system

Abstract

We provide the system. [Solution] A generation means for generating educators' profile information and formulating individualized learning plans based on that profile, An analytical means for collecting learning progress and educational data, and for evaluating educators' performance based on this data, A feedback generation means that proposes improvement plans and next learning steps based on the results of the analysis means, A means of forming a community that provides information sharing and career information with other educators, 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 persona chatbot control method 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] There are problems that it is difficult for educators to secure time for self-skill improvement and to continuously acquire the latest educational methods and specialized knowledge. Also, since there is a lack of information and support necessary for educators to develop their careers, it is difficult to provide a plan optimized for individual needs.

Means for Solving the Problems

[0005] This invention includes a generation means for formulating individualized learning plans based on educators' profile information. Furthermore, it provides an analysis means for evaluating educators' performance by collecting learning progress and educational data and analyzing this data. It supports educators' growth by utilizing a feedback generation means that proposes improvement plans and next learning steps based on the analysis results. In addition, it provides a community-building means for sharing information with other educators and obtaining career-related information, thereby comprehensively supporting educators' career development and skill improvement.

[0006] "Profile information" refers to attribute information such as the individual educator's skills, experience, and goals.

[0007] "Generation method" refers to a function that automatically creates an individualized learning plan based on profile information.

[0008] "Learning progress" refers to the status of a student's learning or training during which they are being taught.

[0009] "Educational data" refers to data that includes the results of lessons conducted by educators and feedback from students.

[0010] "Analysis tools" refer to functions for analyzing collected learning progress and educational data to evaluate the performance of educators.

[0011] "Feedback generation means" refers to a function that, based on the results of analysis, presents improvement suggestions and next steps to educators.

[0012] "Community building means" refers to the function of providing a platform where educators can exchange information with other educators and acquire career information. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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

[0014] 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.

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

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

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

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

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention is a system that utilizes educators' profile information to provide personalized learning plans based on the latest educational trends, in order to support educators' skill development and career advancement. Specific embodiments of this system are shown below.

[0035] System Overview

[0036] This system is a platform designed to help educators hone their skills and advance their careers. Its key functions include profile generation and management, learning plan creation, educational data collection and analysis, feedback generation, and community building and career information provision.

[0037] Program processing

[0038] 1. Generating and managing profile information

[0039] When users first access the system, they create an individual profile. This profile includes information such as skills, experience, areas of responsibility, and career goals.

[0040] The terminal sends the entered information to the server and stores it in the profile database.

[0041] 2. Generating individualized learning plans

[0042] The server generates learning plans based on profile information and references the latest educational trends. The generation engine selects appropriate learning materials and training programs based on the collected data.

[0043] 3. Collection and analysis of educational data

[0044] Users input information such as the progress of classes and training sessions, and learning outcomes.

[0045] The server will analyze this data and use it to evaluate the educator's performance.

[0046] 4. Providing feedback

[0047] Based on the analysis results, the server provides educators with suggestions for improvement and next learning steps in natural language. This allows users to receive a concrete action plan.

[0048] The device visually displays the generated feedback, making it easy for the user to understand.

[0049] 5. Community building and provision of career information

[0050] Users can utilize community features that allow them to interact with other educators and exchange information. This enables them to obtain the latest information on education and career advancement.

[0051] The server utilizes a database to provide information and resources related to career development.

[0052] Specific example

[0053] For example, if a mathematics educator sets "improving problem-solving skills" as a goal in their profile, the system will recommend an appropriate program based on the latest relevant research. The educator takes the program and records their learning progress on their device. After the lesson, the system analyzes student feedback and test results, and the server provides specific suggestions for improvement. This allows educators to take concrete steps to improve the quality of their lessons.

[0054] Thus, the present invention provides a systematic approach to efficiently support educators' continuous skill development and career advancement.

[0055] The following describes the processing flow.

[0056] Step 1:

[0057] When a user first accesses the system, they enter their information on a profile creation screen. This information includes skills, areas of expertise, and career goals.

[0058] Step 2:

[0059] The terminal verifies the entered profile information and sends it to the server as structured data.

[0060] Step 3:

[0061] The server generates and stores the educator's profile in the database based on the received profile information.

[0062] Step 4:

[0063] The server matches profile information with the latest educational trend data to generate a personalized learning plan. Machine learning algorithms may be used in this process.

[0064] Step 5:

[0065] The device visually presents the generated learning plan to the user, who then selects the appropriate training program.

[0066] Step 6:

[0067] Users take the selected training program and input their progress and results via their device.

[0068] Step 7:

[0069] The terminal sends the entered progress data to the server, which then stores it for analysis.

[0070] Step 8:

[0071] The server analyzes accumulated learning progress and lesson data to evaluate the user's educational performance.

[0072] Step 9:

[0073] Based on the analysis results, the server generates feedback for educators and creates specific content to suggest the next learning steps and improvement plans.

[0074] Step 10:

[0075] The device presents the generated feedback to the user in an easy-to-understand format.

[0076] Step 11:

[0077] Users can further their learning based on the feedback they receive and use community features to share information and interact with other educators.

[0078] (Example 1)

[0079] 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."

[0080] For educators to efficiently improve their skills and advance their careers, learning plans tailored to individual characteristics and goals are necessary. However, it is a difficult task for educators to independently gather information based on self-analysis and the latest educational trends, and to construct optimal plans on their own. Furthermore, accurately grasping the progress of learning and immediately deriving appropriate improvement measures is also challenging. Therefore, a comprehensive system is needed to enable educators to efficiently and continuously improve their skills.

[0081] 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.

[0082] In this invention, the server includes a database management means for inputting and storing educator profile information, a plan generation means for referencing the latest educational trends based on the profile information and formulating personalized learning plans using a generation AI model, and an analysis means for collecting educational data and evaluating educators' performance using this data. As a result, educators can easily obtain learning plans tailored to their own characteristics and, furthermore, obtain concrete steps for accurately analyzing and improving their learning outcomes.

[0083] An "educator" is a professional who has the role of teaching knowledge and skills to learners, and aims to improve individual skills and advance their careers.

[0084] "Profile information" is a collection of information that represents the individual characteristics of an educator, including their skills, experience, areas of expertise, and career goals.

[0085] "Database management means" refers to technical means for efficiently storing educators' profile information and for searching, updating, and managing that information as needed.

[0086] "Plan generation means" refers to a technical means for formulating individualized learning plans using a generative AI model based on educators' profile information and the latest educational trends.

[0087] "Educational data" refers to all data related to educational activities, such as the progress of lessons and training conducted by educators, learning outcomes, and student feedback.

[0088] "Analysis tools" are technical means used to evaluate educators' performance based on collected educational data and to analyze learning progress and outcomes.

[0089] The following describes embodiments for carrying out the invention.

[0090] ---

[0091] This invention is designed as a comprehensive support system for educators to improve their skills and advance their careers. The system can be implemented using digital devices (terminals) and a server with an internet connection. Key functions of the system include generating and managing profile information, customizing learning plans, analyzing educational data, providing feedback, and facilitating information sharing.

[0092] First, users input profile information such as their skills, experience, areas of expertise, and career goals via their device. This input information is sent to and stored on a server through a database management system.

[0093] Based on this profile information, the server utilizes a generative AI model to generate personalized learning plans best suited to educators. The generative AI model is input with prompts such as, "Please propose a specific training program based on the latest research data on mathematics education," and then formulates the plan.

[0094] Educational data is collected when users record the progress of lessons and training, learning outcomes, and student feedback through their devices. The server analyzes this collected data to evaluate the educators' performance. Based on the evaluation results, a feedback generation system creates and provides specific improvement suggestions and next steps to the educators in natural language.

[0095] Furthermore, users can exchange information with other educators on the same platform and obtain the latest educational trends and information useful for career development. The server supports the continuous growth of educators by providing career information.

[0096] This allows each educator to learn according to their own goals and receive precise guidance based on the analyzed data, enabling them to take concrete actions to improve educational effectiveness.

[0097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0098] Step 1:

[0099] The user enters profile information into the terminal. This information includes skills, experience, areas of responsibility, and career goals. The terminal formats the entered profile information and sends it to the server. This data is stored on the server using a database management system.

[0100] Step 2:

[0101] The server generates personalized learning plans using a generation AI model based on saved profile information. Specifically, it references the latest educational trend data and inputs a prompt message into the AI ​​model: "Please suggest the optimal training program tailored to the characteristics of the target educator." The AI ​​model processes the data and outputs an appropriate learning plan. The server then provides the generated learning plan to the user.

[0102] Step 3:

[0103] Users input their progress and learning outcomes from their terminals during classes and training programs. The terminals digitize this data and send it to a server. The server receives this educational data and evaluates the educators' performance through data analysis. The results of this analysis are used in the next step.

[0104] Step 4:

[0105] The server generates feedback for educators based on the analyzed performance data. Based on the data obtained using the analysis tools, the next steps and improvement suggestions are written. This feedback is written in natural language and sent to the terminal in a format that is easy for the user to understand. The terminal visually displays this feedback, allowing the user to understand the specific actions they should take next.

[0106] Step 5:

[0107] Users can leverage the system's community features to share information with other educators. The server provides relevant educational trends and career information to support this information sharing. Users can use this information to help them in their career development.

[0108] (Application Example 1)

[0109] 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."

[0110] There is a need for a platform that allows educators to efficiently improve their skills, but currently, there is insufficient provision of learning plans tailored to individual needs and real-time support methods that are aligned with the educational content. As a result, educators' career development and skill acquisition may stagnate.

[0111] 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.

[0112] In this invention, the server includes a generation means for generating characteristic information of educators and formulating individualized learning plans based on those characteristics; an analysis means for collecting learning progress and education-related data and evaluating the educators' performance based on this data; and a support means for providing individualized learning support in real time through an educational support robot. This enables the provision of educational plans tailored to individual needs and real-time educational support.

[0113] "Educator profile information" refers to individual profile information that includes educators' skills, experience, areas of expertise, career goals, and other relevant details.

[0114] An "individualized learning plan" is a customized learning plan developed based on the educator's characteristics, designed to help educators efficiently improve their skills.

[0115] "Education-related data" refers to various types of information related to educational activities, such as the progress of lessons, learning outcomes, and feedback from students.

[0116] "Performance evaluation" is a process of measuring and evaluating the performance of educators based on their educational activities.

[0117] "Feedback generation means" refers to a function that presents educators with areas for improvement and next learning steps based on the analysis results.

[0118] "Community building tools" refer to functions that provide a space where educators can share information and interact with other educators.

[0119] An "educational support robot" is a device that supports educators in educational settings and provides learning support in real time.

[0120] Embodiments of this invention provide a system to support educators in improving their skills and developing their careers. The system primarily includes the development of individualized learning plans based on educators' characteristic information, the collection and analysis of educational data, the generation of feedback, and real-time support functions provided by educational support robots.

[0121] The server generates educator profile information based on a database and develops personalized learning plans. During this process, the server collects education-related data and uses machine learning algorithms to analyze the data and evaluate the educators' performance. Possible cloud services to use include AWS® and Google® Cloud.

[0122] The terminal receives data from the server via a feedback generation mechanism and provides visual or audible feedback to the educator. Natural language processing technology is used to ensure that the information is conveyed in an easily understandable format.

[0123] Users receive real-time support through educational support robots. Specifically, the robots analyze feedback received by educators during lessons and provide appropriate support on the spot. Specialized educational support robots such as NAO and Pepper can be used.

[0124] As a concrete example, a robot analyzes students' reactions in real time during a math lesson. After the lesson, it provides the educator with voice feedback on areas for improvement, which can be used to improve future lessons.

[0125] An example of a prompt might be a question like, "Can you tell me what I could improve after this lesson?" By inputting this prompt into a generative AI model, it would be possible to obtain more detailed feedback.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The user enters the educator's profile on a terminal. The profile includes information such as skills, experience, areas of expertise, and career goals. The terminal sends this information to the server. The server receives the profile data and stores it in a database. A profile is generated based on this input information.

[0129] Step 2:

[0130] The server uses a generative AI model to develop a personalized learning plan based on stored profile information. The server references the latest educational trends and uses machine learning algorithms to match them with the profile and select appropriate learning resources. The output is a personalized learning plan.

[0131] Step 3:

[0132] The server collects data on lesson progress and learning outcomes entered by users. This includes student responses during lessons and grade data. The server analyzes this data to evaluate the educator's performance. The input is progress and outcome data, and the output is the evaluation result.

[0133] Step 4:

[0134] The server generates feedback based on the analysis results. Using a generative AI model, it creates improvement suggestions and next learning steps in natural language. The feedback is sent to the terminal and provided to the user. The input is the evaluation result, and the output is the feedback content.

[0135] Step 5:

[0136] Users conduct lessons using educational support robots. The robots observe students' reactions in real time and provide support based on pre-planned learning schedules. Observation data is sent to a server and used to plan the next lesson.

