system

Abstract

We provide the system. [Solution] A means of receiving information about learner characteristics and target grade level, Based on the aforementioned information, a means for automatically generating educational materials, A means for outputting the generated material to an educational support device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Teachers in junior high schools and senior high schools are overwhelmed with a lot of administrative work, teaching material preparation, and grade management in the educational field, and thus there is a problem that sufficient time for educational activities cannot be ensured. In particular, since the resources for grasping the learning situation of each student and providing appropriate guidance are limited, it is important to streamline these administrative tasks in order to improve the quality of education.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates teaching materials based on the topic and target grade level entered by the teacher. Furthermore, it includes a function to receive, aggregate, and analyze performance data and generate reports to visualize each student's level of understanding. In addition, it has a function to send notices to parents at the appropriate time, a function to manage students' learning progress, a function to organize schedule data and support efficient time management, and a function to propose individualized instruction plans. In this way, it aims to streamline education-related tasks and create an environment in which teachers can concentrate more on educational activities.

[0006] "Teaching materials" refer to documents and materials created for instruction and learning, used in education and training.

[0007] A "topic" refers to the theme or subject matter covered in teaching materials or lessons, and is a key element in determining the specific content that should be conveyed to students.

[0008] "Target grade level" refers to the grade level of students for whom the educational program or materials are intended, and is a standard to ensure that the content and difficulty level are appropriate for that grade level.

[0009] "Teaching materials" refer to materials and tools designed for teachers to use in educational activities and that can be directly used in classroom instruction.

[0010] "Performance data" refers to numerical values ​​and evaluations obtained as results from tests and examinations, and is information used to measure students' learning achievement and understanding.

[0011] "Data aggregation" refers to the act of statistically compiling and organizing data for analysis, and is a method particularly used in the process of calculating total scores or average scores.

[0012] "Analysis" is the process of examining information and data in detail in order to understand their meaning and relationships.

[0013] "Comprehension level" is a measure that assesses how well learners understand and have mastered a particular topic or issue.

[0014] A "report" is a document that summarizes the results of aggregation and analysis and presents them in a visual or documented format.

[0015] "Notices for Parents" refers to information and notifications prepared to convey to parents about their child's learning progress and school activities.

[0016] "Learning status" refers to information indicating what educational activities students are currently engaged in, as well as their progress and level of achievement.

[0017] "Schedule data" refers to information related to timetables and appointments for classes, meetings, and other events at educational institutions.

[0018] An "individualized instruction plan" is a teaching method and plan that is customized to each student's learning needs and abilities. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a system aimed at streamlining the work of teachers in educational settings. This system supports the wide range of tasks performed by teachers, providing an environment that allows them to concentrate on educational activities. The embodiments of this invention are described in detail below.

[0041] This system primarily consists of three components: a server, a terminal, and a user. First, the teacher, acting as the user, uses a terminal to input information about the topic and target grade level of the teaching materials. This then sends the specific requirements for the teaching materials needed for the lesson to the server.

[0042] Based on the information received, the server automatically generates teaching slides and handouts by referencing online resources and internal databases. During this process, the server selects educational materials relevant to the topic and structures them to facilitate efficient lessons. It also sends the generated materials to terminals, making them available for teacher use.

[0043] Furthermore, in managing performance data, users input their performance information into their terminals and send it to a server. The server aggregates the received data and generates a report that analyzes students' understanding. The generated report clearly identifies each student's strengths and weaknesses and is provided as basic material for teachers when developing lesson plans.

[0044] Regarding communication with parents and schedule management, the server also has the function of checking students' learning progress and sending learning progress updates and important announcements to parents at appropriate times. Furthermore, it organizes class and meeting schedules and provides reminders to support teachers in efficient time management.

[0045] For example, if a user needs "Geometry for Junior High School Mathematics" teaching materials, the server automatically generates slides containing figures and example problems using a geometry material template and sends them to the user's device. This process significantly reduces administrative work for teachers and improves the quality of education.

[0046] Thus, the system of the present invention improves work efficiency and enhances the quality of educational activities in educational settings.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users access the system using a terminal and input the topics and target grade levels of the teaching materials required for their lessons. The interface used is intuitive and designed to allow teachers to easily input the necessary information.

[0050] Step 2:

[0051] The terminal transmits user-entered information to the server in real time. During this process, the information is converted to an appropriate data format and processed so that the server can understand it.

[0052] Step 3:

[0053] The server analyzes the received topic and grade level information, and searches internal databases and external online resources. This process utilizes search algorithms to select the most relevant teaching materials.

[0054] Step 4:

[0055] The server automatically generates teaching slides and handouts using teaching material templates based on the search results. This generation process organizes the selected teaching materials into an appropriate format and compiles them into content suitable for educational purposes.

[0056] Step 5:

[0057] The server sends the generated learning materials to the terminal. During this process, a check is performed to verify the data's integrity and format to ensure that the materials are displayed correctly.

[0058] Step 6:

[0059] Users can review the materials generated on their devices and modify or supplement the content as needed. This customization feature allows teachers to provide optimal materials tailored to the specific circumstances of their lessons.

[0060] Step 7:

[0061] When a user enters performance data into a terminal, the terminal sends that data to the server.

[0062] Step 8:

[0063] The server aggregates and analyzes the received performance data and generates a report showing each student's level of understanding. This report includes graphs and charts to highlight students' strengths and areas for improvement.

[0064] Step 9:

[0065] The server sends the generated report to the terminal for user review. At this stage, teachers can adjust their lesson plans based on the report.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] In today's educational environment, teachers are overwhelmed with a variety of tasks, making it difficult for them to focus on effective teaching activities. In particular, preparing teaching materials, analyzing grades, communicating with parents, and managing schedules are time-consuming and labor-intensive, hindering improvements in the quality of education. It is necessary to streamline these tasks and provide teachers with an environment where they can concentrate on their core educational activities.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] This invention includes a server that receives information on the topic and target grade level of teaching materials, and automatically generates teaching materials by referring to online resources and internal information aggregation; a server that receives performance data and aggregates it through statistical analysis; and a server that monitors students' learning progress and sends information to parents at an appropriate time. This reduces the burden on teachers in preparing teaching materials and managing performance, enabling quick and accurate work processing.

[0071] "Teaching materials" refer to materials or sets of materials used to convey learning content in educational activities.

[0072] A "topic" is a term that refers to a specific educational theme or issue, and is the focus of instruction.

[0073] "Target grade level" refers to the age group or grade level of learners following a specific curriculum.

[0074] "Online resources" refer to digital information and content that can be accessed via the internet.

[0075] "Internal information accumulation" refers to a collection of usable knowledge, consisting of data and information stored within a specific system.

[0076] A "generative AI model" is a group of algorithms and programs that generate new information and data structures based on machine learning.

[0077] "Performance data" refers to a numerical record of a learner's learning outcomes, and is the information that forms the basis of evaluation.

[0078] "Statistical analysis" is a mathematical method used to evaluate trends and relationships based on data.

[0079] "Learning status" refers to the state of a learner's current learning progress and level of understanding.

[0080] "Schedule management" refers to the process of organizing schedules and other tasks to ensure efficient time allocation.

[0081] This invention is a system aimed at improving the efficiency of operations in educational settings, and is mainly composed of three components: a server, terminals, and users. This system utilizes a generative AI model to support teachers in a wide range of tasks, providing an environment where they can concentrate on educational activities.

[0082] In the automatic generation of teaching materials,

[0083] The user (teacher) uses a terminal to input information about the topic and target grade level of the teaching material. For example, they might enter the prompt "Geometry in Junior High School Mathematics."

[0084] The terminal immediately sends the entered information to the server.

[0085] Based on the received information, the server references online resources and internal data archives, and automatically generates teaching materials using a generative AI model. For example, it can generate materials that include geometric figures and example problems.

[0086] The server sends the generated teaching materials to the terminals, making them available for teachers to use. This process allows teachers to reduce a lot of administrative work, shorten the time required to prepare teaching materials, and provide high-quality materials.

[0087] In managing performance data,

[0088] The user enters student grade data on the device.

[0089] The terminal sends performance information to the server.

[0090] The server performs statistical analysis and automatically generates reports that visualize students' understanding using generative AI models. This makes it possible to understand students' strengths and weaknesses and to develop more effective teaching plans.

[0091] In terms of contacting parents and managing schedules,

[0092] The server periodically monitors students' learning progress and sends necessary information to parents at the appropriate time.

[0093] Furthermore, by organizing timetables and meeting schedules and sending reminders to their devices, the system supports teachers in efficient time management.

[0094] This system will streamline various tasks in educational settings, enabling the provision of a better educational environment.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] The user uses a terminal to input information about the teaching material's topic and target grade level, as provided by the teacher. This information might be a prompt such as "Geometry in Junior High School Mathematics."

[0098] The terminal converts this information into a digital format and immediately sends it to the server. The input consists of topic and grade information, and the output is the data to be sent to the server.

[0099] Step 2:

[0100] Based on the received topic and target grade level information, the server searches online resources and internal databases to extract relevant educational content. For example, it retrieves geometry lesson templates, appropriate shapes, and example problems from the system.

[0101] The server utilizes a generative AI model to process the extracted content and automatically generate new educational materials. In this process, the input is the extracted educational content, and the output is instructional materials.

[0102] Step 3:

[0103] The server sends the generated learning materials to the terminal. At this time, the materials are formatted into a user-friendly format and made ready for use on the terminal.

[0104] The terminal displays the received teaching materials, making them easily accessible to teachers for use in class. The input is the generated teaching materials, and the output is the displayed teaching materials.

[0105] Step 4:

[0106] Users input student performance information using their devices. This includes, for example, test scores and attendance records.

[0107] The terminal organizes the entered grade data and sends it to the server. In this step, the input is the grade information, and the output is the data sent to the server.

[0108] Step 5:

[0109] The server performs statistical analysis based on the received performance data. Using a generative AI model, it creates reports that highlight each student's strengths and weaknesses.

[0110] As a result, the input is grade data, and the output is a report that visualizes each student's level of understanding.

[0111] Step 6:

[0112] The server monitors students' learning progress and generates messages to report their progress to parents.

[0113] Additionally, reminders will be sent to the device when there are changes to the timetable or important announcements. Input is learning progress information, and output is report messages and reminder information for parents.

[0114] (Application Example 1)

[0115] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0116] In traditional educational settings, there have been challenges such as difficulty in creating appropriate teaching materials tailored to the individual characteristics of learners, difficulty in assessing their level of understanding, and a heavy workload for teachers, making it difficult to maximize educational effectiveness. Furthermore, efficient management of time and communication with parents is also required.

[0117] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0118] In this invention, the server includes means for receiving information on learner characteristics and target grade level and automatically generating educational materials; means for receiving, aggregating, and analyzing learning progress data; and means for managing learners' learning progress and sending notifications to educators. This enables the provision of learning materials optimized for each individual learner, accurate assessment of understanding, and increased efficiency of operations.

[0119] A "learner" refers to an individual receiving education, whose purpose is to acquire specific knowledge or skills.

[0120] "Characteristics" refer to specific attributes or characteristics of learners or learning materials, and are useful information for designing individualized education.

[0121] "Target grade level" refers to the grade level within the curriculum to which the learner belongs, and is a criterion for determining appropriate educational content.

[0122] "Educational materials" refer to information and resources designed to support learners' learning and can be used in classes or for self-study.

[0123] "Educational support devices" refer to devices used in educational settings that play a role in providing learning materials to learners and monitoring their learning progress.

[0124] "Learning progress data" refers to information that shows how well learners understand and are progressing in the learning process.

[0125] "Aggregation and analysis" refers to the process of statistically summarizing learning progress data and evaluating the level of understanding.

[0126] "Visualization" refers to representing data in the form of graphs, charts, and other diagrams to make it easier to understand.

[0127] A "report" is a document that compiles data and information, and is created to convey results and trends.

[0128] The term "education-related personnel" refers to individuals or organizations involved in the education of learners, and is a concept that includes teachers, parents, and others.

[0129] "Sending a message" refers to the act of conveying information to a specific recipient.

[0130] "Time management" refers to efficiently organizing tasks and appointments, allocating time appropriately, and effectively carrying out work or learning.

[0131] The system used to implement this invention primarily consists of three elements: a server, a terminal, and a user. The terminal functions as an educational support device, operated by the user (an educator) as needed. This system uses a Raspberry Pi or equivalent small computer as hardware, and its program is written in Python. A web server using Flask manages data transmission and reception, and TENSORFLOW® is used to analyze learning progress data.

[0132] First, the user inputs information about the learner's characteristics and target grade level from their terminal to the server. Based on this information, the server automatically generates educational materials and provides them to the educational support device. Because the automatically generated materials are optimized for each individual learner, effective learning can be expected.

[0133] Furthermore, the server receives, aggregates, and analyzes learning progress data to generate visualized reports. These visualized reports are useful resources for quickly understanding learners' comprehension levels, allowing educators to use them to more effectively develop individualized instruction plans.

[0134] As a concrete example, the system can automatically generate individualized learning materials for "Geometry in Junior High School Mathematics" and present them to students on their devices. Furthermore, it can analyze learning progress and utilize prompts from a generative AI model ("Generative AI Model: Generate prompts to clearly explain basic geometric shapes") to create an environment for appropriate instruction. This system enables instruction based on the "characteristics" of each individual learner, thereby improving the quality of education.

