System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting the individual data of a student, a means for recording and transmitting the inputted individual data, a means for utilizing a generation model for generating an optimized education program or learning print by using the received individual data and a means for outputting the generated education program or learning print as a digital file.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] Special needs education requires individualized attention tailored to the type and severity of each student's disability, placing a high level of expertise and considerable effort on the part of teachers. However, creating optimal educational programs and study materials for each student every day places a significant burden on teachers. This situation leaves teachers unable to devote sufficient time and effort to their primary teaching duties. Therefore, there is a need for efficient methods to reduce teacher burdens and provide an education optimized for each student. [Means for solving the problem]

[0005] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a means for inputting individual data of students (age, type and degree of disability, and what they can and cannot do), a means for recording and transmitting the input individual data, a means for utilizing a generative model that generates optimized educational programs and study handouts using the received individual data, and a means for outputting the generated educational programs and study handouts as digital files.

[0006] In addition, the present invention also includes a validation means for confirming the validity of the received individual data, and a means for formatting the generated educational programs and study handouts into human-readable formats (PDF, HTML). With this configuration, teachers can automatically obtain optimal educational programs and study handouts simply by inputting individual student data, enabling them to provide appropriate education tailored to each student.

[0007] "Individual student data" refers to information about a specific student, including characteristics such as age, type and degree of disability, and what the student can and cannot do.

[0008] "Input means" refers to the interface or device used to input student data into the system, including, for example, a keyboard or touch screen.

[0009] "Means for recording and transmitting" refers to a device or software that has the function of storing the entered individual student data and transmitting it via communication to another system or server as necessary.

[0010] A "generative model" is an artificial intelligence algorithm or machine learning model that analyzes individual student data and generates optimal educational programs and study materials based on that data.

[0011] A "digital file" is a document in a file format that is created and stored electronically, such as PDF or HTML.

[0012] A "validation means" is a device or software that has the function of verifying the individual student data entered and confirming that the data is accurate and complete.

[0013] A "formatting means" is a device or software that has the function of converting the generated educational program or study handout into a format that is easy for humans to understand (e.g., PDF or HTML). [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a means for inputting individual student data (age, type and degree of disability, and abilities and inabilities), a means for recording and transmitting the input individual data, a means for utilizing a generative model to generate optimized educational programs and study handouts using the received individual data, and a means for outputting the generated educational programs and study handouts as digital files. In addition, the present invention also includes a means for validating the received individual data and a means for formatting the generated educational programs and study handouts into human-readable formats (PDF, HTML).

[0036] Program processing flow

[0037] In the system of the present invention, processing proceeds as follows.

[0038] 1. User provides input data:

[0039] Users (teachers) enter individual student data through the interface of the special needs education system, such as age, type and degree of disability, and what the student can and cannot do, into a form.

[0040] 2. Data transmission by the device:

[0041] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it for transmission. When the send button is clicked, the data is sent to the server.

[0042] 3. Data receipt and validation by the server:

[0043] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[0044] 4. Generation of educational programs and study materials by the server:

[0045] The server passes the successfully validated data to the generative AI model, which generates optimized educational programs and learning handouts based on the data. The generated data is then formatted into user-friendly formats such as PDF and HTML.

[0046] 5. Server generated output:

[0047] The formatted educational programs and handouts are output as digital files and sent to a terminal, where they can be displayed directly or printed as needed.

[0048] 6. Receiving and displaying results by terminal:

[0049] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[0050] Examples:

[0051] Student A's data:

[0052] Age: 10

[0053] Type of disability: Intellectual disability

[0054] Degree of disability: Moderate

[0055] Ability to do: Simple calculations, read and write hiragana

[0056] What they can't do: Understand complex sentences, think abstractly

[0057] User input:

[0058] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the terminal.

[0059] Server Action:

[0060] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model to generate an optimized educational program and learning handouts. The generated data is formatted in PDF format and sent to the device.

[0061] Terminal reception and display:

[0062] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[0063] As described above, the present invention significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[0067] Step 2:

[0068] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[0069] Step 3:

[0070] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for transmission to the server.

[0071] Step 4:

[0072] The device sends the data to the server. The converted JSON data is sent to the server as an HTTP request.

[0073] Step 5:

[0074] The server receives the data. The server receives the HTTP request sent from the terminal and extracts the data.

[0075] Step 6:

[0076] The server validates the data it receives, checking for the validity of the data, for example, whether required fields are missing or whether they contain invalid values.

[0077] Step 7:

[0078] The server calls the generative AI model. If validation is successful, the server passes the data to the generative AI model.

[0079] Step 8:

[0080] The server generates educational programs and study handouts. The generative AI model automatically creates optimal educational programs and study handouts based on individual student data.

[0081] Step 9:

[0082] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[0083] Step 10:

[0084] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[0085] Step 11:

[0086] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[0087] Step 12:

[0088] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[0089] This process flow allows users to easily create personalized educational programs and study handouts, providing optimal learning support for each student.

[0090] Example 1

[0091] 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."

[0092] In special needs education, it is extremely burdensome for teachers to manually create educational programs and learning materials appropriate for each individual student. Furthermore, due to the lack of progress in the management of individual student data and the digitalization of educational resources, it is often difficult to provide efficient and effective educational support. This increases the workload of teachers and leads to problems such as a decline in the quality of education.

[0093] 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.

[0094] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording the input individual data, formatting it into an appropriate data format such as JSON format, and transmitting it; [means for confirming the validity of the received individual data and validating it;] [means for passing successfully validated data to a generative AI model and generating optimized educational programs and study handouts;] [means for formatting the generated educational programs and study handouts into a user-friendly format such as PDF or HTML format; and [means for outputting the formatted educational programs and study handouts as digital files and transmitting them to a terminal.] This significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0095] "Student Individual Data" refers to information specific to each student, such as the student's age, the type and severity of their disability, and what they can and cannot do.

[0096] "Means for input" refers to the interface and operation method used by users (teachers) to input individual student data into the system.

[0097] "Means of recording, formatting into an appropriate data format such as JSON, and transmitting" refers to the function of saving the input data, converting it into a data format as necessary, and transmitting it to the server.

[0098] "Validation and verification measures" refers to the process of verifying that individual data received is accurate and complete.

[0099] "Means of passing data to a generative AI model to generate optimized educational programs and study handouts" refers to the function of using validated data to utilize a generative AI model to create educational programs and study handouts that are optimal for students.

[0100] "Means of formatting into user-friendly formats such as PDF and HTML" refers to the process of converting the generated educational programs and learning handouts into a format that is easy for users to read.

[0101] "Means for outputting as a digital file and sending to a terminal" refers to a mechanism for saving the formatted file in a digital format and sending it to a user's terminal.

[0102] A "prompt" is a text that provides instructions or questions to a generative AI model to generate educational programs or learning materials.

[0103] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a series of processes that automate the collection of individual student data, data validation, generation of optimal educational programs and study handouts, and output of the results. Specific embodiments of this system are described below.

[0104] Users (teachers) enter individual student data through the interface of the special needs education system. Specifically, they access the system using a browser and enter information such as the student's age, type of disability, degree of disability, and what they can and cannot do into an input form. This operation collects information specific to each student.

[0105] The device then uses software to convert the input data into an appropriate data format, such as JSON. Specifically, the device uses a scripting language, such as JavaScript, to convert the input data into JSON. When the "Submit" button is clicked, an AJAX request is made, sending the data to the server.

[0106] The server performs validation to check the validity of the received data. Specifically, the received data is parsed with a JSON parser and checked to see if all required fields are entered correctly and if any inappropriate values ​​are included. If this validation is successful, the server proceeds to the next step.

[0107] The validated data is passed to the generative AI model. The server generates and inputs a prompt to the AI ​​model. This prompt contains specific instructions and questions for generating educational programs and learning handouts. For example, the following prompt is input to the generative AI model:

[0108] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0109] Age: 10

[0110] Type of disability: Intellectual disability

[0111] Degree of disability: Moderate

[0112] Ability to do: Simple calculations, read and write hiragana

[0113] What I can't do: Understand complex sentences, think abstractly

[0114] Please output the generated data in PDF format.

[0115] Based on this prompt, the generative AI model generates educational programs and handouts optimized for the student. The generated educational resources are then formatted into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines.

[0116] Finally, the formatted educational programs and learning handouts are stored as digital files on the server and then sent to the device. The device analyzes the received file URL and displays it in a format that the user can view. The user can use this file to provide educational support to their students.

[0117] As described above, this invention significantly reduces the burden on teachers in special needs education, making it possible to provide efficient and effective education.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Providing input data

[0121] The user enters the student's individual data into an input form. Specifically, the user launches a browser, accesses the special needs education system, and logs in. Then, the user enters data such as the student's age, type of disability, degree of disability, and what the student can and cannot do into the form.

[0122] Input: Individual data such as the student's age, type of disability, severity of disability, and what they can and cannot do.

[0123] Output: Individual student data entered into the form.

[0124] Step 2:

[0125] Formatting data and preparing it for transmission

[0126] The terminal converts the data entered by the user into an appropriate data format, such as JSON. Specifically, it converts the input data into JSON format using a scripting language such as JavaScript. This process is executed when the user clicks the "Submit" button.

[0127] Input: Individual student data entered into the form.

[0128] Output: Individual student data in JSON format.

[0129] Step 3:

[0130] Sending data

[0131] The terminal sends the data converted to JSON format to the server. Specifically, it uses an AJAX request. When the user clicks the "Submit" button, the data is sent to the server via an AJAX request.

[0132] Input: Individual student data converted to JSON format.

[0133] Output: The JSON data sent to the server.

[0134] Step 4:

[0135] Data Receipt and Validation

[0136] The server receives the data sent from the device. It analyzes the received data and checks its validity. Specifically, it uses a JSON parser to analyze the data and checks whether all required fields are entered and whether any inappropriate values ​​are included.

[0137] Input: JSON data sent from the terminal.

[0138] Output: Validated individual student data.

[0139] Step 5:

[0140] Creation of educational programs and study materials

[0141] The server passes the validated data to the generative AI model, which then uses prompts to instruct the model to generate educational programs and learning materials. For example, the following prompts can be used:

[0142] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0143] Age: 10

[0144] Type of disability: Intellectual disability

[0145] Degree of disability: Moderate

[0146] Ability to do: Simple calculations, read and write hiragana

[0147] What I can't do: Understand complex sentences, think abstractly

[0148] Please output the generated data in PDF format.

[0149] Input: Validated individual student data and prompt statement.

[0150] Output: Optimized educational programs and learning handouts created by the generative AI model.

[0151] Step 6:

[0152] Formatting and outputting the product

[0153] The server converts the educational programs and learning handouts obtained from the generative AI model into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines. The converted data is then saved as a digital file.

[0154] Input: Educational programs and learning handouts output from the generative AI model.

[0155] Output: Digital files formatted in PDF and HTML formats.

[0156] Step 7:

[0157] Sending and displaying the product

[0158] The server sends the formatted digital file to the device. Specifically, it sends the URL of the generated file to the device as an HTTP response. The device retrieves this URL and displays the file in a format that the user can view. Specifically, it displays the file using a PDF viewer or HTML viewer.

[0159] Input: URL of a digital file in PDF or HTML format.

[0160] Output: Displayed educational program and study handouts.

[0161] (Application example 1)

[0162] 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."

[0163] In special needs education, there is a need to generate and provide optimal educational programs and study materials tailored to the characteristics of each individual student. However, current systems place a heavy burden on teachers, making it difficult to provide efficient and effective education. In addition, there is a lack of teaching materials that emphasize individuality, making it difficult to select materials that are appropriate for each student. Furthermore, there is the problem that teachers must expend a great deal of effort to verify the validity and quality of the generated teaching materials. There is a need for a system that can solve these issues and provide efficient and effective special needs education.

[0164] 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.

[0165] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording and transmitting the input individual data]; [means for utilizing a generative AI model to generate optimized educational programs and learning tools using the received individual data]; and [means for outputting the generated educational programs and learning tools as digital files and making them available for recommendation and purchase in a virtual store]. This reduces the burden on teachers and makes it possible to quickly provide optimal teaching materials tailored to the characteristics of students. In addition, validation and digital preview functions enable efficient educational support while ensuring the quality of the generated teaching materials.

[0166] "Individual student data" refers to individual information necessary for education, such as a student's age, the type and degree of disability, and what they can and cannot do.

[0167] A "generative AI model" refers to an artificial intelligence model that analyzes individual student data and generates optimal educational programs and learning tools.

[0168] "Validation" refers to the process of verifying that the individual student data received is accurate and that all required fields have been entered correctly.

[0169] "Digital file" means a document or data in electronic form that can be accessed and manipulated by a computer, including formats such as PDF and HTML.

[0170] A "virtual store" is a virtual store operated on the Internet, and in this case refers to an online platform for selling teaching materials and learning tools for special needs education.

[0171] "Recommendation" refers to the act of selecting generated educational programs and learning tools to suit the user's needs and proposing the most suitable ones.

[0172] "Purchase" refers to the act of a user selecting a product in a virtual store and paying for it to acquire it.

[0173] "Digital Preview" refers to the ability for a user to review a generated educational program or learning tool in electronic format prior to purchase.

[0174] "Downloading" refers to the act of transferring and saving data or files from a server to a user's device via the Internet.

[0175] "Formatting" refers to the process of converting generated educational programs and learning tools into user-friendly formats (PDF, HTML).

[0176] MODE FOR CARRYING OUT THE INVENTION

[0177] The embodiment of this invention is a system that generates optimized educational programs and learning tools using individual data of students in special needs education and provides them in a virtual store. The system is composed of the following main components.

[0178] Hardware and Software Configuration

[0179] Hardware

[0180] 1. User device: A device used by teachers or parents to enter student information. This can be a desktop PC, laptop, tablet, smartphone, etc.

[0181] 2. Server: The back-end system that receives and processes data. A cloud server is typically used.

[0182] software

[0183] 1. Frontend: React.js is used to provide the user interface.

[0184] 2. Sending data: To send and receive data, we use HTTP POST requests in JSON format using the Axios library.

[0185] 3. Backend: Use Flask (a Python-based web framework).

[0186] 4. Validation: Use pandas and Marshmallow libraries to check the validity of the data.

[0187] 5. Generative AI model: TensorFlow is used to generate educational programs and learning tools.

[0188] 6. Digital File Formatting: Use ReportLab to convert generated educational programs and learning tools into PDF and HTML formats.

[0189] System operation explanation

[0190] 1. Data entry and submission

[0191] The user (teacher or guardian) enters the student's individual data (age, type and degree of disability, what they can and cannot do) through the user interface, and then clicks the send button to send the data to the server.

[0192] 2. Data processing on the server

[0193] The server validates the received data, specifically checking that all required fields are filled in correctly and that they do not contain any incorrect values.

[0194] 3. Creating teaching materials and formatting files

[0195] Successfully validated data is passed to a generative AI model to generate educational programs and learning tools optimized for each student's characteristics, which are then formatted into PDF or HTML format.

[0196] 4. Recommendations and Purchases

[0197] The generated educational programs and learning tools are recommended in a virtual store, where users can digitally preview them and then purchase them as needed.

[0198] 5. Download and Use

[0199] The purchased learning materials are provided as digital files that users can download and use, and a guide on how to use them is also provided.

[0200] Specific examples

[0201] Student B's data:

[0202] Age: 12

[0203] Type and severity of disability: Autism spectrum disorder, mild

[0204] Specialties: Puzzles, visual tasks

[0205] Weaknesses: Long periods of concentration, complex communication

[0206] Example prompt sentence:

[0207] "Generate visual aids that are ideal for a 12-year-old student with mild autism spectrum disorder. This student excels at puzzles but struggles with long periods of concentration and complex communication."

[0208] This enables the system to quickly generate optimized teaching materials required for special needs education and provide them through a virtual store.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The user uses the device's user interface to enter the student's individual data (age, type and degree of disability, what they can and cannot do) into a form. This data is used in subsequent processing, so it must be entered accurately and in detail.

[0212] Step 2:

[0213] The terminal converts the input data into JSON format and sends it to the server using an HTTP POST request when the submit button is clicked. The technologies used here are React.js and the Axios library. Once the input data is sent to the server, it can be processed in the next step.

[0214] Step 3:

[0215] The server receives the received JSON data and validates it. Specifically, it uses the pandas and Marshmallow libraries to check that the input data contains all required fields and is entered correctly. If the data is invalid, it returns an error message.

[0216] Step 4:

[0217] The server passes the successfully validated data to a generative AI model, which generates optimized educational programs and learning tools using prompts. These prompts are input into the model using TensorFlow. The generated data is then formatted into a digital file in the next step.

[0218] Step 5:

[0219] The server formats the generated educational programs and learning tools into user-friendly formats (PDF, HTML) using the ReportLab library. The formatted digital files are then used for recommendations and purchases in the virtual store.

[0220] Step 6:

[0221] The generated educational programs and learning tools are recommended in a virtual storefront for users to digitally preview and purchase. The virtual storefront is an online platform where the recommended educational materials are presented to users.

[0222] Step 7:

[0223] Users can check out educational materials in a virtual store and make a purchase. Purchased materials can then be downloaded to their devices as digital files. Teachers and parents can provide the downloaded materials to students for use, enabling efficient and effective special needs education.

[0224] Through these steps, the system can generate optimal educational programs and learning tools based on each student's individual data and provide them through a virtual store.

[0225] 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.

[0226] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education, and in particular, by combining an emotion engine that recognizes the user's emotions, it realizes more flexible and adaptive educational support. This system includes the following main means.

