results. The corrected results are then sent back to the server.

[0611] Input: Uploaded document, sentiment analysis results

[0612] Output: Revised document

[0613] Step 6:

[0614] server

[0615] The server receives the corrected document data and sends it to the teacher's terminal.

[0616] Input: Modified document

[0617] Output: Request to send to the teacher's terminal

[0618] Step 7:

[0619] User (Teacher)

[0620] Teachers review the revised documents displayed on their teacher terminals, make any final revisions as needed, and provide feedback to students and parents.

[0621] Input: Modified document

[0622] Output: Reviewed and corrected documents

[0623] Processing steps for generating club activity practice methods and coaching methods

[0624] Step 1:

[0625] User (student)

[0626] Students access the management screen and enter the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0627] Input: Type of club activity, practice content, goals

[0628] Output: Form input data

[0629] Step 2:

[0630] terminal

[0631] The terminal generates a request to send the input information to the server. Specifically, it converts the form input data into JSON format and creates an HTTP POST request.

[0632] Input: Form input data

[0633] Output: Request to send to server

[0634] Step 3:

[0635] server

[0636] The server analyzes the received information and sends a request to the emotion analysis engine to generate a practice plan. This request may include facial expression images and audio data.

[0637] Input: Request to send to server

[0638] Output: Sentiment analysis request

[0639] Step 4:

[0640] Emotion analysis engine

[0641] The emotion analysis engine analyzes students' emotions and sends the results to the generative artificial intelligence system.

[0642] Input: Emotion analysis request (facial expression and voice data)

[0643] Output: Emotion analysis results

[0644] Step 5:

[0645] Generative artificial intelligence

[0646] The generative artificial intelligence generates a practice plan based on specified conditions and emotion analysis results, and sends the results back to the server.

[0647] Input: Practice plan generation request, sentiment analysis results

[0648] Output: Generated practice plan

[0649] Step 6:

[0650] server

[0651] The server receives the generated plan and sends it to the student's device.

[0652] Input: Generated practice plan

[0653] Output: Request to send to student terminals

[0654] Step 7:

[0655] User (student)

[0656] Students review the generated practice plan and use it to improve their actual practice.

[0657] Input: Generated practice plan

[0658] Output: Practice plan to be reviewed and used

[0659] Through the steps described above, the system of the present invention provides appropriate teaching materials, document revisions, and practice plans based on user input information and sentiment analysis results, thereby improving the efficiency and effectiveness of educational activities.

[0660] (Application Example 2)

[0661] 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 device 14 will be referred to as the "terminal."

[0662] This invention aims to improve the quality of customer service and product descriptions in virtual stores. Conventional customer service systems have had difficulty responding flexibly to customer emotions and interests, resulting in a lack of satisfaction. In particular, in virtual stores in an online environment, the quality of real-time customer service is crucial, and appropriate product suggestions that respond to emotions are required. Furthermore, it is important to support store staff and virtual assistants in efficiently handling customer interactions.

[0663] 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.

[0664] In this invention, the server includes means for an emotion engine to analyze the emotions of customers and staff, means for a generative artificial intelligence to generate product suggestions based on the analysis results, and means for transmitting and displaying the generated suggestions on the staff's terminal. This enables flexible responses based on customer emotions.

[0665] A "teacher" is a person whose job is to teach knowledge and skills to students in an educational institution.

[0666] A "student" is a person who belongs to an educational institution and engages in learning.

[0667] "Target grade level" refers to a group of students belonging to a specific academic year as defined in the educational curriculum.

[0668] A "subject" refers to a specific academic field or subject that should be taught in the curriculum.

[0669] "Question format" refers to the format and style of questions used in tests and similar materials.

[0670] "Generative artificial intelligence" refers to artificial intelligence that has the function of generating information based on input data.

[0671] A "test question" is a set of questions or problems used to assess learning outcomes.

[0672] "Learning materials" refer to teaching materials and reference materials used for educational purposes.

[0673] An "emotion engine" is a system or software that analyzes human emotions and utilizes that information.

[0674] A "training plan" is a plan of training methods and procedures to achieve a specific purpose or goal.

[0675] "Product recommendation" refers to the act or content of recommending appropriate products according to the customer's needs and interests.

[0676] A "terminal" is a device or apparatus used for inputting or displaying information.

[0677] A "server" is a centralized management device used for various data processing and management tasks.

[0678] This invention is a system for improving the quality of customer service in virtual stores, utilizing an emotion analysis engine and generative artificial intelligence. This system interacts with customers using terminals such as smart glasses and head-mounted displays, analyzes their emotions in real time, and makes product suggestions based on that analysis.

[0679] System configuration and operation

[0680] hardware

[0681] Device: Smart glasses or head-mounted display (e.g., Microsoft HoloLens®, Google Glass®)

[0682] Server: A server that handles central administration and processing.

[0683] Emotion engine: A device that analyzes emotions from facial expressions, voice, etc. IBM Watson® Tone Analyzer is an example of this.

[0684] software

[0685] Virtual assistant applications: Applications installed on smart glasses or head-mounted displays.

[0686] Emotion analysis software: Functions as an emotion engine and analyzes the user's emotional data.

[0687] Generative artificial intelligence: Uses OpenAI's GPT-4 (registered trademark) and other technologies to generate data based on input information.

[0688] Processing flow and data processing

[0689] 1. Customer information input and sentiment analysis

[0690] User: Store staff wear smart glasses or head-mounted displays and enter customer information. This information includes age, gender, and areas of interest.

[0691] Terminal: An application installed on smart glasses or a head-mounted display sends input information to the server.

[0692] 2. Analysis of emotions

[0693] Server: Receives input information and sends requests to the emotion engine to analyze customer and staff emotions.

[0694] Emotion Engine: Analyzes emotions from facial expressions, voice, etc., and sends the results back to the server.

[0695] 3. Generating Product Proposals

[0696] Server: Based on the results of sentiment analysis and customer information, it sends a request to the generative artificial intelligence to generate appropriate product suggestions.

[0697] Generative artificial intelligence: Generates product suggestions based on specified conditions and emotion analysis results, and returns the results to the server.

[0698] 4. Submitting and displaying proposals

[0699] Server: Sends the generated product suggestions to staff members' devices (smart glasses or head-mounted displays) for display.

[0700] User: Staff members review the displayed product suggestions and explain the suggestions to customers in real time.

[0701] Specific example

[0702] scenario

[0703] Imagine a scenario where a store staff member wears a Microsoft HoloLens and a customer is browsing products in a virtual store. The staff member observes the customer's facial expressions and tone of voice through the HoloLens and analyzes their emotions. Based on the analysis results, they suggest appropriate products.

[0704] Example of a prompt

[0705] Customer sentiment: [Interest]

[0706] Customer attribute: [Female in her 20s]

[0707] Product Category: [Skincare]

[0708] I want a product like this: [A hypoallergenic skincare product for everyday use]

[0709] By having staff directly communicate the generated suggestions to customers via HoloLens, it will be possible to achieve a higher level of customer satisfaction.

[0710] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0711] Step 1:

[0712] The user wears smart glasses or a head-mounted display and enters customer information. This information, including age, gender, and areas of interest, is stored on the device. Input: Customer information. Output: Request to the server.

[0713] Step 2:

[0714] The terminal generates a request to send the entered customer information to the server. The terminal uses an application embedded in smart glasses or a head-mounted display. Input: Customer information. Output: Data transmission to the server.

[0715] Step 3:

[0716] The server sends the received customer information to the emotion engine and requests it to analyze the emotions of the users (staff and customers). Input: Customer information. Output: Request to the emotion engine.

[0717] Step 4:

[0718] The emotion engine analyzes the user's facial expressions and voice tone, and sends the results back to the server. Input: User's facial expression and voice data. Output: Emotion analysis results.

[0719] Step 5:

[0720] The server receives the emotion analysis results and sends a request to the generative artificial intelligence to generate product suggestions based on them. Input: Emotion analysis results. Output: Request to the generative artificial intelligence.

[0721] Step 6:

[0722] A generative artificial intelligence generates product suggestions based on sentiment analysis results and customer information, and sends the results back to the server. Specifically, it selects the most suitable products based on the customer's areas of interest and emotional state. Input: Sentiment analysis results and customer information. Output: Product suggestions.

[0723] Step 7:

[0724] The server sends generated product suggestions to the user's terminal and requests them to display them in real time. Input: Product suggestions. Output: Data sent to the terminal.

[0725] Step 8:

[0726] Users view product suggestions through smart glasses or head-mounted displays, and the system explains the suggestions to customers in real time. Input: Product suggestions. Output: Customer support.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] [Second Embodiment]

[0731] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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).

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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".

[0743] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for club activities. A specific example of this system, based on the following program processing, is shown.

[0744] Tests and document creation

[0745] overview

[0746] This system allows teachers to input the target grade level, subject, and question format, and then a generative artificial intelligence system generates appropriate test questions and learning materials based on that input, which are then sent to and displayed on the teacher's terminal.

[0747] Processing details

[0748] 1. User (Teacher)

[0749] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0750] 2. Terminal

[0751] Generate a request to send the teacher's input information to the server.

[0752] 3. Server

[0753] The server receives the input information and sends a request to the generative artificial intelligence to generate test questions.

[0754] 4. Generative Artificial Intelligence

[0755] Based on the specified conditions, generate test questions of appropriate difficulty and send them to the server.

[0756] 5. Server

[0757] The generated content is sent to the teacher's terminal and displayed.

[0758] 6. User (Teacher)

[0759] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0760] Specific example:

[0761] When a teacher creates a multiplication test for third graders, they input "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), which generates the test questions and sends them back. The teacher then reviews and corrects the generated test before distributing it to the students.

[0762] Creating materials for parents and proofreading essays and book reports.

[0763] overview

[0764] This system allows teachers to upload students' essays and book reports, which are then processed by a generative artificial intelligence system that corrects their grammar and expression, and then sent and displayed on the teacher's terminal.

[0765] Processing details

[0766] 1. User (Teacher)

[0767] Teachers upload student essays and reflection papers from the administration screen.

[0768] 2. Terminal

[0769] Generate a request to send the uploaded file to the server.

[0770] 3. Server

[0771] The server receives the file and sends a request for document modification to the generative artificial intelligence.

[0772] 4. Generative Artificial Intelligence

[0773] The system analyzes uploaded documents, corrects grammar and expression, and sends them to the server.

[0774] 5. Server

[0775] Send the revised document to the teacher's terminal and display it.

[0776] 6. User (Teacher)

[0777] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0778] Specific example:

[0779] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), which corrects grammar and expression. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[0780] Generating practice methods and coaching methods for club activities

[0781] overview

[0782] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence then generates a practice plan based on that input, which is then sent to and displayed on the student's device.

[0783] Processing details

[0784] 1. User (Student)

[0785] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0786] 2. Terminal

[0787] Generate a request to send the student's input information to the server.

[0788] 3. Server

[0789] The server receives the information and sends a request to the generative artificial intelligence to generate a practice plan.

[0790] 4. Generative Artificial Intelligence

[0791] Based on the specified conditions, a practice plan is generated and sent to the server.

[0792] 5. Server

[0793] The generated plan is sent to the student's device for display.

[0794] 6. User (Student)

[0795] Students review the generated practice plan and put it into practice.

[0796] Specific example:

[0797] A student in the soccer club enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence (AI), which generates a specific practice plan and sends it back. The student then reviews the generated practice plan and uses it to improve their training.

[0798] ---

[0799] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[0800] The following describes the processing flow.

[0801] Tests and document creation

[0802] Processing steps

[0803] Step 1:

[0804] User (Teacher)

[0805] The teacher logs into the administration panel and selects the "Create Test" option.

[0806] Step 2:

[0807] User (Teacher)

[0808] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0809] Step 3:

[0810] terminal

[0811] The device generates a request to send the teacher's input information to the server.

[0812] Step 4:

[0813] server

[0814] The server receives the teacher's input information and sends a request to the generative artificial intelligence to generate test questions.

[0815] Step 5:

[0816] Generative artificial intelligence

[0817] A generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and sends the results back to the server.

[0818] Step 6:

[0819] server

[0820] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[0821] Step 7:

[0822] terminal

[0823] The device displays the received test questions, allowing the teacher to review and correct them.

[0824] Step 8:

[0825] User (Teacher)

[0826] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[0827] Creating materials for parents and proofreading essays and book reports.

[0828] Processing steps

[0829] Step 1:

[0830] User (Teacher)

[0831] Teachers upload student essays and reflection papers from the administration screen.

[0832] Step 2:

[0833] terminal

[0834] The device generates a request to send the uploaded file to the server.

[0835] Step 3:

[0836] server

[0837] The server receives the uploaded file and sends a request for document modification to the generative artificial intelligence.

[0838] Step 4:

[0839] Generative artificial intelligence

[0840] The generative artificial intelligence analyzes the uploaded document, corrects its grammar and expression, and returns the results to the server.

[0841] Step 5:

[0842] server

[0843] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[0844] Step 6:

[0845] terminal

[0846] The device displays the received corrections, allowing the teacher to review and make final corrections.

[0847] Step 7:

[0848] User (Teacher)

[0849] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[0850] Generating practice methods and coaching methods for club activities

[0851] Processing steps

[0852] Step 1:

[0853] User (student)

[0854] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[0855] Step 2:

[0856] terminal

[0857] The terminal generates a request to send the student's input information to the server.

[0858] Step 3:

[0859] server

[0860] The server receives the student's input information and sends a request to the generative artificial intelligence to generate a practice plan.

[0861] Step 4:

[0862] Generative artificial intelligence

[0863] The generative artificial intelligence generates a practice plan based on specified conditions and sends the result back to the server.

[0864] Step 5:

[0865] server

[0866] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[0867] Step 6:

[0868] terminal

[0869] The device displays the received practice plan, allowing students to review and practice it.

[0870] Step 7:

[0871] User (student)

[0872] Students review the generated practice plans and use them to improve their actual practice.

[0873] (Example 1)

[0874] 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."

[0875] Traditional methods for creating teaching materials, correcting essays, and developing practice plans for extracurricular activities required teachers and students to do it manually, which was time-consuming and laborious. Furthermore, qualitative evaluation and planning were challenging. In particular, the increased workload on teachers and the resulting limitations on students' opportunities for independent learning made it difficult to achieve efficient educational activities.

[0876] 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.

[0877] In this invention, the server includes means for receiving information input from a user terminal and requesting processing from a generative artificial intelligence; means for returning the generation results received from the generative artificial intelligence to the user terminal; and means for specifying conditions for the generation process to the generative artificial intelligence. This enables the user to efficiently create teaching materials, correct essays, and generate practice plans.

[0878] A "user terminal" is a device operated by a user and is used for interacting with the system.

[0879] A "server" is a computing system that receives requests from user terminals, requests processing from generative artificial intelligence, and sends the results back to the user terminal.

[0880] "Generative artificial intelligence" refers to artificial intelligence models that generate test questions, document revisions, practice plans, and other similar materials based on information input by the user.

[0881] "Test questions or learning materials" refer to educational content created by generative artificial intelligence based on conditions specified by the user (teacher).

[0882] "Document correction" refers to the process by which generative artificial intelligence corrects the grammar and expressions within a document.

[0883] A "practice plan" is a set of specific practice method guidelines created by a generative artificial intelligence system based on the type of club activity, practice content, and goals entered by the student.

[0884] "Input method" refers to the method or interface by which a user provides information to a system.

[0885] "Transmission means" refers to the function that allows a user terminal or server to send information to other devices.

[0886] "Display means" refers to the function that provides the user with information received by the user terminal in a visual manner.

[0887] "Uploading" refers to the process of a user transferring files or data from their local device to a server.

[0888] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for their club activities. This system is realized through the cooperation of user terminals, a server, and generative artificial intelligence.

[0889] First, the teacher logs in using their user terminal and selects the "Create Test" option from the administration screen. Next, they enter the target grade level, subject, and question format, and the program sends this information to the server. The user terminals used are typical personal computers and tablet devices. An example of input would be "3rd Grade," "Mathematics," and "Multiple Choice."

