System

Abstract

A system is provided.SOLUTION: A means for displaying an interface for inputting information on a terminal, a means for transmitting information related to a grade, a teaching material, and a unit input from the terminal to a server, a means for generating an initial guidance plan in the server based on the received information, a means for transmitting the initial guidance plan to the terminal, a means for displaying the received initial guidance plan on the terminal, a means for transmitting a feedback or a correction request input by a user from the terminal to the server, and a means for transmitting the feedback or the correction request from the server to the terminal. A system comprising: means for regenerating an instruction plan based on the received feedback or correction request; means for transmitting the regenerated instruction plan to the terminal; and means for displaying the received regenerated instruction plan in the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] It takes a lot of time and effort for teachers to create lesson plans for each lesson, which is one of the reasons for teachers' long working hours. Furthermore, changes in textbooks and revisions to curriculum guidelines often require teachers to rewrite lesson plans, further increasing the burden on teachers. With traditional methods, lesson plans are created and revised manually, which is inefficient and limits the creativity of teachers. [Means for solving the problem]

[0005] The present invention solves these problems by providing a system for efficiently creating lesson plans using a generative AI model. Specifically, the system includes a means for displaying an interface on a terminal for inputting information, a means for transmitting information about the grade, teaching materials, and units input from the terminal to a server, and a means for generating an initial lesson plan based on the information received at the server. The system also includes a means for transmitting the initial lesson plan to the terminal and displaying the received initial lesson plan at the terminal, and a means for transmitting feedback and revision requests input by the user to the server. The server regenerates the lesson plan based on the received feedback and revision requests and transmits the regenerated lesson plan to the terminal. The terminal displays the received regenerated lesson plan and provides a means for the user to finalize the lesson plan as needed. In this way, teachers can easily make multifaceted revisions and quickly create lesson plans optimized for each lesson.

[0006] A "terminal" is an electronic device that allows teachers to input information and to check, modify, and save the generated lesson plans.

[0007] The "server" is a computer device that generates and regenerates lesson plans based on received information and sends them to the terminal.

[0008] "Information" is data entered by the user regarding the grade, teaching materials, and unit.

[0009] An "interface" is a screen or operating means through which users can input information and confirm and provide feedback on lesson plans.

[0010] A "lesson plan" is a lesson plan written by a teacher when he or she teaches a lesson, and includes an initial lesson plan and a regenerated lesson plan.

[0011] An "initial lesson plan" is the lesson plan that the server first generates using the generative AI model.

[0012] "Feedback" refers to opinions and requests for corrections that the user inputs after checking the initial lesson plan.

[0013] A "request for revision" is a specific change or addition to a lesson plan provided by a user.

[0014] "Regeneration" is the process by which the server regenerates a new lesson plan based on feedback and correction requests received from the user.

[0015] "PDF format" is a type of electronic document format in which final lesson plans are saved. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user. Below, we will explain the program processing of this system from the perspectives of the server, terminal, and user.

[0038] server

[0039] The server is responsible for generating lesson plans using a generative AI model and regenerating the lesson plans based on user feedback.

[0040] First, the server receives information about the grade, teaching materials, and unit from the device. Based on this, the server starts the lesson plan generation process. In this process, the generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[0041] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[0042] Terminal

[0043] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[0044] When the server returns the initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format, and there is a field for the user to enter feedback or correction requests. When correction requests are entered, the device sends them to the server.

[0045] Furthermore, when the revised lesson plan is received, it is displayed again to the user for final confirmation, and finally, a link is provided to allow the user to download the completed lesson plan in PDF format.

[0046] User

[0047] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Then, once they have completed the input, they press the send button.

[0048] The user can review the initial lesson plan sent from the server and enter feedback and requests for revisions to any areas that require revision. For example, they can enter specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[0049] Check the regenerated lesson plan displayed on the device and make any final corrections. Once you have confirmed that all your requests have been reflected, download and save the lesson plan in PDF format.

[0050] In this way, the present invention enables teachers to create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[0054] Step 2:

[0055] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[0056] Step 3:

[0057] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[0058] Step 4:

[0059] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[0060] Step 5:

[0061] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[0062] Step 6:

[0063] The server sends the generated initial lesson plan to the device in JSON format.

[0064] Step 7:

[0065] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[0066] Step 8:

[0067] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[0068] Step 9:

[0069] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[0070] Step 10:

[0071] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[0072] Step 11:

[0073] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[0074] Step 12:

[0075] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[0076] Step 13:

[0077] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[0078] Step 14:

[0079] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[0080] Step 15:

[0081] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[0082] Step 16:

[0083] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[0084] By going through these steps, teachers can create lesson plans effectively and efficiently.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] The traditional lesson plan creation process is time-consuming and laborious for teachers, requiring detailed plans for each grade, teaching material, and unit. This means that teachers spend a lot of time creating lesson plans, which can lead to neglecting to prepare lessons for students. Furthermore, when revisions or adjustments to lesson plans are needed, they must be made manually, which is inefficient and prone to errors.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes means for generating an initial lesson plan using a generative AI model based on the received information, means for regenerating a lesson plan using the generative AI model based on the received feedback and revision requests, and means for providing the final lesson plan in PDF format, thereby improving the efficiency and accuracy of lesson plan creation.

[0090] The "interface for inputting information" refers to the input form or operation screen that is displayed on the terminal so that the user can input information such as the grade, teaching materials, and units.

[0091] A "terminal" is an electronic device operated by a user, such as a PC, tablet, or smartphone.

[0092] The "server" is a central computer system that receives requests from users, processes them, and returns the results, and plays an important role in generating lesson plans using generative AI models.

[0093] A "generative AI model" is a type of model that uses artificial intelligence to generate text data, which is then used to create lesson plans based on specific prompts.

[0094] An "initial lesson plan" is a lesson plan that is first generated using a generative AI model based on the information received by the server.

[0095] "Feedback and correction requests" are opinions and requests for improvements provided by users regarding the initial lesson plan, and are important information for the server to receive and regenerate.

[0096] A "regenerated lesson plan" is a lesson plan that has been regenerated using a generative AI model based on feedback and requests for revisions.

[0097] "PDF format" is an abbreviation for Portable Document Format, and is an electronic file format in which the document layout is fixed.

[0098] A "prompt sentence" is an input sentence used to inform a generative AI model to generate a lesson plan.

[0099] A "link" is a hypertext link that provides access to a specific file or web page that a user can click to download a PDF version of the lesson plan.

[0100] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user and regenerates them in response to feedback. A specific implementation of this system is described below.

[0101] server

[0102] The server serves multiple roles as a central computer system. In particular, it receives information sent by users and generates lesson plans based on that information using a generative AI model. Specifically, it operates as follows:

[0103] First, the server receives information such as the grade, teaching material name, and unit name from the terminal. This process can be performed using Apache or Nginx as a web server, with Python and Flask as the backend. It receives an HTTP POST request, analyzes the information, and stores it in the appropriate variables.

[0104] Based on the received information, the server sends a prompt to the generative AI model (e.g., a generative AI model). This prompt is formed by inserting the received information into a predefined template. For example, a possible prompt might be, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson."

[0105] The generative AI model generates an initial lesson plan based on this prompt sentence, and the generated result is returned to the server, which stores this initial lesson plan in a database, converts it to JSON format, and sends it to the device.

[0106] If the server receives further feedback or correction requests from the user, it sends a new prompt including the feedback to the generative AI model to generate a new lesson plan. This process is repeated as many times as necessary to finally provide the completed lesson plan.

[0107] Terminal

[0108] The terminal provides an interface that makes it easy for users to input information. It has the following main functions:

[0109] First, an interface for entering the grade, teaching material name, and unit name is displayed. This is designed as an HTML form and often uses React or Vue.js as the front-end framework. For example, it provides a drop-down menu for entering the grade and text fields for entering the teaching material name and unit name.

[0110] When the user enters the required information and presses the submit button, the device sends this information to the server as an HTTP POST request using the fetch API or the axios library.

[0111] Receives the initial lesson plan returned from the server and displays it to the user in an easy-to-understand format. Provides a field for inputting feedback or requests for revisions to the lesson plan, and sends it back to the server. Receives a regenerated lesson plan as needed and displays it again to the user.

[0112] Once the final lesson plan is completed, the device will provide a downloadable link in PDF format, which users can click to download the lesson plan as a PDF file.

[0113] User

[0114] The user, i.e., the teacher, accesses the system using a terminal and creates a lesson plan using the following procedure.

[0115] First, enter the necessary information such as the grade, teaching material name, and unit name into the device interface. For example, enter information such as "Grade: 1st year of high school," "Materials used: English textbook," and "Unit name: Unit 3," and then press the send button.

[0116] The teacher reviews the initial lesson plan returned by the server and inputs any necessary corrections or feedback, such as specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[0117] Review the regenerated lesson plan again and make any final adjustments necessary. Once you are sure that all your requests have been reflected, download the lesson plan as a PDF and save it.

[0118] Through this process, teachers can quickly and efficiently create high-quality lesson plans, significantly reducing the amount of time and effort required, and enabling them to provide high-quality lessons to students.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] The terminal displays an interface for the user to input information. The user inputs information such as the grade, teaching material name, and unit name, and presses the send button. At this point, the input is information such as the grade, teaching material name, and unit name, and the output is an HTTP POST request sent to the server.

[0122] Step 2:

[0123] The server receives information about the grade, teaching material name, and unit name sent from the terminal. It receives the HTTP POST request, analyzes the information, and stores it in the appropriate variables. The input is the information sent from the terminal, and the output is the storage of the analyzed information in the variables.

[0124] Step 3:

[0125] The server sends a prompt to the generative AI model based on the received information. This prompt is formed by inserting received information such as the grade, teaching material name, and unit name into a predefined template. For example, the request might read, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson." The input is the analyzed information, and the output is the prompt sent to the generative AI model.

[0126] Step 4:

[0127] The generative AI model generates an initial lesson plan based on the prompt sentence. The generated lesson plan is sent back to the server. The input is the prompt sentence, and the output is the text data of the initial lesson plan.

[0128] Step 5:

[0129] The server receives the initial lesson plan returned from the generative AI model, saves it in a database, converts it to JSON format, and sends it to the device. At this time, it sends it as an HTTP response, setting the Content-Type in the response header to application / json. The input is the text data of the initial lesson plan, and the output is JSON-formatted data sent to the device.

[0130] Step 6:

[0131] The device receives the initial lesson plan returned from the server, analyzes it, and displays it to the user. It formats it into a highly readable format so that the user can check it. The input is the initial lesson plan data in JSON format, and the output is the formatted lesson plan that is displayed to the user.

[0132] Step 7:

[0133] The user checks the displayed initial lesson plan and inputs feedback and requests for revisions. For example, they input specific requests such as "extend the introduction to 20 minutes" or "add pair work," and then press the submit button. The input is the user's feedback and requests for revisions, and the output is an HTTP POST request sent to the server.

[0134] Step 8:

[0135] The server receives and analyzes feedback and correction requests sent from the device. Based on this information, it sends new prompts to the generative AI model and regenerates the lesson plan. The input is the feedback and correction requests, and the output is the new prompt sent to the generative AI model.

[0136] Step 9:

[0137] The generative AI model generates a regenerated lesson plan based on the new prompt sentence and sends it back to the server. The input is the new prompt sentence, and the output is the text data of the regenerated lesson plan.

[0138] Step 10:

[0139] The server receives the text data of the regenerated lesson plan, saves it in the database again, converts it into JSON format, and sends it to the terminal. The input is the text data of the regenerated lesson plan, and the output is JSON format data sent to the terminal.

[0140] Step 11:

[0141] The device receives the regenerated lesson plan returned from the server and displays it to the user. It also provides an interface for accepting confirmation and feedback. The input is the regenerated lesson plan data in JSON format, and the output is a formatted regenerated lesson plan that is displayed to the user.

[0142] Step 12:

[0143] The user checks it again and provides additional feedback if necessary. The server then regenerates it and displays a link on the device that provides the final completed lesson plan in PDF format. Clicking this link allows the user to download the lesson plan as a PDF file. The input is the user's final check and feedback, and the output is a PDF file of the final lesson plan with a download link.

[0144] In this way, the server, terminal, and user each play their respective roles, enabling efficient and accurate creation of high-quality lesson plans.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Factory production processes require the optimization of a wide variety of line, equipment, and product combinations. However, currently, this process is primarily done manually, requiring a great deal of time and effort. Furthermore, the feedback and correction processes for production plans are inefficient. This invention aims to solve these problems and improve the efficiency of creating and correcting production process plans.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes means for providing a display device for inputting information, means for transmitting information related to the line, equipment, and product input from the display device to an electronic device, means for generating an initial process plan in the electronic device based on the received information, means for transmitting the initial process plan to the display device, means for displaying the received initial process plan in the display device, means for transmitting feedback and revision requests input by a user from the display device to the electronic device, means for regenerating a process plan in the electronic device based on the received feedback and revision requests, means for transmitting the regenerated process plan to the display device, and means for displaying the received regenerated process plan in the display device. This enables efficient creation and revision of production process plans for a factory.

[0150] The "display device for inputting information" is a terminal device equipped with a user interface for users to input information about lines, equipment, and products.

[0151] "Display input information" means line, equipment, and product related data provided through a user interface.

[0152] "Electronic device" refers to a server or computer for information processing.

[0153] "Initial process plan" refers to the initial outline plan for the production process of a factory, generated by the server.

[0154] "Regeneration" is the process of recreating a process plan based on received feedback and revision requests.

[0155] "Feedback and revision requests" are evaluations and suggestions for improvement provided by users regarding the initial process plan.

[0156] "PDF Downloadable Link" means a hyperlink that enables the final process plan to be downloaded as a PDF file.

[0157] "Input Area" refers to a dedicated field within the interface where a user can enter feedback or correction requests.

[0158] A "process plan" is a production schedule that includes the necessary steps, time, and resource allocation for the factory line, equipment, and target product.

[0159] This invention is a system for efficiently creating and modifying factory production process plans. The system utilizes display devices, electronics, and generative AI models to generate process plans and regenerate them based on user feedback.

[0160] The display device provides a user interface for users to input information about the line, equipment, and product. Through this interface, users input data related to the production line, the equipment used, and the target product. For example, they input specific information such as "Production Line 1," "Robot Arm X," and "Part A." Once input is complete, the information is transmitted to the electronic device.

[0161] After receiving the information, the electronic device uses the generative AI model to generate an initial process plan, which includes details of each process in the factory, standard times, and required resources. This initial process plan is then sent to a display device and displayed to the user.

[0162] The user reviews the displayed initial process plan and enters feedback and requests for revisions into the display device, including specific requests such as "extend the duration of process 3 to 20 minutes" or "add a new inspection step." The feedback and requests for revisions are then sent back to the electronics, which then uses the generative AI model to regenerate a new process plan.

[0163] The regenerated process plan is sent back to the display device and displayed to the user. The user can then check the regenerated process plan and make any final corrections. Once the user has confirmed that all requests have been reflected, the final process plan can be downloaded in PDF format.

[0164] Hardware and software used

[0165] Hardware: Smartphone or tablet (display device), server (electronic device)

[0166] Software: Generative AI models (e.g., OpenAI GPT)

[0167] The generated data and data calculations are as follows: The display device collects input data from the user and transmits it to the electronic device. The electronic device uses the generative AI model to generate an initial process plan, including details of each process, standard times, and the types of machines and robots to be used. It then regenerates the process plan based on feedback and revision requests.

[0168] Specific examples

[0169] For example, if a factory manager types:

[0170] Production Line: Line 1

[0171] Equipment used: Robot Arm X

[0172] Target product: Part A

[0173] An example of a prompt to input to the generative AI model is:

[0174] Use the generative AI model to generate a production process plan for production line 1, robot arm X, and target product part A. The initial plan should include details of each process, standard times, and required resources.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The user uses the display device to input information about the line, equipment, and product. The input provides specific data such as "Production Line 1," "Robot Arm X," and "Part A." The input information is received by the user interface.

[0178] Step 2:

[0179] The terminal transmits the input information to the electronic device (server). The transmitted data includes all information provided by the user about the production line, the equipment used, and the target product.

[0180] Step 3:

[0181] The server uses a generative AI model to generate an initial process plan based on the received information. The server inputs the data, and the generative AI model calculates and outputs the details, standard times, and required resources for each process. The initial process plan includes specific process steps and required resources.

[0182] Step 4:

[0183] The server sends the created initial process plan to the terminal, which includes the generated process plan as output.

[0184] Step 5:

[0185] The terminal then displays the received initial process plan on a display device, which includes details such as each step of the process, the time it will take, and the equipment to be used.

[0186] Step 6:

[0187] Users can review the initial process plan and provide feedback and correction requests through input fields, including specific requests such as "extend the time required for process 3 to 20 minutes" or "add a new inspection step."

[0188] Step 7:

[0189] The terminal transmits the feedback and correction requests from the user to the server. The transmitted data includes the input feedback and correction requests.

[0190] Step 8:

[0191] The server regenerates the process plan using a generative AI model based on the received feedback and correction requests. The generative AI model then processes and calculates the new data to create a new process plan that reflects the feedback.

[0192] Step 9:

[0193] The server sends the regenerated process plan to the terminal, and includes the regenerated plan as output.

[0194] Step 10:

[0195] The terminal displays the received regenerated process plan on the display device, allowing the user to make final confirmation of the regenerated plan and make further corrections as necessary.

[0196] Step 11:

[0197] Once the final confirmation is complete, the terminal will provide a link to download the final process plan in PDF format, which the user can use to download and use the final plan.

[0198] The above processing steps enable efficient creation and modification of factory production process plans.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] This invention combines a system that enables teachers to efficiently create lesson plans for each lesson with an emotion engine that recognizes the user's emotions. This system allows teachers to create lesson plans more efficiently and in a stress-free environment. Below, we will explain in detail the program processing of this system from the perspectives of the server, terminal, and user.

[0201] server

[0202] The server generates lesson plans using the generative AI model, regenerates the plans based on user feedback, and also automatically corrects lesson plans and provides support content based on information obtained from the emotion engine.

[0203] First, the server receives information about the grade, teaching materials, and unit sent from the device. This information is analyzed and the lesson plan generation process begins. The generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[0204] In addition, the server is equipped with an emotion engine that analyzes the user's emotional state and, if necessary, proposes initial lesson plans and support content.

[0205] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[0206] Terminal

[0207] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[0208] When the server returns an initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format and includes fields for entering feedback and correction requests. In addition, an emotion engine recognizes the user's emotions and automatically suggests feedback and correction requests. For example, if the user is feeling stressed, suggested correction requests are automatically displayed.

[0209] The device sends information from the emotion engine to the server, which then uses this information to regenerate lesson plans and support content, ultimately providing a link to download the completed lesson plans in PDF format.

[0210] User

[0211] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Once they have completed the input, they press the send button.

[0212] The students review the initial lesson plan sent from the server and enter feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[0213] The user checks the regenerated lesson plan displayed on the device and makes any final corrections. During this process, the emotion engine recognizes the user's emotions and may suggest appropriate feedback or correction requests. For example, if the user is feeling stressed, the engine may suggest "simplify this part."