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] The user inputs educational information, such as the learner's characteristics and target grade level, into the terminal. The terminal then sends this information to the server. The input must be text data, and the output must be information data that is appropriately transmitted to the server.

[0138] Step 2:

[0139] The server automatically generates educational materials using a generative AI model, referencing databases and online resources based on the received information. This process utilizes prompts to generate templates for teaching materials appropriate to the target grade level and subject. The input is the information data obtained in the previous step, and the output is the generated teaching material data.

[0140] Step 3:

[0141] The server sends the generated teaching material data to a terminal, which is an educational support device. The terminal receives this data and presents the materials to the user. The input is the teaching material data, and the output is that the teaching materials become viewable on the terminal.

[0142] Step 4:

[0143] The user inputs the learner's learning progress into the terminal, and this data is also sent to the server. The input is progress data, and the output is the transmission to the server.

[0144] Step 5:

[0145] The server aggregates and analyzes learning progress data and generates a visualized report. Here, TensorFlow is used for data analysis and conversion of progress data into charts and graphs. The input is progress data, and the output is a visualized report.

[0146] Step 6:

[0147] The server sends the generated visualization report to the educational support device, making it available for user review on the terminal. The input is the report data, and the output is the ability to view the report on the terminal.

[0148] Step 7:

[0149] Based on the visualized report, the user creates an individualized instruction plan for the learner and inputs information into the terminal to contact educators as needed. Here, the user organizes the information while considering the specific characteristics of the learner. Inputs are feedback on the report and data for communication, and outputs are data for communication with educators.

[0150] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0151] This invention is a system aimed at improving the efficiency of teachers' work and enhancing the user experience in educational settings, providing a means of automating tasks faced by teachers. In particular, by incorporating an emotion engine, it aims to incorporate the emotions of the teacher user and improve the system's responsiveness and the quality of teaching materials.

[0152] This system is structured around three main components: the server, the terminal, and the user, with the emotion engine playing a central role. First, the user accesses the system using a terminal and inputs the topic and target grade level of the teaching materials needed for the lesson. This information is then sent from the terminal to the server.

[0153] The server generates optimal teaching materials using internal databases and external resources based on topic and grade level information. This process incorporates an emotion engine that analyzes user emotion data and incorporates feedback into the material content. For example, if a teacher is experiencing stress, the system adjusts the difficulty level of the materials, providing materials that are considerate of the teacher's burden.

[0154] In grade management, the server receives grade data entered by users from their devices and generates reports showing each student's level of understanding. Here, the emotion engine customizes the way the reports are presented and the content of the analysis based on the teacher's emotional state, providing information in a way that reduces stress.

[0155] Furthermore, the server uses an emotion engine to analyze teachers' daily emotional data and provides rest suggestions and support as needed. This feature helps teachers mitigate stress that may arise during the course of their lessons.

[0156] For example, if a user is deemed stressed while creating teaching materials on the topic of "chemical bonding in high school chemistry," the server will generate concise and easy-to-understand materials to reduce the teacher's burden. The generated materials will then be sent to the teacher's device and made available for use.

[0157] This system enables teachers to perform their duties efficiently, maintain their mental health, and maintain a high quality of education. This invention utilizes an emotional engine to reduce frustration and stress in educational settings, thereby achieving comprehensive educational support.

[0158] The following describes the processing flow.

[0159] Step 1:

[0160] Users use their devices to input information about the teaching material's topic and target grade level. The user interface is designed to allow for easy and accurate information entry through options and input fields.

[0161] Step 2:

[0162] The terminal sends the entered information to the server. During this process, the data is converted into an appropriate format and prepared for processing on the server side.

[0163] Step 3:

[0164] The server searches and references internal databases and online resources based on the received information to identify the most suitable teaching materials. During this process, the emotion engine analyzes the user's emotional state at the time of input and incorporates this into the material generation.

[0165] Step 4:

[0166] The emotion engine collects user emotion data and determines emotional states such as stress and fatigue. Based on this information, the server adjusts the content and difficulty level of the learning materials.

[0167] Step 5:

[0168] The server integrates selected learning material information with feedback from the emotion engine to generate learning slides and handouts. The materials are created in an easy-to-understand format that takes into account the user's emotional state.

[0169] Step 6:

[0170] The generated learning materials are sent to the device and made available for the user to review. The user can customize the learning material content as needed.

[0171] Step 7:

[0172] When a user enters their grade data, the device sends that data to the server. This data is used to visualize each student's level of understanding.

[0173] Step 8:

[0174] The server analyzes performance data and generates reports that show students' comprehension levels and learning trends. The emotion engine monitors the user's emotional state throughout this process and supports the presentation of appropriate information.

[0175] Step 9:

[0176] The generated report is sent to the terminal, making it available for user review and use. The user can then use this information to develop a lesson plan.

[0177] Step 10:

[0178] During lessons and between tasks, the emotion engine continues to analyze the user's emotional data and provides rest recommendations and support information as needed. This allows teachers to manage their own emotions and physical condition while performing their duties.

[0179] (Example 2)

[0180] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0181] In educational settings, teachers spend a significant amount of time on their daily tasks, with curriculum development and assessing student comprehension being particularly burdensome. This can increase teacher stress and potentially lower the quality of education. Therefore, a system is needed to reduce teachers' workload and improve the quality of education.

[0182] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0183] In this invention, the server includes means for receiving information on teaching materials using an information processing device, means for automatically generating teaching materials using a generation AI model based on the received information, and means for analyzing user emotional data and reflecting it in the teaching materials. This reduces the workload of teachers and enables effective educational support that takes emotional stress into consideration.

[0184] An "information processing device" refers to a computer system that receives, processes, and transmits data, and includes terminals and servers used by teachers.

[0185] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate teaching materials based on input data.

[0186] "Teaching materials" refer to learning materials and lesson plans used for educational purposes, and the generated information is used in the educational activities of teachers and students.

[0187] "Emotional data" refers to information indicating teachers' stress levels and emotional states, which the system analyzes and uses for creating teaching materials and providing feedback.

[0188] "Statistical processing" refers to the process of organizing collected data and converting it into numerical indicators such as mean values ​​and distributions.

[0189] "Materials showing learning progress" refers to reports and visual information that show each student's level of understanding and progress in learning content.

[0190] This system aims to streamline the work of teachers in educational institutions and provide high-quality education. The system mainly consists of an information processing unit, a generative AI model, and an emotion engine.

[0191] Teachers, as users, access the system using a terminal. By entering the topic and target grade level of a specific teaching material into this terminal, teachers can begin the material creation process. After the terminal receives the input, this information is sent to the server.

[0192] The server processes incoming information using a generative AI model to create optimal teaching materials. This generative AI model, for example, automatically generates material content using natural language processing techniques. Furthermore, the server analyzes user emotion data using an emotion engine. This emotion data is useful for evaluating teachers' stress levels and emotional states. The analysis results are used as feedback to adjust the content and difficulty level of the teaching materials.

[0193] For example, if a user inputs that they want to create teaching materials on the topic of "chemical bonding in high school chemistry," and the system detects the user's stress level, the server will generate more concise and easy-to-understand teaching materials. These materials are then provided to the user via their terminal.

[0194] In grade management, users input student grades via their devices. The server collects this data and generates reports showing each student's learning progress through statistical processing. The presentation of these reports is adjusted based on the user's sentiment data.

[0195] The server also continuously analyzes teachers' daily emotional data and provides suggestions and support for rest as needed. This feature allows teachers to perform their duties without experiencing excessive stress.

[0196] An example of a prompt message is: "This teacher is stressed out creating teaching materials on chemical bonding in high school chemistry. Please generate materials that will reduce his workload."

[0197] In this way, teachers can work efficiently, take care of their own mental health, and provide high-quality education.

[0198] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0199] Step 1:

[0200] The user inputs the topic and target grade level of the teaching material into the terminal. The terminal receives this information, formats it into a prompt message format, and sends it to the server. This input information may include the topic "Chemical Bonding in High School Chemistry" as an example.

[0201] Step 2:

[0202] The server automatically generates educational materials using a generative AI model based on information received from the terminal. The generative AI model analyzes the input prompt text and performs data calculations to generate the content and structure of the educational materials. The generated educational material content is then generated by the server.

[0203] Step 3:

[0204] The server uses an emotion engine to analyze the user's emotional data. This analysis determines the stress level based on past emotional data and teacher feedback. The analysis results are used to adjust the learning materials, for example, by adjusting the difficulty level of the materials.

[0205] Step 4:

[0206] The server generates the final instructional materials based on the generated teaching materials and the analysis results of the emotional data. In particular, if the user is experiencing high stress levels, the explanations are adjusted to be concise and easy to understand. The final materials are then delivered from the server to the terminal and provided to the user.

[0207] Step 5:

[0208] After class, users enter student grades into a terminal. The server receives the grade data sent from the terminal and performs statistical processing. This statistical processing includes calculations such as the average and distribution of students' comprehension levels.

[0209] Step 6:

[0210] The server generates a student-specific comprehension report based on statistical processing results. The presentation method of the generated report is adjusted according to the user's emotional state. The final report is sent from the server to the terminal and provided to the user.

[0211] Step 7:

[0212] The server analyzes teachers' daily emotional data and processes it to suggest rest and support as needed. Based on this analysis, the server suggests times when teachers need rest and sends notifications to provide support.

[0213] (Application Example 2)

[0214] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0215] In educational settings, teachers and staff experience significant psychological burdens due to their numerous tasks. In particular, creating teaching materials, compiling evaluations, and developing individualized support plans are factors that contribute to emotionally challenging situations. Furthermore, a lack of appropriate material presentation tailored to teachers' emotional states and insufficient support for efficient time management negatively impacts the quality of education and the well-being of teachers and staff.

[0216] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0217] In this invention, the server includes a device for receiving information related to the topic of educational materials and the educational stage to which they apply; a device for automatically generating teaching materials based on the information; and a device for analyzing the teacher's psychological state using an emotion analysis device and optimizing teaching materials or workload. This makes it possible for teachers and staff to improve work efficiency while reducing their psychological burden.

[0218] "Educational materials" are materials provided to learners for educational purposes.

[0219] "Educational stages" refer to the various stages of progress in a learner's educational process.

[0220] "Information" refers to the data and instructions that a system receives.

[0221] "Teaching materials" are educational materials used in classes and learning activities for educational purposes.

[0222] An "emotional analysis device" is a device used to analyze and determine an individual's psychological state.

[0223] "Psychological state" refers to an individual's emotions and mental condition.

[0224] "Workload" refers to the amount of work or the difficulty of tasks assigned to an individual.

[0225] "Optimization" refers to adjusting conditions to achieve the best possible state.

[0226] "Efficient time management" refers to management methods that aim to use time effectively and reduce waste.

[0227] "Burden reduction" refers to alleviating psychological or physical pressure.

[0228] "Business efficiency" refers to the degree to which work and tasks are performed efficiently and effectively.

[0229] To implement this invention, a system is built around a server, a terminal, and a user. The server collects information related to the topics and educational stages of educational materials and analyzes the user's psychological state using an emotion analysis device. Based on this information, it automatically generates teaching materials and further optimizes the workload. Specifically, the server uses a cloud server as the master unit and employs machine learning libraries such as TensorFlow for data processing. It performs emotion analysis and implements a teaching material generation algorithm using a programming language such as Python. The terminal is a smart device capable of installing a dedicated application and is responsible for receiving and displaying relevant data. Through this system, the user can receive appropriate teaching materials and work management support that take their psychological state into consideration.

[0230] As a concrete example, suppose a user selects "Science Experiments in Secondary Education" as the topic for their lesson. If the emotion analysis device detects that the user is feeling fatigued, the server automatically generates concise and easy-to-understand teaching materials for that topic and sends them to the user's device. This allows the user to conduct lessons efficiently while reducing their burden.

[0231] A program that utilizes a generative AI model to generate educational materials that take emotional states into account operates based on the following prompt: "Design and explain an algorithm for generating secondary education science experiment materials that help reduce teacher stress."

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The user uses a terminal to input information about the topic and educational level of the educational material. The input information is sent from the terminal to the server. At this stage, the input identifies the topic name and educational level, and generates JSON format data that is sent to the server as output.

[0235] Step 2:

[0236] The server analyzes the user's psychological state using an emotion analysis device, based on the topics of the received educational materials and educational stage data. Interaction data with the user is input to the emotion analysis model, and evaluation data indicating the user's psychological state (e.g., stress level, fatigue level) is output. This data is used to optimize education.

[0237] Step 3:

[0238] The server generates instructional materials while considering the results of sentiment analysis. A generation AI model is used, taking educational material topics, educational stages, and user psychological state data as input, and generating instructional content as output. The difficulty and complexity of this content are then adjusted according to the user's psychological state.

[0239] Step 4:

[0240] The generated teaching materials are sent from the server to the terminal, making them accessible to the user. Here, the teaching material content is displayed in the terminal's application, and the user utilizes it as digital material to support educational activities. This output is intended as educational material and is in a format that can be viewed and used within the terminal's available storage space.