[0890] The server analyzes the received information and sends the data to a generative artificial intelligence (e.g., OpenAI GPT-3). The generative AI generates test questions based on the provided prompt, "Generate multiple-choice math test questions for third-year students." The generated results are returned to the server in JSON format.

[0891] The server sends the generated test questions to the user's terminal and displays them for the teacher to review. The teacher reviews the generated test questions and makes corrections as needed. They then print the final test questions for distribution to students or share them online.

[0892] Furthermore, teachers upload document files from the management screen to efficiently correct students' essays and book reports. The program sends the uploaded files to the server, which then sends a document correction request to the generative artificial intelligence. The generative AI corrects the grammar and expressions within the document and returns the corrected document to the server. The server sends the results to the user's terminal and displays them for the teacher to review. The teacher reviews the corrections and provides final feedback to the student or their parents.

[0893] Furthermore, students access the administration screen to create practice plans for their club activities, entering the type of club activity, practice content, and goals. The program generates and sends a request to the server. The server sends a practice plan generation request to a generative artificial intelligence. The generative artificial intelligence generates a practice plan based on the specified conditions and sends the result back to the server. The server sends the generated practice plan to the user's terminal and displays it so that the student can actually check it. For example, if "soccer," "dribbling practice," and "physical fitness improvement" are entered, a specific practice plan will be generated.

[0894] These concrete examples enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[0895] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0896] Tests and document creation

[0897] Step 1:

[0898] The user (teacher) logs into the administration panel and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[0899] Input: Login information (username, password), grade level, subject, question format

[0900] Output: Input information is displayed and confirmed on the management screen.

[0901] Step 2:

[0902] The terminal generates a request to send the entered information to the server.

[0903] Input: Information entered by the user (teacher)

[0904] Output: The generated HTTP request is sent to the server.

[0905] Step 3:

[0906] The server receives an HTTP request and parses the information contained in the request body.

[0907] Input: HTTP request sent from the terminal

[0908] Output: The analyzed information is sent to the generative artificial intelligence.

[0909] Step 4:

[0910] Generative artificial intelligence generates test questions based on specified conditions.

[0911] Input: Test creation conditions sent from the server

[0912] Output: Generated test questions (JSON format)

[0913] Step 5:

[0914] The server adjusts the data format as needed based on the generation results received from the generative artificial intelligence and sends them to the user terminal as an HTTP response.

[0915] Input: Generated test questions (JSON format)

[0916] Output: Adjusted test question data

[0917] Step 6:

[0918] The user (teacher) reviews the generated test questions and makes corrections as needed.

[0919] Input: Test question data sent from the server

[0920] Output: Corrected test questions

[0921] Correction of essays and book reports

[0922] Step 1:

[0923] Users (teachers) upload student essays and reflection papers from the administration screen.

[0924] Input: Essay or reflection paper file

[0925] Output: Uploaded files are displayed on the admin screen.

[0926] Step 2:

[0927] The terminal generates a request to send the uploaded file to the server.

[0928] Input: Uploaded document file

[0929] Output: The generated HTTP request is sent to the server.

[0930] Step 3:

[0931] The server receives the file, analyzes its contents and metadata, and sends a document modification request to the generative artificial intelligence.

[0932] Input: Document file sent from the terminal

[0933] Output: The analyzed document data is sent to the generative artificial intelligence.

[0934] Step 4:

[0935] Generative artificial intelligence analyzes uploaded documents and corrects their grammar and expression.

[0936] Input: Document data sent from the server

[0937] Output: Modified document (JSON format)

[0938] Step 5:

[0939] The server receives the modified document and generates a response to display it on the user's terminal.

[0940] Input: Modified document (JSON format)

[0941] Output: Generated response data

[0942] Step 6:

[0943] The user (teacher) reviews the revised document and makes further revisions as needed.

[0944] Input: Modified document sent from the server

[0945] Output: Final revised document

[0946] Creating practice plans for club activities

[0947] Step 1:

[0948] The user (student) accesses the administration screen and selects the "Create Practice Plan" option. They then enter the type of club activity (e.g., soccer), the content of the practice (e.g., dribbling practice), and the goal (e.g., improving physical fitness).

[0949] Input: Type of club activity, practice content, goals

[0950] Output: Input information is displayed and confirmed on the management screen.

[0951] Step 2:

[0952] The terminal generates a request to send the entered information to the server.

[0953] Input: Information entered by the user (student)

[0954] Output: The generated HTTP request is sent to the server.

[0955] Step 3:

[0956] The server receives an HTTP request and parses the information contained in the request body.

[0957] Input: HTTP request sent from the terminal

[0958] Output: The analyzed information is sent to the generative artificial intelligence.

[0959] Step 4:

[0960] Generative artificial intelligence generates a practice plan based on specified conditions.

[0961] Input: Practice plan creation conditions sent from the server

[0962] Output: Generated practice plan (JSON format)

[0963] Step 5:

[0964] The server generates a response to send the generation results received from the generative artificial intelligence to the student terminals.

[0965] Input: Generated practice plan (JSON format)

[0966] Output: Generated response data

[0967] Step 6:

[0968] The user (student) reviews the generated practice plan and puts it into practice.

[0969] Input: Practice plan data sent from the server

[0970] Output: Implemented practice plan

[0971] (Application Example 1)

[0972] 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."

[0973] In today's educational environment, teachers spend a great deal of time and effort on lesson preparation and student evaluation, particularly creating teaching materials, correcting essays, and planning extracurricular activity practices. Furthermore, when providing educational services in physical stores, there is a lack of means to quickly and effectively prepare and distribute teaching materials. This increases the workload on teachers and reduces opportunities for students' independent learning and growth.

[0974] 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.

[0975] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for transmitting and displaying the generated content on a teacher's terminal; and means for creating and distributing teaching materials at physical stores. This makes it possible for teachers to quickly create teaching materials and to effectively provide educational services even at physical stores.

[0976] The system also includes means for teachers to upload students' essays or book reports, means for a generative artificial intelligence to correct the grammar and expression of the uploaded documents, means for sending and displaying the corrected documents on the teacher's terminal, and means for providing real-time corrections to essays and book reports. This streamlines the process of correcting essays and book reports and reduces the burden on teachers.

[0977] Furthermore, the system includes means for students to input the type of club activity and practice content, means for a generative artificial intelligence to generate a practice plan based on the input information, means for transmitting and displaying the generated practice plan on the student's terminal, and means for providing practice plans related to club activities and hobbies. This enables students to spontaneously plan and implement practice plans for their club activities.

[0978] A "teacher terminal" is an electronic device operated by a teacher, used for tasks such as generating test questions and learning materials, correcting essays, and reviewing practice plans.

[0979] "Generative artificial intelligence" refers to a system that includes algorithms and machine learning models for automatically generating test questions, learning materials, practice plans, and document revisions based on input information.

[0980] A "physical store" is a physical location that serves as a place for educational services or learning.

[0981] "Educational materials" refer to materials and tools used for educational purposes, including test questions, worksheets, and drills.

[0982] "Means of creation" refers to methods or devices used to generate information for a specific purpose.

[0983] "Transmission means" refers to functions or devices for transmitting generated information or data to other devices or systems.

[0984] "Display means" refers to devices or software that allow users to visually confirm transmitted information.

[0985] "Essays and book reviews" are written pieces in which students express their own thoughts and feelings, and are evaluated as part of their education.

[0986] "Grammar and expression correction" refers to the process of correcting grammatical errors and improving expressions within a text.

[0987] A "club activity practice plan" refers to a document that outlines the content and plan of practice sessions that students will undertake in their club activities.

[0988] This invention is a system that enables teachers to efficiently create teaching materials and tests, correct students' essays and book reports, and allow students to independently plan club activity practice schedules. This system utilizes generative artificial intelligence.

[0989] Hardware and software to be used

[0990] The hardware used includes teacher and student terminals, as well as display devices in physical stores. Specifically, this includes smartphones, tablets, personal computers, and smart glasses. The software includes Python libraries that implement generative artificial intelligence algorithms, and API servers for data transmission and reception. Specifically, Python, the requests library, and APIs for generative AI models are used.

[0991] Generation of teaching materials and tests

[0992] The server receives input from the teacher regarding the target grade level, subject, and question format, and sends this information to a generative artificial intelligence system to generate appropriate test questions and learning materials. The generated content is then sent to the teacher's terminal and displayed.

[0993] Specific example:

[0994] When a teacher creates a multiplication test for third graders, they input information such as "grade 3," "mathematics," and "multiple choice" from their teacher's terminal. The server receives this information and sends it to a generative artificial intelligence. The AI ​​generates test questions based on this information, sends them to the teacher's terminal via the server, and displays them.

[0995] Example of a prompt:

[0996] grade = 3

[0997] subject = 'mathematics'

[0998] question_type = 'multiple_choice'

[0999] Correction of essays and book reviews

[1000] Teachers upload students' essays and book reports, and a generative artificial intelligence system corrects their grammar and expression. The corrected documents are sent to the teacher's terminal and displayed.

[1001] Specific example:

[1002] When a teacher uploads a student's book report and requests correction from AI, the teacher uploads the report file from their terminal. The server receives it and sends it to the generative artificial intelligence. The AI ​​corrects grammar and expression, and then sends it back to the teacher's terminal via the server for display.

[1003] Example of a prompt:

[1004] essay_text = 'My name is Tanaka.'

[1005] Generating practice plans for club activities

[1006] Students input the type of club activity and practice content, and a generative artificial intelligence generates a personalized practice plan. The generated practice plan is sent to the student's device and displayed.

[1007] Specific example:

[1008] A student in the soccer club inputs "soccer," "dribbling practice," and "physical fitness improvement" to brainstorm new dribbling drills. The server receives this input and sends it to a generative artificial intelligence (AI). The AI ​​generates a specific practice plan, which is then sent to the student's terminal via the server for display.

[1009] Example of a prompt:

[1010] activity = 'soccer'

[1011] goal = 'improve stamina'

[1012] practice_type = 'dribbling'

[1013] These features enable teachers to quickly prepare teaching materials, streamline essay correction, and allow students to independently plan extracurricular activity practice sessions. This leads to more efficient and effective educational activities, reduces the workload on teachers, and promotes independent learning and growth among students.

[1014] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1015] Step 1:

[1016] The user (teacher) logs into a dedicated administration screen and selects the "Create Test" option. They enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The information entered here forms the basis of the data used in subsequent processing steps. The output is an input confirmation screen, where the teacher reviews the entered information.

[1017] Step 2:

[1018] The terminal collects teacher input information and generates a request to send to the server. Specifically, it converts the input data into JSON format and prepares it for making an HTTP request to the appropriate endpoint. As output, appropriately configured request data is generated.

[1019] Step 3:

[1020] The server receives request data sent from the terminal. The server analyzes this data and sends a request to the generative artificial intelligence (AI) to generate test questions. This process involves creating a prompt statement to generate the most suitable test questions based on the input conditions and sending a request to the AI's API. The output is the response data from the AI, which includes the generated test questions.

[1021] Step 4:

[1022] The generative artificial intelligence generates test questions of a specified difficulty level and format based on the received prompt. A pre-trained model is used in this process. The output is the generated test questions, which are then sent back to the server.

[1023] Step 5:

[1024] The server prepares to send the test question data received from the generative artificial intelligence to the terminal. Specifically, it reconstructs the test question data into a format that is easy for the teacher to understand and sends it back to the terminal as an HTTP response. The output is the reconstructed test question data.

[1025] Step 6:

[1026] The terminal displays test question data received from the server to the teacher. The teacher reviews this data, makes corrections as needed, and generates the final test questions. User interaction is emphasized during this correction process. Finally, the corrected test questions are generated and ready to be distributed to students.

[1027] Step 7:

[1028] Users (teachers) upload student essays and reflection papers from the administration screen. These files are analyzed by the system and used in the next step. The output is the uploaded file.

[1029] Step 8:

[1030] The terminal generates a request to send the uploaded essay or review file to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[1031] Step 9:

[1032] The server sends the received essay and reflection files to a generative artificial intelligence system and requests corrections to their grammar and expression. This process involves data analysis and the generation of appropriate prompts. The output is the analyzed data that was sent.

[1033] Step 10:

[1034] The generative artificial intelligence analyzes uploaded essay and review files, correcting grammar and expression. The corrected document data is generated and sent back to the server. The output is the corrected document.

[1035] Step 11:

[1036] The server reconstructs the modified document received from the generative artificial intelligence and prepares it for transmission to the teacher terminal. The output is the reconstructed modified document.

[1037] Step 12:

[1038] The terminal displays the revised document data received from the server to the teacher. The teacher reviews it, makes any necessary final revisions, and returns it to the student. The output is the final revised document.

[1039] Step 13:

[1040] Users (students) access the administration screen and input the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The output is the club activity information entered.

[1041] Step 14:

[1042] The terminal generates a request to send student input information to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[1043] Step 15:

[1044] The server analyzes the received club activity information and sends a request to a generative artificial intelligence system to generate a practice plan. The data is then analyzed, and appropriate prompts are generated. The output is the analyzed data that was sent.

[1045] Step 16:

[1046] The generative artificial intelligence generates a practice plan based on specified conditions and sends it to the server. The generated plan is returned as a data format. The output is the generated practice plan.

[1047] Step 17:

[1048] The server prepares to send the practice plan data received from the generative artificial intelligence to the student's terminal. The reconstructed data is sent to the terminal. The output is the reconstructed practice plan data.

[1049] Step 18:

[1050] The terminal displays practice plan data received from the server to the student. The student reviews and then practices it. The output is the displayed practice plan.

[1051] 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.

[1052] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create club activity practice plans. Furthermore, by incorporating an emotion engine, it adds a function that adjusts the content of tools and materials based on the emotions of the users (teachers and students). This makes it possible to maximize the effectiveness of educational activities. As a specific example of this system, an embodiment based on the following program processing is shown.

[1053] Tests and document creation

[1054] overview

[1055] This system allows teachers to input the target grade level, subject, and question format, and a generative artificial intelligence then generates appropriate test questions and learning materials based on that input. An emotion engine recognizes the user's emotions, adjusts the content accordingly, and then transmits and displays the materials on the teacher's terminal.

[1056] Processing details

[1057] 1. User (Teacher)

[1058] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1059] 2. Terminal

[1060] Generate a request to send the teacher's input information to the server.

[1061] 3. Server

[1062] The server receives the input information and sends a request to the emotion engine to recognize the user's emotions.

[1063] 4. Emotional Engine

[1064] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1065] 5. Server

[1066] The server sends a request to the generative artificial intelligence to generate test questions, along with the results from the emotion engine.

[1067] 6. Generative Artificial Intelligence

[1068] Based on the specified conditions and the results of the emotion engine, test questions of appropriate difficulty are generated and sent back to the server.

[1069] 7. Server

[1070] The generated content is sent to the teacher's terminal and displayed.

[1071] 8. User (Teacher)

[1072] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1073] Specific example:

[1074] When a teacher creates a multiplication test for third graders, they enter "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), and before the AI ​​generates and returns the test questions, an emotion engine analyzes the teacher's emotions. If the teacher is nervous, the difficulty level of the test questions is adjusted to be lower. The teacher then reviews and corrects the generated test before distributing it to the students.

[1075] Creating materials for parents and proofreading essays and book reports.

[1076] overview

[1077] This system allows teachers to upload students' essays and book reports, whereupon a generative artificial intelligence corrects the grammar and expression, an emotion engine recognizes the user's emotions and adjusts the corrections accordingly, and then sends and displays the corrected work on the teacher's terminal.

[1078] Processing details

[1079] 1. User (Teacher)

[1080] Teachers upload student essays and reflection papers from the administration screen.

[1081] 2. Terminal

[1082] Generate a request to send the uploaded file to the server.

[1083] 3. Server

[1084] The server receives the file and sends a request for document revision instructions to the sentiment engine.

[1085] 4. Emotional Engine

[1086] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence.

[1087] 5. Generative Artificial Intelligence

[1088] The uploaded document is analyzed, grammatical and phrasing corrections are made based on the sentiment engine's results, and the results are sent back to the server.

[1089] 6. Server

[1090] Send the revised document to the teacher's terminal and display it.

