system

Abstract

We provide the system. [Solution] Information gathering means for collecting learner learning data, An analysis device that analyzes the learner's progress and level of understanding based on the aforementioned learning data, A means for generating educational materials that generates educational content optimized for learners based on the aforementioned analysis results, A means for delivering the optimized educational content to learners' devices, A response generation means that generates and provides feedback based on the learner's answers in real time, In a smart city environment, a means of use involves using compatible portable devices that allow learning even while on the go, An educational support 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, the method including: 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 conventional educational system, it is difficult to perform individual optimization corresponding to the progress and understanding level of each learner, which may lead to a decline in learning effects and motivation due to general learning content and delayed feedback. In addition, there is a problem that the evaluation work and progress management work of teachers are often carried out manually, resulting in a large workload. Furthermore, it is difficult for guardians to grasp the learning situation of learners at home, and it is difficult to provide effective support.

Means for Solving the Problems

[0005] This invention includes an analysis means for collecting learner learning data and analyzing progress and understanding based on that data. It also includes a content generation means for generating learning content optimized for the learner based on the analysis results. Furthermore, a content distribution means for delivering the generated learning content to the learner's terminal enables individually optimized learning. In addition, it includes a feedback means for generating and presenting real-time feedback based on the learner's answers, thereby solving the aforementioned problems by providing immediate and effective learning support.

[0006] "Data collection means" refers to a function for recording and accumulating learner's learning data, problem answer data, learning time data, and material viewing history data.

[0007] "Analysis tools" refer to functions that evaluate learners' progress and understanding based on collected learning data, and perform analysis using generative models.

[0008] "Content generation means" refers to a function for creating learning content optimized for learners based on analysis results.

[0009] A "content distribution method" is a function that sends generated learning content to the learner's device, thereby making it accessible to the learner.

[0010] A "feedback mechanism" is a function that generates and presents immediate feedback by determining the correctness of the answer entered by the learner. [Brief explanation of the drawing]

[0011] [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The educational support system of this invention is designed to provide integrated educational support for learners, teachers, and parents. The system consists of a server and multiple terminals.

[0033] The server first receives learning data from the learner's device. This data includes problem answer data, learning time data, and material viewing history data. To analyze this data, the server uses a generative model. Analysis using the generative model allows the server to evaluate the learner's progress and level of understanding, and to identify areas where understanding is low.

[0034] Next, the server generates individually optimized educational content based on the analysis results. This generated content includes supplementary practice exercises, explanatory videos, and additional materials on specific topics. This generated content is then delivered from the server to the learner's device.

[0035] The terminal displays learning content received from the server, making it accessible to students. It also has a function to send the results of students' answers to the server. The answer data is used to generate feedback.

[0036] Users (parents or teachers) can receive learning progress reports provided by the server. This allows parents to effectively support their children's learning at home. Meanwhile, teachers receive automated reports that track homework grading and progress, reducing their workload.

[0037] Thus, the system of the present invention provides individually optimized learning support through a complex configuration that includes data collection and analysis, content generation and distribution, and immediate feedback and reporting. For example, for the area "mathematical factorization problems" which is determined to be a low level of understanding, specially designed practice problems are delivered to the learner, and feedback is provided according to their performance.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device collects answer data, study time, and viewing history data when learners answer questions, and sends this data to the server.

[0041] Step 2:

[0042] The server receives data sent from the terminal and stores it in a database. This data serves as foundational information used for later analysis.

[0043] Step 3:

[0044] The server analyzes the accumulated data using a generative model to evaluate the learner's progress and understanding. In this process, it identifies areas where understanding is low and topics requiring further study.

[0045] Step 4:

[0046] Based on the analysis results, the server generates educational content optimized for the learner. This content may include additional practice exercises and explanatory videos.

[0047] Step 5:

[0048] The server delivers the generated optimized content to the learner's device. The delivered content is displayed on the learner's device, allowing the learner to continue their learning.

[0049] Step 6:

[0050] The device displays the content delivered to the learner, guiding them to view it and resubmit their answers. This allows the learner to reinforce areas where they lack understanding.

[0051] Step 7:

[0052] The server receives the answer data that the learner has resubmitted and generates feedback based on it. The feedback includes whether the answer is correct or incorrect and areas for improvement.