[0227] Program processing flow

[0228] 1. User provides input data:

[0229] Users (teachers) enter individual student data (age, type and degree of disability, what they can and cannot do) through the interface of the special needs education system. Input is done using a form, and the required items are filled in on the form.

[0230] 2. Data transmission by the device:

[0231] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it to be sent to the server. When the send button is clicked, the data is sent to the server.

[0232] 3. Emotion engine recognizes user emotions:

[0233] The device analyzes the user's facial expressions and tone of voice when inputting information to recognize emotions. This is done by capturing data in real time using a camera and microphone. Emotional data includes indicators such as the user's stress level and satisfaction.

[0234] 4. Data receipt and validation by the server:

[0235] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[0236] 5. The server invokes the generative AI model, taking into account the emotion data:

[0237] The server passes the successfully validated data to the generative AI model, which analyzes the input student's individual data and also references the user's emotional data to generate optimized educational programs and study handouts. The generated data is then further formatted into user-friendly formats such as PDF and HTML.

[0238] 6. Server output and emotional data recording:

[0239] The formatted educational programs and study handouts are output as digital files and sent to a terminal. Emotional data is also recorded and used as reference material when creating future educational programs.

[0240] 7. Receiving and displaying results by terminal:

[0241] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[0242] Examples:

[0243] Student A's data:

[0244] Age: 10

[0245] Type of disability: Intellectual disability

[0246] Degree of disability: Moderate

[0247] Ability to do: Simple calculations, read and write hiragana

[0248] What they can't do: Understand complex sentences, think abstractly

[0249] User input:

[0250] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the device. At the same time, the device's camera and microphone analyze the user's facial expressions and tone of voice, recording emotional data.

[0251] Emotion Engine Analysis:

[0252] The emotion engine recognizes the user's emotions (e.g., fatigue, stress) and collects them as data, which it then sends to the server.

[0253] Server Action:

[0254] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model, which generates an optimized educational program and learning handouts. Emotional data is also referenced during this process, so it is possible to generate materials of standard difficulty if the teacher is relaxed, or easier materials if the teacher is stressed. The generated data is then formatted into PDF and sent to the device.

[0255] Terminal reception and display:

[0256] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[0257] This process flow automatically generates adaptive educational programs and study handouts that reflect the user's emotions, allowing teachers to efficiently provide the optimal learning environment for each student. This invention contributes to improving the quality of special needs education and reducing the burden on teachers.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[0261] Step 2:

[0262] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[0263] Step 3:

[0264] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for sending to the server.

[0265] Step 4:

[0266] The device activates the emotion engine, and the device's camera and microphone analyze the user's facial expressions and tone of voice to obtain emotional data in real time.

[0267] Step 5:

[0268] The device formats and transmits the emotion data. The acquired emotion data is analyzed, converted into JSON format, and sent to the server.

[0269] Step 6:

[0270] The device sends the data to the server. The formatted individual student data and emotion data are sent to the server as an HTTP request.

[0271] Step 7:

[0272] The server receives the data. The server receives the HTTP request sent from the device and extracts the data.

[0273] Step 8:

[0274] The server validates the data. The server checks the validity of the individual student data received, checking that required fields are entered correctly and that they do not contain any inappropriate values.

[0275] Step 9:

[0276] The server calls the generative AI model and takes into account the emotional data. If validation is successful, the server passes the data to the generative AI model and generates an educational program and learning materials, also referencing the user's emotional data.

[0277] Step 10:

[0278] The server generates educational programs and learning materials. The generative AI model automatically creates optimized educational programs and learning materials based on the student's individual data and emotional data.

[0279] Step 11:

[0280] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[0281] Step 12:

[0282] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[0283] Step 13:

[0284] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[0285] Step 14:

[0286] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[0287] This processing flow allows users to generate personalized educational programs and study handouts that include emotional data generated by the emotion engine, making it possible to provide optimal learning support to each student.

[0288] Example 2

[0289] 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."

[0290] There is a need to provide efficient and effective education while reducing the burden on teachers in special needs education. However, conventional systems have difficulty automatically generating educational programs that fully reflect the characteristics of individual students and the feelings of teachers. Furthermore, there are challenges in developing a system that can provide reliable validation to determine whether the generated educational programs are appropriate, and one that can provide detailed support tailored to the teacher's situation.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes: [means for inputting individual student data (age, type and degree of disability, what the student can and cannot do)]; [means for formatting the input individual data into JSON format and transmitting it; [means for analyzing the user's facial expression and tone of voice at the time of input and recognizing their emotion]; [means for validating the received individual data]; [means for passing successfully validated data to a generative model and generating optimized educational programs and study handouts using the generative AI model]; and [means for formatting the generated educational programs and study handouts into PDF or HTML format and outputting them as digital files]. This makes it possible to automatically generate educational programs and study handouts that are optimal for each student while taking into account the teacher's emotions and output them as digital files.

[0293] "Student Personal Data" refers to specific information about a student, such as age, type and severity of disability, and what the student can and cannot do.

[0294] "Input form" refers to the interface that teachers use to enter individual student data into the system.

[0295] "Emotion recognition means" refers to a means for recognizing emotions by analyzing the user's facial expressions and tone of voice in real time when inputting information.

[0296] "Validation measures" refer to measures to verify that received data meets all required fields and is in the correct format.

[0297] A "generative AI model" refers to an artificial intelligence model that analyzes data entered by the user and generates optimized educational programs and study materials.

[0298] "Digital File" refers to a file format that can be viewed on a digital device, such as PDF or HTML.

[0299] "Emotion Data" refers to data relating to a user's emotions acquired and analyzed by emotion recognition means.

[0300] "Terminal" refers to a device (e.g., a PC or tablet) through which teachers input data and communicate with the system.

[0301] "JSON format" is a text format for structuring and expressing data, and is an abbreviation for JavaScript Object Notation.

[0302] A "generative model" is a model that uses a certain algorithm to generate optimal results (such as educational programs or study materials) based on input data.

[0303] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. In particular, by combining individual student data and teacher emotional data, more flexible and adaptive educational support is realized. This system includes the following main means.

[0304] Providing input data

[0305] First, the user (teacher) enters the student's individual data into the system's input form. This form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, and "moderate" for degree of disability, and would also enter "simple calculations" as a skill and "understanding complex sentences" as a weakness.

[0306] Sending data

[0307] When the user clicks the submit button, the terminal converts the input data to JSON format. This conversion process ensures that all input fields are correctly formatted and converted to the appropriate data type. The terminal then prepares and sends the input data to the server.

[0308] Emotion recognition

[0309] The device uses a camera and microphone to analyze the user's (teacher's) facial expression and tone of voice when inputting data and to recognize emotions. For example, the camera captures the teacher's facial expression, and the microphone captures the tone of voice. The emotion engine analyzes this data and determines the teacher's stress level and fatigue. As a result, emotional data such as "tense," "fatigue," and "relaxed" is generated.

[0310] Data Receipt and Validation

[0311] The server receives the data sent from the device. After receiving it, the server performs data validation, checking whether all required fields are filled in and whether the data format is correct. For example, it checks whether the age is entered correctly as a number, whether the type of disability is within a predetermined category, etc. If an invalid value is included, the server generates an error message and notifies the device.

[0312] Leveraging generative AI models

[0313] The server passes successfully validated data to the generative AI model. The generative AI model uses prompts to analyze the input data and generates optimized educational programs and study handouts based on the results. The model also takes the user's emotional data into consideration. For example, if the teacher is relaxed, it will generate learning materials of normal difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted in PDF or HTML format.

[0314] Output of artifacts and recording of emotion data

[0315] The server outputs the generated educational program and study handouts as digital files and sends them to the device. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the server saves the generated PDF file in a folder dedicated to the user and sends its URL to the device.

[0316] Receiving and displaying results

[0317] The device receives the file sent from the server and displays it in a format that teachers can view. For example, the device opens the generated PDF file so that teachers can view its contents. Based on this, teachers can provide the most appropriate educational program for students.

[0318] Specific examples

[0319] Example prompt sentence:

[0320] "Student A, 10 years old, has an intellectual disability and can do simple calculations and read and write hiragana, but has difficulty understanding complex sentences and thinking abstractly. Generate learning sheets suitable for this student."

[0321] Using this system, teachers can quickly create and provide educational programs and study materials tailored to the characteristics of their students and their own emotional state, thereby improving the quality of special needs education and significantly reducing the burden on teachers.

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] User-provided input data

[0325] The user (teacher) enters individual student data into the system's input form. The input form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, "moderate" for degree of disability, and "simple calculation" for what the student can do and "understand complex sentences" for what they cannot do. The input data is passed to the next step.

[0326] Input: Individual student data

[0327] Output: Individual student data entered in the input form

[0328] Step 2:

[0329] Data transmission by the terminal

[0330] The terminal receives the data entered by the user in the input form and converts it to JSON format. The converted data is prepared to be sent to the server. When the send button is clicked, the terminal sends the JSON formatted data to the server.

[0331] Input: Individual student data entered in the input form

[0332] Data processing: Format data into JSON format

[0333] Output: Data converted to JSON format

[0334] Step 3:

[0335] Recognizing user emotions with an emotion engine

[0336] While the user is entering data, the device's camera and microphone capture the user's facial expressions and tone of voice. This data is sent in real time to the emotion engine for analysis. The emotion engine generates emotion data, such as the user's stress level and fatigue level, which are used in subsequent steps.

[0337] Input: User's facial expression data and tone of voice data

[0338] Data processing: Emotion analysis using an emotion engine

[0339] Output: User emotion data

[0340] Step 4:

[0341] Data reception and validation by the server

[0342] The server receives the JSON-formatted data sent from the device. After receiving it, the server validates the data to ensure all required fields are entered properly. For example, it checks that age is a number and that the type of disability falls within a predefined category. Once the data is validated, it is passed on to the next step.

[0343] Input: Individual student data in JSON format

[0344] Data Calculation: Validation

[0345] Output: Validated data

[0346] Step 5:

[0347] The server invokes the generative AI model and takes emotion data into account.

[0348] The server passes the data that passes validation to the generative AI model. The generative AI model uses prompts to analyze the student's individual data and generate optimized educational programs and study handouts. Emotional data is also taken into consideration during this process. For example, if the teacher is relaxed, it will generate learning materials of medium difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted into PDF or HTML format.

[0349] Input: Validated individual data and user emotion data

[0350] Data Computation: Generating Educational Programs with Generative AI Models

[0351] Output: Optimized educational programs and study materials

[0352] Step 6:

[0353] Server outputs artifacts and records emotion data

[0354] The server outputs the generated educational program and study handouts as digital files and sends them to the terminal. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the generated PDF file can be saved in a folder reserved for teachers, and its URL can be sent to the terminal.

[0355] Input: Optimized educational programs and study materials

[0356] Data processing: Formatting and saving as digital files

[0357] Output: Formatted digital file and recorded emotion data

[0358] Step 7:

[0359] Receiving and displaying results on the device

[0360] The device receives the file sent from the server and displays it in a format that teachers can check. For example, the device displays a link to open the generated PDF file, and the teacher clicks the link to check the contents. Based on this, the teacher can provide the most appropriate educational program for the student.

[0361] Input: URL of a formatted digital file

[0362] Data Calculation: File Display

[0363] Output: Educational programs and learning handouts displayed in a format that teachers can view

[0364] (Application example 2)

[0365] 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."

[0366] Employees perform a wide variety of tasks, and the stress and fatigue that accompany them have a negative impact on work efficiency and safety. While there is a particular need for a system that can grasp an individual's emotional state in real time and provide optimal work instructions, no such system has yet been developed. The present invention aims to develop a system that analyzes an employee's emotional state and provides optimal work instructions that reduce their workload based on that analysis, thereby improving employee efficiency and safety.

[0367] 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 a means for inputting individual data of employees (age, work characteristics and level, what they can and cannot do), a means for recording and transmitting the input individual data, a means for utilizing a generative model that generates optimized work instructions using the received individual data, a means for utilizing an emotion engine that analyzes the emotional state of employees (facial expressions, tone of voice), and a means for outputting the generated work instructions as a digital file. This makes it possible to provide appropriate work instructions based on the individual state and emotions of employees in real time, thereby improving work efficiency and safety.

[0368] "Individual data" refers to individual characteristics and information such as an employee's age, the nature and level of work, and what they can and cannot do.

[0369] The "emotion engine" is a technology that analyzes employees' facial expressions and tone of voice to assess their emotional state in real time.

[0370] A "generative model" is an algorithm or AI that generates optimized work instructions based on individual input data.

[0371] "Validation means" refers to methods or techniques for verifying the validity of individual data received.

[0372] "Digital file" refers to a file recorded in electronic data format, including formats such as PDF and HTML.

[0373] "Means of input" refers to the interface or device through which employees input individual data into the system.

[0374] "Means for recording and transmitting" refers to the technology and methods for storing input data and transmitting it to a server or other system as necessary.

[0375] "Means of generation" refers to the technology and processes used to analyze collected data and create optimized work instructions.

[0376] The "means of analysis" refers to methods or techniques for assessing the emotional state of employees using an emotion engine.

[0377] This invention is a system that analyzes the emotional state of employees in real time and provides optimal work instructions based on that analysis. This system aims to improve work efficiency and safety by automatically generating work instructions based on the individual data and emotional state of employees.

[0378] The system consists of the following main means:

[0379] 1. Means of inputting individual data:

[0380] Users (employees) use tablets or terminals to enter individual data such as age, work characteristics and level, what they can and cannot do, etc. This allows the status and characteristics of each employee to be registered in the system.

[0381] 2. Recording and transmission methods:

[0382] The terminal converts the input data into an appropriate data format, such as JSON, and sends it to the server. Data transmission is performed by operating the interface (such as a send button).

[0383] 3. Analysis method by emotion engine:

[0384] The device is equipped with a camera and microphone, which runs an emotion engine that analyzes employees' facial expressions and tone of voice in real time. The analysis results are quantified as data on the employee's emotional state (e.g., stress, fatigue), and sent to a server.

[0385] 4. Ways to leverage generative models:

[0386] The server inputs the received individual data and emotion data into the generative AI model. The generative AI model takes into account the individual's state and emotion and generates optimal work instructions. The generated work instructions are formatted into PDF, HTML, or other formats and output in a way that employees can easily understand.

[0387] 5. Digital file output method:

[0388] The generated work instructions are generated as digital files and sent to a terminal, where employees can view them and receive instructions that are relevant to the actual work they will be doing.

[0389] The following hardware and software are used to support the operation of the entire system:

[0390] Hardware:

[0391] Tablets and dedicated devices: Used for data entry and display

[0392] Camera and microphone: Used for real-time sentiment analysis

[0393] Central control server: receives data, validates it, and runs AI models

[0394] software:

[0395] OpenCV: Used to process camera images

[0396] EmotionRecognition library: Used for emotion analysis

[0397] JSON format: Used for data formatting

[0398] The requests library: used to send HTTP requests

[0399] Examples of specific prompts include the following:

[0400] Example prompt sentence:

[0401] Generate optimal work instructions based on the following employee data. When doing so, please consider the employee's emotions and provide a low-stress work environment and instructions. Employee data: { 'Age': 35, 'Fatigue level': 'High', 'Stress level': 'Medium', 'Emotion': 'Fatigue', 'Current work': 'Assembly work'}

[0402] In this way, the system has the ability to understand the individual characteristics and emotional state of employees in real time and generate optimal work instructions, thereby reducing the burden on employees and improving work efficiency and safety.

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1:

[0405] Users (employees) enter their individual data, such as age, work characteristics and level, what they can and cannot do, into a tablet or terminal, and this individual data is then registered in the system.

[0406] Step 2:

[0407] The terminal formats each input data into JSON or an appropriate data format, so that the data can be sent to the server.

[0408] Step 3:

[0409] The terminal sends the formatted data to the server. The data is sent when the user clicks the send button. At this point, the role of the terminal becomes important.

[0410] Step 4:

[0411] The device's camera and microphone are activated to capture the employee's facial expressions and tone of voice, which are then analyzed by the emotion engine to generate emotion data. Specifically, OpenCV processes the camera footage, and the EmotionRecognition library evaluates the employee's emotional state.

[0412] Step 5:

[0413] The device sends the generated emotion data in real time to the server, where it is quantified and formatted in JSON format.

[0414] Step 6:

[0415] The server checks the validity of the received individual data and emotion data. Specifically, it performs validation to check whether all required fields have been entered properly and whether any inappropriate values ​​are included.

[0416] Step 7:

[0417] The server inputs the successfully validated data into the generative AI model, which then generates optimal work instructions based on the individual's state and emotions. Specifically, the prompt sentence is passed to the model for analysis.

[0418] Step 8:

[0419] The server formats the generated work instructions into user-friendly formats such as PDF or HTML, making the instructions easy for employees to understand.

[0420] Step 9:

[0421] The server then sends the formatted work instructions as a digital file to the terminal, which provides the work instructions to the employee in real time.

[0422] Step 10:

[0423] The terminal displays the received work instructions so that employees can check them, allowing them to efficiently carry out their work based on the most appropriate work instructions.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a means for inputting individual student data (age, type and degree of disability, and abilities and inabilities), a means for recording and transmitting the input individual data, a means for utilizing a generative model to generate optimized educational programs and study handouts using the received individual data, and a means for outputting the generated educational programs and study handouts as digital files. In addition, the present invention also includes a means for validating the received individual data and a means for formatting the generated educational programs and study handouts into human-readable formats (PDF, HTML).