[1091] 7. User (Teacher)

[1092] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1093] Specific example:

[1094] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), and before the AI ​​corrects grammar and expression, an emotion engine analyzes the teacher's emotions. If the teacher is tired, the correction suggestions are softer; if they are stressed, the approach is more cautious. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[1095] Generating practice methods and coaching methods for club activities

[1096] overview

[1097] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence generates a practice plan based on that input. An emotion engine then recognizes the user's emotions and adjusts the plan accordingly, before sending and displaying it on the student's device.

[1098] Processing details

[1099] 1. User (Student)

[1100] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1101] 2. Terminal

[1102] Generate a request to send the student's input information to the server.

[1103] 3. Server

[1104] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[1105] 4. Emotional Engine

[1106] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results to the generative artificial intelligence system.

[1107] 5. Generative Artificial Intelligence

[1108] Based on the specified conditions and the results from the emotion engine, a practice plan is generated and the results are sent back to the server.

[1109] 6. Server

[1110] The generated plan is sent to the student's device for display.

[1111] 7. User (Student)

[1112] Students review the generated practice plan and use it to improve their actual practice.

[1113] Specific example:

[1114] A soccer club student enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence, and before the AI ​​generates a practice plan, an emotion engine analyzes the student's emotions. If the student is tired, it suggests a plan that includes rest; if highly motivated, it presents a challenging plan. The student reviews the generated practice plan and uses it to improve their training.

[1115] ---

[1116] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth. Furthermore, the introduction of an emotion engine allows for flexible responses based on the user's emotions, improving the quality of the educational environment.

[1117] The following describes the processing flow.

[1118] Tests and document creation

[1119] Processing steps

[1120] Step 1:

[1121] User (Teacher)

[1122] The teacher logs into a dedicated administration panel and selects the "Create Test" option.

[1123] Step 2:

[1124] User (Teacher)

[1125] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1126] Step 3:

[1127] terminal

[1128] The device generates a request to send the teacher's input information to the server.

[1129] Step 4:

[1130] server

[1131] The server receives the input information and sends a request to the emotion engine to recognize the user's (teacher's) emotions.

[1132] Step 5:

[1133] Emotional Engine

[1134] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1135] Step 6:

[1136] server

[1137] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate test questions, incorporating these results.

[1138] Step 7:

[1139] Generative artificial intelligence

[1140] The generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and the teacher's emotional information, and sends the results back to the server.

[1141] Step 8:

[1142] server

[1143] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[1144] Step 9:

[1145] terminal

[1146] The device displays the received test questions, allowing the teacher to review and correct them.

[1147] Step 10:

[1148] User (Teacher)

[1149] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1150] Creating materials for parents and proofreading essays and book reports.

[1151] Processing steps

[1152] Step 1:

[1153] User (Teacher)

[1154] Teachers upload student essays and reflection papers from the administration screen.

[1155] Step 2:

[1156] terminal

[1157] The device generates a request to send the uploaded file to the server.

[1158] Step 3:

[1159] server

[1160] The server receives the uploaded file and sends a request for document revision instructions to the emotion engine.

[1161] Step 4:

[1162] Emotional Engine

[1163] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1164] Step 5:

[1165] server

[1166] The server receives the results from the emotion engine and sends a document revision request to the generative artificial intelligence, incorporating these results.

[1167] Step 6:

[1168] Generative artificial intelligence

[1169] The generative artificial intelligence corrects the grammar and expression of a given document based on the teacher's emotional information, and returns the results to the server.

[1170] Step 7:

[1171] server

[1172] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[1173] Step 8:

[1174] terminal

[1175] The device displays the received corrections, allowing the teacher to review and make final corrections.

[1176] Step 9:

[1177] User (Teacher)

[1178] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1179] Generating practice methods and coaching methods for club activities

[1180] Processing steps

[1181] Step 1:

[1182] User (student)

[1183] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1184] Step 2:

[1185] terminal

[1186] The device generates a request to send the student's input information to the server.

[1187] Step 3:

[1188] server

[1189] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[1190] Step 4:

[1191] Emotional Engine

[1192] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results back to the server.

[1193] Step 5:

[1194] server

[1195] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate a practice plan, incorporating these results.

[1196] Step 6:

[1197] Generative artificial intelligence

[1198] A generative artificial intelligence generates a practice plan based on specified conditions and student emotional information, and sends the result back to the server.

[1199] Step 7:

[1200] server

[1201] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[1202] Step 8:

[1203] terminal

[1204] The device displays the received practice plan, allowing students to review and practice it.

[1205] Step 9:

[1206] User (student)

[1207] Students review the generated practice plans and use them to improve their actual practice.

[1208] (Example 2)

[1209] 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".

[1210] In educational settings, teachers are required to quickly create teaching materials and tests, and to efficiently correct students' essays and book reports. Furthermore, support is needed when students independently create practice plans for extracurricular activities, but there is a lack of effective systems to consistently provide these services. In addition, improving the quality of education requires flexible responses based on the emotions of both teachers and students, but the current system is insufficient to address this.

[1211] 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.

[1212] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for an emotion analysis engine to analyze the teacher's emotions and provide the results to the generative artificial intelligence; means for the generative artificial intelligence to adjust the generated content based on the results of the emotion analysis engine; and means for transmitting and displaying the generated content on the teacher's terminal. This enables teachers to create teaching materials and tests efficiently and effectively, and to adjust educational content based on the user's emotions.

[1213] A "teacher" is a professional who provides education, primarily responsible for teaching subjects to students in educational institutions such as schools.

[1214] "Target grade level" refers to the grade level of students in the curriculum and is information used to indicate the learning stage of students targeted by specific educational content.

[1215] A "subject" refers to a specific academic field or subject taught in the curriculum, and examples include mathematics, Japanese language, and science.

[1216] "Question format" refers to the style of questions in test questions or learning materials, and includes formats such as multiple choice, written response, and fill-in-the-blank.

[1217] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates problems and materials based on given input information.

[1218] An "emotion analysis engine" refers to a technology that analyzes a user's emotions based on their facial expressions, voice, etc., and provides the results to other systems.

[1219] "Users" refer to individuals who utilize this system, and primarily include educators such as teachers and students.

[1220] "Generated content" refers to content such as test questions, practice plans, and learning materials created by a generative artificial intelligence based on input information and sentiment analysis results.

[1221] A "teacher terminal" refers to a device such as a computer or tablet used by a teacher, which displays generated content and other information.

[1222] "Uploading" refers to the act of sending data stored on a local device to a server over a network.

[1223] "Document correction" refers to the process performed by generative artificial intelligence to improve the grammar and expression of uploaded documents.

[1224] A "student" is a person who receives education, and primarily refers to learners who attend educational institutions such as schools.

[1225] A "practice plan" is a detailed plan outlining the practice activities a student plans to undertake, including the type of practice, specific methods, and goals.

[1226] "Student terminals" refer to devices such as computers and tablets used by students, which display generated practice plans and other information.

[1227] This invention provides a system that enables teachers to quickly create teaching materials and tests in educational settings, efficiently correct students' essays and book reports, and allow students to independently create practice plans for extracurricular activities. By incorporating an emotion analysis engine, this system appropriately adjusts its content based on the emotions of the user, both the teacher and the student.

[1228] Tests and document creation

[1229] The user, a teacher, logs into a dedicated administration screen and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The terminal analyzes the teacher's input and generates a request to send it to the server.

[1230] The server sends a request to the emotion analysis engine along with the received input information to obtain the teacher's emotion analysis results. The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice and sends the results back to the server.

[1231] Subsequently, the server sends a request to the generative artificial intelligence to generate test questions. Specifically, information including the results of the sentiment analysis engine is sent. Based on this, the generative artificial intelligence adjusts the difficulty and content of the test questions and generates a series of questions. The generated content is sent to the teacher's terminal via the server, where the teacher reviews the generated materials and makes corrections as needed.

[1232] Specific example:

[1233] If a teacher is creating a multiplication test for third graders, they would enter "3rd grade," "math," and "multiple choice."

[1234] If the emotion analysis engine detects teacher anxiety, the generative artificial intelligence generates test questions with a lower difficulty level.

[1235] The teacher reviews this test and distributes it to the students.

[1236] Creating materials for parents and proofreading essays and book reports.

[1237] The user, a teacher, accesses the administration screen and uploads student essays and reflection papers. The terminal generates a request to send the uploaded files to the server. The server receives the files and sends a request for document revision instructions to the sentiment analysis engine. The sentiment analysis engine analyzes the teacher's emotions and provides the results to the generative artificial intelligence.

[1238] The generative artificial intelligence analyzes uploaded documents and corrects grammar and expression based on the results of the sentiment analysis engine. The corrected results are sent to the teacher's terminal via the server, where the teacher reviews the changes and makes additional corrections as needed. Feedback is then provided to students and parents.

[1239] Specific example:

[1240] A teacher uploads a file of a student's book report to have it corrected.

[1241] If the emotion analysis engine detects teacher fatigue, the generative artificial intelligence will offer correction suggestions in a gentle tone.

[1242] The teacher reviews the corrections and provides feedback to the students.

[1243] Generating practice methods and coaching methods for club activities

[1244] The student user accesses the management screen and enters the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The device generates a request to send the entered information to the server. The server, along with the received information, sends a request to the sentiment analysis engine to analyze the student's emotions.

[1245] The emotion analysis engine analyzes the student's emotions from their facial expressions and voice, and sends the results back to the server. The server sends a request to the generative artificial intelligence to generate a practice plan, including the results from the emotion analysis engine. The generative artificial intelligence generates a practice plan based on the specified conditions and the results of the emotion analysis, and sends the results back to the server. The server sends the generated practice plan to the student's terminal, where the student reviews it and uses it to help with their actual practice.

[1246] Specific example:

[1247] A student in the soccer club types "soccer," "dribbling practice," and "physical fitness improvement" into their input to come up with a new dribbling practice method.

[1248] If the emotion analysis engine detects a high level of student motivation, the generative artificial intelligence generates a challenging practice plan.

[1249] Students review the generated plan and begin practicing.

[1250] As a result, teachers and students can conduct educational activities efficiently and effectively, and the introduction of the emotion analysis engine enables flexible responses based on the user's emotions.

[1251] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1252] Process steps for creating tests and documents

[1253] Step 1:

[1254] User (Teacher)

[1255] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1256] Input: Target grade level, subject, question format

[1257] Output: Form input data

[1258] Step 2:

[1259] terminal

[1260] The terminal generates a request to send to the server based on the teacher's input information. Specifically, it constructs the input information as JSON data and sends it to the server as an HTTP request.

[1261] Input: Form input data

[1262] Output: Request to send to server

[1263] Step 3:

[1264] server

[1265] The server analyzes the incoming request and extracts the necessary data. Next, it sends an emotion recognition request to the emotion analysis engine. This request includes the teacher's facial image and voice data.

[1266] Input: Request to send to server

[1267] Output: Sentiment analysis request

[1268] Step 4:

[1269] Emotion analysis engine

[1270] The emotion analysis engine analyzes the teacher's emotions based on facial images and voice data, and sends the results back to the server. The analysis results include information such as tension, fatigue, and relaxation.

[1271] Input: Sentiment analysis request (facial image and audio data)

[1272] Output: Emotion analysis results (tension, fatigue, relaxation, etc.)

[1273] Step 5:

[1274] server

[1275] The server receives the sentiment analysis results and, along with information on the target grade level, subject, and question format, sends a request to the generative artificial intelligence to generate test questions.

[1276] Input: Sentiment analysis results, target grade level, subject, question format

[1277] Output: Request to generative artificial intelligence

[1278] Step 6:

[1279] Generative artificial intelligence

[1280] The generative artificial intelligence generates test questions based on specified conditions and the results of sentiment analysis. The generated test questions are then sent back to the server.

[1281] Input: Request to a generative artificial intelligence (conditions and sentiment analysis results)

[1282] Output: Generated test questions

[1283] Step 7:

[1284] server

[1285] The server receives the generated test questions, formats them, and sends them to the teacher's terminal.

[1286] Input: Generated test questions

[1287] Output: Request to send to the teacher's terminal

[1288] Step 8:

[1289] User (Teacher)

[1290] The teacher reviews the generated test questions displayed on their teacher's terminal and makes corrections as needed. Finally, they distribute the test to the students.

[1291] Input: Generated test questions

[1292] Output: Confirmed and corrected test questions

[1293] Steps for creating materials for parents and proofreading essays and book reports

[1294] Step 1:

[1295] User (Teacher)

[1296] Teachers access the administration panel and upload student essays and reflection papers.

[1297] Input: Essay or reflection paper file

[1298] Output: Upload request from teacher's terminal

[1299] Step 2:

[1300] terminal

[1301] The terminal generates a request to send to the server based on the uploaded file information. Specifically, it creates an HTTP POST request containing binary data.

[1302] Input: Upload request from teacher's terminal

[1303] Output: Request to send to server

[1304] Step 3:

[1305] server

[1306] The server processes the received file data and sends a request for document revision instructions to the sentiment analysis engine. This request also includes the teacher's facial expressions and voice data.

[1307] Input: Request to send to the server (file data)

[1308] Output: Sentiment analysis request

[1309] Step 4:

[1310] Emotion analysis engine

[1311] The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence system.

[1312] Input: Emotion analysis request (facial expression and voice data)

[1313] Output: Emotion analysis results

[1314] Step 5:

[1315] Generative artificial intelligence

[1316] The generative artificial intelligence analyzes the uploaded document and corrects its grammar and expression based on the sentiment analysis results. The corrected results are then sent back to the server.

[1317] Input: Uploaded document, sentiment analysis results

[1318] Output: Revised document

[1319] Step 6:

[1320] server

[1321] The server receives the corrected document data and sends it to the teacher's terminal.

[1322] Input: Modified document

[1323] Output: Request to send to the teacher's terminal

[1324] Step 7:

[1325] User (Teacher)

[1326] Teachers review the revised documents displayed on their teacher terminals, make any final revisions as needed, and provide feedback to students and parents.

[1327] Input: Modified document

[1328] Output: Reviewed and corrected documents

[1329] Processing steps for generating club activity practice methods and coaching methods

[1330] Step 1:

[1331] User (student)

[1332] Students access the management screen and enter the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1333] Input: Type of club activity, practice content, goals

[1334] Output: Form input data

[1335] Step 2:

[1336] terminal

[1337] The terminal generates a request to send the input information to the server. Specifically, it converts the form input data into JSON format and creates an HTTP POST request.

[1338] Input: Form input data

[1339] Output: Request to send to server

[1340] Step 3:

[1341] server

[1342] The server analyzes the received information and sends a request to the emotion analysis engine to generate a practice plan. This request may include facial expression images and audio data.

[1343] Input: Request to send to server

[1344] Output: Sentiment analysis request

[1345] Step 4:

[1346] Emotion analysis engine

[1347] The emotion analysis engine analyzes students' emotions and sends the results to the generative artificial intelligence system.

[1348] Input: Emotion analysis request (facial expression and voice data)

[1349] Output: Emotion analysis results

[1350] Step 5:

[1351] Generative artificial intelligence

[1352] The generative artificial intelligence generates a practice plan based on specified conditions and emotion analysis results, and sends the results back to the server.

[1353] Input: Practice plan generation request, sentiment analysis results

[1354] Output: Generated practice plan

[1355] Step 6:

[1356] server

[1357] The server receives the generated plan and sends it to the student's device.

[1358] Input: Generated practice plan

[1359] Output: Request to send to student terminals

[1360] Step 7:

[1361] User (student)

[1362] Students review the generated practice plan and use it to improve their actual practice.

[1363] Input: Generated practice plan

[1364] Output: Practice plan to be reviewed and used

[1365] Through the steps described above, the system of the present invention provides appropriate teaching materials, document revisions, and practice plans based on user input information and sentiment analysis results, thereby improving the efficiency and effectiveness of educational activities.

[1366] (Application Example 2)

[1367] 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."