[0053] Step 8:

[0054] The device presents the generated feedback to the learner in real time. This allows the learner to receive immediate evaluation of their answers.

[0055] Step 9:

[0056] Users (parents or teachers) receive learning progress reports delivered from the server, allowing them to understand the learner's situation. This enables them to provide support for home learning and adjust educational policies accordingly.

[0057] (Example 1)

[0058] 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."

[0059] In today's educational environment, while there is a demand for optimal educational support tailored to each learner's progress and level of understanding, conventional systems face the challenge of efficiently generating and distributing individually optimized educational materials. In particular, there are technical limitations that prevent real-time progress analysis and immediate provision of feedback.

[0060] 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.

[0061] In this invention, the server includes means for collecting learner learning information, means for analyzing the learner's progress and level of understanding based on the learning information, and means for generating educational materials based on the analysis results. This enables individually optimized educational support for learners.

[0062] "Learner learning information" refers to various data related to the learning activities undertaken by learners, and specifically includes answer data, time data, and material viewing history data.

[0063] "Means for analyzing progress and understanding" refers to functions and methods for analyzing learners' progress in their learning activities and their level of understanding of knowledge using collected learning information.

[0064] "Means for generating educational materials" refers to methods and technologies for creating learning materials and resources optimized for individual learners based on their analyzed understanding.

[0065] "Means of distribution to a device" refers to the technology or process of transmitting generated educational materials to a learner's terminal so that the learner can view or use them.

[0066] "Means for generating and immediately presenting responses" refers to methods and systems that generate evaluations and comments based on the answers provided by learners and communicate them to the learners in a timely manner.

[0067] A "generative model" refers to an algorithm or program that uses machine learning or artificial intelligence techniques to analyze collected data and evaluate the performance of learners.

[0068] This invention is an integrated system for realizing individually optimized educational support for each learner. The system mainly consists of a server and terminals, and comprehensively handles everything from collecting and analyzing learning information to generating and distributing educational materials and providing feedback.

[0069] The server receives learning information from the learner's device. This learning information includes detailed data such as answer data, time spent learning, and material viewing history. Based on this data, the server performs analysis using a generative AI model. This analysis utilizes machine learning algorithms to evaluate the learner's progress and level of understanding. The analysis results are used to identify which areas the learner needs to deepen their understanding of.

[0070] Next, the server generates individually optimized educational materials based on the analysis results. The generative AI model used here dynamically generates materials based on prompts. An example of such a prompt is, "Generate supplementary materials to deepen understanding of the following areas." The generated materials include supplementary practice problems, explanatory videos, and related learning resources.

[0071] The terminal receives educational materials distributed from the server and displays them in a way that makes them easily accessible and usable by the learner. When a learner answers a question, the answer is sent from the terminal to the server as feedback. This feedback is used on the server for re-evaluation and enables additional educational support.

[0072] Users, including parents and teachers, can receive learning progress reports provided by the server. These reports visually represent learners' progress using graphs and diagrams, supporting effective learning support and progress management. Parents can streamline support at home, and teachers can streamline lesson planning and assessment.

[0073] For example, if analysis indicates a lack of understanding of "mathematical factorization," the server generates practice problems and video materials specifically for factorization and delivers them to the learner's device. The learner's results from working through these materials are sent to the server, and further optimized educational support is continuously provided.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects learning information from the learner's device. It receives answer data, learning time data, and material viewing history data as input. This data is acquired using a secure communication protocol and stored in a database. This data storage serves as the basis for subsequent analysis processing.

[0077] Step 2:

[0078] The server analyzes progress and comprehension using the collected training information. This process utilizes a generative AI model. The model is fed the collected training information as input, and outputs results that evaluate the learner's comprehension and progress. Data analysis is performed using pattern recognition and statistical methods to identify areas that show particular difficulty in comprehension.

[0079] Step 3:

[0080] The server generates educational materials based on the analysis results. The input includes the learner's weaknesses and required skill areas identified through the analysis. Prompts are passed to a generative AI model, which then creates supplementary materials and practice exercises accordingly. The output is personalized educational materials optimized for the learner. These materials are dynamically adjusted, utilizing natural language processing and content generation algorithms throughout the generation process.