[0441] Program processing flow

[0442] In the system of the present invention, processing proceeds as follows.

[0443] 1. User provides input data:

[0444] Users (teachers) enter individual student data through the interface of the special needs education system, such as age, type and degree of disability, and what the student can and cannot do, into a form.

[0445] 2. Data transmission by the device:

[0446] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it for transmission. When the send button is clicked, the data is sent to the server.

[0447] 3. Data receipt and validation by the server:

[0448] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[0449] 4. Generation of educational programs and study materials by the server:

[0450] The server passes the successfully validated data to the generative AI model, which generates optimized educational programs and learning handouts based on the data. The generated data is then formatted into user-friendly formats such as PDF and HTML.

[0451] 5. Server generated output:

[0452] The formatted educational programs and handouts are output as digital files and sent to a terminal, where they can be displayed directly or printed as needed.

[0453] 6. Receiving and displaying results by terminal:

[0454] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[0455] Examples:

[0456] Student A's data:

[0457] Age: 10

[0458] Type of disability: Intellectual disability

[0459] Degree of disability: Moderate

[0460] Ability to do: Simple calculations, read and write hiragana

[0461] What they can't do: Understand complex sentences, think abstractly

[0462] User input:

[0463] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the terminal.

[0464] Server Action:

[0465] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model to generate an optimized educational program and learning handouts. The generated data is formatted in PDF format and sent to the device.

[0466] Terminal reception and display:

[0467] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[0468] As described above, the present invention significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0469] The processing flow will be explained below.

[0470] Step 1:

[0471] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[0472] Step 2:

[0473] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[0474] Step 3:

[0475] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for transmission to the server.

[0476] Step 4:

[0477] The device sends the data to the server. The converted JSON data is sent to the server as an HTTP request.

[0478] Step 5:

[0479] The server receives the data. The server receives the HTTP request sent from the terminal and extracts the data.

[0480] Step 6:

[0481] The server validates the data it receives, checking for the validity of the data, for example, whether required fields are missing or whether they contain invalid values.

[0482] Step 7:

[0483] The server calls the generative AI model. If validation is successful, the server passes the data to the generative AI model.

[0484] Step 8:

[0485] The server generates educational programs and study handouts. The generative AI model automatically creates optimal educational programs and study handouts based on individual student data.

[0486] Step 9:

[0487] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[0488] Step 10:

[0489] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[0490] Step 11:

[0491] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[0492] Step 12:

[0493] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[0494] This process flow allows users to easily create personalized educational programs and study handouts, providing optimal learning support for each student.

[0495] Example 1

[0496] 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."

[0497] In special needs education, it is extremely burdensome for teachers to manually create educational programs and learning materials appropriate for each individual student. Furthermore, due to the lack of progress in the management of individual student data and the digitalization of educational resources, it is often difficult to provide efficient and effective educational support. This increases the workload of teachers and leads to problems such as a decline in the quality of education.

[0498] 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.

[0499] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording the input individual data, formatting it into an appropriate data format such as JSON format, and transmitting it; [means for confirming the validity of the received individual data and validating it;] [means for passing successfully validated data to a generative AI model and generating optimized educational programs and study handouts;] [means for formatting the generated educational programs and study handouts into a user-friendly format such as PDF or HTML format; and [means for outputting the formatted educational programs and study handouts as digital files and transmitting them to a terminal.] This significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0500] "Student Individual Data" refers to information specific to each student, such as the student's age, the type and severity of their disability, and what they can and cannot do.

[0501] "Means for input" refers to the interface and operation method used by users (teachers) to input individual student data into the system.

[0502] "Means of recording, formatting into an appropriate data format such as JSON, and transmitting" refers to the function of saving the input data, converting it into a data format as necessary, and transmitting it to the server.

[0503] "Validation and verification measures" refers to the process of verifying that individual data received is accurate and complete.

[0504] "Means of passing data to a generative AI model to generate optimized educational programs and study handouts" refers to the function of using validated data to utilize a generative AI model to create educational programs and study handouts that are optimal for students.

[0505] "Means of formatting into user-friendly formats such as PDF and HTML" refers to the process of converting the generated educational programs and learning handouts into a format that is easy for users to read.

[0506] "Means for outputting as a digital file and sending to a terminal" refers to a mechanism for saving the formatted file in a digital format and sending it to a user's terminal.

[0507] A "prompt" is a text that provides instructions or questions to a generative AI model to generate educational programs or learning materials.

[0508] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a series of processes that automate the collection of individual student data, data validation, generation of optimal educational programs and study handouts, and output of the results. Specific embodiments of this system are described below.

[0509] Users (teachers) enter individual student data through the interface of the special needs education system. Specifically, they access the system using a browser and enter information such as the student's age, type of disability, degree of disability, and what they can and cannot do into an input form. This operation collects information specific to each student.

[0510] The device then uses software to convert the input data into an appropriate data format, such as JSON. Specifically, the device uses a scripting language, such as JavaScript, to convert the input data into JSON. When the "Submit" button is clicked, an AJAX request is made, sending the data to the server.

[0511] The server performs validation to check the validity of the received data. Specifically, the received data is parsed with a JSON parser and checked to see if all required fields are entered correctly and if any inappropriate values ​​are included. If this validation is successful, the server proceeds to the next step.

[0512] The validated data is passed to the generative AI model. The server generates and inputs a prompt to the AI ​​model. This prompt contains specific instructions and questions for generating educational programs and learning handouts. For example, the following prompt is input to the generative AI model:

[0513] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0514] Age: 10

[0515] Type of disability: Intellectual disability

[0516] Degree of disability: Moderate

[0517] Ability to do: Simple calculations, read and write hiragana

[0518] What I can't do: Understand complex sentences, think abstractly

[0519] Please output the generated data in PDF format.

[0520] Based on this prompt, the generative AI model generates educational programs and handouts optimized for the student. The generated educational resources are then formatted into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines.

[0521] Finally, the formatted educational programs and learning handouts are stored as digital files on the server and then sent to the device. The device analyzes the received file URL and displays it in a format that the user can view. The user can use this file to provide educational support to their students.

[0522] As described above, this invention significantly reduces the burden on teachers in special needs education, making it possible to provide efficient and effective education.

[0523] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0524] Step 1:

[0525] Providing input data

[0526] The user enters the student's individual data into an input form. Specifically, the user launches a browser, accesses the special needs education system, and logs in. Then, the user enters data such as the student's age, type of disability, degree of disability, and what the student can and cannot do into the form.

[0527] Input: Individual data such as the student's age, type of disability, severity of disability, and what they can and cannot do.

[0528] Output: Individual student data entered into the form.

[0529] Step 2:

[0530] Formatting data and preparing it for transmission

[0531] The terminal converts the data entered by the user into an appropriate data format, such as JSON. Specifically, it converts the input data into JSON format using a scripting language such as JavaScript. This process is executed when the user clicks the "Submit" button.

[0532] Input: Individual student data entered into the form.

[0533] Output: Individual student data in JSON format.

[0534] Step 3:

[0535] Sending data

[0536] The terminal sends the data converted to JSON format to the server. Specifically, it uses an AJAX request. When the user clicks the "Submit" button, the data is sent to the server via an AJAX request.

[0537] Input: Individual student data converted to JSON format.

[0538] Output: The JSON data sent to the server.

[0539] Step 4:

[0540] Data Receipt and Validation

[0541] The server receives the data sent from the device. It analyzes the received data and checks its validity. Specifically, it uses a JSON parser to analyze the data and checks whether all required fields are entered and whether any inappropriate values ​​are included.

[0542] Input: JSON data sent from the terminal.

[0543] Output: Validated individual student data.

[0544] Step 5:

[0545] Creation of educational programs and study materials

[0546] The server passes the validated data to the generative AI model, which then uses prompts to instruct the model to generate educational programs and learning materials. For example, the following prompts can be used:

[0547] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0548] Age: 10

[0549] Type of disability: Intellectual disability

[0550] Degree of disability: Moderate

[0551] Ability to do: Simple calculations, read and write hiragana

[0552] What I can't do: Understand complex sentences, think abstractly

[0553] Please output the generated data in PDF format.

[0554] Input: Validated individual student data and prompt statement.

[0555] Output: Optimized educational programs and learning handouts created by the generative AI model.

[0556] Step 6:

[0557] Formatting and outputting the product

[0558] The server converts the educational programs and learning handouts obtained from the generative AI model into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines. The converted data is then saved as a digital file.

[0559] Input: Educational programs and learning handouts output from the generative AI model.

[0560] Output: Digital files formatted in PDF and HTML formats.

[0561] Step 7:

[0562] Sending and displaying the product

[0563] The server sends the formatted digital file to the device. Specifically, it sends the URL of the generated file to the device as an HTTP response. The device retrieves this URL and displays the file in a format that the user can view. Specifically, it displays the file using a PDF viewer or HTML viewer.

[0564] Input: URL of a digital file in PDF or HTML format.

[0565] Output: Displayed educational program and study handouts.

[0566] (Application example 1)

[0567] 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."

[0568] In special needs education, there is a need to generate and provide optimal educational programs and study materials tailored to the characteristics of each individual student. However, current systems place a heavy burden on teachers, making it difficult to provide efficient and effective education. In addition, there is a lack of teaching materials that emphasize individuality, making it difficult to select materials that are appropriate for each student. Furthermore, there is the problem that teachers must expend a great deal of effort to verify the validity and quality of the generated teaching materials. There is a need for a system that can solve these issues and provide efficient and effective special needs education.

[0569] 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.

[0570] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording and transmitting the input individual data]; [means for utilizing a generative AI model to generate optimized educational programs and learning tools using the received individual data]; and [means for outputting the generated educational programs and learning tools as digital files and making them available for recommendation and purchase in a virtual store]. This reduces the burden on teachers and makes it possible to quickly provide optimal teaching materials tailored to the characteristics of students. In addition, validation and digital preview functions enable efficient educational support while ensuring the quality of the generated teaching materials.

[0571] "Individual student data" refers to individual information necessary for education, such as a student's age, the type and degree of disability, and what they can and cannot do.

[0572] A "generative AI model" refers to an artificial intelligence model that analyzes individual student data and generates optimal educational programs and learning tools.

[0573] "Validation" refers to the process of verifying that the individual student data received is accurate and that all required fields have been entered correctly.

[0574] "Digital file" means a document or data in electronic form that can be accessed and manipulated by a computer, including formats such as PDF and HTML.

[0575] A "virtual store" is a virtual store operated on the Internet, and in this case refers to an online platform for selling teaching materials and learning tools for special needs education.

[0576] "Recommendation" refers to the act of selecting generated educational programs and learning tools to suit the user's needs and proposing the most suitable ones.

[0577] "Purchase" refers to the act of a user selecting a product in a virtual store and paying for it to acquire it.

[0578] "Digital Preview" refers to the ability for a user to review a generated educational program or learning tool in electronic format prior to purchase.

[0579] "Downloading" refers to the act of transferring and saving data or files from a server to a user's device via the Internet.

[0580] "Formatting" refers to the process of converting generated educational programs and learning tools into user-friendly formats (PDF, HTML).

[0581] MODE FOR CARRYING OUT THE INVENTION

[0582] The embodiment of this invention is a system that generates optimized educational programs and learning tools using individual data of students in special needs education and provides them in a virtual store. The system is composed of the following main components.

[0583] Hardware and Software Configuration

[0584] Hardware

[0585] 1. User device: A device used by teachers or parents to enter student information. This can be a desktop PC, laptop, tablet, smartphone, etc.

[0586] 2. Server: The back-end system that receives and processes data. A cloud server is typically used.

[0587] software

[0588] 1. Frontend: React.js is used to provide the user interface.

[0589] 2. Sending data: To send and receive data, we use HTTP POST requests in JSON format using the Axios library.

[0590] 3. Backend: Use Flask (a Python-based web framework).

[0591] 4. Validation: Use pandas and Marshmallow libraries to check the validity of the data.

[0592] 5. Generative AI model: TensorFlow is used to generate educational programs and learning tools.

[0593] 6. Digital File Formatting: Use ReportLab to convert generated educational programs and learning tools into PDF and HTML formats.

[0594] System operation explanation

[0595] 1. Data entry and submission

[0596] The user (teacher or guardian) enters the student's individual data (age, type and degree of disability, what they can and cannot do) through the user interface, and then clicks the send button to send the data to the server.

[0597] 2. Data processing on the server

[0598] The server validates the received data, specifically checking that all required fields are filled in correctly and that they do not contain any incorrect values.

[0599] 3. Creating teaching materials and formatting files

[0600] Successfully validated data is passed to a generative AI model to generate educational programs and learning tools optimized for each student's characteristics, which are then formatted into PDF or HTML format.

[0601] 4. Recommendations and Purchases

[0602] The generated educational programs and learning tools are recommended in a virtual store, where users can digitally preview them and then purchase them as needed.

[0603] 5. Download and Use

[0604] The purchased learning materials are provided as digital files that users can download and use, and a guide on how to use them is also provided.

[0605] Specific examples

[0606] Student B's data:

[0607] Age: 12

[0608] Type and severity of disability: Autism spectrum disorder, mild

[0609] Specialties: Puzzles, visual tasks

[0610] Weaknesses: Long periods of concentration, complex communication

[0611] Example prompt sentence:

[0612] "Generate visual aids that are ideal for a 12-year-old student with mild autism spectrum disorder. This student excels at puzzles but struggles with long periods of concentration and complex communication."

[0613] This enables the system to quickly generate optimized teaching materials required for special needs education and provide them through a virtual store.

[0614] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0615] Step 1:

[0616] The user uses the device's user interface to enter the student's individual data (age, type and degree of disability, what they can and cannot do) into a form. This data is used in subsequent processing, so it must be entered accurately and in detail.

[0617] Step 2:

[0618] The terminal converts the input data into JSON format and sends it to the server using an HTTP POST request when the submit button is clicked. The technologies used here are React.js and the Axios library. Once the input data is sent to the server, it can be processed in the next step.

[0619] Step 3:

[0620] The server receives the received JSON data and validates it. Specifically, it uses the pandas and Marshmallow libraries to check that the input data contains all required fields and is entered correctly. If the data is invalid, it returns an error message.

[0621] Step 4:

[0622] The server passes the successfully validated data to a generative AI model, which generates optimized educational programs and learning tools using prompts. These prompts are input into the model using TensorFlow. The generated data is then formatted into a digital file in the next step.

[0623] Step 5:

[0624] The server formats the generated educational programs and learning tools into user-friendly formats (PDF, HTML) using the ReportLab library. The formatted digital files are then used for recommendations and purchases in the virtual store.

[0625] Step 6:

[0626] The generated educational programs and learning tools are recommended in a virtual storefront for users to digitally preview and purchase. The virtual storefront is an online platform where the recommended educational materials are presented to users.

[0627] Step 7:

[0628] Users can check out educational materials in a virtual store and make a purchase. Purchased materials can then be downloaded to their devices as digital files. Teachers and parents can provide the downloaded materials to students for use, enabling efficient and effective special needs education.

[0629] Through these steps, the system can generate optimal educational programs and learning tools based on each student's individual data and provide them through a virtual store.

[0630] 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.

[0631] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education, and in particular, by combining an emotion engine that recognizes the user's emotions, it realizes more flexible and adaptive educational support. This system includes the following main means.

[0632] Program processing flow

[0633] 1. User provides input data:

[0634] Users (teachers) enter individual student data (age, type and degree of disability, what they can and cannot do) through the interface of the special needs education system. Input is done using a form, and the required items are filled in on the form.

[0635] 2. Data transmission by the device:

[0636] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it to be sent to the server. When the send button is clicked, the data is sent to the server.

[0637] 3. Emotion engine recognizes user emotions:

[0638] The device analyzes the user's facial expressions and tone of voice when inputting information to recognize emotions. This is done by capturing data in real time using a camera and microphone. Emotional data includes indicators such as the user's stress level and satisfaction.

[0639] 4. Data receipt and validation by the server:

[0640] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[0641] 5. The server invokes the generative AI model, taking into account the emotion data:

[0642] The server passes the successfully validated data to the generative AI model, which analyzes the input student's individual data and also references the user's emotional data to generate optimized educational programs and study handouts. The generated data is then further formatted into user-friendly formats such as PDF and HTML.

[0643] 6. Server output and emotional data recording:

[0644] The formatted educational programs and study handouts are output as digital files and sent to a terminal. Emotional data is also recorded and used as reference material when creating future educational programs.

[0645] 7. Receiving and displaying results by terminal:

[0646] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[0647] Examples:

[0648] Student A's data:

[0649] Age: 10

[0650] Type of disability: Intellectual disability

[0651] Degree of disability: Moderate

[0652] Ability to do: Simple calculations, read and write hiragana

[0653] What they can't do: Understand complex sentences, think abstractly

[0654] User input:

[0655] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the device. At the same time, the device's camera and microphone analyze the user's facial expressions and tone of voice, recording emotional data.

[0656] Emotion Engine Analysis:

[0657] The emotion engine recognizes the user's emotions (e.g., fatigue, stress) and collects them as data, which it then sends to the server.

[0658] Server Action:

[0659] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model, which generates an optimized educational program and learning handouts. Emotional data is also referenced during this process, so it is possible to generate materials of standard difficulty if the teacher is relaxed, or easier materials if the teacher is stressed. The generated data is then formatted into PDF and sent to the device.

[0660] Terminal reception and display:

[0661] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[0662] This process flow automatically generates adaptive educational programs and study handouts that reflect the user's emotions, allowing teachers to efficiently provide the optimal learning environment for each student. This invention contributes to improving the quality of special needs education and reducing the burden on teachers.