[0137] 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.

[0138] This invention provides a more personalized learning experience by combining an emotion engine with a system that supports educators' skill development and career advancement. In addition to its core functions of creating individualized learning plans based on profile information and evaluating learning progress, this system recognizes the user's emotional state and adjusts feedback and learning content accordingly.

[0139] System Overview

[0140] This system consists of the following main components: a profile generation and management module, a learning plan generation module, a data analysis module, a feedback generation module, a community building module, and an emotion engine. The emotion engine is responsible for recognizing emotions in real time from user input data and interactions and reflecting them in the overall system functionality.

[0141] Program processing

[0142] 1. Profile generation and management

[0143] Users create a profile by entering their skills, goals, and activity history. This information is used to generate a personalized learning plan.

[0144] The terminal formats the input information and sends it to the server.

[0145] 2. Generating individualized learning plans

[0146] The server generates an optimal learning plan based on profile information. The emotion engine allows for flexible adjustments based on the user's emotional state.

[0147] 3. Collection and analysis of educational data

[0148] Users input data obtained during learning and classes, and also provide emotional feedback.

[0149] The server analyzes this data to identify areas for improvement in order to enhance the quality of education.

[0150] 4. How the Emotion Engine Works

[0151] The emotion engine within the server analyzes user input data and interaction logs to evaluate their emotional state at any given time.

[0152] The feedback generation module uses information from the emotion engine to present the user with the most appropriate feedback and learning steps.

[0153] 5. Community building and provision of career information

[0154] Users can participate in the community and exchange ideas with other educators. In this process, smooth communication is facilitated by support from an emotional engine.

[0155] The server uses sentiment data to provide users with appropriate resources for career advancement.

[0156] Specific example

[0157] For example, if the emotion engine detects that a user is experiencing stress, the system will make adjustments to reduce the burden on the learning plan. Specifically, this could involve increasing the amount of relaxation-oriented content or using a softer tone for feedback. In this way, the present invention makes it possible to make the educator's skill development process more effective and personalized by incorporating emotion analysis.

[0158] The following describes the processing flow.

[0159] Step 1:

[0160] Upon initial access, users enter personal information, educational goals, and current skill levels into the system. This creates an educator profile.

[0161] Step 2:

[0162] The terminal sends the entered user information to the server and records it in the profile database.

[0163] Step 3:

[0164] Based on profile information, the server generates personalized learning plans that are tailored to the latest educational trends and the user's goals.

[0165] Step 4:

[0166] The emotion engine on the server analyzes user interaction data and input information being trained to evaluate the user's emotional state in real time.

[0167] Step 5:

[0168] The server reflects the results of the emotion engine and dynamically adjusts the learning plan according to the user's emotional state. For example, if the user is stressed, it may lower the difficulty level of the learning material or increase the amount of relaxation content.

[0169] Step 6:

[0170] Users progress through a learning plan provided by the system, deepening their learning as they receive feedback at each step. This feedback includes emotionally adaptive advice from an emotion engine.

[0171] Step 7:

[0172] The device receives feedback input and learning progress reports from the user and transfers the data to the server.

[0173] Step 8:

[0174] The server continuously analyzes learning progress data and feedback information to comprehensively evaluate the educators' performance.

[0175] Step 9:

[0176] Based on the analysis results and the evaluation of the emotion engine, the server generates and provides detailed feedback to the user, including the next learning steps.

[0177] Step 10:

[0178] Users can leverage community features to share knowledge and experience with other educators and utilize career information provided by the server to advance their careers. Throughout this process, the emotion engine supports smooth communication.

[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] Conventional educational support systems have struggled to provide individualized learning experiences because they do not adequately consider the emotional states and unique characteristics of individual educators. Furthermore, they suffer from a lack of intuitive feedback and difficulty in facilitating smooth information exchange among educators. This invention aims to solve these problems and provide a system that more effectively supports educators' skill development and career advancement.

[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 generating characteristic information of educators and constructing personalized learning plans based on that characteristic information; means for collecting learning progress and education-related information and evaluating the educators' performance based on this information; and emotion analysis means for recognizing the educators' emotional state in real time and adjusting the learning plan and feedback. This makes it possible to provide flexible learning plans that take into account the emotional state of each educator and to generate appropriate feedback.

[0184] "Characteristic information" refers to data specific to each individual educator, such as their skills, goals, and activity history.

[0185] A "learning plan" is a schedule that is individualized based on the educator's characteristics and includes the goals to be achieved and the specific learning content.

[0186] "Analysis tools" refer to technical means used to analyze collected data and evaluate educators' learning progress and performance.

[0187] A "feedback generation method" refers to a method or technique for proposing specific improvement plans or next learning steps to educators based on the results of an analysis.

[0188] "Emotional analysis tools" are means of recognizing the emotional state of educators in real time and adjusting learning plans and feedback accordingly.

[0189] "Community-building tools" refer to methods and platforms for educators to exchange information with other professionals and provide them with professional information.

[0190] This invention is a system for providing learning experiences tailored to the individual needs of educators. This system incorporates various technologies, including the generation of feature information and the use of emotion analysis methods.

[0191] Hardware and software configuration

[0192] The server plays a central role in processing input data from educators and generating learning plans. This involves using database management systems (e.g., MySQL®) and programming languages ​​such as Python and R to run analytical algorithms.

[0193] The emotion engine is a software component that analyzes user characteristic information and real-time interaction data to determine the emotional state of educators. Machine learning techniques (e.g., TENSORFLOW®) are used for this analysis.

[0194] The device provides an interface for users to input characteristic information and emotional feedback as educators. This is done through a web browser or a dedicated application.

[0195] Users receive personalized learning plans and feedback by entering their profile data into their device. This provides support for educational purposes.

[0196] Specific example

[0197] For example, if a user inputs feedback into the emotion engine stating that "the current learning load is high and I'm feeling stressed," the system immediately re-evaluates the learning plan and makes adjustments, such as increasing the amount of relaxation content. In this way, the system aims to provide a highly flexible educational experience.

[0198] Example of a prompt

[0199] "I feel my stress levels are high. How can I adjust the learning content?"

[0200] This invention provides an innovative approach for educators to continuously improve their skills and builds a support system that can adapt to diverse situations.

[0201] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0202] Step 1:

[0203] Users input profile information, including their skills, goals, and activity history. This forms the basic data necessary for generating characteristic information. This input data from the user is sent to the server via the terminal. The terminal performs preprocessing to ensure a standardized input format and accurate data transmission to the server.

[0204] Step 2:

[0205] The server receives profile data sent from the terminal and stores it in a database. At this stage, the server checks the integrity of the data and supplements any incomplete data. The server also uses a generative AI model to generate feature information and creates a personalized learning plan based on each user's profile. As output, a learning plan tailored to each individual educator is generated.

[0206] Step 3:

[0207] The user engages in learning activities according to a learning plan, inputting progress data and their emotional state into the module. Emotional feedback is also provided as needed during these activities. The input data is organized by the terminal and sent back to the server for analysis.

[0208] Step 4:

[0209] The server analyzes learning progress data and sentiment feedback submitted by the user. This analysis utilizes machine learning algorithms to evaluate educational effectiveness and identify emotional states. The server uses a generative AI model to adjust the learning plan as needed based on the analysis results. The output includes an improved learning plan and personalized feedback.

[0210] Step 5:

[0211] The feedback generation module operates on the server and generates appropriate feedback for the user based on information obtained from the emotion engine. Using natural language processing techniques, the feedback is provided in easy-to-understand language. The output includes feedback that provides guidance on the next learning step.

[0212] Step 6:

[0213] Users have the opportunity to exchange information with other educators through community features. Here, communication support based on sentiment analysis is provided, and networking support is offered. In addition, the server provides occupational information tailored to the user's career path. As an output, users can gain new knowledge and perspectives through interaction with others.

[0214] (Application Example 2)

[0215] 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".

[0216] Traditional educator support systems do not provide personalized learning support that takes into account the emotional state of individual educators. As a result, educators may not be able to adjust their learning appropriately when they experience stress or anxiety, leading to insufficient learning outcomes. There is a need to improve this situation and more effectively support educators' skill development.

[0217] 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.

[0218] In this invention, the server includes a generation means for generating educator profile information and formulating an individualized learning plan based on that profile; an analysis means for collecting learning progress and educational data and evaluating the educator's performance based on this data; a feedback generation means for proposing improvement plans and next learning steps based on the results of the analysis means; an emotion analysis means for analyzing the educator's emotional state in real time using an emotion analysis function; an adaptation means for adjusting the learning plan and feedback content based on the results of the emotion analysis means; and a community formation means for sharing information with other educators and providing career information. This makes it possible to optimize the learning experience based on the emotional state of each educator.

[0219] A "profile generation method" is a function that collects and analyzes information such as educators' skills, goals, and activity history to create data that forms the basis for developing individualized learning plans.

[0220] "Analysis tools" refer to functions that collect learning progress and educational data, evaluate educators' performance based on this data, and present the results as feedback or suggestions for improvement.

[0221] A "feedback generation method" is a function that provides educators with specific improvement suggestions and next learning steps based on the results of the analysis method.

[0222] "Emotional analysis tools" are functions that analyze the emotional state of educators in real time, evaluating their emotions using data such as their facial expressions and voice.

[0223] "Adaptive measures" refer to the function of adjusting learning plans and feedback content based on the results of emotion analysis measures, thereby providing a learning experience that matches the educator's current emotional state.

[0224] "Community building tools" refer to functions that promote information sharing among educators and provide career information, supporting opinion exchange and resource provision through networks.

[0225] This embodiment of the invention is a system that supports the skill development of educators, and effectively combines profile generation means, analysis means, feedback generation means, emotion analysis means, adaptation means, and community formation means.

[0226] The server creates personalized profiles using a profile generation mechanism based on data such as skills, goals, and activity history provided by educators. These profiles are stored and managed using database management software. Subsequently, an analysis mechanism evaluates the learning history and progress data, and a feedback generation mechanism provides suggestions for improvement and the next learning steps.

[0227] Furthermore, the server utilizes emotion analysis tools to recognize the educator's emotional state in real time by analyzing their voice and image data. This emotion analysis uses software such as voice recognition APIs and facial recognition APIs. Based on the emotional state, adaptive tools adjust the learning plan and feedback content to provide an optimal learning experience tailored to the educator's condition.

[0228] The device provides an interface for educators to share information with other educators through community building tools and to obtain career information. It also enables smooth communication using a networking API.

[0229] For example, if an emotion analysis tool detects that an educator is feeling stressed, adaptive tools will recommend content that reduces the learning load. Furthermore, the community allows educators to share relaxation techniques with other educators.

[0230] An example of a prompt for a generative AI model is, "Suggest how a robot could respond by providing feedback to help an educator who is nervous before a presentation relax." Based on this prompt, the generative AI model can derive appropriate support measures.

[0231] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0232] Step 1:

[0233] The user inputs their skills, goals, and activity history through the device. The device formats this profile information and sends it to the server. This is the process of verifying the input data, converting it to the appropriate format, and then sending it.

[0234] Step 2:

[0235] The server uses a profile generation mechanism to create an individualized learning plan based on the received profile information. During this process, input data is recorded in a database, and an optimized learning plan is generated using an algorithm.

[0236] Step 3:

[0237] As users engage in learning activities, they input progress and feedback into their devices. The devices collect this data and send it to the server. This entire process involves appropriately encoding and transmitting the input data.

[0238] Step 4:

[0239] The server uses analytical tools to analyze the collected data and evaluate the performance of educators. It uses input data to perform analysis with a scoring model and records the results in a database.

[0240] Step 5:

[0241] Based on the analysis results, the server uses a feedback generation mechanism to present the user with suggestions for improvement and the next learning steps. This involves generating appropriate advice and recommended content and sending it to the device.

[0242] Step 6:

[0243] The device presents the received feedback to the user, clearly displaying specific learning content and steps. This step presents information in a format that is easy for the user to understand.

[0244] Step 7:

[0245] The server utilizes emotion analysis techniques to detect the user's emotional state in real time from their voice and image data. It processes the input data using an emotion analysis algorithm to evaluate the current emotional state.

[0246] Step 8:

[0247] Based on the results of the emotion analysis, the server uses adaptive mechanisms to adjust the learning plan and feedback content. It determines appropriate learning load and content according to the emotion evaluation and sends them to the terminal.

[0248] Step 9:

[0249] Users connect to the community through their devices and share information with other users. The platform provides the necessary UI for community participation and facilitates smooth interaction.

[0250] Step 10:

[0251] The server provides users with carrier information and resources through community building mechanisms. It also manages appropriate data access and sharing functions on the network.

[0252] Through these steps, the user's learning experience becomes personalized, enabling flexible support tailored to their emotional state.