[0081] Step 4:

[0082] The server distributes the generated educational materials to the learner's device. The input is the generated teaching materials. An appropriate communication protocol is used for distribution, and the materials are configured to be displayed on the learner's device. The output is educational content accessible to the learner.

[0083] Step 5:

[0084] The device collects the results of learners' use of learning materials. Input includes the results of questions answered by the learner and their learning history. This data is sent to a server for analysis. Sending this data is important for continuously tracking the learner's progress.

[0085] Step 6:

[0086] The server re-analyzes the learner's response data. The input is the learner's most recent response data. This re-analysis evaluates the newly acquired learner's progress and identifies any further educational support needed. The output is a learner progress report, which is an important source of information for parents and teachers.

[0087] (Application Example 1)

[0088] 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."

[0089] While there is a need for more efficient progress management and individualized instruction in educational settings, conventional systems have limitations in providing adequate real-time learning support and reporting. Furthermore, there is a lack of flexible mechanisms to accommodate learning in mobile environments.

[0090] 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.

[0091] In this invention, the server includes information gathering means for collecting learner learning data, an analysis device for analyzing the learner's progress and level of understanding based on the learning data, and material generation means for generating educational content optimized for the learner based on the analysis results. This enables individually optimized educational support for learners and allows them to continue learning even while on the go.

[0092] "Information gathering means" refers to the functional unit that collects learner learning data, and acquires data including problem answer information, learning time information, and material viewing history information.

[0093] The term "analysis device" refers to a functional unit that analyzes collected learning data and evaluates the learner's progress and level of understanding.

[0094] The "material generation means" is a functional unit that creates educational content optimized for learners based on analysis results, and provides individualized learning materials and supplementary content.

[0095] The "educational material distribution means" is a functional unit that transfers generated educational content to learners' devices and makes it accessible.

[0096] The "response generation means" is a functional unit that creates immediate feedback based on the learner's answer and presents it to the learner.

[0097] "Means of use" refers to a functional component that is compatible with portable devices, enabling learners to continue learning while on the go or in different locations within a smart city environment.

[0098] The system for implementing this invention consists of three main components: a server, a learner's terminal, and a user (teacher / parent).

[0099] The server is equipped with information gathering means, analysis devices, material generation means, and material distribution means. The information gathering means collects problem answer information, study time information, and material viewing history information collected through learners' devices such as smartphones and tablets and stores them on the server. This makes it possible to track learners' learning activities in detail.

[0100] The analysis device uses collected data and a generative AI model to analyze learners' understanding and progress. Based on this analysis, the material generation system creates individually optimized educational content. This content may include video explanations, practice problems tailored to proficiency levels, and additional learning materials.

[0101] The educational material distribution system transmits generated educational content to learners' devices in real time, allowing learners to use the content immediately. Accordingly, the response generation system provides instant feedback based on the answers, supporting the learners' learning.

[0102] The system's purpose is to enable learners to continue their studies wherever they are in a smart city environment, maintaining access to learning through mobile devices such as smartphones and tablets. Users will be responsible for viewing progress reports provided by the server and providing appropriate support to learners.

[0103] As a concrete example, when a middle school student is learning about factorization in mathematics, this system allows them to access practice problems via their smartphone and submit their answers to the server. This answer data is then analyzed on the server, and supplementary materials are provided as needed.

[0104] Examples of prompts for a generative AI model are as follows:

[0105] Please use the following data to evaluate the learner's level of understanding.

[0106] Answer data: 'Question 1': 'Correct', 'Question 2': 'Incorrect'

[0107] Study time: 1 hour

[0108] Please also submit proposals for creating educational content based on the evaluation results.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The terminal collects learner's answer data, study time, and material viewing history, and sends this data to the server. The input is learner activity data, and the output is data packets sent to the server. The terminal provides an interface for data collection and efficiently transmits the data.

[0112] Step 2:

[0113] The server stores the received data using information gathering tools and inputs the data into the analysis device. The input is training data transmitted from the terminal, and the output is data organized in an analyzable format. The server manages the data using a database system.

[0114] Step 3:

[0115] The analysis system on the server uses a generative AI model to evaluate the learner's progress and understanding. The input is organized training data, and the output is an understanding evaluation score. In this process, the generative AI model analyzes the data using prompt sentences. Specifically, the model identifies patterns and quantifies the learning status.