[0214] Once teachers have confirmed that all requests have been reflected, they can download and save the lesson plan in PDF format. This system not only enables teachers to create high-quality lesson plans quickly and efficiently, but also reduces stress with the support of the emotion engine.

[0215] Through these steps, the present invention enables teachers to create lesson plans effectively and efficiently, enabling them to provide high-quality lessons to students.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[0219] Step 2:

[0220] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[0221] Step 3:

[0222] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[0223] Step 4:

[0224] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[0225] Step 5:

[0226] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[0227] Step 6:

[0228] The server uses an emotion engine to recognize the user's emotional state, which is done in parallel with the generation of the initial lesson plan.

[0229] Step 7:

[0230] Based on the information obtained from the emotion engine, the server modifies the initial lesson plan as needed and adds supportive content, including, for example, suggestions to simplify the overall lesson plan if the user is feeling stressed.

[0231] Step 8:

[0232] The server sends the generated or modified initial lesson plan to the device in JSON format.

[0233] Step 9:

[0234] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[0235] Step 10:

[0236] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[0237] Step 11:

[0238] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[0239] Step 12:

[0240] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[0241] Step 13:

[0242] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[0243] Step 14:

[0244] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[0245] Step 15:

[0246] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[0247] Step 16:

[0248] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[0249] Step 17:

[0250] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[0251] Step 18:

[0252] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[0253] Step 19:

[0254] The emotion engine continuously monitors the user's emotions and provides supportive content as needed. For example, if the user is feeling stressed, it will display advice on relaxation techniques and stress management.

[0255] Through these steps, teachers can create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students. Support from the emotion engine reduces stress for teachers and creates a better educational environment.

[0256] Example 2

[0257] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0258] Conventional lesson plan creation systems require teachers to manually create lesson plans, requiring a great deal of time and effort. Furthermore, they lack support that takes into account the psychological state and emotions of teachers, which can result in increased stress for teachers and affect the quality of instruction. To address these issues, a system was needed that would enable teachers to create lesson plans more efficiently and provide appropriate support according to their emotional state.

[0259] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for displaying an interface for inputting information, means for communicating information on the education level, type of teaching material, and education unit input from the terminal, means for generating an initial education plan based on the received information, means for transmitting the initial education plan to the terminal, means for communicating feedback and revision requests input by the user from the terminal, means for regenerating the education plan generated based on the received feedback and revision requests, means for transmitting the regenerated education plan to the terminal, means for displaying the regenerated education plan received on the terminal, means for analyzing the user's emotions using an emotion analysis engine, and means for revising the content of the education plan based on the user's emotional state and suggesting advice and support content. This enables teachers to quickly and efficiently create high-quality education plans and provide instruction while reducing stress with the support of the emotion engine.

[0260] An "interface" is a means, such as a screen or input form, through which a user provides input information.

[0261] A "terminal" is a device such as a computer or tablet that a user operates and uses to input information.

[0262] A "server" is a computer system on a network that receives and processes information sent from a terminal.

[0263] "Education level" is information indicating the grade or educational stage.

[0264] "Type of teaching material" is information indicating the category of educational materials or texts to be used.

[0265] An "educational unit" is information that indicates a specific topic or section that is covered in an educational curriculum.

[0266] An "initial teaching plan" is the first lesson plan created by the server using a generative AI model.

[0267] "Communication" refers to sending and receiving information between a terminal and a server.

[0268] "Feedback" refers to opinions and information on improvements to the initial education plan provided by the user.

[0269] "Request for revision" is information about a specific request for change made by a user to the initial training plan.

[0270] "Regeneration" is the process of regenerating the educational plan based on feedback and requests for revisions.

[0271] An "emotion analysis engine" is a software component for recognizing and analyzing a user's emotional state.

[0272] "Advice" refers to teaching plans and improvement suggestions based on the sentiment analysis engine.

[0273] "Support content" refers to additional materials and actions that complement the educational plan and support teachers' teaching activities.

[0274] This invention relates to a system that allows educators to efficiently create lesson plans, and further combines it with an emotion engine that recognizes the user's emotions. Details for specifically implementing this system are provided below.

[0275] Hardware and Software Configuration

[0276] server

[0277] The server is a high-performance computer system that functions as the center of information processing. This server is equipped with a generative AI model and an emotion analysis engine. The generative AI model automatically generates teaching plans, and the emotion analysis engine is used to analyze the emotional state of teachers.

[0278] Terminal

[0279] A terminal is a device used by teachers to input information and check generated lesson plans. Specific hardware examples include personal computers, tablets, and smartphones. Software that provides a user interface is installed on the terminal.

[0280] User

[0281] The user (in this case, a teacher) operates a terminal to input the information necessary to create a lesson plan, and provides feedback and requests for revisions. Based on the information entered by the user, the server generates and regenerates a lesson plan.

[0282] What the program does

[0283] Entering initial information

[0284] Users use the terminal to enter information such as educational level, type of teaching material, and teaching unit. This information can be easily entered through an input form. For example, it can be entered in the format of "Grade: 1st year of high school," "Teaching material: English textbook," and "Unit name: Unit 3."

[0285] Transmission and processing of information

[0286] The device sends the input information to the server, which then uses the generative AI model to generate an initial lesson plan based on the received information.

[0287] Example prompt sentence:

[0288] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[0289] Displaying and feedback on initial lesson plans

[0290] The generated initial lesson plan is sent from the server to the device, which displays it to the user. The user can review the displayed initial lesson plan and enter feedback and requests for revisions. For example, specific requests such as "extend the introduction to 20 minutes" or "add pair work" can be included.

[0291] Sentiment Analysis and Regeneration

[0292] When the server receives feedback or correction requests from the user, it uses an emotion analysis engine to analyze the user's emotional state. Based on the user's emotional state, it adjusts the content of the educational plan and suggests advice and support content. For example, if the user is feeling stressed, it will suggest corrections to reduce the user's stress.

[0293] View and download the final lesson plan

[0294] The regenerated lesson plan is sent from the server to the device again and displayed to the user. Once the lesson plan has been confirmed to reflect all requests, the device provides a link to download the lesson plan in PDF format.

[0295] This configuration allows teachers to quickly and efficiently create high-quality lesson plans, and the support provided by the emotion analysis engine enables them to teach effectively while reducing stress.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1:

[0298] The user enters information into the device. The user enters information such as grade, teaching materials, and unit name into the input form displayed on the device's interface. This input form is designed with an intuitive UI, and information is entered in the format of, for example, "Grade: 1st year of high school," "Teaching materials used: English textbook," and "Unit name: Unit 3." This operation collects the initial information.

[0299] input:

[0300] Grade: 1st year of high school

[0301] Materials used: English textbook

[0302] Unit Name: Unit 3

[0303] output:

[0304] Information entered into the device.

[0305] Step 2:

[0306] The device sends the input information to the server. When the user clicks the send button, the device sends the input information to the server in JSON format or similar.

[0307] Specific behavior:

[0308] The terminal sends the following data:

[0309] {

[0310] "grade": "first year of high school",

[0311] "material": "English text",

[0312] "unit": "Unit 3"

[0313] }

[0314] input:

[0315] Information entered into the device.

[0316] output:

[0317] Information received by the server.

[0318] Step 3:

[0319] The server generates an initial lesson plan using a generative AI model. The server analyzes the received information and inputs it as a prompt to the generative AI model. Based on this prompt, the generative AI model automatically generates an initial lesson plan.

[0320] Examples:

[0321] Prompt statement:

[0322] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[0323] input:

[0324] {

[0325] "grade": "first year of high school",

[0326] "material": "English text",

[0327] "unit": "Unit 3"

[0328] }

[0329] output:

[0330] Initial guidance plan.

[0331] Step 4:

[0332] The server sends the initial lesson plan to the terminal, which then displays the generated initial lesson plan to the user.

[0333] Specific behavior:

[0334] The server transmits the generated lesson plan, which is received and displayed by the terminal.

[0335] Generated lesson plan:

[0336] 1. Introduction to the lesson (10 minutes)

[0337] 2. Introduction to the topic (15 minutes)

[0338] 3. Exercises (20 minutes)

[0339] 4. Reflection (10 min)

[0340] input:

[0341] Initial guidance plan.

[0342] output:

[0343] The initial lesson plan displayed on the device.

[0344] Step 5:

[0345] The user inputs feedback and requests for revisions to the initial lesson plan. The user reviews the initial lesson plan and inputs feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[0346] input:

[0347] Initial guidance plan.

[0348] output:

[0349] Feedback and correction requests.

[0350] Step 6:

[0351] The device sends feedback and correction requests to the server. After the user enters feedback and correction requests, the device sends them to the server. The data is again sent in JSON format.

[0352] Specific behavior:

[0353] The device sends the following feedback and correction requests to the server:

[0354] {

[0355] "feedback": "Extend introduction to 20 minutes",

[0356] "request": "Add pair work"

[0357] }

[0358] input:

[0359] Feedback and correction requests.

[0360] output:

[0361] Feedback and correction requests received by the server.

[0362] Step 7:

[0363] The server generates a regenerated lesson plan and analyzes the user's emotional state using an emotion analysis engine. Depending on the emotional state, if the user is feeling stressed, a correction plan to reduce the burden is suggested. The regenerated lesson plan is created.

[0364] Specific behavior:

[0365] The server uses an emotion analysis engine to determine the user's emotional state and suggests corrections as needed based on a generative AI model.

[0366] input:

[0367] Feedback and correction requests, sentiment analysis results.

[0368] output:

[0369] The regenerated lesson plan.

[0370] Step 8:

[0371] The server transmits the regenerated lesson plan to the terminal, which then displays it to the user.

[0372] Specific behavior:

[0373] The server transmits the generated regenerated lesson plan, which is received and displayed by the terminal.

[0374] Regenerated lesson plan:

[0375] 1. Introduction to the lesson (20 minutes)

[0376] 2. Introduction to the topic (10 minutes)

[0377] 3. Pair work (15 minutes)

[0378] 4. Exercises (20 minutes)

[0379] 5. Reflection (10 min.)

[0380] input:

[0381] The regenerated lesson plan.

[0382] output:

[0383] The regenerated lesson plan displayed on the device.

[0384] Step 9:

[0385] The user checks the final lesson plan and downloads it in PDF format. After checking, the user clicks the download link on their device to save the final lesson plan in PDF format.

[0386] Specific behavior:

[0387] Click the download link displayed on your device and save the PDF.

[0388] PDF file:

[0389] First Year High School English Unit 3 Lesson Plan

[0390] 1. Introduction to the lesson (20 minutes)

[0391] 2. Introduction to the topic (10 minutes)

[0392] 3. Pair work (15 minutes)

[0393] 4. Exercises (20 minutes)

[0394] 5. Reflection (10 min.)

[0395] input:

[0396] The regenerated lesson plan.

[0397] output:

[0398] Final lesson plan in PDF format.

[0399] (Application example 2)

[0400] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0401] Conventional lesson plan creation systems make it difficult for teachers to efficiently create detailed lesson plans for each lesson, placing a heavy burden on busy teachers in particular. Furthermore, lesson plans are not appropriately revised based on the teacher's emotional state, and an environment that can reduce stress is not provided. As a result, the reduction of teacher workload and improvement of lesson quality are not fully achieved.

[0402] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for displaying an interface for inputting information on a terminal; means for transmitting information on the grade, teaching material, and unit input from the terminal to the server; means for generating, in the server, an initial lesson plan using a generative model based on the received information; means for transmitting the initial lesson plan to the terminal; means for displaying, in the terminal, the received initial lesson plan; means for transmitting feedback and revision requests input by the user from the terminal to the server; means for regenerating, in the server, a lesson plan based on the received feedback and revision requests; means for transmitting the regenerated lesson plan to the terminal; means for analyzing the user's emotional state using an emotion engine and automatically revising the lesson plan and revision suggestions based on the emotion data; means for providing support content based on information obtained from the emotion engine; and means for displaying, in the terminal, the received regenerated lesson plan. This will enable teachers to create lesson plans efficiently, and will also enable them to receive automatic corrections and support content based on their emotional state, enabling them to deliver high-quality lessons while reducing stress.

[0403] An "interface" is a screen or input means that makes it easy for users to input information.

[0404] A "terminal" is an electronic device used by a user (for example, a smartphone or a head-mounted display).

[0405] "Server" means a central processing unit for generating and regenerating lesson plans.

[0406] "Grade" refers to the year in the educational curriculum that the student is in.

[0407] "Teaching materials" refer to textbooks and supplementary materials used in classes.

[0408] A "unit" is a specific theme or division of content within an educational curriculum.

[0409] "Generative model" refers to artificial intelligence technology for creating initial lesson plans and regenerative lesson plans.

[0410] An "initial lesson plan" is a lesson plan that the generative model first creates based on the received information.

[0411] "Feedback" refers to evaluations and requests for revisions to the initial lesson plan provided by the user.

[0412] "Regeneration" is the process of recreating a lesson plan based on received feedback and revision requests.

[0413] An "emotion engine" is a technology that analyzes the user's emotional state and adjusts the system's behavior based on that data.

[0414] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[0415] "Support content" refers to additional educational or instructional materials provided to support the user's work.

[0416] A "prompt sentence" is an instruction sentence or initial data that the generative model uses to create a lesson plan.

[0417] In order to put the present invention into practice, it is important that the server, terminal, and user each play their respective roles appropriately. Specific embodiments will be described below from the viewpoints of the server, terminal, and user.

[0418] server

[0419] The server is mainly responsible for generating and regenerating lesson plans and processing the emotion engine. Specifically, it uses the following hardware and software:

[0420] Hardware: high performance server, camera and microphone for emotion analysis

[0421] software:

[0422] Generative AI models (e.g., OpenAI GPT-4)

[0423] Emotion engine (e.g. EmotionML)

[0424] Backend server frameworks (e.g., Node.js)

[0425] Database (e.g. MongoDB)

[0426] The server first receives information about the grade, teaching materials, and unit sent from the device. The received data is passed to the generative AI model as a prompt, and an initial lesson plan is generated. For example, the following prompt sentences are used:

[0427] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[0428] Lesson theme: How to make questions using interrogative words

[0429] Lesson goal: To enable students to create basic questions that include self-introductions.

[0430] Introduction: 10 minutes

[0431] Main topic: 30 minutes

[0432] Exercise: 20 minutes

[0433] Reflection: 10 minutes

[0434] Teacher's emotional state: Normal

[0435] feedback:

[0436] Introduction extended to 15 minutes

[0437] Reduced exercise time by 10 minutes

[0438] The generated initial lesson plan is sent to the device and displayed to the user. When the user provides feedback, the server regenerates the lesson plan using the generative AI model. During regeneration, the emotion engine analyzes the user's emotional state and automatically corrects the plan based on the emotion data. Finally, the server sends the completed lesson plan to the device and provides a link to download it in PDF format.

[0439] Terminal

[0440] A terminal provides an interface that allows users to easily input information. Specifically, the terminal uses the following hardware and software:

[0441] Hardware: Smartphones and head-mounted displays

[0442] Software: Web front-end framework (e.g., React.js)

[0443] The device first collects information about the grade, teaching materials, and unit entered by the user and sends it to the server. The initial lesson plan returned from the server is displayed in an easy-to-read format and includes a field for the user to enter feedback. The device also collects user emotional data using a camera and microphone for emotion analysis and sends this data to the server.

[0444] User

[0445] The user inputs the necessary information using the device, and checks and provides feedback on the lesson plan.

[0446] 1. Enter the grade, teaching materials, and unit information. For example, "Grade: 1st year of high school," "Teaching materials: English textbook," and "Unit name: Unit 3."

[0447] 2. Check the initial lesson plan sent from the server.

[0448] 3. Enter your feedback. For example, "Extend the introduction to 20 minutes" or "Add pair work."

[0449] 4. Check the regenerated lesson plan and make any final corrections.

[0450] 5. Download and save the final lesson plan as a PDF.

[0451] This system allows teachers to create high-quality lesson plans quickly and efficiently, and the emotional engine helps reduce stress, enabling teachers to deliver effective, high-quality lessons.

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] The user uses the terminal to input information about the grade, teaching materials, and unit. For example, the user might input "Grade: 1st year of junior high school," "Teaching materials: English textbook," and "Unit name: Unit 3." This input data is collected by the terminal and sent to the server.

[0455] Step 2:

[0456] The terminal sends the input information to the server. At this time, the information about the input grade, teaching material, and unit is transferred to the server as a data packet. If the input data is "Grade: 1st year of junior high school," "Teaching material: English textbook," and "Unit name: Unit 3," a prompt sentence is generated based on this.

[0457] Step 3:

[0458] The server sends a prompt to the generative AI model based on the received information. For example, the prompt text might look like this:

[0459] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[0460] Lesson theme: How to make questions using interrogative words

[0461] Lesson goal: To enable students to create basic questions that include self-introductions.

[0462] Introduction: 10 minutes

[0463] Main topic: 30 minutes

[0464] Exercise: 20 minutes

[0465] Reflection: 10 minutes

[0466] Teacher's emotional state: Normal

[0467] feedback:

[0468] Introduction extended to 15 minutes

[0469] Reduced exercise time by 10 minutes

[0470] The server generates an initial lesson plan based on a generative AI model (e.g., OpenAI GPT-4). During this generation process, each lesson phase is proposed based on the received information.

[0471] Step 4:

[0472] The server then sends the generated initial lesson plan to the device. The data is structured in an easy-to-read format. For example, the initial lesson plan is divided into phases such as an introduction to the lesson, the main topic, exercises, and reflection.

[0473] Step 5:

[0474] The device displays the received initial lesson plan to the user. The user can review this display and input feedback or requests for revisions. For example, they can input feedback such as "extend the introduction to 20 minutes" or "add pair work."

[0475] Step 6:

[0476] The terminal transmits the feedback and correction requests entered by the user to the server, and this feedback data is transferred to the server as an additional data packet.

[0477] Step 7:

[0478] The server regenerates the lesson plan based on the received feedback and correction requests. This process uses the generative AI model again, and corrections are made based on the feedback. Furthermore, the emotion engine analyzes the user's emotional state, and the lesson plan is automatically corrected based on the emotion data.

[0479] Step 8:

[0480] The server then sends the regenerated lesson plan to the device. The final lesson plan is also provided in PDF format, and a download link is generated if necessary.

[0481] Step 9:

[0482] The terminal displays the received regenerated lesson plan to the user and also provides a download link in PDF format. The user can check the final lesson plan and download it if necessary.

[0483] By following these steps, teachers can create lesson plans efficiently and effectively and receive support tailored to their emotional state, enabling them to deliver high-quality lessons.

[0484] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0491] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0495] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0496] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0497] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0498] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0499] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0500] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user. Below, we will explain the program processing of this system from the perspectives of the server, terminal, and user.

[0501] server

[0502] The server is responsible for generating lesson plans using a generative AI model and regenerating the lesson plans based on user feedback.

[0503] First, the server receives information about the grade, teaching materials, and unit from the device. Based on this, the server starts the lesson plan generation process. In this process, the generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[0504] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[0505] Terminal

[0506] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[0507] When the server returns the initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format, and there is a field for the user to enter feedback or correction requests. When correction requests are entered, the device sends them to the server.

[0508] Furthermore, when the revised lesson plan is received, it is displayed again to the user for final confirmation, and finally, a link is provided to allow the user to download the completed lesson plan in PDF format.