[0663] The processing flow will be explained below.

[0664] Step 1:

[0665] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[0666] Step 2:

[0667] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[0668] Step 3:

[0669] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for sending to the server.

[0670] Step 4:

[0671] The device activates the emotion engine, and the device's camera and microphone analyze the user's facial expressions and tone of voice to obtain emotional data in real time.

[0672] Step 5:

[0673] The device formats and transmits the emotion data. The acquired emotion data is analyzed, converted into JSON format, and sent to the server.

[0674] Step 6:

[0675] The device sends the data to the server. The formatted individual student data and emotion data are sent to the server as an HTTP request.

[0676] Step 7:

[0677] The server receives the data. The server receives the HTTP request sent from the device and extracts the data.

[0678] Step 8:

[0679] The server validates the data. The server checks the validity of the individual student data received, checking that required fields are entered correctly and that they do not contain any inappropriate values.

[0680] Step 9:

[0681] The server calls the generative AI model and takes into account the emotional data. If validation is successful, the server passes the data to the generative AI model and generates an educational program and learning materials, also referencing the user's emotional data.

[0682] Step 10:

[0683] The server generates educational programs and learning materials. The generative AI model automatically creates optimized educational programs and learning materials based on the student's individual data and emotional data.

[0684] Step 11:

[0685] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[0686] Step 12:

[0687] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[0688] Step 13:

[0689] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[0690] Step 14:

[0691] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[0692] This processing flow allows users to generate personalized educational programs and study handouts that include emotional data generated by the emotion engine, making it possible to provide optimal learning support to each student.

[0693] Example 2

[0694] 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."

[0695] There is a need to provide efficient and effective education while reducing the burden on teachers in special needs education. However, conventional systems have difficulty automatically generating educational programs that fully reflect the characteristics of individual students and the feelings of teachers. Furthermore, there are challenges in developing a system that can provide reliable validation to determine whether the generated educational programs are appropriate, and one that can provide detailed support tailored to the teacher's situation.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0697] In this invention, the server includes: [means for inputting individual student data (age, type and degree of disability, what the student can and cannot do)]; [means for formatting the input individual data into JSON format and transmitting it; [means for analyzing the user's facial expression and tone of voice at the time of input and recognizing their emotion]; [means for validating the received individual data]; [means for passing successfully validated data to a generative model and generating optimized educational programs and study handouts using the generative AI model]; and [means for formatting the generated educational programs and study handouts into PDF or HTML format and outputting them as digital files]. This makes it possible to automatically generate educational programs and study handouts that are optimal for each student while taking into account the teacher's emotions and output them as digital files.

[0698] "Student Personal Data" refers to specific information about a student, such as age, type and severity of disability, and what the student can and cannot do.

[0699] "Input form" refers to the interface that teachers use to enter individual student data into the system.

[0700] "Emotion recognition means" refers to a means for recognizing emotions by analyzing the user's facial expressions and tone of voice in real time when inputting information.

[0701] "Validation measures" refer to measures to verify that received data meets all required fields and is in the correct format.

[0702] A "generative AI model" refers to an artificial intelligence model that analyzes data entered by the user and generates optimized educational programs and study materials.

[0703] "Digital File" refers to a file format that can be viewed on a digital device, such as PDF or HTML.

[0704] "Emotion Data" refers to data relating to a user's emotions acquired and analyzed by emotion recognition means.

[0705] "Terminal" refers to a device (e.g., a PC or tablet) through which teachers input data and communicate with the system.

[0706] "JSON format" is a text format for structuring and expressing data, and is an abbreviation for JavaScript Object Notation.

[0707] A "generative model" is a model that uses a certain algorithm to generate optimal results (such as educational programs or study materials) based on input data.

[0708] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. In particular, by combining individual student data and teacher emotional data, more flexible and adaptive educational support is realized. This system includes the following main means.

[0709] Providing input data

[0710] First, the user (teacher) enters the student's individual data into the system's input form. This form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, and "moderate" for degree of disability, and would also enter "simple calculations" as a skill and "understanding complex sentences" as a weakness.

[0711] Sending data

[0712] When the user clicks the submit button, the terminal converts the input data to JSON format. This conversion process ensures that all input fields are correctly formatted and converted to the appropriate data type. The terminal then prepares and sends the input data to the server.

[0713] Emotion recognition

[0714] The device uses a camera and microphone to analyze the user's (teacher's) facial expression and tone of voice when inputting data and to recognize emotions. For example, the camera captures the teacher's facial expression, and the microphone captures the tone of voice. The emotion engine analyzes this data and determines the teacher's stress level and fatigue. As a result, emotional data such as "tense," "fatigue," and "relaxed" is generated.

[0715] Data Receipt and Validation

[0716] The server receives the data sent from the device. After receiving it, the server performs data validation, checking whether all required fields are filled in and whether the data format is correct. For example, it checks whether the age is entered correctly as a number, whether the type of disability is within a predetermined category, etc. If an invalid value is included, the server generates an error message and notifies the device.

[0717] Leveraging generative AI models

[0718] The server passes successfully validated data to the generative AI model. The generative AI model uses prompts to analyze the input data and generates optimized educational programs and study handouts based on the results. The model also takes the user's emotional data into consideration. For example, if the teacher is relaxed, it will generate learning materials of normal difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted in PDF or HTML format.

[0719] Output of artifacts and recording of emotion data

[0720] The server outputs the generated educational program and study handouts as digital files and sends them to the device. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the server saves the generated PDF file in a folder dedicated to the user and sends its URL to the device.

[0721] Receiving and displaying results

[0722] The device receives the file sent from the server and displays it in a format that teachers can view. For example, the device opens the generated PDF file so that teachers can view its contents. Based on this, teachers can provide the most appropriate educational program for students.

[0723] Specific examples

[0724] Example prompt sentence:

[0725] "Student A, 10 years old, has an intellectual disability and can do simple calculations and read and write hiragana, but has difficulty understanding complex sentences and thinking abstractly. Generate learning sheets suitable for this student."

[0726] Using this system, teachers can quickly create and provide educational programs and study materials tailored to the characteristics of their students and their own emotional state, thereby improving the quality of special needs education and significantly reducing the burden on teachers.

[0727] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0728] Step 1:

[0729] User-provided input data

[0730] The user (teacher) enters individual student data into the system's input form. The input form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, "moderate" for degree of disability, and "simple calculation" for what the student can do and "understand complex sentences" for what they cannot do. The input data is passed to the next step.

[0731] Input: Individual student data

[0732] Output: Individual student data entered in the input form

[0733] Step 2:

[0734] Data transmission by the terminal

[0735] The terminal receives the data entered by the user in the input form and converts it to JSON format. The converted data is prepared to be sent to the server. When the send button is clicked, the terminal sends the JSON formatted data to the server.

[0736] Input: Individual student data entered in the input form

[0737] Data processing: Format data into JSON format

[0738] Output: Data converted to JSON format

[0739] Step 3:

[0740] Recognizing user emotions with an emotion engine

[0741] While the user is entering data, the device's camera and microphone capture the user's facial expressions and tone of voice. This data is sent in real time to the emotion engine for analysis. The emotion engine generates emotion data, such as the user's stress level and fatigue level, which are used in subsequent steps.

[0742] Input: User's facial expression data and tone of voice data

[0743] Data processing: Emotion analysis using an emotion engine

[0744] Output: User emotion data

[0745] Step 4:

[0746] Data reception and validation by the server

[0747] The server receives the JSON-formatted data sent from the device. After receiving it, the server validates the data to ensure all required fields are entered properly. For example, it checks that age is a number and that the type of disability falls within a predefined category. Once the data is validated, it is passed on to the next step.

[0748] Input: Individual student data in JSON format

[0749] Data Calculation: Validation

[0750] Output: Validated data

[0751] Step 5:

[0752] The server invokes the generative AI model and takes emotion data into account.

[0753] The server passes the data that passes validation to the generative AI model. The generative AI model uses prompts to analyze the student's individual data and generate optimized educational programs and study handouts. Emotional data is also taken into consideration during this process. For example, if the teacher is relaxed, it will generate learning materials of medium difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted into PDF or HTML format.

[0754] Input: Validated individual data and user emotion data

[0755] Data Computation: Generating Educational Programs with Generative AI Models

[0756] Output: Optimized educational programs and study materials

[0757] Step 6:

[0758] Server outputs artifacts and records emotion data

[0759] The server outputs the generated educational program and study handouts as digital files and sends them to the terminal. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the generated PDF file can be saved in a folder reserved for teachers, and its URL can be sent to the terminal.

[0760] Input: Optimized educational programs and study materials

[0761] Data processing: Formatting and saving as digital files

[0762] Output: Formatted digital file and recorded emotion data

[0763] Step 7:

[0764] Receiving and displaying results on the device

[0765] The device receives the file sent from the server and displays it in a format that teachers can check. For example, the device displays a link to open the generated PDF file, and the teacher clicks the link to check the contents. Based on this, the teacher can provide the most appropriate educational program for the student.

[0766] Input: URL of a formatted digital file

[0767] Data Calculation: File Display

[0768] Output: Educational programs and learning handouts displayed in a format that teachers can view

[0769] (Application example 2)

[0770] 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."

[0771] Employees perform a wide variety of tasks, and the stress and fatigue that accompany them have a negative impact on work efficiency and safety. While there is a particular need for a system that can grasp an individual's emotional state in real time and provide optimal work instructions, no such system has yet been developed. The present invention aims to develop a system that analyzes an employee's emotional state and provides optimal work instructions that reduce their workload based on that analysis, thereby improving employee efficiency and safety.

[0772] 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 a means for inputting individual data of employees (age, work characteristics and level, what they can and cannot do), a means for recording and transmitting the input individual data, a means for utilizing a generative model that generates optimized work instructions using the received individual data, a means for utilizing an emotion engine that analyzes the emotional state of employees (facial expressions, tone of voice), and a means for outputting the generated work instructions as a digital file. This makes it possible to provide appropriate work instructions based on the individual state and emotions of employees in real time, thereby improving work efficiency and safety.

[0773] "Individual data" refers to individual characteristics and information such as an employee's age, the nature and level of work, and what they can and cannot do.

[0774] The "emotion engine" is a technology that analyzes employees' facial expressions and tone of voice to assess their emotional state in real time.

[0775] A "generative model" is an algorithm or AI that generates optimized work instructions based on individual input data.

[0776] "Validation means" refers to methods or techniques for verifying the validity of individual data received.

[0777] "Digital file" refers to a file recorded in electronic data format, including formats such as PDF and HTML.

[0778] "Means of input" refers to the interface or device through which employees input individual data into the system.

[0779] "Means for recording and transmitting" refers to the technology and methods for storing input data and transmitting it to a server or other system as necessary.

[0780] "Means of generation" refers to the technology and processes used to analyze collected data and create optimized work instructions.

[0781] The "means of analysis" refers to methods or techniques for assessing the emotional state of employees using an emotion engine.

[0782] This invention is a system that analyzes the emotional state of employees in real time and provides optimal work instructions based on that analysis. This system aims to improve work efficiency and safety by automatically generating work instructions based on the individual data and emotional state of employees.

[0783] The system consists of the following main means:

[0784] 1. Means of inputting individual data:

[0785] Users (employees) use tablets or terminals to enter individual data such as age, work characteristics and level, what they can and cannot do, etc. This allows the status and characteristics of each employee to be registered in the system.

[0786] 2. Recording and transmission methods:

[0787] The terminal converts the input data into an appropriate data format, such as JSON, and sends it to the server. Data transmission is performed by operating the interface (such as a send button).

[0788] 3. Analysis method by emotion engine:

[0789] The device is equipped with a camera and microphone, which runs an emotion engine that analyzes employees' facial expressions and tone of voice in real time. The analysis results are quantified as data on the employee's emotional state (e.g., stress, fatigue), and sent to a server.

[0790] 4. Ways to leverage generative models:

[0791] The server inputs the received individual data and emotion data into the generative AI model. The generative AI model takes into account the individual's state and emotion and generates optimal work instructions. The generated work instructions are formatted into PDF, HTML, or other formats and output in a way that employees can easily understand.

[0792] 5. Digital file output method:

[0793] The generated work instructions are generated as digital files and sent to a terminal, where employees can view them and receive instructions that are relevant to the actual work they will be doing.

[0794] The following hardware and software are used to support the operation of the entire system:

[0795] Hardware:

[0796] Tablets and dedicated devices: Used for data entry and display

[0797] Camera and microphone: Used for real-time sentiment analysis

[0798] Central control server: receives data, validates it, and runs AI models

[0799] software:

[0800] OpenCV: Used to process camera images

[0801] EmotionRecognition library: Used for emotion analysis

[0802] JSON format: Used for data formatting

[0803] The requests library: used to send HTTP requests

[0804] Examples of specific prompts include the following:

[0805] Example prompt sentence:

[0806] Generate optimal work instructions based on the following employee data. When doing so, please consider the employee's emotions and provide a low-stress work environment and instructions. Employee data: { 'Age': 35, 'Fatigue level': 'High', 'Stress level': 'Medium', 'Emotion': 'Fatigue', 'Current work': 'Assembly work'}

[0807] In this way, the system has the ability to understand the individual characteristics and emotional state of employees in real time and generate optimal work instructions, thereby reducing the burden on employees and improving work efficiency and safety.

[0808] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0809] Step 1:

[0810] Users (employees) enter their individual data, such as age, work characteristics and level, what they can and cannot do, into a tablet or terminal, and this individual data is then registered in the system.

[0811] Step 2:

[0812] The terminal formats each input data into JSON or an appropriate data format, so that the data can be sent to the server.

[0813] Step 3:

[0814] The terminal sends the formatted data to the server. The data is sent when the user clicks the send button. At this point, the role of the terminal becomes important.

[0815] Step 4:

[0816] The device's camera and microphone are activated to capture the employee's facial expressions and tone of voice, which are then analyzed by the emotion engine to generate emotion data. Specifically, OpenCV processes the camera footage, and the EmotionRecognition library evaluates the employee's emotional state.

[0817] Step 5:

[0818] The device sends the generated emotion data in real time to the server, where it is quantified and formatted in JSON format.

[0819] Step 6:

[0820] The server checks the validity of the received individual data and emotion data. Specifically, it performs validation to check whether all required fields have been entered properly and whether any inappropriate values ​​are included.

[0821] Step 7:

[0822] The server inputs the successfully validated data into the generative AI model, which then generates optimal work instructions based on the individual's state and emotions. Specifically, the prompt sentence is passed to the model for analysis.

[0823] Step 8:

[0824] The server formats the generated work instructions into user-friendly formats such as PDF or HTML, making the instructions easy for employees to understand.

[0825] Step 9:

[0826] The server then sends the formatted work instructions as a digital file to the terminal, which provides the work instructions to the employee in real time.

[0827] Step 10:

[0828] The terminal displays the received work instructions so that employees can check them, allowing them to efficiently carry out their work based on the most appropriate work instructions.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] [Third embodiment]

[0833] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0834] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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."

[0845] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a means for inputting individual student data (age, type and degree of disability, and abilities and inabilities), a means for recording and transmitting the input individual data, a means for utilizing a generative model to generate optimized educational programs and study handouts using the received individual data, and a means for outputting the generated educational programs and study handouts as digital files. In addition, the present invention also includes a means for validating the received individual data and a means for formatting the generated educational programs and study handouts into human-readable formats (PDF, HTML).

[0846] Program processing flow

[0847] In the system of the present invention, processing proceeds as follows.

[0848] 1. User provides input data:

[0849] Users (teachers) enter individual student data through the interface of the special needs education system, such as age, type and degree of disability, and what the student can and cannot do, into a form.

[0850] 2. Data transmission by the device:

[0851] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it for transmission. When the send button is clicked, the data is sent to the server.

[0852] 3. Data receipt and validation by the server:

[0853] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[0854] 4. Generation of educational programs and study materials by the server:

[0855] The server passes the successfully validated data to the generative AI model, which generates optimized educational programs and learning handouts based on the data. The generated data is then formatted into user-friendly formats such as PDF and HTML.

[0856] 5. Server generated output:

[0857] The formatted educational programs and handouts are output as digital files and sent to a terminal, where they can be displayed directly or printed as needed.

[0858] 6. Receiving and displaying results by terminal:

[0859] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[0860] Examples:

[0861] Student A's data:

[0862] Age: 10

[0863] Type of disability: Intellectual disability

[0864] Degree of disability: Moderate

[0865] Ability to do: Simple calculations, read and write hiragana

[0866] What they can't do: Understand complex sentences, think abstractly

[0867] User input:

[0868] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the terminal.

[0869] Server Action:

[0870] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model to generate an optimized educational program and learning handouts. The generated data is formatted in PDF format and sent to the device.

[0871] Terminal reception and display:

[0872] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[0873] As described above, the present invention significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0874] The processing flow will be explained below.

[0875] Step 1:

[0876] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[0877] Step 2:

[0878] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[0879] Step 3:

[0880] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for transmission to the server.

[0881] Step 4:

[0882] The device sends the data to the server. The converted JSON data is sent to the server as an HTTP request.

[0883] Step 5:

[0884] The server receives the data. The server receives the HTTP request sent from the terminal and extracts the data.

[0885] Step 6:

[0886] The server validates the data it receives, checking for the validity of the data, for example, whether required fields are missing or whether they contain invalid values.

[0887] Step 7:

[0888] The server calls the generative AI model. If validation is successful, the server passes the data to the generative AI model.