[1368] This invention aims to improve the quality of customer service and product descriptions in virtual stores. Conventional customer service systems have had difficulty responding flexibly to customer emotions and interests, resulting in a lack of satisfaction. In particular, in virtual stores in an online environment, the quality of real-time customer service is crucial, and appropriate product suggestions that respond to emotions are required. Furthermore, it is important to support store staff and virtual assistants in efficiently handling customer interactions.

[1369] 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.

[1370] In this invention, the server includes means for an emotion engine to analyze the emotions of customers and staff, means for a generative artificial intelligence to generate product suggestions based on the analysis results, and means for transmitting and displaying the generated suggestions on the staff's terminal. This enables flexible responses based on customer emotions.

[1371] A "teacher" is a person whose job is to teach knowledge and skills to students in an educational institution.

[1372] A "student" is a person who belongs to an educational institution and engages in learning.

[1373] "Target grade level" refers to a group of students belonging to a specific academic year as defined in the educational curriculum.

[1374] A "subject" refers to a specific academic field or subject that should be taught in the curriculum.

[1375] "Question format" refers to the format and style of questions used in tests and similar materials.

[1376] "Generative artificial intelligence" refers to artificial intelligence that has the function of generating information based on input data.

[1377] A "test question" is a set of questions or problems used to assess learning outcomes.

[1378] "Learning materials" refer to teaching materials and reference materials used for educational purposes.

[1379] An "emotion engine" is a system or software that analyzes human emotions and utilizes that information.

[1380] A "training plan" is a plan of training methods and procedures to achieve a specific purpose or goal.

[1381] "Product recommendation" refers to the act or content of recommending appropriate products according to the customer's needs and interests.

[1382] A "terminal" is a device or apparatus used for inputting or displaying information.

[1383] A "server" is a centralized management device used for various data processing and management tasks.

[1384] This invention is a system for improving the quality of customer service in virtual stores, utilizing an emotion analysis engine and generative artificial intelligence. This system interacts with customers using terminals such as smart glasses and head-mounted displays, analyzes their emotions in real time, and makes product suggestions based on that analysis.

[1385] System configuration and operation

[1386] hardware

[1387] Device: Smart glasses or head-mounted display (e.g., Microsoft HoloLens, Google Glass)

[1388] Server: A server that handles central administration and processing.

[1389] Emotion engine: A device that analyzes emotions from facial expressions, voice, etc. IBM Watson Tone Analyzer is an example of this.

[1390] software

[1391] Virtual assistant applications: Applications installed on smart glasses or head-mounted displays.

[1392] Emotion analysis software: Functions as an emotion engine and analyzes the user's emotional data.

[1393] Generative artificial intelligence: Uses OpenAI's GPT-4 and other methods to generate data based on input information.

[1394] Processing flow and data processing

[1395] 1. Customer information input and sentiment analysis

[1396] User: Store staff wear smart glasses or head-mounted displays and enter customer information. This information includes age, gender, and areas of interest.

[1397] Terminal: An application installed on smart glasses or a head-mounted display sends input information to the server.

[1398] 2. Analysis of emotions

[1399] Server: Receives input information and sends requests to the emotion engine to analyze customer and staff emotions.

[1400] Emotion Engine: Analyzes emotions from facial expressions, voice, etc., and sends the results back to the server.

[1401] 3. Generating Product Proposals

[1402] Server: Based on the sentiment analysis results and customer information, it sends a request to the generative artificial intelligence to generate appropriate product suggestions.

[1403] Generative artificial intelligence: Generates product suggestions based on specified conditions and emotion analysis results, and returns the results to the server.

[1404] 4. Submitting and displaying proposals

[1405] Server: Sends the generated product suggestions to staff members' devices (smart glasses or head-mounted displays) for display.

[1406] User: Staff members review the displayed product suggestions and explain the suggestions to customers in real time.

[1407] Specific example

[1408] scenario

[1409] Imagine a scenario where a store staff member wears a Microsoft HoloLens and a customer is browsing products in a virtual store. The staff member observes the customer's facial expressions and tone of voice through the HoloLens and analyzes their emotions. Based on the analysis results, they suggest appropriate products.

[1410] Example of a prompt

[1411] Customer sentiment: [Interest]

[1412] Customer attribute: [Female in her 20s]

[1413] Product Category: [Skincare]

[1414] I want a product like this: [A hypoallergenic skincare product for everyday use]

[1415] By having staff directly communicate the generated suggestions to customers via HoloLens, it will be possible to achieve a higher level of customer satisfaction.

[1416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1417] Step 1:

[1418] The user wears smart glasses or a head-mounted display and enters customer information. This information, including age, gender, and areas of interest, is stored on the device. Input: Customer information. Output: Request to the server.

[1419] Step 2:

[1420] The terminal generates a request to send the entered customer information to the server. The terminal uses an application embedded in smart glasses or a head-mounted display. Input: Customer information. Output: Data transmission to the server.

[1421] Step 3:

[1422] The server sends the received customer information to the emotion engine and requests an analysis of the user's (staff and customer) emotions. Input: Customer information. Output: Request to the emotion engine.

[1423] Step 4:

[1424] The emotion engine analyzes the user's facial expressions and voice tone, and sends the results back to the server. Input: User's facial expression and voice data. Output: Emotion analysis results.

[1425] Step 5:

[1426] The server receives the emotion analysis results and sends a request to the generative artificial intelligence to generate product suggestions based on them. Input: Emotion analysis results. Output: Request to the generative artificial intelligence.

[1427] Step 6:

[1428] A generative artificial intelligence generates product suggestions based on emotion analysis results and customer information, and sends the results back to the server. Specifically, it selects the most suitable products based on the customer's areas of interest and emotional state. Input: Emotion analysis results and customer information. Output: Product suggestions.

[1429] Step 7:

[1430] The server sends generated product suggestions to the user's terminal and requests them to display them in real time. Input: Product suggestions. Output: Data sent to the terminal.

[1431] Step 8:

[1432] Users view product suggestions through smart glasses or head-mounted displays, and the suggestions are explained to customers in real time. Input: Product suggestions. Output: Customer support.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] [Third Embodiment]

[1437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1438] 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.

[1439] 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).

[1440] 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.

[1441] 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.

[1442] 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).

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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".

[1449] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for club activities. A specific example of this system, based on the following program processing, is shown.

[1450] Tests and document creation

[1451] overview

[1452] This system allows teachers to input the target grade level, subject, and question format, and then a generative artificial intelligence system generates appropriate test questions and learning materials based on that input, which are then sent to and displayed on the teacher's terminal.

[1453] Processing details

[1454] 1. User (Teacher)

[1455] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1456] 2. Terminal

[1457] Generate a request to send the teacher's input information to the server.

[1458] 3. Server

[1459] The server receives the input information and sends a request to the generative artificial intelligence to generate test questions.

[1460] 4. Generative Artificial Intelligence

[1461] Based on the specified conditions, generate test questions of appropriate difficulty and send them to the server.

[1462] 5. Server

[1463] The generated content is sent to the teacher's terminal and displayed.

[1464] 6. User (Teacher)

[1465] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1466] Specific example:

[1467] When a teacher creates a multiplication test for third graders, they input "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), which generates the test questions and sends them back. The teacher then reviews and corrects the generated test before distributing it to the students.

[1468] Creating materials for parents and proofreading essays and book reports.

[1469] overview

[1470] This system allows teachers to upload students' essays and book reports, which are then processed by a generative artificial intelligence system that corrects their grammar and expression, and then sent and displayed on the teacher's terminal.

[1471] Processing details

[1472] 1. User (Teacher)

[1473] Teachers upload student essays and reflection papers from the administration screen.

[1474] 2. Terminal

[1475] Generate a request to send the uploaded file to the server.

[1476] 3. Server

[1477] The server receives the file and sends a request for document modification to the generative artificial intelligence.

[1478] 4. Generative Artificial Intelligence

[1479] The system analyzes uploaded documents, corrects grammar and expression, and sends them to the server.

[1480] 5. Server

[1481] Send the revised document to the teacher's terminal and display it.

[1482] 6. User (Teacher)

[1483] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1484] Specific example:

[1485] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), which corrects grammar and expression. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[1486] Generating practice methods and coaching methods for club activities

[1487] overview

[1488] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence then generates a practice plan based on that input, which is then sent to and displayed on the student's device.

[1489] Processing details

[1490] 1. User (Student)

[1491] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1492] 2. Terminal

[1493] Generate a request to send the student's input information to the server.

[1494] 3. Server

[1495] The server receives the information and sends a request to the generative artificial intelligence to generate a practice plan.

[1496] 4. Generative Artificial Intelligence

[1497] Based on the specified conditions, a practice plan is generated and sent to the server.

[1498] 5. Server

[1499] The generated plan is sent to the student's device for display.

[1500] 6. User (Student)

[1501] Students review the generated practice plan and put it into practice.

[1502] Specific example:

[1503] A student in the soccer club enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence (AI), which generates a specific practice plan and sends it back. The student then reviews the generated practice plan and uses it to improve their training.

[1504] ---

[1505] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[1506] The following describes the processing flow.

[1507] Tests and document creation

[1508] Processing steps

[1509] Step 1:

[1510] User (Teacher)

[1511] The teacher logs into the administration panel and selects the "Create Test" option.

[1512] Step 2:

[1513] User (Teacher)

[1514] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1515] Step 3:

[1516] terminal

[1517] The device generates a request to send the teacher's input information to the server.

[1518] Step 4:

[1519] server

[1520] The server receives the teacher's input information and sends a request to the generative artificial intelligence to generate test questions.

[1521] Step 5:

[1522] Generative artificial intelligence

[1523] A generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and sends the results back to the server.

[1524] Step 6:

[1525] server

[1526] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[1527] Step 7:

[1528] terminal

[1529] The device displays the received test questions, allowing the teacher to review and correct them.

[1530] Step 8:

[1531] User (Teacher)

[1532] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1533] Creating materials for parents and proofreading essays and book reports.

[1534] Processing steps

[1535] Step 1:

[1536] User (Teacher)

[1537] Teachers upload student essays and reflection papers from the administration screen.

[1538] Step 2:

[1539] terminal

[1540] The device generates a request to send the uploaded file to the server.

[1541] Step 3:

[1542] server

[1543] The server receives the uploaded file and sends a request for document modification to the generative artificial intelligence.

[1544] Step 4:

[1545] Generative artificial intelligence

[1546] The generative artificial intelligence analyzes the uploaded document, corrects its grammar and expression, and returns the results to the server.

[1547] Step 5:

[1548] server

[1549] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[1550] Step 6:

[1551] terminal

[1552] The device displays the received corrections, allowing the teacher to review and make final corrections.

[1553] Step 7:

[1554] User (Teacher)

[1555] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1556] Generating practice methods and coaching methods for club activities

[1557] Processing steps

[1558] Step 1:

[1559] User (student)

[1560] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1561] Step 2:

[1562] terminal

[1563] The terminal generates a request to send the student's input information to the server.

[1564] Step 3:

[1565] server

[1566] The server receives the student's input information and sends a request to the generative artificial intelligence to generate a practice plan.

[1567] Step 4:

[1568] Generative artificial intelligence

[1569] The generative artificial intelligence generates a practice plan based on specified conditions and sends the result back to the server.

[1570] Step 5:

[1571] server

[1572] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[1573] Step 6:

[1574] terminal

[1575] The device displays the received practice plan, allowing students to review and practice it.

[1576] Step 7:

[1577] User (student)

[1578] Students review the generated practice plans and use them to improve their actual practice.

[1579] (Example 1)

[1580] 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."

[1581] Traditional methods for creating teaching materials, correcting essays, and developing practice plans for extracurricular activities required teachers and students to do it manually, which was time-consuming and laborious. Furthermore, qualitative evaluation and planning were challenging. In particular, the increased workload on teachers and the resulting limitations on students' opportunities for independent learning made it difficult to achieve efficient educational activities.

[1582] 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.

[1583] In this invention, the server includes means for receiving information input from a user terminal and requesting processing from a generative artificial intelligence; means for returning the generation results received from the generative artificial intelligence to the user terminal; and means for specifying conditions for the generation process to the generative artificial intelligence. This enables the user to efficiently create teaching materials, correct essays, and generate practice plans.

[1584] A "user terminal" is a device operated by a user and is used for interacting with the system.

[1585] A "server" is a computing system that receives requests from user terminals, requests processing from generative artificial intelligence, and sends the results back to the user terminal.

[1586] "Generative artificial intelligence" refers to artificial intelligence models that generate test questions, document revisions, practice plans, and other similar materials based on information input by the user.

[1587] "Test questions or learning materials" refer to educational content created by generative artificial intelligence based on conditions specified by the user (teacher).

[1588] "Document correction" refers to the process by which generative artificial intelligence corrects the grammar and expressions within a document.

[1589] A "practice plan" is a set of specific practice method guidelines created by a generative artificial intelligence system based on the type of club activity, practice content, and goals entered by the student.

[1590] "Input method" refers to the method or interface by which a user provides information to a system.

[1591] "Transmission means" refers to the function that allows a user terminal or server to send information to other devices.

[1592] "Display means" refers to the function that provides the user with information received by the user terminal in a visual manner.

[1593] "Uploading" refers to the process of a user transferring files or data from their local device to a server.

[1594] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for their club activities. This system is realized through the cooperation of user terminals, a server, and generative artificial intelligence.

[1595] First, the teacher logs in using their user terminal and selects the "Create Test" option from the administration screen. Next, they enter the target grade level, subject, and question format, and the program sends this information to the server. The user terminals used are typical personal computers and tablet devices. An example of input would be "3rd Grade," "Mathematics," and "Multiple Choice."

[1596] The server analyzes the received information and sends the data to a generative artificial intelligence (e.g., OpenAI GPT-3). The generative AI generates test questions based on the provided prompt, "Generate multiple-choice math test questions for third-year students." The generated results are returned to the server in JSON format.

[1597] The server sends the generated test questions to the user's terminal and displays them for the teacher to review. The teacher reviews the generated test questions and makes corrections as needed. They then print the final test questions for distribution to students or share them online.

[1598] Furthermore, teachers upload document files from the management screen to efficiently correct students' essays and book reports. The program sends the uploaded files to the server, which then sends a document correction request to the generative artificial intelligence. The generative AI corrects the grammar and expressions within the document and returns the corrected document to the server. The server sends the results to the user's terminal and displays them for the teacher to review. The teacher reviews the corrections and provides final feedback to the student or their parents.

[1599] Furthermore, students access the administration screen to create practice plans for their club activities, entering the type of club activity, practice content, and goals. The program generates and sends a request to the server. The server sends a practice plan generation request to a generative artificial intelligence. The generative artificial intelligence generates a practice plan based on the specified conditions and sends the result back to the server. The server sends the generated practice plan to the user's terminal and displays it so that the student can actually check it. For example, if "soccer," "dribbling practice," and "physical fitness improvement" are entered, a specific practice plan will be generated.

[1600] These concrete examples enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[1601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1602] Tests and document creation

[1603] Step 1:

[1604] The user (teacher) logs into the administration panel and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1605] Input: Login information (username, password), grade level, subject, question format

[1606] Output: Input information is displayed and confirmed on the management screen.

[1607] Step 2:

[1608] The terminal generates a request to send the entered information to the server.

[1609] Input: Information entered by the user (teacher)

[1610] Output: The generated HTTP request is sent to the server.

[1611] Step 3:

[1612] The server receives an HTTP request and parses the information contained in the request body.

[1613] Input: HTTP request sent from the terminal

[1614] Output: The analyzed information is sent to the generative artificial intelligence.

[1615] Step 4:

[1616] Generative artificial intelligence generates test questions based on specified conditions.

[1617] Input: Test creation conditions sent from the server

[1618] Output: Generated test questions (JSON format)

[1619] Step 5:

[1620] The server adjusts the data format as needed based on the generation results received from the generative artificial intelligence and sends them to the user terminal as an HTTP response.

[1621] Input: Generated test questions (JSON format)

[1622] Output: Adjusted test question data

[1623] Step 6:

[1624] The user (teacher) reviews the generated test questions and makes corrections as needed.

[1625] Input: Test question data sent from the server

[1626] Output: Corrected test questions

[1627] Correction of essays and book reports

[1628] Step 1:

[1629] Users (teachers) upload student essays and reflection papers from the administration screen.