[0241] Step 5:

[0242] As users engage in educational activities, response and progress data are continuously sent from their devices to the server. User interaction and learning progress data are considered as input, and the server performs actions such as additional optimization and suggesting new learning materials as needed. This process continues in real time, enhancing the effectiveness of the education.

[0243] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0244] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0245] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0249] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0250] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0251] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0252] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0253] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0254] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0255] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0257] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0258] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] This invention is a system aimed at streamlining the work of teachers in educational settings. This system supports the wide range of tasks performed by teachers, providing an environment that allows them to concentrate on educational activities. The embodiments of this invention are described in detail below.

[0260] This system primarily consists of three components: a server, a terminal, and a user. First, the teacher, acting as the user, uses a terminal to input information about the topic and target grade level of the teaching materials. This then sends the specific requirements for the teaching materials needed for the lesson to the server.

[0261] Based on the information received, the server automatically generates teaching slides and handouts by referencing online resources and internal databases. During this process, the server selects educational materials relevant to the topic and structures them to facilitate efficient lessons. It also sends the generated materials to terminals, making them available for teacher use.

[0262] Furthermore, in managing performance data, users input their performance information into their terminals and send it to a server. The server aggregates the received data and generates a report that analyzes students' understanding. The generated report clearly identifies each student's strengths and weaknesses and is provided as basic material for teachers when developing lesson plans.

[0263] Regarding communication with parents and schedule management, the server also has the function of checking students' learning progress and sending learning progress updates and important announcements to parents at appropriate times. Furthermore, it organizes class and meeting schedules and provides reminders to support teachers in efficient time management.

[0264] For example, if a user needs "Geometry for Junior High School Mathematics" teaching materials, the server automatically generates slides containing figures and example problems using a geometry material template and sends them to the user's device. This process significantly reduces administrative work for teachers and improves the quality of education.

[0265] Thus, the system of the present invention improves work efficiency and enhances the quality of educational activities in educational settings.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] Users access the system using a terminal and input the topics and target grade levels of the teaching materials required for their lessons. The interface used is intuitive and designed to allow teachers to easily input the necessary information.

[0269] Step 2:

[0270] The terminal transmits user-entered information to the server in real time. During this process, the information is converted to an appropriate data format and processed so that the server can understand it.

[0271] Step 3:

[0272] The server analyzes the received topic and grade level information, and searches internal databases and external online resources. This process utilizes search algorithms to select the most relevant teaching materials.

[0273] Step 4:

[0274] The server automatically generates teaching slides and handouts using teaching material templates based on the search results. This generation process organizes the selected teaching materials into an appropriate format and compiles them into content suitable for educational purposes.

[0275] Step 5:

[0276] The server sends the generated learning materials to the terminal. During this process, a check is performed to verify the data's integrity and format to ensure that the materials are displayed correctly.

[0277] Step 6:

[0278] Users can review the materials generated on their devices and modify or supplement the content as needed. This customization feature allows teachers to provide optimal materials tailored to the specific circumstances of their lessons.

[0279] Step 7:

[0280] When a user enters performance data into a terminal, the terminal sends that data to the server.

[0281] Step 8:

[0282] The server aggregates and analyzes the received performance data and generates a report indicating the understanding level of each student. This report includes graphs and charts for indicating the strengths of the students and areas that need improvement.

[0283] Step 9:

[0284] The server sends the generated report to the terminal for the user to confirm. At this stage, the teacher can adjust the teaching plan based on the report.

[0285] (Example 1)

[0286] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In modern educational settings, teachers are faced with diverse tasks and have difficulty concentrating on effective educational activities. In particular, preparing teaching materials, analyzing performance evaluations, communicating with parents, and managing schedules require time and effort, and are obstacles to improving the quality of education. It is necessary to streamline these tasks and provide an environment in which teachers can concentrate on their original educational activities.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0289] In this invention, the server includes means for receiving information regarding the topic of teaching materials and the target school year, and automatically generating teaching materials for guidance by referring to online resources and internal information integration; means for receiving performance data and performing aggregation through statistical analysis; and means for monitoring the learning status of students and transmitting contact information to parents at an appropriate time. Thereby, the burden on teachers for preparing teaching materials and managing performance is reduced, and rapid and accurate business processing becomes possible.

[0290] "Teaching materials" refer to materials or groups of materials used to convey learning content in educational activities.

[0291] A "topic" is a term that refers to a specific educational theme or issue, and is the focus of instruction.

[0292] "Target grade level" refers to the age group or grade level of learners following a specific curriculum.

[0293] "Online resources" refer to digital information and content that can be accessed via the internet.

[0294] "Internal information accumulation" refers to a collection of usable knowledge, consisting of data and information stored within a specific system.

[0295] A "generative AI model" is a group of algorithms and programs that generate new information and data structures based on machine learning.

[0296] "Performance data" refers to a numerical record of a learner's learning outcomes, and is the information that forms the basis of evaluation.

[0297] "Statistical analysis" is a mathematical method used to evaluate trends and relationships based on data.

[0298] "Learning status" refers to the state of a learner's current learning progress and level of understanding.

[0299] "Schedule management" refers to the process of organizing schedules and other tasks to ensure efficient time allocation.

[0300] This invention is a system aimed at improving the efficiency of operations in educational settings, and is mainly composed of three components: a server, terminals, and users. This system utilizes a generative AI model to support teachers in a wide range of tasks, providing an environment where they can concentrate on educational activities.

[0301] In the automatic generation of teaching materials,

[0302] The user (teacher) uses the terminal to input information about the topic of teaching materials and the target school year. For example, the user inputs a prompt sentence such as "Geometry in middle school mathematics".

[0303] The terminal immediately sends the input information to the server.

[0304] Based on the received information, the server refers to online resources and internal information integration, and uses the generative AI model to automatically generate teaching materials for guidance. For example, it generates teaching materials including geometric figures and problem examples in geometry.

[0305] The server sends the generated teaching materials to the terminal, making them available for the teacher to use. Through this process, the teacher can streamline many administrative tasks, shorten the preparation time for teaching materials, and provide high-quality teaching materials.

[0306] In the management of academic performance data,

[0307] The user inputs the academic performance data of students using the terminal.

[0308] The terminal sends the performance information to the server.

[0309] The server conducts statistical analysis and uses the generative AI model to automatically generate a report that visualizes the understanding level of students. This enables the teacher to grasp the strengths and weaknesses of students and more effectively formulate teaching plans.

[0310] In communicating with parents and managing schedules,

[0311] The server regularly monitors the learning status of students and sends the necessary information to parents at appropriate times.

[0312] In addition, by organizing class schedules and meeting schedules and sending reminders to the terminal, it supports teachers in efficient time management.

[0313] This system will streamline various tasks in educational settings, enabling the provision of a better educational environment.

[0314] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0315] Step 1:

[0316] The user uses a terminal to input information about the teaching material's topic and target grade level, as provided by the teacher. This information might be a prompt such as "Geometry in Junior High School Mathematics."

[0317] The terminal converts this information into a digital format and immediately sends it to the server. The input consists of topic and grade information, and the output is the data to be sent to the server.

[0318] Step 2:

[0319] Based on the received topic and target grade level information, the server searches online resources and internal databases to extract relevant educational content. For example, it retrieves geometry lesson templates, appropriate shapes, and example problems from the system.

[0320] The server utilizes a generative AI model to process the extracted content and automatically generate new educational materials. In this process, the input is the extracted educational content, and the output is instructional materials.

[0321] Step 3:

[0322] The server sends the generated learning materials to the terminal. At this time, the materials are formatted into a user-friendly format and made ready for use on the terminal.

[0323] The terminal displays the received teaching materials, making them easily accessible to teachers for use in class. The input is the generated teaching materials, and the output is the displayed teaching materials.

[0324] Step 4:

[0325] Users input student performance information using their devices. This includes, for example, test scores and attendance records.

[0326] The terminal organizes the entered grade data and sends it to the server. In this step, the input is the grade information, and the output is the data sent to the server.

[0327] Step 5:

[0328] The server performs statistical analysis based on the received performance data. Using a generative AI model, it creates reports that highlight each student's strengths and weaknesses.

[0329] As a result, the input is grade data, and the output is a report that visualizes each student's level of understanding.

[0330] Step 6:

[0331] The server monitors students' learning progress and generates messages to report their progress to parents.

[0332] Additionally, reminders will be sent to the device when there are changes to the timetable or important announcements. Input is learning progress information, and output is report messages and reminder information for parents.

[0333] (Application Example 1)

[0334] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0335] In traditional educational settings, there have been challenges such as difficulty in creating appropriate teaching materials tailored to the individual characteristics of learners, difficulty in assessing their level of understanding, and a heavy workload for teachers, making it difficult to maximize educational effectiveness. Furthermore, efficient management of time and communication with parents is also required.

[0336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0337] In this invention, the server includes means for receiving information on learner characteristics and target grade level and automatically generating educational materials; means for receiving, aggregating, and analyzing learning progress data; and means for managing learners' learning progress and sending notifications to educators. This enables the provision of learning materials optimized for each individual learner, accurate assessment of understanding, and increased efficiency of operations.

[0338] A "learner" refers to an individual receiving education, whose purpose is to acquire specific knowledge or skills.

[0339] "Characteristics" refer to specific attributes or characteristics of learners or learning materials, and are useful information for designing individualized education.

[0340] "Target grade level" refers to the grade level within the curriculum to which the learner belongs, and is a criterion for determining appropriate educational content.

[0341] "Educational materials" refer to information and resources designed to support learners' learning and can be used in classes or for self-study.

[0342] "Educational support devices" refer to devices used in educational settings that play a role in providing learning materials to learners and monitoring their learning progress.

[0343] "Learning progress data" refers to information that shows how well learners understand and are progressing in the learning process.

[0344] "Aggregation and analysis" refers to the process of statistically summarizing learning progress data and evaluating the level of understanding.

[0345] "Visualization" refers to representing data in the form of graphs, charts, and other diagrams to make it easier to understand.

[0346] A "report" is a document that compiles data and information, and is created to convey results and trends.

[0347] The term "education-related personnel" refers to individuals or organizations involved in the education of learners, and is a concept that includes teachers, parents, and others.

[0348] "Sending a message" refers to the act of conveying information to a specific recipient.

[0349] "Time management" refers to efficiently organizing tasks and appointments, allocating time appropriately, and effectively carrying out work or learning.

[0350] The system used to implement this invention primarily consists of three elements: a server, a terminal, and a user. The terminal functions as an educational support device, operated by the user (an educator) as needed. This system uses a Raspberry Pi or equivalent small computer as hardware, and its program is written in Python. A web server using Flask manages data transmission and reception, and TensorFlow is used to analyze learning progress data.

[0351] First, the user inputs information about the learner's characteristics and target grade level from their terminal to the server. Based on this information, the server automatically generates educational materials and provides them to the educational support device. Because the automatically generated materials are optimized for each individual learner, effective learning can be expected.

[0352] Furthermore, the server receives, aggregates, and analyzes learning progress data to generate visualized reports. These visualized reports are useful resources for quickly understanding learners' comprehension levels, allowing educators to use them to more effectively develop individualized instruction plans.

[0353] As a concrete example, the system can automatically generate individualized learning materials for "Geometry in Junior High School Mathematics" and present them to students on their devices. Furthermore, it can analyze learning progress and utilize prompts from a generative AI model ("Generative AI Model: Generate prompts to clearly explain basic geometric shapes") to create an environment for appropriate instruction. This system enables instruction based on the "characteristics" of each individual learner, thereby improving the quality of education.

[0354] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0355] Step 1:

[0356] The user inputs educational information, such as the learner's characteristics and target grade level, into the terminal. The terminal then sends this information to the server. The input must be text data, and the output must be information data that is appropriately transmitted to the server.

[0357] Step 2:

[0358] The server automatically generates educational materials using a generative AI model, referencing databases and online resources based on the received information. This process utilizes prompts to generate templates for teaching materials appropriate to the target grade level and subject. The input is the information data obtained in the previous step, and the output is the generated teaching material data.

[0359] Step 3:

[0360] The server sends the generated teaching material data to a terminal, which is an educational support device. The terminal receives this data and presents the materials to the user. The input is the teaching material data, and the output is that the teaching materials become viewable on the terminal.

[0361] Step 4:

[0362] The user inputs the learner's learning progress into the terminal, and this data is also sent to the server. The input is progress data, and the output is the transmission to the server.

[0363] Step 5:

[0364] The server aggregates and analyzes learning progress data and generates a visualized report. Here, TensorFlow is used for data analysis and conversion of progress data into charts and graphs. The input is progress data, and the output is a visualized report.

[0365] Step 6:

[0366] The server sends the generated visualization report to the educational support device, making it available for user review on the terminal. The input is the report data, and the output is the ability to view the report on the terminal.

[0367] Step 7:

[0368] Based on the visualized report, the user creates an individualized instruction plan for the learner and inputs information into the terminal to contact educators as needed. Here, the user organizes the information while considering the specific characteristics of the learner. Inputs are feedback on the report and data for communication, and outputs are data for communication with educators.