[0253] 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.

[0254] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0255] 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.

[0256] [Second Embodiment]

[0257] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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).

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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".

[0269] This invention is a system that utilizes educators' profile information to provide personalized learning plans based on the latest educational trends, in order to support educators' skill development and career advancement. Specific embodiments of this system are shown below.

[0270] System Overview

[0271] This system is a platform designed to help educators hone their skills and advance their careers. Its key functions include profile generation and management, learning plan creation, educational data collection and analysis, feedback generation, and community building and career information provision.

[0272] Program processing

[0273] 1. Generating and managing profile information

[0274] When users first access the system, they create an individual profile. This profile includes information such as skills, experience, areas of responsibility, and career goals.

[0275] The terminal sends the entered information to the server and stores it in the profile database.

[0276] 2. Generating individualized learning plans

[0277] The server generates learning plans based on profile information and references the latest educational trends. The generation engine selects appropriate learning materials and training programs based on the collected data.

[0278] 3. Collection and analysis of educational data

[0279] Users input information such as the progress of classes and training sessions, and learning outcomes.

[0280] The server will analyze this data and use it to evaluate the educator's performance.

[0281] 4. Providing feedback

[0282] Based on the analysis results, the server provides educators with suggestions for improvement and next learning steps in natural language. This allows users to receive a concrete action plan.

[0283] The terminal visually displays the generated feedback so that it can be easily understood by the user.

[0284] 5. Community Formation and Provision of Career Information

[0285] Users utilize a community function that enables them to communicate with other educators and exchange information, thereby obtaining the latest educational information and information related to career advancement.

[0286] The server utilizes a database to provide information and resources related to career formation.

[0287] Specific Example

[0288] For example, when an educator teaching mathematics sets "improvement of problem-solving ability" as a goal in their profile, the system recommends an appropriate program while referring to the latest relevant research. The educator takes the program and records the progress of their learning on the terminal. After the class, the server presents specific areas for improvement based on the feedback from students and test results regarding the class content. This enables the educator to take specific steps to enhance the quality of their classes.

[0289] In this way, the present invention provides a systematic approach for efficiently supporting the continuous skill improvement and career formation of educators.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] When the user first accesses, they input their information on the profile creation screen. The information includes skills, areas of responsibility, and career goals.

[0293] Step 2:

[0294] The terminal checks the input profile information and sends it to the server as structured data.

[0295] Step 3:

[0296] Based on the received profile information, the server generates and stores the educator's profile in the database.

[0297] Step 4:

[0298] The server matches the profile information with the latest education trend data and generates an individualized learning plan. In this process, machine learning algorithms may be used.

[0299] Step 5:

[0300] The terminal visually presents the generated learning plan to the user, and the user selects an appropriate training program.

[0301] Step 6:

[0302] The user takes the selected training program and enters its progress and results through the terminal.

[0303] Step 7:

[0304] The terminal sends the input progress data to the server, and the server stores it for analysis.

[0305] Step 8:

[0306] The server analyzes the accumulated learning progress and course data to evaluate the user's educational performance.

[0307] Step 9:

[0308] Based on the analysis results, the server generates feedback for the educator and creates specific content for proposing the next learning steps and improvement plans.

[0309] Step 10:

[0310] The device presents the generated feedback to the user in an easy-to-understand format.

[0311] Step 11:

[0312] Users can further their learning based on the feedback they receive and use community features to share information and interact with other educators.

[0313] (Example 1)

[0314] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0315] For educators to efficiently improve their skills and advance their careers, learning plans tailored to individual characteristics and goals are necessary. However, it is a difficult task for educators to independently gather information based on self-analysis and the latest educational trends, and to construct optimal plans on their own. Furthermore, accurately grasping the progress of learning and immediately deriving appropriate improvement measures is also challenging. Therefore, a comprehensive system is needed to enable educators to efficiently and continuously improve their skills.

[0316] 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.

[0317] In this invention, the server includes a database management means for inputting and storing educator profile information, a plan generation means for referencing the latest educational trends based on the profile information and formulating personalized learning plans using a generation AI model, and an analysis means for collecting educational data and evaluating educators' performance using this data. As a result, educators can easily obtain learning plans tailored to their own characteristics and, furthermore, obtain concrete steps for accurately analyzing and improving their learning outcomes.

[0318] An "educator" is a professional who has the role of teaching knowledge and skills to learners, and aims to improve individual skills and advance their careers.

[0319] "Profile information" is a collection of information that represents the individual characteristics of an educator, including their skills, experience, areas of expertise, and career goals.

[0320] "Database management means" refers to technical means for efficiently storing educators' profile information and for searching, updating, and managing that information as needed.

[0321] "Plan generation means" refers to a technical means for formulating individualized learning plans using a generative AI model based on educators' profile information and the latest educational trends.

[0322] "Educational data" refers to all data related to educational activities, such as the progress of lessons and training conducted by educators, learning outcomes, and student feedback.

[0323] "Analysis tools" are technical means used to evaluate educators' performance based on collected educational data and to analyze learning progress and outcomes.

[0324] The following describes embodiments for carrying out the invention.

[0325] ---

[0326] This invention is designed as a comprehensive support system for educators to improve their skills and advance their careers. The system can be implemented using digital devices (terminals) and a server with an internet connection. Key functions of the system include generating and managing profile information, customizing learning plans, analyzing educational data, providing feedback, and facilitating information sharing.

[0327] First, users input profile information such as their skills, experience, areas of expertise, and career goals via their device. This input information is sent to and stored on a server through a database management system.

[0328] Based on this profile information, the server utilizes a generative AI model to generate personalized learning plans best suited to educators. The generative AI model is input with prompts such as, "Please propose a specific training program based on the latest research data on mathematics education," and then formulates the plan.

[0329] Educational data is collected when users record the progress of lessons and training, learning outcomes, and student feedback through their devices. The server analyzes this collected data to evaluate the educators' performance. Based on the evaluation results, a feedback generation system creates and provides specific improvement suggestions and next steps to the educators in natural language.

[0330] Furthermore, users can exchange information with other educators on the same platform and obtain the latest educational trends and information useful for career development. The server supports the continuous growth of educators by providing career information.

[0331] This allows each educator to learn according to their own goals and receive precise guidance based on the analyzed data, enabling them to take concrete actions to improve educational effectiveness.

[0332] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0333] Step 1:

[0334] The user enters profile information into the terminal. This information includes skills, experience, areas of responsibility, and career goals. The terminal formats the entered profile information and sends it to the server. This data is stored on the server using a database management system.

[0335] Step 2:

[0336] The server generates personalized learning plans using a generation AI model based on saved profile information. Specifically, it references the latest educational trend data and inputs a prompt message into the AI ​​model: "Please suggest the optimal training program tailored to the characteristics of the target educator." The AI ​​model processes the data and outputs an appropriate learning plan. The server then provides the generated learning plan to the user.

[0337] Step 3:

[0338] Users input their progress and learning outcomes from their terminals during classes and training programs. The terminals digitize this data and send it to a server. The server receives this educational data and evaluates the educators' performance through data analysis. The results of this analysis are used in the next step.

[0339] Step 4:

[0340] The server generates feedback for educators based on the analyzed performance data. Based on the data obtained using the analysis tools, the next steps and improvement suggestions are written. This feedback is written in natural language and sent to the terminal in a format that is easy for the user to understand. The terminal visually displays this feedback, allowing the user to understand the specific actions they should take next.

[0341] Step 5:

[0342] Users can leverage the system's community features to share information with other educators. The server provides relevant educational trends and career information to support this information sharing. Users can use this information to help them in their career development.

[0343] (Application Example 1)

[0344] 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."

[0345] There is a need for a platform that allows educators to efficiently improve their skills, but currently, there is insufficient provision of learning plans tailored to individual needs and real-time support methods that are aligned with the educational content. As a result, educators' career development and skill acquisition may stagnate.

[0346] 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.

[0347] In this invention, the server includes a generation means for generating characteristic information of educators and formulating individualized learning plans based on those characteristics; an analysis means for collecting learning progress and education-related data and evaluating the educators' performance based on this data; and a support means for providing individualized learning support in real time through an educational support robot. This enables the provision of educational plans tailored to individual needs and real-time educational support.

[0348] "Educator profile information" refers to individual profile information that includes educators' skills, experience, areas of expertise, career goals, and other relevant details.

[0349] An "individualized learning plan" is a customized learning plan developed based on the educator's characteristics, designed to help educators efficiently improve their skills.

[0350] "Education-related data" refers to various types of information related to educational activities, such as the progress of lessons, learning outcomes, and feedback from students.

[0351] "Performance evaluation" is a process of measuring and evaluating the performance of educators based on their educational activities.

[0352] "Feedback generation means" refers to a function that presents educators with areas for improvement and next learning steps based on the analysis results.

[0353] "Community building tools" refer to functions that provide a space where educators can share information and interact with other educators.

[0354] An "educational support robot" is a device that supports educators in educational settings and provides learning support in real time.

[0355] Embodiments of this invention provide a system to support educators in improving their skills and developing their careers. The system primarily includes the development of individualized learning plans based on educators' characteristic information, the collection and analysis of educational data, the generation of feedback, and real-time support functions provided by educational support robots.

[0356] The server generates educator profile information based on a database and develops personalized learning plans. During this process, the server collects educational data and uses machine learning algorithms to analyze the data and evaluate the educators' performance. Possible cloud services to use include AWS and Google Cloud.

[0357] The terminal receives data from the server via a feedback generation mechanism and provides visual or audible feedback to the educator. Natural language processing technology is used to ensure that the information is conveyed in an easily understandable format.

[0358] Users receive real-time support through educational support robots. Specifically, the robots analyze feedback received by educators during lessons and provide appropriate support on the spot. Specialized educational support robots such as NAO and Pepper can be used.

[0359] As a concrete example, a robot analyzes students' reactions in real time during a math lesson. After the lesson, it provides the educator with voice feedback on areas for improvement, which can be used to improve future lessons.

[0360] An example of a prompt might be a question like, "Can you tell me what I could improve after this lesson?" By inputting this prompt into a generative AI model, it would be possible to obtain more detailed feedback.

[0361] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0362] Step 1:

[0363] The user enters the educator's profile on a terminal. The profile includes information such as skills, experience, areas of expertise, and career goals. The terminal sends this information to the server. The server receives the profile data and stores it in a database. A profile is generated based on this input information.

[0364] Step 2:

[0365] The server uses a generative AI model to develop a personalized learning plan based on stored profile information. The server references the latest educational trends and uses machine learning algorithms to match them with the profile and select appropriate learning resources. The output is a personalized learning plan.

[0366] Step 3:

[0367] The server collects data on lesson progress and learning outcomes entered by users. This includes student responses during lessons and grade data. The server analyzes this data to evaluate the educator's performance. The input is progress and outcome data, and the output is the evaluation result.

[0368] Step 4:

[0369] The server generates feedback based on the analysis results. Using a generative AI model, it creates improvement suggestions and next learning steps in natural language. The feedback is sent to the terminal and provided to the user. The input is the evaluation result, and the output is the feedback content.

[0370] Step 5:

[0371] Users conduct lessons using educational support robots. The robots observe students' reactions in real time and provide support based on pre-planned learning schedules. Observation data is sent to a server and used to plan the next lesson.

[0372] 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.

[0373] This invention provides a more personalized learning experience by combining an emotion engine with a system that supports educators' skill development and career advancement. In addition to its core functions of creating individualized learning plans based on profile information and evaluating learning progress, this system recognizes the user's emotional state and adjusts feedback and learning content accordingly.

[0374] System Overview

[0375] This system consists of the following main components: a profile generation and management module, a learning plan generation module, a data analysis module, a feedback generation module, a community building module, and an emotion engine. The emotion engine is responsible for recognizing emotions in real time from user input data and interactions and reflecting them in the overall system functionality.

[0376] Program processing

[0377] 1. Profile generation and management

[0378] Users create a profile by entering their skills, goals, and activity history. This information is used to generate a personalized learning plan.

[0379] The terminal formats the input information and sends it to the server.

[0380] 2. Generating individualized learning plans

[0381] The server generates an optimal learning plan based on profile information. The emotion engine allows for flexible adjustments based on the user's emotional state.

[0382] 3. Collection and analysis of educational data

[0383] Users input data obtained during learning and classes, and also provide emotional feedback.

[0384] The server analyzes this data to identify areas for improvement in order to enhance the quality of education.

[0385] 4. How the Emotion Engine Works

[0386] The emotion engine within the server analyzes user input data and interaction logs to evaluate their emotional state at any given time.

[0387] The feedback generation module uses information from the emotion engine to present the user with the most appropriate feedback and learning steps.

[0388] 5. Community building and provision of career information

[0389] Users can participate in the community and exchange ideas with other educators. In this process, smooth communication is facilitated by support from an emotional engine.