[0509] User

[0510] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Then, once they have completed the input, they press the send button.

[0511] The user can review the initial lesson plan sent from the server and enter feedback and requests for revisions to any areas that require revision. For example, they can enter specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[0512] Check the regenerated lesson plan displayed on the device and make any final corrections. Once you have confirmed that all your requests have been reflected, download and save the lesson plan in PDF format.

[0513] In this way, the present invention enables teachers to create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students.

[0514] The processing flow will be explained below.

[0515] Step 1:

[0516] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[0517] Step 2:

[0518] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[0519] Step 3:

[0520] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[0521] Step 4:

[0522] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[0523] Step 5:

[0524] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[0525] Step 6:

[0526] The server sends the generated initial lesson plan to the device in JSON format.

[0527] Step 7:

[0528] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[0529] Step 8:

[0530] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[0531] Step 9:

[0532] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[0533] Step 10:

[0534] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[0535] Step 11:

[0536] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[0537] Step 12:

[0538] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[0539] Step 13:

[0540] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[0541] Step 14:

[0542] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[0543] Step 15:

[0544] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[0545] Step 16:

[0546] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[0547] By going through these steps, teachers can create lesson plans effectively and efficiently.

[0548] Example 1

[0549] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] The traditional lesson plan creation process is time-consuming and laborious for teachers, requiring detailed plans for each grade, teaching material, and unit. This means that teachers spend a lot of time creating lesson plans, which can lead to neglecting to prepare lessons for students. Furthermore, when revisions or adjustments to lesson plans are needed, they must be made manually, which is inefficient and prone to errors.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0552] In this invention, the server includes means for generating an initial lesson plan using a generative AI model based on the received information, means for regenerating a lesson plan using the generative AI model based on the received feedback and revision requests, and means for providing the final lesson plan in PDF format, thereby improving the efficiency and accuracy of lesson plan creation.

[0553] The "interface for inputting information" refers to the input form or operation screen that is displayed on the terminal so that the user can input information such as the grade, teaching materials, and units.

[0554] A "terminal" is an electronic device operated by a user, such as a PC, tablet, or smartphone.

[0555] The "server" is a central computer system that receives requests from users, processes them, and returns the results, and plays an important role in generating lesson plans using generative AI models.

[0556] A "generative AI model" is a type of model that uses artificial intelligence to generate text data, which is then used to create lesson plans based on specific prompts.

[0557] An "initial lesson plan" is a lesson plan that is first generated using a generative AI model based on the information received by the server.

[0558] "Feedback and correction requests" are opinions and requests for improvements provided by users regarding the initial lesson plan, and are important information for the server to receive and regenerate.

[0559] A "regenerated lesson plan" is a lesson plan that has been regenerated using a generative AI model based on feedback and requests for revisions.

[0560] "PDF format" is an abbreviation for Portable Document Format, and is an electronic file format in which the document layout is fixed.

[0561] A "prompt sentence" is an input sentence used to inform a generative AI model to generate a lesson plan.

[0562] A "link" is a hypertext link that provides access to a specific file or web page that a user can click to download a PDF version of the lesson plan.

[0563] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user and regenerates them in response to feedback. A specific implementation of this system is described below.

[0564] server

[0565] The server serves multiple roles as a central computer system. In particular, it receives information sent by users and generates lesson plans based on that information using a generative AI model. Specifically, it operates as follows:

[0566] First, the server receives information such as the grade, teaching material name, and unit name from the terminal. This process can be performed using Apache or Nginx as a web server, with Python and Flask as the backend. It receives an HTTP POST request, analyzes the information, and stores it in the appropriate variables.

[0567] Based on the received information, the server sends a prompt to the generative AI model (e.g., a generative AI model). This prompt is formed by inserting the received information into a predefined template. For example, a possible prompt might be, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson."

[0568] The generative AI model generates an initial lesson plan based on this prompt sentence, and the generated result is returned to the server, which stores this initial lesson plan in a database, converts it to JSON format, and sends it to the device.

[0569] If the server receives further feedback or correction requests from the user, it sends a new prompt including the feedback to the generative AI model to generate a new lesson plan. This process is repeated as many times as necessary to finally provide the completed lesson plan.

[0570] Terminal

[0571] The terminal provides an interface that makes it easy for users to input information. It has the following main functions:

[0572] First, an interface for entering the grade, teaching material name, and unit name is displayed. This is designed as an HTML form and often uses React or Vue.js as the front-end framework. For example, it provides a drop-down menu for entering the grade and text fields for entering the teaching material name and unit name.

[0573] When the user enters the required information and presses the submit button, the device sends this information to the server as an HTTP POST request using the fetch API or the axios library.

[0574] Receives the initial lesson plan returned from the server and displays it to the user in an easy-to-understand format. Provides a field for inputting feedback or requests for revisions to the lesson plan, and sends it back to the server. Receives a regenerated lesson plan as needed and displays it again to the user.

[0575] Once the final lesson plan is completed, the device will provide a downloadable link in PDF format, which users can click to download the lesson plan as a PDF file.

[0576] User

[0577] The user, i.e., the teacher, accesses the system using a terminal and creates a lesson plan using the following procedure.

[0578] First, enter the necessary information such as the grade, teaching material name, and unit name into the device interface. For example, enter information such as "Grade: 1st year of high school," "Materials used: English textbook," and "Unit name: Unit 3," and then press the send button.

[0579] The teacher reviews the initial lesson plan returned by the server and inputs any necessary corrections or feedback, such as specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[0580] Review the regenerated lesson plan again and make any final adjustments necessary. Once you are sure that all your requests have been reflected, download the lesson plan as a PDF and save it.

[0581] Through this process, teachers can quickly and efficiently create high-quality lesson plans, significantly reducing the amount of time and effort required, and enabling them to provide high-quality lessons to students.

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1:

[0584] The terminal displays an interface for the user to input information. The user inputs information such as the grade, teaching material name, and unit name, and presses the send button. At this point, the input is information such as the grade, teaching material name, and unit name, and the output is an HTTP POST request sent to the server.

[0585] Step 2:

[0586] The server receives information about the grade, teaching material name, and unit name sent from the terminal. It receives the HTTP POST request, analyzes the information, and stores it in the appropriate variables. The input is the information sent from the terminal, and the output is the storage of the analyzed information in the variables.

[0587] Step 3:

[0588] The server sends a prompt to the generative AI model based on the received information. This prompt is formed by inserting received information such as the grade, teaching material name, and unit name into a predefined template. For example, the request might read, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson." The input is the analyzed information, and the output is the prompt sent to the generative AI model.

[0589] Step 4:

[0590] The generative AI model generates an initial lesson plan based on the prompt sentence. The generated lesson plan is sent back to the server. The input is the prompt sentence, and the output is the text data of the initial lesson plan.

[0591] Step 5:

[0592] The server receives the initial lesson plan returned from the generative AI model, saves it in a database, converts it to JSON format, and sends it to the device. At this time, it sends it as an HTTP response, setting the Content-Type in the response header to application / json. The input is the text data of the initial lesson plan, and the output is JSON-formatted data sent to the device.

[0593] Step 6:

[0594] The device receives the initial lesson plan returned from the server, analyzes it, and displays it to the user. It formats it into a highly readable format so that the user can check it. The input is the initial lesson plan data in JSON format, and the output is the formatted lesson plan that is displayed to the user.

[0595] Step 7:

[0596] The user checks the displayed initial lesson plan and inputs feedback and requests for revisions. For example, they input specific requests such as "extend the introduction to 20 minutes" or "add pair work," and then press the submit button. The input is the user's feedback and requests for revisions, and the output is an HTTP POST request sent to the server.

[0597] Step 8:

[0598] The server receives and analyzes feedback and correction requests sent from the device. Based on this information, it sends new prompts to the generative AI model and regenerates the lesson plan. The input is the feedback and correction requests, and the output is the new prompt sent to the generative AI model.

[0599] Step 9:

[0600] The generative AI model generates a regenerated lesson plan based on the new prompt sentence and sends it back to the server. The input is the new prompt sentence, and the output is the text data of the regenerated lesson plan.

[0601] Step 10:

[0602] The server receives the text data of the regenerated lesson plan, saves it in the database again, converts it into JSON format, and sends it to the terminal. The input is the text data of the regenerated lesson plan, and the output is JSON format data sent to the terminal.

[0603] Step 11:

[0604] The device receives the regenerated lesson plan returned from the server and displays it to the user. It also provides an interface for accepting confirmation and feedback. The input is the regenerated lesson plan data in JSON format, and the output is a formatted regenerated lesson plan that is displayed to the user.

[0605] Step 12:

[0606] The user checks it again and provides additional feedback if necessary. The server then regenerates it and displays a link on the device that provides the final completed lesson plan in PDF format. Clicking this link allows the user to download the lesson plan as a PDF file. The input is the user's final check and feedback, and the output is a PDF file of the final lesson plan with a download link.

[0607] In this way, the server, terminal, and user each play their respective roles, enabling efficient and accurate creation of high-quality lesson plans.

[0608] (Application example 1)

[0609] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0610] Factory production processes require the optimization of a wide variety of line, equipment, and product combinations. However, currently, this process is primarily done manually, requiring a great deal of time and effort. Furthermore, the feedback and correction processes for production plans are inefficient. This invention aims to solve these problems and improve the efficiency of creating and correcting production process plans.

[0611] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0612] In this invention, the server includes means for providing a display device for inputting information, means for transmitting information related to the line, equipment, and product input from the display device to an electronic device, means for generating an initial process plan in the electronic device based on the received information, means for transmitting the initial process plan to the display device, means for displaying the received initial process plan in the display device, means for transmitting feedback and revision requests input by a user from the display device to the electronic device, means for regenerating a process plan in the electronic device based on the received feedback and revision requests, means for transmitting the regenerated process plan to the display device, and means for displaying the received regenerated process plan in the display device. This enables efficient creation and revision of production process plans for a factory.

[0613] The "display device for inputting information" is a terminal device equipped with a user interface for users to input information about lines, equipment, and products.

[0614] "Display input information" means line, equipment, and product related data provided through a user interface.

[0615] "Electronic device" refers to a server or computer for information processing.

[0616] "Initial process plan" refers to the initial outline plan for the production process of a factory, generated by the server.

[0617] "Regeneration" is the process of recreating a process plan based on received feedback and revision requests.

[0618] "Feedback and revision requests" are evaluations and suggestions for improvement provided by users regarding the initial process plan.

[0619] "PDF Downloadable Link" means a hyperlink that enables the final process plan to be downloaded as a PDF file.

[0620] "Input Area" refers to a dedicated field within the interface where a user can enter feedback or correction requests.

[0621] A "process plan" is a production schedule that includes the necessary steps, time, and resource allocation for the factory line, equipment, and target product.

[0622] This invention is a system for efficiently creating and modifying factory production process plans. The system utilizes display devices, electronics, and generative AI models to generate process plans and regenerate them based on user feedback.

[0623] The display device provides a user interface for users to input information about the line, equipment, and product. Through this interface, users input data related to the production line, the equipment used, and the target product. For example, they input specific information such as "Production Line 1," "Robot Arm X," and "Part A." Once input is complete, the information is transmitted to the electronic device.

[0624] After receiving the information, the electronic device uses the generative AI model to generate an initial process plan, which includes details of each process in the factory, standard times, and required resources. This initial process plan is then sent to a display device and displayed to the user.

[0625] The user reviews the displayed initial process plan and enters feedback and requests for revisions into the display device, including specific requests such as "extend the duration of process 3 to 20 minutes" or "add a new inspection step." The feedback and requests for revisions are then sent back to the electronics, which then uses the generative AI model to regenerate a new process plan.

[0626] The regenerated process plan is sent back to the display device and displayed to the user. The user can then check the regenerated process plan and make any final corrections. Once the user has confirmed that all requests have been reflected, the final process plan can be downloaded in PDF format.

[0627] Hardware and software used

[0628] Hardware: Smartphone or tablet (display device), server (electronic device)

[0629] Software: Generative AI models (e.g., OpenAI GPT)

[0630] The generated data and data calculations are as follows: The display device collects input data from the user and transmits it to the electronic device. The electronic device uses the generative AI model to generate an initial process plan, including details of each process, standard times, and the types of machines and robots to be used. It then regenerates the process plan based on feedback and revision requests.

[0631] Specific examples

[0632] For example, if a factory manager types:

[0633] Production Line: Line 1

[0634] Equipment used: Robot Arm X

[0635] Target product: Part A

[0636] An example of a prompt to input to the generative AI model is:

[0637] Use the generative AI model to generate a production process plan for production line 1, robot arm X, and target product part A. The initial plan should include details of each process, standard times, and required resources.

[0638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0639] Step 1:

[0640] The user uses the display device to input information about the line, equipment, and product. The input provides specific data such as "Production Line 1," "Robot Arm X," and "Part A." The input information is received by the user interface.

[0641] Step 2:

[0642] The terminal transmits the input information to the electronic device (server). The transmitted data includes all information provided by the user about the production line, the equipment used, and the target product.

[0643] Step 3:

[0644] The server uses a generative AI model to generate an initial process plan based on the received information. The server inputs the data, and the generative AI model calculates and outputs the details, standard times, and required resources for each process. The initial process plan includes specific process steps and required resources.

[0645] Step 4:

[0646] The server sends the created initial process plan to the terminal, which includes the generated process plan as output.

[0647] Step 5:

[0648] The terminal then displays the received initial process plan on a display device, which includes details such as each step of the process, the time it will take, and the equipment to be used.

[0649] Step 6:

[0650] Users can review the initial process plan and provide feedback and correction requests through input fields, including specific requests such as "extend the time required for process 3 to 20 minutes" or "add a new inspection step."

[0651] Step 7:

[0652] The terminal transmits the feedback and correction requests from the user to the server. The transmitted data includes the input feedback and correction requests.

[0653] Step 8:

[0654] The server regenerates the process plan using a generative AI model based on the received feedback and correction requests. The generative AI model then processes and calculates the new data to create a new process plan that reflects the feedback.

[0655] Step 9:

[0656] The server sends the regenerated process plan to the terminal, and includes the regenerated plan as output.

[0657] Step 10:

[0658] The terminal displays the received regenerated process plan on the display device, allowing the user to make final confirmation of the regenerated plan and make further corrections as necessary.

[0659] Step 11:

[0660] Once the final confirmation is complete, the terminal will provide a link to download the final process plan in PDF format, which the user can use to download and use the final plan.

[0661] The above processing steps enable efficient creation and modification of factory production process plans.

[0662] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0663] This invention combines a system that enables teachers to efficiently create lesson plans for each lesson with an emotion engine that recognizes the user's emotions. This system allows teachers to create lesson plans more efficiently and in a stress-free environment. Below, we will explain in detail the program processing of this system from the perspectives of the server, terminal, and user.

[0664] server

[0665] The server generates lesson plans using the generative AI model, regenerates the plans based on user feedback, and also automatically corrects lesson plans and provides support content based on information obtained from the emotion engine.

[0666] First, the server receives information about the grade, teaching materials, and unit sent from the device. This information is analyzed and the lesson plan generation process begins. The generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[0667] In addition, the server is equipped with an emotion engine that analyzes the user's emotional state and, if necessary, proposes initial lesson plans and support content.

[0668] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[0669] Terminal

[0670] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[0671] When the server returns an initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format and includes fields for entering feedback and correction requests. In addition, an emotion engine recognizes the user's emotions and automatically suggests feedback and correction requests. For example, if the user is feeling stressed, suggested correction requests are automatically displayed.

[0672] The device sends information from the emotion engine to the server, which then uses this information to regenerate lesson plans and support content, ultimately providing a link to download the completed lesson plans in PDF format.

[0673] User

[0674] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Once they have completed the input, they press the send button.

[0675] The students review the initial lesson plan sent from the server and enter feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[0676] The user checks the regenerated lesson plan displayed on the device and makes any final corrections. During this process, the emotion engine recognizes the user's emotions and may suggest appropriate feedback or correction requests. For example, if the user is feeling stressed, the engine may suggest "simplify this part."

[0677] Once teachers have confirmed that all requests have been reflected, they can download and save the lesson plan in PDF format. This system not only enables teachers to create high-quality lesson plans quickly and efficiently, but also reduces stress with the support of the emotion engine.

[0678] Through these steps, the present invention enables teachers to create lesson plans effectively and efficiently, enabling them to provide high-quality lessons to students.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[0682] Step 2:

[0683] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[0684] Step 3:

[0685] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[0686] Step 4:

[0687] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[0688] Step 5:

[0689] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[0690] Step 6:

[0691] The server uses an emotion engine to recognize the user's emotional state, which is done in parallel with the generation of the initial lesson plan.

[0692] Step 7:

[0693] Based on the information obtained from the emotion engine, the server modifies the initial lesson plan as needed and adds supportive content, including, for example, suggestions to simplify the overall lesson plan if the user is feeling stressed.

[0694] Step 8:

[0695] The server sends the generated or modified initial lesson plan to the device in JSON format.

[0696] Step 9:

[0697] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[0698] Step 10:

[0699] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[0700] Step 11:

[0701] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[0702] Step 12:

[0703] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[0704] Step 13:

[0705] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[0706] Step 14:

[0707] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[0708] Step 15:

[0709] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[0710] Step 16:

[0711] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[0712] Step 17:

[0713] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[0714] Step 18:

[0715] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[0716] Step 19:

[0717] The emotion engine continuously monitors the user's emotions and provides supportive content as needed. For example, if the user is feeling stressed, it will display advice on relaxation techniques and stress management.

[0718] Through these steps, teachers can create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students. Support from the emotion engine reduces stress for teachers and creates a better educational environment.

[0719] Example 2

[0720] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0721] Conventional lesson plan creation systems require teachers to manually create lesson plans, requiring a great deal of time and effort. Furthermore, they lack support that takes into account the psychological state and emotions of teachers, which can result in increased stress for teachers and affect the quality of instruction. To address these issues, a system was needed that would enable teachers to create lesson plans more efficiently and provide appropriate support according to their emotional state.

[0722] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for displaying an interface for inputting information, means for communicating information on the education level, type of teaching material, and education unit input from the terminal, means for generating an initial education plan based on the received information, means for transmitting the initial education plan to the terminal, means for communicating feedback and revision requests input by the user from the terminal, means for regenerating the education plan generated based on the received feedback and revision requests, means for transmitting the regenerated education plan to the terminal, means for displaying the regenerated education plan received on the terminal, means for analyzing the user's emotions using an emotion analysis engine, and means for revising the content of the education plan based on the user's emotional state and suggesting advice and support content. This enables teachers to quickly and efficiently create high-quality education plans and provide instruction while reducing stress with the support of the emotion engine.

[0723] An "interface" is a means, such as a screen or input form, through which a user provides input information.

[0724] A "terminal" is a device such as a computer or tablet that a user operates and uses to input information.

[0725] A "server" is a computer system on a network that receives and processes information sent from a terminal.

[0726] "Education level" is information indicating the grade or educational stage.

[0727] "Type of teaching material" is information indicating the category of educational materials or texts to be used.

[0728] An "educational unit" is information that indicates a specific topic or section that is covered in an educational curriculum.

[0729] An "initial teaching plan" is the first lesson plan created by the server using a generative AI model.