[0369] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0370] This invention is a system aimed at improving the efficiency of teachers' work and enhancing the user experience in educational settings, providing a means of automating tasks faced by teachers. In particular, by incorporating an emotion engine, it aims to incorporate the emotions of the teacher user and improve the system's responsiveness and the quality of teaching materials.

[0371] This system is structured around three main components: the server, the terminal, and the user, with the emotion engine playing a central role. First, the user accesses the system using a terminal and inputs the topic and target grade level of the teaching materials needed for the lesson. This information is then sent from the terminal to the server.

[0372] The server generates optimal teaching materials using internal databases and external resources based on topic and grade level information. This process incorporates an emotion engine that analyzes user emotion data and incorporates feedback into the material content. For example, if a teacher is experiencing stress, the system adjusts the difficulty level of the materials, providing materials that are considerate of the teacher's burden.

[0373] In grade management, the server receives grade data entered by users from their devices and generates reports showing each student's level of understanding. Here, the emotion engine customizes the way the reports are presented and the content of the analysis based on the teacher's emotional state, providing information in a way that reduces stress.

[0374] Furthermore, the server uses an emotion engine to analyze teachers' daily emotional data and provides rest suggestions and support as needed. This feature helps teachers mitigate stress that may arise during the course of their lessons.

[0375] For example, if a user is deemed stressed while creating teaching materials on the topic of "chemical bonding in high school chemistry," the server will generate concise and easy-to-understand materials to reduce the teacher's burden. The generated materials will then be sent to the teacher's device and made available for use.

[0376] This system enables teachers to perform their duties efficiently, maintain their mental health, and maintain a high quality of education. This invention utilizes an emotional engine to reduce frustration and stress in educational settings, thereby achieving comprehensive educational support.

[0377] The following describes the processing flow.

[0378] Step 1:

[0379] Users use their devices to input information about the teaching material's topic and target grade level. The user interface is designed to allow for easy and accurate information entry through options and input fields.

[0380] Step 2:

[0381] The terminal sends the entered information to the server. During this process, the data is converted into an appropriate format and prepared for processing on the server side.

[0382] Step 3:

[0383] The server searches and references internal databases and online resources based on the received information to identify the most suitable teaching materials. During this process, the emotion engine analyzes the user's emotional state at the time of input and incorporates this into the material generation.

[0384] Step 4:

[0385] The emotion engine collects user emotion data and determines emotional states such as stress and fatigue. Based on this information, the server adjusts the content and difficulty level of the learning materials.

[0386] Step 5:

[0387] The server integrates selected learning material information with feedback from the emotion engine to generate learning slides and handouts. The materials are created in an easy-to-understand format that takes into account the user's emotional state.

[0388] Step 6:

[0389] The generated learning materials are sent to the device and made available for the user to review. The user can customize the learning material content as needed.

[0390] Step 7:

[0391] When a user enters their grade data, the device sends that data to the server. This data is used to visualize each student's level of understanding.

[0392] Step 8:

[0393] The server analyzes performance data and generates reports that show students' comprehension levels and learning trends. The emotion engine monitors the user's emotional state throughout this process and supports the presentation of appropriate information.

[0394] Step 9:

[0395] The generated report is sent to the terminal, making it available for user review and use. The user can then use this information to develop a lesson plan.

[0396] Step 10:

[0397] During lessons and between tasks, the emotion engine continues to analyze the user's emotional data and provides rest recommendations and support information as needed. This allows teachers to manage their own emotions and physical condition while performing their duties.

[0398] (Example 2)

[0399] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0400] In educational settings, teachers spend a significant amount of time on their daily tasks, with curriculum development and assessing student comprehension being particularly burdensome. This can increase teacher stress and potentially lower the quality of education. Therefore, a system is needed to reduce teachers' workload and improve the quality of education.

[0401] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0402] In this invention, the server includes means for receiving information on teaching materials using an information processing device, means for automatically generating teaching materials using a generation AI model based on the received information, and means for analyzing user emotional data and reflecting it in the teaching materials. This reduces the workload of teachers and enables effective educational support that takes emotional stress into consideration.

[0403] An "information processing device" refers to a computer system that receives, processes, and transmits data, and includes terminals and servers used by teachers.

[0404] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate teaching materials based on input data.

[0405] "Teaching materials" refer to learning materials and lesson plans used for educational purposes, and the generated information is used in the educational activities of teachers and students.

[0406] "Emotional data" refers to information indicating teachers' stress levels and emotional states, which the system analyzes and uses for creating teaching materials and providing feedback.

[0407] "Statistical processing" refers to the process of organizing collected data and converting it into numerical indicators such as mean values ​​and distributions.

[0408] "Materials showing learning progress" refers to reports and visual information that show each student's level of understanding and progress in learning content.

[0409] This system aims to streamline the work of teachers in educational institutions and provide high-quality education. The system mainly consists of an information processing unit, a generative AI model, and an emotion engine.

[0410] Teachers, as users, access the system using a terminal. By entering the topic and target grade level of a specific teaching material into this terminal, teachers can begin the material creation process. After the terminal receives the input, this information is sent to the server.

[0411] The server processes incoming information using a generative AI model to create optimal teaching materials. This generative AI model, for example, automatically generates material content using natural language processing techniques. Furthermore, the server analyzes user emotion data using an emotion engine. This emotion data is useful for evaluating teachers' stress levels and emotional states. The analysis results are used as feedback to adjust the content and difficulty level of the teaching materials.

[0412] For example, if a user inputs that they want to create teaching materials on the topic of "chemical bonding in high school chemistry," and the system detects the user's stress level, the server will generate more concise and easy-to-understand teaching materials. These materials are then provided to the user via their terminal.

[0413] In grade management, users input student grades via their devices. The server collects this data and generates reports showing each student's learning progress through statistical processing. The presentation of these reports is adjusted based on the user's sentiment data.

[0414] The server also continuously analyzes teachers' daily emotional data and provides suggestions and support for rest as needed. This feature allows teachers to perform their duties without experiencing excessive stress.

[0415] An example of a prompt message is: "This teacher is stressed out creating teaching materials on chemical bonding in high school chemistry. Please generate materials that will reduce his workload."

[0416] In this way, teachers can work efficiently, take care of their own mental health, and provide high-quality education.

[0417] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0418] Step 1:

[0419] The user inputs the topic and target grade level of the teaching material into the terminal. The terminal receives this information, formats it into a prompt message format, and sends it to the server. This input information may include the topic "Chemical Bonding in High School Chemistry" as an example.

[0420] Step 2:

[0421] The server automatically generates educational materials using a generative AI model based on information received from the terminal. The generative AI model analyzes the input prompt text and performs data calculations to generate the content and structure of the educational materials. The generated educational material content is then generated by the server.

[0422] Step 3:

[0423] The server uses an emotion engine to analyze the user's emotional data. This analysis determines the stress level based on past emotional data and teacher feedback. The analysis results are used to adjust the learning materials, for example, by adjusting the difficulty level of the materials.

[0424] Step 4:

[0425] The server generates the final instructional materials based on the generated teaching materials and the analysis results of the emotional data. In particular, if the user is experiencing high stress levels, the explanations are adjusted to be concise and easy to understand. The final materials are then delivered from the server to the terminal and provided to the user.

[0426] Step 5:

[0427] After class, users enter student grades into a terminal. The server receives the grade data sent from the terminal and performs statistical processing. This statistical processing includes calculations such as the average and distribution of students' comprehension levels.

[0428] Step 6:

[0429] The server generates a student-specific comprehension report based on statistical processing results. The presentation method of the generated report is adjusted according to the user's emotional state. The final report is sent from the server to the terminal and provided to the user.

[0430] Step 7:

[0431] The server analyzes teachers' daily emotional data and processes it to suggest rest and support as needed. Based on this analysis, the server suggests times when teachers need rest and sends notifications to provide support.

[0432] (Application Example 2)

[0433] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0434] In educational settings, teachers and staff experience significant psychological burdens due to their numerous tasks. In particular, creating teaching materials, compiling evaluations, and developing individualized support plans are factors that contribute to emotionally challenging situations. Furthermore, a lack of appropriate material presentation tailored to teachers' emotional states and insufficient support for efficient time management negatively impacts the quality of education and the well-being of teachers and staff.

[0435] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0436] In this invention, the server includes a device for receiving information related to the topic of educational materials and the educational stage to which they apply; a device for automatically generating teaching materials based on the information; and a device for analyzing the teacher's psychological state using an emotion analysis device and optimizing teaching materials or workload. This makes it possible for teachers and staff to improve work efficiency while reducing their psychological burden.

[0437] "Educational materials" are materials provided to learners for educational purposes.

[0438] "Educational stages" refer to the various stages of progress in a learner's educational process.

[0439] "Information" refers to the data and instructions that a system receives.

[0440] "Teaching materials" are educational materials used in classes and learning activities for educational purposes.

[0441] An "emotional analysis device" is a device used to analyze and determine an individual's psychological state.

[0442] "Psychological state" refers to an individual's emotions and mental condition.

[0443] "Workload" refers to the amount of work or the difficulty of tasks assigned to an individual.

[0444] "Optimization" refers to adjusting conditions to achieve the best possible state.

[0445] "Efficient time management" refers to management methods that aim to use time effectively and reduce waste.

[0446] "Burden reduction" refers to alleviating psychological or physical pressure.

[0447] "Business efficiency" refers to the degree to which work and tasks are performed efficiently and effectively.

[0448] To implement this invention, a system is built around a server, a terminal, and a user. The server collects information related to the topics and educational stages of educational materials and analyzes the user's psychological state using an emotion analysis device. Based on this information, it automatically generates teaching materials and further optimizes the workload. Specifically, the server uses a cloud server as the master unit and employs machine learning libraries such as TensorFlow for data processing. It performs emotion analysis and implements a teaching material generation algorithm using a programming language such as Python. The terminal is a smart device capable of installing a dedicated application and is responsible for receiving and displaying relevant data. Through this system, the user can receive appropriate teaching materials and work management support that take their psychological state into consideration.

[0449] As a concrete example, suppose a user selects "Science Experiments in Secondary Education" as the topic for their lesson. If the emotion analysis device detects that the user is feeling fatigued, the server automatically generates concise and easy-to-understand teaching materials for that topic and sends them to the user's device. This allows the user to conduct lessons efficiently while reducing their burden.

[0450] A program that utilizes a generative AI model to generate educational materials that take emotional states into account operates based on the following prompt: "Design and explain an algorithm for generating secondary education science experiment materials that help reduce teacher stress."

[0451] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0452] Step 1:

[0453] The user uses a terminal to input information about the topic and educational level of the educational material. The input information is sent from the terminal to the server. At this stage, the input identifies the topic name and educational level, and generates JSON format data that is sent to the server as output.

[0454] Step 2:

[0455] The server analyzes the user's psychological state using an emotion analysis device, based on the topics of the received educational materials and educational stage data. Interaction data with the user is input to the emotion analysis model, and evaluation data indicating the user's psychological state (e.g., stress level, fatigue level) is output. This data is used to optimize education.

[0456] Step 3:

[0457] The server generates instructional materials while considering the results of sentiment analysis. A generation AI model is used, taking educational material topics, educational stages, and user psychological state data as input, and generating instructional content as output. The difficulty and complexity of this content are then adjusted according to the user's psychological state.

[0458] Step 4:

[0459] The generated teaching materials are sent from the server to the terminal, making them accessible to the user. Here, the teaching material content is displayed in the terminal's application, and the user utilizes it as digital material to support educational activities. This output is intended as educational material and is in a format that can be viewed and used within the terminal's available storage space.

[0460] Step 5:

[0461] As users engage in educational activities, response and progress data are continuously sent from their devices to the server. User interaction and learning progress data are considered as input, and the server performs actions such as additional optimization and suggesting new learning materials as needed. This process continues in real time, enhancing the effectiveness of the education.

[0462] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0463] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0464] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0465] [Third Embodiment]

[0466] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0467] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0468] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0469] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0470] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0471] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0472] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0473] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0474] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0475] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0476] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0477] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0478] This invention is a system aimed at streamlining the work of teachers in educational settings. This system supports the wide range of tasks performed by teachers, providing an environment that allows them to concentrate on educational activities. The embodiments of this invention are described in detail below.

[0479] This system primarily consists of three components: a server, a terminal, and a user. First, the teacher, acting as the user, uses a terminal to input information about the topic and target grade level of the teaching materials. This then sends the specific requirements for the teaching materials needed for the lesson to the server.

[0480] Based on the information received, the server automatically generates teaching slides and handouts by referencing online resources and internal databases. During this process, the server selects educational materials relevant to the topic and structures them to facilitate efficient lessons. It also sends the generated materials to terminals, making them available for teacher use.

[0481] Furthermore, in managing performance data, users input their performance information into their terminals and send it to a server. The server aggregates the received data and generates a report that analyzes students' understanding. The generated report clearly identifies each student's strengths and weaknesses and is provided as basic material for teachers when developing lesson plans.

[0482] Regarding communication with parents and schedule management, the server also has the function of checking students' learning progress and sending learning progress updates and important announcements to parents at appropriate times. Furthermore, it organizes class and meeting schedules and provides reminders to support teachers in efficient time management.