[0390] The server uses sentiment data to provide users with appropriate resources for career advancement.

[0391] Specific example

[0392] For example, if the emotion engine detects that a user is experiencing stress, the system will make adjustments to reduce the burden on the learning plan. Specifically, this could involve increasing the amount of relaxation-oriented content or using a softer tone for feedback. In this way, the present invention makes it possible to make the educator's skill development process more effective and personalized by incorporating emotion analysis.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] Upon initial access, users enter personal information, educational goals, and current skill levels into the system. This creates an educator profile.

[0396] Step 2:

[0397] The terminal sends the entered user information to the server and records it in the profile database.

[0398] Step 3:

[0399] Based on profile information, the server generates personalized learning plans that are tailored to the latest educational trends and the user's goals.

[0400] Step 4:

[0401] The emotion engine on the server analyzes user interaction data and input information being trained to evaluate the user's emotional state in real time.

[0402] Step 5:

[0403] The server reflects the results of the emotion engine and dynamically adjusts the learning plan according to the user's emotional state. For example, if the user is stressed, it may lower the difficulty level of the learning material or increase the amount of relaxation content.

[0404] Step 6:

[0405] Users progress through a learning plan provided by the system, deepening their learning as they receive feedback at each step. This feedback includes emotionally adaptive advice from an emotion engine.

[0406] Step 7:

[0407] The device receives feedback input and learning progress reports from the user and transfers the data to the server.

[0408] Step 8:

[0409] The server continuously analyzes learning progress data and feedback information to comprehensively evaluate the educators' performance.

[0410] Step 9:

[0411] Based on the analysis results and the evaluation of the emotion engine, the server generates and provides detailed feedback to the user, including the next learning steps.

[0412] Step 10:

[0413] Users can leverage community features to share knowledge and experience with other educators and utilize career information provided by the server to advance their careers. Throughout this process, the emotion engine supports smooth communication.

[0414] (Example 2)

[0415] 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".

[0416] Conventional educational support systems have struggled to provide individualized learning experiences because they do not adequately consider the emotional states and unique characteristics of individual educators. Furthermore, they suffer from a lack of intuitive feedback and difficulty in facilitating smooth information exchange among educators. This invention aims to solve these problems and provide a system that more effectively supports educators' skill development and career advancement.

[0417] 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.

[0418] In this invention, the server includes means for generating characteristic information of educators and constructing personalized learning plans based on that characteristic information; means for collecting learning progress and education-related information and evaluating the educators' performance based on this information; and emotion analysis means for recognizing the educators' emotional state in real time and adjusting the learning plan and feedback. This makes it possible to provide flexible learning plans that take into account the emotional state of each educator and to generate appropriate feedback.

[0419] "Characteristic information" refers to data specific to each individual educator, such as their skills, goals, and activity history.

[0420] A "learning plan" is a schedule that is individualized based on the educator's characteristics and includes the goals to be achieved and the specific learning content.

[0421] "Analysis tools" refer to technical means used to analyze collected data and evaluate educators' learning progress and performance.

[0422] A "feedback generation method" refers to a method or technique for proposing specific improvement plans or next learning steps to educators based on the results of an analysis.

[0423] "Emotional analysis tools" are means of recognizing the emotional state of educators in real time and adjusting learning plans and feedback accordingly.

[0424] "Community-building tools" refer to methods and platforms for educators to exchange information with other professionals and provide them with professional information.

[0425] This invention is a system for providing learning experiences tailored to the individual needs of educators. This system incorporates various technologies, including the generation of feature information and the use of emotion analysis methods.

[0426] Hardware and software configuration

[0427] The server plays a central role in processing input data from educators and generating learning plans. This involves using database management systems (e.g., MySQL) and programming languages ​​such as Python or R to run analytical algorithms.

[0428] The emotion engine is a software component that analyzes user characteristic information and real-time interaction data to determine the emotional state of educators. Machine learning techniques (e.g., TensorFlow) are used for this analysis.

[0429] The device provides an interface for users to input characteristic information and emotional feedback as educators. This is done through a web browser or a dedicated application.

[0430] Users receive personalized learning plans and feedback by entering their profile data into their device. This provides support for educational purposes.

[0431] Specific example

[0432] For example, if a user inputs feedback into the emotion engine stating that "the current learning load is high and I'm feeling stressed," the system immediately re-evaluates the learning plan and makes adjustments, such as increasing the amount of relaxation content. In this way, the system aims to provide a highly flexible educational experience.

[0433] Example of a prompt

[0434] "I feel my stress levels are high. How can I adjust the learning content?"

[0435] This invention provides an innovative approach for educators to continuously improve their skills and builds a support system that can adapt to diverse situations.

[0436] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0437] Step 1:

[0438] Users input profile information, including their skills, goals, and activity history. This forms the basic data necessary for generating characteristic information. This input data from the user is sent to the server via the terminal. The terminal performs preprocessing to ensure a standardized input format and accurate data transmission to the server.

[0439] Step 2:

[0440] The server receives profile data sent from the terminal and stores it in a database. At this stage, the server checks the integrity of the data and supplements any incomplete data. The server also uses a generative AI model to generate feature information and creates a personalized learning plan based on each user's profile. As output, a learning plan tailored to each individual educator is generated.

[0441] Step 3:

[0442] The user engages in learning activities according to a learning plan, inputting progress data and their emotional state into the module. Emotional feedback is also provided as needed during these activities. The input data is organized by the terminal and sent back to the server for analysis.

[0443] Step 4:

[0444] The server analyzes learning progress data and sentiment feedback submitted by the user. This analysis utilizes machine learning algorithms to evaluate educational effectiveness and identify emotional states. The server uses a generative AI model to adjust the learning plan as needed based on the analysis results. The output includes an improved learning plan and personalized feedback.

[0445] Step 5:

[0446] The feedback generation module operates on the server and generates appropriate feedback for the user based on information obtained from the emotion engine. Using natural language processing techniques, the feedback is provided in easy-to-understand language. The output includes feedback that provides guidance on the next learning step.

[0447] Step 6:

[0448] Users have the opportunity to exchange information with other educators through community features. Here, communication support based on sentiment analysis is provided, and networking support is offered. In addition, the server provides occupational information tailored to the user's career path. As an output, users can gain new knowledge and perspectives through interaction with others.

[0449] (Application Example 2)

[0450] 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."

[0451] Traditional educator support systems do not provide personalized learning support that takes into account the emotional state of individual educators. As a result, educators may not be able to adjust their learning appropriately when they experience stress or anxiety, leading to insufficient learning outcomes. There is a need to improve this situation and more effectively support educators' skill development.

[0452] 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.

[0453] In this invention, the server includes a generation means for generating educator profile information and formulating an individualized learning plan based on that profile; an analysis means for collecting learning progress and educational data and evaluating the educator's performance based on this data; a feedback generation means for proposing improvement plans and next learning steps based on the results of the analysis means; an emotion analysis means for analyzing the educator's emotional state in real time using an emotion analysis function; an adaptation means for adjusting the learning plan and feedback content based on the results of the emotion analysis means; and a community formation means for sharing information with other educators and providing career information. This makes it possible to optimize the learning experience based on the emotional state of each educator.

[0454] A "profile generation method" is a function that collects and analyzes information such as educators' skills, goals, and activity history to create data that forms the basis for developing individualized learning plans.

[0455] "Analysis tools" refer to functions that collect learning progress and educational data, evaluate educators' performance based on this data, and present the results as feedback or suggestions for improvement.

[0456] A "feedback generation method" is a function that provides educators with specific improvement suggestions and next learning steps based on the results of the analysis method.

[0457] "Emotional analysis tools" are functions that analyze the emotional state of educators in real time, evaluating their emotions using data such as their facial expressions and voice.

[0458] "Adaptive measures" refer to the function of adjusting learning plans and feedback content based on the results of emotion analysis measures, thereby providing a learning experience that matches the educator's current emotional state.

[0459] "Community building tools" refer to functions that promote information sharing among educators and provide career information, supporting opinion exchange and resource provision through networks.

[0460] This embodiment of the invention is a system that supports the skill development of educators, and effectively combines profile generation means, analysis means, feedback generation means, emotion analysis means, adaptation means, and community formation means.

[0461] The server creates personalized profiles using a profile generation mechanism based on data such as skills, goals, and activity history provided by educators. These profiles are stored and managed using database management software. Subsequently, an analysis mechanism evaluates the learning history and progress data, and a feedback generation mechanism provides suggestions for improvement and the next learning steps.

[0462] Furthermore, the server utilizes emotion analysis tools to recognize the educator's emotional state in real time by analyzing their voice and image data. This emotion analysis uses software such as voice recognition APIs and facial recognition APIs. Based on the emotional state, adaptive tools adjust the learning plan and feedback content to provide an optimal learning experience tailored to the educator's condition.

[0463] The device provides an interface for educators to share information with other educators through community building tools and to obtain career information. It also enables smooth communication using a networking API.

[0464] For example, if an emotion analysis tool detects that an educator is feeling stressed, adaptive tools will recommend content that reduces the learning load. Furthermore, the community allows educators to share relaxation techniques with other educators.

[0465] An example of a prompt for a generative AI model is, "Suggest how a robot could respond by providing feedback to help an educator who is nervous before a presentation relax." Based on this prompt, the generative AI model can derive appropriate support measures.

[0466] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0467] Step 1:

[0468] The user inputs their skills, goals, and activity history through the device. The device formats this profile information and sends it to the server. This is the process of verifying the input data, converting it to the appropriate format, and then sending it.

[0469] Step 2:

[0470] The server uses a profile generation mechanism to create an individualized learning plan based on the received profile information. During this process, input data is recorded in a database, and an optimized learning plan is generated using an algorithm.

[0471] Step 3:

[0472] As users engage in learning activities, they input progress and feedback into their devices. The devices collect this data and send it to the server. This entire process involves appropriately encoding and transmitting the input data.

[0473] Step 4:

[0474] The server uses analytical tools to analyze the collected data and evaluate the performance of educators. It uses input data to perform analysis with a scoring model and records the results in a database.

[0475] Step 5:

[0476] Based on the analysis results, the server uses a feedback generation mechanism to present the user with suggestions for improvement and the next learning steps. This involves generating appropriate advice and recommended content and sending it to the device.

[0477] Step 6:

[0478] The device presents the received feedback to the user, clearly displaying specific learning content and steps. This step presents information in a format that is easy for the user to understand.

[0479] Step 7:

[0480] The server utilizes emotion analysis techniques to detect the user's emotional state in real time from their voice and image data. It processes the input data using an emotion analysis algorithm to evaluate the current emotional state.

[0481] Step 8:

[0482] Based on the results of the emotion analysis, the server uses adaptive mechanisms to adjust the learning plan and feedback content. It determines appropriate learning load and content according to the emotion evaluation and sends them to the terminal.

[0483] Step 9:

[0484] Users connect to the community through their devices and share information with other users. The platform provides the necessary UI for community participation and facilitates smooth interaction.

[0485] Step 10:

[0486] The server provides users with carrier information and resources through community building mechanisms. It also manages appropriate data access and sharing functions on the network.

[0487] Through these steps, the user's learning experience becomes personalized, enabling flexible support tailored to their emotional state.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] [Third Embodiment]

[0492] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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".

[0504] This invention is a system that utilizes educators' profile information to provide personalized learning plans based on the latest educational trends, in order to support educators' skill development and career advancement. Specific embodiments of this system are shown below.

[0505] System Overview

[0506] This system is a platform designed to help educators hone their skills and advance their careers. Its key functions include profile generation and management, learning plan creation, educational data collection and analysis, feedback generation, and community building and career information provision.

[0507] Program processing

[0508] 1. Generating and managing profile information

[0509] When users first access the system, they create an individual profile. This profile includes information such as skills, experience, areas of responsibility, and career goals.

[0510] The terminal sends the entered information to the server and stores it in the profile database.

[0511] 2. Generating individualized learning plans

[0512] The server generates learning plans based on profile information and references the latest educational trends. The generation engine selects appropriate learning materials and training programs based on the collected data.

[0513] 3. Collection and analysis of educational data

[0514] Users input information such as the progress of classes and training sessions, and learning outcomes.

[0515] The server will analyze this data and use it to evaluate the educator's performance.

[0516] 4. Providing feedback

[0517] Based on the analysis results, the server provides educators with suggestions for improvement and next learning steps in natural language. This allows users to receive a concrete action plan.

[0518] The device visually displays the generated feedback, making it easy for the user to understand.

[0519] 5. Community building and provision of career information

[0520] Users can utilize community features that allow them to interact with other educators and exchange information. This enables them to obtain the latest information on education and career advancement.

[0521] The server utilizes a database to provide information and resources related to career development.