[1630] Input: Essay or reflection paper file

[1631] Output: Uploaded files are displayed on the admin screen.

[1632] Step 2:

[1633] The terminal generates a request to send the uploaded file to the server.

[1634] Input: Uploaded document file

[1635] Output: The generated HTTP request is sent to the server.

[1636] Step 3:

[1637] The server receives the file, analyzes its contents and metadata, and sends a document modification request to the generative artificial intelligence.

[1638] Input: Document file sent from the terminal

[1639] Output: The analyzed document data is sent to the generative artificial intelligence.

[1640] Step 4:

[1641] Generative artificial intelligence analyzes uploaded documents and corrects their grammar and expression.

[1642] Input: Document data sent from the server

[1643] Output: Modified document (JSON format)

[1644] Step 5:

[1645] The server receives the modified document and generates a response to display it on the user's terminal.

[1646] Input: Modified document (JSON format)

[1647] Output: Generated response data

[1648] Step 6:

[1649] The user (teacher) reviews the revised document and makes further revisions as needed.

[1650] Input: Modified document sent from the server

[1651] Output: Final revised document

[1652] Creating practice plans for club activities

[1653] Step 1:

[1654] The user (student) accesses the administration screen and selects the "Create Practice Plan" option. They then enter the type of club activity (e.g., soccer), the content of the practice (e.g., dribbling practice), and the goal (e.g., improving physical fitness).

[1655] Input: Type of club activity, practice content, goals

[1656] Output: Input information is displayed and confirmed on the management screen.

[1657] Step 2:

[1658] The terminal generates a request to send the entered information to the server.

[1659] Input: Information entered by the user (student)

[1660] Output: The generated HTTP request is sent to the server.

[1661] Step 3:

[1662] The server receives an HTTP request and parses the information contained in the request body.

[1663] Input: HTTP request sent from the terminal

[1664] Output: The analyzed information is sent to the generative artificial intelligence.

[1665] Step 4:

[1666] Generative artificial intelligence generates a practice plan based on specified conditions.

[1667] Input: Practice plan creation conditions sent from the server

[1668] Output: Generated practice plan (JSON format)

[1669] Step 5:

[1670] The server generates a response to send the generation results received from the generative artificial intelligence to the student terminals.

[1671] Input: Generated practice plan (JSON format)

[1672] Output: Generated response data

[1673] Step 6:

[1674] The user (student) reviews the generated practice plan and puts it into practice.

[1675] Input: Practice plan data sent from the server

[1676] Output: Implemented practice plan

[1677] (Application Example 1)

[1678] 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."

[1679] In today's educational environment, teachers spend a great deal of time and effort on lesson preparation and student evaluation, particularly creating teaching materials, correcting essays, and planning extracurricular activity practices. Furthermore, when providing educational services in physical stores, there is a lack of means to quickly and effectively prepare and distribute teaching materials. This increases the workload on teachers and reduces opportunities for students' independent learning and growth.

[1680] 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.

[1681] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for transmitting and displaying the generated content on a teacher's terminal; and means for creating and distributing teaching materials at physical stores. This makes it possible for teachers to quickly create teaching materials and to effectively provide educational services even at physical stores.

[1682] The system also includes means for teachers to upload students' essays or book reports, means for a generative artificial intelligence to correct the grammar and expression of the uploaded documents, means for sending and displaying the corrected documents on the teacher's terminal, and means for providing real-time corrections to essays and book reports. This streamlines the process of correcting essays and book reports and reduces the burden on teachers.

[1683] Furthermore, the system includes means for students to input the type of club activity and practice content, means for a generative artificial intelligence to generate a practice plan based on the input information, means for transmitting and displaying the generated practice plan on the student's terminal, and means for providing practice plans related to club activities and hobbies. This enables students to spontaneously plan and implement practice plans for their club activities.

[1684] A "teacher terminal" is an electronic device operated by a teacher, used for tasks such as generating test questions and learning materials, correcting essays, and reviewing practice plans.

[1685] "Generative artificial intelligence" refers to a system that includes algorithms and machine learning models for automatically generating test questions, learning materials, practice plans, and document revisions based on input information.

[1686] A "physical store" is a physical location that serves as a place for educational services or learning.

[1687] "Educational materials" refer to materials and tools used for educational purposes, including test questions, worksheets, and drills.

[1688] "Means of creation" refers to methods or devices used to generate information for a specific purpose.

[1689] "Transmission means" refers to functions or devices for transmitting generated information or data to other devices or systems.

[1690] "Display means" refers to devices or software that allow users to visually confirm transmitted information.

[1691] "Essays and book reviews" are written pieces in which students express their own thoughts and feelings, and are evaluated as part of their education.

[1692] "Grammar and expression correction" refers to the process of correcting grammatical errors and improving expressions within a text.

[1693] A "club activity practice plan" refers to a document that outlines the content and plan of practice sessions that students will undertake in their club activities.

[1694] This invention is a system that enables teachers to efficiently create teaching materials and tests, correct students' essays and book reports, and allow students to independently plan club activity practice schedules. This system utilizes generative artificial intelligence.

[1695] Hardware and software to be used

[1696] The hardware used includes teacher and student terminals, as well as display devices in physical stores. Specifically, this includes smartphones, tablets, personal computers, and smart glasses. The software includes Python libraries that implement generative artificial intelligence algorithms, and API servers for data transmission and reception. Specifically, Python, the requests library, and APIs for generative AI models are used.

[1697] Generation of teaching materials and tests

[1698] The server receives input from the teacher regarding the target grade level, subject, and question format, and sends this information to a generative artificial intelligence system to generate appropriate test questions and learning materials. The generated content is then sent to the teacher's terminal and displayed.

[1699] Specific example:

[1700] When a teacher creates a multiplication test for third graders, they input information such as "grade 3," "mathematics," and "multiple choice" from their teacher's terminal. The server receives this information and sends it to a generative artificial intelligence. The AI ​​generates test questions based on this information, sends them to the teacher's terminal via the server, and displays them.

[1701] Example of a prompt:

[1702] grade = 3

[1703] subject = 'mathematics'

[1704] question_type = 'multiple_choice'

[1705] Correction of essays and book reviews

[1706] Teachers upload students' essays and book reports, and a generative artificial intelligence system corrects their grammar and expression. The corrected documents are sent to the teacher's terminal and displayed.

[1707] Specific example:

[1708] When a teacher uploads a student's book report and requests correction from AI, the teacher uploads the report file from their terminal. The server receives it and sends it to the generative artificial intelligence. The AI ​​corrects grammar and expression, and then sends it back to the teacher's terminal via the server for display.

[1709] Example of a prompt:

[1710] essay_text = 'My name is Tanaka.'

[1711] Generating practice plans for club activities

[1712] Students input the type of club activity and practice content, and a generative artificial intelligence generates a personalized practice plan. The generated practice plan is sent to the student's device and displayed.

[1713] Specific example:

[1714] A student in the soccer club inputs "soccer," "dribbling practice," and "physical fitness improvement" to brainstorm new dribbling drills. The server receives this input and sends it to a generative artificial intelligence (AI). The AI ​​generates a specific practice plan, which is then sent to the student's terminal via the server for display.

[1715] Example of a prompt:

[1716] activity = 'soccer'

[1717] goal = 'improve stamina'

[1718] practice_type = 'dribbling'

[1719] These features enable teachers to quickly prepare teaching materials, streamline essay correction, and allow students to independently plan extracurricular activity practice sessions. This leads to more efficient and effective educational activities, reduces the workload on teachers, and promotes independent learning and growth among students.

[1720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1721] Step 1:

[1722] The user (teacher) logs into a dedicated administration screen and selects the "Create Test" option. They enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The information entered here forms the basis of the data used in subsequent processing steps. The output is an input confirmation screen, where the teacher reviews the entered information.

[1723] Step 2:

[1724] The terminal collects teacher input information and generates a request to send to the server. Specifically, it converts the input data into JSON format and prepares it for making an HTTP request to the appropriate endpoint. As output, appropriately configured request data is generated.

[1725] Step 3:

[1726] The server receives request data sent from the terminal. The server analyzes this data and sends a request to the generative artificial intelligence (AI) to generate test questions. This process involves creating a prompt statement to generate the most suitable test questions based on the input conditions and sending a request to the AI's API. The output is the response data from the AI, which includes the generated test questions.

[1727] Step 4:

[1728] The generative artificial intelligence generates test questions of a specified difficulty level and format based on the received prompt. A pre-trained model is used in this process. The output is the generated test questions, which are then sent back to the server.

[1729] Step 5:

[1730] The server prepares to send the test question data received from the generative artificial intelligence to the terminal. Specifically, it reconstructs the test question data into a format that is easy for the teacher to understand and sends it back to the terminal as an HTTP response. The output is the reconstructed test question data.

[1731] Step 6:

[1732] The terminal displays test question data received from the server to the teacher. The teacher reviews this data, makes corrections as needed, and generates the final test questions. User interaction is emphasized during this correction process. Finally, the corrected test questions are generated and ready to be distributed to students.

[1733] Step 7:

[1734] Users (teachers) upload student essays and reflection papers from the administration screen. These files are analyzed by the system and used in the next step. The output is the uploaded file.

[1735] Step 8:

[1736] The terminal generates a request to send the uploaded essay or review file to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[1737] Step 9:

[1738] The server sends the received essay and reflection files to a generative artificial intelligence system and requests corrections to their grammar and expression. This process involves data analysis and the generation of appropriate prompts. The output is the analyzed data that was sent.

[1739] Step 10:

[1740] The generative artificial intelligence analyzes uploaded essay and review files, correcting grammar and expression. The corrected document data is generated and sent back to the server. The output is the corrected document.

[1741] Step 11:

[1742] The server reconstructs the modified document received from the generative artificial intelligence and prepares it for transmission to the teacher terminal. The output is the reconstructed modified document.

[1743] Step 12:

[1744] The terminal displays the revised document data received from the server to the teacher. The teacher reviews it, makes any necessary final revisions, and returns it to the student. The output is the final revised document.

[1745] Step 13:

[1746] Users (students) access the administration screen and input the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The output is the club activity information entered.

[1747] Step 14:

[1748] The terminal generates a request to send student input information to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[1749] Step 15:

[1750] The server analyzes the received club activity information and sends a request to a generative artificial intelligence system to generate a practice plan. The data is then analyzed, and appropriate prompts are generated. The output is the analyzed data that was sent.

[1751] Step 16:

[1752] The generative artificial intelligence generates a practice plan based on specified conditions and sends it to the server. The generated plan is returned as a data format. The output is the generated practice plan.

[1753] Step 17:

[1754] The server prepares to send the practice plan data received from the generative artificial intelligence to the student's terminal. The reconstructed data is sent to the terminal. The output is the reconstructed practice plan data.

[1755] Step 18:

[1756] The terminal displays practice plan data received from the server to the student. The student reviews and then practices it. The output is the displayed practice plan.

[1757] 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.

[1758] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create club activity practice plans. Furthermore, by incorporating an emotion engine, it adds a function that adjusts the content of tools and materials based on the emotions of the users (teachers and students). This makes it possible to maximize the effectiveness of educational activities. As a specific example of this system, an embodiment based on the following program processing is shown.

[1759] Tests and document creation

[1760] overview

[1761] This system allows teachers to input the target grade level, subject, and question format, and a generative artificial intelligence then generates appropriate test questions and learning materials based on that input. An emotion engine recognizes the user's emotions, adjusts the content accordingly, and then transmits and displays the materials on the teacher's terminal.

[1762] Processing details

[1763] 1. User (Teacher)

[1764] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1765] 2. Terminal

[1766] Generate a request to send the teacher's input information to the server.

[1767] 3. Server

[1768] The server receives the input information and sends a request to the emotion engine to recognize the user's emotions.

[1769] 4. Emotional Engine

[1770] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1771] 5. Server

[1772] The server sends a request to the generative artificial intelligence to generate test questions, along with the results from the emotion engine.

[1773] 6. Generative Artificial Intelligence

[1774] Based on the specified conditions and the results of the emotion engine, test questions of appropriate difficulty are generated and sent back to the server.

[1775] 7. Server

[1776] The generated content is sent to the teacher's terminal and displayed.

[1777] 8. User (Teacher)

[1778] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1779] Specific example:

[1780] When a teacher creates a multiplication test for third graders, they enter "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), and before the AI ​​generates and returns the test questions, an emotion engine analyzes the teacher's emotions. If the teacher is nervous, the difficulty level of the test questions is adjusted to be lower. The teacher then reviews and corrects the generated test before distributing it to the students.

[1781] Creating materials for parents and proofreading essays and book reports.

[1782] overview

[1783] This system allows teachers to upload students' essays and book reports, whereupon a generative artificial intelligence corrects the grammar and expression, an emotion engine recognizes the user's emotions and adjusts the corrections accordingly, and then sends and displays the corrected work on the teacher's terminal.

[1784] Processing details

[1785] 1. User (Teacher)

[1786] Teachers upload student essays and reflection papers from the administration screen.

[1787] 2. Terminal

[1788] Generate a request to send the uploaded file to the server.

[1789] 3. Server

[1790] The server receives the file and sends a request for document revision instructions to the sentiment engine.

[1791] 4. Emotional Engine

[1792] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence.

[1793] 5. Generative Artificial Intelligence

[1794] The uploaded document is analyzed, grammatical and phrasing corrections are made based on the sentiment engine's results, and the results are sent back to the server.

[1795] 6. Server

[1796] Send the revised document to the teacher's terminal and display it.

[1797] 7. User (Teacher)

[1798] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1799] Specific example:

[1800] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), and before the AI ​​corrects grammar and expression, an emotion engine analyzes the teacher's emotions. If the teacher is tired, the correction suggestions are softer; if they are stressed, the approach is more cautious. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[1801] Generating practice methods and coaching methods for club activities

[1802] overview

[1803] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence generates a practice plan based on that input. An emotion engine then recognizes the user's emotions and adjusts the plan accordingly, before sending and displaying it on the student's device.

[1804] Processing details

[1805] 1. User (Student)

[1806] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1807] 2. Terminal

[1808] Generate a request to send the student's input information to the server.

[1809] 3. Server

[1810] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[1811] 4. Emotional Engine

[1812] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results to the generative artificial intelligence system.

[1813] 5. Generative Artificial Intelligence

[1814] Based on the specified conditions and the results from the emotion engine, a practice plan is generated and the results are sent back to the server.

[1815] 6. Server

[1816] The generated plan is sent to the student's device for display.

[1817] 7. User (Student)

[1818] Students review the generated practice plan and use it to improve their actual practice.

[1819] Specific example:

[1820] A soccer club student enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence, and before the AI ​​generates a practice plan, an emotion engine analyzes the student's emotions. If the student is tired, it suggests a plan that includes rest; if highly motivated, it presents a challenging plan. The student reviews the generated practice plan and uses it to improve their training.

[1821] ---

[1822] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth. Furthermore, the introduction of an emotion engine allows for flexible responses based on the user's emotions, improving the quality of the educational environment.

[1823] The following describes the processing flow.

[1824] Tests and document creation

[1825] Processing steps

[1826] Step 1:

[1827] User (Teacher)

[1828] The teacher logs into a dedicated administration panel and selects the "Create Test" option.

[1829] Step 2:

[1830] User (Teacher)

[1831] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1832] Step 3:

[1833] terminal

[1834] The device generates a request to send the teacher's input information to the server.

[1835] Step 4:

[1836] server

[1837] The server receives the input information and sends a request to the emotion engine to recognize the user's (teacher's) emotions.

[1838] Step 5:

[1839] Emotional Engine

[1840] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1841] Step 6:

[1842] server

[1843] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate test questions, incorporating these results.

[1844] Step 7:

[1845] Generative artificial intelligence

[1846] The generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and the teacher's emotional information, and sends the results back to the server.

[1847] Step 8:

[1848] server

[1849] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[1850] Step 9:

[1851] terminal

[1852] The device displays the received test questions, allowing the teacher to review and correct them.

[1853] Step 10:

[1854] User (Teacher)

[1855] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[1856] Creating materials for parents and proofreading essays and book reports.