[0483] For example, if a user needs "Geometry for Junior High School Mathematics" teaching materials, the server automatically generates slides containing figures and example problems using a geometry material template and sends them to the user's device. This process significantly reduces administrative work for teachers and improves the quality of education.

[0484] Thus, the system of the present invention improves work efficiency and enhances the quality of educational activities in educational settings.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] Users access the system using a terminal and input the topics and target grade levels of the teaching materials required for their lessons. The interface used is intuitive and designed to allow teachers to easily input the necessary information.

[0488] Step 2:

[0489] The terminal transmits user-entered information to the server in real time. During this process, the information is converted to an appropriate data format and processed so that the server can understand it.

[0490] Step 3:

[0491] The server analyzes the received topic and grade level information, and searches internal databases and external online resources. This process utilizes search algorithms to select the most relevant teaching materials.

[0492] Step 4:

[0493] The server automatically generates teaching slides and handouts using teaching material templates based on the search results. This generation process organizes the selected teaching materials into an appropriate format and compiles them into content suitable for educational purposes.

[0494] Step 5:

[0495] The server sends the generated learning materials to the terminal. During this process, a check is performed to verify the data's integrity and format to ensure that the materials are displayed correctly.

[0496] Step 6:

[0497] Users can review the materials generated on their devices and modify or supplement the content as needed. This customization feature allows teachers to provide optimal materials tailored to the specific circumstances of their lessons.

[0498] Step 7:

[0499] When a user enters performance data into a terminal, the terminal sends that data to the server.

[0500] Step 8:

[0501] The server aggregates and analyzes the received performance data and generates a report showing each student's level of understanding. This report includes graphs and charts to highlight students' strengths and areas for improvement.

[0502] Step 9:

[0503] The server sends the generated report to the terminal for user review. At this stage, teachers can adjust their lesson plans based on the report.

[0504] (Example 1)

[0505] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] In today's educational environment, teachers are overwhelmed with a variety of tasks, making it difficult for them to focus on effective teaching activities. In particular, preparing teaching materials, analyzing grades, communicating with parents, and managing schedules are time-consuming and labor-intensive, hindering improvements in the quality of education. It is necessary to streamline these tasks and provide teachers with an environment where they can concentrate on their core educational activities.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0508] This invention includes a server that receives information on the topic and target grade level of teaching materials, and automatically generates teaching materials by referring to online resources and internal information aggregation; a server that receives performance data and aggregates it through statistical analysis; and a server that monitors students' learning progress and sends information to parents at an appropriate time. This reduces the burden on teachers in preparing teaching materials and managing performance, enabling quick and accurate work processing.

[0509] "Teaching materials" refer to materials or sets of materials used to convey learning content in educational activities.

[0510] A "topic" is a term that refers to a specific educational theme or issue, and is the focus of instruction.

[0511] "Target grade level" refers to the age group or grade level of learners following a specific curriculum.

[0512] "Online resources" refer to digital information and content that can be accessed via the internet.

[0513] "Internal information accumulation" refers to a collection of usable knowledge, consisting of data and information stored within a specific system.

[0514] A "generative AI model" is a group of algorithms and programs that generate new information and data structures based on machine learning.

[0515] "Performance data" refers to a numerical record of a learner's learning outcomes, and is the information that forms the basis of evaluation.

[0516] "Statistical analysis" is a mathematical method used to evaluate trends and relationships based on data.

[0517] "Learning status" refers to the state of a learner's current learning progress and level of understanding.

[0518] "Schedule management" refers to the process of organizing schedules and other tasks to ensure efficient time allocation.

[0519] This invention is a system aimed at improving the efficiency of operations in educational settings, and is mainly composed of three components: a server, terminals, and users. This system utilizes a generative AI model to support teachers in a wide range of tasks, providing an environment where they can concentrate on educational activities.

[0520] In the automatic generation of teaching materials,

[0521] The user (teacher) uses a terminal to input information about the topic and target grade level of the teaching material. For example, they might enter the prompt "Geometry in Junior High School Mathematics."

[0522] The terminal immediately sends the entered information to the server.

[0523] Based on the received information, the server references online resources and internal data archives, and automatically generates teaching materials using a generative AI model. For example, it can generate materials that include geometric figures and example problems.

[0524] The server sends the generated teaching materials to the terminals, making them available for teachers to use. This process allows teachers to reduce a lot of administrative work, shorten the time required to prepare teaching materials, and provide high-quality materials.

[0525] In managing performance data,

[0526] The user enters student grade data on the device.

[0527] The terminal sends performance information to the server.

[0528] The server performs statistical analysis and automatically generates reports that visualize students' understanding using generative AI models. This makes it possible to understand students' strengths and weaknesses and to develop more effective teaching plans.

[0529] In terms of contacting parents and managing schedules,

[0530] The server periodically monitors students' learning progress and sends necessary information to parents at the appropriate time.

[0531] Furthermore, by organizing timetables and meeting schedules and sending reminders to their devices, the system supports teachers in efficient time management.

[0532] This system will streamline various tasks in educational settings, enabling the provision of a better educational environment.

[0533] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0534] Step 1:

[0535] The user uses a terminal to input information about the teaching material's topic and target grade level, as provided by the teacher. This information might be a prompt such as "Geometry in Junior High School Mathematics."

[0536] The terminal converts this information into a digital format and immediately sends it to the server. The input consists of topic and grade information, and the output is the data to be sent to the server.

[0537] Step 2:

[0538] Based on the received topic and target grade level information, the server searches online resources and internal databases to extract relevant educational content. For example, it retrieves geometry lesson templates, appropriate shapes, and example problems from the system.

[0539] The server utilizes a generative AI model to process the extracted content and automatically generate new educational materials. In this process, the input is the extracted educational content, and the output is instructional materials.

[0540] Step 3:

[0541] The server sends the generated learning materials to the terminal. At this time, the materials are formatted into a user-friendly format and made ready for use on the terminal.

[0542] The terminal displays the received teaching materials, making them easily accessible to teachers for use in class. The input is the generated teaching materials, and the output is the displayed teaching materials.

[0543] Step 4:

[0544] Users input student performance information using their devices. This includes, for example, test scores and attendance records.

[0545] The terminal organizes the entered grade data and sends it to the server. In this step, the input is the grade information, and the output is the data sent to the server.

[0546] Step 5:

[0547] The server performs statistical analysis based on the received performance data. Using a generative AI model, it creates reports that highlight each student's strengths and weaknesses.

[0548] As a result, the input is grade data, and the output is a report that visualizes each student's level of understanding.

[0549] Step 6:

[0550] The server monitors students' learning progress and generates messages to report their progress to parents.

[0551] Additionally, reminders will be sent to the device when there are changes to the timetable or important announcements. Input is learning progress information, and output is report messages and reminder information for parents.

[0552] (Application Example 1)

[0553] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] In traditional educational settings, there have been challenges such as difficulty in creating appropriate teaching materials tailored to the individual characteristics of learners, difficulty in assessing their level of understanding, and a heavy workload for teachers, making it difficult to maximize educational effectiveness. Furthermore, efficient management of time and communication with parents is also required.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0556] In this invention, the server includes means for receiving information on learner characteristics and target grade level and automatically generating educational materials; means for receiving, aggregating, and analyzing learning progress data; and means for managing learners' learning progress and sending notifications to educators. This enables the provision of learning materials optimized for each individual learner, accurate assessment of understanding, and increased efficiency of operations.

[0557] A "learner" refers to an individual receiving education, whose purpose is to acquire specific knowledge or skills.

[0558] "Characteristics" refer to specific attributes or characteristics of learners or learning materials, and are useful information for designing individualized education.

[0559] "Target grade level" refers to the grade level within the curriculum to which the learner belongs, and is a criterion for determining appropriate educational content.

[0560] "Educational materials" refer to information and resources designed to support learners' learning and can be used in classes or for self-study.

[0561] "Educational support devices" refer to devices used in educational settings that play a role in providing learning materials to learners and monitoring their learning progress.

[0562] "Learning progress data" refers to information that shows how well learners understand and are progressing in the learning process.

[0563] "Aggregation and analysis" refers to the process of statistically summarizing learning progress data and evaluating the level of understanding.

[0564] "Visualization" refers to representing data in the form of graphs, charts, and other diagrams to make it easier to understand.

[0565] A "report" is a document that compiles data and information, and is created to convey results and trends.

[0566] The term "education-related personnel" refers to individuals or organizations involved in the education of learners, and is a concept that includes teachers, parents, and others.

[0567] "Sending a message" refers to the act of conveying information to a specific recipient.

[0568] "Time management" refers to efficiently organizing tasks and appointments, allocating time appropriately, and effectively carrying out work or learning.

[0569] The system used to implement this invention primarily consists of three elements: a server, a terminal, and a user. The terminal functions as an educational support device, operated by the user (an educator) as needed. This system uses a Raspberry Pi or equivalent small computer as hardware, and its program is written in Python. A web server using Flask manages data transmission and reception, and TensorFlow is used to analyze learning progress data.

[0570] First, the user inputs information about the learner's characteristics and target grade level from their terminal to the server. Based on this information, the server automatically generates educational materials and provides them to the educational support device. Because the automatically generated materials are optimized for each individual learner, effective learning can be expected.

[0571] Furthermore, the server receives, aggregates, and analyzes learning progress data to generate visualized reports. These visualized reports are useful resources for quickly understanding learners' comprehension levels, allowing educators to use them to more effectively develop individualized instruction plans.

[0572] As a concrete example, the system can automatically generate individualized learning materials for "Geometry in Junior High School Mathematics" and present them to students on their devices. Furthermore, it can analyze learning progress and utilize prompts from a generative AI model ("Generative AI Model: Generate prompts to clearly explain basic geometric shapes") to create an environment for appropriate instruction. This system enables instruction based on the "characteristics" of each individual learner, thereby improving the quality of education.

[0573] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0574] Step 1:

[0575] The user inputs educational information, such as the learner's characteristics and target grade level, into the terminal. The terminal then sends this information to the server. The input must be text data, and the output must be information data that is appropriately transmitted to the server.

[0576] Step 2:

[0577] The server automatically generates educational materials using a generative AI model, referencing databases and online resources based on the received information. This process utilizes prompts to generate templates for teaching materials appropriate to the target grade level and subject. The input is the information data obtained in the previous step, and the output is the generated teaching material data.

[0578] Step 3:

[0579] The server sends the generated teaching material data to a terminal, which is an educational support device. The terminal receives this data and presents the materials to the user. The input is the teaching material data, and the output is that the teaching materials become viewable on the terminal.

[0580] Step 4:

[0581] The user inputs the learner's learning progress into the terminal, and this data is also sent to the server. The input is progress data, and the output is the transmission to the server.

[0582] Step 5:

[0583] The server aggregates and analyzes learning progress data and generates a visualized report. Here, TensorFlow is used for data analysis and conversion of progress data into charts and graphs. The input is progress data, and the output is a visualized report.

[0584] Step 6:

[0585] The server sends the generated visualization report to the educational support device, making it available for user review on the terminal. The input is the report data, and the output is the ability to view the report on the terminal.

[0586] Step 7:

[0587] Based on the visualized report, the user creates an individualized instruction plan for the learner and inputs information into the terminal to contact educators as needed. Here, the user organizes the information while considering the specific characteristics of the learner. Inputs are feedback on the report and data for communication, and outputs are data for communication with educators.

[0588] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0589] This invention is a system aimed at improving the efficiency of teachers' work and enhancing the user experience in educational settings, providing a means of automating tasks faced by teachers. In particular, by incorporating an emotion engine, it aims to incorporate the emotions of the teacher user and improve the system's responsiveness and the quality of teaching materials.

[0590] This system is structured around three main components: the server, the terminal, and the user, with the emotion engine playing a central role. First, the user accesses the system using a terminal and inputs the topic and target grade level of the teaching materials needed for the lesson. This information is then sent from the terminal to the server.

[0591] The server generates optimal teaching materials using internal databases and external resources based on topic and grade level information. This process incorporates an emotion engine that analyzes user emotion data and incorporates feedback into the material content. For example, if a teacher is experiencing stress, the system adjusts the difficulty level of the materials, providing materials that are considerate of the teacher's burden.

[0592] In grade management, the server receives grade data entered by users from their devices and generates reports showing each student's level of understanding. Here, the emotion engine customizes the way the reports are presented and the content of the analysis based on the teacher's emotional state, providing information in a way that reduces stress.

[0593] Furthermore, the server uses an emotion engine to analyze teachers' daily emotional data and provides rest suggestions and support as needed. This feature helps teachers mitigate stress that may arise during the course of their lessons.

[0594] For example, if a user is deemed stressed while creating teaching materials on the topic of "chemical bonding in high school chemistry," the server will generate concise and easy-to-understand materials to reduce the teacher's burden. The generated materials will then be sent to the teacher's device and made available for use.

[0595] This system enables teachers to perform their duties efficiently, maintain their mental health, and maintain a high quality of education. This invention utilizes an emotional engine to reduce frustration and stress in educational settings, thereby achieving comprehensive educational support.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users use their devices to input information about the teaching material's topic and target grade level. The user interface is designed to allow for easy and accurate information entry through options and input fields.

[0599] Step 2:

[0600] The terminal sends the entered information to the server. During this process, the data is converted into an appropriate format and prepared for processing on the server side.

