system

Abstract

We provide the system. [Solution] A means of acquiring information for collecting data on children's learning progress and data from local educational institutions, A data processing means for integrating and organizing the collected data, An artificial intelligence model means for generating learning plans and career paths based on organized data, A display means for presenting the generated learning plan and career path to the user, A means of collecting user feedback on the presented learning plan and career path, A means of updating the artificial intelligence model based on user feedback, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern educational environment, it is difficult to appropriately manage the balance between children's learning and play. Especially when parents are busy, there is a problem that sufficient learning support cannot be provided to children, and an appropriate educational policy according to the abilities and interests of individual students cannot be proposed. Also, due to insufficient information on the entrance examination situation for each region, there is a problem that appropriate advice cannot be provided in future career choices.

Means for Solving the Problems

[0005] This invention provides a system that efficiently collects and integrates information on children's learning progress and local educational institutions, and then uses an artificial intelligence model to individually optimize and propose learning plans and career paths based on the generated data. Specifically, it collects student learning progress data and local data using an information acquisition means, processes the data using a data processing means, and then generates learning plans and career paths using an artificial intelligence model means. This is then presented to the user using a display means, and the artificial intelligence model is continuously updated based on user input obtained through a feedback means, thereby realizing the most suitable educational proposals for each individual student.

[0006] "Information acquisition means" refers to methods for collecting data on children's learning progress and data from local educational institutions, and includes functions that acquire information using APIs or data input interfaces.

[0007] "Data processing means" refers to the means for organizing and integrating collected data, and includes functions for unifying data formats and performing processing to correct duplication and omissions.

[0008] "Artificial intelligence modeling" refers to a function equipped with AI technology that is used to suggest optimal learning plans and career paths for children based on compiled data.

[0009] "Display means" refers to a means of communicating the generated learning plan or career path to the user, and includes a function that provides visual information in an intuitively understandable manner.

[0010] "Feedback mechanisms" refer to a means of collecting opinions and evaluations of suggestions generated by users, and a function that uses this feedback to improve the AI ​​model.

[0011] "Methods for updating artificial intelligence models" refers to methods that include a process for collecting user feedback data, incorporating it into improvements to the AI ​​model, and aiming to improve the accuracy of future suggestions. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is implemented as a system that utilizes advanced data analysis techniques and artificial intelligence models to support children's learning and growth.

[0034] First, the server automatically collects learning progress data and test results in real time from schools and local educational institutions using information acquisition methods. This data is stored in a database and used in subsequent processing.

[0035] Next, the server uses data processing tools to clean the collected data. This involves removing duplicate data and filling in missing data. This process creates a unified dataset, which forms the basis for analysis by the AI ​​model.

[0036] Using this well-organized data, the server generates personalized learning plans and career paths for each child through an artificial intelligence model. The AI ​​model analyzes past data and current trends to provide suggestions tailored to each child's characteristics. For example, a student with exceptional math skills might be suggested to participate in the International Mathematical Olympiad.

[0037] The generated learning plan and career path are delivered to the terminal via a display device and presented to the user. The terminal uses a GUI to provide information in a visually easy-to-understand format, making it easy for the user to comprehend the suggestions.

[0038] Furthermore, users can use their devices to provide feedback on the presented plans and paths. For example, if they feel the study schedule is unrealistic, they can enter a comment to that effect. This feedback is sent to the server in real time.

[0039] Finally, the server collects user feedback obtained through feedback mechanisms and incorporates it into the AI ​​model. This gradually improves the model, making adjustments so that future suggestions better match user needs.

[0040] This entire system enables effective learning support tailored to each individual child, allowing parents and children themselves to actively contribute to improving the educational environment.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server uses APIs and data entry interfaces to retrieve student learning data from educational institutions and store it in a database. This includes data such as grades, attendance information, and assignment progress.

[0044] Step 2:

[0045] The server cleans the collected data using data processing tools. Specifically, it removes duplicate data and fills in missing data. It also standardizes the data format as needed.

[0046] Step 3:

[0047] The server inputs the prepared data into an artificial intelligence model to analyze each student's learning progress and characteristics. Based on this individual data, the model generates an optimal learning plan and career path.

[0048] Step 4:

[0049] The server delivers the generated learning plan and career path to the terminal using a display device. The terminal then displays this information graphically to the user, making it easy to understand intuitively.

[0050] Step 5:

[0051] Users provide feedback on the presented learning plans and career paths through their devices. For example, they can input suggestions for revisions to the proposed learning schedule or provide evaluations of the career path suggestions.

[0052] Step 6:

[0053] The server collects feedback data sent by users and incorporates it into the artificial intelligence model through a feedback mechanism. This process updates the model and improves accuracy in subsequent suggestions.

[0054] (Example 1)

[0055] 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."

[0056] Traditionally, there has been a problem in effectively providing educational support tailored to the individual characteristics and progress of each learner. In particular, there is a lack of information processing and feedback systems that can collect learner data in real time and use that data to create optimal educational plans. This has led to problems such as inconsistent quality of education and difficulty in receiving appropriate career paths and learning support tailored to individual characteristics.

[0057] 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.

[0058] In this invention, the server includes an information acquisition mechanism for aggregating learner's educational progress data and educational institution information, a data manipulation mechanism for processing and organizing the aggregated information, and an artificial intelligence model mechanism for formulating educational plans and career paths based on the organized information. This enables effective learning support tailored to individual characteristics by automatically formulating and providing an educational plan optimized for each learner to the user.

[0059] An "information acquisition mechanism" is a technical means for aggregating data on learners' educational progress and information from educational institutions.

[0060] A "data manipulation mechanism" is a technical means for processing and organizing aggregated information, removing duplicate information, and supplementing missing information.

[0061] An "artificial intelligence model mechanism" is a technical means for formulating optimal educational plans and career paths based on compiled information.

[0062] A "display mechanism" is a technical means of visually presenting formulated educational plans and career paths to users.

[0063] A "mechanism for collecting opinions" is a technical means that has the function of allowing users to provide feedback on the presented educational plan and career path.

[0064] The "mechanism for improving artificial intelligence models" is a technical means to update artificial intelligence models based on collected user feedback and improve the accuracy of future model formulations.

[0065] This invention is built as a system that provides educational support tailored to individual learners. The system operates primarily based on the interaction between servers, terminals, and users.

[0066] The server uses an information acquisition mechanism to collect learner progress data and educational institution information. This involves using devices with APIs and internet connectivity, and the information is securely stored in a database. For example, learning records can be automatically retrieved from the educational institution's management system.

[0067] Next, the server uses Python and Pandas libraries to process the aggregated data using a data manipulation mechanism. This standardizes the format of the information and removes duplicate information and fills in missing information.

[0068] Based on the compiled data, the server uses an artificial intelligence modeling mechanism to formulate educational plans and career paths. AI platforms such as TENSORFLOW® and PyTorch are used to analyze trends from past learner data and generate personalized plans. For example, learners who demonstrate strong abilities in mathematics can be offered additional learning materials and events to provide further challenges.

[0069] The generated educational plan and career guidance are transmitted to the terminal via a display mechanism. The terminal uses a graphical user interface (GUI) to present the suggestions to the learner in a visually easy-to-understand format. The learning schedule and career guidance are displayed on the tablet or PC screen.

[0070] Users can provide feedback on the plan presented through their device. This feedback is entered through the interface and sent to the server in real time. The feedback includes specific opinions as text comments, such as "I don't think the learning plan is realistic."

[0071] Ultimately, the server improves its artificial intelligence model based on the collected feedback. Through this iterative process, the model gradually evolves, and subsequent suggestions become more aligned with the learner's needs. For example, if the prompt is "Student A is good at science and wants to learn more about it," the model has the ability to generate a customized educational plan based on this information.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server uses an information retrieval mechanism to collect learner data from educational institutions. Input is grade information via APIs or data transfer protocols, and output is raw learning status data. This process accesses the educational institution's database and downloads learner grades and attendance information.

[0075] Step 2:

[0076] The server cleans the collected data using a data manipulation mechanism. The input is raw training data, and the output is a formatted dataset. The specific processing uses the Pandas library to remove duplicate data and impute missing data using preceding and succeeding data.

[0077] Step 3:

[0078] The server generates educational plans using an artificial intelligence model mechanism. The input is a formatted dataset, and the output is a personalized educational plan. The AI ​​model analyzes the data and constructs a suitable plan by comparing it with each learner's past performance. For example, a learner with excellent math skills will be suggested a more challenging next-level material.

[0079] Step 4:

[0080] The server sends the generated educational plan to the terminal. The input is the personalized educational plan, and the output is visually displayed data on the user's terminal. Specifically, it displays the new plan and career path to the learner in an easy-to-understand manner using a GUI-based dashboard.

[0081] Step 5:

[0082] The user uses a terminal to input feedback on the presented plan. The input is the user's text comment, and the output is feedback data transferred to the server. For example, the user might input the opinion "The plan is not feasible" as a comment on the terminal.

[0083] Step 6:

[0084] The server improves the artificial intelligence model based on feedback. The input is user feedback data, and the output is the updated AI model. Retraining is performed to reflect the results of the feedback, enabling the provision of more accurate training plans in the future.

[0085] (Application Example 1)

[0086] 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."

[0087] In systems designed to support children's learning activities, achieving both effectiveness and individual optimization is a challenge. Conventional systems were time-consuming to collect and analyze data, and their standardized learning plans failed to adapt to individual needs. Therefore, there is a need to simultaneously achieve real-time situation monitoring and optimal support for each individual learner.

[0088] 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.

[0089] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables interactive support using an educational device and facilitates feedback using prompt statements.

[0090] "Information acquisition means" refers to devices or processes for collecting data on children's learning progress and data from educational institutions.

[0091] "Data processing means" refers to the process of integrating collected data, eliminating missing or duplicate data, and converting it into organized data.

[0092] An "artificial intelligence model" is an algorithm or program that generates learning plans and career paths optimized for children based on compiled data.

[0093] "Display means" refers to a device or interface for visually presenting the generated learning plan or career path to the user.

[0094] A "feedback collection method" is a process or device that allows users to provide opinions and evaluations regarding the presented learning plan or career path.

[0095] An "educational device" is an automated device equipped with functions to support learning while interacting with the user.

[0096] A "prompt message" is a set of instructions or questions generated to encourage user feedback.

[0097] The system for implementing this invention consists of three main elements: a server, an educational device, and a user terminal.

[0098] The server uses data acquisition methods to collect children's learning progress data and data from local educational institutions in real time. The collected data is cleaned and integrated using Pandas, a data processing software that uses Python. During data processing, missing data is imputed and duplicate data is removed, transforming the data into a unified dataset.

[0099] Next, the server uses the prepared data to generate learning plans and career paths tailored to each individual child, employing an AI model built with TensorFlow as an artificial intelligence modeling tool. This AI model provides specific suggestions based on existing learning data and current trends.

[0100] The generated learning plan and career path information are delivered to the user's terminal via a display device. On the user's terminal, a GUI is used to visually display the learning plan, providing information in a user-friendly format. Visualization libraries such as TKinter are used for displaying the information on the terminal.

[0101] The educational robot interacts with the user, or learner, in a conversational manner, supporting the progress of the learning plan. Based on the input feedback, the educational robot sends information to the server, and the AI ​​model is adjusted in real time. As a result, the AI ​​model reflects the user's opinions, making subsequent suggestions more adaptive.

[0102] Prompts serve to help users provide feedback. For example, a prompt like, "Have you finished studying this unit? Please tell us what you think," might be used. This prompt makes it easier for users to spontaneously provide feedback.

[0103] In this way, it becomes possible to provide individually optimized learning support. For example, if a learner takes a test in a certain subject and it is determined that they perform well in mathematics, they may be prompted with a message such as, "Would you like a mathematics reinforcement plan?" This allows learners to more efficiently develop their strengths.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1: Information Gathering

[0106] The server collects data on children's learning progress from schools and local educational institutions through information acquisition methods. Inputs include student grades, attendance information, and class progress. This information is retrieved via APIs and other data interfaces and sent to the server. The output is in the form of raw learning data. The server uses this in the next processing step.

[0107] Step 2: Data Processing

[0108] The server prepares the collected training data using data processing tools. In this step, Pandas is used to clean the data, remove duplicates, and impute missing data. The input is the raw data obtained in step 1, and the output is a prepared dataset in a unified format. This dataset is used as the basis for the AI ​​model.