[0522] Specific example

[0523] For example, if a mathematics educator sets "improving problem-solving skills" as a goal in their profile, the system will recommend an appropriate program based on the latest relevant research. The educator takes the program and records their learning progress on their device. After the lesson, the system analyzes student feedback and test results, and the server provides specific suggestions for improvement. This allows educators to take concrete steps to improve the quality of their lessons.

[0524] Thus, the present invention provides a systematic approach to efficiently support educators' continuous skill development and career advancement.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] When a user first accesses the system, they enter their information on a profile creation screen. This information includes skills, areas of expertise, and career goals.

[0528] Step 2:

[0529] The terminal verifies the entered profile information and sends it to the server as structured data.

[0530] Step 3:

[0531] The server generates and stores the educator's profile in the database based on the received profile information.

[0532] Step 4:

[0533] The server matches profile information with the latest educational trend data to generate a personalized learning plan. Machine learning algorithms may be used in this process.

[0534] Step 5:

[0535] The device visually presents the generated learning plan to the user, who then selects the appropriate training program.

[0536] Step 6:

[0537] Users take the selected training program and input their progress and results via their device.

[0538] Step 7:

[0539] The terminal sends the entered progress data to the server, which then stores it for analysis.

[0540] Step 8:

[0541] The server analyzes accumulated learning progress and lesson data to evaluate the user's educational performance.

[0542] Step 9:

[0543] Based on the analysis results, the server generates feedback for educators and creates specific content to suggest the next learning steps and improvement plans.

[0544] Step 10:

[0545] The device presents the generated feedback to the user in an easy-to-understand format.

[0546] Step 11:

[0547] Users can further their learning based on the feedback they receive and use community features to share information and interact with other educators.

[0548] (Example 1)

[0549] 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."

[0550] For educators to efficiently improve their skills and advance their careers, learning plans tailored to individual characteristics and goals are necessary. However, it is a difficult task for educators to independently gather information based on self-analysis and the latest educational trends, and to construct optimal plans on their own. Furthermore, accurately grasping the progress of learning and immediately deriving appropriate improvement measures is also challenging. Therefore, a comprehensive system is needed to enable educators to efficiently and continuously improve their skills.

[0551] 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.

[0552] In this invention, the server includes a database management means for inputting and storing educator profile information, a plan generation means for referencing the latest educational trends based on the profile information and formulating personalized learning plans using a generation AI model, and an analysis means for collecting educational data and evaluating educators' performance using this data. As a result, educators can easily obtain learning plans tailored to their own characteristics and, furthermore, obtain concrete steps for accurately analyzing and improving their learning outcomes.

[0553] An "educator" is a professional who has the role of teaching knowledge and skills to learners, and aims to improve individual skills and advance their careers.

[0554] "Profile information" is a collection of information that represents the individual characteristics of an educator, including their skills, experience, areas of expertise, and career goals.

[0555] "Database management means" refers to technical means for efficiently storing educators' profile information and for searching, updating, and managing that information as needed.

[0556] "Plan generation means" refers to a technical means for formulating individualized learning plans using a generative AI model based on educators' profile information and the latest educational trends.

[0557] "Educational data" refers to all data related to educational activities, such as the progress of lessons and training conducted by educators, learning outcomes, and student feedback.

[0558] "Analysis tools" are technical means used to evaluate educators' performance based on collected educational data and to analyze learning progress and outcomes.

[0559] The following describes embodiments for carrying out the invention.

[0560] ---

[0561] This invention is designed as a comprehensive support system for educators to improve their skills and advance their careers. The system can be implemented using digital devices (terminals) and a server with an internet connection. Key functions of the system include generating and managing profile information, customizing learning plans, analyzing educational data, providing feedback, and facilitating information sharing.

[0562] First, users input profile information such as their skills, experience, areas of expertise, and career goals via their device. This input information is sent to and stored on a server through a database management system.

[0563] Based on this profile information, the server utilizes a generative AI model to generate personalized learning plans best suited to educators. The generative AI model is input with prompts such as, "Please propose a specific training program based on the latest research data on mathematics education," and then formulates the plan.

[0564] Educational data is collected when users record the progress of lessons and training, learning outcomes, and student feedback through their devices. The server analyzes this collected data to evaluate the educators' performance. Based on the evaluation results, a feedback generation system creates and provides specific improvement suggestions and next steps to the educators in natural language.

[0565] Furthermore, users can exchange information with other educators on the same platform and obtain the latest educational trends and information useful for career development. The server supports the continuous growth of educators by providing career information.

[0566] This allows each educator to learn according to their own goals and receive precise guidance based on the analyzed data, enabling them to take concrete actions to improve educational effectiveness.

[0567] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0568] Step 1:

[0569] The user enters profile information into the terminal. This information includes skills, experience, areas of responsibility, and career goals. The terminal formats the entered profile information and sends it to the server. This data is stored on the server using a database management system.

[0570] Step 2:

[0571] The server generates personalized learning plans using a generation AI model based on saved profile information. Specifically, it references the latest educational trend data and inputs a prompt message into the AI ​​model: "Please suggest the optimal training program tailored to the characteristics of the target educator." The AI ​​model processes the data and outputs an appropriate learning plan. The server then provides the generated learning plan to the user.

[0572] Step 3:

[0573] Users input their progress and learning outcomes from their terminals during classes and training programs. The terminals digitize this data and send it to a server. The server receives this educational data and evaluates the educators' performance through data analysis. The results of this analysis are used in the next step.

[0574] Step 4:

[0575] The server generates feedback for educators based on the analyzed performance data. Based on the data obtained using the analysis tools, the next steps and improvement suggestions are written. This feedback is written in natural language and sent to the terminal in a format that is easy for the user to understand. The terminal visually displays this feedback, allowing the user to understand the specific actions they should take next.

[0576] Step 5:

[0577] Users can leverage the system's community features to share information with other educators. The server provides relevant educational trends and career information to support this information sharing. Users can use this information to help them in their career development.

[0578] (Application Example 1)

[0579] 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."

[0580] There is a need for a platform that allows educators to efficiently improve their skills, but currently, there is insufficient provision of learning plans tailored to individual needs and real-time support methods that are aligned with the educational content. As a result, educators' career development and skill acquisition may stagnate.

[0581] 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.

[0582] In this invention, the server includes a generation means for generating characteristic information of educators and formulating individualized learning plans based on those characteristics; an analysis means for collecting learning progress and education-related data and evaluating the educators' performance based on this data; and a support means for providing individualized learning support in real time through an educational support robot. This enables the provision of educational plans tailored to individual needs and real-time educational support.

[0583] "Educator profile information" refers to individual profile information that includes educators' skills, experience, areas of expertise, career goals, and other relevant details.

[0584] An "individualized learning plan" is a customized learning plan developed based on the educator's characteristics, designed to help educators efficiently improve their skills.

[0585] "Education-related data" refers to various types of information related to educational activities, such as the progress of lessons, learning outcomes, and feedback from students.

[0586] "Performance evaluation" is a process of measuring and evaluating the performance of educators based on their educational activities.

[0587] "Feedback generation means" refers to a function that presents educators with areas for improvement and next learning steps based on the analysis results.

[0588] "Community building tools" refer to functions that provide a space where educators can share information and interact with other educators.

[0589] An "educational support robot" is a device that supports educators in educational settings and provides learning support in real time.

[0590] Embodiments of this invention provide a system to support educators in improving their skills and developing their careers. The system primarily includes the development of individualized learning plans based on educators' characteristic information, the collection and analysis of educational data, the generation of feedback, and real-time support functions provided by educational support robots.

[0591] The server generates educator profile information based on a database and develops personalized learning plans. During this process, the server collects educational data and uses machine learning algorithms to analyze the data and evaluate the educators' performance. Possible cloud services to use include AWS and Google Cloud.

[0592] The terminal receives data from the server via a feedback generation mechanism and provides visual or audible feedback to the educator. Natural language processing technology is used to ensure that the information is conveyed in an easily understandable format.

[0593] Users receive real-time support through educational support robots. Specifically, the robots analyze feedback received by educators during lessons and provide appropriate support on the spot. Specialized educational support robots such as NAO and Pepper can be used.

[0594] As a concrete example, a robot analyzes students' reactions in real time during a math lesson. After the lesson, it provides the educator with voice feedback on areas for improvement, which can be used to improve future lessons.

[0595] An example of a prompt might be a question like, "Can you tell me what I could improve after this lesson?" By inputting this prompt into a generative AI model, it would be possible to obtain more detailed feedback.

[0596] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0597] Step 1:

[0598] The user enters the educator's profile on a terminal. The profile includes information such as skills, experience, areas of expertise, and career goals. The terminal sends this information to the server. The server receives the profile data and stores it in a database. A profile is generated based on this input information.

[0599] Step 2:

[0600] The server uses a generative AI model to develop a personalized learning plan based on stored profile information. The server references the latest educational trends and uses machine learning algorithms to match them with the profile and select appropriate learning resources. The output is a personalized learning plan.

[0601] Step 3:

[0602] The server collects data on lesson progress and learning outcomes entered by users. This includes student responses during lessons and grade data. The server analyzes this data to evaluate the educator's performance. The input is progress and outcome data, and the output is the evaluation result.

[0603] Step 4:

[0604] The server generates feedback based on the analysis results. Using a generative AI model, it creates improvement suggestions and next learning steps in natural language. The feedback is sent to the terminal and provided to the user. The input is the evaluation result, and the output is the feedback content.

[0605] Step 5:

[0606] Users conduct lessons using educational support robots. The robots observe students' reactions in real time and provide support based on pre-planned learning schedules. Observation data is sent to a server and used to plan the next lesson.

[0607] 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.

[0608] This invention provides a more personalized learning experience by combining an emotion engine with a system that supports educators' skill development and career advancement. In addition to its core functions of creating individualized learning plans based on profile information and evaluating learning progress, this system recognizes the user's emotional state and adjusts feedback and learning content accordingly.

[0609] System Overview

[0610] This system consists of the following main components: a profile generation and management module, a learning plan generation module, a data analysis module, a feedback generation module, a community building module, and an emotion engine. The emotion engine is responsible for recognizing emotions in real time from user input data and interactions and reflecting them in the overall system functionality.

[0611] Program processing

[0612] 1. Profile generation and management

[0613] Users create a profile by entering their skills, goals, and activity history. This information is used to generate a personalized learning plan.

[0614] The terminal formats the input information and sends it to the server.

[0615] 2. Generating individualized learning plans

[0616] The server generates an optimal learning plan based on profile information. The emotion engine allows for flexible adjustments based on the user's emotional state.

[0617] 3. Collection and analysis of educational data

[0618] Users input data obtained during learning and classes, and also provide emotional feedback.

[0619] The server analyzes this data to identify areas for improvement in order to enhance the quality of education.

[0620] 4. How the Emotion Engine Works

[0621] The emotion engine within the server analyzes user input data and interaction logs to evaluate their emotional state at any given time.

[0622] The feedback generation module uses information from the emotion engine to present the user with the most appropriate feedback and learning steps.

[0623] 5. Community building and provision of career information

[0624] Users can participate in the community and exchange ideas with other educators. In this process, smooth communication is facilitated by support from an emotional engine.

[0625] The server uses sentiment data to provide users with appropriate resources for career advancement.

[0626] Specific example

[0627] For example, if the emotion engine detects that a user is experiencing stress, the system will make adjustments to reduce the burden on the learning plan. Specifically, this could involve increasing the amount of relaxation-oriented content or using a softer tone for feedback. In this way, the present invention makes it possible to make the educator's skill development process more effective and personalized by incorporating emotion analysis.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] Upon initial access, users enter personal information, educational goals, and current skill levels into the system. This creates an educator profile.

[0631] Step 2:

[0632] The terminal sends the entered user information to the server and records it in the profile database.

[0633] Step 3:

[0634] Based on profile information, the server generates personalized learning plans that are tailored to the latest educational trends and the user's goals.

[0635] Step 4:

[0636] The emotion engine on the server analyzes user interaction data and input information being trained to evaluate the user's emotional state in real time.

[0637] Step 5:

[0638] The server reflects the results of the emotion engine and dynamically adjusts the learning plan according to the user's emotional state. For example, if the user is stressed, it may lower the difficulty level of the learning material or increase the amount of relaxation content.

[0639] Step 6:

[0640] Users progress through a learning plan provided by the system, deepening their learning as they receive feedback at each step. This feedback includes emotionally adaptive advice from an emotion engine.

[0641] Step 7:

[0642] The device receives feedback input and learning progress reports from the user and transfers the data to the server.

[0643] Step 8:

[0644] The server continuously analyzes learning progress data and feedback information to comprehensively evaluate the educators' performance.

[0645] Step 9:

[0646] Based on the analysis results and the evaluation of the emotion engine, the server generates and provides detailed feedback to the user, including the next learning steps.