[0601] Step 3:

[0602] The server searches and references internal databases and online resources based on the received information to identify the most suitable teaching materials. During this process, the emotion engine analyzes the user's emotional state at the time of input and incorporates this into the material generation.

[0603] Step 4:

[0604] The emotion engine collects user emotion data and determines emotional states such as stress and fatigue. Based on this information, the server adjusts the content and difficulty level of the learning materials.

[0605] Step 5:

[0606] The server integrates selected learning material information with feedback from the emotion engine to generate learning slides and handouts. The materials are created in an easy-to-understand format that takes into account the user's emotional state.

[0607] Step 6:

[0608] The generated learning materials are sent to the device and made available for the user to review. The user can customize the learning material content as needed.

[0609] Step 7:

[0610] When a user enters their grade data, the device sends that data to the server. This data is used to visualize each student's level of understanding.

[0611] Step 8:

[0612] The server analyzes performance data and generates reports that show students' comprehension levels and learning trends. The emotion engine monitors the user's emotional state throughout this process and supports the presentation of appropriate information.

[0613] Step 9:

[0614] The generated report is sent to the terminal, making it available for user review and use. The user can then use this information to develop a lesson plan.

[0615] Step 10:

[0616] During lessons and between tasks, the emotion engine continues to analyze the user's emotional data and provides rest recommendations and support information as needed. This allows teachers to manage their own emotions and physical condition while performing their duties.

[0617] (Example 2)

[0618] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0619] In educational settings, teachers spend a significant amount of time on their daily tasks, with curriculum development and assessing student comprehension being particularly burdensome. This can increase teacher stress and potentially lower the quality of education. Therefore, a system is needed to reduce teachers' workload and improve the quality of education.

[0620] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0621] In this invention, the server includes means for receiving information on teaching materials using an information processing device, means for automatically generating teaching materials using a generation AI model based on the received information, and means for analyzing user emotional data and reflecting it in the teaching materials. This reduces the workload of teachers and enables effective educational support that takes emotional stress into consideration.

[0622] An "information processing device" refers to a computer system that receives, processes, and transmits data, and includes terminals and servers used by teachers.

[0623] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate teaching materials based on input data.

[0624] "Teaching materials" refer to learning materials and lesson plans used for educational purposes, and the generated information is used in the educational activities of teachers and students.

[0625] "Emotional data" refers to information indicating teachers' stress levels and emotional states, which the system analyzes and uses for creating teaching materials and providing feedback.

[0626] "Statistical processing" refers to the process of organizing collected data and converting it into numerical indicators such as mean values ​​and distributions.

[0627] "Materials showing learning progress" refers to reports and visual information that show each student's level of understanding and progress in learning content.

[0628] This system aims to streamline the work of teachers in educational institutions and provide high-quality education. The system mainly consists of an information processing unit, a generative AI model, and an emotion engine.

[0629] Teachers, as users, access the system using a terminal. By entering the topic and target grade level of a specific teaching material into this terminal, teachers can begin the material creation process. After the terminal receives the input, this information is sent to the server.

[0630] The server processes incoming information using a generative AI model to create optimal teaching materials. This generative AI model, for example, automatically generates material content using natural language processing techniques. Furthermore, the server analyzes user emotion data using an emotion engine. This emotion data is useful for evaluating teachers' stress levels and emotional states. The analysis results are used as feedback to adjust the content and difficulty level of the teaching materials.

[0631] For example, if a user inputs that they want to create teaching materials on the topic of "chemical bonding in high school chemistry," and the system detects the user's stress level, the server will generate more concise and easy-to-understand teaching materials. These materials are then provided to the user via their terminal.

[0632] In grade management, users input student grades via their devices. The server collects this data and generates reports showing each student's learning progress through statistical processing. The presentation of these reports is adjusted based on the user's sentiment data.

[0633] The server also continuously analyzes teachers' daily emotional data and provides suggestions and support for rest as needed. This feature allows teachers to perform their duties without experiencing excessive stress.

[0634] An example of a prompt message is: "This teacher is stressed out creating teaching materials on chemical bonding in high school chemistry. Please generate materials that will reduce his workload."

[0635] In this way, teachers can work efficiently, take care of their own mental health, and provide high-quality education.

[0636] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0637] Step 1:

[0638] The user inputs the topic and target grade level of the teaching material into the terminal. The terminal receives this information, formats it into a prompt message format, and sends it to the server. This input information may include the topic "Chemical Bonding in High School Chemistry" as an example.

[0639] Step 2:

[0640] The server automatically generates educational materials using a generative AI model based on information received from the terminal. The generative AI model analyzes the input prompt text and performs data calculations to generate the content and structure of the educational materials. The generated educational material content is then generated by the server.

[0641] Step 3:

[0642] The server uses an emotion engine to analyze the user's emotional data. This analysis determines the stress level based on past emotional data and teacher feedback. The analysis results are used to adjust the learning materials, for example, by adjusting the difficulty level of the materials.

[0643] Step 4:

[0644] The server generates the final instructional materials based on the generated teaching materials and the analysis results of the emotional data. In particular, if the user is experiencing high stress levels, the explanations are adjusted to be concise and easy to understand. The final materials are then delivered from the server to the terminal and provided to the user.

[0645] Step 5:

[0646] After class, users enter student grades into a terminal. The server receives the grade data sent from the terminal and performs statistical processing. This statistical processing includes calculations such as the average and distribution of students' comprehension levels.

[0647] Step 6:

[0648] The server generates a student-specific comprehension report based on statistical processing results. The presentation method of the generated report is adjusted according to the user's emotional state. The final report is sent from the server to the terminal and provided to the user.

[0649] Step 7:

[0650] The server analyzes teachers' daily emotional data and processes it to suggest rest and support as needed. Based on this analysis, the server suggests times when teachers need rest and sends notifications to provide support.

[0651] (Application Example 2)

[0652] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0653] In educational settings, teachers and staff experience significant psychological burdens due to their numerous tasks. In particular, creating teaching materials, compiling evaluations, and developing individualized support plans are factors that contribute to emotionally challenging situations. Furthermore, a lack of appropriate material presentation tailored to teachers' emotional states and insufficient support for efficient time management negatively impacts the quality of education and the well-being of teachers and staff.

[0654] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0655] In this invention, the server includes a device for receiving information related to the topic of educational materials and the educational stage to which they apply; a device for automatically generating teaching materials based on the information; and a device for analyzing the teacher's psychological state using an emotion analysis device and optimizing teaching materials or workload. This makes it possible for teachers and staff to improve work efficiency while reducing their psychological burden.

[0656] "Educational materials" are materials provided to learners for educational purposes.

[0657] "Educational stages" refer to the various stages of progress in a learner's educational process.

[0658] "Information" refers to the data and instructions that a system receives.

[0659] "Teaching materials" are educational materials used in classes and learning activities for educational purposes.

[0660] An "emotional analysis device" is a device used to analyze and determine an individual's psychological state.

[0661] "Psychological state" refers to an individual's emotions and mental condition.

[0662] "Workload" refers to the amount of work or the difficulty of tasks assigned to an individual.

[0663] "Optimization" refers to adjusting conditions to achieve the best possible state.

[0664] "Efficient time management" refers to management methods that aim to use time effectively and reduce waste.

[0665] "Burden reduction" refers to alleviating psychological or physical pressure.

[0666] "Business efficiency" refers to the degree to which work and tasks are performed efficiently and effectively.

[0667] To implement this invention, a system is built around a server, a terminal, and a user. The server collects information related to the topics and educational stages of educational materials and analyzes the user's psychological state using an emotion analysis device. Based on this information, it automatically generates teaching materials and further optimizes the workload. Specifically, the server uses a cloud server as the master unit and employs machine learning libraries such as TensorFlow for data processing. It performs emotion analysis and implements a teaching material generation algorithm using a programming language such as Python. The terminal is a smart device capable of installing a dedicated application and is responsible for receiving and displaying relevant data. Through this system, the user can receive appropriate teaching materials and work management support that take their psychological state into consideration.

[0668] As a concrete example, suppose a user selects "Science Experiments in Secondary Education" as the topic for their lesson. If the emotion analysis device detects that the user is feeling fatigued, the server automatically generates concise and easy-to-understand teaching materials for that topic and sends them to the user's device. This allows the user to conduct lessons efficiently while reducing their burden.

[0669] A program that utilizes a generative AI model to generate educational materials that take emotional states into account operates based on the following prompt: "Design and explain an algorithm for generating secondary education science experiment materials that help reduce teacher stress."

[0670] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0671] Step 1:

[0672] The user uses a terminal to input information about the topic and educational level of the educational material. The input information is sent from the terminal to the server. At this stage, the input identifies the topic name and educational level, and generates JSON format data that is sent to the server as output.

[0673] Step 2:

[0674] The server analyzes the user's psychological state using an emotion analysis device, based on the topics of the received educational materials and educational stage data. Interaction data with the user is input to the emotion analysis model, and evaluation data indicating the user's psychological state (e.g., stress level, fatigue level) is output. This data is used to optimize education.

[0675] Step 3:

[0676] The server generates instructional materials while considering the results of sentiment analysis. A generation AI model is used, taking educational material topics, educational stages, and user psychological state data as input, and generating instructional content as output. The difficulty and complexity of this content are then adjusted according to the user's psychological state.

[0677] Step 4:

[0678] The generated teaching materials are sent from the server to the terminal, making them accessible to the user. Here, the teaching material content is displayed in the terminal's application, and the user utilizes it as digital material to support educational activities. This output is intended as educational material and is in a format that can be viewed and used within the terminal's available storage space.

[0679] Step 5:

[0680] As users engage in educational activities, response and progress data are continuously sent from their devices to the server. User interaction and learning progress data are considered as input, and the server performs actions such as additional optimization and suggesting new learning materials as needed. This process continues in real time, enhancing the effectiveness of the education.

[0681] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0682] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0683] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0684] [Fourth Embodiment]

[0685] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0686] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0687] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0688] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0689] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0690] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0691] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0692] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0693] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0694] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0695] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0696] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0697] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] This invention is a system aimed at streamlining the work of teachers in educational settings. This system supports the wide range of tasks performed by teachers, providing an environment that allows them to concentrate on educational activities. The embodiments of this invention are described in detail below.

[0699] This system primarily consists of three components: a server, a terminal, and a user. First, the teacher, acting as the user, uses a terminal to input information about the topic and target grade level of the teaching materials. This then sends the specific requirements for the teaching materials needed for the lesson to the server.

[0700] Based on the information received, the server automatically generates teaching slides and handouts by referencing online resources and internal databases. During this process, the server selects educational materials relevant to the topic and structures them to facilitate efficient lessons. It also sends the generated materials to terminals, making them available for teacher use.

[0701] Furthermore, in managing performance data, users input their performance information into their terminals and send it to a server. The server aggregates the received data and generates a report that analyzes students' understanding. The generated report clearly identifies each student's strengths and weaknesses and is provided as basic material for teachers when developing lesson plans.

[0702] Regarding communication with parents and schedule management, the server also has the function of checking students' learning progress and sending learning progress updates and important announcements to parents at appropriate times. Furthermore, it organizes class and meeting schedules and provides reminders to support teachers in efficient time management.

[0703] For example, if a user needs "Geometry for Junior High School Mathematics" teaching materials, the server automatically generates slides containing figures and example problems using a geometry material template and sends them to the user's device. This process significantly reduces administrative work for teachers and improves the quality of education.

[0704] Thus, the system of the present invention improves work efficiency and enhances the quality of educational activities in educational settings.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] Users access the system using a terminal and input the topics and target grade levels of the teaching materials required for their lessons. The interface used is intuitive and designed to allow teachers to easily input the necessary information.

[0708] Step 2:

[0709] The terminal transmits user-entered information to the server in real time. During this process, the information is converted to an appropriate data format and processed so that the server can understand it.

[0710] Step 3:

[0711] The server analyzes the received topic and grade level information, and searches internal databases and external online resources. This process utilizes search algorithms to select the most relevant teaching materials.

[0712] Step 4:

[0713] The server automatically generates teaching slides and handouts using teaching material templates based on the search results. This generation process organizes the selected teaching materials into an appropriate format and compiles them into content suitable for educational purposes.

[0714] Step 5:

[0715] The server sends the generated learning materials to the terminal. During this process, a check is performed to verify the data's integrity and format to ensure that the materials are displayed correctly.

[0716] Step 6:

[0717] Users can review the materials generated on their devices and modify or supplement the content as needed. This customization feature allows teachers to provide optimal materials tailored to the specific circumstances of their lessons.

[0718] Step 7:

[0719] When a user enters performance data into a terminal, the terminal sends that data to the server.

[0720] Step 8:

[0721] The server aggregates and analyzes the received performance data and generates a report showing each student's level of understanding. This report includes graphs and charts to highlight students' strengths and areas for improvement.

[0722] Step 9:

[0723] The server sends the generated report to the terminal for user review. At this stage, teachers can adjust their lesson plans based on the report.