[0116] Step 4:

[0117] The server uses a material generation system to create optimized educational content based on evaluation results. The input is the comprehension evaluation score, and the output is educational material tailored to the learner. The generation process produces content selected by AI.

[0118] Step 5:

[0119] The server transmits the generated educational content to the terminal via a material distribution system. The input is educational materials, and the output is learning content displayed on the terminal. The server utilizes the network infrastructure to communicate data quickly.

[0120] Step 6:

[0121] The terminal presents educational content received from the server to the learner. Input is data sent from the server, and output is information displayed on the user interface. The terminal appropriately displays visual content and provides a learning experience.

[0122] Step 7:

[0123] Users (teachers or parents) receive feedback from the server based on the learner's progress and provide appropriate learning support. This feedback includes the accuracy rate of answers and advice for solving learning tasks. The input is progress reports from the server, and the output is the user's support actions. Throughout this process, users obtain information through digital devices.

[0124] 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.

[0125] The educational support system according to the present invention provides personalized learning support by collecting and analyzing both learner learning data and emotional data. The system consists of a server, multiple terminals, and an emotional engine.

[0126] The server receives learning data and emotional data transmitted from the learner's device. Learning data includes problem answer data, learning time data, and material viewing history data. Emotional data, on the other hand, is based on the learner's facial expression data and voice data collected via an emotional engine. The server analyzes this data to evaluate not only the learner's progress and understanding, but also their motivation and stress levels during learning.

[0127] Next, the server generates learning content optimized for the learner from the collected and analyzed data. This includes feedback and encouraging messages that take emotional states into consideration, as well as interactive content to maintain motivation. The generated content is then delivered from the server to the learner's device.

[0128] The terminal displays learning content received from the server, making it accessible to students. The terminal also has a facial recognition camera and microphone that transmit the learner's emotional state to an emotion engine. When a learner answers a question, the emotional information is sent back to the server along with the answer data, allowing for real-time monitoring of the learner's state.

[0129] Users can receive feedback that reflects their emotional state. For example, if it is determined that their concentration on learning is declining, they may be presented with interactions suggesting short breaks or recommending simple exercises to relax.

[0130] Thus, the system of the present invention uses data obtained from the emotion engine to realize individually optimized educational support for learners. For example, if it is determined that a learner is experiencing stress when faced with a problem on "equations of motion in physics" that they do not understand well, the system can deliver a temporary interruption to help them relax or an encouragement message to maintain their motivation.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device records answer data, study time, and viewing history of learning materials when learners answer questions. Furthermore, the device transmits facial expression data and voice data collected through the camera and microphone to the emotion engine.

[0134] Step 2:

[0135] The emotion engine analyzes facial expression and voice data transmitted from the device to estimate the learner's emotional state. The estimated emotional state is evaluated as motivation and stress levels and sent to the server.

[0136] Step 3:

[0137] The server receives learning data from the terminal and emotional data from the emotion engine, and stores them in a database. This provides the foundation for comprehensively understanding the learner's learning progress and emotional state.

[0138] Step 4:

[0139] The server analyzes the accumulated data to evaluate the learner's progress, comprehension, and emotional state. During this process, it identifies areas where comprehension is weak or where stress levels are high, thus hindering learning.

[0140] Step 5:

[0141] Based on the analysis results, the server generates educational content optimized for the learner. This content includes practice exercises and videos to address areas of misunderstanding, as well as emotionally sensitive feedback and motivational messages.

[0142] Step 6:

[0143] The server delivers optimized content to the learner's device. The device then displays this content for the learner to access immediately.

[0144] Step 7:

[0145] The device assists learners in progressing through their studies using the content. Each time a learner answers a question, the device collects the result and sentiment data again and sends it to the server.

[0146] Step 8:

[0147] The server generates feedback using newly received answer data and sentiment data. This feedback includes whether the answer is correct or incorrect, suggestions for improvement, and emotionally-based encouragement and break suggestions.

[0148] Step 9:

[0149] The device presents the generated feedback to the learner in real time. This allows the learner to enhance their learning effectiveness and manage their mental burden appropriately.

[0150] Step 10:

[0151] Users (parents or teachers) can receive regular reports from the server regarding learning progress and emotional state, which can be used to support their child at home and in the classroom.