[0647] Step 10:

[0648] Users can leverage community features to share knowledge and experience with other educators and utilize career information provided by the server to advance their careers. Throughout this process, the emotion engine supports smooth communication.

[0649] (Example 2)

[0650] 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."

[0651] Conventional educational support systems have struggled to provide individualized learning experiences because they do not adequately consider the emotional states and unique characteristics of individual educators. Furthermore, they suffer from a lack of intuitive feedback and difficulty in facilitating smooth information exchange among educators. This invention aims to solve these problems and provide a system that more effectively supports educators' skill development and career advancement.

[0652] 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.

[0653] In this invention, the server includes means for generating characteristic information of educators and constructing personalized learning plans based on that characteristic information; means for collecting learning progress and education-related information and evaluating the educators' performance based on this information; and emotion analysis means for recognizing the educators' emotional state in real time and adjusting the learning plan and feedback. This makes it possible to provide flexible learning plans that take into account the emotional state of each educator and to generate appropriate feedback.

[0654] "Characteristic information" refers to data specific to each individual educator, such as their skills, goals, and activity history.

[0655] A "learning plan" is a schedule that is individualized based on the educator's characteristics and includes the goals to be achieved and the specific learning content.

[0656] "Analysis tools" refer to technical means used to analyze collected data and evaluate educators' learning progress and performance.

[0657] A "feedback generation method" refers to a method or technique for proposing specific improvement plans or next learning steps to educators based on the results of an analysis.

[0658] "Emotional analysis tools" are means of recognizing the emotional state of educators in real time and adjusting learning plans and feedback accordingly.

[0659] "Community-building tools" refer to methods and platforms for educators to exchange information with other professionals and provide them with professional information.

[0660] This invention is a system for providing learning experiences tailored to the individual needs of educators. This system incorporates various technologies, including the generation of feature information and the use of emotion analysis methods.

[0661] Hardware and software configuration

[0662] The server plays a central role in processing input data from educators and generating learning plans. This involves using database management systems (e.g., MySQL) and programming languages ​​such as Python or R to run analytical algorithms.

[0663] The emotion engine is a software component that analyzes user characteristic information and real-time interaction data to determine the emotional state of educators. Machine learning techniques (e.g., TensorFlow) are used for this analysis.

[0664] The device provides an interface for users to input characteristic information and emotional feedback as educators. This is done through a web browser or a dedicated application.

[0665] Users receive personalized learning plans and feedback by entering their profile data into their device. This provides support for educational purposes.

[0666] Specific example

[0667] For example, if a user inputs feedback into the emotion engine stating that "the current learning load is high and I'm feeling stressed," the system immediately re-evaluates the learning plan and makes adjustments, such as increasing the amount of relaxation content. In this way, the system aims to provide a highly flexible educational experience.

[0668] Example of a prompt

[0669] "I feel my stress levels are high. How can I adjust the learning content?"

[0670] This invention provides an innovative approach for educators to continuously improve their skills and builds a support system that can adapt to diverse situations.

[0671] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0672] Step 1:

[0673] Users input profile information, including their skills, goals, and activity history. This forms the basic data necessary for generating characteristic information. This input data from the user is sent to the server via the terminal. The terminal performs preprocessing to ensure a standardized input format and accurate data transmission to the server.

[0674] Step 2:

[0675] The server receives profile data sent from the terminal and stores it in a database. At this stage, the server checks the integrity of the data and supplements any incomplete data. The server also uses a generative AI model to generate feature information and creates a personalized learning plan based on each user's profile. As output, a learning plan tailored to each individual educator is generated.

[0676] Step 3:

[0677] The user engages in learning activities according to a learning plan, inputting progress data and their emotional state into the module. Emotional feedback is also provided as needed during these activities. The input data is organized by the terminal and sent back to the server for analysis.

[0678] Step 4:

[0679] The server analyzes learning progress data and sentiment feedback submitted by the user. This analysis utilizes machine learning algorithms to evaluate educational effectiveness and identify emotional states. The server uses a generative AI model to adjust the learning plan as needed based on the analysis results. The output includes an improved learning plan and personalized feedback.

[0680] Step 5:

[0681] The feedback generation module operates on the server and generates appropriate feedback for the user based on information obtained from the emotion engine. Using natural language processing techniques, the feedback is provided in easy-to-understand language. The output includes feedback that provides guidance on the next learning step.

[0682] Step 6:

[0683] Users have the opportunity to exchange information with other educators through community features. Here, communication support based on sentiment analysis is provided, and networking support is offered. In addition, the server provides occupational information tailored to the user's career path. As an output, users can gain new knowledge and perspectives through interaction with others.

[0684] (Application Example 2)

[0685] 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."

[0686] Traditional educator support systems do not provide personalized learning support that takes into account the emotional state of individual educators. As a result, educators may not be able to adjust their learning appropriately when they experience stress or anxiety, leading to insufficient learning outcomes. There is a need to improve this situation and more effectively support educators' skill development.

[0687] 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.

[0688] In this invention, the server includes a generation means for generating educator profile information and formulating an individualized learning plan based on that profile; an analysis means for collecting learning progress and educational data and evaluating the educator's performance based on this data; a feedback generation means for proposing improvement plans and next learning steps based on the results of the analysis means; an emotion analysis means for analyzing the educator's emotional state in real time using an emotion analysis function; an adaptation means for adjusting the learning plan and feedback content based on the results of the emotion analysis means; and a community formation means for sharing information with other educators and providing career information. This makes it possible to optimize the learning experience based on the emotional state of each educator.

[0689] A "profile generation method" is a function that collects and analyzes information such as educators' skills, goals, and activity history to create data that forms the basis for developing individualized learning plans.

[0690] "Analysis tools" refer to functions that collect learning progress and educational data, evaluate educators' performance based on this data, and present the results as feedback or suggestions for improvement.

[0691] A "feedback generation method" is a function that provides educators with specific improvement suggestions and next learning steps based on the results of the analysis method.

[0692] "Emotional analysis tools" are functions that analyze the emotional state of educators in real time, evaluating their emotions using data such as their facial expressions and voice.

[0693] "Adaptive measures" refer to the function of adjusting learning plans and feedback content based on the results of emotion analysis measures, thereby providing a learning experience that matches the educator's current emotional state.

[0694] "Community building tools" refer to functions that promote information sharing among educators and provide career information, supporting opinion exchange and resource provision through networks.

[0695] This embodiment of the invention is a system that supports the skill development of educators, and effectively combines profile generation means, analysis means, feedback generation means, emotion analysis means, adaptation means, and community formation means.

[0696] The server creates personalized profiles using a profile generation mechanism based on data such as skills, goals, and activity history provided by educators. These profiles are stored and managed using database management software. Subsequently, an analysis mechanism evaluates the learning history and progress data, and a feedback generation mechanism provides suggestions for improvement and the next learning steps.

[0697] Furthermore, the server utilizes emotion analysis tools to recognize the educator's emotional state in real time by analyzing their voice and image data. This emotion analysis uses software such as voice recognition APIs and facial recognition APIs. Based on the emotional state, adaptive tools adjust the learning plan and feedback content to provide an optimal learning experience tailored to the educator's condition.

[0698] The device provides an interface for educators to share information with other educators through community building tools and to obtain career information. It also enables smooth communication using a networking API.

[0699] For example, if an emotion analysis tool detects that an educator is feeling stressed, adaptive tools will recommend content that reduces the learning load. Furthermore, the community allows educators to share relaxation techniques with other educators.

[0700] An example of a prompt for a generative AI model is, "Suggest how a robot could respond by providing feedback to help an educator who is nervous before a presentation relax." Based on this prompt, the generative AI model can derive appropriate support measures.

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The user inputs their skills, goals, and activity history through the device. The device formats this profile information and sends it to the server. This is the process of verifying the input data, converting it to the appropriate format, and then sending it.

[0704] Step 2:

[0705] The server uses a profile generation mechanism to create an individualized learning plan based on the received profile information. During this process, input data is recorded in a database, and an optimized learning plan is generated using an algorithm.

[0706] Step 3:

[0707] As users engage in learning activities, they input progress and feedback into their devices. The devices collect this data and send it to the server. This entire process involves appropriately encoding and transmitting the input data.

[0708] Step 4:

[0709] The server uses analytical tools to analyze the collected data and evaluate the performance of educators. It uses input data to perform analysis with a scoring model and records the results in a database.

[0710] Step 5:

[0711] Based on the analysis results, the server uses a feedback generation mechanism to present the user with suggestions for improvement and the next learning steps. This involves generating appropriate advice and recommended content and sending it to the device.

[0712] Step 6:

[0713] The device presents the received feedback to the user, clearly displaying specific learning content and steps. This step presents information in a format that is easy for the user to understand.

[0714] Step 7:

[0715] The server utilizes emotion analysis techniques to detect the user's emotional state in real time from their voice and image data. It processes the input data using an emotion analysis algorithm to evaluate the current emotional state.

[0716] Step 8:

[0717] Based on the results of the emotion analysis, the server uses adaptive mechanisms to adjust the learning plan and feedback content. It determines appropriate learning load and content according to the emotion evaluation and sends them to the terminal.

[0718] Step 9:

[0719] Users connect to the community through their devices and share information with other users. The platform provides the necessary UI for community participation and facilitates smooth interaction.

[0720] Step 10:

[0721] The server provides users with carrier information and resources through community building mechanisms. It also manages appropriate data access and sharing functions on the network.

[0722] Through these steps, the user's learning experience becomes personalized, enabling flexible support tailored to their emotional state.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] [Fourth Embodiment]

[0727] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0728] 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.

[0729] 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).

[0730] 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.

[0731] 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.

[0732] 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).

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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".

[0740] This invention is a system that utilizes educators' profile information to provide personalized learning plans based on the latest educational trends, in order to support educators' skill development and career advancement. Specific embodiments of this system are shown below.

[0741] System Overview

[0742] This system is a platform designed to help educators hone their skills and advance their careers. Its key functions include profile generation and management, learning plan creation, educational data collection and analysis, feedback generation, and community building and career information provision.

[0743] Program processing

[0744] 1. Generating and managing profile information

[0745] When users first access the system, they create an individual profile. This profile includes information such as skills, experience, areas of responsibility, and career goals.

[0746] The terminal sends the entered information to the server and stores it in the profile database.

[0747] 2. Generating individualized learning plans

[0748] The server generates learning plans based on profile information and references the latest educational trends. The generation engine selects appropriate learning materials and training programs based on the collected data.

[0749] 3. Collection and analysis of educational data

[0750] Users input information such as the progress of classes and training sessions, and learning outcomes.

[0751] The server will analyze this data and use it to evaluate the educator's performance.

[0752] 4. Providing feedback

[0753] Based on the analysis results, the server provides educators with suggestions for improvement and next learning steps in natural language. This allows users to receive a concrete action plan.

[0754] The device visually displays the generated feedback, making it easy for the user to understand.

[0755] 5. Community building and provision of career information

[0756] Users can utilize community features that allow them to interact with other educators and exchange information. This enables them to obtain the latest information on education and career advancement.

[0757] The server utilizes a database to provide information and resources related to career development.

[0758] Specific example

[0759] For example, if a mathematics educator sets "improving problem-solving skills" as a goal in their profile, the system will recommend an appropriate program based on the latest relevant research. The educator takes the program and records their learning progress on their device. After the lesson, the system analyzes student feedback and test results, and the server provides specific suggestions for improvement. This allows educators to take concrete steps to improve the quality of their lessons.

[0760] Thus, the present invention provides a systematic approach to efficiently support educators' continuous skill development and career advancement.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] When a user first accesses the system, they enter their information on a profile creation screen. This information includes skills, areas of expertise, and career goals.

[0764] Step 2:

[0765] The terminal verifies the entered profile information and sends it to the server as structured data.

[0766] Step 3:

[0767] The server generates and stores the educator's profile in the database based on the received profile information.

[0768] Step 4:

[0769] The server matches profile information with the latest educational trend data to generate a personalized learning plan. Machine learning algorithms may be used in this process.

[0770] Step 5:

[0771] The device visually presents the generated learning plan to the user, who then selects the appropriate training program.

[0772] Step 6:

[0773] Users take the selected training program and input their progress and results via their device.

[0774] Step 7:

[0775] The terminal sends the entered progress data to the server, which then stores it for analysis.

[0776] Step 8:

[0777] The server analyzes accumulated learning progress and lesson data to evaluate the user's educational performance.

[0778] Step 9:

[0779] Based on the analysis results, the server generates feedback for educators and creates specific content to suggest the next learning steps and improvement plans.

[0780] Step 10:

[0781] The device presents the generated feedback to the user in an easy-to-understand format.

[0782] Step 11:

[0783] Users can further their learning based on the feedback they receive and use community features to share information and interact with other educators.