[1857] Processing steps

[1858] Step 1:

[1859] User (Teacher)

[1860] Teachers upload student essays and reflection papers from the administration screen.

[1861] Step 2:

[1862] terminal

[1863] The device generates a request to send the uploaded file to the server.

[1864] Step 3:

[1865] server

[1866] The server receives the uploaded file and sends a request for document revision instructions to the emotion engine.

[1867] Step 4:

[1868] Emotional Engine

[1869] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[1870] Step 5:

[1871] server

[1872] The server receives the results from the emotion engine and sends a document revision request to the generative artificial intelligence, incorporating these results.

[1873] Step 6:

[1874] Generative artificial intelligence

[1875] The generative artificial intelligence corrects the grammar and expression of a given document based on the teacher's emotional information, and returns the results to the server.

[1876] Step 7:

[1877] server

[1878] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[1879] Step 8:

[1880] terminal

[1881] The device displays the received corrections, allowing the teacher to review and make final corrections.

[1882] Step 9:

[1883] User (Teacher)

[1884] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[1885] Generating practice methods and coaching methods for club activities

[1886] Processing steps

[1887] Step 1:

[1888] User (student)

[1889] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[1890] Step 2:

[1891] terminal

[1892] The device generates a request to send the student's input information to the server.

[1893] Step 3:

[1894] server

[1895] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[1896] Step 4:

[1897] Emotional Engine

[1898] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results back to the server.

[1899] Step 5:

[1900] server

[1901] The server receives the results from the emotion engine and sends a request to the generative artificial intelligence to generate a practice plan, incorporating these results.

[1902] Step 6:

[1903] Generative artificial intelligence

[1904] A generative artificial intelligence generates a practice plan based on specified conditions and student emotional information, and sends the result back to the server.

[1905] Step 7:

[1906] server

[1907] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[1908] Step 8:

[1909] terminal

[1910] The device displays the received practice plan, allowing students to review and practice it.

[1911] Step 9:

[1912] User (student)

[1913] Students review the generated practice plans and use them to improve their actual practice.

[1914] (Example 2)

[1915] 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."

[1916] In educational settings, teachers are required to quickly create teaching materials and tests, and to efficiently correct students' essays and book reports. Furthermore, support is needed when students independently create practice plans for extracurricular activities, but there is a lack of effective systems to consistently provide these services. In addition, improving the quality of education requires flexible responses based on the emotions of both teachers and students, but the current system is insufficient to address this.

[1917] 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.

[1918] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for an emotion analysis engine to analyze the teacher's emotions and provide the results to the generative artificial intelligence; means for the generative artificial intelligence to adjust the generated content based on the results of the emotion analysis engine; and means for transmitting and displaying the generated content on the teacher's terminal. This enables teachers to create teaching materials and tests efficiently and effectively, and to adjust educational content based on the user's emotions.

[1919] A "teacher" is a professional who provides education, primarily responsible for teaching subjects to students in educational institutions such as schools.

[1920] "Target grade level" refers to the grade level of students in the curriculum and is information used to indicate the learning stage of students targeted by specific educational content.

[1921] A "subject" refers to a specific academic field or subject taught in the curriculum, and examples include mathematics, Japanese language, and science.

[1922] "Question format" refers to the style of questions in test questions or learning materials, and includes formats such as multiple choice, written response, and fill-in-the-blank.

[1923] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates problems and materials based on given input information.

[1924] An "emotion analysis engine" refers to a technology that analyzes a user's emotions based on their facial expressions, voice, etc., and provides the results to other systems.

[1925] "Users" refer to individuals who utilize this system, and primarily include educators such as teachers and students.

[1926] "Generated content" refers to content such as test questions, practice plans, and learning materials created by a generative artificial intelligence based on input information and sentiment analysis results.

[1927] A "teacher terminal" refers to a device such as a computer or tablet used by a teacher, which displays generated content and other information.

[1928] "Uploading" refers to the act of sending data stored on a local device to a server over a network.

[1929] "Document correction" refers to the process performed by generative artificial intelligence to improve the grammar and expression of uploaded documents.

[1930] A "student" is a person who receives education, and primarily refers to learners who attend educational institutions such as schools.

[1931] A "practice plan" is a detailed plan outlining the practice activities a student plans to undertake, including the type of practice, specific methods, and goals.

[1932] "Student terminals" refer to devices such as computers and tablets used by students, which display generated practice plans and other information.

[1933] This invention provides a system that enables teachers to quickly create teaching materials and tests in educational settings, efficiently correct students' essays and book reports, and allow students to independently create practice plans for extracurricular activities. By incorporating an emotion analysis engine, this system appropriately adjusts its content based on the emotions of the user, both the teacher and the student.

[1934] Tests and document creation

[1935] The user, a teacher, logs into a dedicated administration screen and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The terminal analyzes the teacher's input and generates a request to send it to the server.

[1936] The server sends a request to the emotion analysis engine along with the received input information to obtain the teacher's emotion analysis results. The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice and sends the results back to the server.

[1937] Subsequently, the server sends a request to the generative artificial intelligence to generate test questions. Specifically, information including the results of the sentiment analysis engine is sent. Based on this, the generative artificial intelligence adjusts the difficulty and content of the test questions and generates a series of questions. The generated content is sent to the teacher's terminal via the server, where the teacher reviews the generated materials and makes corrections as needed.

[1938] Specific example:

[1939] If a teacher is creating a multiplication test for third graders, they would enter "3rd grade," "math," and "multiple choice."

[1940] If the emotion analysis engine detects teacher anxiety, the generative artificial intelligence generates test questions with a lower difficulty level.

[1941] The teacher reviews this test and distributes it to the students.

[1942] Creating materials for parents and proofreading essays and book reports.

[1943] The user, a teacher, accesses the administration screen and uploads student essays and reflection papers. The terminal generates a request to send the uploaded files to the server. The server receives the files and sends a request for document revision instructions to the sentiment analysis engine. The sentiment analysis engine analyzes the teacher's emotions and provides the results to the generative artificial intelligence.

[1944] The generative artificial intelligence analyzes uploaded documents and corrects grammar and expression based on the results of the sentiment analysis engine. The corrected results are sent to the teacher's terminal via the server, where the teacher reviews the changes and makes additional corrections as needed. Feedback is then provided to students and parents.

[1945] Specific example:

[1946] A teacher uploads a file of a student's book report to have it corrected.

[1947] If the emotion analysis engine detects teacher fatigue, the generative artificial intelligence will offer correction suggestions in a gentle tone.

[1948] The teacher reviews the corrections and provides feedback to the students.

[1949] Generating practice methods and coaching methods for club activities

[1950] The student user accesses the management screen and enters the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The device generates a request to send the entered information to the server. The server, along with the received information, sends a request to the sentiment analysis engine to analyze the student's emotions.

[1951] The emotion analysis engine analyzes the student's emotions from their facial expressions and voice, and sends the results back to the server. The server sends a request to the generative artificial intelligence to generate a practice plan, including the results from the emotion analysis engine. The generative artificial intelligence generates a practice plan based on the specified conditions and the results of the emotion analysis, and sends the results back to the server. The server sends the generated practice plan to the student's terminal, where the student reviews it and uses it to help with their actual practice.

[1952] Specific example:

[1953] A student in the soccer club types "soccer," "dribbling practice," and "physical fitness improvement" into their input to come up with a new dribbling practice method.

[1954] If the emotion analysis engine detects a high level of student motivation, the generative artificial intelligence generates a challenging practice plan.

[1955] Students review the generated plan and begin practicing.

[1956] As a result, teachers and students can conduct educational activities efficiently and effectively, and the introduction of the emotion analysis engine enables flexible responses based on the user's emotions.

[1957] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1958] Process steps for creating tests and documents

[1959] Step 1:

[1960] User (Teacher)

[1961] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[1962] Input: Target grade level, subject, question format

[1963] Output: Form input data

[1964] Step 2:

[1965] terminal

[1966] The terminal generates a request to send to the server based on the teacher's input information. Specifically, it constructs the input information as JSON data and sends it to the server as an HTTP request.

[1967] Input: Form input data

[1968] Output: Request to send to server

[1969] Step 3:

[1970] server

[1971] The server analyzes the incoming request and extracts the necessary data. Next, it sends an emotion recognition request to the emotion analysis engine. This request includes the teacher's facial image and voice data.

[1972] Input: Request to send to server

[1973] Output: Sentiment analysis request

[1974] Step 4:

[1975] Emotion analysis engine

[1976] The emotion analysis engine analyzes the teacher's emotions based on facial images and voice data, and sends the results back to the server. The analysis results include information such as tension, fatigue, and relaxation.

[1977] Input: Sentiment analysis request (facial image and audio data)

[1978] Output: Emotion analysis results (tension, fatigue, relaxation, etc.)

[1979] Step 5:

[1980] server

[1981] The server receives the sentiment analysis results and, along with information on the target grade level, subject, and question format, sends a request to the generative artificial intelligence to generate test questions.

[1982] Input: Sentiment analysis results, target grade level, subject, question format

[1983] Output: Request to generative artificial intelligence

[1984] Step 6:

[1985] Generative artificial intelligence

[1986] The generative artificial intelligence generates test questions based on specified conditions and the results of sentiment analysis. The generated test questions are then sent back to the server.

[1987] Input: Request to a generative artificial intelligence (conditions and sentiment analysis results)

[1988] Output: Generated test questions

[1989] Step 7:

[1990] server

[1991] The server receives the generated test questions, formats them, and sends them to the teacher's terminal.

[1992] Input: Generated test questions

[1993] Output: Request to send to the teacher's terminal

[1994] Step 8:

[1995] User (Teacher)

[1996] The teacher reviews the generated test questions displayed on their teacher's terminal and makes corrections as needed. Finally, they distribute the test to the students.

[1997] Input: Generated test questions

[1998] Output: Confirmed and corrected test questions

[1999] Steps for creating materials for parents and proofreading essays and book reports

[2000] Step 1:

[2001] User (Teacher)

[2002] Teachers access the administration panel and upload student essays and reflection papers.

[2003] Input: Essay or reflection paper file

[2004] Output: Upload request from teacher's terminal

[2005] Step 2:

[2006] terminal

[2007] The terminal generates a request to send to the server based on the uploaded file information. Specifically, it creates an HTTP POST request containing binary data.

[2008] Input: Upload request from teacher's terminal

[2009] Output: Request to send to server

[2010] Step 3:

[2011] server

[2012] The server processes the received file data and sends a request for document revision instructions to the sentiment analysis engine. This request also includes the teacher's facial expressions and voice data.

[2013] Input: Request to send to the server (file data)

[2014] Output: Sentiment analysis request

[2015] Step 4:

[2016] Emotion analysis engine

[2017] The emotion analysis engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence system.

[2018] Input: Emotion analysis request (facial expression and voice data)

[2019] Output: Emotion analysis results

[2020] Step 5:

[2021] Generative artificial intelligence

[2022] The generative artificial intelligence analyzes the uploaded document and corrects its grammar and expression based on the sentiment analysis results. The corrected results are then sent back to the server.

[2023] Input: Uploaded document, sentiment analysis results

[2024] Output: Revised document

[2025] Step 6:

[2026] server

[2027] The server receives the corrected document data and sends it to the teacher's terminal.

[2028] Input: Modified document

[2029] Output: Request to send to the teacher's terminal

[2030] Step 7:

[2031] User (Teacher)

[2032] Teachers review the revised documents displayed on their teacher terminals, make any final revisions as needed, and provide feedback to students and parents.

[2033] Input: Modified document

[2034] Output: Reviewed and corrected documents

[2035] Processing steps for generating club activity practice methods and coaching methods

[2036] Step 1:

[2037] User (student)

[2038] Students access the management screen and enter the type of club activity (e.g., soccer), desired practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[2039] Input: Type of club activity, practice content, goals

[2040] Output: Form input data

[2041] Step 2:

[2042] terminal

[2043] The terminal generates a request to send the input information to the server. Specifically, it converts the form input data into JSON format and creates an HTTP POST request.

[2044] Input: Form input data

[2045] Output: Request to send to server

[2046] Step 3:

[2047] server

[2048] The server analyzes the received information and sends a request to the emotion analysis engine to generate a practice plan. This request may include facial expression images and audio data.

[2049] Input: Request to send to server

[2050] Output: Sentiment analysis request

[2051] Step 4:

[2052] Emotion analysis engine

[2053] The emotion analysis engine analyzes students' emotions and sends the results to the generative artificial intelligence system.

[2054] Input: Emotion analysis request (facial expression and voice data)

[2055] Output: Emotion analysis results

[2056] Step 5:

[2057] Generative artificial intelligence

[2058] The generative artificial intelligence generates a practice plan based on specified conditions and emotion analysis results, and sends the results back to the server.

[2059] Input: Practice plan generation request, sentiment analysis results

[2060] Output: Generated practice plan

[2061] Step 6:

[2062] server

[2063] The server receives the generated plan and sends it to the student's device.

[2064] Input: Generated practice plan

[2065] Output: Request to send to student terminals

[2066] Step 7:

[2067] User (student)

[2068] Students review the generated practice plan and use it to improve their actual practice.

[2069] Input: Generated practice plan

[2070] Output: Practice plan to be reviewed and used

[2071] Through the steps described above, the system of the present invention provides appropriate teaching materials, document revisions, and practice plans based on user input information and sentiment analysis results, thereby improving the efficiency and effectiveness of educational activities.

[2072] (Application Example 2)

[2073] 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."

[2074] This invention aims to improve the quality of customer service and product descriptions in virtual stores. Conventional customer service systems have had difficulty responding flexibly to customer emotions and interests, resulting in a lack of satisfaction. In particular, in virtual stores in an online environment, the quality of real-time customer service is crucial, and appropriate product suggestions that respond to emotions are required. Furthermore, it is important to support store staff and virtual assistants in efficiently handling customer interactions.

[2075] 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.

[2076] In this invention, the server includes means for an emotion engine to analyze the emotions of customers and staff, means for a generative artificial intelligence to generate product suggestions based on the analysis results, and means for transmitting and displaying the generated suggestions on the staff's terminal. This enables flexible responses based on customer emotions.

[2077] A "teacher" is a person whose job is to teach knowledge and skills to students in an educational institution.

[2078] A "student" is a person who belongs to an educational institution and engages in learning.

[2079] "Target grade level" refers to a group of students belonging to a specific academic year as defined in the educational curriculum.

[2080] A "subject" refers to a specific academic field or subject that should be taught in the curriculum.

[2081] "Question format" refers to the format and style of questions used in tests and similar materials.

[2082] "Generative artificial intelligence" refers to artificial intelligence that has the function of generating information based on input data.

[2083] A "test question" is a set of questions or problems used to assess learning outcomes.

[2084] "Learning materials" refer to teaching materials and reference materials used for educational purposes.

[2085] An "emotion engine" is a system or software that analyzes human emotions and utilizes that information.

[2086] A "training plan" is a plan of training methods and procedures to achieve a specific purpose or goal.

[2087] "Product recommendation" refers to the act or content of recommending appropriate products according to the customer's needs and interests.

[2088] A "terminal" is a device or apparatus used for inputting or displaying information.

[2089] A "server" is a centralized management device used for various data processing and management tasks.

[2090] This invention is a system for improving the quality of customer service in virtual stores, utilizing an emotion analysis engine and generative artificial intelligence. This system interacts with customers using terminals such as smart glasses and head-mounted displays, analyzes their emotions in real time, and makes product suggestions based on that analysis.

[2091] System configuration and operation

[2092] hardware

[2093] Device: Smart glasses or head-mounted display (e.g., Microsoft HoloLens, Google Glass)

[2094] Server: A server that handles central administration and processing.

[2095] Emotion engine: A device that analyzes emotions from facial expressions, voice, etc. IBM Watson Tone Analyzer is an example of this.

[2096] software

[2097] Virtual assistant applications: Applications installed on smart glasses or head-mounted displays.

[2098] Emotion analysis software: Functions as an emotion engine and analyzes the user's emotional data.

[2099] Generative artificial intelligence: Uses OpenAI's GPT-4 and other methods to generate data based on input information.