[0730] "Communication" refers to sending and receiving information between a terminal and a server.

[0731] "Feedback" refers to opinions and information on improvements to the initial education plan provided by the user.

[0732] "Request for revision" is information about a specific request for change made by a user to the initial training plan.

[0733] "Regeneration" is the process of regenerating the educational plan based on feedback and requests for revisions.

[0734] An "emotion analysis engine" is a software component for recognizing and analyzing a user's emotional state.

[0735] "Advice" refers to teaching plans and improvement suggestions based on the sentiment analysis engine.

[0736] "Support content" refers to additional materials and actions that complement the educational plan and support teachers' teaching activities.

[0737] This invention relates to a system that allows educators to efficiently create lesson plans, and further combines it with an emotion engine that recognizes the user's emotions. Details for specifically implementing this system are provided below.

[0738] Hardware and Software Configuration

[0739] server

[0740] The server is a high-performance computer system that functions as the center of information processing. This server is equipped with a generative AI model and an emotion analysis engine. The generative AI model automatically generates teaching plans, and the emotion analysis engine is used to analyze the emotional state of teachers.

[0741] Terminal

[0742] A terminal is a device used by teachers to input information and check generated lesson plans. Specific hardware examples include personal computers, tablets, and smartphones. Software that provides a user interface is installed on the terminal.

[0743] User

[0744] The user (in this case, a teacher) operates a terminal to input the information necessary to create a lesson plan, and provides feedback and requests for revisions. Based on the information entered by the user, the server generates and regenerates a lesson plan.

[0745] What the program does

[0746] Entering initial information

[0747] Users use the terminal to enter information such as educational level, type of teaching material, and teaching unit. This information can be easily entered through an input form. For example, it can be entered in the format of "Grade: 1st year of high school," "Teaching material: English textbook," and "Unit name: Unit 3."

[0748] Transmission and processing of information

[0749] The device sends the input information to the server, which then uses the generative AI model to generate an initial lesson plan based on the received information.

[0750] Example prompt sentence:

[0751] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[0752] Displaying and feedback on initial lesson plans

[0753] The generated initial lesson plan is sent from the server to the device, which displays it to the user. The user can review the displayed initial lesson plan and enter feedback and requests for revisions. For example, specific requests such as "extend the introduction to 20 minutes" or "add pair work" can be included.

[0754] Sentiment Analysis and Regeneration

[0755] When the server receives feedback or correction requests from the user, it uses an emotion analysis engine to analyze the user's emotional state. Based on the user's emotional state, it adjusts the content of the educational plan and suggests advice and support content. For example, if the user is feeling stressed, it will suggest corrections to reduce the user's stress.

[0756] View and download the final lesson plan

[0757] The regenerated lesson plan is sent from the server to the device again and displayed to the user. Once the lesson plan has been confirmed to reflect all requests, the device provides a link to download the lesson plan in PDF format.

[0758] This configuration allows teachers to quickly and efficiently create high-quality lesson plans, and the support provided by the emotion analysis engine enables them to teach effectively while reducing stress.

[0759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] The user enters information into the device. The user enters information such as grade, teaching materials, and unit name into the input form displayed on the device's interface. This input form is designed with an intuitive UI, and information is entered in the format of, for example, "Grade: 1st year of high school," "Teaching materials used: English textbook," and "Unit name: Unit 3." This operation collects the initial information.

[0762] input:

[0763] Grade: 1st year of high school

[0764] Materials used: English textbook

[0765] Unit Name: Unit 3

[0766] output:

[0767] Information entered into the device.

[0768] Step 2:

[0769] The device sends the input information to the server. When the user clicks the send button, the device sends the input information to the server in JSON format or similar.

[0770] Specific behavior:

[0771] The terminal sends the following data:

[0772] {

[0773] "grade": "first year of high school",

[0774] "material": "English text",

[0775] "unit": "Unit 3"

[0776] }

[0777] input:

[0778] Information entered into the device.

[0779] output:

[0780] Information received by the server.

[0781] Step 3:

[0782] The server generates an initial lesson plan using a generative AI model. The server analyzes the received information and inputs it as a prompt to the generative AI model. Based on this prompt, the generative AI model automatically generates an initial lesson plan.

[0783] Examples:

[0784] Prompt statement:

[0785] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[0786] input:

[0787] {

[0788] "grade": "first year of high school",

[0789] "material": "English text",

[0790] "unit": "Unit 3"

[0791] }

[0792] output:

[0793] Initial guidance plan.

[0794] Step 4:

[0795] The server sends the initial lesson plan to the terminal, which then displays the generated initial lesson plan to the user.

[0796] Specific behavior:

[0797] The server transmits the generated lesson plan, which is received and displayed by the terminal.

[0798] Generated lesson plan:

[0799] 1. Introduction to the lesson (10 minutes)

[0800] 2. Introduction to the topic (15 minutes)

[0801] 3. Exercises (20 minutes)

[0802] 4. Reflection (10 min)

[0803] input:

[0804] Initial guidance plan.

[0805] output:

[0806] The initial lesson plan displayed on the device.

[0807] Step 5:

[0808] The user inputs feedback and requests for revisions to the initial lesson plan. The user reviews the initial lesson plan and inputs feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[0809] input:

[0810] Initial guidance plan.

[0811] output:

[0812] Feedback and correction requests.

[0813] Step 6:

[0814] The device sends feedback and correction requests to the server. After the user enters feedback and correction requests, the device sends them to the server. The data is again sent in JSON format.

[0815] Specific behavior:

[0816] The device sends the following feedback and correction requests to the server:

[0817] {

[0818] "feedback": "Extend introduction to 20 minutes",

[0819] "request": "Add pair work"

[0820] }

[0821] input:

[0822] Feedback and correction requests.

[0823] output:

[0824] Feedback and correction requests received by the server.

[0825] Step 7:

[0826] The server generates a regenerated lesson plan and analyzes the user's emotional state using an emotion analysis engine. Depending on the emotional state, if the user is feeling stressed, a correction plan to reduce the burden is suggested. The regenerated lesson plan is created.

[0827] Specific behavior:

[0828] The server uses an emotion analysis engine to determine the user's emotional state and suggests corrections as needed based on a generative AI model.

[0829] input:

[0830] Feedback and correction requests, sentiment analysis results.

[0831] output:

[0832] The regenerated lesson plan.

[0833] Step 8:

[0834] The server transmits the regenerated lesson plan to the terminal, which then displays it to the user.

[0835] Specific behavior:

[0836] The server transmits the generated regenerated lesson plan, which is received and displayed by the terminal.

[0837] Regenerated lesson plan:

[0838] 1. Introduction to the lesson (20 minutes)

[0839] 2. Introduction to the topic (10 minutes)

[0840] 3. Pair work (15 minutes)

[0841] 4. Exercises (20 minutes)

[0842] 5. Reflection (10 min.)

[0843] input:

[0844] The regenerated lesson plan.

[0845] output:

[0846] The regenerated lesson plan displayed on the device.

[0847] Step 9:

[0848] The user checks the final lesson plan and downloads it in PDF format. After checking, the user clicks the download link on their device to save the final lesson plan in PDF format.

[0849] Specific behavior:

[0850] Click the download link displayed on your device and save the PDF.

[0851] PDF file:

[0852] First Year High School English Unit 3 Lesson Plan

[0853] 1. Introduction to the lesson (20 minutes)

[0854] 2. Introduction to the topic (10 minutes)

[0855] 3. Pair work (15 minutes)

[0856] 4. Exercises (20 minutes)

[0857] 5. Reflection (10 min.)

[0858] input:

[0859] The regenerated lesson plan.

[0860] output:

[0861] Final lesson plan in PDF format.

[0862] (Application example 2)

[0863] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0864] Conventional lesson plan creation systems make it difficult for teachers to efficiently create detailed lesson plans for each lesson, placing a heavy burden on busy teachers in particular. Furthermore, lesson plans are not appropriately revised based on the teacher's emotional state, and an environment that can reduce stress is not provided. As a result, the reduction of teacher workload and improvement of lesson quality are not fully achieved.

[0865] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for displaying an interface for inputting information on a terminal; means for transmitting information on the grade, teaching material, and unit input from the terminal to the server; means for generating, in the server, an initial lesson plan using a generative model based on the received information; means for transmitting the initial lesson plan to the terminal; means for displaying, in the terminal, the received initial lesson plan; means for transmitting feedback and revision requests input by the user from the terminal to the server; means for regenerating, in the server, a lesson plan based on the received feedback and revision requests; means for transmitting the regenerated lesson plan to the terminal; means for analyzing the user's emotional state using an emotion engine and automatically revising the lesson plan and revision suggestions based on the emotion data; means for providing support content based on information obtained from the emotion engine; and means for displaying, in the terminal, the received regenerated lesson plan. This will enable teachers to create lesson plans efficiently, and will also enable them to receive automatic corrections and support content based on their emotional state, enabling them to deliver high-quality lessons while reducing stress.

[0866] An "interface" is a screen or input means that makes it easy for users to input information.

[0867] A "terminal" is an electronic device used by a user (for example, a smartphone or a head-mounted display).

[0868] "Server" means a central processing unit for generating and regenerating lesson plans.

[0869] "Grade" refers to the year in the educational curriculum that the student is in.

[0870] "Teaching materials" refer to textbooks and supplementary materials used in classes.

[0871] A "unit" is a specific theme or division of content within an educational curriculum.

[0872] "Generative model" refers to artificial intelligence technology for creating initial lesson plans and regenerative lesson plans.

[0873] An "initial lesson plan" is a lesson plan that the generative model first creates based on the received information.

[0874] "Feedback" refers to evaluations and requests for revisions to the initial lesson plan provided by the user.

[0875] "Regeneration" is the process of recreating a lesson plan based on received feedback and revision requests.

[0876] An "emotion engine" is a technology that analyzes the user's emotional state and adjusts the system's behavior based on that data.

[0877] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[0878] "Support content" refers to additional educational or instructional materials provided to support the user's work.

[0879] A "prompt sentence" is an instruction sentence or initial data that the generative model uses to create a lesson plan.

[0880] In order to put the present invention into practice, it is important that the server, terminal, and user each play their respective roles appropriately. Specific embodiments will be described below from the viewpoints of the server, terminal, and user.

[0881] server

[0882] The server is mainly responsible for generating and regenerating lesson plans and processing the emotion engine. Specifically, it uses the following hardware and software:

[0883] Hardware: high performance server, camera and microphone for emotion analysis

[0884] software:

[0885] Generative AI models (e.g., OpenAI GPT-4)

[0886] Emotion engine (e.g. EmotionML)

[0887] Backend server frameworks (e.g., Node.js)

[0888] Database (e.g. MongoDB)

[0889] The server first receives information about the grade, teaching materials, and unit sent from the device. The received data is passed to the generative AI model as a prompt, and an initial lesson plan is generated. For example, the following prompt sentences are used:

[0890] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[0891] Lesson theme: How to make questions using interrogative words

[0892] Lesson goal: To enable students to create basic questions that include self-introductions.

[0893] Introduction: 10 minutes

[0894] Main topic: 30 minutes

[0895] Exercise: 20 minutes

[0896] Reflection: 10 minutes

[0897] Teacher's emotional state: Normal

[0898] feedback:

[0899] Introduction extended to 15 minutes

[0900] Reduced exercise time by 10 minutes

[0901] The generated initial lesson plan is sent to the device and displayed to the user. When the user provides feedback, the server regenerates the lesson plan using the generative AI model. During regeneration, the emotion engine analyzes the user's emotional state and automatically corrects the plan based on the emotion data. Finally, the server sends the completed lesson plan to the device and provides a link to download it in PDF format.

[0902] Terminal

[0903] A terminal provides an interface that allows users to easily input information. Specifically, the terminal uses the following hardware and software:

[0904] Hardware: Smartphones and head-mounted displays

[0905] Software: Web front-end framework (e.g., React.js)

[0906] The device first collects information about the grade, teaching materials, and unit entered by the user and sends it to the server. The initial lesson plan returned from the server is displayed in an easy-to-read format and includes a field for the user to enter feedback. The device also collects user emotional data using a camera and microphone for emotion analysis and sends this data to the server.

[0907] User

[0908] The user inputs the necessary information using the device, and checks and provides feedback on the lesson plan.

[0909] 1. Enter the grade, teaching materials, and unit information. For example, "Grade: 1st year of high school," "Teaching materials: English textbook," and "Unit name: Unit 3."

[0910] 2. Check the initial lesson plan sent from the server.

[0911] 3. Enter your feedback. For example, "Extend the introduction to 20 minutes" or "Add pair work."

[0912] 4. Check the regenerated lesson plan and make any final corrections.

[0913] 5. Download and save the final lesson plan as a PDF.

[0914] This system allows teachers to create high-quality lesson plans quickly and efficiently, and the emotional engine helps reduce stress, enabling teachers to deliver effective, high-quality lessons.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The user uses the terminal to input information about the grade, teaching materials, and unit. For example, the user might input "Grade: 1st year of junior high school," "Teaching materials: English textbook," and "Unit name: Unit 3." This input data is collected by the terminal and sent to the server.

[0918] Step 2:

[0919] The terminal sends the input information to the server. At this time, the information about the input grade, teaching material, and unit is transferred to the server as a data packet. If the input data is "Grade: 1st year of junior high school," "Teaching material: English textbook," and "Unit name: Unit 3," a prompt sentence is generated based on this.

[0920] Step 3:

[0921] The server sends a prompt to the generative AI model based on the received information. For example, the prompt text might look like this:

[0922] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[0923] Lesson theme: How to make questions using interrogative words

[0924] Lesson goal: To enable students to create basic questions that include self-introductions.

[0925] Introduction: 10 minutes

[0926] Main topic: 30 minutes

[0927] Exercise: 20 minutes

[0928] Reflection: 10 minutes

[0929] Teacher's emotional state: Normal

[0930] feedback:

[0931] Introduction extended to 15 minutes

[0932] Reduced exercise time by 10 minutes

[0933] The server generates an initial lesson plan based on a generative AI model (e.g., OpenAI GPT-4). During this generation process, each lesson phase is proposed based on the received information.

[0934] Step 4:

[0935] The server then sends the generated initial lesson plan to the device. The data is structured in an easy-to-read format. For example, the initial lesson plan is divided into phases such as an introduction to the lesson, the main topic, exercises, and reflection.

[0936] Step 5:

[0937] The device displays the received initial lesson plan to the user. The user can review this display and input feedback or requests for revisions. For example, they can input feedback such as "extend the introduction to 20 minutes" or "add pair work."

[0938] Step 6:

[0939] The terminal transmits the feedback and correction requests entered by the user to the server, and this feedback data is transferred to the server as an additional data packet.

[0940] Step 7:

[0941] The server regenerates the lesson plan based on the received feedback and correction requests. This process uses the generative AI model again, and corrections are made based on the feedback. Furthermore, the emotion engine analyzes the user's emotional state, and the lesson plan is automatically corrected based on the emotion data.

[0942] Step 8:

[0943] The server then sends the regenerated lesson plan to the device. The final lesson plan is also provided in PDF format, and a download link is generated if necessary.

[0944] Step 9:

[0945] The terminal displays the received regenerated lesson plan to the user and also provides a download link in PDF format. The user can check the final lesson plan and download it if necessary.

[0946] By following these steps, teachers can create lesson plans efficiently and effectively and receive support tailored to their emotional state, enabling them to deliver high-quality lessons.

[0947] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0948] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0949] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0950] [Third embodiment]

[0951] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0952] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0953] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0954] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0955] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0956] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0957] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0958] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0959] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0960] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0961] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0962] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0963] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user. Below, we will explain the program processing of this system from the perspectives of the server, terminal, and user.

[0964] server

[0965] The server is responsible for generating lesson plans using a generative AI model and regenerating the lesson plans based on user feedback.

[0966] First, the server receives information about the grade, teaching materials, and unit from the device. Based on this, the server starts the lesson plan generation process. In this process, the generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[0967] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[0968] Terminal

[0969] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[0970] When the server returns the initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format, and there is a field for the user to enter feedback or correction requests. When correction requests are entered, the device sends them to the server.

[0971] Furthermore, when the revised lesson plan is received, it is displayed again to the user for final confirmation, and finally, a link is provided to allow the user to download the completed lesson plan in PDF format.

[0972] User

[0973] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Then, once they have completed the input, they press the send button.

[0974] The user can review the initial lesson plan sent from the server and enter feedback and requests for revisions to any areas that require revision. For example, they can enter specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[0975] Check the regenerated lesson plan displayed on the device and make any final corrections. Once you have confirmed that all your requests have been reflected, download and save the lesson plan in PDF format.

[0976] In this way, the present invention enables teachers to create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students.

[0977] The processing flow will be explained below.

[0978] Step 1:

[0979] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[0980] Step 2:

[0981] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[0982] Step 3:

[0983] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[0984] Step 4:

[0985] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[0986] Step 5:

[0987] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[0988] Step 6:

[0989] The server sends the generated initial lesson plan to the device in JSON format.

[0990] Step 7:

[0991] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[0992] Step 8:

[0993] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[0994] Step 9:

[0995] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[0996] Step 10:

[0997] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[0998] Step 11:

[0999] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[1000] Step 12:

[1001] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[1002] Step 13:

[1003] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[1004] Step 14:

[1005] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[1006] Step 15:

[1007] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[1008] Step 16:

[1009] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[1010] By going through these steps, teachers can create lesson plans effectively and efficiently.

[1011] Example 1

[1012] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1013] The traditional lesson plan creation process is time-consuming and laborious for teachers, requiring detailed plans for each grade, teaching material, and unit. This means that teachers spend a lot of time creating lesson plans, which can lead to neglecting to prepare lessons for students. Furthermore, when revisions or adjustments to lesson plans are needed, they must be made manually, which is inefficient and prone to errors.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1015] In this invention, the server includes means for generating an initial lesson plan using a generative AI model based on the received information, means for regenerating a lesson plan using the generative AI model based on the received feedback and revision requests, and means for providing the final lesson plan in PDF format, thereby improving the efficiency and accuracy of lesson plan creation.

[1016] The "interface for inputting information" refers to the input form or operation screen that is displayed on the terminal so that the user can input information such as the grade, teaching materials, and units.

[1017] A "terminal" is an electronic device operated by a user, such as a PC, tablet, or smartphone.

[1018] The "server" is a central computer system that receives requests from users, processes them, and returns the results, and plays an important role in generating lesson plans using generative AI models.

[1019] A "generative AI model" is a type of model that uses artificial intelligence to generate text data, which is then used to create lesson plans based on specific prompts.

[1020] An "initial lesson plan" is a lesson plan that is first generated using a generative AI model based on the information received by the server.

[1021] "Feedback and correction requests" are opinions and requests for improvements provided by users regarding the initial lesson plan, and are important information for the server to receive and regenerate.

[1022] A "regenerated lesson plan" is a lesson plan that has been regenerated using a generative AI model based on feedback and requests for revisions.

[1023] "PDF format" is an abbreviation for Portable Document Format, and is an electronic file format in which the document layout is fixed.

[1024] A "prompt sentence" is an input sentence used to inform a generative AI model to generate a lesson plan.

[1025] A "link" is a hypertext link that provides access to a specific file or web page that a user can click to download a PDF version of the lesson plan.