[0784] (Example 1)

[0785] 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".

[0786] For educators to efficiently improve their skills and advance their careers, learning plans tailored to individual characteristics and goals are necessary. However, it is a difficult task for educators to independently gather information based on self-analysis and the latest educational trends, and to construct optimal plans on their own. Furthermore, accurately grasping the progress of learning and immediately deriving appropriate improvement measures is also challenging. Therefore, a comprehensive system is needed to enable educators to efficiently and continuously improve their skills.

[0787] 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.

[0788] In this invention, the server includes a database management means for inputting and storing educator profile information, a plan generation means for referencing the latest educational trends based on the profile information and formulating personalized learning plans using a generation AI model, and an analysis means for collecting educational data and evaluating educators' performance using this data. As a result, educators can easily obtain learning plans tailored to their own characteristics and, furthermore, obtain concrete steps for accurately analyzing and improving their learning outcomes.

[0789] An "educator" is a professional who has the role of teaching knowledge and skills to learners, and aims to improve individual skills and advance their careers.

[0790] "Profile information" is a collection of information that represents the individual characteristics of an educator, including their skills, experience, areas of expertise, and career goals.

[0791] "Database management means" refers to technical means for efficiently storing educators' profile information and for searching, updating, and managing that information as needed.

[0792] "Plan generation means" refers to a technical means for formulating individualized learning plans using a generative AI model based on educators' profile information and the latest educational trends.

[0793] "Educational data" refers to all data related to educational activities, such as the progress of lessons and training conducted by educators, learning outcomes, and student feedback.

[0794] "Analysis tools" are technical means used to evaluate educators' performance based on collected educational data and to analyze learning progress and outcomes.

[0795] The following describes embodiments for carrying out the invention.

[0796] ---

[0797] This invention is designed as a comprehensive support system for educators to improve their skills and advance their careers. The system can be implemented using digital devices (terminals) and a server with an internet connection. Key functions of the system include generating and managing profile information, customizing learning plans, analyzing educational data, providing feedback, and facilitating information sharing.

[0798] First, users input profile information such as their skills, experience, areas of expertise, and career goals via their device. This input information is sent to and stored on a server through a database management system.

[0799] Based on this profile information, the server utilizes a generative AI model to generate personalized learning plans best suited to educators. The generative AI model is input with prompts such as, "Please propose a specific training program based on the latest research data on mathematics education," and then formulates the plan.

[0800] Educational data is collected when users record the progress of lessons and training, learning outcomes, and student feedback through their devices. The server analyzes this collected data to evaluate the educators' performance. Based on the evaluation results, a feedback generation system creates and provides specific improvement suggestions and next steps to the educators in natural language.

[0801] Furthermore, users can exchange information with other educators on the same platform and obtain the latest educational trends and information useful for career development. The server supports the continuous growth of educators by providing career information.

[0802] This allows each educator to learn according to their own goals and receive precise guidance based on the analyzed data, enabling them to take concrete actions to improve educational effectiveness.

[0803] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0804] Step 1:

[0805] The user enters profile information into the terminal. This information includes skills, experience, areas of responsibility, and career goals. The terminal formats the entered profile information and sends it to the server. This data is stored on the server using a database management system.

[0806] Step 2:

[0807] The server generates personalized learning plans using a generation AI model based on saved profile information. Specifically, it references the latest educational trend data and inputs a prompt message into the AI ​​model: "Please suggest the optimal training program tailored to the characteristics of the target educator." The AI ​​model processes the data and outputs an appropriate learning plan. The server then provides the generated learning plan to the user.

[0808] Step 3:

[0809] Users input their progress and learning outcomes from their terminals during classes and training programs. The terminals digitize this data and send it to a server. The server receives this educational data and evaluates the educators' performance through data analysis. The results of this analysis are used in the next step.

[0810] Step 4:

[0811] The server generates feedback for educators based on the analyzed performance data. Based on the data obtained using the analysis tools, the next steps and improvement suggestions are written. This feedback is written in natural language and sent to the terminal in a format that is easy for the user to understand. The terminal visually displays this feedback, allowing the user to understand the specific actions they should take next.

[0812] Step 5:

[0813] Users can leverage the system's community features to share information with other educators. The server provides relevant educational trends and career information to support this information sharing. Users can use this information to help them in their career development.

[0814] (Application Example 1)

[0815] 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".

[0816] There is a need for a platform that allows educators to efficiently improve their skills, but currently, there is insufficient provision of learning plans tailored to individual needs and real-time support methods that are aligned with the educational content. As a result, educators' career development and skill acquisition may stagnate.

[0817] 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.

[0818] In this invention, the server includes a generation means for generating characteristic information of educators and formulating individualized learning plans based on those characteristics; an analysis means for collecting learning progress and education-related data and evaluating the educators' performance based on this data; and a support means for providing individualized learning support in real time through an educational support robot. This enables the provision of educational plans tailored to individual needs and real-time educational support.

[0819] "Educator profile information" refers to individual profile information that includes educators' skills, experience, areas of expertise, career goals, and other relevant details.

[0820] An "individualized learning plan" is a customized learning plan developed based on the educator's characteristics, designed to help educators efficiently improve their skills.

[0821] "Education-related data" refers to various types of information related to educational activities, such as the progress of lessons, learning outcomes, and feedback from students.

[0822] "Performance evaluation" is a process of measuring and evaluating the performance of educators based on their educational activities.

[0823] "Feedback generation means" refers to a function that presents educators with areas for improvement and next learning steps based on the analysis results.

[0824] "Community building tools" refer to functions that provide a space where educators can share information and interact with other educators.

[0825] An "educational support robot" is a device that supports educators in educational settings and provides learning support in real time.

[0826] Embodiments of this invention provide a system to support educators in improving their skills and developing their careers. The system primarily includes the development of individualized learning plans based on educators' characteristic information, the collection and analysis of educational data, the generation of feedback, and real-time support functions provided by educational support robots.

[0827] The server generates educator profile information based on a database and develops personalized learning plans. During this process, the server collects educational data and uses machine learning algorithms to analyze the data and evaluate the educators' performance. Possible cloud services to use include AWS and Google Cloud.

[0828] The terminal receives data from the server via a feedback generation mechanism and provides visual or audible feedback to the educator. Natural language processing technology is used to ensure that the information is conveyed in an easily understandable format.

[0829] Users receive real-time support through educational support robots. Specifically, the robots analyze feedback received by educators during lessons and provide appropriate support on the spot. Specialized educational support robots such as NAO and Pepper can be used.

[0830] As a concrete example, a robot analyzes students' reactions in real time during a math lesson. After the lesson, it provides the educator with voice feedback on areas for improvement, which can be used to improve future lessons.

[0831] An example of a prompt might be a question like, "Can you tell me what I could improve after this lesson?" By inputting this prompt into a generative AI model, it would be possible to obtain more detailed feedback.

[0832] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0833] Step 1:

[0834] The user enters the educator's profile on a terminal. The profile includes information such as skills, experience, areas of expertise, and career goals. The terminal sends this information to the server. The server receives the profile data and stores it in a database. A profile is generated based on this input information.

[0835] Step 2:

[0836] The server uses a generative AI model to develop a personalized learning plan based on stored profile information. The server references the latest educational trends and uses machine learning algorithms to match them with the profile and select appropriate learning resources. The output is a personalized learning plan.

[0837] Step 3:

[0838] The server collects data on lesson progress and learning outcomes entered by users. This includes student responses during lessons and grade data. The server analyzes this data to evaluate the educator's performance. The input is progress and outcome data, and the output is the evaluation result.

[0839] Step 4:

[0840] The server generates feedback based on the analysis results. Using a generative AI model, it creates improvement suggestions and next learning steps in natural language. The feedback is sent to the terminal and provided to the user. The input is the evaluation result, and the output is the feedback content.

[0841] Step 5:

[0842] Users conduct lessons using educational support robots. The robots observe students' reactions in real time and provide support based on pre-planned learning schedules. Observation data is sent to a server and used to plan the next lesson.

[0843] 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.

[0844] This invention provides a more personalized learning experience by combining an emotion engine with a system that supports educators' skill development and career advancement. In addition to its core functions of creating individualized learning plans based on profile information and evaluating learning progress, this system recognizes the user's emotional state and adjusts feedback and learning content accordingly.

[0845] System Overview

[0846] This system consists of the following main components: a profile generation and management module, a learning plan generation module, a data analysis module, a feedback generation module, a community building module, and an emotion engine. The emotion engine is responsible for recognizing emotions in real time from user input data and interactions and reflecting them in the overall system functionality.

[0847] Program processing

[0848] 1. Profile generation and management

[0849] Users create a profile by entering their skills, goals, and activity history. This information is used to generate a personalized learning plan.

[0850] The terminal formats the input information and sends it to the server.

[0851] 2. Generating individualized learning plans

[0852] The server generates an optimal learning plan based on profile information. The emotion engine allows for flexible adjustments based on the user's emotional state.

[0853] 3. Collection and analysis of educational data

[0854] Users input data obtained during learning and classes, and also provide emotional feedback.

[0855] The server analyzes this data to identify areas for improvement in order to enhance the quality of education.

[0856] 4. How the Emotion Engine Works

[0857] The emotion engine within the server analyzes user input data and interaction logs to evaluate their emotional state at any given time.

[0858] The feedback generation module uses information from the emotion engine to present the user with the most appropriate feedback and learning steps.

[0859] 5. Community building and provision of career information

[0860] Users can participate in the community and exchange ideas with other educators. In this process, smooth communication is facilitated by support from an emotional engine.

[0861] The server uses sentiment data to provide users with appropriate resources for career advancement.

[0862] Specific example

[0863] For example, if the emotion engine detects that a user is experiencing stress, the system will make adjustments to reduce the burden on the learning plan. Specifically, this could involve increasing the amount of relaxation-oriented content or using a softer tone for feedback. In this way, the present invention makes it possible to make the educator's skill development process more effective and personalized by incorporating emotion analysis.

[0864] The following describes the processing flow.

[0865] Step 1:

[0866] Upon initial access, users enter personal information, educational goals, and current skill levels into the system. This creates an educator profile.

[0867] Step 2:

[0868] The terminal sends the entered user information to the server and records it in the profile database.

[0869] Step 3:

[0870] Based on profile information, the server generates personalized learning plans that are tailored to the latest educational trends and the user's goals.

[0871] Step 4:

[0872] The emotion engine on the server analyzes user interaction data and input information being trained to evaluate the user's emotional state in real time.

[0873] Step 5:

[0874] The server reflects the results of the emotion engine and dynamically adjusts the learning plan according to the user's emotional state. For example, if the user is stressed, it may lower the difficulty level of the learning material or increase the amount of relaxation content.

[0875] Step 6:

[0876] Users progress through a learning plan provided by the system, deepening their learning as they receive feedback at each step. This feedback includes emotionally adaptive advice from an emotion engine.

[0877] Step 7:

[0878] The device receives feedback input and learning progress reports from the user and transfers the data to the server.

[0879] Step 8:

[0880] The server continuously analyzes learning progress data and feedback information to comprehensively evaluate the educators' performance.

[0881] Step 9:

[0882] Based on the analysis results and the evaluation of the emotion engine, the server generates and provides detailed feedback to the user, including the next learning steps.

[0883] Step 10:

[0884] Users can leverage community features to share knowledge and experience with other educators and utilize career information provided by the server to advance their careers. Throughout this process, the emotion engine supports smooth communication.

[0885] (Example 2)

[0886] 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".

[0887] Conventional educational support systems have struggled to provide individualized learning experiences because they do not adequately consider the emotional states and unique characteristics of individual educators. Furthermore, they suffer from a lack of intuitive feedback and difficulty in facilitating smooth information exchange among educators. This invention aims to solve these problems and provide a system that more effectively supports educators' skill development and career advancement.

[0888] 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.

[0889] In this invention, the server includes means for generating characteristic information of educators and constructing personalized learning plans based on that characteristic information; means for collecting learning progress and education-related information and evaluating the educators' performance based on this information; and emotion analysis means for recognizing the educators' emotional state in real time and adjusting the learning plan and feedback. This makes it possible to provide flexible learning plans that take into account the emotional state of each educator and to generate appropriate feedback.

[0890] "Characteristic information" refers to data specific to each individual educator, such as their skills, goals, and activity history.

[0891] A "learning plan" is a schedule that is individualized based on the educator's characteristics and includes the goals to be achieved and the specific learning content.

[0892] "Analysis tools" refer to technical means used to analyze collected data and evaluate educators' learning progress and performance.

[0893] A "feedback generation method" refers to a method or technique for proposing specific improvement plans or next learning steps to educators based on the results of an analysis.

[0894] "Emotional analysis tools" are means of recognizing the emotional state of educators in real time and adjusting learning plans and feedback accordingly.