[2100] Processing flow and data processing

[2101] 1. Customer information input and sentiment analysis

[2102] User: Store staff wear smart glasses or head-mounted displays and enter customer information. This information includes age, gender, and areas of interest.

[2103] Terminal: An application installed on smart glasses or a head-mounted display sends input information to the server.

[2104] 2. Analysis of emotions

[2105] Server: Receives input information and sends requests to the emotion engine to analyze customer and staff emotions.

[2106] Emotion Engine: Analyzes emotions from facial expressions, voice, etc., and sends the results back to the server.

[2107] 3. Generating Product Proposals

[2108] Server: Based on the sentiment analysis results and customer information, it sends a request to the generative artificial intelligence to generate appropriate product suggestions.

[2109] Generative artificial intelligence: Generates product suggestions based on specified conditions and emotion analysis results, and returns the results to the server.

[2110] 4. Submitting and displaying proposals

[2111] Server: Sends the generated product suggestions to staff members' devices (smart glasses or head-mounted displays) for display.

[2112] User: Staff members review the displayed product suggestions and explain the suggestions to customers in real time.

[2113] Specific example

[2114] scenario

[2115] Imagine a scenario where a store staff member wears a Microsoft HoloLens and a customer is browsing products in a virtual store. The staff member observes the customer's facial expressions and tone of voice through the HoloLens and analyzes their emotions. Based on the analysis results, they suggest appropriate products.

[2116] Example of a prompt

[2117] Customer sentiment: [Interest]

[2118] Customer attribute: [Female in her 20s]

[2119] Product Category: [Skincare]

[2120] I want a product like this: [A hypoallergenic skincare product for everyday use]

[2121] By having staff directly communicate the generated suggestions to customers via HoloLens, it will be possible to achieve a higher level of customer satisfaction.

[2122] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2123] Step 1:

[2124] The user wears smart glasses or a head-mounted display and enters customer information. This information, including age, gender, and areas of interest, is stored on the device. Input: Customer information. Output: Request to the server.

[2125] Step 2:

[2126] The terminal generates a request to send the entered customer information to the server. The terminal uses an application embedded in smart glasses or a head-mounted display. Input: Customer information. Output: Data transmission to the server.

[2127] Step 3:

[2128] The server sends the received customer information to the emotion engine and requests an analysis of the user's (staff and customer) emotions. Input: Customer information. Output: Request to the emotion engine.

[2129] Step 4:

[2130] The emotion engine analyzes the user's facial expressions and voice tone, and sends the results back to the server. Input: User's facial expression and voice data. Output: Emotion analysis results.

[2131] Step 5:

[2132] The server receives the emotion analysis results and sends a request to the generative artificial intelligence to generate product suggestions based on them. Input: Emotion analysis results. Output: Request to the generative artificial intelligence.

[2133] Step 6:

[2134] A generative artificial intelligence generates product suggestions based on emotion analysis results and customer information, and sends the results back to the server. Specifically, it selects the most suitable products based on the customer's areas of interest and emotional state. Input: Emotion analysis results and customer information. Output: Product suggestions.

[2135] Step 7:

[2136] The server sends generated product suggestions to the user's terminal and requests them to display them in real time. Input: Product suggestions. Output: Data sent to the terminal.

[2137] Step 8:

[2138] Users view product suggestions through smart glasses or head-mounted displays, and the suggestions are explained to customers in real time. Input: Product suggestions. Output: Customer support.

[2139] 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.

[2140] 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.

[2141] 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.

[2142] [Fourth Embodiment]

[2143] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2144] 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.

[2145] 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).

[2146] 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.

[2147] 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.

[2148] 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).

[2149] 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.

[2150] 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.

[2151] 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.

[2152] 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.

[2153] 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.

[2154] 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.

[2155] 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".

[2156] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for club activities. A specific example of this system, based on the following program processing, is shown.

[2157] Tests and document creation

[2158] overview

[2159] This system allows teachers to input the target grade level, subject, and question format, and then a generative artificial intelligence system generates appropriate test questions and learning materials based on that input, which are then sent to and displayed on the teacher's terminal.

[2160] Processing details

[2161] 1. User (Teacher)

[2162] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[2163] 2. Terminal

[2164] Generate a request to send the teacher's input information to the server.

[2165] 3. Server

[2166] The server receives the input information and sends a request to the generative artificial intelligence to generate test questions.

[2167] 4. Generative Artificial Intelligence

[2168] Based on the specified conditions, generate test questions of appropriate difficulty and send them to the server.

[2169] 5. Server

[2170] The generated content is sent to the teacher's terminal and displayed.

[2171] 6. User (Teacher)

[2172] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[2173] Specific example:

[2174] When a teacher creates a multiplication test for third graders, they input "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), which generates the test questions and sends them back. The teacher then reviews and corrects the generated test before distributing it to the students.

[2175] Creating materials for parents and proofreading essays and book reports.

[2176] overview

[2177] This system allows teachers to upload students' essays and book reports, which are then processed by a generative artificial intelligence system that corrects their grammar and expression, and then sent and displayed on the teacher's terminal.

[2178] Processing details

[2179] 1. User (Teacher)

[2180] Teachers upload student essays and reflection papers from the administration screen.

[2181] 2. Terminal

[2182] Generate a request to send the uploaded file to the server.

[2183] 3. Server

[2184] The server receives the file and sends a request for document modification to the generative artificial intelligence.

[2185] 4. Generative Artificial Intelligence

[2186] The system analyzes uploaded documents, corrects grammar and expression, and sends them to the server.

[2187] 5. Server

[2188] Send the revised document to the teacher's terminal and display it.

[2189] 6. User (Teacher)

[2190] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[2191] Specific example:

[2192] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), which corrects grammar and expression. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[2193] Generating practice methods and coaching methods for club activities

[2194] overview

[2195] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence then generates a practice plan based on that input, which is then sent to and displayed on the student's device.

[2196] Processing details

[2197] 1. User (Student)

[2198] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[2199] 2. Terminal

[2200] Generate a request to send the student's input information to the server.

[2201] 3. Server

[2202] The server receives the information and sends a request to the generative artificial intelligence to generate a practice plan.

[2203] 4. Generative Artificial Intelligence

[2204] Based on the specified conditions, a practice plan is generated and sent to the server.

[2205] 5. Server

[2206] The generated plan is sent to the student's device for display.

[2207] 6. User (Student)

[2208] Students review the generated practice plan and put it into practice.

[2209] Specific example:

[2210] A student in the soccer club enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence (AI), which generates a specific practice plan and sends it back. The student then reviews the generated practice plan and uses it to improve their training.

[2211] ---

[2212] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[2213] The following describes the processing flow.

[2214] Tests and document creation

[2215] Processing steps

[2216] Step 1:

[2217] User (Teacher)

[2218] The teacher logs into the administration panel and selects the "Create Test" option.

[2219] Step 2:

[2220] User (Teacher)

[2221] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[2222] Step 3:

[2223] terminal

[2224] The device generates a request to send the teacher's input information to the server.

[2225] Step 4:

[2226] server

[2227] The server receives the teacher's input information and sends a request to the generative artificial intelligence to generate test questions.

[2228] Step 5:

[2229] Generative artificial intelligence

[2230] A generative artificial intelligence generates test questions of appropriate difficulty based on specified conditions and sends the results back to the server.

[2231] Step 6:

[2232] server

[2233] The server sends the test questions received from the generative artificial intelligence to the teacher's terminal.

[2234] Step 7:

[2235] terminal

[2236] The device displays the received test questions, allowing the teacher to review and correct them.

[2237] Step 8:

[2238] User (Teacher)

[2239] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[2240] Creating materials for parents and proofreading essays and book reports.

[2241] Processing steps

[2242] Step 1:

[2243] User (Teacher)

[2244] Teachers upload student essays and reflection papers from the administration screen.

[2245] Step 2:

[2246] terminal

[2247] The device generates a request to send the uploaded file to the server.

[2248] Step 3:

[2249] server

[2250] The server receives the uploaded file and sends a request for document modification to the generative artificial intelligence.

[2251] Step 4:

[2252] Generative artificial intelligence

[2253] The generative artificial intelligence analyzes the uploaded document, corrects its grammar and expression, and returns the results to the server.

[2254] Step 5:

[2255] server

[2256] The server sends the corrected document received from the generative artificial intelligence to the teacher terminal.

[2257] Step 6:

[2258] terminal

[2259] The device displays the received corrections, allowing the teacher to review and make final corrections.

[2260] Step 7:

[2261] User (Teacher)

[2262] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[2263] Generating practice methods and coaching methods for club activities

[2264] Processing steps

[2265] Step 1:

[2266] User (student)

[2267] Students access the management screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[2268] Step 2:

[2269] terminal

[2270] The terminal generates a request to send the student's input information to the server.

[2271] Step 3:

[2272] server

[2273] The server receives the student's input information and sends a request to the generative artificial intelligence to generate a practice plan.

[2274] Step 4:

[2275] Generative artificial intelligence

[2276] The generative artificial intelligence generates a practice plan based on specified conditions and sends the result back to the server.

[2277] Step 5:

[2278] server

[2279] The server sends the practice plan received from the generative artificial intelligence to the student's terminal.

[2280] Step 6:

[2281] terminal

[2282] The device displays the received practice plan, allowing students to review and practice it.

[2283] Step 7:

[2284] User (student)

[2285] Students review the generated practice plans and use them to improve their actual practice.

[2286] (Example 1)

[2287] 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".

[2288] Traditional methods for creating teaching materials, correcting essays, and developing practice plans for extracurricular activities required teachers and students to do it manually, which was time-consuming and laborious. Furthermore, qualitative evaluation and planning were challenging. In particular, the increased workload on teachers and the resulting limitations on students' opportunities for independent learning made it difficult to achieve efficient educational activities.

[2289] 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.

[2290] In this invention, the server includes means for receiving information input from a user terminal and requesting processing from a generative artificial intelligence; means for returning the generation results received from the generative artificial intelligence to the user terminal; and means for specifying conditions for the generation process to the generative artificial intelligence. This enables the user to efficiently create teaching materials, correct essays, and generate practice plans.

[2291] A "user terminal" is a device operated by a user and is used for interacting with the system.

[2292] A "server" is a computing system that receives requests from user terminals, requests processing from generative artificial intelligence, and sends the results back to the user terminal.

[2293] "Generative artificial intelligence" refers to artificial intelligence models that generate test questions, document revisions, practice plans, and other similar materials based on information input by the user.

[2294] "Test questions or learning materials" refer to educational content created by generative artificial intelligence based on conditions specified by the user (teacher).

[2295] "Document correction" refers to the process by which generative artificial intelligence corrects the grammar and expressions within a document.

[2296] A "practice plan" is a set of specific practice method guidelines created by a generative artificial intelligence system based on the type of club activity, practice content, and goals entered by the student.

[2297] "Input method" refers to the method or interface by which a user provides information to a system.

[2298] "Transmission means" refers to the function that allows a user terminal or server to send information to other devices.

[2299] "Display means" refers to the function that provides the user with information received by the user terminal in a visual manner.

[2300] "Uploading" refers to the process of a user transferring files or data from their local device to a server.

[2301] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create practice plans for their club activities. This system is realized through the cooperation of user terminals, a server, and generative artificial intelligence.

[2302] First, the teacher logs in using their user terminal and selects the "Create Test" option from the administration screen. Next, they enter the target grade level, subject, and question format, and the program sends this information to the server. The user terminals used are typical personal computers and tablet devices. An example of input would be "3rd Grade," "Mathematics," and "Multiple Choice."

[2303] The server analyzes the received information and sends the data to a generative artificial intelligence (e.g., OpenAI GPT-3). The generative AI generates test questions based on the provided prompt, "Generate multiple-choice math test questions for third-year students." The generated results are returned to the server in JSON format.

[2304] The server sends the generated test questions to the user's terminal and displays them for the teacher to review. The teacher reviews the generated test questions and makes corrections as needed. They then print the final test questions for distribution to students or share them online.

[2305] Furthermore, teachers upload document files from the management screen to efficiently correct students' essays and book reports. The program sends the uploaded files to the server, which then sends a document correction request to the generative artificial intelligence. The generative AI corrects the grammar and expressions within the document and returns the corrected document to the server. The server sends the results to the user's terminal and displays them for the teacher to review. The teacher reviews the corrections and provides final feedback to the student or their parents.

[2306] Furthermore, students access the administration screen to create practice plans for their club activities, entering the type of club activity, practice content, and goals. The program generates and sends a request to the server. The server sends a practice plan generation request to a generative artificial intelligence. The generative artificial intelligence generates a practice plan based on the specified conditions and sends the result back to the server. The server sends the generated practice plan to the user's terminal and displays it so that the student can actually check it. For example, if "soccer," "dribbling practice," and "physical fitness improvement" are entered, a specific practice plan will be generated.

[2307] These concrete examples enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth.

[2308] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2309] Tests and document creation

[2310] Step 1:

[2311] The user (teacher) logs into the administration panel and selects the "Create Test" option. They then enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[2312] Input: Login information (username, password), grade level, subject, question format

[2313] Output: Input information is displayed and confirmed on the management screen.

[2314] Step 2:

[2315] The terminal generates a request to send the entered information to the server.

[2316] Input: Information entered by the user (teacher)

[2317] Output: The generated HTTP request is sent to the server.

[2318] Step 3:

[2319] The server receives an HTTP request and parses the information contained in the request body.

[2320] Input: HTTP request sent from the terminal

[2321] Output: The analyzed information is sent to the generative artificial intelligence.

[2322] Step 4:

[2323] Generative artificial intelligence generates test questions based on specified conditions.

[2324] Input: Test creation conditions sent from the server

[2325] Output: Generated test questions (JSON format)

[2326] Step 5:

[2327] The server adjusts the data format as needed based on the generation results received from the generative artificial intelligence and sends them to the user terminal as an HTTP response.

[2328] Input: Generated test questions (JSON format)

[2329] Output: Adjusted test question data

[2330] Step 6:

[2331] The user (teacher) reviews the generated test questions and makes corrections as needed.

[2332] Input: Test question data sent from the server

[2333] Output: Corrected test questions

[2334] Correction of essays and book reports

[2335] Step 1:

[2336] Users (teachers) upload student essays and reflection papers from the administration screen.

[2337] Input: Essay or reflection paper file

[2338] Output: Uploaded files are displayed on the admin screen.

[2339] Step 2:

[2340] The terminal generates a request to send the uploaded file to the server.

[2341] Input: Uploaded document file

[2342] Output: The generated HTTP request is sent to the server.

[2343] Step 3:

[2344] The server receives the file, analyzes its contents and metadata, and sends a document modification request to the generative artificial intelligence.

[2345] Input: Document file sent from the terminal

[2346] Output: The analyzed document data is sent to the generative artificial intelligence.

[2347] Step 4:

[2348] Generative artificial intelligence analyzes uploaded documents and corrects their grammar and expression.

[2349] Input: Document data sent from the server

[2350] Output: Modified document (JSON format)

[2351] Step 5:

[2352] The server receives the modified document and generates a response to display it on the user's terminal.

[2353] Input: Modified document (JSON format)

[2354] Output: Generated response data

[2355] Step 6:

[2356] The user (teacher) reviews the revised document and makes further revisions as needed.

[2357] Input: Modified document sent from the server

[2358] Output: Final revised document

[2359] Creating practice plans for club activities

[2360] Step 1:

[2361] The user (student) accesses the administration screen and selects the "Create Practice Plan" option. They then enter the type of club activity (e.g., soccer), the content of the practice (e.g., dribbling practice), and the goal (e.g., improving physical fitness).

[2362] Input: Type of club activity, practice content, goals

[2363] Output: Input information is displayed and confirmed on the management screen.

[2364] Step 2:

[2365] The terminal generates a request to send the entered information to the server.

[2366] Input: Information entered by the user (student)

[2367] Output: The generated HTTP request is sent to the server.

[2368] Step 3:

[2369] The server receives an HTTP request and parses the information contained in the request body.

[2370] Input: HTTP request sent from the terminal

[2371] Output: The analyzed information is sent to the generative artificial intelligence.