[0724] (Example 1)

[0725] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] In today's educational environment, teachers are overwhelmed with a variety of tasks, making it difficult for them to focus on effective teaching activities. In particular, preparing teaching materials, analyzing grades, communicating with parents, and managing schedules are time-consuming and labor-intensive, hindering improvements in the quality of education. It is necessary to streamline these tasks and provide teachers with an environment where they can concentrate on their core educational activities.

[0727] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0728] This invention includes a server that receives information on the topic and target grade level of teaching materials, and automatically generates teaching materials by referring to online resources and internal information aggregation; a server that receives performance data and aggregates it through statistical analysis; and a server that monitors students' learning progress and sends information to parents at an appropriate time. This reduces the burden on teachers in preparing teaching materials and managing performance, enabling quick and accurate work processing.

[0729] "Teaching materials" refer to materials or sets of materials used to convey learning content in educational activities.

[0730] A "topic" is a term that refers to a specific educational theme or issue, and is the focus of instruction.

[0731] "Target grade level" refers to the age group or grade level of learners following a specific curriculum.

[0732] "Online resources" refer to digital information and content that can be accessed via the internet.

[0733] "Internal information accumulation" refers to a collection of usable knowledge, consisting of data and information stored within a specific system.

[0734] A "generative AI model" is a group of algorithms and programs that generate new information and data structures based on machine learning.

[0735] "Performance data" refers to a numerical record of a learner's learning outcomes, and is the information that forms the basis of evaluation.

[0736] "Statistical analysis" is a mathematical method used to evaluate trends and relationships based on data.

[0737] "Learning status" refers to the state of a learner's current learning progress and level of understanding.

[0738] "Schedule management" refers to the process of organizing schedules and other tasks to ensure efficient time allocation.

[0739] This invention is a system aimed at improving the efficiency of operations in educational settings, and is mainly composed of three components: a server, terminals, and users. This system utilizes a generative AI model to support teachers in a wide range of tasks, providing an environment where they can concentrate on educational activities.

[0740] In the automatic generation of teaching materials,

[0741] The user (teacher) uses a terminal to input information about the topic and target grade level of the teaching material. For example, they might enter the prompt "Geometry in Junior High School Mathematics."

[0742] The terminal immediately sends the entered information to the server.

[0743] Based on the received information, the server references online resources and internal data archives, and automatically generates teaching materials using a generative AI model. For example, it can generate materials that include geometric figures and example problems.

[0744] The server sends the generated teaching materials to the terminals, making them available for teachers to use. This process allows teachers to reduce a lot of administrative work, shorten the time required to prepare teaching materials, and provide high-quality materials.

[0745] In managing performance data,

[0746] The user enters student grade data on the device.

[0747] The terminal sends performance information to the server.

[0748] The server performs statistical analysis and automatically generates reports that visualize students' understanding using generative AI models. This makes it possible to understand students' strengths and weaknesses and to develop more effective teaching plans.

[0749] In terms of contacting parents and managing schedules,

[0750] The server periodically monitors students' learning progress and sends necessary information to parents at the appropriate time.

[0751] Furthermore, by organizing timetables and meeting schedules and sending reminders to their devices, the system supports teachers in efficient time management.

[0752] This system will streamline various tasks in educational settings, enabling the provision of a better educational environment.

[0753] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0754] Step 1:

[0755] The user uses a terminal to input information about the teaching material's topic and target grade level, as provided by the teacher. This information might be a prompt such as "Geometry in Junior High School Mathematics."

[0756] The terminal converts this information into a digital format and immediately sends it to the server. The input consists of topic and grade information, and the output is the data to be sent to the server.

[0757] Step 2:

[0758] Based on the received topic and target grade level information, the server searches online resources and internal databases to extract relevant educational content. For example, it retrieves geometry lesson templates, appropriate shapes, and example problems from the system.

[0759] The server utilizes a generative AI model to process the extracted content and automatically generate new educational materials. In this process, the input is the extracted educational content, and the output is instructional materials.

[0760] Step 3:

[0761] The server sends the generated learning materials to the terminal. At this time, the materials are formatted into a user-friendly format and made ready for use on the terminal.

[0762] The terminal displays the received teaching materials, making them easily accessible to teachers for use in class. The input is the generated teaching materials, and the output is the displayed teaching materials.

[0763] Step 4:

[0764] Users input student performance information using their devices. This includes, for example, test scores and attendance records.

[0765] The terminal organizes the entered grade data and sends it to the server. In this step, the input is the grade information, and the output is the data sent to the server.

[0766] Step 5:

[0767] The server performs statistical analysis based on the received performance data. Using a generative AI model, it creates reports that highlight each student's strengths and weaknesses.

[0768] As a result, the input is grade data, and the output is a report that visualizes each student's level of understanding.

[0769] Step 6:

[0770] The server monitors students' learning progress and generates messages to report their progress to parents.

[0771] Additionally, reminders will be sent to the device when there are changes to the timetable or important announcements. Input is learning progress information, and output is report messages and reminder information for parents.

[0772] (Application Example 1)

[0773] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0774] In traditional educational settings, there have been challenges such as difficulty in creating appropriate teaching materials tailored to the individual characteristics of learners, difficulty in assessing their level of understanding, and a heavy workload for teachers, making it difficult to maximize educational effectiveness. Furthermore, efficient management of time and communication with parents is also required.

[0775] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0776] In this invention, the server includes means for receiving information on learner characteristics and target grade level and automatically generating educational materials; means for receiving, aggregating, and analyzing learning progress data; and means for managing learners' learning progress and sending notifications to educators. This enables the provision of learning materials optimized for each individual learner, accurate assessment of understanding, and increased efficiency of operations.

[0777] A "learner" refers to an individual receiving education, whose purpose is to acquire specific knowledge or skills.

[0778] "Characteristics" refer to specific attributes or characteristics of learners or learning materials, and are useful information for designing individualized education.

[0779] "Target grade level" refers to the grade level within the curriculum to which the learner belongs, and is a criterion for determining appropriate educational content.

[0780] "Educational materials" refer to information and resources designed to support learners' learning and can be used in classes or for self-study.

[0781] "Educational support devices" refer to devices used in educational settings that play a role in providing learning materials to learners and monitoring their learning progress.

[0782] "Learning progress data" refers to information that shows how well learners understand and are progressing in the learning process.

[0783] "Aggregation and analysis" refers to the process of statistically summarizing learning progress data and evaluating the level of understanding.

[0784] "Visualization" refers to representing data in the form of graphs, charts, and other diagrams to make it easier to understand.

[0785] A "report" is a document that compiles data and information, and is created to convey results and trends.

[0786] The term "education-related personnel" refers to individuals or organizations involved in the education of learners, and is a concept that includes teachers, parents, and others.

[0787] "Sending a message" refers to the act of conveying information to a specific recipient.

[0788] "Time management" refers to efficiently organizing tasks and appointments, allocating time appropriately, and effectively carrying out work or learning.

[0789] The system used to implement this invention primarily consists of three elements: a server, a terminal, and a user. The terminal functions as an educational support device, operated by the user (an educator) as needed. This system uses a Raspberry Pi or equivalent small computer as hardware, and its program is written in Python. A web server using Flask manages data transmission and reception, and TensorFlow is used to analyze learning progress data.

[0790] First, the user inputs information about the learner's characteristics and target grade level from their terminal to the server. Based on this information, the server automatically generates educational materials and provides them to the educational support device. Because the automatically generated materials are optimized for each individual learner, effective learning can be expected.

[0791] Furthermore, the server receives, aggregates, and analyzes learning progress data to generate visualized reports. These visualized reports are useful resources for quickly understanding learners' comprehension levels, allowing educators to use them to more effectively develop individualized instruction plans.

[0792] As a concrete example, the system can automatically generate individualized learning materials for "Geometry in Junior High School Mathematics" and present them to students on their devices. Furthermore, it can analyze learning progress and utilize prompts from a generative AI model ("Generative AI Model: Generate prompts to clearly explain basic geometric shapes") to create an environment for appropriate instruction. This system enables instruction based on the "characteristics" of each individual learner, thereby improving the quality of education.

[0793] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0794] Step 1:

[0795] The user inputs educational information, such as the learner's characteristics and target grade level, into the terminal. The terminal then sends this information to the server. The input must be text data, and the output must be information data that is appropriately transmitted to the server.

[0796] Step 2:

[0797] The server automatically generates educational materials using a generative AI model, referencing databases and online resources based on the received information. This process utilizes prompts to generate templates for teaching materials appropriate to the target grade level and subject. The input is the information data obtained in the previous step, and the output is the generated teaching material data.

[0798] Step 3:

[0799] The server sends the generated teaching material data to a terminal, which is an educational support device. The terminal receives this data and presents the materials to the user. The input is the teaching material data, and the output is that the teaching materials become viewable on the terminal.

[0800] Step 4:

[0801] The user inputs the learner's learning progress into the terminal, and this data is also sent to the server. The input is progress data, and the output is the transmission to the server.

[0802] Step 5:

[0803] The server aggregates and analyzes learning progress data and generates a visualized report. Here, TensorFlow is used for data analysis and conversion of progress data into charts and graphs. The input is progress data, and the output is a visualized report.

[0804] Step 6:

[0805] The server sends the generated visualization report to the educational support device, making it available for user review on the terminal. The input is the report data, and the output is the ability to view the report on the terminal.

[0806] Step 7:

[0807] Based on the visualized report, the user creates an individualized instruction plan for the learner and inputs information into the terminal to contact educators as needed. Here, the user organizes the information while considering the specific characteristics of the learner. Inputs are feedback on the report and data for communication, and outputs are data for communication with educators.

[0808] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0809] This invention is a system aimed at improving the efficiency of teachers' work and enhancing the user experience in educational settings, providing a means of automating tasks faced by teachers. In particular, by incorporating an emotion engine, it aims to incorporate the emotions of the teacher user and improve the system's responsiveness and the quality of teaching materials.

[0810] This system is structured around three main components: the server, the terminal, and the user, with the emotion engine playing a central role. First, the user accesses the system using a terminal and inputs the topic and target grade level of the teaching materials needed for the lesson. This information is then sent from the terminal to the server.

[0811] The server generates optimal teaching materials using internal databases and external resources based on topic and grade level information. This process incorporates an emotion engine that analyzes user emotion data and incorporates feedback into the material content. For example, if a teacher is experiencing stress, the system adjusts the difficulty level of the materials, providing materials that are considerate of the teacher's burden.

[0812] In grade management, the server receives grade data entered by users from their devices and generates reports showing each student's level of understanding. Here, the emotion engine customizes the way the reports are presented and the content of the analysis based on the teacher's emotional state, providing information in a way that reduces stress.

[0813] Furthermore, the server uses an emotion engine to analyze teachers' daily emotional data and provides rest suggestions and support as needed. This feature helps teachers mitigate stress that may arise during the course of their lessons.

[0814] For example, if a user is deemed stressed while creating teaching materials on the topic of "chemical bonding in high school chemistry," the server will generate concise and easy-to-understand materials to reduce the teacher's burden. The generated materials will then be sent to the teacher's device and made available for use.

[0815] This system enables teachers to perform their duties efficiently, maintain their mental health, and maintain a high quality of education. This invention utilizes an emotional engine to reduce frustration and stress in educational settings, thereby achieving comprehensive educational support.

[0816] The following describes the processing flow.

[0817] Step 1:

[0818] Users use their devices to input information about the teaching material's topic and target grade level. The user interface is designed to allow for easy and accurate information entry through options and input fields.

[0819] Step 2:

[0820] The terminal sends the entered information to the server. During this process, the data is converted into an appropriate format and prepared for processing on the server side.

[0821] Step 3:

[0822] The server searches and references internal databases and online resources based on the received information to identify the most suitable teaching materials. During this process, the emotion engine analyzes the user's emotional state at the time of input and incorporates this into the material generation.

[0823] Step 4:

[0824] The emotion engine collects user emotion data and determines emotional states such as stress and fatigue. Based on this information, the server adjusts the content and difficulty level of the learning materials.

[0825] Step 5:

[0826] The server integrates selected learning material information with feedback from the emotion engine to generate learning slides and handouts. The materials are created in an easy-to-understand format that takes into account the user's emotional state.

[0827] Step 6:

[0828] The generated learning materials are sent to the device and made available for the user to review. The user can customize the learning material content as needed.

[0829] Step 7:

[0830] When a user enters their grade data, the device sends that data to the server. This data is used to visualize each student's level of understanding.

[0831] Step 8:

[0832] The server analyzes performance data and generates reports that show students' comprehension levels and learning trends. The emotion engine monitors the user's emotional state throughout this process and supports the presentation of appropriate information.

[0833] Step 9:

[0834] The generated report is sent to the terminal, making it available for user review and use. The user can then use this information to develop a lesson plan.

[0835] Step 10:

[0836] During lessons and between tasks, the emotion engine continues to analyze the user's emotional data and provides rest recommendations and support information as needed. This allows teachers to manage their own emotions and physical condition while performing their duties.

[0837] (Example 2)

[0838] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0839] In educational settings, teachers spend a significant amount of time on their daily tasks, with curriculum development and assessing student comprehension being particularly burdensome. This can increase teacher stress and potentially lower the quality of education. Therefore, a system is needed to reduce teachers' workload and improve the quality of education.