[0895] "Community-building tools" refer to methods and platforms for educators to exchange information with other professionals and provide them with professional information.

[0896] This invention is a system for providing learning experiences tailored to the individual needs of educators. This system incorporates various technologies, including the generation of feature information and the use of emotion analysis methods.

[0897] Hardware and software configuration

[0898] The server plays a central role in processing input data from educators and generating learning plans. This involves using database management systems (e.g., MySQL) and programming languages ​​such as Python or R to run analytical algorithms.

[0899] The emotion engine is a software component that analyzes user characteristic information and real-time interaction data to determine the emotional state of educators. Machine learning techniques (e.g., TensorFlow) are used for this analysis.

[0900] The device provides an interface for users to input characteristic information and emotional feedback as educators. This is done through a web browser or a dedicated application.

[0901] Users receive personalized learning plans and feedback by entering their profile data into their device. This provides support for educational purposes.

[0902] Specific example

[0903] For example, if a user inputs feedback into the emotion engine stating that "the current learning load is high and I'm feeling stressed," the system immediately re-evaluates the learning plan and makes adjustments, such as increasing the amount of relaxation content. In this way, the system aims to provide a highly flexible educational experience.

[0904] Example of a prompt

[0905] "I feel my stress levels are high. How can I adjust the learning content?"

[0906] This invention provides an innovative approach for educators to continuously improve their skills and builds a support system that can adapt to diverse situations.

[0907] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0908] Step 1:

[0909] Users input profile information, including their skills, goals, and activity history. This forms the basic data necessary for generating characteristic information. This input data from the user is sent to the server via the terminal. The terminal performs preprocessing to ensure a standardized input format and accurate data transmission to the server.

[0910] Step 2:

[0911] The server receives profile data sent from the terminal and stores it in a database. At this stage, the server checks the integrity of the data and supplements any incomplete data. The server also uses a generative AI model to generate feature information and creates a personalized learning plan based on each user's profile. As output, a learning plan tailored to each individual educator is generated.

[0912] Step 3:

[0913] The user engages in learning activities according to a learning plan, inputting progress data and their emotional state into the module. Emotional feedback is also provided as needed during these activities. The input data is organized by the terminal and sent back to the server for analysis.

[0914] Step 4:

[0915] The server analyzes learning progress data and sentiment feedback submitted by the user. This analysis utilizes machine learning algorithms to evaluate educational effectiveness and identify emotional states. The server uses a generative AI model to adjust the learning plan as needed based on the analysis results. The output includes an improved learning plan and personalized feedback.

[0916] Step 5:

[0917] The feedback generation module operates on the server and generates appropriate feedback for the user based on information obtained from the emotion engine. Using natural language processing techniques, the feedback is provided in easy-to-understand language. The output includes feedback that provides guidance on the next learning step.

[0918] Step 6:

[0919] Users have the opportunity to exchange information with other educators through community features. Here, communication support based on sentiment analysis is provided, and networking support is offered. In addition, the server provides occupational information tailored to the user's career path. As an output, users can gain new knowledge and perspectives through interaction with others.

[0920] (Application Example 2)

[0921] 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".

[0922] Traditional educator support systems do not provide personalized learning support that takes into account the emotional state of individual educators. As a result, educators may not be able to adjust their learning appropriately when they experience stress or anxiety, leading to insufficient learning outcomes. There is a need to improve this situation and more effectively support educators' skill development.

[0923] 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.

[0924] In this invention, the server includes a generation means for generating educator profile information and formulating an individualized learning plan based on that profile; an analysis means for collecting learning progress and educational data and evaluating the educator's performance based on this data; a feedback generation means for proposing improvement plans and next learning steps based on the results of the analysis means; an emotion analysis means for analyzing the educator's emotional state in real time using an emotion analysis function; an adaptation means for adjusting the learning plan and feedback content based on the results of the emotion analysis means; and a community formation means for sharing information with other educators and providing career information. This makes it possible to optimize the learning experience based on the emotional state of each educator.

[0925] A "profile generation method" is a function that collects and analyzes information such as educators' skills, goals, and activity history to create data that forms the basis for developing individualized learning plans.

[0926] "Analysis tools" refer to functions that collect learning progress and educational data, evaluate educators' performance based on this data, and present the results as feedback or suggestions for improvement.

[0927] A "feedback generation method" is a function that provides educators with specific improvement suggestions and next learning steps based on the results of the analysis method.

[0928] "Emotional analysis tools" are functions that analyze the emotional state of educators in real time, evaluating their emotions using data such as their facial expressions and voice.

[0929] "Adaptive measures" refer to the function of adjusting learning plans and feedback content based on the results of emotion analysis measures, thereby providing a learning experience that matches the educator's current emotional state.

[0930] "Community building tools" refer to functions that promote information sharing among educators and provide career information, supporting opinion exchange and resource provision through networks.

[0931] This embodiment of the invention is a system that supports the skill development of educators, and effectively combines profile generation means, analysis means, feedback generation means, emotion analysis means, adaptation means, and community formation means.

[0932] The server creates personalized profiles using a profile generation mechanism based on data such as skills, goals, and activity history provided by educators. These profiles are stored and managed using database management software. Subsequently, an analysis mechanism evaluates the learning history and progress data, and a feedback generation mechanism provides suggestions for improvement and the next learning steps.

[0933] Furthermore, the server utilizes emotion analysis tools to recognize the educator's emotional state in real time by analyzing their voice and image data. This emotion analysis uses software such as voice recognition APIs and facial recognition APIs. Based on the emotional state, adaptive tools adjust the learning plan and feedback content to provide an optimal learning experience tailored to the educator's condition.

[0934] The device provides an interface for educators to share information with other educators through community building tools and to obtain career information. It also enables smooth communication using a networking API.

[0935] For example, if an emotion analysis tool detects that an educator is feeling stressed, adaptive tools will recommend content that reduces the learning load. Furthermore, the community allows educators to share relaxation techniques with other educators.

[0936] An example of a prompt for a generative AI model is, "Suggest how a robot could respond by providing feedback to help an educator who is nervous before a presentation relax." Based on this prompt, the generative AI model can derive appropriate support measures.

[0937] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0938] Step 1:

[0939] The user inputs their skills, goals, and activity history through the device. The device formats this profile information and sends it to the server. This is the process of verifying the input data, converting it to the appropriate format, and then sending it.

[0940] Step 2:

[0941] The server uses a profile generation mechanism to create an individualized learning plan based on the received profile information. During this process, input data is recorded in a database, and an optimized learning plan is generated using an algorithm.

[0942] Step 3:

[0943] As users engage in learning activities, they input progress and feedback into their devices. The devices collect this data and send it to the server. This entire process involves appropriately encoding and transmitting the input data.

[0944] Step 4:

[0945] The server uses analytical tools to analyze the collected data and evaluate the performance of educators. It uses input data to perform analysis with a scoring model and records the results in a database.

[0946] Step 5:

[0947] Based on the analysis results, the server uses a feedback generation mechanism to present the user with suggestions for improvement and the next learning steps. This involves generating appropriate advice and recommended content and sending it to the device.

[0948] Step 6:

[0949] The device presents the received feedback to the user, clearly displaying specific learning content and steps. This step presents information in a format that is easy for the user to understand.

[0950] Step 7:

[0951] The server utilizes emotion analysis techniques to detect the user's emotional state in real time from their voice and image data. It processes the input data using an emotion analysis algorithm to evaluate the current emotional state.

[0952] Step 8:

[0953] Based on the results of the emotion analysis, the server uses adaptive mechanisms to adjust the learning plan and feedback content. It determines appropriate learning load and content according to the emotion evaluation and sends them to the terminal.

[0954] Step 9:

[0955] Users connect to the community through their devices and share information with other users. The platform provides the necessary UI for community participation and facilitates smooth interaction.

[0956] Step 10:

[0957] The server provides users with carrier information and resources through community building mechanisms. It also manages appropriate data access and sharing functions on the network.

[0958] Through these steps, the user's learning experience becomes personalized, enabling flexible support tailored to their emotional state.

[0959] 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.

[0960] 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.

[0961] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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."

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0980] The following is further disclosed regarding the embodiments described above.

[0981] (Claim 1)

[0982] A generation means for generating educators' profile information and formulating individualized learning plans based on that profile,

[0983] An analytical means for collecting learning progress and educational data, and for evaluating educators' performance based on this data,

[0984] A feedback generation means that proposes improvement plans and next learning steps based on the results of the analysis means,

[0985] A means of forming a community that provides information sharing and career information with other educators,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, which uses natural language processing technology to provide feedback in an easily understandable natural language.

[0989] (Claim 3)

[0990] The system according to claim 1, which proposes the necessary skill set for educators based on the latest educational trends and research findings.

[0991] "Example 1"

[0992] (Claim 1)

[0993] A database management system for inputting and storing educators' profile information,

[0994] A plan generation method that uses a generative AI model to formulate an individualized learning plan by referencing the latest educational trends based on profile information,

[0995] A means for collecting educational data and using this data to analyze and evaluate the performance of educators,

[0996] Based on the results of the analysis means, a feedback generation means provides improvement suggestions and next learning steps in natural language,

[0997] A means of sharing information that provides interaction with other educators and career information,

[0998] A system that includes this.

[0999] (Claim 2)

[1000] The system according to claim 1, which uses natural language processing to visually display the generated feedback in a way that is easy for the user to understand.

[1001] (Claim 3)

[1002] The system according to claim 1, which inputs prompt sentences into a generative AI model and proposes the necessary skill set for educators based on the latest educational trends and research findings.

[1003] "Application Example 1"

[1004] (Claim 1)

[1005] A generation means for generating characteristic information of educators and formulating individualized learning plans based on those characteristics,

[1006] An analytical tool for collecting learning progress and education-related data, and for evaluating the performance of educators based on this data,

[1007] Based on the results of the analysis means, a feedback generation means is provided to propose improvement plans and the next learning stage.

[1008] A means of forming a community that provides information exchange and professional information with other educators,

[1009] A support system that provides real-time individualized learning support through educational support robots,

[1010] A system that includes this.

[1011] (Claim 2)

[1012] The system according to claim 1, which uses natural language processing technology to provide feedback in an easily understandable natural language.

[1013] (Claim 3)

[1014] The system according to claim 1, which proposes a set of skills necessary for educators based on the latest educational trends and research findings.

[1015] "Example 2 of combining an emotion engine"

[1016] (Claim 1)

[1017] A generation means for generating characteristic information of educators and constructing an individualized learning plan based on that characteristic information,

[1018] An analytical means for collecting learning progress and education-related information, and for evaluating the performance of educators based on this information,

[1019] A feedback generation means that proposes improvement plans and next learning steps based on the results of the analysis means,

[1020] A sentiment analysis tool that recognizes the emotional state of educators in real time and adjusts learning plans and feedback accordingly.

[1021] A means of forming a community that provides information exchange and professional information with other educators,

[1022] A system that includes this.

[1023] (Claim 2)

[1024] The system according to claim 1, which uses natural language processing technology to provide feedback in easily understandable natural language and adds feedback that has been adjusted based on sentiment analysis.

[1025] (Claim 3)

[1026] The system according to claim 1, which proposes a set of abilities necessary for educators based on the latest educational trends and research results, and personalizes the proposed content through emotion analysis.

[1027] "Application example 2 when combining with an emotional engine"

[1028] (Claim 1)

[1029] A generation means for generating educators' profile information and formulating individualized learning plans based on that profile,

[1030] An analytical means for collecting learning progress and educational data, and for evaluating educators' performance based on this data,

[1031] A feedback generation means that proposes improvement plans and next learning steps based on the results of the analysis means,

[1032] An emotion analysis method that uses emotion analysis functions to analyze the emotional state of educators in real time,

[1033] Adaptive means to adjust learning plans and feedback content based on the results of emotion analysis,

[1034] A means of forming a community that provides information sharing and career information with other educators,

[1035] A system that includes this.

[1036] (Claim 2)

[1037] The system according to claim 1, which uses natural language processing technology to provide feedback in easily understandable natural language and takes into account the results of sentiment analysis.

[1038] (Claim 3)

[1039] The system according to claim 1, which proposes the necessary skill set for educators based on the latest educational trends and research findings, and performs adjustments based on emotional state. [Explanation of Symbols]

[1040] 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 generation means for generating educators' profile information and formulating individualized learning plans based on that profile, An analytical means for collecting learning progress and educational data, and for evaluating educators' performance based on this data, A feedback generation means that proposes improvement plans and next learning steps based on the results of the analysis means, A means of forming a community that provides information sharing and career information with other educators, A system that includes this.

2. The system according to claim 1, which uses natural language processing technology to provide feedback in an easily understandable natural language.

3. The system according to claim 1, which proposes the necessary skill set for educators based on the latest educational trends and research findings.

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