[2372] Step 4:

[2373] Generative artificial intelligence generates a practice plan based on specified conditions.

[2374] Input: Practice plan creation conditions sent from the server

[2375] Output: Generated practice plan (JSON format)

[2376] Step 5:

[2377] The server generates a response to send the generation results received from the generative artificial intelligence to the student terminals.

[2378] Input: Generated practice plan (JSON format)

[2379] Output: Generated response data

[2380] Step 6:

[2381] The user (student) reviews the generated practice plan and puts it into practice.

[2382] Input: Practice plan data sent from the server

[2383] Output: Implemented practice plan

[2384] (Application Example 1)

[2385] 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".

[2386] In today's educational environment, teachers spend a great deal of time and effort on lesson preparation and student evaluation, particularly creating teaching materials, correcting essays, and planning extracurricular activity practices. Furthermore, when providing educational services in physical stores, there is a lack of means to quickly and effectively prepare and distribute teaching materials. This increases the workload on teachers and reduces opportunities for students' independent learning and growth.

[2387] 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.

[2388] In this invention, the server includes means for teachers to input the target grade level, subject, and question format; means for a generative artificial intelligence to generate test questions or learning materials based on the input information; means for transmitting and displaying the generated content on a teacher's terminal; and means for creating and distributing teaching materials at physical stores. This makes it possible for teachers to quickly create teaching materials and to effectively provide educational services even at physical stores.

[2389] The system also includes means for teachers to upload students' essays or book reports, means for a generative artificial intelligence to correct the grammar and expression of the uploaded documents, means for sending and displaying the corrected documents on the teacher's terminal, and means for providing real-time corrections to essays and book reports. This streamlines the process of correcting essays and book reports and reduces the burden on teachers.

[2390] Furthermore, the system includes means for students to input the type of club activity and practice content, means for a generative artificial intelligence to generate a practice plan based on the input information, means for transmitting and displaying the generated practice plan on the student's terminal, and means for providing practice plans related to club activities and hobbies. This enables students to spontaneously plan and implement practice plans for their club activities.

[2391] A "teacher terminal" is an electronic device operated by a teacher, used for tasks such as generating test questions and learning materials, correcting essays, and reviewing practice plans.

[2392] "Generative artificial intelligence" refers to a system that includes algorithms and machine learning models for automatically generating test questions, learning materials, practice plans, and document revisions based on input information.

[2393] A "physical store" is a physical location that serves as a place for educational services or learning.

[2394] "Educational materials" refer to materials and tools used for educational purposes, including test questions, worksheets, and drills.

[2395] "Means of creation" refers to methods or devices used to generate information for a specific purpose.

[2396] "Transmission means" refers to functions or devices for transmitting generated information or data to other devices or systems.

[2397] "Display means" refers to devices or software that allow users to visually confirm transmitted information.

[2398] "Essays and book reviews" are written pieces in which students express their own thoughts and feelings, and are evaluated as part of their education.

[2399] "Grammar and expression correction" refers to the process of correcting grammatical errors and improving expressions within a text.

[2400] A "club activity practice plan" refers to a document that outlines the content and plan of practice sessions that students will undertake in their club activities.

[2401] This invention is a system that enables teachers to efficiently create teaching materials and tests, correct students' essays and book reports, and allow students to independently plan club activity practice schedules. This system utilizes generative artificial intelligence.

[2402] Hardware and software to be used

[2403] The hardware used includes teacher and student terminals, as well as display devices in physical stores. Specifically, this includes smartphones, tablets, personal computers, and smart glasses. The software includes Python libraries that implement generative artificial intelligence algorithms, and API servers for data transmission and reception. Specifically, Python, the requests library, and APIs for generative AI models are used.

[2404] Generation of teaching materials and tests

[2405] The server receives input from the teacher regarding the target grade level, subject, and question format, and sends this information to a generative artificial intelligence system to generate appropriate test questions and learning materials. The generated content is then sent to the teacher's terminal and displayed.

[2406] Specific example:

[2407] When a teacher creates a multiplication test for third graders, they input information such as "grade 3," "mathematics," and "multiple choice" from their teacher's terminal. The server receives this information and sends it to a generative artificial intelligence. The AI ​​generates test questions based on this information, sends them to the teacher's terminal via the server, and displays them.

[2408] Example of a prompt:

[2409] grade = 3

[2410] subject = 'mathematics'

[2411] question_type = 'multiple_choice'

[2412] Correction of essays and book reviews

[2413] Teachers upload students' essays and book reports, and a generative artificial intelligence system corrects their grammar and expression. The corrected documents are sent to the teacher's terminal and displayed.

[2414] Specific example:

[2415] When a teacher uploads a student's book report and requests correction from AI, the teacher uploads the report file from their terminal. The server receives it and sends it to the generative artificial intelligence. The AI ​​corrects grammar and expression, and then sends it back to the teacher's terminal via the server for display.

[2416] Example of a prompt:

[2417] essay_text = 'My name is Tanaka.'

[2418] Generating practice plans for club activities

[2419] Students input the type of club activity and practice content, and a generative artificial intelligence generates a personalized practice plan. The generated practice plan is sent to the student's device and displayed.

[2420] Specific example:

[2421] A student in the soccer club inputs "soccer," "dribbling practice," and "physical fitness improvement" to brainstorm new dribbling drills. The server receives this input and sends it to a generative artificial intelligence (AI). The AI ​​generates a specific practice plan, which is then sent to the student's terminal via the server for display.

[2422] Example of a prompt:

[2423] activity = 'soccer'

[2424] goal = 'improve stamina'

[2425] practice_type = 'dribbling'

[2426] These features enable teachers to quickly prepare teaching materials, streamline essay correction, and allow students to independently plan extracurricular activity practice sessions. This leads to more efficient and effective educational activities, reduces the workload on teachers, and promotes independent learning and growth among students.

[2427] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2428] Step 1:

[2429] The user (teacher) logs into a dedicated administration screen and selects the "Create Test" option. They enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice). The information entered here forms the basis of the data used in subsequent processing steps. The output is an input confirmation screen, where the teacher reviews the entered information.

[2430] Step 2:

[2431] The terminal collects teacher input information and generates a request to send to the server. Specifically, it converts the input data into JSON format and prepares it for making an HTTP request to the appropriate endpoint. As output, appropriately configured request data is generated.

[2432] Step 3:

[2433] The server receives request data sent from the terminal. The server analyzes this data and sends a request to the generative artificial intelligence (AI) to generate test questions. This process involves creating a prompt statement to generate the most suitable test questions based on the input conditions and sending a request to the AI's API. The output is the response data from the AI, which includes the generated test questions.

[2434] Step 4:

[2435] The generative artificial intelligence generates test questions of a specified difficulty level and format based on the received prompt. A pre-trained model is used in this process. The output is the generated test questions, which are then sent back to the server.

[2436] Step 5:

[2437] The server prepares to send the test question data received from the generative artificial intelligence to the terminal. Specifically, it reconstructs the test question data into a format that is easy for the teacher to understand and sends it back to the terminal as an HTTP response. The output is the reconstructed test question data.

[2438] Step 6:

[2439] The terminal displays test question data received from the server to the teacher. The teacher reviews this data, makes corrections as needed, and generates the final test questions. User interaction is emphasized during this correction process. Finally, the corrected test questions are generated and ready to be distributed to students.

[2440] Step 7:

[2441] Users (teachers) upload student essays and reflection papers from the administration screen. These files are analyzed by the system and used in the next step. The output is the uploaded file.

[2442] Step 8:

[2443] The terminal generates a request to send the uploaded essay or review file to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[2444] Step 9:

[2445] The server sends the received essay and reflection files to a generative artificial intelligence system and requests corrections to their grammar and expression. This process involves data analysis and the generation of appropriate prompts. The output is the analyzed data that was sent.

[2446] Step 10:

[2447] The generative artificial intelligence analyzes uploaded essay and review files, correcting grammar and expression. The corrected document data is generated and sent back to the server. The output is the corrected document.

[2448] Step 11:

[2449] The server reconstructs the modified document received from the generative artificial intelligence and prepares it for transmission to the teacher terminal. The output is the reconstructed modified document.

[2450] Step 12:

[2451] The terminal displays the revised document data received from the server to the teacher. The teacher reviews it, makes any necessary final revisions, and returns it to the student. The output is the final revised document.

[2452] Step 13:

[2453] Users (students) access the administration screen and input the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness). The output is the club activity information entered.

[2454] Step 14:

[2455] The terminal generates a request to send student input information to the server. The converted data is sent to the server in JSON format. The output is the correct request data.

[2456] Step 15:

[2457] The server analyzes the received club activity information and sends a request to a generative artificial intelligence system to generate a practice plan. The data is then analyzed, and appropriate prompts are generated. The output is the analyzed data that was sent.

[2458] Step 16:

[2459] The generative artificial intelligence generates a practice plan based on specified conditions and sends it to the server. The generated plan is returned as a data format. The output is the generated practice plan.

[2460] Step 17:

[2461] The server prepares to send the practice plan data received from the generative artificial intelligence to the student's terminal. The reconstructed data is sent to the terminal. The output is the reconstructed practice plan data.

[2462] Step 18:

[2463] The terminal displays practice plan data received from the server to the student. The student reviews and then practices it. The output is the displayed practice plan.

[2464] 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.

[2465] This invention provides a system that enables teachers to quickly create teaching materials and tests, efficiently correct students' essays and book reports, and allow students to independently create club activity practice plans. Furthermore, by incorporating an emotion engine, it adds a function that adjusts the content of tools and materials based on the emotions of the users (teachers and students). This makes it possible to maximize the effectiveness of educational activities. As a specific example of this system, an embodiment based on the following program processing is shown.

[2466] Tests and document creation

[2467] overview

[2468] This system allows teachers to input the target grade level, subject, and question format, and a generative artificial intelligence then generates appropriate test questions and learning materials based on that input. An emotion engine recognizes the user's emotions, adjusts the content accordingly, and then transmits and displays the materials on the teacher's terminal.

[2469] Processing details

[2470] 1. User (Teacher)

[2471] The teacher logs into a dedicated administration panel and selects the "Create Test" option. Next, they enter the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[2472] 2. Terminal

[2473] Generate a request to send the teacher's input information to the server.

[2474] 3. Server

[2475] The server receives the input information and sends a request to the emotion engine to recognize the user's emotions.

[2476] 4. Emotional Engine

[2477] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server.

[2478] 5. Server

[2479] The server sends a request to the generative artificial intelligence to generate test questions, along with the results from the emotion engine.

[2480] 6. Generative Artificial Intelligence

[2481] Based on the specified conditions and the results of the emotion engine, test questions of appropriate difficulty are generated and sent back to the server.

[2482] 7. Server

[2483] The generated content is sent to the teacher's terminal and displayed.

[2484] 8. User (Teacher)

[2485] The teacher reviews the generated materials, makes any necessary corrections, and distributes them to the students.

[2486] Specific example:

[2487] When a teacher creates a multiplication test for third graders, they enter "Grade 3," "Mathematics," and "Multiple Choice" into the management screen. The server sends this information to a generative artificial intelligence (AI), and before the AI ​​generates and returns the test questions, an emotion engine analyzes the teacher's emotions. If the teacher is nervous, the difficulty level of the test questions is adjusted to be lower. The teacher then reviews and corrects the generated test before distributing it to the students.

[2488] Creating materials for parents and proofreading essays and book reports.

[2489] overview

[2490] This system allows teachers to upload students' essays and book reports, whereupon a generative artificial intelligence corrects the grammar and expression, an emotion engine recognizes the user's emotions and adjusts the corrections accordingly, and then sends and displays the corrected work on the teacher's terminal.

[2491] Processing details

[2492] 1. User (Teacher)

[2493] Teachers upload student essays and reflection papers from the administration screen.

[2494] 2. Terminal

[2495] Generate a request to send the uploaded file to the server.

[2496] 3. Server

[2497] The server receives the file and sends a request for document revision instructions to the sentiment engine.

[2498] 4. Emotional Engine

[2499] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results to the generative artificial intelligence.

[2500] 5. Generative Artificial Intelligence

[2501] The uploaded document is analyzed, grammatical and phrasing corrections are made based on the sentiment engine's results, and the results are sent back to the server.

[2502] 6. Server

[2503] Send the revised document to the teacher's terminal and display it.

[2504] 7. User (Teacher)

[2505] The teacher reviews the revised version, makes final corrections as needed, and provides feedback to the students and their parents.

[2506] Specific example:

[2507] Teachers upload student book reports to an administration panel for correction. The server sends the file to a generative artificial intelligence (AI), and before the AI ​​corrects grammar and expression, an emotion engine analyzes the teacher's emotions. If the teacher is tired, the correction suggestions are softer; if they are stressed, the approach is more cautious. The teacher reviews the corrected report, makes any further revisions, and returns it to the student.

[2508] Generating practice methods and coaching methods for club activities

[2509] overview

[2510] This system allows students to input the type of club activity and desired practice content, and a generative artificial intelligence generates a practice plan based on that input. An emotion engine then recognizes the user's emotions and adjusts the plan accordingly, before sending and displaying it on the student's device.

[2511] Processing details

[2512] 1. User (Student)

[2513] Students access the administration screen and enter the type of club activity (e.g., soccer), practice content (e.g., dribbling practice), and goals (e.g., improving physical fitness).

[2514] 2. Terminal

[2515] Generate a request to send the student's input information to the server.

[2516] 3. Server

[2517] The server receives the information and sends a request to the emotion engine to generate a practice plan.

[2518] 4. Emotional Engine

[2519] The emotion engine analyzes students' emotions from their facial expressions and voices, and sends the results to the generative artificial intelligence system.

[2520] 5. Generative Artificial Intelligence

[2521] Based on the specified conditions and the results from the emotion engine, a practice plan is generated and the results are sent back to the server.

[2522] 6. Server

[2523] The generated plan is sent to the student's device for display.

[2524] 7. User (Student)

[2525] Students review the generated practice plan and use it to improve their actual practice.

[2526] Specific example:

[2527] A soccer club student enters "soccer," "dribbling practice," and "physical fitness improvement" into the management screen to devise a new dribbling practice method. The server sends the information to a generative artificial intelligence, and before the AI ​​generates a practice plan, an emotion engine analyzes the student's emotions. If the student is tired, it suggests a plan that includes rest; if highly motivated, it presents a challenging plan. The student reviews the generated practice plan and uses it to improve their training.

[2528] ---

[2529] These embodiments enable teachers and students to conduct educational activities efficiently and effectively. Teachers can reduce their workload, and students can promote independent learning and growth. Furthermore, the introduction of an emotion engine allows for flexible responses based on the user's emotions, improving the quality of the educational environment.

[2530] The following describes the processing flow.

[2531] Tests and document creation

[2532] Processing steps

[2533] Step 1:

[2534] User (Teacher)

[2535] The teacher logs into a dedicated administration panel and selects the "Create Test" option.

[2536] Step 2:

[2537] User (Teacher)

[2538] The teacher enters the target grade level (e.g., 3rd grade), subject (e.g., mathematics), and question format (e.g., multiple choice).

[2539] Step 3:

[2540] terminal

[2541] The device generates a request to send the teacher's input information to the server.

[2542] Step 4:

[2543] server

[2544] The server receives the input information and sends a request to the emotion engine to recognize the user's (teacher's) emotions.

[2545] Step 5:

[2546] Emotional Engine

[2547] The emotion engine analyzes the teacher's emotions from their facial expressions and voice, and sends the results back to the server. ...

Claims

1. A means for teachers to input the target grade level, subject, and question format, A means by which a generative artificial intelligence generates test questions or learning materials based on input information, A means of sending and displaying the generated content on the teacher's terminal, A system that includes this.

2. A means for teachers to upload students' essays or reflections, A means for a generative artificial intelligence to correct the grammar and expression of an uploaded document, A means of sending and displaying the revised document on the teacher's terminal, The system according to claim 1, including the following:

3. A means for students to input the type of club activity and practice content, A means by which a generative artificial intelligence generates a practice plan based on input information, A means of sending and displaying the generated practice plan on the student's device, The system according to claim 1, including the following:

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