[1026] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user and regenerates them in response to feedback. A specific implementation of this system is described below.

[1027] server

[1028] The server serves multiple roles as a central computer system. In particular, it receives information sent by users and generates lesson plans based on that information using a generative AI model. Specifically, it operates as follows:

[1029] First, the server receives information such as the grade, teaching material name, and unit name from the terminal. This process can be performed using Apache or Nginx as a web server, with Python and Flask as the backend. It receives an HTTP POST request, analyzes the information, and stores it in the appropriate variables.

[1030] Based on the received information, the server sends a prompt to the generative AI model (e.g., a generative AI model). This prompt is formed by inserting the received information into a predefined template. For example, a possible prompt might be, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson."

[1031] The generative AI model generates an initial lesson plan based on this prompt sentence, and the generated result is returned to the server, which stores this initial lesson plan in a database, converts it to JSON format, and sends it to the device.

[1032] If the server receives further feedback or correction requests from the user, it sends a new prompt including the feedback to the generative AI model to generate a new lesson plan. This process is repeated as many times as necessary to finally provide the completed lesson plan.

[1033] Terminal

[1034] The terminal provides an interface that makes it easy for users to input information. It has the following main functions:

[1035] First, an interface for entering the grade, teaching material name, and unit name is displayed. This is designed as an HTML form and often uses React or Vue.js as the front-end framework. For example, it provides a drop-down menu for entering the grade and text fields for entering the teaching material name and unit name.

[1036] When the user enters the required information and presses the submit button, the device sends this information to the server as an HTTP POST request using the fetch API or the axios library.

[1037] Receives the initial lesson plan returned from the server and displays it to the user in an easy-to-understand format. Provides a field for inputting feedback or requests for revisions to the lesson plan, and sends it back to the server. Receives a regenerated lesson plan as needed and displays it again to the user.

[1038] Once the final lesson plan is completed, the device will provide a downloadable link in PDF format, which users can click to download the lesson plan as a PDF file.

[1039] User

[1040] The user, i.e., the teacher, accesses the system using a terminal and creates a lesson plan using the following procedure.

[1041] First, enter the necessary information such as the grade, teaching material name, and unit name into the device interface. For example, enter information such as "Grade: 1st year of high school," "Materials used: English textbook," and "Unit name: Unit 3," and then press the send button.

[1042] The teacher reviews the initial lesson plan returned by the server and inputs any necessary corrections or feedback, such as specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[1043] Review the regenerated lesson plan again and make any final adjustments necessary. Once you are sure that all your requests have been reflected, download the lesson plan as a PDF and save it.

[1044] Through this process, teachers can quickly and efficiently create high-quality lesson plans, significantly reducing the amount of time and effort required, and enabling them to provide high-quality lessons to students.

[1045] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1046] Step 1:

[1047] The terminal displays an interface for the user to input information. The user inputs information such as the grade, teaching material name, and unit name, and presses the send button. At this point, the input is information such as the grade, teaching material name, and unit name, and the output is an HTTP POST request sent to the server.

[1048] Step 2:

[1049] The server receives information about the grade, teaching material name, and unit name sent from the terminal. It receives the HTTP POST request, analyzes the information, and stores it in the appropriate variables. The input is the information sent from the terminal, and the output is the storage of the analyzed information in the variables.

[1050] Step 3:

[1051] The server sends a prompt to the generative AI model based on the received information. This prompt is formed by inserting received information such as the grade, teaching material name, and unit name into a predefined template. For example, the request might read, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson." The input is the analyzed information, and the output is the prompt sent to the generative AI model.

[1052] Step 4:

[1053] The generative AI model generates an initial lesson plan based on the prompt sentence. The generated lesson plan is sent back to the server. The input is the prompt sentence, and the output is the text data of the initial lesson plan.

[1054] Step 5:

[1055] The server receives the initial lesson plan returned from the generative AI model, saves it in a database, converts it to JSON format, and sends it to the device. At this time, it sends it as an HTTP response, setting the Content-Type in the response header to application / json. The input is the text data of the initial lesson plan, and the output is JSON-formatted data sent to the device.

[1056] Step 6:

[1057] The device receives the initial lesson plan returned from the server, analyzes it, and displays it to the user. It formats it into a highly readable format so that the user can check it. The input is the initial lesson plan data in JSON format, and the output is the formatted lesson plan that is displayed to the user.

[1058] Step 7:

[1059] The user checks the displayed initial lesson plan and inputs feedback and requests for revisions. For example, they input specific requests such as "extend the introduction to 20 minutes" or "add pair work," and then press the submit button. The input is the user's feedback and requests for revisions, and the output is an HTTP POST request sent to the server.

[1060] Step 8:

[1061] The server receives and analyzes feedback and correction requests sent from the device. Based on this information, it sends new prompts to the generative AI model and regenerates the lesson plan. The input is the feedback and correction requests, and the output is the new prompt sent to the generative AI model.

[1062] Step 9:

[1063] The generative AI model generates a regenerated lesson plan based on the new prompt sentence and sends it back to the server. The input is the new prompt sentence, and the output is the text data of the regenerated lesson plan.

[1064] Step 10:

[1065] The server receives the text data of the regenerated lesson plan, saves it in the database again, converts it into JSON format, and sends it to the terminal. The input is the text data of the regenerated lesson plan, and the output is JSON format data sent to the terminal.

[1066] Step 11:

[1067] The device receives the regenerated lesson plan returned from the server and displays it to the user. It also provides an interface for accepting confirmation and feedback. The input is the regenerated lesson plan data in JSON format, and the output is a formatted regenerated lesson plan that is displayed to the user.

[1068] Step 12:

[1069] The user checks it again and provides additional feedback if necessary. The server then regenerates it and displays a link on the device that provides the final completed lesson plan in PDF format. Clicking this link allows the user to download the lesson plan as a PDF file. The input is the user's final check and feedback, and the output is a PDF file of the final lesson plan with a download link.

[1070] In this way, the server, terminal, and user each play their respective roles, enabling efficient and accurate creation of high-quality lesson plans.

[1071] (Application example 1)

[1072] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1073] Factory production processes require the optimization of a wide variety of line, equipment, and product combinations. However, currently, this process is primarily done manually, requiring a great deal of time and effort. Furthermore, the feedback and correction processes for production plans are inefficient. This invention aims to solve these problems and improve the efficiency of creating and correcting production process plans.

[1074] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1075] In this invention, the server includes means for providing a display device for inputting information, means for transmitting information related to the line, equipment, and product input from the display device to an electronic device, means for generating an initial process plan in the electronic device based on the received information, means for transmitting the initial process plan to the display device, means for displaying the received initial process plan in the display device, means for transmitting feedback and revision requests input by a user from the display device to the electronic device, means for regenerating a process plan in the electronic device based on the received feedback and revision requests, means for transmitting the regenerated process plan to the display device, and means for displaying the received regenerated process plan in the display device. This enables efficient creation and revision of production process plans for a factory.

[1076] The "display device for inputting information" is a terminal device equipped with a user interface for users to input information about lines, equipment, and products.

[1077] "Display input information" means line, equipment, and product related data provided through a user interface.

[1078] "Electronic device" refers to a server or computer for information processing.

[1079] "Initial process plan" refers to the initial outline plan for the production process of a factory, generated by the server.

[1080] "Regeneration" is the process of recreating a process plan based on received feedback and revision requests.

[1081] "Feedback and revision requests" are evaluations and suggestions for improvement provided by users regarding the initial process plan.

[1082] "PDF Downloadable Link" means a hyperlink that enables the final process plan to be downloaded as a PDF file.

[1083] "Input Area" refers to a dedicated field within the interface where a user can enter feedback or correction requests.

[1084] A "process plan" is a production schedule that includes the necessary steps, time, and resource allocation for the factory line, equipment, and target product.

[1085] This invention is a system for efficiently creating and modifying factory production process plans. The system utilizes display devices, electronics, and generative AI models to generate process plans and regenerate them based on user feedback.

[1086] The display device provides a user interface for users to input information about the line, equipment, and product. Through this interface, users input data related to the production line, the equipment used, and the target product. For example, they input specific information such as "Production Line 1," "Robot Arm X," and "Part A." Once input is complete, the information is transmitted to the electronic device.

[1087] After receiving the information, the electronic device uses the generative AI model to generate an initial process plan, which includes details of each process in the factory, standard times, and required resources. This initial process plan is then sent to a display device and displayed to the user.

[1088] The user reviews the displayed initial process plan and enters feedback and requests for revisions into the display device, including specific requests such as "extend the duration of process 3 to 20 minutes" or "add a new inspection step." The feedback and requests for revisions are then sent back to the electronics, which then uses the generative AI model to regenerate a new process plan.

[1089] The regenerated process plan is sent back to the display device and displayed to the user. The user can then check the regenerated process plan and make any final corrections. Once the user has confirmed that all requests have been reflected, the final process plan can be downloaded in PDF format.

[1090] Hardware and software used

[1091] Hardware: Smartphone or tablet (display device), server (electronic device)

[1092] Software: Generative AI models (e.g., OpenAI GPT)

[1093] The generated data and data calculations are as follows: The display device collects input data from the user and transmits it to the electronic device. The electronic device uses the generative AI model to generate an initial process plan, including details of each process, standard times, and the types of machines and robots to be used. It then regenerates the process plan based on feedback and revision requests.

[1094] Specific examples

[1095] For example, if a factory manager types:

[1096] Production Line: Line 1

[1097] Equipment used: Robot Arm X

[1098] Target product: Part A

[1099] An example of a prompt to input to the generative AI model is:

[1100] Use the generative AI model to generate a production process plan for production line 1, robot arm X, and target product part A. The initial plan should include details of each process, standard times, and required resources.

[1101] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1102] Step 1:

[1103] The user uses the display device to input information about the line, equipment, and product. The input provides specific data such as "Production Line 1," "Robot Arm X," and "Part A." The input information is received by the user interface.

[1104] Step 2:

[1105] The terminal transmits the input information to the electronic device (server). The transmitted data includes all information provided by the user about the production line, the equipment used, and the target product.

[1106] Step 3:

[1107] The server uses a generative AI model to generate an initial process plan based on the received information. The server inputs the data, and the generative AI model calculates and outputs the details, standard times, and required resources for each process. The initial process plan includes specific process steps and required resources.

[1108] Step 4:

[1109] The server sends the created initial process plan to the terminal, which includes the generated process plan as output.

[1110] Step 5:

[1111] The terminal then displays the received initial process plan on a display device, which includes details such as each step of the process, the time it will take, and the equipment to be used.

[1112] Step 6:

[1113] Users can review the initial process plan and provide feedback and correction requests through input fields, including specific requests such as "extend the time required for process 3 to 20 minutes" or "add a new inspection step."

[1114] Step 7:

[1115] The terminal transmits the feedback and correction requests from the user to the server. The transmitted data includes the input feedback and correction requests.

[1116] Step 8:

[1117] The server regenerates the process plan using a generative AI model based on the received feedback and correction requests. The generative AI model then processes and calculates the new data to create a new process plan that reflects the feedback.

[1118] Step 9:

[1119] The server sends the regenerated process plan to the terminal, and includes the regenerated plan as output.

[1120] Step 10:

[1121] The terminal displays the received regenerated process plan on the display device, allowing the user to make final confirmation of the regenerated plan and make further corrections as necessary.

[1122] Step 11:

[1123] Once the final confirmation is complete, the terminal will provide a link to download the final process plan in PDF format, which the user can use to download and use the final plan.

[1124] The above processing steps enable efficient creation and modification of factory production process plans.

[1125] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1126] This invention combines a system that enables teachers to efficiently create lesson plans for each lesson with an emotion engine that recognizes the user's emotions. This system allows teachers to create lesson plans more efficiently and in a stress-free environment. Below, we will explain in detail the program processing of this system from the perspectives of the server, terminal, and user.

[1127] server

[1128] The server generates lesson plans using the generative AI model, regenerates the plans based on user feedback, and also automatically corrects lesson plans and provides support content based on information obtained from the emotion engine.

[1129] First, the server receives information about the grade, teaching materials, and unit sent from the device. This information is analyzed and the lesson plan generation process begins. The generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[1130] In addition, the server is equipped with an emotion engine that analyzes the user's emotional state and, if necessary, proposes initial lesson plans and support content.

[1131] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[1132] Terminal

[1133] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[1134] When the server returns an initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format and includes fields for entering feedback and correction requests. In addition, an emotion engine recognizes the user's emotions and automatically suggests feedback and correction requests. For example, if the user is feeling stressed, suggested correction requests are automatically displayed.

[1135] The device sends information from the emotion engine to the server, which then uses this information to regenerate lesson plans and support content, ultimately providing a link to download the completed lesson plans in PDF format.

[1136] User

[1137] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Once they have completed the input, they press the send button.

[1138] The students review the initial lesson plan sent from the server and enter feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[1139] The user checks the regenerated lesson plan displayed on the device and makes any final corrections. During this process, the emotion engine recognizes the user's emotions and may suggest appropriate feedback or correction requests. For example, if the user is feeling stressed, the engine may suggest "simplify this part."

[1140] Once teachers have confirmed that all requests have been reflected, they can download and save the lesson plan in PDF format. This system not only enables teachers to create high-quality lesson plans quickly and efficiently, but also reduces stress with the support of the emotion engine.

[1141] Through these steps, the present invention enables teachers to create lesson plans effectively and efficiently, enabling them to provide high-quality lessons to students.

[1142] The processing flow will be explained below.

[1143] Step 1:

[1144] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[1145] Step 2:

[1146] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[1147] Step 3:

[1148] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[1149] Step 4:

[1150] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[1151] Step 5:

[1152] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[1153] Step 6:

[1154] The server uses an emotion engine to recognize the user's emotional state, which is done in parallel with the generation of the initial lesson plan.

[1155] Step 7:

[1156] Based on the information obtained from the emotion engine, the server modifies the initial lesson plan as needed and adds supportive content, including, for example, suggestions to simplify the overall lesson plan if the user is feeling stressed.

[1157] Step 8:

[1158] The server sends the generated or modified initial lesson plan to the device in JSON format.

[1159] Step 9:

[1160] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[1161] Step 10:

[1162] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[1163] Step 11:

[1164] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[1165] Step 12:

[1166] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[1167] Step 13:

[1168] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[1169] Step 14:

[1170] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[1171] Step 15:

[1172] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[1173] Step 16:

[1174] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[1175] Step 17:

[1176] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[1177] Step 18:

[1178] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[1179] Step 19:

[1180] The emotion engine continuously monitors the user's emotions and provides supportive content as needed. For example, if the user is feeling stressed, it will display advice on relaxation techniques and stress management.

[1181] Through these steps, teachers can create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students. Support from the emotion engine reduces stress for teachers and creates a better educational environment.

[1182] Example 2

[1183] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1184] Conventional lesson plan creation systems require teachers to manually create lesson plans, requiring a great deal of time and effort. Furthermore, they lack support that takes into account the psychological state and emotions of teachers, which can result in increased stress for teachers and affect the quality of instruction. To address these issues, a system was needed that would enable teachers to create lesson plans more efficiently and provide appropriate support according to their emotional state.

[1185] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for displaying an interface for inputting information, means for communicating information on the education level, type of teaching material, and education unit input from the terminal, means for generating an initial education plan based on the received information, means for transmitting the initial education plan to the terminal, means for communicating feedback and revision requests input by the user from the terminal, means for regenerating the education plan generated based on the received feedback and revision requests, means for transmitting the regenerated education plan to the terminal, means for displaying the regenerated education plan received on the terminal, means for analyzing the user's emotions using an emotion analysis engine, and means for revising the content of the education plan based on the user's emotional state and suggesting advice and support content. This enables teachers to quickly and efficiently create high-quality education plans and provide instruction while reducing stress with the support of the emotion engine.

[1186] An "interface" is a means, such as a screen or input form, through which a user provides input information.

[1187] A "terminal" is a device such as a computer or tablet that a user operates and uses to input information.

[1188] A "server" is a computer system on a network that receives and processes information sent from a terminal.

[1189] "Education level" is information indicating the grade or educational stage.

[1190] "Type of teaching material" is information indicating the category of educational materials or texts to be used.

[1191] An "educational unit" is information that indicates a specific topic or section that is covered in an educational curriculum.

[1192] An "initial teaching plan" is the first lesson plan created by the server using a generative AI model.

[1193] "Communication" refers to sending and receiving information between a terminal and a server.

[1194] "Feedback" refers to opinions and information on improvements to the initial education plan provided by the user.

[1195] "Request for revision" is information about a specific request for change made by a user to the initial training plan.

[1196] "Regeneration" is the process of regenerating the educational plan based on feedback and requests for revisions.

[1197] An "emotion analysis engine" is a software component for recognizing and analyzing a user's emotional state.

[1198] "Advice" refers to teaching plans and improvement suggestions based on the sentiment analysis engine.

[1199] "Support content" refers to additional materials and actions that complement the educational plan and support teachers' teaching activities.

[1200] This invention relates to a system that allows educators to efficiently create lesson plans, and further combines it with an emotion engine that recognizes the user's emotions. Details for specifically implementing this system are provided below.

[1201] Hardware and Software Configuration

[1202] server

[1203] The server is a high-performance computer system that functions as the center of information processing. This server is equipped with a generative AI model and an emotion analysis engine. The generative AI model automatically generates teaching plans, and the emotion analysis engine is used to analyze the emotional state of teachers.

[1204] Terminal

[1205] A terminal is a device used by teachers to input information and check generated lesson plans. Specific hardware examples include personal computers, tablets, and smartphones. Software that provides a user interface is installed on the terminal.

[1206] User

[1207] The user (in this case, a teacher) operates a terminal to input the information necessary to create a lesson plan, and provides feedback and requests for revisions. Based on the information entered by the user, the server generates and regenerates a lesson plan.

[1208] What the program does

[1209] Entering initial information

[1210] Users use the terminal to enter information such as educational level, type of teaching material, and teaching unit. This information can be easily entered through an input form. For example, it can be entered in the format of "Grade: 1st year of high school," "Teaching material: English textbook," and "Unit name: Unit 3."

[1211] Transmission and processing of information

[1212] The device sends the input information to the server, which then uses the generative AI model to generate an initial lesson plan based on the received information.

[1213] Example prompt sentence:

[1214] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[1215] Displaying and feedback on initial lesson plans

[1216] The generated initial lesson plan is sent from the server to the device, which displays it to the user. The user can review the displayed initial lesson plan and enter feedback and requests for revisions. For example, specific requests such as "extend the introduction to 20 minutes" or "add pair work" can be included.

[1217] Sentiment Analysis and Regeneration

[1218] When the server receives feedback or correction requests from the user, it uses an emotion analysis engine to analyze the user's emotional state. Based on the user's emotional state, it adjusts the content of the educational plan and suggests advice and support content. For example, if the user is feeling stressed, it will suggest corrections to reduce the user's stress.

[1219] View and download the final lesson plan

[1220] The regenerated lesson plan is sent from the server to the device again and displayed to the user. Once the lesson plan has been confirmed to reflect all requests, the device provides a link to download the lesson plan in PDF format.

[1221] This configuration allows teachers to quickly and efficiently create high-quality lesson plans, and the support provided by the emotion analysis engine enables them to teach effectively while reducing stress.