[0109] Step 3: Generate a learning plan

[0110] The server receives the prepared data and generates a learning plan using an artificial intelligence model. The AI ​​model, built using TensorFlow, analyzes past training data and current trends. The input is the prepared data, and the output is a personalized learning plan. This plan suggests specific subjects and tasks based on the user's characteristics.

[0111] Step 4: Presenting a learning plan

[0112] The server sends the generated learning plan to the user's terminal. The terminal uses a display device to provide information visually through a GUI powered by TKinter. The input is the learning plan generated in step 3, and the output is the visualized information. This allows the user to review the plan details and understand what to learn next.

[0113] Step 5: Gathering Feedback

[0114] Users provide feedback on their learning plans through their devices. Through the feedback collection mechanism, users input information about the feasibility and areas for improvement of the plan. This prompt includes user-guided questions such as, "Have you finished studying this unit? Please share your thoughts." The input is the user's opinion, and the output is feedback data.

[0115] Step 6: Update the model

[0116] The server updates the artificial intelligence model based on user feedback. The AI ​​model incorporates the newly acquired feedback data to make future suggestions more appropriate. In this process, the input is the feedback collected in step 5, and the output is the updated AI model. This makes it possible to provide an optimized learning plan at all times.

[0117] 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.

[0118] This invention is implemented as a system that combines an emotion engine to provide more advanced support for children's learning. The system mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, and an emotion engine.

[0119] First, the server utilizes information acquisition methods to collect real-time information on students' learning progress and academic performance from schools and local educational institutions. This data includes test results and attendance information obtained via APIs. The collected data is stored in a database and used for subsequent processing.

[0120] The server then uses data processing tools to remove duplicate data, fill in missing data, and standardize data formats. This process prepares the student data for analysis.

[0121] Next, the server uses an artificial intelligence model to analyze the prepared data and generate an optimized learning plan and career path for each student. The generated plan is dynamically adapted based on past data and the current situation.

[0122] The generated plan and route are transmitted to the terminal via a display device and visually displayed on the screen. The terminal provides information graphically to help the user understand the proposal.

[0123] Furthermore, users can express their emotional reactions to the content provided through the emotion engine. The emotion engine uses voice analysis and facial recognition to evaluate the user's emotional state in real time. For example, it can obtain emotional data such as "surprise" or "satisfaction."

[0124] The server integrates user feedback entered via the terminal with emotional data recognized by the emotion engine, and uses this information to update the artificial intelligence model for future suggestions. For example, if a user expresses dissatisfaction with a plan, a new plan can be generated based on that information.

[0125] In this way, this system adds an emotional element to feedback, enabling more personalized educational suggestions than conventional systems. This allows parents and students to manage the educational environment more effectively and lead to optimal learning outcomes.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server collects student learning progress and performance data from educational institutions through various data acquisition methods. This data is automatically retrieved using an API and stored in a database. This information includes student test results, attendance records, and assignment submission status.

[0129] Step 2:

[0130] The server uses data processing tools to organize the collected data. Specifically, it removes duplicate data, fills in missing data, and standardizes the data format. This makes the data suitable for analysis.

[0131] Step 3:

[0132] The server inputs data into an artificial intelligence model to generate individualized learning plans and career paths for each student. The AI ​​model analyzes past performance and trends to create dynamic plans. For example, it can suggest even higher-level math to students who excel in mathematics.

[0133] Step 4:

[0134] The terminal presents the user with a learning plan received from the server via a display device. The information is displayed on the screen in a visually organized manner to facilitate user understanding.

[0135] Step 5:

[0136] The device uses an emotion engine to recognize the user's emotions in real time. While the user is viewing the learning plan, it analyzes changes in voice and facial expressions and evaluates their emotional response. For example, if the user shows "surprise" or "confusion," that information is recorded.

[0137] Step 6:

[0138] Users can input their opinions and emotional feedback on the presented learning plan through their device. For example, if they feel a suggestion is inappropriate, they can explain their specific reasons in the comments section.

[0139] Step 7:

[0140] The server aggregates feedback and sentiment data sent from the terminal and updates the artificial intelligence model. This update ensures that the next suggestion reflects the user's emotions and opinions.

[0141] (Example 2)

[0142] 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".

[0143] In today's educational environment, individualized learning support is a crucial issue. Traditional education systems struggle to accurately understand students' learning progress and provide individually optimized learning plans, and they lack mechanisms for effectively incorporating feedback. As a result, students often don't receive the educational support they truly need, leading to decreased learning efficiency.

[0144] 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.

[0145] In this invention, the server includes data collection means for acquiring educational data and information on local educational institutions, information processing means for integrating and organizing the collected information, and artificial intelligence modeling means for creating educational plans and career paths based on the organized information. This makes it possible to dynamically generate learning plans optimized for individual students and to flexibly update educational suggestions while taking into account user feedback and sentiment data.

[0146] "Data collection means" refers to functions or devices used to acquire educational data and information on local educational institutions.

[0147] "Information processing means" refers to a function or device used to integrate and organize collected information.

[0148] "Artificial intelligence model means" refers to a function or device that includes machine learning technology used to create educational plans and career paths based on compiled information.

[0149] "Display means" refers to a device or method used to present the created educational plan and career path to the user.

[0150] "Means for evaluating and collecting emotional feedback" refers to a function or device used to analyze a user's emotional state and collect feedback on it.

[0151] "Means for updating the model" refers to a function or device used to modify or improve an artificial intelligence model based on user feedback and sentiment data.

[0152] This invention is a system in which a server collects educational data and information from local educational institutions and generates personalized learning plans. First, the server uses an information acquisition means to obtain students' learning status and performance information in real time via an API. This makes it possible to organize and store data from educational institutions in a database on the server.

[0153] Next, the server uses information processing tools to organize the collected data. This process involves removing duplicate data, filling in missing data, and standardizing the format to ensure data accuracy and consistency. Database queries and scripts are used for this process.

[0154] Based on the compiled data, the server generates learning plans and career paths using artificial intelligence models. The AI ​​technology used here applies past learning data and algorithms to provide the most appropriate educational suggestions for each student. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0155] The generated plan is sent to the device and presented to the user through a display device. The device visually displays the data using a graphical user interface so that the user can easily understand the information. In this process, web technologies such as React and Vue.js are commonly used.

[0156] Subsequently, users can provide feedback on the displayed learning plan. To capture emotional responses, a means of evaluating and collecting emotional feedback is used, analyzing emotional data from the user's voice and facial expressions. This information is used to update the artificial intelligence model and improve the accuracy of the plan.

[0157] For example, if feedback is received regarding an elementary school math drill, such as "Please include more fraction problems," this feedback will be reflected in the artificial intelligence model, and adjustments will be made to the next learning plan.

[0158] An example of a prompt message that can utilize this technology is: "Please create a math learning plan for elementary school students, focusing particularly on fractions. Please include recommended materials and specific topics."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server uses an information retrieval method to obtain student learning status and grade information through the educational institution's API. The input requires the API endpoint URL and authentication information, and data is collected using HTTP requests. The output is student information data in JSON or XML format. Specifically, the server automatically executes this request periodically to retrieve new data in a timely manner.

[0162] Step 2:

[0163] The server processes the acquired JSON or XML data using information processing tools. Student information data is provided as input, and the output is normalized data that can be stored in a database. The server performs data normalization, imputation of missing values ​​(e.g., median imputation), and standardization of formatting. Specifically, scripts and SQL queries are used to remove duplicate data and standardize data formats.

[0164] Step 3:

[0165] The server uses artificial intelligence models with pre-prepared data as input to generate learning plans and career paths. The output is an optimal educational plan for each individual student, with the AI ​​model making predictions using historical data and algorithms. Specifically, it uses machine learning libraries (e.g., TensorFlow and PyTorch) to analyze data and create educational plans.

[0166] Step 4:

[0167] The server sends the generated learning plan to the terminal and presents it to the user via a display device. The input is the generated learning plan, and the output is a visual display on the user's terminal. Specifically, web technologies (e.g., React or Vue.js) are used to display the plan in a dashboard or infographic format.

[0168] Step 5:

[0169] Users provide feedback on the learning plan displayed through their device. Input includes the user's emotional responses and text feedback, while output is feedback data. Specifically, users interact with the interface, inputting text and selecting options.

[0170] Step 6:

[0171] The server collects user feedback and emotional feedback data to update the model. It uses the feedback data as input to refine the new model. The output is the updated artificial intelligence model. Specifically, it analyzes the feedback data and automates model retraining.

[0172] (Application Example 2)

[0173] 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".

[0174] In supporting children's learning, there is a problem in providing individualized learning experiences. Traditional learning systems can grasp the learner's progress and suggest an optimal learning plan, but they cannot adapt the plan in real time while considering the user's emotions and reactions. In particular, understanding the learner's emotional state is important in order to effectively promote the maintenance of motivation and the overcoming of weak subjects.

[0175] 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.

[0176] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables a comprehensive understanding of the learner's learning status and emotional state, and the proposal of a dynamic learning plan based on this understanding.

[0177] "Information acquisition means" refers to functions for acquiring data on learners' learning progress and data on educational institutions.

[0178] "Data processing means" refers to functions that integrate, organize, and prepare collected information into a format suitable for analysis.

[0179] An "artificial intelligence model" is a function that generates optimal educational plans and career paths for learners based on compiled data.

[0180] "Display means" refers to a function for visually presenting the generated educational plan or career path to the user.

[0181] A "feedback mechanism" is a function for collecting user reactions to the educational plans and career paths presented.

[0182] An "emotion recognition tool" is a function that analyzes the user's emotional state from their facial expressions and voice, and collects that data.

[0183] "Emotional data" is a collection of information that indicates a user's emotional state, and the system uses this data to understand the user's mental state.

[0184] "Means of improvement" refers to features that utilize feedback and sentiment data to continuously update artificial intelligence models.

[0185] The system for implementing this invention mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, emotion recognition means, and a server for enabling these to function smoothly.

[0186] The server first acquires data in real time from learners and educational institutions through various information acquisition methods. This includes automated data acquisition using APIs and user-input interfaces. The collected data is stored in a database on the server.

[0187] Next, the server uses data processing tools to integrate the collected data, remove duplicate information and fill in missing information, and prepare it in a format suitable for analysis. The prepared data is then analyzed by artificial intelligence models to generate personalized educational plans and career paths optimized for the learner. In this process, the plan is dynamically adapted based on past performance data and current circumstances.

[0188] The generated educational plans and career paths are transmitted to the user's terminal via a display device and presented to the user in a visually verifiable format. The terminal employs a graphical user interface to facilitate user understanding of the proposed content.

[0189] Users can input feedback on displayed suggestions via their device, and this feedback is sent to the server. Simultaneously, emotion recognition technology is used to collect emotional data from the user's facial expressions and voice, and this data is analyzed in real time. The collected emotional data and feedback are integrated and used in a process to improve the artificial intelligence model on the server, which will help in suggesting future educational plans.

[0190] For example, if a child shows anxiety about a subject they struggle with, the system uses that information to suggest more engaging content or methods for the next session. This increases the child's motivation to learn. A specific example of a prompt might be, "If the child shows anxiety about a math problem, suggest words of encouragement."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server collects data from learners and educational institutions using various data acquisition methods. It uses data from APIs and data entry interfaces as input. Output is stored in a database in the form of student grades, attendance information, etc. After data acquisition, the data is organized on the server and prepared for subsequent processes.

[0194] Step 2:

[0195] The server organizes the data collected by the data processing system, removing duplicate data, filling in missing data, and standardizing data formats. The input is raw data retrieved from the database, and the output is well-organized data suitable for analysis. Through this process, the server establishes a stable data environment.

[0196] Step 3:

[0197] The server analyzes data prepared using artificial intelligence modeling tools. The input is processed data, and the generating AI model outputs a learning plan and path optimized for each user. At this stage, the AI ​​evaluates past learning history and current status and proposes a dynamic plan.

[0198] Step 4:

[0199] The generated learning plan and career path are sent from the server to the terminal and presented to the user through a display device. The input is AI-generated plan data, and the output is a graphical display that the user can visually understand. The terminal provides a user-friendly interface.