[0889] Step 8:

[0890] The server generates educational programs and study handouts. The generative AI model automatically creates optimal educational programs and study handouts based on individual student data.

[0891] Step 9:

[0892] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[0893] Step 10:

[0894] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[0895] Step 11:

[0896] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[0897] Step 12:

[0898] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[0899] This process flow allows users to easily create personalized educational programs and study handouts, providing optimal learning support for each student.

[0900] Example 1

[0901] 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."

[0902] In special needs education, it is extremely burdensome for teachers to manually create educational programs and learning materials appropriate for each individual student. Furthermore, due to the lack of progress in the management of individual student data and the digitalization of educational resources, it is often difficult to provide efficient and effective educational support. This increases the workload of teachers and leads to problems such as a decline in the quality of education.

[0903] 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.

[0904] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording the input individual data, formatting it into an appropriate data format such as JSON format, and transmitting it; [means for confirming the validity of the received individual data and validating it;] [means for passing successfully validated data to a generative AI model and generating optimized educational programs and study handouts;] [means for formatting the generated educational programs and study handouts into a user-friendly format such as PDF or HTML format; and [means for outputting the formatted educational programs and study handouts as digital files and transmitting them to a terminal.] This significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[0905] "Student Individual Data" refers to information specific to each student, such as the student's age, the type and severity of their disability, and what they can and cannot do.

[0906] "Means for input" refers to the interface and operation method used by users (teachers) to input individual student data into the system.

[0907] "Means of recording, formatting into an appropriate data format such as JSON, and transmitting" refers to the function of saving the input data, converting it into a data format as necessary, and transmitting it to the server.

[0908] "Validation and verification measures" refers to the process of verifying that individual data received is accurate and complete.

[0909] "Means of passing data to a generative AI model to generate optimized educational programs and study handouts" refers to the function of using validated data to utilize a generative AI model to create educational programs and study handouts that are optimal for students.

[0910] "Means of formatting into user-friendly formats such as PDF and HTML" refers to the process of converting the generated educational programs and learning handouts into a format that is easy for users to read.

[0911] "Means for outputting as a digital file and sending to a terminal" refers to a mechanism for saving the formatted file in a digital format and sending it to a user's terminal.

[0912] A "prompt" is a text that provides instructions or questions to a generative AI model to generate educational programs or learning materials.

[0913] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a series of processes that automate the collection of individual student data, data validation, generation of optimal educational programs and study handouts, and output of the results. Specific embodiments of this system are described below.

[0914] Users (teachers) enter individual student data through the interface of the special needs education system. Specifically, they access the system using a browser and enter information such as the student's age, type of disability, degree of disability, and what they can and cannot do into an input form. This operation collects information specific to each student.

[0915] The device then uses software to convert the input data into an appropriate data format, such as JSON. Specifically, the device uses a scripting language, such as JavaScript, to convert the input data into JSON. When the "Submit" button is clicked, an AJAX request is made, sending the data to the server.

[0916] The server performs validation to check the validity of the received data. Specifically, the received data is parsed with a JSON parser and checked to see if all required fields are entered correctly and if any inappropriate values ​​are included. If this validation is successful, the server proceeds to the next step.

[0917] The validated data is passed to the generative AI model. The server generates and inputs a prompt to the AI ​​model. This prompt contains specific instructions and questions for generating educational programs and learning handouts. For example, the following prompt is input to the generative AI model:

[0918] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0919] Age: 10

[0920] Type of disability: Intellectual disability

[0921] Degree of disability: Moderate

[0922] Ability to do: Simple calculations, read and write hiragana

[0923] What I can't do: Understand complex sentences, think abstractly

[0924] Please output the generated data in PDF format.

[0925] Based on this prompt, the generative AI model generates educational programs and handouts optimized for the student. The generated educational resources are then formatted into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines.

[0926] Finally, the formatted educational programs and learning handouts are stored as digital files on the server and then sent to the device. The device analyzes the received file URL and displays it in a format that the user can view. The user can use this file to provide educational support to their students.

[0927] As described above, this invention significantly reduces the burden on teachers in special needs education, making it possible to provide efficient and effective education.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1:

[0930] Providing input data

[0931] The user enters the student's individual data into an input form. Specifically, the user launches a browser, accesses the special needs education system, and logs in. Then, the user enters data such as the student's age, type of disability, degree of disability, and what the student can and cannot do into the form.

[0932] Input: Individual data such as the student's age, type of disability, severity of disability, and what they can and cannot do.

[0933] Output: Individual student data entered into the form.

[0934] Step 2:

[0935] Formatting data and preparing it for transmission

[0936] The terminal converts the data entered by the user into an appropriate data format, such as JSON. Specifically, it converts the input data into JSON format using a scripting language such as JavaScript. This process is executed when the user clicks the "Submit" button.

[0937] Input: Individual student data entered into the form.

[0938] Output: Individual student data in JSON format.

[0939] Step 3:

[0940] Sending data

[0941] The terminal sends the data converted to JSON format to the server. Specifically, it uses an AJAX request. When the user clicks the "Submit" button, the data is sent to the server via an AJAX request.

[0942] Input: Individual student data converted to JSON format.

[0943] Output: The JSON data sent to the server.

[0944] Step 4:

[0945] Data Receipt and Validation

[0946] The server receives the data sent from the device. It analyzes the received data and checks its validity. Specifically, it uses a JSON parser to analyze the data and checks whether all required fields are entered and whether any inappropriate values ​​are included.

[0947] Input: JSON data sent from the terminal.

[0948] Output: Validated individual student data.

[0949] Step 5:

[0950] Creation of educational programs and study materials

[0951] The server passes the validated data to the generative AI model, which then uses prompts to instruct the model to generate educational programs and learning materials. For example, the following prompts can be used:

[0952] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[0953] Age: 10

[0954] Type of disability: Intellectual disability

[0955] Degree of disability: Moderate

[0956] Ability to do: Simple calculations, read and write hiragana

[0957] What I can't do: Understand complex sentences, think abstractly

[0958] Please output the generated data in PDF format.

[0959] Input: Validated individual student data and prompt statement.

[0960] Output: Optimized educational programs and learning handouts created by the generative AI model.

[0961] Step 6:

[0962] Formatting and outputting the product

[0963] The server converts the educational programs and learning handouts obtained from the generative AI model into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines. The converted data is then saved as a digital file.

[0964] Input: Educational programs and learning handouts output from the generative AI model.

[0965] Output: Digital files formatted in PDF and HTML formats.

[0966] Step 7:

[0967] Sending and displaying the product

[0968] The server sends the formatted digital file to the device. Specifically, it sends the URL of the generated file to the device as an HTTP response. The device retrieves this URL and displays the file in a format that the user can view. Specifically, it displays the file using a PDF viewer or HTML viewer.

[0969] Input: URL of a digital file in PDF or HTML format.

[0970] Output: Displayed educational program and study handouts.

[0971] (Application example 1)

[0972] 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."

[0973] In special needs education, there is a need to generate and provide optimal educational programs and study materials tailored to the characteristics of each individual student. However, current systems place a heavy burden on teachers, making it difficult to provide efficient and effective education. In addition, there is a lack of teaching materials that emphasize individuality, making it difficult to select materials that are appropriate for each student. Furthermore, there is the problem that teachers must expend a great deal of effort to verify the validity and quality of the generated teaching materials. There is a need for a system that can solve these issues and provide efficient and effective special needs education.

[0974] 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.

[0975] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording and transmitting the input individual data]; [means for utilizing a generative AI model to generate optimized educational programs and learning tools using the received individual data]; and [means for outputting the generated educational programs and learning tools as digital files and making them available for recommendation and purchase in a virtual store]. This reduces the burden on teachers and makes it possible to quickly provide optimal teaching materials tailored to the characteristics of students. In addition, validation and digital preview functions enable efficient educational support while ensuring the quality of the generated teaching materials.

[0976] "Individual student data" refers to individual information necessary for education, such as a student's age, the type and degree of disability, and what they can and cannot do.

[0977] A "generative AI model" refers to an artificial intelligence model that analyzes individual student data and generates optimal educational programs and learning tools.

[0978] "Validation" refers to the process of verifying that the individual student data received is accurate and that all required fields have been entered correctly.

[0979] "Digital file" means a document or data in electronic form that can be accessed and manipulated by a computer, including formats such as PDF and HTML.

[0980] A "virtual store" is a virtual store operated on the Internet, and in this case refers to an online platform for selling teaching materials and learning tools for special needs education.

[0981] "Recommendation" refers to the act of selecting generated educational programs and learning tools to suit the user's needs and proposing the most suitable ones.

[0982] "Purchase" refers to the act of a user selecting a product in a virtual store and paying for it to acquire it.

[0983] "Digital Preview" refers to the ability for a user to review a generated educational program or learning tool in electronic format prior to purchase.

[0984] "Downloading" refers to the act of transferring and saving data or files from a server to a user's device via the Internet.

[0985] "Formatting" refers to the process of converting generated educational programs and learning tools into user-friendly formats (PDF, HTML).

[0986] MODE FOR CARRYING OUT THE INVENTION

[0987] The embodiment of this invention is a system that generates optimized educational programs and learning tools using individual data of students in special needs education and provides them in a virtual store. The system is composed of the following main components.

[0988] Hardware and Software Configuration

[0989] Hardware

[0990] 1. User device: A device used by teachers or parents to enter student information. This can be a desktop PC, laptop, tablet, smartphone, etc.

[0991] 2. Server: The back-end system that receives and processes data. A cloud server is typically used.

[0992] software

[0993] 1. Frontend: React.js is used to provide the user interface.

[0994] 2. Sending data: To send and receive data, we use HTTP POST requests in JSON format using the Axios library.

[0995] 3. Backend: Use Flask (a Python-based web framework).

[0996] 4. Validation: Use pandas and Marshmallow libraries to check the validity of the data.

[0997] 5. Generative AI model: TensorFlow is used to generate educational programs and learning tools.

[0998] 6. Digital File Formatting: Use ReportLab to convert generated educational programs and learning tools into PDF and HTML formats.

[0999] System operation explanation

[1000] 1. Data entry and submission

[1001] The user (teacher or guardian) enters the student's individual data (age, type and degree of disability, what they can and cannot do) through the user interface, and then clicks the send button to send the data to the server.

[1002] 2. Data processing on the server

[1003] The server validates the received data, specifically checking that all required fields are filled in correctly and that they do not contain any incorrect values.

[1004] 3. Creating teaching materials and formatting files

[1005] Successfully validated data is passed to a generative AI model to generate educational programs and learning tools optimized for each student's characteristics, which are then formatted into PDF or HTML format.

[1006] 4. Recommendations and Purchases

[1007] The generated educational programs and learning tools are recommended in a virtual store, where users can digitally preview them and then purchase them as needed.

[1008] 5. Download and Use

[1009] The purchased learning materials are provided as digital files that users can download and use, and a guide on how to use them is also provided.

[1010] Specific examples

[1011] Student B's data:

[1012] Age: 12

[1013] Type and severity of disability: Autism spectrum disorder, mild

[1014] Specialties: Puzzles, visual tasks

[1015] Weaknesses: Long periods of concentration, complex communication

[1016] Example prompt sentence:

[1017] "Generate visual aids that are ideal for a 12-year-old student with mild autism spectrum disorder. This student excels at puzzles but struggles with long periods of concentration and complex communication."

[1018] This enables the system to quickly generate optimized teaching materials required for special needs education and provide them through a virtual store.

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

[1020] Step 1:

[1021] The user uses the device's user interface to enter the student's individual data (age, type and degree of disability, what they can and cannot do) into a form. This data is used in subsequent processing, so it must be entered accurately and in detail.

[1022] Step 2:

[1023] The terminal converts the input data into JSON format and sends it to the server using an HTTP POST request when the submit button is clicked. The technologies used here are React.js and the Axios library. Once the input data is sent to the server, it can be processed in the next step.

[1024] Step 3:

[1025] The server receives the received JSON data and validates it. Specifically, it uses the pandas and Marshmallow libraries to check that the input data contains all required fields and is entered correctly. If the data is invalid, it returns an error message.

[1026] Step 4:

[1027] The server passes the successfully validated data to a generative AI model, which generates optimized educational programs and learning tools using prompts. These prompts are input into the model using TensorFlow. The generated data is then formatted into a digital file in the next step.

[1028] Step 5:

[1029] The server formats the generated educational programs and learning tools into user-friendly formats (PDF, HTML) using the ReportLab library. The formatted digital files are then used for recommendations and purchases in the virtual store.

[1030] Step 6:

[1031] The generated educational programs and learning tools are recommended in a virtual storefront for users to digitally preview and purchase. The virtual storefront is an online platform where the recommended educational materials are presented to users.

[1032] Step 7:

[1033] Users can check out educational materials in a virtual store and make a purchase. Purchased materials can then be downloaded to their devices as digital files. Teachers and parents can provide the downloaded materials to students for use, enabling efficient and effective special needs education.

[1034] Through these steps, the system can generate optimal educational programs and learning tools based on each student's individual data and provide them through a virtual store.

[1035] 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.

[1036] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education, and in particular, by combining an emotion engine that recognizes the user's emotions, it realizes more flexible and adaptive educational support. This system includes the following main means.

[1037] Program processing flow

[1038] 1. User provides input data:

[1039] Users (teachers) enter individual student data (age, type and degree of disability, what they can and cannot do) through the interface of the special needs education system. Input is done using a form, and the required items are filled in on the form.

[1040] 2. Data transmission by the device:

[1041] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it to be sent to the server. When the send button is clicked, the data is sent to the server.

[1042] 3. Emotion engine recognizes user emotions:

[1043] The device analyzes the user's facial expressions and tone of voice when inputting information to recognize emotions. This is done by capturing data in real time using a camera and microphone. Emotional data includes indicators such as the user's stress level and satisfaction.

[1044] 4. Data receipt and validation by the server:

[1045] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[1046] 5. The server invokes the generative AI model, taking into account the emotion data:

[1047] The server passes the successfully validated data to the generative AI model, which analyzes the input student's individual data and also references the user's emotional data to generate optimized educational programs and study handouts. The generated data is then further formatted into user-friendly formats such as PDF and HTML.

[1048] 6. Server output and emotional data recording:

[1049] The formatted educational programs and study handouts are output as digital files and sent to a terminal. Emotional data is also recorded and used as reference material when creating future educational programs.

[1050] 7. Receiving and displaying results by terminal:

[1051] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[1052] Examples:

[1053] Student A's data:

[1054] Age: 10

[1055] Type of disability: Intellectual disability

[1056] Degree of disability: Moderate

[1057] Ability to do: Simple calculations, read and write hiragana

[1058] What they can't do: Understand complex sentences, think abstractly

[1059] User input:

[1060] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the device. At the same time, the device's camera and microphone analyze the user's facial expressions and tone of voice, recording emotional data.

[1061] Emotion Engine Analysis:

[1062] The emotion engine recognizes the user's emotions (e.g., fatigue, stress) and collects them as data, which it then sends to the server.

[1063] Server Action:

[1064] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model, which generates an optimized educational program and learning handouts. Emotional data is also referenced during this process, so it is possible to generate materials of standard difficulty if the teacher is relaxed, or easier materials if the teacher is stressed. The generated data is then formatted into PDF and sent to the device.

[1065] Terminal reception and display:

[1066] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[1067] This process flow automatically generates adaptive educational programs and study handouts that reflect the user's emotions, allowing teachers to efficiently provide the optimal learning environment for each student. This invention contributes to improving the quality of special needs education and reducing the burden on teachers.

[1068] The processing flow will be explained below.

[1069] Step 1:

[1070] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[1071] Step 2:

[1072] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[1073] Step 3:

[1074] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for sending to the server.

[1075] Step 4:

[1076] The device activates the emotion engine, and the device's camera and microphone analyze the user's facial expressions and tone of voice to obtain emotional data in real time.

[1077] Step 5:

[1078] The device formats and transmits the emotion data. The acquired emotion data is analyzed, converted into JSON format, and sent to the server.

[1079] Step 6:

[1080] The device sends the data to the server. The formatted individual student data and emotion data are sent to the server as an HTTP request.

[1081] Step 7:

[1082] The server receives the data. The server receives the HTTP request sent from the device and extracts the data.

[1083] Step 8:

[1084] The server validates the data. The server checks the validity of the individual student data received, checking that required fields are entered correctly and that they do not contain any inappropriate values.

[1085] Step 9:

[1086] The server calls the generative AI model and takes into account the emotional data. If validation is successful, the server passes the data to the generative AI model and generates an educational program and learning materials, also referencing the user's emotional data.

[1087] Step 10:

[1088] The server generates educational programs and learning materials. The generative AI model automatically creates optimized educational programs and learning materials based on the student's individual data and emotional data.

[1089] Step 11:

[1090] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[1091] Step 12:

[1092] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[1093] Step 13:

[1094] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[1095] Step 14:

[1096] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[1097] This processing flow allows users to generate personalized educational programs and study handouts that include emotional data generated by the emotion engine, making it possible to provide optimal learning support to each student.

[1098] Example 2

[1099] 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."