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] The user enters information into the device. The user enters information such as grade, teaching materials, and unit name into the input form displayed on the device's interface. This input form is designed with an intuitive UI, and information is entered in the format of, for example, "Grade: 1st year of high school," "Teaching materials used: English textbook," and "Unit name: Unit 3." This operation collects the initial information.

[1225] input:

[1226] Grade: 1st year of high school

[1227] Materials used: English textbook

[1228] Unit Name: Unit 3

[1229] output:

[1230] Information entered into the device.

[1231] Step 2:

[1232] The device sends the input information to the server. When the user clicks the send button, the device sends the input information to the server in JSON format or similar.

[1233] Specific behavior:

[1234] The terminal sends the following data:

[1235] {

[1236] "grade": "first year of high school",

[1237] "material": "English text",

[1238] "unit": "Unit 3"

[1239] }

[1240] input:

[1241] Information entered into the device.

[1242] output:

[1243] Information received by the server.

[1244] Step 3:

[1245] The server generates an initial lesson plan using a generative AI model. The server analyzes the received information and inputs it as a prompt to the generative AI model. Based on this prompt, the generative AI model automatically generates an initial lesson plan.

[1246] Examples:

[1247] Prompt statement:

[1248] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[1249] input:

[1250] {

[1251] "grade": "first year of high school",

[1252] "material": "English text",

[1253] "unit": "Unit 3"

[1254] }

[1255] output:

[1256] Initial guidance plan.

[1257] Step 4:

[1258] The server sends the initial lesson plan to the terminal, which then displays the generated initial lesson plan to the user.

[1259] Specific behavior:

[1260] The server transmits the generated lesson plan, which is received and displayed by the terminal.

[1261] Generated lesson plan:

[1262] 1. Introduction to the lesson (10 minutes)

[1263] 2. Introduction to the topic (15 minutes)

[1264] 3. Exercises (20 minutes)

[1265] 4. Reflection (10 min)

[1266] input:

[1267] Initial guidance plan.

[1268] output:

[1269] The initial lesson plan displayed on the device.

[1270] Step 5:

[1271] The user inputs feedback and requests for revisions to the initial lesson plan. The user reviews the initial lesson plan and inputs feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[1272] input:

[1273] Initial guidance plan.

[1274] output:

[1275] Feedback and correction requests.

[1276] Step 6:

[1277] The device sends feedback and correction requests to the server. After the user enters feedback and correction requests, the device sends them to the server. The data is again sent in JSON format.

[1278] Specific behavior:

[1279] The device sends the following feedback and correction requests to the server:

[1280] {

[1281] "feedback": "Extend introduction to 20 minutes",

[1282] "request": "Add pair work"

[1283] }

[1284] input:

[1285] Feedback and correction requests.

[1286] output:

[1287] Feedback and correction requests received by the server.

[1288] Step 7:

[1289] The server generates a regenerated lesson plan and analyzes the user's emotional state using an emotion analysis engine. Depending on the emotional state, if the user is feeling stressed, a correction plan to reduce the burden is suggested. The regenerated lesson plan is created.

[1290] Specific behavior:

[1291] The server uses an emotion analysis engine to determine the user's emotional state and suggests corrections as needed based on a generative AI model.

[1292] input:

[1293] Feedback and correction requests, sentiment analysis results.

[1294] output:

[1295] The regenerated lesson plan.

[1296] Step 8:

[1297] The server transmits the regenerated lesson plan to the terminal, which then displays it to the user.

[1298] Specific behavior:

[1299] The server transmits the generated regenerated lesson plan, which is received and displayed by the terminal.

[1300] Regenerated lesson plan:

[1301] 1. Introduction to the lesson (20 minutes)

[1302] 2. Introduction to the topic (10 minutes)

[1303] 3. Pair work (15 minutes)

[1304] 4. Exercises (20 minutes)

[1305] 5. Reflection (10 min.)

[1306] input:

[1307] The regenerated lesson plan.

[1308] output:

[1309] The regenerated lesson plan displayed on the device.

[1310] Step 9:

[1311] The user checks the final lesson plan and downloads it in PDF format. After checking, the user clicks the download link on their device to save the final lesson plan in PDF format.

[1312] Specific behavior:

[1313] Click the download link displayed on your device and save the PDF.

[1314] PDF file:

[1315] First Year High School English Unit 3 Lesson Plan

[1316] 1. Introduction to the lesson (20 minutes)

[1317] 2. Introduction to the topic (10 minutes)

[1318] 3. Pair work (15 minutes)

[1319] 4. Exercises (20 minutes)

[1320] 5. Reflection (10 min.)

[1321] input:

[1322] The regenerated lesson plan.

[1323] output:

[1324] Final lesson plan in PDF format.

[1325] (Application example 2)

[1326] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1327] Conventional lesson plan creation systems make it difficult for teachers to efficiently create detailed lesson plans for each lesson, placing a heavy burden on busy teachers in particular. Furthermore, lesson plans are not appropriately revised based on the teacher's emotional state, and an environment that can reduce stress is not provided. As a result, the reduction of teacher workload and improvement of lesson quality are not fully achieved.

[1328] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for displaying an interface for inputting information on a terminal; means for transmitting information on the grade, teaching material, and unit input from the terminal to the server; means for generating, in the server, an initial lesson plan using a generative model based on the received information; means for transmitting the initial lesson plan to the terminal; means for displaying, in the terminal, the received initial lesson plan; means for transmitting feedback and revision requests input by the user from the terminal to the server; means for regenerating, in the server, a lesson plan based on the received feedback and revision requests; means for transmitting the regenerated lesson plan to the terminal; means for analyzing the user's emotional state using an emotion engine and automatically revising the lesson plan and revision suggestions based on the emotion data; means for providing support content based on information obtained from the emotion engine; and means for displaying, in the terminal, the received regenerated lesson plan. This will enable teachers to create lesson plans efficiently, and will also enable them to receive automatic corrections and support content based on their emotional state, enabling them to deliver high-quality lessons while reducing stress.

[1329] An "interface" is a screen or input means that makes it easy for users to input information.

[1330] A "terminal" is an electronic device used by a user (for example, a smartphone or a head-mounted display).

[1331] "Server" means a central processing unit for generating and regenerating lesson plans.

[1332] "Grade" refers to the year in the educational curriculum that the student is in.

[1333] "Teaching materials" refer to textbooks and supplementary materials used in classes.

[1334] A "unit" is a specific theme or division of content within an educational curriculum.

[1335] "Generative model" refers to artificial intelligence technology for creating initial lesson plans and regenerative lesson plans.

[1336] An "initial lesson plan" is a lesson plan that the generative model first creates based on the received information.

[1337] "Feedback" refers to evaluations and requests for revisions to the initial lesson plan provided by the user.

[1338] "Regeneration" is the process of recreating a lesson plan based on received feedback and revision requests.

[1339] An "emotion engine" is a technology that analyzes the user's emotional state and adjusts the system's behavior based on that data.

[1340] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[1341] "Support content" refers to additional educational or instructional materials provided to support the user's work.

[1342] A "prompt sentence" is an instruction sentence or initial data that the generative model uses to create a lesson plan.

[1343] In order to put the present invention into practice, it is important that the server, terminal, and user each play their respective roles appropriately. Specific embodiments will be described below from the viewpoints of the server, terminal, and user.

[1344] server

[1345] The server is mainly responsible for generating and regenerating lesson plans and processing the emotion engine. Specifically, it uses the following hardware and software:

[1346] Hardware: high performance server, camera and microphone for emotion analysis

[1347] software:

[1348] Generative AI models (e.g., OpenAI GPT-4)

[1349] Emotion engine (e.g. EmotionML)

[1350] Backend server frameworks (e.g., Node.js)

[1351] Database (e.g. MongoDB)

[1352] The server first receives information about the grade, teaching materials, and unit sent from the device. The received data is passed to the generative AI model as a prompt, and an initial lesson plan is generated. For example, the following prompt sentences are used:

[1353] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[1354] Lesson theme: How to make questions using interrogative words

[1355] Lesson goal: To enable students to create basic questions that include self-introductions.

[1356] Introduction: 10 minutes

[1357] Main topic: 30 minutes

[1358] Exercise: 20 minutes

[1359] Reflection: 10 minutes

[1360] Teacher's emotional state: Normal

[1361] feedback:

[1362] Introduction extended to 15 minutes

[1363] Reduced exercise time by 10 minutes

[1364] The generated initial lesson plan is sent to the device and displayed to the user. When the user provides feedback, the server regenerates the lesson plan using the generative AI model. During regeneration, the emotion engine analyzes the user's emotional state and automatically corrects the plan based on the emotion data. Finally, the server sends the completed lesson plan to the device and provides a link to download it in PDF format.

[1365] Terminal

[1366] A terminal provides an interface that allows users to easily input information. Specifically, the terminal uses the following hardware and software:

[1367] Hardware: Smartphones and head-mounted displays

[1368] Software: Web front-end framework (e.g., React.js)

[1369] The device first collects information about the grade, teaching materials, and unit entered by the user and sends it to the server. The initial lesson plan returned from the server is displayed in an easy-to-read format and includes a field for the user to enter feedback. The device also collects user emotional data using a camera and microphone for emotion analysis and sends this data to the server.

[1370] User

[1371] The user inputs the necessary information using the device, and checks and provides feedback on the lesson plan.

[1372] 1. Enter the grade, teaching materials, and unit information. For example, "Grade: 1st year of high school," "Teaching materials: English textbook," and "Unit name: Unit 3."

[1373] 2. Check the initial lesson plan sent from the server.

[1374] 3. Enter your feedback. For example, "Extend the introduction to 20 minutes" or "Add pair work."

[1375] 4. Check the regenerated lesson plan and make any final corrections.

[1376] 5. Download and save the final lesson plan as a PDF.

[1377] This system allows teachers to create high-quality lesson plans quickly and efficiently, and the emotional engine helps reduce stress, enabling teachers to deliver effective, high-quality lessons.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The user uses the terminal to input information about the grade, teaching materials, and unit. For example, the user might input "Grade: 1st year of junior high school," "Teaching materials: English textbook," and "Unit name: Unit 3." This input data is collected by the terminal and sent to the server.

[1381] Step 2:

[1382] The terminal sends the input information to the server. At this time, the information about the input grade, teaching material, and unit is transferred to the server as a data packet. If the input data is "Grade: 1st year of junior high school," "Teaching material: English textbook," and "Unit name: Unit 3," a prompt sentence is generated based on this.

[1383] Step 3:

[1384] The server sends a prompt to the generative AI model based on the received information. For example, the prompt text might look like this:

[1385] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[1386] Lesson theme: How to make questions using interrogative words

[1387] Lesson goal: To enable students to create basic questions that include self-introductions.

[1388] Introduction: 10 minutes

[1389] Main topic: 30 minutes

[1390] Exercise: 20 minutes

[1391] Reflection: 10 minutes

[1392] Teacher's emotional state: Normal

[1393] feedback:

[1394] Introduction extended to 15 minutes

[1395] Reduced exercise time by 10 minutes

[1396] The server generates an initial lesson plan based on a generative AI model (e.g., OpenAI GPT-4). During this generation process, each lesson phase is proposed based on the received information.

[1397] Step 4:

[1398] The server then sends the generated initial lesson plan to the device. The data is structured in an easy-to-read format. For example, the initial lesson plan is divided into phases such as an introduction to the lesson, the main topic, exercises, and reflection.

[1399] Step 5:

[1400] The device displays the received initial lesson plan to the user. The user can review this display and input feedback or requests for revisions. For example, they can input feedback such as "extend the introduction to 20 minutes" or "add pair work."

[1401] Step 6:

[1402] The terminal transmits the feedback and correction requests entered by the user to the server, and this feedback data is transferred to the server as an additional data packet.

[1403] Step 7:

[1404] The server regenerates the lesson plan based on the received feedback and correction requests. This process uses the generative AI model again, and corrections are made based on the feedback. Furthermore, the emotion engine analyzes the user's emotional state, and the lesson plan is automatically corrected based on the emotion data.

[1405] Step 8:

[1406] The server then sends the regenerated lesson plan to the device. The final lesson plan is also provided in PDF format, and a download link is generated if necessary.

[1407] Step 9:

[1408] The terminal displays the received regenerated lesson plan to the user and also provides a download link in PDF format. The user can check the final lesson plan and download it if necessary.

[1409] By following these steps, teachers can create lesson plans efficiently and effectively and receive support tailored to their emotional state, enabling them to deliver high-quality lessons.

[1410] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1411] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1412] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1413] [Fourth embodiment]

[1414] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1415] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1416] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1417] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1418] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1420] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1421] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1422] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1423] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1424] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1425] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1426] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1427] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user. Below, we will explain the program processing of this system from the perspectives of the server, terminal, and user.

[1428] server

[1429] The server is responsible for generating lesson plans using a generative AI model and regenerating the lesson plans based on user feedback.

[1430] First, the server receives information about the grade, teaching materials, and unit from the device. Based on this, the server starts the lesson plan generation process. In this process, the generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[1431] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[1432] Terminal

[1433] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[1434] When the server returns the initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format, and there is a field for the user to enter feedback or correction requests. When correction requests are entered, the device sends them to the server.

[1435] Furthermore, when the revised lesson plan is received, it is displayed again to the user for final confirmation, and finally, a link is provided to allow the user to download the completed lesson plan in PDF format.

[1436] User

[1437] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Then, once they have completed the input, they press the send button.

[1438] The user can review the initial lesson plan sent from the server and enter feedback and requests for revisions to any areas that require revision. For example, they can enter specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[1439] Check the regenerated lesson plan displayed on the device and make any final corrections. Once you have confirmed that all your requests have been reflected, download and save the lesson plan in PDF format.

[1440] In this way, the present invention enables teachers to create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students.

[1441] The processing flow will be explained below.

[1442] Step 1:

[1443] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[1444] Step 2:

[1445] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[1446] Step 3:

[1447] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[1448] Step 4:

[1449] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[1450] Step 5:

[1451] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[1452] Step 6:

[1453] The server sends the generated initial lesson plan to the device in JSON format.

[1454] Step 7:

[1455] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[1456] Step 8:

[1457] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[1458] Step 9:

[1459] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[1460] Step 10:

[1461] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[1462] Step 11:

[1463] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[1464] Step 12:

[1465] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[1466] Step 13:

[1467] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[1468] Step 14:

[1469] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[1470] Step 15:

[1471] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[1472] Step 16:

[1473] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[1474] By going through these steps, teachers can create lesson plans effectively and efficiently.

[1475] Example 1

[1476] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1477] The traditional lesson plan creation process is time-consuming and laborious for teachers, requiring detailed plans for each grade, teaching material, and unit. This means that teachers spend a lot of time creating lesson plans, which can lead to neglecting to prepare lessons for students. Furthermore, when revisions or adjustments to lesson plans are needed, they must be made manually, which is inefficient and prone to errors.

[1478] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1479] In this invention, the server includes means for generating an initial lesson plan using a generative AI model based on the received information, means for regenerating a lesson plan using the generative AI model based on the received feedback and revision requests, and means for providing the final lesson plan in PDF format, thereby improving the efficiency and accuracy of lesson plan creation.

[1480] The "interface for inputting information" refers to the input form or operation screen that is displayed on the terminal so that the user can input information such as the grade, teaching materials, and units.

[1481] A "terminal" is an electronic device operated by a user, such as a PC, tablet, or smartphone.

[1482] The "server" is a central computer system that receives requests from users, processes them, and returns the results, and plays an important role in generating lesson plans using generative AI models.

[1483] A "generative AI model" is a type of model that uses artificial intelligence to generate text data, which is then used to create lesson plans based on specific prompts.

[1484] An "initial lesson plan" is a lesson plan that is first generated using a generative AI model based on the information received by the server.

[1485] "Feedback and correction requests" are opinions and requests for improvements provided by users regarding the initial lesson plan, and are important information for the server to receive and regenerate.

[1486] A "regenerated lesson plan" is a lesson plan that has been regenerated using a generative AI model based on feedback and requests for revisions.

[1487] "PDF format" is an abbreviation for Portable Document Format, and is an electronic file format in which the document layout is fixed.

[1488] A "prompt sentence" is an input sentence used to inform a generative AI model to generate a lesson plan.

[1489] A "link" is a hypertext link that provides access to a specific file or web page that a user can click to download a PDF version of the lesson plan.

[1490] This invention relates to a system that allows teachers to efficiently create lesson plans for each lesson. This system uses a generative AI model to generate lesson plans based on information entered by the user and regenerates them in response to feedback. A specific implementation of this system is described below.

[1491] server

[1492] The server serves multiple roles as a central computer system. In particular, it receives information sent by users and generates lesson plans based on that information using a generative AI model. Specifically, it operates as follows:

[1493] First, the server receives information such as the grade, teaching material name, and unit name from the terminal. This process can be performed using Apache or Nginx as a web server, with Python and Flask as the backend. It receives an HTTP POST request, analyzes the information, and stores it in the appropriate variables.

[1494] Based on the received information, the server sends a prompt to the generative AI model (e.g., a generative AI model). This prompt is formed by inserting the received information into a predefined template. For example, a possible prompt might be, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson."

[1495] The generative AI model generates an initial lesson plan based on this prompt sentence, and the generated result is returned to the server, which stores this initial lesson plan in a database, converts it to JSON format, and sends it to the device.

[1496] If the server receives further feedback or correction requests from the user, it sends a new prompt including the feedback to the generative AI model to generate a new lesson plan. This process is repeated as many times as necessary to finally provide the completed lesson plan.

[1497] Terminal

[1498] The terminal provides an interface that makes it easy for users to input information. It has the following main functions:

[1499] First, an interface for entering the grade, teaching material name, and unit name is displayed. This is designed as an HTML form and often uses React or Vue.js as the front-end framework. For example, it provides a drop-down menu for entering the grade and text fields for entering the teaching material name and unit name.

[1500] When the user enters the required information and presses the submit button, the device sends this information to the server as an HTTP POST request using the fetch API or the axios library.

[1501] Receives the initial lesson plan returned from the server and displays it to the user in an easy-to-understand format. Provides a field for inputting feedback or requests for revisions to the lesson plan, and sends it back to the server. Receives a regenerated lesson plan as needed and displays it again to the user.

[1502] Once the final lesson plan is completed, the device will provide a downloadable link in PDF format, which users can click to download the lesson plan as a PDF file.

[1503] User

[1504] The user, i.e., the teacher, accesses the system using a terminal and creates a lesson plan using the following procedure.

[1505] First, enter the necessary information such as the grade, teaching material name, and unit name into the device interface. For example, enter information such as "Grade: 1st year of high school," "Materials used: English textbook," and "Unit name: Unit 3," and then press the send button.

[1506] The teacher reviews the initial lesson plan returned by the server and inputs any necessary corrections or feedback, such as specific requests such as "extend the introduction to 20 minutes" or "add pair work."

[1507] Review the regenerated lesson plan again and make any final adjustments necessary. Once you are sure that all your requests have been reflected, download the lesson plan as a PDF and save it.

[1508] Through this process, teachers can quickly and efficiently create high-quality lesson plans, significantly reducing the amount of time and effort required, and enabling them to provide high-quality lessons to students.