[0200] Step 5:

[0201] Users provide feedback on the plan displayed through their device. This feedback can be in the form of text or voice, and the feedback data is sent to the server as output. This feedback is then used for subsequent improvements.

[0202] Step 6:

[0203] The server uses emotion recognition technology to acquire emotional data from the user's facial expressions and voice. The input is emotional information obtained through the camera and microphone, and the analysis results are stored as output along with feedback. The system analyzes the user's mental state in real time.

[0204] Step 7:

[0205] Finally, the server integrates feedback and sentiment data to update the artificial intelligence model. The input is the user's sentiment data and feedback data, and the improved AI model is output. This will result in more personalized suggestions for future sessions.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is implemented as a system that utilizes advanced data analysis techniques and artificial intelligence models to support children's learning and growth.

[0223] First, the server automatically collects learning progress data and test results in real time from schools and local educational institutions using information acquisition methods. This data is stored in a database and used in subsequent processing.

[0224] Next, the server uses data processing tools to clean the collected data. This involves removing duplicate data and filling in missing data. This process creates a unified dataset, which forms the basis for analysis by the AI ​​model.

[0225] Using this well-organized data, the server generates personalized learning plans and career paths for each child through an artificial intelligence model. The AI ​​model analyzes past data and current trends to provide suggestions tailored to each child's characteristics. For example, a student with exceptional math skills might be suggested to participate in the International Mathematical Olympiad.

[0226] The generated learning plan and career path are delivered to the terminal via a display device and presented to the user. The terminal uses a GUI to provide information in a visually easy-to-understand format, making it easy for the user to comprehend the suggestions.

[0227] Furthermore, users can use their devices to provide feedback on the presented plans and paths. For example, if they feel the study schedule is unrealistic, they can enter a comment to that effect. This feedback is sent to the server in real time.

[0228] Finally, the server collects user feedback obtained through feedback mechanisms and incorporates it into the AI ​​model. This gradually improves the model, making adjustments so that future suggestions better match user needs.

[0229] This entire system enables effective learning support tailored to each individual child, allowing parents and children themselves to actively contribute to improving the educational environment.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server uses APIs and data entry interfaces to retrieve student learning data from educational institutions and store it in a database. This includes data such as grades, attendance information, and assignment progress.

[0233] Step 2:

[0234] The server cleans the collected data using data processing tools. Specifically, it removes duplicate data and fills in missing data. It also standardizes the data format as needed.

[0235] Step 3:

[0236] The server inputs the prepared data into an artificial intelligence model to analyze each student's learning progress and characteristics. Based on this individual data, the model generates an optimal learning plan and career path.

[0237] Step 4:

[0238] The server delivers the generated learning plan and career path to the terminal using a display device. The terminal then displays this information graphically to the user, making it easy to understand intuitively.

[0239] Step 5:

[0240] Users provide feedback on the presented learning plans and career paths through their devices. For example, they can input suggestions for revisions to the proposed learning schedule or provide evaluations of the career path suggestions.

[0241] Step 6:

[0242] The server collects feedback data sent by users and incorporates it into the artificial intelligence model through a feedback mechanism. This process updates the model and improves accuracy in subsequent suggestions.

[0243] (Example 1)

[0244] 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."

[0245] Traditionally, there has been a problem in effectively providing educational support tailored to the individual characteristics and progress of each learner. In particular, there is a lack of information processing and feedback systems that can collect learner data in real time and use that data to create optimal educational plans. This has led to problems such as inconsistent quality of education and difficulty in receiving appropriate career paths and learning support tailored to individual characteristics.

[0246] 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.

[0247] In this invention, the server includes an information acquisition mechanism for aggregating learner's educational progress data and educational institution information, a data manipulation mechanism for processing and organizing the aggregated information, and an artificial intelligence model mechanism for formulating educational plans and career paths based on the organized information. This enables effective learning support tailored to individual characteristics by automatically formulating and providing an educational plan optimized for each learner to the user.

[0248] An "information acquisition mechanism" is a technical means for aggregating data on learners' educational progress and information from educational institutions.

[0249] A "data manipulation mechanism" is a technical means for processing and organizing aggregated information, removing duplicate information, and supplementing missing information.

[0250] An "artificial intelligence model mechanism" is a technical means for formulating optimal educational plans and career paths based on compiled information.

[0251] A "display mechanism" is a technical means of visually presenting formulated educational plans and career paths to users.

[0252] A "mechanism for collecting opinions" is a technical means that has the function of allowing users to provide feedback on the presented educational plan and career path.

[0253] The "mechanism for improving artificial intelligence models" is a technical means to update artificial intelligence models based on collected user feedback and improve the accuracy of future model formulations.

[0254] This invention is built as a system that provides educational support tailored to individual learners. The system operates primarily based on the interaction between servers, terminals, and users.

[0255] The server uses an information acquisition mechanism to collect learner progress data and educational institution information. This involves using devices with APIs and internet connectivity, and the information is securely stored in a database. For example, learning records can be automatically retrieved from the educational institution's management system.

[0256] Next, the server uses Python and Pandas libraries to process the aggregated data using a data manipulation mechanism. This standardizes the format of the information and removes duplicate information and fills in missing information.

[0257] Based on the compiled data, the server uses an artificial intelligence modeling mechanism to develop educational plans and career paths. AI platforms such as TensorFlow and PyTorch are used to analyze trends from past learner data and generate personalized plans. For example, learners who demonstrate strong abilities in mathematics can be offered additional learning materials and events to provide further challenges.

[0258] The generated educational plan and career guidance are transmitted to the terminal via a display mechanism. The terminal uses a graphical user interface (GUI) to present the suggestions to the learner in a visually easy-to-understand format. The learning schedule and career guidance are displayed on the tablet or PC screen.

[0259] Users can provide feedback on the plan presented through their device. This feedback is entered through the interface and sent to the server in real time. The feedback includes specific opinions as text comments, such as "I don't think the learning plan is realistic."

[0260] Ultimately, the server improves its artificial intelligence model based on the collected feedback. Through this iterative process, the model gradually evolves, and subsequent suggestions become more aligned with the learner's needs. For example, if the prompt is "Student A is good at science and wants to learn more about it," the model has the ability to generate a customized educational plan based on this information.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server uses an information retrieval mechanism to collect learner data from educational institutions. Input is grade information via APIs or data transfer protocols, and output is raw learning status data. This process accesses the educational institution's database and downloads learner grades and attendance information.

[0264] Step 2:

[0265] The server cleans the collected data using a data manipulation mechanism. The input is raw training data, and the output is a formatted dataset. The specific processing uses the Pandas library to remove duplicate data and impute missing data using preceding and succeeding data.

[0266] Step 3:

[0267] The server generates educational plans using an artificial intelligence model mechanism. The input is a formatted dataset, and the output is a personalized educational plan. The AI ​​model analyzes the data and constructs a suitable plan by comparing it with each learner's past performance. For example, a learner with excellent math skills will be suggested a more challenging next-level material.

[0268] Step 4:

[0269] The server sends the generated educational plan to the terminal. The input is the personalized educational plan, and the output is visually displayed data on the user's terminal. Specifically, it displays the new plan and career path to the learner in an easy-to-understand manner using a GUI-based dashboard.

[0270] Step 5:

[0271] The user uses a terminal to input feedback on the presented plan. The input is the user's text comment, and the output is feedback data transferred to the server. For example, the user might input the opinion "The plan is not feasible" as a comment on the terminal.

[0272] Step 6:

[0273] The server improves the artificial intelligence model based on feedback. The input is user feedback data, and the output is the updated AI model. Retraining is performed to reflect the results of the feedback, enabling the provision of more accurate training plans in the future.

[0274] (Application Example 1)

[0275] 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."

[0276] In a system for supporting children's learning activities, it is an issue to achieve its effectiveness and individual optimization. In conventional systems, since it took time to collect and analyze data and the learning plans were uniform, they could not adapt to individual needs. For this reason, it is required to simultaneously achieve real-time situation awareness and optimal support for individual learners.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0278] In this invention, the server includes an information acquisition means, a data processing means, and an artificial intelligence model means. Thereby, it becomes possible to promote interactive support using an educational body and feedback using prompt sentences.

[0279] The "information acquisition means" is a device or process for collecting children's learning situation data and data from educational institutions.

[0280] The "data processing means" is a process for integrating the collected data and converting it into organized data that eliminates deficiencies and duplicates.

[0281] The "artificial intelligence model means" is an algorithm or program for generating a learning plan and course optimized for children based on the organized data.

[0282] The "display means" is a device or interface for visually presenting the generated learning plan and course to the user.

[0283] The "feedback collection means" is a process or device that enables users to provide opinions and evaluations on the presented learning plan and course.

[0284] The "educational body" is an automatic device equipped with a function for providing learning support while interacting with the user.

[0285] A "prompt sentence" is a sentence of instructions or questions generated to facilitate user feedback.

[0286] The system for implementing this invention consists of three main elements: a server, an educational institution, and a user terminal.

[0287] The server uses information acquisition means to collect in real-time data on children's learning status and data from local educational institutions. The collected data is cleaned and integrated using Pandas, which is data processing software using Python. In data processing, missing data is supplemented and duplicate data is deleted, and it is converted into a unified dataset.

[0288] Next, based on the prepared data, the server uses an AI model constructed by leveraging TensorFlow as artificial intelligence model means to generate a learning plan and a career path suitable for each individual child. This AI model makes specific proposals based on existing learning data and current trends.

[0289] The generated learning plan and career path information are distributed to the user terminal through display means. On the user terminal, a GUI is used to visually display the learning plan and provide information in a format that is easy for the user to understand. Visualization libraries such as TKinter are used for the terminal display.

[0290] The educational institution interacts with the user, that is, the learner, in an interactive manner and supports the progress of the learning plan. Based on the input feedback, the educational institution sends information to the server, and the AI model is adjusted in real-time. As a result, the AI model reflects the user's opinions and subsequent proposals become more adaptable.

[0291] Prompts serve to help users provide feedback. For example, a prompt like, "Have you finished studying this unit? Please tell us what you think," might be used. This prompt makes it easier for users to spontaneously provide feedback.

[0292] In this way, it becomes possible to provide individually optimized learning support. For example, if a learner takes a test in a certain subject and it is determined that they perform well in mathematics, they may be prompted with a message such as, "Would you like a mathematics reinforcement plan?" This allows learners to more efficiently develop their strengths.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1: Information Gathering

[0295] The server collects data on children's learning progress from schools and local educational institutions through information acquisition methods. Inputs include student grades, attendance information, and class progress. This information is retrieved via APIs and other data interfaces and sent to the server. The output is in the form of raw learning data. The server uses this in the next processing step.

[0296] Step 2: Data Processing

[0297] The server prepares the collected training data using data processing tools. In this step, Pandas is used to clean the data, remove duplicates, and impute missing data. The input is the raw data obtained in step 1, and the output is a prepared dataset in a unified format. This dataset is used as the basis for the AI ​​model.

[0298] Step 3: Generate a learning plan

[0299] The server receives the prepared data and generates a learning plan using an artificial intelligence model. The AI ​​model, built using TensorFlow, analyzes past training data and current trends. The input is the prepared data, and the output is a personalized learning plan. This plan suggests specific subjects and tasks based on the user's characteristics.

[0300] Step 4: Presenting a learning plan

[0301] The server sends the generated learning plan to the user's terminal. The terminal uses a display device to provide information visually through a GUI powered by TKinter. The input is the learning plan generated in step 3, and the output is the visualized information. This allows the user to review the plan details and understand what to learn next.

[0302] Step 5: Gathering Feedback

[0303] Users provide feedback on their learning plans through their devices. Through the feedback collection mechanism, users input information about the feasibility and areas for improvement of the plan. This prompt includes user-guided questions such as, "Have you finished studying this unit? Please share your thoughts." The input is the user's opinion, and the output is feedback data.

[0304] Step 6: Update the model

[0305] The server updates the artificial intelligence model based on user feedback. The AI ​​model incorporates the newly acquired feedback data to make future suggestions more appropriate. In this process, the input is the feedback collected in step 5, and the output is the updated AI model. This makes it possible to provide an optimized learning plan at all times.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0307] This invention is implemented as a system combined with an emotion engine in order to more highly support children's learning. The system mainly consists of an information acquisition means, a data processing means, an artificial intelligence model means, a display means, a feedback means, and an emotion engine.