[1100] There is a need to provide efficient and effective education while reducing the burden on teachers in special needs education. However, conventional systems have difficulty automatically generating educational programs that fully reflect the characteristics of individual students and the feelings of teachers. Furthermore, there are challenges in developing a system that can provide reliable validation to determine whether the generated educational programs are appropriate, and one that can provide detailed support tailored to the teacher's situation.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1102] In this invention, the server includes: [means for inputting individual student data (age, type and degree of disability, what the student can and cannot do)]; [means for formatting the input individual data into JSON format and transmitting it; [means for analyzing the user's facial expression and tone of voice at the time of input and recognizing their emotion]; [means for validating the received individual data]; [means for passing successfully validated data to a generative model and generating optimized educational programs and study handouts using the generative AI model]; and [means for formatting the generated educational programs and study handouts into PDF or HTML format and outputting them as digital files]. This makes it possible to automatically generate educational programs and study handouts that are optimal for each student while taking into account the teacher's emotions and output them as digital files.

[1103] "Student Personal Data" refers to specific information about a student, such as age, type and severity of disability, and what the student can and cannot do.

[1104] "Input form" refers to the interface that teachers use to enter individual student data into the system.

[1105] "Emotion recognition means" refers to a means for recognizing emotions by analyzing the user's facial expressions and tone of voice in real time when inputting information.

[1106] "Validation measures" refer to measures to verify that received data meets all required fields and is in the correct format.

[1107] A "generative AI model" refers to an artificial intelligence model that analyzes data entered by the user and generates optimized educational programs and study materials.

[1108] "Digital File" refers to a file format that can be viewed on a digital device, such as PDF or HTML.

[1109] "Emotion Data" refers to data relating to a user's emotions acquired and analyzed by emotion recognition means.

[1110] "Terminal" refers to a device (e.g., a PC or tablet) through which teachers input data and communicate with the system.

[1111] "JSON format" is a text format for structuring and expressing data, and is an abbreviation for JavaScript Object Notation.

[1112] A "generative model" is a model that uses a certain algorithm to generate optimal results (such as educational programs or study materials) based on input data.

[1113] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. In particular, by combining individual student data and teacher emotional data, more flexible and adaptive educational support is realized. This system includes the following main means.

[1114] Providing input data

[1115] First, the user (teacher) enters the student's individual data into the system's input form. This form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, and "moderate" for degree of disability, and would also enter "simple calculations" as a skill and "understanding complex sentences" as a weakness.

[1116] Sending data

[1117] When the user clicks the submit button, the terminal converts the input data to JSON format. This conversion process ensures that all input fields are correctly formatted and converted to the appropriate data type. The terminal then prepares and sends the input data to the server.

[1118] Emotion recognition

[1119] The device uses a camera and microphone to analyze the user's (teacher's) facial expression and tone of voice when inputting data and to recognize emotions. For example, the camera captures the teacher's facial expression, and the microphone captures the tone of voice. The emotion engine analyzes this data and determines the teacher's stress level and fatigue. As a result, emotional data such as "tense," "fatigue," and "relaxed" is generated.

[1120] Data Receipt and Validation

[1121] The server receives the data sent from the device. After receiving it, the server performs data validation, checking whether all required fields are filled in and whether the data format is correct. For example, it checks whether the age is entered correctly as a number, whether the type of disability is within a predetermined category, etc. If an invalid value is included, the server generates an error message and notifies the device.

[1122] Leveraging generative AI models

[1123] The server passes successfully validated data to the generative AI model. The generative AI model uses prompts to analyze the input data and generates optimized educational programs and study handouts based on the results. The model also takes the user's emotional data into consideration. For example, if the teacher is relaxed, it will generate learning materials of normal difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted in PDF or HTML format.

[1124] Output of artifacts and recording of emotion data

[1125] The server outputs the generated educational program and study handouts as digital files and sends them to the device. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the server saves the generated PDF file in a folder dedicated to the user and sends its URL to the device.

[1126] Receiving and displaying results

[1127] The device receives the file sent from the server and displays it in a format that teachers can view. For example, the device opens the generated PDF file so that teachers can view its contents. Based on this, teachers can provide the most appropriate educational program for students.

[1128] Specific examples

[1129] Example prompt sentence:

[1130] "Student A, 10 years old, has an intellectual disability and can do simple calculations and read and write hiragana, but has difficulty understanding complex sentences and thinking abstractly. Generate learning sheets suitable for this student."

[1131] Using this system, teachers can quickly create and provide educational programs and study materials tailored to the characteristics of their students and their own emotional state, thereby improving the quality of special needs education and significantly reducing the burden on teachers.

[1132] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] User-provided input data

[1135] The user (teacher) enters individual student data into the system's input form. The input form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, "moderate" for degree of disability, and "simple calculation" for what the student can do and "understand complex sentences" for what they cannot do. The input data is passed to the next step.

[1136] Input: Individual student data

[1137] Output: Individual student data entered in the input form

[1138] Step 2:

[1139] Data transmission by the terminal

[1140] The terminal receives the data entered by the user in the input form and converts it to JSON format. The converted data is prepared to be sent to the server. When the send button is clicked, the terminal sends the JSON formatted data to the server.

[1141] Input: Individual student data entered in the input form

[1142] Data processing: Format data into JSON format

[1143] Output: Data converted to JSON format

[1144] Step 3:

[1145] Recognizing user emotions with an emotion engine

[1146] While the user is entering data, the device's camera and microphone capture the user's facial expressions and tone of voice. This data is sent in real time to the emotion engine for analysis. The emotion engine generates emotion data, such as the user's stress level and fatigue level, which are used in subsequent steps.

[1147] Input: User's facial expression data and tone of voice data

[1148] Data processing: Emotion analysis using an emotion engine

[1149] Output: User emotion data

[1150] Step 4:

[1151] Data reception and validation by the server

[1152] The server receives the JSON-formatted data sent from the device. After receiving it, the server validates the data to ensure all required fields are entered properly. For example, it checks that age is a number and that the type of disability falls within a predefined category. Once the data is validated, it is passed on to the next step.

[1153] Input: Individual student data in JSON format

[1154] Data Calculation: Validation

[1155] Output: Validated data

[1156] Step 5:

[1157] The server invokes the generative AI model and takes emotion data into account.

[1158] The server passes the data that passes validation to the generative AI model. The generative AI model uses prompts to analyze the student's individual data and generate optimized educational programs and study handouts. Emotional data is also taken into consideration during this process. For example, if the teacher is relaxed, it will generate learning materials of medium difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted into PDF or HTML format.

[1159] Input: Validated individual data and user emotion data

[1160] Data Computation: Generating Educational Programs with Generative AI Models

[1161] Output: Optimized educational programs and study materials

[1162] Step 6:

[1163] Server outputs artifacts and records emotion data

[1164] The server outputs the generated educational program and study handouts as digital files and sends them to the terminal. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the generated PDF file can be saved in a folder reserved for teachers, and its URL can be sent to the terminal.

[1165] Input: Optimized educational programs and study materials

[1166] Data processing: Formatting and saving as digital files

[1167] Output: Formatted digital file and recorded emotion data

[1168] Step 7:

[1169] Receiving and displaying results on the device

[1170] The device receives the file sent from the server and displays it in a format that teachers can check. For example, the device displays a link to open the generated PDF file, and the teacher clicks the link to check the contents. Based on this, the teacher can provide the most appropriate educational program for the student.

[1171] Input: URL of a formatted digital file

[1172] Data Calculation: File Display

[1173] Output: Educational programs and learning handouts displayed in a format that teachers can view

[1174] (Application example 2)

[1175] 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."

[1176] Employees perform a wide variety of tasks, and the stress and fatigue that accompany them have a negative impact on work efficiency and safety. While there is a particular need for a system that can grasp an individual's emotional state in real time and provide optimal work instructions, no such system has yet been developed. The present invention aims to develop a system that analyzes an employee's emotional state and provides optimal work instructions that reduce their workload based on that analysis, thereby improving employee efficiency and safety.

[1177] 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 a means for inputting individual data of employees (age, work characteristics and level, what they can and cannot do), a means for recording and transmitting the input individual data, a means for utilizing a generative model that generates optimized work instructions using the received individual data, a means for utilizing an emotion engine that analyzes the emotional state of employees (facial expressions, tone of voice), and a means for outputting the generated work instructions as a digital file. This makes it possible to provide appropriate work instructions based on the individual state and emotions of employees in real time, thereby improving work efficiency and safety.

[1178] "Individual data" refers to individual characteristics and information such as an employee's age, the nature and level of work, and what they can and cannot do.

[1179] The "emotion engine" is a technology that analyzes employees' facial expressions and tone of voice to assess their emotional state in real time.

[1180] A "generative model" is an algorithm or AI that generates optimized work instructions based on individual input data.

[1181] "Validation means" refers to methods or techniques for verifying the validity of individual data received.

[1182] "Digital file" refers to a file recorded in electronic data format, including formats such as PDF and HTML.

[1183] "Means of input" refers to the interface or device through which employees input individual data into the system.

[1184] "Means for recording and transmitting" refers to the technology and methods for storing input data and transmitting it to a server or other system as necessary.

[1185] "Means of generation" refers to the technology and processes used to analyze collected data and create optimized work instructions.

[1186] The "means of analysis" refers to methods or techniques for assessing the emotional state of employees using an emotion engine.

[1187] This invention is a system that analyzes the emotional state of employees in real time and provides optimal work instructions based on that analysis. This system aims to improve work efficiency and safety by automatically generating work instructions based on the individual data and emotional state of employees.

[1188] The system consists of the following main means:

[1189] 1. Means of inputting individual data:

[1190] Users (employees) use tablets or terminals to enter individual data such as age, work characteristics and level, what they can and cannot do, etc. This allows the status and characteristics of each employee to be registered in the system.

[1191] 2. Recording and transmission methods:

[1192] The terminal converts the input data into an appropriate data format, such as JSON, and sends it to the server. Data transmission is performed by operating the interface (such as a send button).

[1193] 3. Analysis method by emotion engine:

[1194] The device is equipped with a camera and microphone, which runs an emotion engine that analyzes employees' facial expressions and tone of voice in real time. The analysis results are quantified as data on the employee's emotional state (e.g., stress, fatigue), and sent to a server.

[1195] 4. Ways to leverage generative models:

[1196] The server inputs the received individual data and emotion data into the generative AI model. The generative AI model takes into account the individual's state and emotion and generates optimal work instructions. The generated work instructions are formatted into PDF, HTML, or other formats and output in a way that employees can easily understand.

[1197] 5. Digital file output method:

[1198] The generated work instructions are generated as digital files and sent to a terminal, where employees can view them and receive instructions that are relevant to the actual work they will be doing.

[1199] The following hardware and software are used to support the operation of the entire system:

[1200] Hardware:

[1201] Tablets and dedicated devices: Used for data entry and display

[1202] Camera and microphone: Used for real-time sentiment analysis

[1203] Central control server: receives data, validates it, and runs AI models

[1204] software:

[1205] OpenCV: Used to process camera images

[1206] EmotionRecognition library: Used for emotion analysis

[1207] JSON format: Used for data formatting

[1208] The requests library: used to send HTTP requests

[1209] Examples of specific prompts include the following:

[1210] Example prompt sentence:

[1211] Generate optimal work instructions based on the following employee data. When doing so, please consider the employee's emotions and provide a low-stress work environment and instructions. Employee data: { 'Age': 35, 'Fatigue level': 'High', 'Stress level': 'Medium', 'Emotion': 'Fatigue', 'Current work': 'Assembly work'}

[1212] In this way, the system has the ability to understand the individual characteristics and emotional state of employees in real time and generate optimal work instructions, thereby reducing the burden on employees and improving work efficiency and safety.

[1213] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1214] Step 1:

[1215] Users (employees) enter their individual data, such as age, work characteristics and level, what they can and cannot do, into a tablet or terminal, and this individual data is then registered in the system.

[1216] Step 2:

[1217] The terminal formats each input data into JSON or an appropriate data format, so that the data can be sent to the server.

[1218] Step 3:

[1219] The terminal sends the formatted data to the server. The data is sent when the user clicks the send button. At this point, the role of the terminal becomes important.

[1220] Step 4:

[1221] The device's camera and microphone are activated to capture the employee's facial expressions and tone of voice, which are then analyzed by the emotion engine to generate emotion data. Specifically, OpenCV processes the camera footage, and the EmotionRecognition library evaluates the employee's emotional state.

[1222] Step 5:

[1223] The device sends the generated emotion data in real time to the server, where it is quantified and formatted in JSON format.

[1224] Step 6:

[1225] The server checks the validity of the received individual data and emotion data. Specifically, it performs validation to check whether all required fields have been entered properly and whether any inappropriate values ​​are included.

[1226] Step 7:

[1227] The server inputs the successfully validated data into the generative AI model, which then generates optimal work instructions based on the individual's state and emotions. Specifically, the prompt sentence is passed to the model for analysis.

[1228] Step 8:

[1229] The server formats the generated work instructions into user-friendly formats such as PDF or HTML, making the instructions easy for employees to understand.

[1230] Step 9:

[1231] The server then sends the formatted work instructions as a digital file to the terminal, which provides the work instructions to the employee in real time.

[1232] Step 10:

[1233] The terminal displays the received work instructions so that employees can check them, allowing them to efficiently carry out their work based on the most appropriate work instructions.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] [Fourth embodiment]

[1238] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1239] 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.

[1240] 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).

[1241] 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.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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."

[1251] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a means for inputting individual student data (age, type and degree of disability, and abilities and inabilities), a means for recording and transmitting the input individual data, a means for utilizing a generative model to generate optimized educational programs and study handouts using the received individual data, and a means for outputting the generated educational programs and study handouts as digital files. In addition, the present invention also includes a means for validating the received individual data and a means for formatting the generated educational programs and study handouts into human-readable formats (PDF, HTML).

[1252] Program processing flow

[1253] In the system of the present invention, processing proceeds as follows.

[1254] 1. User provides input data:

[1255] Users (teachers) enter individual student data through the interface of the special needs education system, such as age, type and degree of disability, and what the student can and cannot do, into a form.

[1256] 2. Data transmission by the device:

[1257] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it for transmission. When the send button is clicked, the data is sent to the server.

[1258] 3. Data receipt and validation by the server:

[1259] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[1260] 4. Generation of educational programs and study materials by the server:

[1261] The server passes the successfully validated data to the generative AI model, which generates optimized educational programs and learning handouts based on the data. The generated data is then formatted into user-friendly formats such as PDF and HTML.

[1262] 5. Server generated output:

[1263] The formatted educational programs and handouts are output as digital files and sent to a terminal, where they can be displayed directly or printed as needed.

[1264] 6. Receiving and displaying results by terminal:

[1265] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[1266] Examples:

[1267] Student A's data:

[1268] Age: 10

[1269] Type of disability: Intellectual disability

[1270] Degree of disability: Moderate

[1271] Ability to do: Simple calculations, read and write hiragana

[1272] What they can't do: Understand complex sentences, think abstractly

[1273] User input:

[1274] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the terminal.

[1275] Server Action:

[1276] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model to generate an optimized educational program and learning handouts. The generated data is formatted in PDF format and sent to the device.

[1277] Terminal reception and display:

[1278] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[1279] As described above, the present invention significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[1283] Step 2:

[1284] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[1285] Step 3:

[1286] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for transmission to the server.

[1287] Step 4:

[1288] The device sends the data to the server. The converted JSON data is sent to the server as an HTTP request.

[1289] Step 5:

[1290] The server receives the data. The server receives the HTTP request sent from the terminal and extracts the data.

[1291] Step 6:

[1292] The server validates the data it receives, checking for the validity of the data, for example, whether required fields are missing or whether they contain invalid values.

[1293] Step 7:

[1294] The server calls the generative AI model. If validation is successful, the server passes the data to the generative AI model.

[1295] Step 8:

[1296] The server generates educational programs and study handouts. The generative AI model automatically creates optimal educational programs and study handouts based on individual student data.

[1297] Step 9:

[1298] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[1299] Step 10:

[1300] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[1301] Step 11:

[1302] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[1303] Step 12:

[1304] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[1305] This process flow allows users to easily create personalized educational programs and study handouts, providing optimal learning support for each student.

[1306] Example 1

[1307] 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."

[1308] In special needs education, it is extremely burdensome for teachers to manually create educational programs and learning handouts suited to each individual student. Furthermore, due to the lack of progress in the management of individual student data and the digitalization of educational resources, it is often difficult to provide efficient and effective educational support. This increases the workload of teachers and leads to problems such as a decline in the quality of education.

[1309] 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.

[1310] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording the input individual data, formatting it into an appropriate data format such as JSON format, and transmitting it; [means for confirming the validity of the received individual data and validating it;] [means for passing successfully validated data to a generative AI model and generating optimized educational programs and study handouts;] [means for formatting the generated educational programs and study handouts into a user-friendly format such as PDF or HTML format; and [means for outputting the formatted educational programs and study handouts as digital files and transmitting them to a terminal.] This significantly reduces the burden on teachers in special needs education and enables the provision of efficient and effective education.

[1311] "Student Individual Data" refers to information specific to each student, such as the student's age, the type and severity of their disability, and what they can and cannot do.

[1312] "Means for input" refers to the interface and operation method used by users (teachers) to input individual student data into the system.

[1313] "Means of recording, formatting into an appropriate data format such as JSON, and transmitting" refers to the function of saving the input data, converting it into a data format as necessary, and transmitting it to the server.

[1314] "Validation and verification measures" refers to the process of verifying that individual data received is accurate and complete.

[1315] "Means of passing data to a generative AI model to generate optimized educational programs and study handouts" refers to the function of using validated data to utilize a generative AI model to create educational programs and study handouts that are optimal for students.

[1316] "Means of formatting into user-friendly formats such as PDF and HTML" refers to the process of converting the generated educational programs and learning handouts into a format that is easy for users to read.