[1509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1510] Step 1:

[1511] The terminal displays an interface for the user to input information. The user inputs information such as the grade, teaching material name, and unit name, and presses the send button. At this point, the input is information such as the grade, teaching material name, and unit name, and the output is an HTTP POST request sent to the server.

[1512] Step 2:

[1513] The server receives information about the grade, teaching material name, and unit name sent from the terminal. It receives the HTTP POST request, analyzes the information, and stores it in the appropriate variables. The input is the information sent from the terminal, and the output is the storage of the analyzed information in the variables.

[1514] Step 3:

[1515] The server sends a prompt to the generative AI model based on the received information. This prompt is formed by inserting received information such as the grade, teaching material name, and unit name into a predefined template. For example, the request might read, "Please create an English lesson plan for first-year high school students. The teaching material to be used is 'English Textbook' and the unit is 'Unit 3'. The lesson plan should include the following elements: an introduction to the lesson, an explanation of the unit's theme, related exercises, and a review of the lesson." The input is the analyzed information, and the output is the prompt sent to the generative AI model.

[1516] Step 4:

[1517] The generative AI model generates an initial lesson plan based on the prompt sentence. The generated lesson plan is sent back to the server. The input is the prompt sentence, and the output is the text data of the initial lesson plan.

[1518] Step 5:

[1519] The server receives the initial lesson plan returned from the generative AI model, saves it in a database, converts it to JSON format, and sends it to the device. At this time, it sends it as an HTTP response, setting the Content-Type in the response header to application / json. The input is the text data of the initial lesson plan, and the output is JSON-formatted data sent to the device.

[1520] Step 6:

[1521] The device receives the initial lesson plan returned from the server, analyzes it, and displays it to the user. It formats it into a highly readable format so that the user can check it. The input is the initial lesson plan data in JSON format, and the output is the formatted lesson plan that is displayed to the user.

[1522] Step 7:

[1523] The user checks the displayed initial lesson plan and inputs feedback and requests for revisions. For example, they input specific requests such as "extend the introduction to 20 minutes" or "add pair work," and then press the submit button. The input is the user's feedback and requests for revisions, and the output is an HTTP POST request sent to the server.

[1524] Step 8:

[1525] The server receives and analyzes feedback and correction requests sent from the device. Based on this information, it sends new prompts to the generative AI model and regenerates the lesson plan. The input is the feedback and correction requests, and the output is the new prompt sent to the generative AI model.

[1526] Step 9:

[1527] The generative AI model generates a regenerated lesson plan based on the new prompt sentence and sends it back to the server. The input is the new prompt sentence, and the output is the text data of the regenerated lesson plan.

[1528] Step 10:

[1529] The server receives the text data of the regenerated lesson plan, saves it in the database again, converts it into JSON format, and sends it to the terminal. The input is the text data of the regenerated lesson plan, and the output is JSON format data sent to the terminal.

[1530] Step 11:

[1531] The device receives the regenerated lesson plan returned from the server and displays it to the user. It also provides an interface for accepting confirmation and feedback. The input is the regenerated lesson plan data in JSON format, and the output is a formatted regenerated lesson plan that is displayed to the user.

[1532] Step 12:

[1533] The user checks it again and provides additional feedback if necessary. The server then regenerates it and displays a link on the device that provides the final completed lesson plan in PDF format. Clicking this link allows the user to download the lesson plan as a PDF file. The input is the user's final check and feedback, and the output is a PDF file of the final lesson plan with a download link.

[1534] In this way, the server, terminal, and user each play their respective roles, enabling efficient and accurate creation of high-quality lesson plans.

[1535] (Application example 1)

[1536] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] Factory production processes require the optimization of a wide variety of line, equipment, and product combinations. However, currently, this process is primarily done manually, requiring a great deal of time and effort. Furthermore, the feedback and correction processes for production plans are inefficient. This invention aims to solve these problems and improve the efficiency of creating and correcting production process plans.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1539] In this invention, the server includes means for providing a display device for inputting information, means for transmitting information related to the line, equipment, and product input from the display device to an electronic device, means for generating an initial process plan in the electronic device based on the received information, means for transmitting the initial process plan to the display device, means for displaying the received initial process plan in the display device, means for transmitting feedback and revision requests input by a user from the display device to the electronic device, means for regenerating a process plan in the electronic device based on the received feedback and revision requests, means for transmitting the regenerated process plan to the display device, and means for displaying the received regenerated process plan in the display device. This enables efficient creation and revision of production process plans for a factory.

[1540] The "display device for inputting information" is a terminal device equipped with a user interface for users to input information about lines, equipment, and products.

[1541] "Display input information" means line, equipment, and product related data provided through a user interface.

[1542] "Electronic device" refers to a server or computer for information processing.

[1543] "Initial process plan" refers to the initial outline plan for the production process of a factory, generated by the server.

[1544] "Regeneration" is the process of recreating a process plan based on received feedback and revision requests.

[1545] "Feedback and revision requests" are evaluations and suggestions for improvement provided by users regarding the initial process plan.

[1546] "PDF Downloadable Link" means a hyperlink that enables the final process plan to be downloaded as a PDF file.

[1547] "Input Area" refers to a dedicated field within the interface where a user can enter feedback or correction requests.

[1548] A "process plan" is a production schedule that includes the necessary steps, time, and resource allocation for the factory line, equipment, and target product.

[1549] This invention is a system for efficiently creating and modifying factory production process plans. The system utilizes display devices, electronics, and generative AI models to generate process plans and regenerate them based on user feedback.

[1550] The display device provides a user interface for users to input information about the line, equipment, and product. Through this interface, users input data related to the production line, the equipment used, and the target product. For example, they input specific information such as "Production Line 1," "Robot Arm X," and "Part A." Once input is complete, the information is transmitted to the electronic device.

[1551] After receiving the information, the electronic device uses the generative AI model to generate an initial process plan, which includes details of each process in the factory, standard times, and required resources. This initial process plan is then sent to a display device and displayed to the user.

[1552] The user reviews the displayed initial process plan and enters feedback and requests for revisions into the display device, including specific requests such as "extend the duration of process 3 to 20 minutes" or "add a new inspection step." The feedback and requests for revisions are then sent back to the electronics, which then uses the generative AI model to regenerate a new process plan.

[1553] The regenerated process plan is sent back to the display device and displayed to the user. The user can then check the regenerated process plan and make any final corrections. Once the user has confirmed that all requests have been reflected, the final process plan can be downloaded in PDF format.

[1554] Hardware and software used

[1555] Hardware: Smartphone or tablet (display device), server (electronic device)

[1556] Software: Generative AI models (e.g., OpenAI GPT)

[1557] The generated data and data calculations are as follows: The display device collects input data from the user and transmits it to the electronic device. The electronic device uses the generative AI model to generate an initial process plan, including details of each process, standard times, and the types of machines and robots to be used. It then regenerates the process plan based on feedback and revision requests.

[1558] Specific examples

[1559] For example, if a factory manager types:

[1560] Production Line: Line 1

[1561] Equipment used: Robot Arm X

[1562] Target product: Part A

[1563] An example of a prompt to input to the generative AI model is:

[1564] Use the generative AI model to generate a production process plan for production line 1, robot arm X, and target product part A. The initial plan should include details of each process, standard times, and required resources.

[1565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1566] Step 1:

[1567] The user uses the display device to input information about the line, equipment, and product. The input provides specific data such as "Production Line 1," "Robot Arm X," and "Part A." The input information is received by the user interface.

[1568] Step 2:

[1569] The terminal transmits the input information to the electronic device (server). The transmitted data includes all information provided by the user about the production line, the equipment used, and the target product.

[1570] Step 3:

[1571] The server uses a generative AI model to generate an initial process plan based on the received information. The server inputs the data, and the generative AI model calculates and outputs the details, standard times, and required resources for each process. The initial process plan includes specific process steps and required resources.

[1572] Step 4:

[1573] The server sends the created initial process plan to the terminal, which includes the generated process plan as output.

[1574] Step 5:

[1575] The terminal then displays the received initial process plan on a display device, which includes details such as each step of the process, the time it will take, and the equipment to be used.

[1576] Step 6:

[1577] Users can review the initial process plan and provide feedback and correction requests through input fields, including specific requests such as "extend the time required for process 3 to 20 minutes" or "add a new inspection step."

[1578] Step 7:

[1579] The terminal transmits the feedback and correction requests from the user to the server. The transmitted data includes the input feedback and correction requests.

[1580] Step 8:

[1581] The server regenerates the process plan using a generative AI model based on the received feedback and correction requests. The generative AI model then processes and calculates the new data to create a new process plan that reflects the feedback.

[1582] Step 9:

[1583] The server sends the regenerated process plan to the terminal, and includes the regenerated plan as output.

[1584] Step 10:

[1585] The terminal displays the received regenerated process plan on the display device, allowing the user to make final confirmation of the regenerated plan and make further corrections as necessary.

[1586] Step 11:

[1587] Once the final confirmation is complete, the terminal will provide a link to download the final process plan in PDF format, which the user can use to download and use the final plan.

[1588] The above processing steps enable efficient creation and modification of factory production process plans.

[1589] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1590] This invention combines a system that enables teachers to efficiently create lesson plans for each lesson with an emotion engine that recognizes the user's emotions. This system allows teachers to create lesson plans more efficiently and in a stress-free environment. Below, we will explain in detail the program processing of this system from the perspectives of the server, terminal, and user.

[1591] server

[1592] The server generates lesson plans using the generative AI model, regenerates the plans based on user feedback, and also automatically corrects lesson plans and provides support content based on information obtained from the emotion engine.

[1593] First, the server receives information about the grade, teaching materials, and unit sent from the device. This information is analyzed and the lesson plan generation process begins. The generative AI model creates an initial lesson plan based on the received information. This initial lesson plan includes various teaching phases, such as a lesson introduction, explanation of the subject, exercises, and review.

[1594] In addition, the server is equipped with an emotion engine that analyzes the user's emotional state and, if necessary, proposes initial lesson plans and support content.

[1595] The generated initial lesson plan is sent from the server to the device and displayed to the user (teacher). When the user sends feedback or requests for revisions, the server receives this and regenerates the lesson plan using the generative AI model again. Once the regenerated new lesson plan is complete, it is sent back to the device.

[1596] Terminal

[1597] The terminal provides an interface that makes it easy for users to input information. An input form is displayed on the screen, allowing users to input information such as grade level, teaching materials, and unit name. Once input is complete and the send button is pressed, the terminal sends this information to the server.

[1598] When the server returns an initial lesson plan, the device displays it to the user (teacher). The lesson plan is provided in a highly visible format and includes fields for entering feedback and correction requests. In addition, an emotion engine recognizes the user's emotions and automatically suggests feedback and correction requests. For example, if the user is feeling stressed, suggested correction requests are automatically displayed.

[1599] The device sends information from the emotion engine to the server, which then uses this information to regenerate lesson plans and support content, ultimately providing a link to download the completed lesson plans in PDF format.

[1600] User

[1601] The user (teacher) uses the terminal to input the necessary information. For example, they enter information such as "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3" into the interface. Once they have completed the input, they press the send button.

[1602] The students review the initial lesson plan sent from the server and enter feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[1603] The user checks the regenerated lesson plan displayed on the device and makes any final corrections. During this process, the emotion engine recognizes the user's emotions and may suggest appropriate feedback or correction requests. For example, if the user is feeling stressed, the engine may suggest "simplify this part."

[1604] Once teachers have confirmed that all requests have been reflected, they can download and save the lesson plan in PDF format. This system not only enables teachers to create high-quality lesson plans quickly and efficiently, but also reduces stress with the support of the emotion engine.

[1605] Through these steps, the present invention enables teachers to create lesson plans effectively and efficiently, enabling them to provide high-quality lessons to students.

[1606] The processing flow will be explained below.

[1607] Step 1:

[1608] The terminal displays a form for entering the grade, teaching materials, and unit name. This form has input fields for "grade," "teaching materials used," and "unit name."

[1609] Step 2:

[1610] The user enters the necessary information into the input form displayed on the terminal. For example, they enter "grade: first year of high school," "teaching material: English textbook," and "unit name: Unit 3."

[1611] Step 3:

[1612] The terminal sends the information entered by the user to the server by pressing the "Submit" button on the form.

[1613] Step 4:

[1614] The server receives the information on the grade, teaching materials, and unit name sent from the device, analyzes this information, and starts the lesson plan generation process.

[1615] Step 5:

[1616] The server uses a generative AI model to generate an initial lesson plan based on the received information, which includes a lesson introduction, topic explanation, exercises, and reflection phases.

[1617] Step 6:

[1618] The server uses an emotion engine to recognize the user's emotional state, which is done in parallel with the generation of the initial lesson plan.

[1619] Step 7:

[1620] Based on the information obtained from the emotion engine, the server modifies the initial lesson plan as needed and adds supportive content, including, for example, suggestions to simplify the overall lesson plan if the user is feeling stressed.

[1621] Step 8:

[1622] The server sends the generated or modified initial lesson plan to the device in JSON format.

[1623] Step 9:

[1624] The terminal displays the received initial lesson plan to the user. The lesson plan is displayed in a highly visible format so that the user can easily check it.

[1625] Step 10:

[1626] The user can review the initial lesson plan and provide feedback and requests for revisions, such as specific requests such as "extending the introduction to 20 minutes" or "adding pair work."

[1627] Step 11:

[1628] The terminal sends the feedback and correction requests entered by the user to the server by pressing the "Send" button in the feedback input field.

[1629] Step 12:

[1630] The server receives feedback and correction requests sent from the devices and starts the process of regenerating lesson plans based on this information.

[1631] Step 13:

[1632] The server uses the generative AI model to regenerate lesson plans that reflect the feedback and revision requests. The regenerated lesson plans reflect the user's requests.

[1633] Step 14:

[1634] The server sends the regenerated lesson plan to the device. The lesson plan is again sent in JSON format.

[1635] Step 15:

[1636] The terminal displays the regenerated lesson plan to the user in a format that is easy for the user to check.

[1637] Step 16:

[1638] The user checks the regenerated lesson plan for the last time and determines whether additional corrections are necessary. If necessary, the user inputs the correction request again.

[1639] Step 17:

[1640] The server repeats the regeneration process as necessary, and when it finally completes a satisfactory lesson plan, it sends it to the device.

[1641] Step 18:

[1642] The device will display the final lesson plan to the user and provide a link to download it in PDF format, which the user can use to download and save the lesson plan.

[1643] Step 19:

[1644] The emotion engine continuously monitors the user's emotions and provides supportive content as needed. For example, if the user is feeling stressed, it will display advice on relaxation techniques and stress management.

[1645] Through these steps, teachers can create lesson plans quickly and efficiently, enabling them to provide high-quality lessons to students. Support from the emotion engine reduces stress for teachers and creates a better educational environment.

[1646] Example 2

[1647] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1648] Conventional lesson plan creation systems require teachers to manually create lesson plans, requiring a great deal of time and effort. Furthermore, they lack support that takes into account the psychological state and emotions of teachers, which can result in increased stress for teachers and affect the quality of instruction. To address these issues, a system was needed that would enable teachers to create lesson plans more efficiently and provide appropriate support according to their emotional state.

[1649] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for displaying an interface for inputting information, means for communicating information on the education level, type of teaching material, and education unit input from the terminal, means for generating an initial education plan based on the received information, means for transmitting the initial education plan to the terminal, means for communicating feedback and revision requests input by the user from the terminal, means for regenerating the education plan generated based on the received feedback and revision requests, means for transmitting the regenerated education plan to the terminal, means for displaying the regenerated education plan received on the terminal, means for analyzing the user's emotions using an emotion analysis engine, and means for revising the content of the education plan based on the user's emotional state and suggesting advice and support content. This enables teachers to quickly and efficiently create high-quality education plans and provide instruction while reducing stress with the support of the emotion engine.

[1650] An "interface" is a means, such as a screen or input form, through which a user provides input information.

[1651] A "terminal" is a device such as a computer or tablet that a user operates and uses to input information.

[1652] A "server" is a computer system on a network that receives and processes information sent from a terminal.

[1653] "Education level" is information indicating the grade or educational stage.

[1654] "Type of teaching material" is information indicating the category of educational materials or texts to be used.

[1655] An "educational unit" is information that indicates a specific topic or section that is covered in an educational curriculum.

[1656] An "initial teaching plan" is the first lesson plan created by the server using a generative AI model.

[1657] "Communication" refers to sending and receiving information between a terminal and a server.

[1658] "Feedback" refers to opinions and information on improvements to the initial education plan provided by the user.

[1659] "Request for revision" is information about a specific request for change made by a user to the initial training plan.

[1660] "Regeneration" is the process of regenerating the educational plan based on feedback and requests for revisions.

[1661] An "emotion analysis engine" is a software component for recognizing and analyzing a user's emotional state.

[1662] "Advice" refers to teaching plans and improvement suggestions based on the sentiment analysis engine.

[1663] "Support content" refers to additional materials and actions that complement the educational plan and support teachers' teaching activities.

[1664] This invention relates to a system that allows educators to efficiently create lesson plans, and further combines it with an emotion engine that recognizes the user's emotions. Details for specifically implementing this system are provided below.

[1665] Hardware and Software Configuration

[1666] server

[1667] The server is a high-performance computer system that functions as the center of information processing. This server is equipped with a generative AI model and an emotion analysis engine. The generative AI model automatically generates teaching plans, and the emotion analysis engine is used to analyze the emotional state of teachers.

[1668] Terminal

[1669] A terminal is a device used by teachers to input information and check generated lesson plans. Specific hardware examples include personal computers, tablets, and smartphones. Software that provides a user interface is installed on the terminal.

[1670] User

[1671] The user (in this case, a teacher) operates a terminal to input the information necessary to create a lesson plan, and provides feedback and requests for revisions. Based on the information entered by the user, the server generates and regenerates a lesson plan.

[1672] What the program does

[1673] Entering initial information

[1674] Users use the terminal to enter information such as educational level, type of teaching material, and teaching unit. This information can be easily entered through an input form. For example, it can be entered in the format of "Grade: 1st year of high school," "Teaching material: English textbook," and "Unit name: Unit 3."

[1675] Transmission and processing of information

[1676] The device sends the input information to the server, which then uses the generative AI model to generate an initial lesson plan based on the received information.

[1677] Example prompt sentence:

[1678] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[1679] Displaying and feedback on initial lesson plans

[1680] The generated initial lesson plan is sent from the server to the device, which displays it to the user. The user can review the displayed initial lesson plan and enter feedback and requests for revisions. For example, specific requests such as "extend the introduction to 20 minutes" or "add pair work" can be included.

[1681] Sentiment Analysis and Regeneration

[1682] When the server receives feedback or correction requests from the user, it uses an emotion analysis engine to analyze the user's emotional state. Based on the user's emotional state, it adjusts the content of the educational plan and suggests advice and support content. For example, if the user is feeling stressed, it will suggest corrections to reduce the user's stress.

[1683] View and download the final lesson plan

[1684] The regenerated lesson plan is sent from the server to the device again and displayed to the user. Once the lesson plan has been confirmed to reflect all requests, the device provides a link to download the lesson plan in PDF format.

[1685] This configuration allows teachers to quickly and efficiently create high-quality lesson plans, and the support provided by the emotion analysis engine enables them to teach effectively while reducing stress.