[0308] First, the server utilizes the information acquisition means to collect the learning status and grade information of students from schools and regional educational institutions in real time. This data includes test results and attendance information obtained via an API. The collected data is stored in a database and used in subsequent processing.

[0309] Subsequently, the server uses the data processing means to delete duplicate data, complement missing data, and unify the data format. Through this process, the target student data is arranged in a state suitable for analysis.

[0310] Next, the server analyzes the prepared data using the artificial intelligence model means and generates an optimized learning plan and career path for each student. The generated plan is dynamically adapted based on past data and the current situation.

[0311] =]] The generated plan and career path are transmitted to the terminal via the display means and visually displayed on the screen. The terminal provides information graphically so that the user can easily understand the proposal.

[0312] Furthermore, the user can express an emotional reaction to the content provided through the emotion engine. The emotion engine evaluates the user's emotional state in real time using voice analysis and facial expression recognition. For example, emotion data such as "surprise" and "satisfaction" can be obtained.

[0313] The server integrates user feedback entered via the terminal with emotional data recognized by the emotion engine, and uses this information to update the artificial intelligence model for future suggestions. For example, if a user expresses dissatisfaction with a plan, a new plan can be generated based on that information.

[0314] In this way, this system adds an emotional element to feedback, enabling more personalized educational suggestions than conventional systems. This allows parents and students to manage the educational environment more effectively and lead to optimal learning outcomes.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The server collects student learning progress and performance data from educational institutions through various data acquisition methods. This data is automatically retrieved using an API and stored in a database. This information includes student test results, attendance records, and assignment submission status.

[0318] Step 2:

[0319] The server uses data processing tools to organize the collected data. Specifically, it removes duplicate data, fills in missing data, and standardizes the data format. This makes the data suitable for analysis.

[0320] Step 3:

[0321] The server inputs data into an artificial intelligence model to generate individualized learning plans and career paths for each student. The AI ​​model analyzes past performance and trends to create dynamic plans. For example, it can suggest even higher-level math to students who excel in mathematics.

[0322] Step 4:

[0323] The terminal presents the user with a learning plan received from the server via a display device. The information is displayed on the screen in a visually organized manner to facilitate user understanding.

[0324] Step 5:

[0325] The device uses an emotion engine to recognize the user's emotions in real time. While the user is viewing the learning plan, it analyzes changes in voice and facial expressions and evaluates their emotional response. For example, if the user shows "surprise" or "confusion," that information is recorded.

[0326] Step 6:

[0327] Users can input their opinions and emotional feedback on the presented learning plan through their device. For example, if they feel a suggestion is inappropriate, they can explain their specific reasons in the comments section.

[0328] Step 7:

[0329] The server aggregates feedback and sentiment data sent from the terminal and updates the artificial intelligence model. This update ensures that the next suggestion reflects the user's emotions and opinions.

[0330] (Example 2)

[0331] 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".

[0332] In today's educational environment, individualized learning support is a crucial issue. Traditional education systems struggle to accurately understand students' learning progress and provide individually optimized learning plans, and they lack mechanisms for effectively incorporating feedback. As a result, students often don't receive the educational support they truly need, leading to decreased learning efficiency.

[0333] 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.

[0334] In this invention, the server includes data collection means for acquiring educational data and information on local educational institutions, information processing means for integrating and organizing the collected information, and artificial intelligence modeling means for creating educational plans and career paths based on the organized information. This makes it possible to dynamically generate learning plans optimized for individual students and to flexibly update educational suggestions while taking into account user feedback and sentiment data.

[0335] "Data collection means" refers to functions or devices used to acquire educational data and information on local educational institutions.

[0336] "Information processing means" refers to a function or device used to integrate and organize collected information.

[0337] "Artificial intelligence model means" refers to a function or device that includes machine learning technology used to create educational plans and career paths based on compiled information.

[0338] "Display means" refers to a device or method used to present the created educational plan and career path to the user.

[0339] "Means for evaluating and collecting emotional feedback" refers to a function or device used to analyze a user's emotional state and collect feedback on it.

[0340] "Means for updating the model" refers to a function or device used to modify or improve an artificial intelligence model based on user feedback and sentiment data.

[0341] This invention is a system in which a server collects educational data and information from local educational institutions and generates personalized learning plans. First, the server uses an information acquisition means to obtain students' learning status and performance information in real time via an API. This makes it possible to organize and store data from educational institutions in a database on the server.

[0342] Next, the server uses information processing tools to organize the collected data. This process involves removing duplicate data, filling in missing data, and standardizing the format to ensure data accuracy and consistency. Database queries and scripts are used for this process.

[0343] Based on the compiled data, the server generates learning plans and career paths using artificial intelligence models. The AI ​​technology used here applies past learning data and algorithms to provide the most appropriate educational suggestions for each student. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0344] The generated plan is sent to the device and presented to the user through a display device. The device visually displays the data using a graphical user interface so that the user can easily understand the information. In this process, web technologies such as React and Vue.js are commonly used.

[0345] Subsequently, users can provide feedback on the displayed learning plan. To capture emotional responses, a means of evaluating and collecting emotional feedback is used, analyzing emotional data from the user's voice and facial expressions. This information is used to update the artificial intelligence model and improve the accuracy of the plan.

[0346] For example, if feedback is received regarding an elementary school math drill, such as "Please include more fraction problems," this feedback will be reflected in the artificial intelligence model, and adjustments will be made to the next learning plan.

[0347] An example of a prompt message that can utilize this technology is: "Please create a math learning plan for elementary school students, focusing particularly on fractions. Please include recommended materials and specific topics."

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server uses an information retrieval method to obtain student learning status and grade information through the educational institution's API. The input requires the API endpoint URL and authentication information, and data is collected using HTTP requests. The output is student information data in JSON or XML format. Specifically, the server automatically executes this request periodically to retrieve new data in a timely manner.

[0351] Step 2:

[0352] The server processes the acquired JSON or XML data using information processing tools. Student information data is provided as input, and the output is normalized data that can be stored in a database. The server performs data normalization, imputation of missing values ​​(e.g., median imputation), and standardization of formatting. Specifically, scripts and SQL queries are used to remove duplicate data and standardize data formats.

[0353] Step 3:

[0354] The server uses artificial intelligence models with pre-prepared data as input to generate learning plans and career paths. The output is an optimal educational plan for each individual student, with the AI ​​model making predictions using historical data and algorithms. Specifically, it uses machine learning libraries (e.g., TensorFlow and PyTorch) to analyze data and create educational plans.

[0355] Step 4:

[0356] The server sends the generated learning plan to the terminal and presents it to the user via a display device. The input is the generated learning plan, and the output is a visual display on the user's terminal. Specifically, web technologies (e.g., React or Vue.js) are used to display the plan in a dashboard or infographic format.

[0357] Step 5:

[0358] Users provide feedback on the learning plan displayed through their device. Input includes the user's emotional responses and text feedback, while output is feedback data. Specifically, users interact with the interface, inputting text and selecting options.

[0359] Step 6:

[0360] The server collects user feedback and emotional feedback data to update the model. It uses the feedback data as input to refine the new model. The output is the updated artificial intelligence model. Specifically, it analyzes the feedback data and automates model retraining.

[0361] (Application Example 2)

[0362] 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."

[0363] In supporting children's learning, there is a problem in providing individualized learning experiences. Traditional learning systems can grasp the learner's progress and suggest an optimal learning plan, but they cannot adapt the plan in real time while considering the user's emotions and reactions. In particular, understanding the learner's emotional state is important in order to effectively promote the maintenance of motivation and the overcoming of weak subjects.

[0364] 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.

[0365] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables a comprehensive understanding of the learner's learning status and emotional state, and the proposal of a dynamic learning plan based on this understanding.

[0366] "Information acquisition means" refers to functions for acquiring data on learners' learning progress and data on educational institutions.

[0367] "Data processing means" refers to functions that integrate, organize, and prepare collected information into a format suitable for analysis.

[0368] An "artificial intelligence model" is a function that generates optimal educational plans and career paths for learners based on compiled data.

[0369] "Display means" refers to a function for visually presenting the generated educational plan or career path to the user.

[0370] A "feedback mechanism" is a function for collecting user reactions to the educational plans and career paths presented.

[0371] An "emotion recognition tool" is a function that analyzes the user's emotional state from their facial expressions and voice, and collects that data.

[0372] "Emotional data" is a collection of information that indicates a user's emotional state, and the system uses this data to understand the user's mental state.

[0373] "Means of improvement" refers to features that utilize feedback and sentiment data to continuously update artificial intelligence models.

[0374] The system for implementing this invention mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, emotion recognition means, and a server for enabling these to function smoothly.

[0375] The server first acquires data in real time from learners and educational institutions through various information acquisition methods. This includes automated data acquisition using APIs and user-input interfaces. The collected data is stored in a database on the server.

[0376] Next, the server uses data processing tools to integrate the collected data, remove duplicate information and fill in missing information, and prepare it in a format suitable for analysis. The prepared data is then analyzed by artificial intelligence models to generate personalized educational plans and career paths optimized for the learner. In this process, the plan is dynamically adapted based on past performance data and current circumstances.

[0377] The generated educational plans and career paths are transmitted to the user's terminal via a display device and presented to the user in a visually verifiable format. The terminal employs a graphical user interface to facilitate user understanding of the proposed content.

[0378] Users can input feedback on displayed suggestions via their device, and this feedback is sent to the server. Simultaneously, emotion recognition technology is used to collect emotional data from the user's facial expressions and voice, and this data is analyzed in real time. The collected emotional data and feedback are integrated and used in a process to improve the artificial intelligence model on the server, which will help in suggesting future educational plans.

[0379] For example, if a child shows anxiety about a subject they struggle with, the system uses that information to suggest more engaging content or methods for the next session. This increases the child's motivation to learn. A specific example of a prompt might be, "If the child shows anxiety about a math problem, suggest words of encouragement."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server collects data from learners and educational institutions using various data acquisition methods. It uses data from APIs and data entry interfaces as input. Output is stored in a database in the form of student grades, attendance information, etc. After data acquisition, the data is organized on the server and prepared for subsequent processes.

[0383] Step 2:

[0384] The server organizes the data collected by the data processing system, removing duplicate data, filling in missing data, and standardizing data formats. The input is raw data retrieved from the database, and the output is well-organized data suitable for analysis. Through this process, the server establishes a stable data environment.

[0385] Step 3:

[0386] The server analyzes data prepared using artificial intelligence modeling tools. The input is processed data, and the generating AI model outputs a learning plan and path optimized for each user. At this stage, the AI ​​evaluates past learning history and current status and proposes a dynamic plan.

[0387] Step 4:

[0388] The generated learning plan and career path are sent from the server to the terminal and presented to the user through a display device. The input is AI-generated plan data, and the output is a graphical display that the user can visually understand. The terminal provides a user-friendly interface.

[0389] Step 5:

[0390] Users provide feedback on the plan displayed through their device. This feedback can be in the form of text or voice, and the feedback data is sent to the server as output. This feedback is then used for subsequent improvements.

[0391] Step 6:

[0392] The server uses emotion recognition technology to acquire emotional data from the user's facial expressions and voice. The input is emotional information obtained through the camera and microphone, and the analysis results are stored as output along with feedback. The system analyzes the user's mental state in real time.

[0393] Step 7:

[0394] Finally, the server integrates feedback and sentiment data to update the artificial intelligence model. The input is the user's sentiment data and feedback data, and the improved AI model is output. This will result in more personalized suggestions for future sessions.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] This invention is implemented as a system that utilizes advanced data analysis techniques and artificial intelligence models to support children's learning and growth.

[0412] First, the server automatically collects learning progress data and test results in real time from schools and local educational institutions using information acquisition methods. This data is stored in a database and used in subsequent processing.

[0413] Next, the server uses data processing tools to clean the collected data. This involves removing duplicate data and filling in missing data. This process creates a unified dataset, which forms the basis for analysis by the AI ​​model.

[0414] Using this well-organized data, the server generates personalized learning plans and career paths for each child through an artificial intelligence model. The AI ​​model analyzes past data and current trends to provide suggestions tailored to each child's characteristics. For example, a student with exceptional math skills might be suggested to participate in the International Mathematical Olympiad.

[0415] The generated learning plan and career path are delivered to the terminal via a display device and presented to the user. The terminal uses a GUI to provide information in a visually easy-to-understand format, making it easy for the user to comprehend the suggestions.