[1317] "Means for outputting as a digital file and sending to a terminal" refers to a mechanism for saving the formatted file in a digital format and sending it to a user's terminal.

[1318] A "prompt" is a text that provides instructions or questions to a generative AI model to generate educational programs or learning materials.

[1319] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. This system includes a series of processes that automate the collection of individual student data, data validation, generation of optimal educational programs and study handouts, and output of the results. Specific embodiments of this system are described below.

[1320] Users (teachers) enter individual student data through the interface of the special needs education system. Specifically, they access the system using a browser and enter information such as the student's age, type of disability, degree of disability, and what they can and cannot do into an input form. This operation collects information specific to each student.

[1321] The device then uses software to convert the input data into an appropriate data format, such as JSON. Specifically, the device uses a scripting language, such as JavaScript, to convert the input data into JSON. When the "Submit" button is clicked, an AJAX request is made, sending the data to the server.

[1322] The server performs validation to check the validity of the received data. Specifically, the received data is parsed with a JSON parser and checked to see if all required fields are entered correctly and if any inappropriate values ​​are included. If this validation is successful, the server proceeds to the next step.

[1323] The validated data is passed to the generative AI model. The server generates and inputs a prompt to the AI ​​model. This prompt contains specific instructions and questions for generating educational programs and learning handouts. For example, the following prompt is input to the generative AI model:

[1324] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[1325] Age: 10

[1326] Type of disability: Intellectual disability

[1327] Degree of disability: Moderate

[1328] Ability to do: Simple calculations, read and write hiragana

[1329] What I can't do: Understand complex sentences, think abstractly

[1330] Please output the generated data in PDF format.

[1331] Based on this prompt, the generative AI model generates educational programs and handouts optimized for the student. The generated educational resources are then formatted into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines.

[1332] Finally, the formatted educational programs and learning handouts are stored as digital files on the server and then sent to the device. The device analyzes the received file URL and displays it in a format that the user can view. The user can use this file to provide educational support to their students.

[1333] As described above, this invention significantly reduces the burden on teachers in special needs education, making it possible to provide efficient and effective education.

[1334] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] Providing input data

[1337] The user enters the student's individual data into an input form. Specifically, the user launches a browser, accesses the special needs education system, and logs in. Then, the user enters data such as the student's age, type of disability, degree of disability, and what the student can and cannot do into the form.

[1338] Input: Individual data such as the student's age, type of disability, severity of disability, and what they can and cannot do.

[1339] Output: Individual student data entered into the form.

[1340] Step 2:

[1341] Formatting data and preparing it for transmission

[1342] The terminal converts the data entered by the user into an appropriate data format, such as JSON. Specifically, it converts the input data into JSON format using a scripting language such as JavaScript. This process is executed when the user clicks the "Submit" button.

[1343] Input: Individual student data entered into the form.

[1344] Output: Individual student data in JSON format.

[1345] Step 3:

[1346] Sending data

[1347] The terminal sends the data converted to JSON format to the server. Specifically, it uses an AJAX request. When the user clicks the "Submit" button, the data is sent to the server via an AJAX request.

[1348] Input: Individual student data converted to JSON format.

[1349] Output: The JSON data sent to the server.

[1350] Step 4:

[1351] Data Receipt and Validation

[1352] The server receives the data sent from the device. It analyzes the received data and checks its validity. Specifically, it uses a JSON parser to analyze the data and checks whether all required fields are entered and whether any inappropriate values ​​are included.

[1353] Input: JSON data sent from the terminal.

[1354] Output: Validated individual student data.

[1355] Step 5:

[1356] Creation of educational programs and study materials

[1357] The server passes the validated data to the generative AI model, which then uses prompts to instruct the model to generate educational programs and learning materials. For example, the following prompts can be used:

[1358] "Generate the most suitable learning handout for a 10-year-old student with intellectual disabilities based on the following data:

[1359] Age: 10

[1360] Type of disability: Intellectual disability

[1361] Degree of disability: Moderate

[1362] Ability to do: Simple calculations, read and write hiragana

[1363] What I can't do: Understand complex sentences, think abstractly

[1364] Please output the generated data in PDF format.

[1365] Input: Validated individual student data and prompt statement.

[1366] Output: Optimized educational programs and learning handouts created by the generative AI model.

[1367] Step 6:

[1368] Formatting and outputting the product

[1369] The server converts the educational programs and learning handouts obtained from the generative AI model into user-friendly formats such as PDF and HTML using PDF generation software and HTML template engines. The converted data is then saved as a digital file.

[1370] Input: Educational programs and learning handouts output from the generative AI model.

[1371] Output: Digital files formatted in PDF and HTML formats.

[1372] Step 7:

[1373] Sending and displaying the product

[1374] The server sends the formatted digital file to the device. Specifically, it sends the URL of the generated file to the device as an HTTP response. The device retrieves this URL and displays the file in a format that the user can view. Specifically, it displays the file using a PDF viewer or HTML viewer.

[1375] Input: URL of a digital file in PDF or HTML format.

[1376] Output: Displayed educational program and study handouts.

[1377] (Application example 1)

[1378] 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."

[1379] In special needs education, there is a need to generate and provide optimal educational programs and study materials tailored to the characteristics of each individual student. However, current systems place a heavy burden on teachers, making it difficult to provide efficient and effective education. In addition, there is a lack of teaching materials that emphasize individuality, making it difficult to select materials that are appropriate for each student. Furthermore, there is the problem that teachers must expend a great deal of effort to verify the validity and quality of the generated teaching materials. There is a need for a system that can solve these issues and provide efficient and effective special needs education.

[1380] 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.

[1381] In this invention, the server includes: [means for inputting individual data of students (age, type and degree of disability, what they can and cannot do)]; [means for recording and transmitting the input individual data]; [means for utilizing a generative AI model to generate optimized educational programs and learning tools using the received individual data]; and [means for outputting the generated educational programs and learning tools as digital files and making them available for recommendation and purchase in a virtual store]. This reduces the burden on teachers and makes it possible to quickly provide optimal teaching materials tailored to the characteristics of students. In addition, validation and digital preview functions enable efficient educational support while ensuring the quality of the generated teaching materials.

[1382] "Individual student data" refers to individual information necessary for education, such as a student's age, the type and degree of disability, and what they can and cannot do.

[1383] A "generative AI model" refers to an artificial intelligence model that analyzes individual student data and generates optimal educational programs and learning tools.

[1384] "Validation" refers to the process of verifying that the individual student data received is accurate and that all required fields have been entered correctly.

[1385] "Digital file" means a document or data in electronic form that can be accessed and manipulated by a computer, including formats such as PDF and HTML.

[1386] A "virtual store" is a virtual store operated on the Internet, and in this case refers to an online platform for selling teaching materials and learning tools for special needs education.

[1387] "Recommendation" refers to the act of selecting generated educational programs and learning tools to suit the user's needs and proposing the most suitable ones.

[1388] "Purchase" refers to the act of a user selecting a product in a virtual store and paying for it to acquire it.

[1389] "Digital Preview" refers to the ability for a user to review a generated educational program or learning tool in electronic format prior to purchase.

[1390] "Downloading" refers to the act of transferring and saving data or files from a server to a user's device via the Internet.

[1391] "Formatting" refers to the process of converting generated educational programs and learning tools into user-friendly formats (PDF, HTML).

[1392] MODE FOR CARRYING OUT THE INVENTION

[1393] The embodiment of this invention is a system that generates optimized educational programs and learning tools using individual data of students in special needs education and provides them in a virtual store. The system is composed of the following main components.

[1394] Hardware and Software Configuration

[1395] Hardware

[1396] 1. User device: A device used by teachers or parents to enter student information. This can be a desktop PC, laptop, tablet, smartphone, etc.

[1397] 2. Server: The back-end system that receives and processes data. A cloud server is typically used.

[1398] software

[1399] 1. Frontend: React.js is used to provide the user interface.

[1400] 2. Sending data: To send and receive data, we use HTTP POST requests in JSON format using the Axios library.

[1401] 3. Backend: Use Flask (a Python-based web framework).

[1402] 4. Validation: Use pandas and Marshmallow libraries to check the validity of the data.

[1403] 5. Generative AI model: TensorFlow is used to generate educational programs and learning tools.

[1404] 6. Digital File Formatting: Use ReportLab to convert generated educational programs and learning tools into PDF and HTML formats.

[1405] System operation explanation

[1406] 1. Data entry and submission

[1407] The user (teacher or guardian) enters the student's individual data (age, type and degree of disability, what they can and cannot do) through the user interface, and then clicks the send button to send the data to the server.

[1408] 2. Data processing on the server

[1409] The server validates the received data, specifically checking that all required fields are filled in correctly and that they do not contain any incorrect values.

[1410] 3. Creating teaching materials and formatting files

[1411] Successfully validated data is passed to a generative AI model to generate educational programs and learning tools optimized for each student's characteristics, which are then formatted into PDF or HTML format.

[1412] 4. Recommendations and Purchases

[1413] The generated educational programs and learning tools are recommended in a virtual store, where users can digitally preview them and then purchase them as needed.

[1414] 5. Download and Use

[1415] The purchased learning materials are provided as digital files that users can download and use, and a guide on how to use them is also provided.

[1416] Specific examples

[1417] Student B's data:

[1418] Age: 12

[1419] Type and severity of disability: Autism spectrum disorder, mild

[1420] Specialties: Puzzles, visual tasks

[1421] Weaknesses: Long periods of concentration, complex communication

[1422] Example prompt sentence:

[1423] "Generate visual aids that are ideal for a 12-year-old student with mild autism spectrum disorder. This student excels at puzzles but struggles with long periods of concentration and complex communication."

[1424] This enables the system to quickly generate optimized teaching materials required for special needs education and provide them through a virtual store.

[1425] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1426] Step 1:

[1427] The user uses the device's user interface to enter the student's individual data (age, type and degree of disability, what they can and cannot do) into a form. This data is used in subsequent processing, so it must be entered accurately and in detail.

[1428] Step 2:

[1429] The terminal converts the input data into JSON format and sends it to the server using an HTTP POST request when the submit button is clicked. The technologies used here are React.js and the Axios library. Once the input data is sent to the server, it can be processed in the next step.

[1430] Step 3:

[1431] The server receives the received JSON data and validates it. Specifically, it uses the pandas and Marshmallow libraries to check that the input data contains all required fields and is entered correctly. If the data is invalid, it returns an error message.

[1432] Step 4:

[1433] The server passes the successfully validated data to a generative AI model, which generates optimized educational programs and learning tools using prompts. These prompts are input into the model using TensorFlow. The generated data is then formatted into a digital file in the next step.

[1434] Step 5:

[1435] The server formats the generated educational programs and learning tools into user-friendly formats (PDF, HTML) using the ReportLab library. The formatted digital files are then used for recommendations and purchases in the virtual store.

[1436] Step 6:

[1437] The generated educational programs and learning tools are recommended in a virtual storefront for users to digitally preview and purchase. The virtual storefront is an online platform where the recommended educational materials are presented to users.

[1438] Step 7:

[1439] Users can check out educational materials in a virtual store and make a purchase. Purchased materials can then be downloaded to their devices as digital files. Teachers and parents can provide the downloaded materials to students for use, enabling efficient and effective special needs education.

[1440] Through these steps, the system can generate optimal educational programs and learning tools based on each student's individual data and provide them through a virtual store.

[1441] 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.

[1442] The present invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education, and in particular, by combining an emotion engine that recognizes the user's emotions, it realizes more flexible and adaptive educational support. This system includes the following main means.

[1443] Program processing flow

[1444] 1. User provides input data:

[1445] Users (teachers) enter individual student data (age, type and degree of disability, what they can and cannot do) through the interface of the special needs education system. Input is done using a form, and the required items are filled in on the form.

[1446] 2. Data transmission by the device:

[1447] The terminal formats the input data into an appropriate data format, such as JSON, and prepares it to be sent to the server. When the send button is clicked, the data is sent to the server.

[1448] 3. Emotion engine recognizes user emotions:

[1449] The device analyzes the user's facial expressions and tone of voice when inputting information to recognize emotions. This is done by capturing data in real time using a camera and microphone. Emotional data includes indicators such as the user's stress level and satisfaction.

[1450] 4. Data receipt and validation by the server:

[1451] The server receives the data sent from the device and checks the validity of the data, for example, checking that all required fields are filled in correctly and that they do not contain any inappropriate values.

[1452] 5. The server invokes the generative AI model, taking into account the emotion data:

[1453] The server passes the successfully validated data to the generative AI model, which analyzes the input student's individual data and also references the user's emotional data to generate optimized educational programs and study handouts. The generated data is then further formatted into user-friendly formats such as PDF and HTML.

[1454] 6. Server output and emotional data recording:

[1455] The formatted educational programs and study handouts are output as digital files and sent to a terminal. Emotional data is also recorded and used as reference material when creating future educational programs.

[1456] 7. Receiving and displaying results by terminal:

[1457] The device receives the files sent from the server and displays them in a format that teachers can view, allowing teachers to quickly and efficiently provide students with the most suitable educational programs and study materials.

[1458] Examples:

[1459] Student A's data:

[1460] Age: 10

[1461] Type of disability: Intellectual disability

[1462] Degree of disability: Moderate

[1463] Ability to do: Simple calculations, read and write hiragana

[1464] What they can't do: Understand complex sentences, think abstractly

[1465] User input:

[1466] The user (teacher) enters Student A's information into the system's input form and clicks the submit button. This data is converted to JSON format on the device. At the same time, the device's camera and microphone analyze the user's facial expressions and tone of voice, recording emotional data.

[1467] Emotion Engine Analysis:

[1468] The emotion engine recognizes the user's emotions (e.g., fatigue, stress) and collects them as data, which it then sends to the server.

[1469] Server Action:

[1470] The server receives the data sent from the device and performs validation. If validation is successful, the data is passed to the generative AI model, which generates an optimized educational program and learning handouts. Emotional data is also referenced during this process, so it is possible to generate materials of standard difficulty if the teacher is relaxed, or easier materials if the teacher is stressed. The generated data is then formatted into PDF and sent to the device.

[1471] Terminal reception and display:

[1472] The device receives the generated PDF file and displays it for the teacher to review. The teacher then provides learning support to Student A based on this information.

[1473] This process flow automatically generates adaptive educational programs and study handouts that reflect the user's emotions, allowing teachers to efficiently provide the optimal learning environment for each student. This invention contributes to improving the quality of special needs education and reducing the burden on teachers.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] The user logs in to the system. The teacher accesses the login screen of the special needs education system and enters the user name and password for authentication.

[1477] Step 2:

[1478] The user enters the student's individual data. After logging in, the user enters the student's individual information (age, type and degree of disability, what they can and cannot do) into the form and clicks the "Submit" button.

[1479] Step 3:

[1480] The terminal formats the input data. The terminal converts the student's individual data into JSON format and prepares it for sending to the server.

[1481] Step 4:

[1482] The device activates the emotion engine, and the device's camera and microphone analyze the user's facial expressions and tone of voice to obtain emotional data in real time.

[1483] Step 5:

[1484] The device formats and transmits the emotion data. The acquired emotion data is analyzed, converted into JSON format, and sent to the server.

[1485] Step 6:

[1486] The device sends the data to the server. The formatted individual student data and emotion data are sent to the server as an HTTP request.

[1487] Step 7:

[1488] The server receives the data. The server receives the HTTP request sent from the device and extracts the data.

[1489] Step 8:

[1490] The server validates the data. The server checks the validity of the individual student data received, checking that required fields are entered correctly and that they do not contain any inappropriate values.

[1491] Step 9:

[1492] The server calls the generative AI model and takes into account the emotional data. If validation is successful, the server passes the data to the generative AI model and generates an educational program and learning materials, also referencing the user's emotional data.

[1493] Step 10:

[1494] The server generates educational programs and learning materials. The generative AI model automatically creates optimized educational programs and learning materials based on the student's individual data and emotional data.

[1495] Step 11:

[1496] The server formats the generated results: it converts the generated educational programs and study handouts into a human-readable format (e.g., PDF or HTML).

[1497] Step 12:

[1498] The server sends the formatted data to the device, and returns the formatted digital file (PDF or HTML) to the device as an HTTP response.

[1499] Step 13:

[1500] The terminal receives the data. The terminal receives the HTTP response from the server and obtains the generated educational program and study handouts.

[1501] Step 14:

[1502] The terminal displays the results. The terminal displays the received file so that the teacher can check it, and if necessary, print it out or display it on a tablet terminal.

[1503] This processing flow allows users to generate personalized educational programs and study handouts that include emotional data generated by the emotion engine, making it possible to provide optimal learning support to each student.

[1504] Example 2

[1505] 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."

[1506] There is a need to provide efficient and effective education while reducing the burden on teachers in special needs education. However, conventional systems have difficulty automatically generating educational programs that fully reflect the characteristics of individual students and the feelings of teachers. Furthermore, there are challenges in developing a system that can provide reliable validation to determine whether the generated educational programs are appropriate, and one that can provide detailed support tailored to the teacher's situation.

[1507] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1508] In this invention, the server includes: [means for inputting individual student data (age, type and degree of disability, what the student can and cannot do)]; [means for formatting the input individual data into JSON format and transmitting it; [means for analyzing the user's facial expression and tone of voice at the time of input and recognizing their emotion]; [means for validating the received individual data]; [means for passing successfully validated data to a generative model and generating optimized educational programs and study handouts using the generative AI model]; and [means for formatting the generated educational programs and study handouts into PDF or HTML format and outputting them as digital files]. This makes it possible to automatically generate educational programs and study handouts that are optimal for each student while taking into account the teacher's emotions and output them as digital files.