[0840] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0841] In this invention, the server includes means for receiving information on teaching materials using an information processing device, means for automatically generating teaching materials using a generation AI model based on the received information, and means for analyzing user emotional data and reflecting it in the teaching materials. This reduces the workload of teachers and enables effective educational support that takes emotional stress into consideration.

[0842] An "information processing device" refers to a computer system that receives, processes, and transmits data, and includes terminals and servers used by teachers.

[0843] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate teaching materials based on input data.

[0844] "Teaching materials" refer to learning materials and lesson plans used for educational purposes, and the generated information is used in the educational activities of teachers and students.

[0845] "Emotional data" refers to information indicating teachers' stress levels and emotional states, which the system analyzes and uses for creating teaching materials and providing feedback.

[0846] "Statistical processing" refers to the process of organizing collected data and converting it into numerical indicators such as mean values ​​and distributions.

[0847] "Materials showing learning progress" refers to reports and visual information that show each student's level of understanding and progress in learning content.

[0848] This system aims to streamline the work of teachers in educational institutions and provide high-quality education. The system mainly consists of an information processing unit, a generative AI model, and an emotion engine.

[0849] Teachers, as users, access the system using a terminal. By entering the topic and target grade level of a specific teaching material into this terminal, teachers can begin the material creation process. After the terminal receives the input, this information is sent to the server.

[0850] The server processes incoming information using a generative AI model to create optimal teaching materials. This generative AI model, for example, automatically generates material content using natural language processing techniques. Furthermore, the server analyzes user emotion data using an emotion engine. This emotion data is useful for evaluating teachers' stress levels and emotional states. The analysis results are used as feedback to adjust the content and difficulty level of the teaching materials.

[0851] For example, if a user inputs that they want to create teaching materials on the topic of "chemical bonding in high school chemistry," and the system detects the user's stress level, the server will generate more concise and easy-to-understand teaching materials. These materials are then provided to the user via their terminal.

[0852] In grade management, users input student grades via their devices. The server collects this data and generates reports showing each student's learning progress through statistical processing. The presentation of these reports is adjusted based on the user's sentiment data.

[0853] The server also continuously analyzes teachers' daily emotional data and provides suggestions and support for rest as needed. This feature allows teachers to perform their duties without experiencing excessive stress.

[0854] An example of a prompt message is: "This teacher is stressed out creating teaching materials on chemical bonding in high school chemistry. Please generate materials that will reduce his workload."

[0855] In this way, teachers can work efficiently, take care of their own mental health, and provide high-quality education.

[0856] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0857] Step 1:

[0858] The user inputs the topic and target grade level of the teaching material into the terminal. The terminal receives this information, formats it into a prompt message format, and sends it to the server. This input information may include the topic "Chemical Bonding in High School Chemistry" as an example.

[0859] Step 2:

[0860] The server automatically generates educational materials using a generative AI model based on information received from the terminal. The generative AI model analyzes the input prompt text and performs data calculations to generate the content and structure of the educational materials. The generated educational material content is then generated by the server.

[0861] Step 3:

[0862] The server uses an emotion engine to analyze the user's emotional data. This analysis determines the stress level based on past emotional data and teacher feedback. The analysis results are used to adjust the learning materials, for example, by adjusting the difficulty level of the materials.

[0863] Step 4:

[0864] The server generates the final instructional materials based on the generated teaching materials and the analysis results of the emotional data. In particular, if the user is experiencing high stress levels, the explanations are adjusted to be concise and easy to understand. The final materials are then delivered from the server to the terminal and provided to the user.

[0865] Step 5:

[0866] After class, users enter student grades into a terminal. The server receives the grade data sent from the terminal and performs statistical processing. This statistical processing includes calculations such as the average and distribution of students' comprehension levels.

[0867] Step 6:

[0868] The server generates a student-specific comprehension report based on statistical processing results. The presentation method of the generated report is adjusted according to the user's emotional state. The final report is sent from the server to the terminal and provided to the user.

[0869] Step 7:

[0870] The server analyzes teachers' daily emotional data and processes it to suggest rest and support as needed. Based on this analysis, the server suggests times when teachers need rest and sends notifications to provide support.

[0871] (Application Example 2)

[0872] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0873] In educational settings, teachers and staff experience significant psychological burdens due to their numerous tasks. In particular, creating teaching materials, compiling evaluations, and developing individualized support plans are factors that contribute to emotionally challenging situations. Furthermore, a lack of appropriate material presentation tailored to teachers' emotional states and insufficient support for efficient time management negatively impacts the quality of education and the well-being of teachers and staff.

[0874] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0875] In this invention, the server includes a device for receiving information related to the topic of educational materials and the educational stage to which they apply; a device for automatically generating teaching materials based on the information; and a device for analyzing the teacher's psychological state using an emotion analysis device and optimizing teaching materials or workload. This makes it possible for teachers and staff to improve work efficiency while reducing their psychological burden.

[0876] "Educational materials" are materials provided to learners for educational purposes.

[0877] "Educational stages" refer to the various stages of progress in a learner's educational process.

[0878] "Information" refers to the data and instructions that a system receives.

[0879] "Teaching materials" are educational materials used in classes and learning activities for educational purposes.

[0880] An "emotional analysis device" is a device used to analyze and determine an individual's psychological state.

[0881] "Psychological state" refers to an individual's emotions and mental condition.

[0882] "Workload" refers to the amount of work or the difficulty of tasks assigned to an individual.

[0883] "Optimization" refers to adjusting conditions to achieve the best possible state.

[0884] "Efficient time management" refers to management methods that aim to use time effectively and reduce waste.

[0885] "Burden reduction" refers to alleviating psychological or physical pressure.

[0886] "Business efficiency" refers to the degree to which work and tasks are performed efficiently and effectively.

[0887] To implement this invention, a system is built around a server, a terminal, and a user. The server collects information related to the topics and educational stages of educational materials and analyzes the user's psychological state using an emotion analysis device. Based on this information, it automatically generates teaching materials and further optimizes the workload. Specifically, the server uses a cloud server as the master unit and employs machine learning libraries such as TensorFlow for data processing. It performs emotion analysis and implements a teaching material generation algorithm using a programming language such as Python. The terminal is a smart device capable of installing a dedicated application and is responsible for receiving and displaying relevant data. Through this system, the user can receive appropriate teaching materials and work management support that take their psychological state into consideration.

[0888] As a concrete example, suppose a user selects "Science Experiments in Secondary Education" as the topic for their lesson. If the emotion analysis device detects that the user is feeling fatigued, the server automatically generates concise and easy-to-understand teaching materials for that topic and sends them to the user's device. This allows the user to conduct lessons efficiently while reducing their burden.

[0889] A program that utilizes a generative AI model to generate educational materials that take emotional states into account operates based on the following prompt: "Design and explain an algorithm for generating secondary education science experiment materials that help reduce teacher stress."

[0890] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0891] Step 1:

[0892] The user uses a terminal to input information about the topic and educational level of the educational material. The input information is sent from the terminal to the server. At this stage, the input identifies the topic name and educational level, and generates JSON format data that is sent to the server as output.

[0893] Step 2:

[0894] The server analyzes the user's psychological state using an emotion analysis device, based on the topics of the received educational materials and educational stage data. Interaction data with the user is input to the emotion analysis model, and evaluation data indicating the user's psychological state (e.g., stress level, fatigue level) is output. This data is used to optimize education.

[0895] Step 3:

[0896] The server generates instructional materials while considering the results of sentiment analysis. A generation AI model is used, taking educational material topics, educational stages, and user psychological state data as input, and generating instructional content as output. The difficulty and complexity of this content are then adjusted according to the user's psychological state.

[0897] Step 4:

[0898] The generated teaching materials are sent from the server to the terminal, making them accessible to the user. Here, the teaching material content is displayed in the terminal's application, and the user utilizes it as digital material to support educational activities. This output is intended as educational material and is in a format that can be viewed and used within the terminal's available storage space.

[0899] Step 5:

[0900] As users engage in educational activities, response and progress data are continuously sent from their devices to the server. User interaction and learning progress data are considered as input, and the server performs actions such as additional optimization and suggesting new learning materials as needed. This process continues in real time, enhancing the effectiveness of the education.

[0901] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0902] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0903] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0904] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0905] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0906] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0907] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0908] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0909] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0910] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0911] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0912] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0913] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0914] 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.

[0915] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0916] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0917] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0918] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0919] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0920] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0921] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0922] The following is further disclosed regarding the embodiments described above.

[0923] (Claim 1)

[0924] A means of receiving information about the topics and target grade levels of teaching materials,

[0925] Based on the aforementioned information, a means for automatically generating teaching materials,

[0926] A means of outputting the generated teaching materials,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] A means for receiving, aggregating, and analyzing performance data,

[0930] A means of generating reports to visualize each student's level of understanding,

[0931] A means of outputting the generated report,

[0932] The system according to claim 1, comprising:

[0933] (Claim 3)

[0934] A means of managing students' learning progress in order to send important information to parents at the appropriate time,

[0935] A means to organize schedule data and support teachers in efficient time management,

[0936] Methods for proposing individualized instruction plans,

[0937] The system according to claim 1, comprising:

[0938] "Example 1"

[0939] (Claim 1)

[0940] A means of receiving information about the topics and target grade levels of teaching materials,

[0941] Based on the aforementioned information, a means for automatically generating teaching materials by referring to online resources and internal information collections,

[0942] A means of constructing educational materials using a generative AI model and transmitting the generated materials to a distribution device for output,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] A means of receiving performance data and aggregating it through statistical analysis,

[0946] A method for generating reports to visualize each student's level of learning using a generative AI model,

[0947] The system according to claim 1, wherein the generated report is transmitted to a distribution device and output.

[0948] (Claim 3)

[0949] A means of monitoring students' learning progress and sending timely information to parents,

[0950] A means of managing timetables and supporting teachers in efficient schedule management,

[0951] The system according to claim 1, which generates an introduction plan to support the instructional plan.

[0952] "Application Example 1"

[0953] (Claim 1)

[0954] A means of receiving information about learner characteristics and target grade level,

[0955] Based on the aforementioned information, a means for automatically generating educational materials,

[0956] A means for outputting the generated material to an educational support device,

[0957] A system that includes this.

[0958] (Claim 2)

[0959] A means for receiving, aggregating, and analyzing learning progress data,

[0960] A means of generating a report to visualize the level of understanding for each learner,

[0961] A means for outputting the generated report to an educational support device,

[0962] The system according to claim 1, comprising:

[0963] (Claim 3)

[0964] A means of managing learners' progress and sending notifications to educators,

[0965] Organizing schedule information and providing means to support efficient time management in educational support devices,

[0966] Methods for proposing individualized instruction plans,

[0967] The system according to claim 1, comprising:

[0968] "Example 2 of combining an emotion engine"

[0969] (Claim 1)

[0970] A means of receiving information about teaching materials using an information processing device,

[0971] A means for automatically generating teaching materials using a generation AI model based on the received information,

[0972] A means for analyzing user emotional data and reflecting it in the aforementioned teaching materials,

[0973] A means for outputting the generated teaching materials via an information processing device,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] A means for receiving data on learning performance, and for performing statistical processing and analysis,

[0977] A means of adjusting the information presentation method based on user sentiment data,

[0978] A means of creating materials that show the learning progress of individual students,

[0979] A means for outputting the generated material via an information processing device,

[0980] The system according to claim 1, comprising:

[0981] (Claim 3)

[0982] A means of analyzing the user's daily emotional data and suggesting rest or support as needed,

[0983] The system according to claim 1, comprising:

[0984] "Application example 2 when combining with an emotional engine"

[0985] (Claim 1)

[0986] A device for receiving information related to the topic of educational materials and the educational level to which they apply,

[0987] Based on the aforementioned information, a device that automatically generates teaching materials,

[0988] A device that provides generated teaching materials,

[0989] A device that uses an emotion analysis device to analyze the psychological state of teachers and optimize teaching materials or workload,

[0990] A system that includes this.

[0991] (Claim 2)

[0992] A device that receives evaluation data and performs statistical processing and analysis,

[0993] A device that generates reports to visualize the level of understanding for each learner,

[0994] A device that provides the generated report,

[0995] A device that uses an emotion analysis device to analyze the psychology of faculty and staff, and individually adjusts the display or content of the report,

[0996] The system according to claim 1, comprising:

[0997] (Claim 3)

[0998] A device for managing learners' learning progress in order to send communications to parents at the appropriate time,

[0999] A device that organizes time data and supports educators in efficient time management,

[1000] A device that proposes individualized support plans,

[1001] A device that uses an emotion analysis device to monitor the emotional state of workers and makes efficient rest suggestions,

[1002] The system according to claim 1, comprising: [Explanation of Symbols]

[1003] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information about learner characteristics and target grade level, Based on the aforementioned information, a means for automatically generating educational materials, A means for outputting the generated material to an educational support device, A system that includes this.

2. A means for receiving, aggregating, and analyzing learning progress data, A means of generating a report to visualize the level of understanding for each learner, A means for outputting the generated report to an educational support device, The system according to claim 1, comprising:

3. A means of managing learners' progress and sending notifications to educators, Organizing schedule information and providing means to support efficient time management in educational support devices, Methods for proposing individualized instruction plans, The system according to claim 1, comprising:

Images