[0416] Furthermore, users can use their devices to provide feedback on the presented plans and paths. For example, if they feel the study schedule is unrealistic, they can enter a comment to that effect. This feedback is sent to the server in real time.

[0417] Finally, the server collects user feedback obtained through feedback mechanisms and incorporates it into the AI ​​model. This gradually improves the model, making adjustments so that future suggestions better match user needs.

[0418] This entire system enables effective learning support tailored to each individual child, allowing parents and children themselves to actively contribute to improving the educational environment.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server uses APIs and data entry interfaces to retrieve student learning data from educational institutions and store it in a database. This includes data such as grades, attendance information, and assignment progress.

[0422] Step 2:

[0423] The server cleans the collected data using data processing tools. Specifically, it removes duplicate data and fills in missing data. It also standardizes the data format as needed.

[0424] Step 3:

[0425] The server inputs the prepared data into an artificial intelligence model to analyze each student's learning progress and characteristics. Based on this individual data, the model generates an optimal learning plan and career path.

[0426] Step 4:

[0427] The server delivers the generated learning plan and career path to the terminal using a display device. The terminal then displays this information graphically to the user, making it easy to understand intuitively.

[0428] Step 5:

[0429] Users provide feedback on the presented learning plans and career paths through their devices. For example, they can input suggestions for revisions to the proposed learning schedule or provide evaluations of the career path suggestions.

[0430] Step 6:

[0431] The server collects feedback data sent by users and incorporates it into the artificial intelligence model through a feedback mechanism. This process updates the model and improves accuracy in subsequent suggestions.

[0432] (Example 1)

[0433] 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."

[0434] Traditionally, there has been a problem in effectively providing educational support tailored to the individual characteristics and progress of each learner. In particular, there is a lack of information processing and feedback systems that can collect learner data in real time and use that data to create optimal educational plans. This has led to problems such as inconsistent quality of education and difficulty in receiving appropriate career paths and learning support tailored to individual characteristics.

[0435] 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.

[0436] In this invention, the server includes an information acquisition mechanism for aggregating learner's educational progress data and educational institution information, a data manipulation mechanism for processing and organizing the aggregated information, and an artificial intelligence model mechanism for formulating educational plans and career paths based on the organized information. This enables effective learning support tailored to individual characteristics by automatically formulating and providing an educational plan optimized for each learner to the user.

[0437] An "information acquisition mechanism" is a technical means for aggregating data on learners' educational progress and information from educational institutions.

[0438] A "data manipulation mechanism" is a technical means for processing and organizing aggregated information, removing duplicate information, and supplementing missing information.

[0439] An "artificial intelligence model mechanism" is a technical means for formulating optimal educational plans and career paths based on compiled information.

[0440] A "display mechanism" is a technical means of visually presenting formulated educational plans and career paths to users.

[0441] A "mechanism for collecting opinions" is a technical means that has the function of allowing users to provide feedback on the presented educational plan and career path.

[0442] The "mechanism for improving artificial intelligence models" is a technical means to update artificial intelligence models based on collected user feedback and improve the accuracy of future model formulations.

[0443] This invention is built as a system that provides educational support tailored to individual learners. The system operates primarily based on the interaction between servers, terminals, and users.

[0444] The server uses an information acquisition mechanism to collect learner progress data and educational institution information. This involves using devices with APIs and internet connectivity, and the information is securely stored in a database. For example, learning records can be automatically retrieved from the educational institution's management system.

[0445] Next, the server uses Python and Pandas libraries to process the aggregated data using a data manipulation mechanism. This standardizes the format of the information and removes duplicate information and fills in missing information.

[0446] Based on the compiled data, the server uses an artificial intelligence modeling mechanism to develop educational plans and career paths. AI platforms such as TensorFlow and PyTorch are used to analyze trends from past learner data and generate personalized plans. For example, learners who demonstrate strong abilities in mathematics can be offered additional learning materials and events to provide further challenges.

[0447] The generated educational plan and career guidance are transmitted to the terminal via a display mechanism. The terminal uses a graphical user interface (GUI) to present the suggestions to the learner in a visually easy-to-understand format. The learning schedule and career guidance are displayed on the tablet or PC screen.

[0448] Users can provide feedback on the plan presented through their device. This feedback is entered through the interface and sent to the server in real time. The feedback includes specific opinions as text comments, such as "I don't think the learning plan is realistic."

[0449] Ultimately, the server improves its artificial intelligence model based on the collected feedback. Through this iterative process, the model gradually evolves, and subsequent suggestions become more aligned with the learner's needs. For example, if the prompt is "Student A is good at science and wants to learn more about it," the model has the ability to generate a customized educational plan based on this information.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The server uses an information retrieval mechanism to collect learner data from educational institutions. Input is grade information via APIs or data transfer protocols, and output is raw learning status data. This process accesses the educational institution's database and downloads learner grades and attendance information.

[0453] Step 2:

[0454] The server cleans the collected data using a data manipulation mechanism. The input is raw training data, and the output is a formatted dataset. The specific processing uses the Pandas library to remove duplicate data and impute missing data using preceding and succeeding data.

[0455] Step 3:

[0456] The server generates educational plans using an artificial intelligence model mechanism. The input is a formatted dataset, and the output is a personalized educational plan. The AI ​​model analyzes the data and constructs a suitable plan by comparing it with each learner's past performance. For example, a learner with excellent math skills will be suggested a more challenging next-level material.

[0457] Step 4:

[0458] The server sends the generated educational plan to the terminal. The input is the personalized educational plan, and the output is visually displayed data on the user's terminal. Specifically, it displays the new plan and career path to the learner in an easy-to-understand manner using a GUI-based dashboard.

[0459] Step 5:

[0460] The user uses a terminal to input feedback on the presented plan. The input is the user's text comment, and the output is feedback data transferred to the server. For example, the user might input the opinion "The plan is not feasible" as a comment on the terminal.

[0461] Step 6:

[0462] The server improves the artificial intelligence model based on feedback. The input is user feedback data, and the output is the updated AI model. Retraining is performed to reflect the results of the feedback, enabling the provision of more accurate training plans in the future.

[0463] (Application Example 1)

[0464] 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."

[0465] In systems designed to support children's learning activities, achieving both effectiveness and individual optimization is a challenge. Conventional systems were time-consuming to collect and analyze data, and their standardized learning plans failed to adapt to individual needs. Therefore, there is a need to simultaneously achieve real-time situation monitoring and optimal support for each individual learner.

[0466] 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.

[0467] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables interactive support using an educational device and facilitates feedback using prompt statements.

[0468] "Information acquisition means" refers to devices or processes for collecting data on children's learning progress and data from educational institutions.

[0469] "Data processing means" refers to the process of integrating collected data, eliminating missing or duplicate data, and converting it into organized data.

[0470] An "artificial intelligence model" is an algorithm or program that generates learning plans and career paths optimized for children based on compiled data.

[0471] "Display means" refers to a device or interface for visually presenting the generated learning plan or career path to the user.

[0472] A "feedback collection method" is a process or device that allows users to provide opinions and evaluations regarding the presented learning plan or career path.

[0473] An "educational device" is an automated device equipped with functions to support learning while interacting with the user.

[0474] A "prompt message" is a set of instructions or questions generated to encourage user feedback.

[0475] The system for implementing this invention consists of three main elements: a server, an educational device, and a user terminal.

[0476] The server uses data acquisition methods to collect children's learning progress data and data from local educational institutions in real time. The collected data is cleaned and integrated using Pandas, a data processing software that uses Python. During data processing, missing data is imputed and duplicate data is removed, transforming the data into a unified dataset.

[0477] Next, the server uses the prepared data to generate learning plans and career paths tailored to each individual child, employing an AI model built with TensorFlow as an artificial intelligence modeling tool. This AI model provides specific suggestions based on existing learning data and current trends.

[0478] The generated learning plan and career path information are delivered to the user's terminal via a display device. On the user's terminal, a GUI is used to visually display the learning plan, providing information in a user-friendly format. Visualization libraries such as TKinter are used for displaying the information on the terminal.

[0479] The educational robot interacts with the user, or learner, in a conversational manner, supporting the progress of the learning plan. Based on the input feedback, the educational robot sends information to the server, and the AI ​​model is adjusted in real time. As a result, the AI ​​model reflects the user's opinions, making subsequent suggestions more adaptive.

[0480] Prompts serve to help users provide feedback. For example, a prompt like, "Have you finished studying this unit? Please tell us what you think," might be used. This prompt makes it easier for users to spontaneously provide feedback.

[0481] In this way, it becomes possible to provide individually optimized learning support. For example, if a learner takes a test in a certain subject and it is determined that they perform well in mathematics, they may be prompted with a message such as, "Would you like a mathematics reinforcement plan?" This allows learners to more efficiently develop their strengths.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1: Information Gathering

[0484] The server collects data on children's learning progress from schools and local educational institutions through information acquisition methods. Inputs include student grades, attendance information, and class progress. This information is retrieved via APIs and other data interfaces and sent to the server. The output is in the form of raw learning data. The server uses this in the next processing step.

[0485] Step 2: Data Processing

[0486] The server prepares the collected training data using data processing tools. In this step, Pandas is used to clean the data, remove duplicates, and impute missing data. The input is the raw data obtained in step 1, and the output is a prepared dataset in a unified format. This dataset is used as the basis for the AI ​​model.

[0487] Step 3: Generate a learning plan

[0488] The server receives the prepared data and generates a learning plan using an artificial intelligence model. The AI ​​model, built using TensorFlow, analyzes past training data and current trends. The input is the prepared data, and the output is a personalized learning plan. This plan suggests specific subjects and tasks based on the user's characteristics.

[0489] Step 4: Presenting a learning plan

[0490] The server sends the generated learning plan to the user's terminal. The terminal uses a display device to provide information visually through a GUI powered by TKinter. The input is the learning plan generated in step 3, and the output is the visualized information. This allows the user to review the plan details and understand what to learn next.

[0491] Step 5: Gathering Feedback

[0492] Users provide feedback on their learning plans through their devices. Through the feedback collection mechanism, users input information about the feasibility and areas for improvement of the plan. This prompt includes user-guided questions such as, "Have you finished studying this unit? Please share your thoughts." The input is the user's opinion, and the output is feedback data.

[0493] Step 6: Update the model

[0494] The server updates the artificial intelligence model based on user feedback. The AI ​​model incorporates the newly acquired feedback data to make future suggestions more appropriate. In this process, the input is the feedback collected in step 5, and the output is the updated AI model. This makes it possible to provide an optimized learning plan at all times.

[0495] 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.

[0496] This invention is implemented as a system that combines an emotion engine to provide more advanced support for children's learning. The system mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, and an emotion engine.

[0497] First, the server utilizes information acquisition methods to collect real-time information on students' learning progress and academic performance from schools and local educational institutions. This data includes test results and attendance information obtained via APIs. The collected data is stored in a database and used for subsequent processing.

[0498] The server then uses data processing tools to remove duplicate data, fill in missing data, and standardize data formats. This process prepares the student data for analysis.

[0499] Next, the server uses an artificial intelligence model to analyze the prepared data and generate an optimized learning plan and career path for each student. The generated plan is dynamically adapted based on past data and the current situation.

[0500] The generated plan and route are transmitted to the terminal via a display device and visually displayed on the screen. The terminal provides information graphically to help the user understand the proposal.

[0501] Furthermore, users can express their emotional reactions to the content provided through the emotion engine. The emotion engine uses voice analysis and facial recognition to evaluate the user's emotional state in real time. For example, it can obtain emotional data such as "surprise" or "satisfaction."

[0502] The server integrates user feedback entered via the terminal with emotional data recognized by the emotion engine, and uses this information to update the artificial intelligence model for future suggestions. For example, if a user expresses dissatisfaction with a plan, a new plan can be generated based on that information.

[0503] In this way, this system adds an emotional element to feedback, enabling more personalized educational suggestions than conventional systems. This allows parents and students to manage the educational environment more effectively and lead to optimal learning outcomes.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The server collects student learning progress and performance data from educational institutions through various data acquisition methods. This data is automatically retrieved using an API and stored in a database. This information includes student test results, attendance records, and assignment submission status.