[1686] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1687] Step 1:

[1688] The user enters information into the device. The user enters information such as grade, teaching materials, and unit name into the input form displayed on the device's interface. This input form is designed with an intuitive UI, and information is entered in the format of, for example, "Grade: 1st year of high school," "Teaching materials used: English textbook," and "Unit name: Unit 3." This operation collects the initial information.

[1689] input:

[1690] Grade: 1st year of high school

[1691] Materials used: English textbook

[1692] Unit Name: Unit 3

[1693] output:

[1694] Information entered into the device.

[1695] Step 2:

[1696] The device sends the input information to the server. When the user clicks the send button, the device sends the input information to the server in JSON format or similar.

[1697] Specific behavior:

[1698] The terminal sends the following data:

[1699] {

[1700] "grade": "first year of high school",

[1701] "material": "English text",

[1702] "unit": "Unit 3"

[1703] }

[1704] input:

[1705] Information entered into the device.

[1706] output:

[1707] Information received by the server.

[1708] Step 3:

[1709] The server generates an initial lesson plan using a generative AI model. The server analyzes the received information and inputs it as a prompt to the generative AI model. Based on this prompt, the generative AI model automatically generates an initial lesson plan.

[1710] Examples:

[1711] Prompt statement:

[1712] "Please create an English lesson plan for first-year high school students. The materials to be used will be an English textbook, and the subject will be Unit 3."

[1713] input:

[1714] {

[1715] "grade": "first year of high school",

[1716] "material": "English text",

[1717] "unit": "Unit 3"

[1718] }

[1719] output:

[1720] Initial guidance plan.

[1721] Step 4:

[1722] The server sends the initial lesson plan to the terminal, which then displays the generated initial lesson plan to the user.

[1723] Specific behavior:

[1724] The server transmits the generated lesson plan, which is received and displayed by the terminal.

[1725] Generated lesson plan:

[1726] 1. Introduction to the lesson (10 minutes)

[1727] 2. Introduction to the topic (15 minutes)

[1728] 3. Exercises (20 minutes)

[1729] 4. Reflection (10 min)

[1730] input:

[1731] Initial guidance plan.

[1732] output:

[1733] The initial lesson plan displayed on the device.

[1734] Step 5:

[1735] The user inputs feedback and requests for revisions to the initial lesson plan. The user reviews the initial lesson plan and inputs feedback and requests for revisions, such as "extend the introduction to 20 minutes" or "add pair work."

[1736] input:

[1737] Initial guidance plan.

[1738] output:

[1739] Feedback and correction requests.

[1740] Step 6:

[1741] The device sends feedback and correction requests to the server. After the user enters feedback and correction requests, the device sends them to the server. The data is again sent in JSON format.

[1742] Specific behavior:

[1743] The device sends the following feedback and correction requests to the server:

[1744] {

[1745] "feedback": "Extend introduction to 20 minutes",

[1746] "request": "Add pair work"

[1747] }

[1748] input:

[1749] Feedback and correction requests.

[1750] output:

[1751] Feedback and correction requests received by the server.

[1752] Step 7:

[1753] The server generates a regenerated lesson plan and analyzes the user's emotional state using an emotion analysis engine. Depending on the emotional state, if the user is feeling stressed, a correction plan to reduce the burden is suggested. The regenerated lesson plan is created.

[1754] Specific behavior:

[1755] The server uses an emotion analysis engine to determine the user's emotional state and suggests corrections as needed based on a generative AI model.

[1756] input:

[1757] Feedback and correction requests, sentiment analysis results.

[1758] output:

[1759] The regenerated lesson plan.

[1760] Step 8:

[1761] The server transmits the regenerated lesson plan to the terminal, which then displays it to the user.

[1762] Specific behavior:

[1763] The server transmits the generated regenerated lesson plan, which is received and displayed by the terminal.

[1764] Regenerated lesson plan:

[1765] 1. Introduction to the lesson (20 minutes)

[1766] 2. Introduction to the topic (10 minutes)

[1767] 3. Pair work (15 minutes)

[1768] 4. Exercises (20 minutes)

[1769] 5. Reflection (10 min.)

[1770] input:

[1771] The regenerated lesson plan.

[1772] output:

[1773] The regenerated lesson plan displayed on the device.

[1774] Step 9:

[1775] The user checks the final lesson plan and downloads it in PDF format. After checking, the user clicks the download link on their device to save the final lesson plan in PDF format.

[1776] Specific behavior:

[1777] Click the download link displayed on your device and save the PDF.

[1778] PDF file:

[1779] First Year High School English Unit 3 Lesson Plan

[1780] 1. Introduction to the lesson (20 minutes)

[1781] 2. Introduction to the topic (10 minutes)

[1782] 3. Pair work (15 minutes)

[1783] 4. Exercises (20 minutes)

[1784] 5. Reflection (10 min.)

[1785] input:

[1786] The regenerated lesson plan.

[1787] output:

[1788] Final lesson plan in PDF format.

[1789] (Application example 2)

[1790] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1791] Conventional lesson plan creation systems make it difficult for teachers to efficiently create detailed lesson plans for each lesson, placing a heavy burden on busy teachers in particular. Furthermore, lesson plans are not appropriately revised based on the teacher's emotional state, and an environment that can reduce stress is not provided. As a result, the reduction of teacher workload and improvement of lesson quality are not fully achieved.

[1792] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for displaying an interface for inputting information on a terminal; means for transmitting information on the grade, teaching material, and unit input from the terminal to the server; means for generating, in the server, an initial lesson plan using a generative model based on the received information; means for transmitting the initial lesson plan to the terminal; means for displaying, in the terminal, the received initial lesson plan; means for transmitting feedback and revision requests input by the user from the terminal to the server; means for regenerating, in the server, a lesson plan based on the received feedback and revision requests; means for transmitting the regenerated lesson plan to the terminal; means for analyzing the user's emotional state using an emotion engine and automatically revising the lesson plan and revision suggestions based on the emotion data; means for providing support content based on information obtained from the emotion engine; and means for displaying, in the terminal, the received regenerated lesson plan. This will enable teachers to create lesson plans efficiently, and will also enable them to receive automatic corrections and support content based on their emotional state, enabling them to deliver high-quality lessons while reducing stress.

[1793] An "interface" is a screen or input means that makes it easy for users to input information.

[1794] A "terminal" is an electronic device used by a user (for example, a smartphone or a head-mounted display).

[1795] "Server" means a central processing unit for generating and regenerating lesson plans.

[1796] "Grade" refers to the year in the educational curriculum that the student is in.

[1797] "Teaching materials" refer to textbooks and supplementary materials used in classes.

[1798] A "unit" is a specific theme or division of content within an educational curriculum.

[1799] "Generative model" refers to artificial intelligence technology for creating initial lesson plans and regenerative lesson plans.

[1800] An "initial lesson plan" is a lesson plan that the generative model first creates based on the received information.

[1801] "Feedback" refers to evaluations and requests for revisions to the initial lesson plan provided by the user.

[1802] "Regeneration" is the process of recreating a lesson plan based on received feedback and revision requests.

[1803] An "emotion engine" is a technology that analyzes the user's emotional state and adjusts the system's behavior based on that data.

[1804] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[1805] "Support content" refers to additional educational or instructional materials provided to support the user's work.

[1806] A "prompt sentence" is an instruction sentence or initial data that the generative model uses to create a lesson plan.

[1807] In order to put the present invention into practice, it is important that the server, terminal, and user each play their respective roles appropriately. Specific embodiments will be described below from the viewpoints of the server, terminal, and user.

[1808] server

[1809] The server is mainly responsible for generating and regenerating lesson plans and processing the emotion engine. Specifically, it uses the following hardware and software:

[1810] Hardware: high performance server, camera and microphone for emotion analysis

[1811] software:

[1812] Generative AI models (e.g., OpenAI GPT-4)

[1813] Emotion engine (e.g. EmotionML)

[1814] Backend server frameworks (e.g., Node.js)

[1815] Database (e.g. MongoDB)

[1816] The server first receives information about the grade, teaching materials, and unit sent from the device. The received data is passed to the generative AI model as a prompt, and an initial lesson plan is generated. For example, the following prompt sentences are used:

[1817] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[1818] Lesson theme: How to make questions using interrogative words

[1819] Lesson goal: To enable students to create basic questions that include self-introductions.

[1820] Introduction: 10 minutes

[1821] Main topic: 30 minutes

[1822] Exercise: 20 minutes

[1823] Reflection: 10 minutes

[1824] Teacher's emotional state: Normal

[1825] feedback:

[1826] Introduction extended to 15 minutes

[1827] Reduced exercise time by 10 minutes

[1828] The generated initial lesson plan is sent to the device and displayed to the user. When the user provides feedback, the server regenerates the lesson plan using the generative AI model. During regeneration, the emotion engine analyzes the user's emotional state and automatically corrects the plan based on the emotion data. Finally, the server sends the completed lesson plan to the device and provides a link to download it in PDF format.

[1829] Terminal

[1830] A terminal provides an interface that allows users to easily input information. Specifically, the terminal uses the following hardware and software:

[1831] Hardware: Smartphones and head-mounted displays

[1832] Software: Web front-end framework (e.g., React.js)

[1833] The device first collects information about the grade, teaching materials, and unit entered by the user and sends it to the server. The initial lesson plan returned from the server is displayed in an easy-to-read format and includes a field for the user to enter feedback. The device also collects user emotional data using a camera and microphone for emotion analysis and sends this data to the server.

[1834] User

[1835] The user inputs the necessary information using the device, and checks and provides feedback on the lesson plan.

[1836] 1. Enter the grade, teaching materials, and unit information. For example, "Grade: 1st year of high school," "Teaching materials: English textbook," and "Unit name: Unit 3."

[1837] 2. Check the initial lesson plan sent from the server.

[1838] 3. Enter your feedback. For example, "Extend the introduction to 20 minutes" or "Add pair work."

[1839] 4. Check the regenerated lesson plan and make any final corrections.

[1840] 5. Download and save the final lesson plan as a PDF.

[1841] This system allows teachers to create high-quality lesson plans quickly and efficiently, and the emotional engine helps reduce stress, enabling teachers to deliver effective, high-quality lessons.

[1842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1843] Step 1:

[1844] The user uses the terminal to input information about the grade, teaching materials, and unit. For example, the user might input "Grade: 1st year of junior high school," "Teaching materials: English textbook," and "Unit name: Unit 3." This input data is collected by the terminal and sent to the server.

[1845] Step 2:

[1846] The terminal sends the input information to the server. At this time, the information about the input grade, teaching material, and unit is transferred to the server as a data packet. If the input data is "Grade: 1st year of junior high school," "Teaching material: English textbook," and "Unit name: Unit 3," a prompt sentence is generated based on this.

[1847] Step 3:

[1848] The server sends a prompt to the generative AI model based on the received information. For example, the prompt text might look like this:

[1849] Please create a lesson plan for an English class for first-year junior high school students based on the following information.

[1850] Lesson theme: How to make questions using interrogative words

[1851] Lesson goal: To enable students to create basic questions that include self-introductions.

[1852] Introduction: 10 minutes

[1853] Main topic: 30 minutes

[1854] Exercise: 20 minutes

[1855] Reflection: 10 minutes

[1856] Teacher's emotional state: Normal

[1857] feedback:

[1858] Introduction extended to 15 minutes

[1859] Reduced exercise time by 10 minutes

[1860] The server generates an initial lesson plan based on a generative AI model (e.g., OpenAI GPT-4). During this generation process, each lesson phase is proposed based on the received information.

[1861] Step 4:

[1862] The server then sends the generated initial lesson plan to the device. The data is structured in an easy-to-read format. For example, the initial lesson plan is divided into phases such as an introduction to the lesson, the main topic, exercises, and reflection.

[1863] Step 5:

[1864] The device displays the received initial lesson plan to the user. The user can review this display and input feedback or requests for revisions. For example, they can input feedback such as "extend the introduction to 20 minutes" or "add pair work."

[1865] Step 6:

[1866] The terminal transmits the feedback and correction requests entered by the user to the server, and this feedback data is transferred to the server as an additional data packet.

[1867] Step 7:

[1868] The server regenerates the lesson plan based on the received feedback and correction requests. This process uses the generative AI model again, and corrections are made based on the feedback. Furthermore, the emotion engine analyzes the user's emotional state, and the lesson plan is automatically corrected based on the emotion data.

[1869] Step 8:

[1870] The server then sends the regenerated lesson plan to the device. The final lesson plan is also provided in PDF format, and a download link is generated if necessary.

[1871] Step 9:

[1872] The terminal displays the received regenerated lesson plan to the user and also provides a download link in PDF format. The user can check the final lesson plan and download it if necessary.

[1873] By following these steps, teachers can create lesson plans efficiently and effectively and receive support tailored to their emotional state, enabling them to deliver high-quality lessons.

[1874] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1875] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1876] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1877] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1878] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1879] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1880] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1881] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1882] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1883] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1884] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1885] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1886] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1887] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1888] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1889] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1890] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1891] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1892] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1893] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1894] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1895] The following is further disclosed regarding the above embodiment.

[1896] (Claim 1)

[1897] means for displaying an interface on a terminal for inputting information;

[1898] means for transmitting information about the grade, teaching materials, and units input from the terminal to a server;

[1899] means in the server for generating an initial lesson plan based on the received information;

[1900] means for transmitting the initial lesson plan to the terminal;

[1901] a means for displaying the received initial lesson plan in the terminal;

[1902] means for transmitting feedback and correction requests input by the user from the terminal to the server;

[1903] a means for regenerating a lesson plan based on the received feedback and correction requests in the server;

[1904] means for transmitting the regenerated lesson plan to the terminal;

[1905] means for displaying the received regenerated lesson plan in the terminal;

[1906] A system including:

[1907] (Claim 2)

[1908] 10. The system of claim 1, wherein the initial lesson plan or the regenerated lesson plan provides a downloadable link in PDF format.

[1909] (Claim 3)

[1910] The system according to claim 1, wherein the terminal provides an input field for feedback or requests for revision to the lesson plan.

[1911] "Example 1"

[1912] (Claim 1)

[1913] means for displaying an interface on a terminal for inputting information;

[1914] means for transmitting information about the grade, teaching materials, and units input from the terminal to a server;

[1915] a means for generating an initial lesson plan using a generative AI model based on the received information in the server;

[1916] means for transmitting the initial lesson plan to the terminal;

[1917] a means for displaying the received initial lesson plan in the terminal;

[1918] means for transmitting feedback and correction requests input by the user from the terminal to the server;

[1919] a means for regenerating a lesson plan based on the received feedback and correction requests using a generative AI model in the server;

[1920] means for transmitting the regenerated lesson plan to the terminal;

[1921] means for displaying the received regenerated lesson plan in the terminal;

[1922] a means for providing said final lesson plan in PDF format;

[1923] A system including:

[1924] (Claim 2)

[1925] 2. The system of claim 1, wherein the lesson plan is generated by a generative AI model using a prompt sentence.

[1926] (Claim 3)

[1927] The system according to claim 1, wherein the terminal provides an input field for feedback or requests for revision to the lesson plan.

[1928] "Application Example 1"

[1929] (Claim 1)

[1930] means for providing a display for inputting information;

[1931] means for transmitting information about the line, equipment, and product input from the display device to an electronic device;

[1932] means for generating an initial process plan based on the received information in the electronic device;

[1933] means for transmitting the initial process plan to the display device;

[1934] means for displaying the received initial process plan in the display device;

[1935] means for transmitting feedback and correction requests input by a user from said display device to said electronic device;

[1936] means for regenerating a process plan based on the received feedback and modification requests in the electronic device;

[1937] means for transmitting the regenerated process plan to the display device;

[1938] means for displaying the received regenerated process plan on the display device;

[1939] A system including:

[1940] (Claim 2)

[1941] 10. The system of claim 1, wherein the initial process plan or the regenerated process plan provides a downloadable link in PDF format.

[1942] (Claim 3)

[1943] 10. The system of claim 1, wherein the display device provides an input area for feedback and requests for modification of the process plan.

[1944] "Example 2: Combining Emotion Engines"

[1945] (Claim 1)

[1946] means for displaying an interface for inputting information;

[1947] means for communicating information on the educational level, type of teaching material, and educational unit input from the terminal;

[1948] means for generating an initial education plan based on the received information in the server;

[1949] means for transmitting the initial training plan to the terminal;

[1950] a means for displaying the received initial training plan in the terminal;

[1951] means for communicating feedback and correction requests input by the user from said terminal;

[1952] a means for regenerating the generated training plan based on the received feedback and correction requests in the server;

[1953] means for transmitting the regenerated training plan to the terminal;

[1954] a means for displaying the received regenerated training plan in the terminal;

[1955] A means for analyzing user emotions using an emotion analysis engine;

[1956] a means for modifying the content of the educational plan based on the emotional state of the user and suggesting advice and support content;

[1957] A system including:

[1958] (Claim 2)

[1959] 10. The system of claim 1, wherein the initial or regenerated educational plan provides a downloadable link in PDF format.

[1960] (Claim 3)

[1961] 2. The system according to claim 1, wherein the terminal provides an input field for feedback or requests for revisions to the training plan.

[1962] "Application example 2 when combining emotion engines"

[1963] New Claims

[1964] (Claim 1)

[1965] means for displaying an interface on a terminal for inputting information;

[1966] means for transmitting information about the grade, teaching materials, and units input from the terminal to a server;

[1967] means in the server for generating an initial lesson plan using a generative model based on the received information;

[1968] means for transmitting the initial lesson plan to the terminal;

[1969] a means for displaying the received initial lesson plan in the terminal;

[1970] means for transmitting feedback and correction requests input by the user from the terminal to the server;

[1971] a means for regenerating a lesson plan based on the received feedback and correction requests in the server;

[1972] means for transmitting the regenerated lesson plan to the terminal;

[1973] a means for analyzing the user's emotional state using an emotion engine and automatically modifying the lesson plan or correction suggestion based on the emotion data;

[1974] means for providing support content based on information obtained from the emotion engine;

[1975] means for displaying the received regenerated lesson plan in the terminal;

[1976] A system including:

[1977] (Claim 2)

[1978] 10. The system of claim 1, wherein the initial lesson plan or the regenerated lesson plan provides a downloadable link in PDF format.

[1979] (Claim 3)

[1980] The system according to claim 1, wherein the terminal provides an input field for feedback or requests for revision to the lesson plan. [Explanation of symbols]

[1981] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for displaying an interface on a terminal for inputting information; means for transmitting information about the grade, teaching materials, and units input from the terminal to a server; means in the server for generating an initial lesson plan based on the received information; means for transmitting the initial lesson plan to the terminal; a means for displaying the received initial lesson plan in the terminal; means for transmitting feedback and correction requests input by the user from the terminal to the server; a means for regenerating a lesson plan based on the received feedback and correction requests in the server; means for transmitting the regenerated lesson plan to the terminal; means for displaying the received regenerated lesson plan in the terminal; A system including:

2. The system of claim 1 , wherein the initial lesson plan or the regenerated lesson plan provides a downloadable link in PDF format.

3. The system according to claim 1 , wherein the terminal provides an input field for feedback or requests for revision to the lesson plan.

Images