[1509] "Student Personal Data" refers to specific information about a student, such as age, type and severity of disability, and what the student can and cannot do.

[1510] "Input form" refers to the interface that teachers use to enter individual student data into the system.

[1511] "Emotion recognition means" refers to a means for recognizing emotions by analyzing the user's facial expressions and tone of voice in real time when inputting information.

[1512] "Validation measures" refer to measures to verify that received data meets all required fields and is in the correct format.

[1513] A "generative AI model" refers to an artificial intelligence model that analyzes data entered by the user and generates optimized educational programs and study materials.

[1514] "Digital File" refers to a file format that can be viewed on a digital device, such as PDF or HTML.

[1515] "Emotion Data" refers to data relating to a user's emotions acquired and analyzed by emotion recognition means.

[1516] "Terminal" refers to a device (e.g., a PC or tablet) through which teachers input data and communicate with the system.

[1517] "JSON format" is a text format for structuring and expressing data, and is an abbreviation for JavaScript Object Notation.

[1518] A "generative model" is a model that uses a certain algorithm to generate optimal results (such as educational programs or study materials) based on input data.

[1519] This invention is a system for reducing the burden on teachers in special needs education and providing efficient and effective education. In particular, by combining individual student data and teacher emotional data, more flexible and adaptive educational support is realized. This system includes the following main means.

[1520] Providing input data

[1521] First, the user (teacher) enters the student's individual data into the system's input form. This form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, and "moderate" for degree of disability, and would also enter "simple calculations" as a skill and "understanding complex sentences" as a weakness.

[1522] Sending data

[1523] When the user clicks the submit button, the terminal converts the input data to JSON format. This conversion process ensures that all input fields are correctly formatted and converted to the appropriate data type. The terminal then prepares and sends the input data to the server.

[1524] Emotion recognition

[1525] The device uses a camera and microphone to analyze the user's (teacher's) facial expression and tone of voice when inputting data and to recognize emotions. For example, the camera captures the teacher's facial expression, and the microphone captures the tone of voice. The emotion engine analyzes this data and determines the teacher's stress level and fatigue. As a result, emotional data such as "tense," "fatigue," and "relaxed" is generated.

[1526] Data Receipt and Validation

[1527] The server receives the data sent from the device. After receiving it, the server performs data validation, checking whether all required fields are filled in and whether the data format is correct. For example, it checks whether the age is entered correctly as a number, whether the type of disability is within a predetermined category, etc. If an invalid value is included, the server generates an error message and notifies the device.

[1528] Leveraging generative AI models

[1529] The server passes successfully validated data to the generative AI model. The generative AI model uses prompts to analyze the input data and generates optimized educational programs and study handouts based on the results. The model also takes the user's emotional data into consideration. For example, if the teacher is relaxed, it will generate learning materials of normal difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted in PDF or HTML format.

[1530] Output of artifacts and recording of emotion data

[1531] The server outputs the generated educational program and study handouts as digital files and sends them to the device. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the server saves the generated PDF file in a folder dedicated to the user and sends its URL to the device.

[1532] Receiving and displaying results

[1533] The device receives the file sent from the server and displays it in a format that teachers can view. For example, the device opens the generated PDF file so that teachers can view its contents. Based on this, teachers can provide the most appropriate educational program for students.

[1534] Specific examples

[1535] Example prompt sentence:

[1536] "Student A, 10 years old, has an intellectual disability and can do simple calculations and read and write hiragana, but has difficulty understanding complex sentences and thinking abstractly. Generate learning sheets suitable for this student."

[1537] Using this system, teachers can quickly create and provide educational programs and study materials tailored to the characteristics of their students and their own emotional state, thereby improving the quality of special needs education and significantly reducing the burden on teachers.

[1538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1539] Step 1:

[1540] User-provided input data

[1541] The user (teacher) enters individual student data into the system's input form. The input form includes items such as age, type and degree of disability, and what the student can and cannot do. For example, if a teacher is entering information about a 10-year-old student A with an intellectual disability, the teacher would enter "10" for age, "intellectual disability" for type of disability, "moderate" for degree of disability, and "simple calculation" for what the student can do and "understand complex sentences" for what they cannot do. The input data is passed to the next step.

[1542] Input: Individual student data

[1543] Output: Individual student data entered in the input form

[1544] Step 2:

[1545] Data transmission by the terminal

[1546] The terminal receives the data entered by the user in the input form and converts it to JSON format. The converted data is prepared to be sent to the server. When the send button is clicked, the terminal sends the JSON formatted data to the server.

[1547] Input: Individual student data entered in the input form

[1548] Data processing: Format data into JSON format

[1549] Output: Data converted to JSON format

[1550] Step 3:

[1551] Recognizing user emotions with an emotion engine

[1552] While the user is entering data, the device's camera and microphone capture the user's facial expressions and tone of voice. This data is sent in real time to the emotion engine for analysis. The emotion engine generates emotion data, such as the user's stress level and fatigue level, which are used in subsequent steps.

[1553] Input: User's facial expression data and tone of voice data

[1554] Data processing: Emotion analysis using an emotion engine

[1555] Output: User emotion data

[1556] Step 4:

[1557] Data reception and validation by the server

[1558] The server receives the JSON-formatted data sent from the device. After receiving it, the server validates the data to ensure all required fields are entered properly. For example, it checks that age is a number and that the type of disability falls within a predefined category. Once the data is validated, it is passed on to the next step.

[1559] Input: Individual student data in JSON format

[1560] Data Calculation: Validation

[1561] Output: Validated data

[1562] Step 5:

[1563] The server invokes the generative AI model and takes emotion data into account.

[1564] The server passes the data that passes validation to the generative AI model. The generative AI model uses prompts to analyze the student's individual data and generate optimized educational programs and study handouts. Emotional data is also taken into consideration during this process. For example, if the teacher is relaxed, it will generate learning materials of medium difficulty, but if the teacher is stressed, it will generate easy learning materials. The generated data is formatted into PDF or HTML format.

[1565] Input: Validated individual data and user emotion data

[1566] Data Computation: Generating Educational Programs with Generative AI Models

[1567] Output: Optimized educational programs and study materials

[1568] Step 6:

[1569] Server outputs artifacts and records emotion data

[1570] The server outputs the generated educational program and study handouts as digital files and sends them to the terminal. At the same time, emotional data is also recorded and used as reference material for creating future educational programs. For example, the generated PDF file can be saved in a folder reserved for teachers, and its URL can be sent to the terminal.

[1571] Input: Optimized educational programs and study materials

[1572] Data processing: Formatting and saving as digital files

[1573] Output: Formatted digital file and recorded emotion data

[1574] Step 7:

[1575] Receiving and displaying results on the device

[1576] The device receives the file sent from the server and displays it in a format that teachers can check. For example, the device displays a link to open the generated PDF file, and the teacher clicks the link to check the contents. Based on this, the teacher can provide the most appropriate educational program for the student.

[1577] Input: URL of a formatted digital file

[1578] Data Calculation: File Display

[1579] Output: Educational programs and learning handouts displayed in a format that teachers can view

[1580] (Application example 2)

[1581] 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."

[1582] Employees perform a wide variety of tasks, and the stress and fatigue that accompany them have a negative impact on work efficiency and safety. While there is a particular need for a system that can grasp an individual's emotional state in real time and provide optimal work instructions, no such system has yet been developed. The present invention aims to develop a system that analyzes an employee's emotional state and provides optimal work instructions that reduce their workload based on that analysis, thereby improving employee efficiency and safety.

[1583] 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 a means for inputting individual data of employees (age, work characteristics and level, what they can and cannot do), a means for recording and transmitting the input individual data, a means for utilizing a generative model that generates optimized work instructions using the received individual data, a means for utilizing an emotion engine that analyzes the emotional state of employees (facial expressions, tone of voice), and a means for outputting the generated work instructions as a digital file. This makes it possible to provide appropriate work instructions based on the individual state and emotions of employees in real time, thereby improving work efficiency and safety.

[1584] "Individual data" refers to individual characteristics and information such as an employee's age, the nature and level of work, and what they can and cannot do.

[1585] The "emotion engine" is a technology that analyzes employees' facial expressions and tone of voice to assess their emotional state in real time.

[1586] A "generative model" is an algorithm or AI that generates optimized work instructions based on individual input data.

[1587] "Validation means" refers to methods or techniques for verifying the validity of individual data received.

[1588] "Digital file" refers to a file recorded in electronic data format, including formats such as PDF and HTML.

[1589] "Means of input" refers to the interface or device through which employees input individual data into the system.

[1590] "Means for recording and transmitting" refers to the technology and methods for storing input data and transmitting it to a server or other system as necessary.

[1591] "Means of generation" refers to the technology and processes used to analyze collected data and create optimized work instructions.

[1592] The "means of analysis" refers to methods or techniques for assessing the emotional state of employees using an emotion engine.

[1593] This invention is a system that analyzes the emotional state of employees in real time and provides optimal work instructions based on that analysis. This system aims to improve work efficiency and safety by automatically generating work instructions based on the individual data and emotional state of employees.

[1594] The system consists of the following main means:

[1595] 1. Means of inputting individual data:

[1596] Users (employees) use tablets or terminals to enter individual data such as age, work characteristics and level, what they can and cannot do, etc. This allows the status and characteristics of each employee to be registered in the system.

[1597] 2. Recording and transmission methods:

[1598] The terminal converts the input data into an appropriate data format, such as JSON, and sends it to the server. Data transmission is performed by operating the interface (such as a send button).

[1599] 3. Analysis method by emotion engine:

[1600] The device is equipped with a camera and microphone, which runs an emotion engine that analyzes employees' facial expressions and tone of voice in real time. The analysis results are quantified as data on the employee's emotional state (e.g., stress, fatigue), and sent to a server.

[1601] 4. Ways to leverage generative models:

[1602] The server inputs the received individual data and emotion data into the generative AI model. The generative AI model takes into account the individual's state and emotion and generates optimal work instructions. The generated work instructions are formatted into PDF, HTML, or other formats and output in a way that employees can easily understand.

[1603] 5. Digital file output method:

[1604] The generated work instructions are generated as digital files and sent to a terminal, where employees can view them and receive instructions that are relevant to the actual work they will be doing.

[1605] The following hardware and software are used to support the operation of the entire system:

[1606] Hardware:

[1607] Tablets and dedicated devices: Used for data entry and display

[1608] Camera and microphone: Used for real-time sentiment analysis

[1609] Central control server: receives data, validates it, and runs AI models

[1610] software:

[1611] OpenCV: Used to process camera images

[1612] EmotionRecognition library: Used for emotion analysis

[1613] JSON format: Used for data formatting

[1614] The requests library: used to send HTTP requests

[1615] Examples of specific prompts include the following:

[1616] Example prompt sentence:

[1617] Generate optimal work instructions based on the following employee data. When doing so, please consider the employee's emotions and provide a low-stress work environment and instructions. Employee data: { 'Age': 35, 'Fatigue level': 'High', 'Stress level': 'Medium', 'Emotion': 'Fatigue', 'Current work': 'Assembly work'}

[1618] In this way, the system has the ability to understand the individual characteristics and emotional state of employees in real time and generate optimal work instructions, thereby reducing the burden on employees and improving work efficiency and safety.

[1619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1620] Step 1:

[1621] Users (employees) enter their individual data, such as age, work characteristics and level, what they can and cannot do, into a tablet or terminal, and this individual data is then registered in the system.

[1622] Step 2:

[1623] The terminal formats each input data into JSON or an appropriate data format, so that the data can be sent to the server.

[1624] Step 3:

[1625] The terminal sends the formatted data to the server. The data is sent when the user clicks the send button. At this point, the role of the terminal becomes important.

[1626] Step 4:

[1627] The device's camera and microphone are activated to capture the employee's facial expressions and tone of voice, which are then analyzed by the emotion engine to generate emotion data. Specifically, OpenCV processes the camera footage, and the EmotionRecognition library evaluates the employee's emotional state.

[1628] Step 5:

[1629] The device sends the generated emotion data in real time to the server, where it is quantified and formatted in JSON format.

[1630] Step 6:

[1631] The server checks the validity of the received individual data and emotion data. Specifically, it performs validation to check whether all required fields have been entered properly and whether any inappropriate values ​​are included.

[1632] Step 7:

[1633] The server inputs the successfully validated data into the generative AI model, which then generates optimal work instructions based on the individual's state and emotions. Specifically, the prompt sentence is passed to the model for analysis.

[1634] Step 8:

[1635] The server formats the generated work instructions into user-friendly formats such as PDF or HTML, making the instructions easy for employees to understand.

[1636] Step 9:

[1637] The server then sends the formatted work instructions as a digital file to the terminal, which provides the work instructions to the employee in real time.

[1638] Step 10:

[1639] The terminal displays the received work instructions so that employees can check them, allowing them to efficiently carry out their work based on the most appropriate work instructions.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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).

[1647] 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.

[1648] 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."

[1649] 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.

[1650] 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).

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] The following is further disclosed regarding the above embodiment.

[1662] (Claim 1)

[1663] [Method for inputting individual student data (age, type and degree of disability, what students can and cannot do)]

[1664] [Means for recording and transmitting the individual data entered];

[1665] [Means of utilizing generative models to generate optimized educational programs and learning materials using received individual data],

[1666] [Means for outputting the generated educational programs and learning handouts as digital files],

[1667] A system including:

[1668] (Claim 2)

[1669] 2. The system of claim 1, further comprising: a validation means for verifying the validity of the received individual data.

[1670] (Claim 3)

[1671] The system of claim 1, further comprising: a means for formatting the generated educational program or study handout into a human-readable format (PDF, HTML).

[1672] "Example 1"

[1673] (Claim 1)

[1674] [Means for entering individual student data (age, type and degree of disability, what they can and cannot do) and

[1675] [Means for recording the individual data entered, formatting it into an appropriate data format such as JSON, and sending it;

[1676] [Means for confirming and validating the validity of the individual data received; and

[1677] [Means for passing successfully validated data to the AI ​​model to generate optimized educational programs and learning materials;

[1678] [Means for formatting the generated educational programs and learning handouts into user-friendly formats such as PDF and HTML,

[1679] A system including a means for outputting formatted educational programs and study handouts as digital files and transmitting them to a terminal.

[1680] (Claim 2)

[1681] [Means for displaying the received file in a format that can be confirmed by the user] The system according to claim 1.

[1682] (Claim 3)

[1683] [The system according to claim 1 includes a means for generating optimal prompt sentences based on individual student data and inputting the sentences into a generative AI model.

[1684] "Application Example 1"

[1685] (Claim 1)

[1686] [Means for entering individual student data (age, type and degree of disability, what they can and cannot do) and

[1687] [Means for recording and transmitting the input individual data;

[1688] [Means for utilizing generative AI models that generate optimized educational programs and learning tools using the received individualized data; and

[1689] [Means for outputting the generated educational programs and learning tools as digital files and making them available for recommendation and purchase in a virtual store;

[1690] A system including:

[1691] (Claim 2)

[1692] 2. The system of claim 1, further comprising: a validation means for verifying the validity of the received individual data.

[1693] (Claim 3)

[1694] 10. The system of claim 1, further comprising: means for formatting the generated educational programs and learning tools into a human-readable format (PDF, HTML) for digital preview and download.

[1695] "Example 2: Combining Emotion Engines"

[1696] (Claim 1)

[1697] [Means for entering individual student data (age, type and degree of disability, what they can and cannot do) and

[1698] [Means for formatting the input data into JSON format and sending it;

[1699] [An emotion recognition means for analyzing the user's facial expression and tone of voice when inputting and recognizing their emotions;

[1700] [Validation means for verifying the validity of the received individual data; and

[1701] [Means for passing successfully validated data to a generative model and using the generative AI model to generate optimized educational programs and learning materials;

[1702] [Means for formatting the generated educational programs and learning handouts into PDF or HTML format and outputting them as digital files;

[1703] A system including:

[1704] (Claim 2)

[1705] [The system according to claim 1 includes a means for recording emotional data input by the user and for referring to the data when generating an educational program.]

[1706] (Claim 3)

[1707] [The system according to claim 1 includes a means for transmitting the generated educational program and study printouts to the terminal and displaying them in a format that can be confirmed by the user.]

[1708] "Application example 2 when combining emotion engines"

[1709] (Claim 1)

[1710] [Means for entering individual employee data (age, work characteristics / level, what you can / cannot do)]

[1711] [Means for recording and transmitting the individual data entered];

[1712] [A means for utilizing a generative model to generate optimized work instructions using the received individual data];

[1713] [Methods that utilize an emotion engine that analyzes employees' emotional states (facial expressions, tone of voice)],

[1714] [Means for outputting the generated work instructions as a digital file];

[1715] A system including:

[1716] (Claim 2)

[1717] The system of claim 1, further comprising: [a validation means for confirming the validity of the received individual data].

[1718] (Claim 3)

[1719] 10. The system of claim 1, further comprising: means for formatting the generated work instructions into a human-readable format (PDF, HTML). [Explanation of symbols]

[1720] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of inputting individual student data; means for recording and transmitting the input individual data; A means for utilizing a generative model that generates optimized educational programs and learning materials using the received individual data; A means for outputting the generated educational program and learning handouts as digital files; A system including:

2. 10. The system of claim 1, further comprising validation means for verifying the validity of the received individual data.

3. 10. The system of claim 1, further comprising means for formatting the generated educational program or study handout into a human-readable format.

Images