[0507] Step 2:

[0508] The server uses data processing tools to organize the collected data. Specifically, it removes duplicate data, fills in missing data, and standardizes the data format. This makes the data suitable for analysis.

[0509] Step 3:

[0510] The server inputs data into an artificial intelligence model to generate individualized learning plans and career paths for each student. The AI ​​model analyzes past performance and trends to create dynamic plans. For example, it can suggest even higher-level math to students who excel in mathematics.

[0511] Step 4:

[0512] The terminal presents the user with a learning plan received from the server via a display device. The information is displayed on the screen in a visually organized manner to facilitate user understanding.

[0513] Step 5:

[0514] The device uses an emotion engine to recognize the user's emotions in real time. While the user is viewing the learning plan, it analyzes changes in voice and facial expressions and evaluates their emotional response. For example, if the user shows "surprise" or "confusion," that information is recorded.

[0515] Step 6:

[0516] Users can input their opinions and emotional feedback on the presented learning plan through their device. For example, if they feel a suggestion is inappropriate, they can explain their specific reasons in the comments section.

[0517] Step 7:

[0518] The server aggregates feedback and sentiment data sent from the terminal and updates the artificial intelligence model. This update ensures that the next suggestion reflects the user's emotions and opinions.

[0519] (Example 2)

[0520] 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."

[0521] In today's educational environment, individualized learning support is a crucial issue. Traditional education systems struggle to accurately understand students' learning progress and provide individually optimized learning plans, and they lack mechanisms for effectively incorporating feedback. As a result, students often don't receive the educational support they truly need, leading to decreased learning efficiency.

[0522] 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.

[0523] In this invention, the server includes data collection means for acquiring educational data and information on local educational institutions, information processing means for integrating and organizing the collected information, and artificial intelligence modeling means for creating educational plans and career paths based on the organized information. This makes it possible to dynamically generate learning plans optimized for individual students and to flexibly update educational suggestions while taking into account user feedback and sentiment data.

[0524] "Data collection means" refers to functions or devices used to acquire educational data and information on local educational institutions.

[0525] "Information processing means" refers to a function or device used to integrate and organize collected information.

[0526] "Artificial intelligence model means" refers to a function or device that includes machine learning technology used to create educational plans and career paths based on compiled information.

[0527] "Display means" refers to a device or method used to present the created educational plan and career path to the user.

[0528] "Means for evaluating and collecting emotional feedback" refers to a function or device used to analyze a user's emotional state and collect feedback on it.

[0529] "Means for updating the model" refers to a function or device used to modify or improve an artificial intelligence model based on user feedback and sentiment data.

[0530] This invention is a system in which a server collects educational data and information from local educational institutions and generates personalized learning plans. First, the server uses an information acquisition means to obtain students' learning status and performance information in real time via an API. This makes it possible to organize and store data from educational institutions in a database on the server.

[0531] Next, the server uses information processing tools to organize the collected data. This process involves removing duplicate data, filling in missing data, and standardizing the format to ensure data accuracy and consistency. Database queries and scripts are used for this process.

[0532] Based on the compiled data, the server generates learning plans and career paths using artificial intelligence models. The AI ​​technology used here applies past learning data and algorithms to provide the most appropriate educational suggestions for each student. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0533] The generated plan is sent to the device and presented to the user through a display device. The device visually displays the data using a graphical user interface so that the user can easily understand the information. In this process, web technologies such as React and Vue.js are commonly used.

[0534] Subsequently, users can provide feedback on the displayed learning plan. To capture emotional responses, a means of evaluating and collecting emotional feedback is used, analyzing emotional data from the user's voice and facial expressions. This information is used to update the artificial intelligence model and improve the accuracy of the plan.

[0535] For example, if feedback is received regarding an elementary school math drill, such as "Please include more fraction problems," this feedback will be reflected in the artificial intelligence model, and adjustments will be made to the next learning plan.

[0536] An example of a prompt message that can utilize this technology is: "Please create a math learning plan for elementary school students, focusing particularly on fractions. Please include recommended materials and specific topics."

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The server uses an information retrieval method to obtain student learning status and grade information through the educational institution's API. The input requires the API endpoint URL and authentication information, and data is collected using HTTP requests. The output is student information data in JSON or XML format. Specifically, the server automatically executes this request periodically to retrieve new data in a timely manner.

[0540] Step 2:

[0541] The server processes the acquired JSON or XML data using information processing tools. Student information data is provided as input, and the output is normalized data that can be stored in a database. The server performs data normalization, imputation of missing values ​​(e.g., median imputation), and standardization of formatting. Specifically, scripts and SQL queries are used to remove duplicate data and standardize data formats.

[0542] Step 3:

[0543] The server uses artificial intelligence models with pre-prepared data as input to generate learning plans and career paths. The output is an optimal educational plan for each individual student, with the AI ​​model making predictions using historical data and algorithms. Specifically, it uses machine learning libraries (e.g., TensorFlow and PyTorch) to analyze data and create educational plans.

[0544] Step 4:

[0545] The server sends the generated learning plan to the terminal and presents it to the user via a display device. The input is the generated learning plan, and the output is a visual display on the user's terminal. Specifically, web technologies (e.g., React or Vue.js) are used to display the plan in a dashboard or infographic format.

[0546] Step 5:

[0547] Users provide feedback on the learning plan displayed through their device. Input includes the user's emotional responses and text feedback, while output is feedback data. Specifically, users interact with the interface, inputting text and selecting options.

[0548] Step 6:

[0549] The server collects user feedback and emotional feedback data to update the model. It uses the feedback data as input to refine the new model. The output is the updated artificial intelligence model. Specifically, it analyzes the feedback data and automates model retraining.

[0550] (Application Example 2)

[0551] 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."

[0552] In supporting children's learning, there is a problem in providing individualized learning experiences. Traditional learning systems can grasp the learner's progress and suggest an optimal learning plan, but they cannot adapt the plan in real time while considering the user's emotions and reactions. In particular, understanding the learner's emotional state is important in order to effectively promote the maintenance of motivation and the overcoming of weak subjects.

[0553] 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.

[0554] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables a comprehensive understanding of the learner's learning status and emotional state, and the proposal of a dynamic learning plan based on this understanding.

[0555] "Information acquisition means" refers to functions for acquiring data on learners' learning progress and data on educational institutions.

[0556] "Data processing means" refers to functions that integrate, organize, and prepare collected information into a format suitable for analysis.

[0557] An "artificial intelligence model" is a function that generates optimal educational plans and career paths for learners based on compiled data.

[0558] "Display means" refers to a function for visually presenting the generated educational plan or career path to the user.

[0559] A "feedback mechanism" is a function for collecting user reactions to the educational plans and career paths presented.

[0560] An "emotion recognition tool" is a function that analyzes the user's emotional state from their facial expressions and voice, and collects that data.

[0561] "Emotional data" is a collection of information that indicates a user's emotional state, and the system uses this data to understand the user's mental state.

[0562] "Means of improvement" refers to features that utilize feedback and sentiment data to continuously update artificial intelligence models.

[0563] The system for implementing this invention mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, emotion recognition means, and a server for enabling these to function smoothly.

[0564] The server first acquires data in real time from learners and educational institutions through various information acquisition methods. This includes automated data acquisition using APIs and user-input interfaces. The collected data is stored in a database on the server.

[0565] Next, the server uses data processing tools to integrate the collected data, remove duplicate information and fill in missing information, and prepare it in a format suitable for analysis. The prepared data is then analyzed by artificial intelligence models to generate personalized educational plans and career paths optimized for the learner. In this process, the plan is dynamically adapted based on past performance data and current circumstances.

[0566] The generated educational plans and career paths are transmitted to the user's terminal via a display device and presented to the user in a visually verifiable format. The terminal employs a graphical user interface to facilitate user understanding of the proposed content.

[0567] Users can input feedback on displayed suggestions via their device, and this feedback is sent to the server. Simultaneously, emotion recognition technology is used to collect emotional data from the user's facial expressions and voice, and this data is analyzed in real time. The collected emotional data and feedback are integrated and used in a process to improve the artificial intelligence model on the server, which will help in suggesting future educational plans.

[0568] For example, if a child shows anxiety about a subject they struggle with, the system uses that information to suggest more engaging content or methods for the next session. This increases the child's motivation to learn. A specific example of a prompt might be, "If the child shows anxiety about a math problem, suggest words of encouragement."

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server collects data from learners and educational institutions using various data acquisition methods. It uses data from APIs and data entry interfaces as input. Output is stored in a database in the form of student grades, attendance information, etc. After data acquisition, the data is organized on the server and prepared for subsequent processes.

[0572] Step 2:

[0573] The server organizes the data collected by the data processing system, removing duplicate data, filling in missing data, and standardizing data formats. The input is raw data retrieved from the database, and the output is well-organized data suitable for analysis. Through this process, the server establishes a stable data environment.

[0574] Step 3:

[0575] The server analyzes data prepared using artificial intelligence modeling tools. The input is processed data, and the generating AI model outputs a learning plan and path optimized for each user. At this stage, the AI ​​evaluates past learning history and current status and proposes a dynamic plan.

[0576] Step 4:

[0577] The generated learning plan and career path are sent from the server to the terminal and presented to the user through a display device. The input is AI-generated plan data, and the output is a graphical display that the user can visually understand. The terminal provides a user-friendly interface.

[0578] Step 5:

[0579] Users provide feedback on the plan displayed through their device. This feedback can be in the form of text or voice, and the feedback data is sent to the server as output. This feedback is then used for subsequent improvements.

[0580] Step 6:

[0581] The server uses emotion recognition technology to acquire emotional data from the user's facial expressions and voice. The input is emotional information obtained through the camera and microphone, and the analysis results are stored as output along with feedback. The system analyzes the user's mental state in real time.

[0582] Step 7:

[0583] Finally, the server integrates feedback and sentiment data to update the artificial intelligence model. The input is the user's sentiment data and feedback data, and the improved AI model is output. This will result in more personalized suggestions for future sessions.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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".

[0601] This invention is implemented as a system that utilizes advanced data analysis techniques and artificial intelligence models to support children's learning and growth.

[0602] First, the server automatically collects learning progress data and test results in real time from schools and local educational institutions using information acquisition methods. This data is stored in a database and used in subsequent processing.

[0603] Next, the server uses data processing tools to clean the collected data. This involves removing duplicate data and filling in missing data. This process creates a unified dataset, which forms the basis for analysis by the AI ​​model.

[0604] Using this well-organized data, the server generates personalized learning plans and career paths for each child through an artificial intelligence model. The AI ​​model analyzes past data and current trends to provide suggestions tailored to each child's characteristics. For example, a student with exceptional math skills might be suggested to participate in the International Mathematical Olympiad.

[0605] The generated learning plan and career path are delivered to the terminal via a display device and presented to the user. The terminal uses a GUI to provide information in a visually easy-to-understand format, making it easy for the user to comprehend the suggestions.

[0606] Furthermore, users can use their devices to provide feedback on the presented plans and paths. For example, if they feel the study schedule is unrealistic, they can enter a comment to that effect. This feedback is sent to the server in real time.

[0607] Finally, the server collects user feedback obtained through feedback mechanisms and incorporates it into the AI ​​model. This gradually improves the model, making adjustments so that future suggestions better match user needs.

[0608] This entire system enables effective learning support tailored to each individual child, allowing parents and children themselves to actively contribute to improving the educational environment.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server uses APIs and data entry interfaces to retrieve student learning data from educational institutions and store it in a database. This includes data such as grades, attendance information, and assignment progress.

[0612] Step 2:

[0613] The server cleans the collected data using data processing tools. Specifically, it removes duplicate data and fills in missing data. It also standardizes the data format as needed.

[0614] Step 3:

[0615] The server inputs the prepared data into an artificial intelligence model to analyze each student's learning progress and characteristics. Based on this individual data, the model generates an optimal learning plan and career path.

[0616] Step 4:

[0617] The server delivers the generated learning plan and career path to the terminal using a display device. The terminal then displays this information graphically to the user, making it easy to understand intuitively.

[0618] Step 5:

[0619] Users provide feedback on the presented learning plans and career paths through their devices. For example, they can input suggestions for revisions to the proposed learning schedule or provide evaluations of the career path suggestions.

[0620] Step 6:

[0621] The server collects feedback data sent by users and incorporates it into the artificial intelligence model through a feedback mechanism. This process updates the model and improves accuracy in subsequent suggestions.

[0622] (Example 1)

[0623] 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".

[0624] Traditionally, there has been a problem in effectively providing educational support tailored to the individual characteristics and progress of each learner. In particular, there is a lack of information processing and feedback systems that can collect learner data in real time and use that data to create optimal educational plans. This has led to problems such as inconsistent quality of education and difficulty in receiving appropriate career paths and learning support tailored to individual characteristics.

[0625] 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.

[0626] In this invention, the server includes an information acquisition mechanism for aggregating learner's educational progress data and educational institution information, a data manipulation mechanism for processing and organizing the aggregated information, and an artificial intelligence model mechanism for formulating educational plans and career paths based on the organized information. This enables effective learning support tailored to individual characteristics by automatically formulating and providing an educational plan optimized for each learner to the user.

[0627] An "information acquisition mechanism" is a technical means for aggregating data on learners' educational progress and information from educational institutions.

[0628] A "data manipulation mechanism" is a technical means for processing and organizing aggregated information, removing duplicate information, and supplementing missing information.

[0629] An "artificial intelligence model mechanism" is a technical means for formulating optimal educational plans and career paths based on compiled information.

[0630] A "display mechanism" is a technical means of visually presenting formulated educational plans and career paths to users.

[0631] A "mechanism for collecting opinions" is a technical means that has the function of allowing users to provide feedback on the presented educational plan and career path.

[0632] The "mechanism for improving artificial intelligence models" is a technical means to update artificial intelligence models based on collected user feedback and improve the accuracy of future model formulations.

[0633] This invention is built as a system that provides educational support tailored to individual learners. The system operates primarily based on the interaction between servers, terminals, and users.

[0634] The server uses an information acquisition mechanism to collect learner progress data and educational institution information. This involves using devices with APIs and internet connectivity, and the information is securely stored in a database. For example, learning records can be automatically retrieved from the educational institution's management system.

[0635] Next, the server uses Python and Pandas libraries to process the aggregated data using a data manipulation mechanism. This standardizes the format of the information and removes duplicate information and fills in missing information.

[0636] Based on the compiled data, the server uses an artificial intelligence modeling mechanism to develop educational plans and career paths. AI platforms such as TensorFlow and PyTorch are used to analyze trends from past learner data and generate personalized plans. For example, learners who demonstrate strong abilities in mathematics can be offered additional learning materials and events to provide further challenges.

[0637] The generated educational plan and career guidance are transmitted to the terminal via a display mechanism. The terminal uses a graphical user interface (GUI) to present the suggestions to the learner in a visually easy-to-understand format. The learning schedule and career guidance are displayed on the tablet or PC screen.

[0638] Users can provide feedback on the plan presented through their device. This feedback is entered through the interface and sent to the server in real time. The feedback includes specific opinions as text comments, such as "I don't think the learning plan is realistic."

[0639] Ultimately, the server improves its artificial intelligence model based on the collected feedback. Through this iterative process, the model gradually evolves, and subsequent suggestions become more aligned with the learner's needs. For example, if the prompt is "Student A is good at science and wants to learn more about it," the model has the ability to generate a customized educational plan based on this information.

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The server uses an information retrieval mechanism to collect learner data from educational institutions. Input is grade information via APIs or data transfer protocols, and output is raw learning status data. This process accesses the educational institution's database and downloads learner grades and attendance information.

[0643] Step 2:

[0644] The server cleans the collected data using a data manipulation mechanism. The input is raw training data, and the output is a formatted dataset. The specific processing uses the Pandas library to remove duplicate data and impute missing data using preceding and succeeding data.

[0645] Step 3:

[0646] The server generates educational plans using an artificial intelligence model mechanism. The input is a formatted dataset, and the output is a personalized educational plan. The AI ​​model analyzes the data and constructs a suitable plan by comparing it with each learner's past performance. For example, a learner with excellent math skills will be suggested a more challenging next-level material.

[0647] Step 4:

[0648] The server sends the generated educational plan to the terminal. The input is the personalized educational plan, and the output is visually displayed data on the user's terminal. Specifically, it displays the new plan and career path to the learner in an easy-to-understand manner using a GUI-based dashboard.

[0649] Step 5:

[0650] The user uses a terminal to input feedback on the presented plan. The input is the user's text comment, and the output is feedback data transferred to the server. For example, the user might input the opinion "The plan is not feasible" as a comment on the terminal.

[0651] Step 6:

[0652] The server improves the artificial intelligence model based on feedback. The input is user feedback data, and the output is the updated AI model. Retraining is performed to reflect the results of the feedback, enabling the provision of more accurate training plans in the future.

[0653] (Application Example 1)

[0654] 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".

[0655] In systems designed to support children's learning activities, achieving both effectiveness and individual optimization is a challenge. Conventional systems were time-consuming to collect and analyze data, and their standardized learning plans failed to adapt to individual needs. Therefore, there is a need to simultaneously achieve real-time situation monitoring and optimal support for each individual learner.

[0656] 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.

[0657] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables interactive support using an educational device and facilitates feedback using prompt statements.

[0658] "Information acquisition means" refers to devices or processes for collecting data on children's learning progress and data from educational institutions.

[0659] "Data processing means" refers to the process of integrating collected data, eliminating missing or duplicate data, and converting it into organized data.

[0660] An "artificial intelligence model" is an algorithm or program that generates learning plans and career paths optimized for children based on compiled data.

[0661] "Display means" refers to a device or interface for visually presenting the generated learning plan or career path to the user.

[0662] A "feedback collection method" is a process or device that allows users to provide opinions and evaluations regarding the presented learning plan or career path.

[0663] An "educational device" is an automated device equipped with functions to support learning while interacting with the user.

[0664] A "prompt message" is a set of instructions or questions generated to encourage user feedback.

[0665] The system for implementing this invention consists of three main elements: a server, an educational device, and a user terminal.

[0666] The server uses data acquisition methods to collect children's learning progress data and data from local educational institutions in real time. The collected data is cleaned and integrated using Pandas, a data processing software that uses Python. During data processing, missing data is imputed and duplicate data is removed, transforming the data into a unified dataset.

[0667] Next, the server uses the prepared data to generate learning plans and career paths tailored to each individual child, employing an AI model built with TensorFlow as an artificial intelligence modeling tool. This AI model provides specific suggestions based on existing learning data and current trends.

[0668] The generated learning plan and career path information are delivered to the user's terminal via a display device. On the user's terminal, a GUI is used to visually display the learning plan, providing information in a user-friendly format. Visualization libraries such as TKinter are used for displaying the information on the terminal.

[0669] The educational robot interacts with the user, or learner, in a conversational manner, supporting the progress of the learning plan. Based on the input feedback, the educational robot sends information to the server, and the AI ​​model is adjusted in real time. As a result, the AI ​​model reflects the user's opinions, making subsequent suggestions more adaptive.

[0670] Prompts serve to help users provide feedback. For example, a prompt like, "Have you finished studying this unit? Please tell us what you think," might be used. This prompt makes it easier for users to spontaneously provide feedback.

[0671] In this way, it becomes possible to provide individually optimized learning support. For example, if a learner takes a test in a certain subject and it is determined that they perform well in mathematics, they may be prompted with a message such as, "Would you like a mathematics reinforcement plan?" This allows learners to more efficiently develop their strengths.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1: Information Gathering

[0674] The server collects data on children's learning progress from schools and local educational institutions through information acquisition methods. Inputs include student grades, attendance information, and class progress. This information is retrieved via APIs and other data interfaces and sent to the server. The output is in the form of raw learning data. The server uses this in the next processing step.

[0675] Step 2: Data Processing

[0676] The server prepares the collected training data using data processing tools. In this step, Pandas is used to clean the data, remove duplicates, and impute missing data. The input is the raw data obtained in step 1, and the output is a prepared dataset in a unified format. This dataset is used as the basis for the AI ​​model.

[0677] Step 3: Generate a learning plan

[0678] The server receives the prepared data and generates a learning plan using an artificial intelligence model. The AI ​​model, built using TensorFlow, analyzes past training data and current trends. The input is the prepared data, and the output is a personalized learning plan. This plan suggests specific subjects and tasks based on the user's characteristics.

[0679] Step 4: Presenting a learning plan

[0680] The server sends the generated learning plan to the user's terminal. The terminal uses a display device to provide information visually through a GUI powered by TKinter. The input is the learning plan generated in step 3, and the output is the visualized information. This allows the user to review the plan details and understand what to learn next.

[0681] Step 5: Gathering Feedback

[0682] Users provide feedback on their learning plans through their devices. Through the feedback collection mechanism, users input information about the feasibility and areas for improvement of the plan. This prompt includes user-guided questions such as, "Have you finished studying this unit? Please share your thoughts." The input is the user's opinion, and the output is feedback data.

[0683] Step 6: Update the model

[0684] The server updates the artificial intelligence model based on user feedback. The AI ​​model incorporates the newly acquired feedback data to make future suggestions more appropriate. In this process, the input is the feedback collected in step 5, and the output is the updated AI model. This makes it possible to provide an optimized learning plan at all times.

[0685] 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.

[0686] This invention is implemented as a system that combines an emotion engine to provide more advanced support for children's learning. The system mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, and an emotion engine.

[0687] First, the server utilizes information acquisition methods to collect real-time information on students' learning progress and academic performance from schools and local educational institutions. This data includes test results and attendance information obtained via APIs. The collected data is stored in a database and used for subsequent processing.

[0688] The server then uses data processing tools to remove duplicate data, fill in missing data, and standardize data formats. This process prepares the student data for analysis.

[0689] Next, the server uses an artificial intelligence model to analyze the prepared data and generate an optimized learning plan and career path for each student. The generated plan is dynamically adapted based on past data and the current situation.

[0690] The generated plan and route are transmitted to the terminal via a display device and visually displayed on the screen. The terminal provides information graphically to help the user understand the proposal.

[0691] Furthermore, users can express their emotional reactions to the content provided through the emotion engine. The emotion engine uses voice analysis and facial recognition to evaluate the user's emotional state in real time. For example, it can obtain emotional data such as "surprise" or "satisfaction."

[0692] The server integrates user feedback entered via the terminal with emotional data recognized by the emotion engine, and uses this information to update the artificial intelligence model for future suggestions. For example, if a user expresses dissatisfaction with a plan, a new plan can be generated based on that information.

[0693] In this way, this system adds an emotional element to feedback, enabling more personalized educational suggestions than conventional systems. This allows parents and students to manage the educational environment more effectively and lead to optimal learning outcomes.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The server collects student learning progress and performance data from educational institutions through various data acquisition methods. This data is automatically retrieved using an API and stored in a database. This information includes student test results, attendance records, and assignment submission status.

[0697] Step 2:

[0698] The server uses data processing tools to organize the collected data. Specifically, it removes duplicate data, fills in missing data, and standardizes the data format. This makes the data suitable for analysis.

[0699] Step 3:

[0700] The server inputs data into an artificial intelligence model to generate individualized learning plans and career paths for each student. The AI ​​model analyzes past performance and trends to create dynamic plans. For example, it can suggest even higher-level math to students who excel in mathematics.

[0701] Step 4:

[0702] The terminal presents the user with a learning plan received from the server via a display device. The information is displayed on the screen in a visually organized manner to facilitate user understanding.

[0703] Step 5:

[0704] The device uses an emotion engine to recognize the user's emotions in real time. While the user is viewing the learning plan, it analyzes changes in voice and facial expressions and evaluates their emotional response. For example, if the user shows "surprise" or "confusion," that information is recorded.

[0705] Step 6:

[0706] Users can input their opinions and emotional feedback on the presented learning plan through their device. For example, if they feel a suggestion is inappropriate, they can explain their specific reasons in the comments section.

[0707] Step 7:

[0708] The server aggregates feedback and sentiment data sent from the terminal and updates the artificial intelligence model. This update ensures that the next suggestion reflects the user's emotions and opinions.

[0709] (Example 2)

[0710] 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".

[0711] In today's educational environment, individualized learning support is a crucial issue. Traditional education systems struggle to accurately understand students' learning progress and provide individually optimized learning plans, and they lack mechanisms for effectively incorporating feedback. As a result, students often don't receive the educational support they truly need, leading to decreased learning efficiency.

[0712] 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.

[0713] In this invention, the server includes data collection means for acquiring educational data and information on local educational institutions, information processing means for integrating and organizing the collected information, and artificial intelligence modeling means for creating educational plans and career paths based on the organized information. This makes it possible to dynamically generate learning plans optimized for individual students and to flexibly update educational suggestions while taking into account user feedback and sentiment data.

[0714] "Data collection means" refers to functions or devices used to acquire educational data and information on local educational institutions.

[0715] "Information processing means" refers to a function or device used to integrate and organize collected information.

[0716] "Artificial intelligence model means" refers to a function or device that includes machine learning technology used to create educational plans and career paths based on compiled information.

[0717] "Display means" refers to a device or method used to present the created educational plan and career path to the user.

[0718] "Means for evaluating and collecting emotional feedback" refers to a function or device used to analyze a user's emotional state and collect feedback on it.

[0719] "Means for updating the model" refers to a function or device used to modify or improve an artificial intelligence model based on user feedback and sentiment data.

[0720] This invention is a system in which a server collects educational data and information from local educational institutions and generates personalized learning plans. First, the server uses an information acquisition means to obtain students' learning status and performance information in real time via an API. This makes it possible to organize and store data from educational institutions in a database on the server.

[0721] Next, the server uses information processing tools to organize the collected data. This process involves removing duplicate data, filling in missing data, and standardizing the format to ensure data accuracy and consistency. Database queries and scripts are used for this process.

[0722] Based on the compiled data, the server generates learning plans and career paths using artificial intelligence models. The AI ​​technology used here applies past learning data and algorithms to provide the most appropriate educational suggestions for each student. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0723] The generated plan is sent to the device and presented to the user through a display device. The device visually displays the data using a graphical user interface so that the user can easily understand the information. In this process, web technologies such as React and Vue.js are commonly used.

[0724] Subsequently, users can provide feedback on the displayed learning plan. To capture emotional responses, a means of evaluating and collecting emotional feedback is used, analyzing emotional data from the user's voice and facial expressions. This information is used to update the artificial intelligence model and improve the accuracy of the plan.

[0725] For example, if feedback is received regarding an elementary school math drill, such as "Please include more fraction problems," this feedback will be reflected in the artificial intelligence model, and adjustments will be made to the next learning plan.

[0726] An example of a prompt message that can utilize this technology is: "Please create a math learning plan for elementary school students, focusing particularly on fractions. Please include recommended materials and specific topics."

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The server uses an information retrieval method to obtain student learning status and grade information through the educational institution's API. The input requires the API endpoint URL and authentication information, and data is collected using HTTP requests. The output is student information data in JSON or XML format. Specifically, the server automatically executes this request periodically to retrieve new data in a timely manner.

[0730] Step 2:

[0731] The server processes the acquired JSON or XML data using information processing tools. Student information data is provided as input, and the output is normalized data that can be stored in a database. The server performs data normalization, imputation of missing values ​​(e.g., median imputation), and standardization of formatting. Specifically, scripts and SQL queries are used to remove duplicate data and standardize data formats.

[0732] Step 3:

[0733] The server uses artificial intelligence models with pre-prepared data as input to generate learning plans and career paths. The output is an optimal educational plan for each individual student, with the AI ​​model making predictions using historical data and algorithms. Specifically, it uses machine learning libraries (e.g., TensorFlow and PyTorch) to analyze data and create educational plans.

[0734] Step 4:

[0735] The server sends the generated learning plan to the terminal and presents it to the user via a display device. The input is the generated learning plan, and the output is a visual display on the user's terminal. Specifically, web technologies (e.g., React or Vue.js) are used to display the plan in a dashboard or infographic format.

[0736] Step 5:

[0737] Users provide feedback on the learning plan displayed through their device. Input includes the user's emotional responses and text feedback, while output is feedback data. Specifically, users interact with the interface, inputting text and selecting options.

[0738] Step 6:

[0739] The server collects user feedback and emotional feedback data to update the model. It uses the feedback data as input to refine the new model. The output is the updated artificial intelligence model. Specifically, it analyzes the feedback data and automates model retraining.

[0740] (Application Example 2)

[0741] 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".

[0742] In supporting children's learning, there is a problem in providing individualized learning experiences. Traditional learning systems can grasp the learner's progress and suggest an optimal learning plan, but they cannot adapt the plan in real time while considering the user's emotions and reactions. In particular, understanding the learner's emotional state is important in order to effectively promote the maintenance of motivation and the overcoming of weak subjects.

[0743] 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.

[0744] In this invention, the server includes information acquisition means, data processing means, and artificial intelligence model means. This enables a comprehensive understanding of the learner's learning status and emotional state, and the proposal of a dynamic learning plan based on this understanding.

[0745] "Information acquisition means" refers to functions for acquiring data on learners' learning progress and data on educational institutions.

[0746] "Data processing means" refers to functions that integrate, organize, and prepare collected information into a format suitable for analysis.

[0747] An "artificial intelligence model" is a function that generates optimal educational plans and career paths for learners based on compiled data.

[0748] "Display means" refers to a function for visually presenting the generated educational plan or career path to the user.

[0749] A "feedback mechanism" is a function for collecting user reactions to the educational plans and career paths presented.

[0750] An "emotion recognition tool" is a function that analyzes the user's emotional state from their facial expressions and voice, and collects that data.

[0751] "Emotional data" is a collection of information that indicates a user's emotional state, and the system uses this data to understand the user's mental state.

[0752] "Means of improvement" refers to features that utilize feedback and sentiment data to continuously update artificial intelligence models.

[0753] The system for implementing this invention mainly consists of information acquisition means, data processing means, artificial intelligence model means, display means, feedback means, emotion recognition means, and a server for enabling these to function smoothly.

[0754] The server first acquires data in real time from learners and educational institutions through various information acquisition methods. This includes automated data acquisition using APIs and user-input interfaces. The collected data is stored in a database on the server.

[0755] Next, the server uses data processing tools to integrate the collected data, remove duplicate information and fill in missing information, and prepare it in a format suitable for analysis. The prepared data is then analyzed by artificial intelligence models to generate personalized educational plans and career paths optimized for the learner. In this process, the plan is dynamically adapted based on past performance data and current circumstances.

[0756] The generated educational plans and career paths are transmitted to the user's terminal via a display device and presented to the user in a visually verifiable format. The terminal employs a graphical user interface to facilitate user understanding of the proposed content.

[0757] Users can input feedback on displayed suggestions via their device, and this feedback is sent to the server. Simultaneously, emotion recognition technology is used to collect emotional data from the user's facial expressions and voice, and this data is analyzed in real time. The collected emotional data and feedback are integrated and used in a process to improve the artificial intelligence model on the server, which will help in suggesting future educational plans.

[0758] For example, if a child shows anxiety about a subject they struggle with, the system uses that information to suggest more engaging content or methods for the next session. This increases the child's motivation to learn. A specific example of a prompt might be, "If the child shows anxiety about a math problem, suggest words of encouragement."

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The server collects data from learners and educational institutions using various data acquisition methods. It uses data from APIs and data entry interfaces as input. Output is stored in a database in the form of student grades, attendance information, etc. After data acquisition, the data is organized on the server and prepared for subsequent processes.

[0762] Step 2:

[0763] The server organizes the data collected by the data processing system, removing duplicate data, filling in missing data, and standardizing data formats. The input is raw data retrieved from the database, and the output is well-organized data suitable for analysis. Through this process, the server establishes a stable data environment.

[0764] Step 3:

[0765] The server analyzes data prepared using artificial intelligence modeling tools. The input is processed data, and the generating AI model outputs a learning plan and path optimized for each user. At this stage, the AI ​​evaluates past learning history and current status and proposes a dynamic plan.

[0766] Step 4:

[0767] The generated learning plan and career path are sent from the server to the terminal and presented to the user through a display device. The input is AI-generated plan data, and the output is a graphical display that the user can visually understand. The terminal provides a user-friendly interface.

[0768] Step 5:

[0769] Users provide feedback on the plan displayed through their device. This feedback can be in the form of text or voice, and the feedback data is sent to the server as output. This feedback is then used for subsequent improvements.

[0770] Step 6:

[0771] The server uses emotion recognition technology to acquire emotional data from the user's facial expressions and voice. The input is emotional information obtained through the camera and microphone, and the analysis results are stored as output along with feedback. The system analyzes the user's mental state in real time.

[0772] Step 7:

[0773] Finally, the server integrates feedback and sentiment data to update the artificial intelligence model. The input is the user's sentiment data and feedback data, and the improved AI model is output. This will result in more personalized suggestions for future sessions.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of acquiring information for collecting data on children's learning progress and data from local educational institutions,

[0798] A data processing means for integrating and organizing the collected data,

[0799] An artificial intelligence model means for generating learning plans and career paths based on organized data,

[0800] A display means for presenting the generated learning plan and career path to the user,

[0801] A means of collecting user feedback on the presented learning plan and career path,

[0802] A means of updating the artificial intelligence model based on user feedback,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the information acquisition means is a means for updating student performance information in real time using an API and a data input interface.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the data processing means is a means for unifying the format of data, removing duplicate data, and supplementing missing data.

[0808] "Example 1"

[0809] (Claim 1)

[0810] An information acquisition mechanism for aggregating learner's educational progress data and information from educational institutions,

[0811] A data manipulation mechanism for processing and organizing aggregated information,

[0812] An artificial intelligence model mechanism that formulates educational plans and career paths based on well-organized information,

[0813] A display mechanism that presents the formulated educational plan and career path to users,

[0814] A mechanism for collecting user opinions on the presented educational plan and career paths,

[0815] A mechanism to improve artificial intelligence models based on user feedback,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the information acquisition mechanism is a mechanism that immediately updates learner performance information using a standardized interface and an information input interface.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the data manipulation mechanism is a mechanism that performs information format standardization, removal of duplicate information, and supplementation of missing information.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means of acquiring information for collecting data on children's learning progress and data from local educational institutions,

[0824] A data processing means for integrating and organizing the collected data,

[0825] An artificial intelligence model means for generating learning plans and career paths based on organized data,

[0826] A display means for presenting the generated learning plan and career path to the user,

[0827] A means of collecting user feedback on the presented learning plan and career path,

[0828] A means of updating the artificial intelligence model based on user feedback,

[0829] A means of providing functions that allow interaction with users through educational devices and support their learning plans,

[0830] A means of generating prompt statements and providing support to facilitate user feedback,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the information acquisition means is a means for dynamically updating learner evaluation information using a data transmission interface.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the data processing means is a means for standardizing the data format, eliminating repetitive data, and improving missing data.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A data collection method for obtaining educational data and information on local educational institutions,

[0839] Information processing means for integrating and organizing collected information,

[0840] An artificial intelligence model that creates educational plans and career paths based on compiled information,

[0841] A display means for presenting the created educational plan and career path to the user,

[0842] A means of evaluating and collecting users' emotional feedback on the presented educational plan and career path,

[0843] A means of updating an artificial intelligence model based on user feedback and sentiment data,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the data collection means is a means for supplementing educational performance information in real time using an interface and an information input function.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the information processing means is a means for unifying the format of information, removing duplicate information, and supplementing missing information.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means of acquiring information to obtain data on children's learning progress and data from local educational institutions,

[0852] A data processing means for integrating and organizing the collected data,

[0853] An artificial intelligence model means for generating educational plans and career paths based on organized data,

[0854] A display means for presenting the generated educational plan and career path to the user,

[0855] A feedback mechanism for recording the user's response to the presented educational plan and career path,

[0856] An emotion recognition method for analyzing the user's emotional state,

[0857] A means of improving artificial intelligence models based on user feedback and sentiment data,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the information acquisition means is a means for updating learner performance information in real time via a data interface.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the data processing means is a means for unifying the format of information, removing duplicate information, and supplementing missing information. [Explanation of symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring information for collecting data on children's learning progress and data from local educational institutions, A data processing means for integrating and organizing the collected data, An artificial intelligence model means for generating learning plans and career paths based on organized data, A display means for presenting the generated learning plan and career path to the user, A means of collecting user feedback on the presented learning plan and career path, A means of updating the artificial intelligence model based on user feedback, A system that includes this.

2. The system according to claim 1, wherein the information acquisition means is a means for updating student performance information in real time using an API and a data input interface.

3. The system according to claim 1, wherein the data processing means is a means for unifying the format of data, removing duplicate data, and supplementing missing data.

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