[0152] (Example 2)

[0153] 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".

[0154] While conventional educational support systems evaluate learners' progress and comprehension based on their learning data, they have struggled to provide individually optimized learning support that takes into account learners' emotional states. Therefore, there is a lack of flexible learning support that takes into account emotional aspects such as stress and decreased motivation that learners experience during their studies.

[0155] 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.

[0156] In this invention, the server includes data collection means for collecting learner learning data and emotional data; analysis means for evaluating the learner's progress, understanding, and emotional state based on the learning data and emotional data; and content generation means for generating learning content individually optimized for the learner based on the evaluation results. This makes it possible to consider the learner's emotional state in real time and provide more effective and individually optimized learning support.

[0157] "Data collection methods" refer to processes and devices for efficiently collecting learners' learning data and emotional data.

[0158] "Analysis tools" refer to processes and functions for evaluating learners' progress, comprehension, and emotional state based on collected learning and emotional data.

[0159] "Content generation methods" refer to the processes and tools used to create personalized learning content for learners based on analyzed data.

[0160] "Content distribution means" refers to the process and methods for delivering generated learning content to learners' information devices.

[0161] "Feedback methods" refer to processes and mechanisms for providing appropriate feedback in real time based on learners' answers and sentiment data.

[0162] The educational support system of the present invention aims to improve the learning experience by providing learners with individually optimized learning content. This system comprises a server, terminals, and an emotion engine.

[0163] The server has the primary function of collecting learner learning data and emotional data. Learning data includes problem answer data, learning time data, and material viewing history data, while emotional data includes facial expression data and voice data. The server uses machine learning algorithms and data analysis software to evaluate learners' progress, comprehension, and emotional state using this data. This allows the server to extract important information to improve the quality of education and generate educational content tailored to learners' needs. It also uses generative AI models to create encouraging messages and feedback that are appropriate to the learner's emotional state.

[0164] The terminal acts as the interface with the learner and transfers collected data to the server. The terminal also collects data in real time using a facial recognition camera and microphone, and transmits the learner's emotional state to the emotion engine. When the learner answers a question, the emotional information is sent back to the server along with the answer data, and personalized content based on the results is delivered to the terminal.

[0165] Users receive customized learning content and feedback delivered via their devices, allowing them to progress in their learning. For example, if the system detects a decrease in concentration, it can suggest a break or provide motivational messages.

[0166] For example, when a user is learning "equations of motion in physics," if the system detects the user's stress level, the server will either stream a relaxing video or suggest a deep breathing exercise. In this way, learners can progress through their studies efficiently at their own pace.

[0167] An example of a specific prompt for a generative AI model is, "Provide guidance for generating content that will provide appropriate feedback based on the learner's current emotional state." By using such prompts, the generative AI can specify the most suitable support for each individual learner.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The device collects the learner's problem-solving data, study time data, and learning material viewing history data. Simultaneously, it acquires the learner's facial expression data and voice data using a facial recognition camera and microphone. All of this data is sent to the emotion engine and transferred to the server as input data to understand the learner's emotional state.

[0171] Step 2:

[0172] The server receives learning data and sentiment data sent from the terminal and stores them in a database. Next, it uses machine learning algorithms to analyze this data and evaluate the learner's progress, comprehension, stress level, and motivation status. Based on the input data, it outputs analysis results, including the aforementioned evaluations.

[0173] Step 3:

[0174] The server generates learning content that is individually optimized for each learner based on the analysis results. This generation uses a generative AI model and includes encouraging messages and feedback tailored to the learner's emotional state. The input for content generation is the analysis results, and the output is customized learning content and feedback.

[0175] Step 4:

[0176] The server delivers the generated learning content to the learner's device. At this stage, notifications and scheduling are managed to ensure that the device displays the appropriate content at the optimal time for the learner. The input is the generated content, and the output is the delivery of the content to the learner's device.

[0177] Step 5:

[0178] Users progress through their learning using optimized content delivered to their devices. Based on feedback received during learning, users can check their understanding and motivation in real time. The user's learning activity is monitored again on the device, and the process is repeated, returning to step 1. The inputs are the delivered content and user feedback, and the output is the update of the learning progress.

[0179] (Application Example 2)

[0180] 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".

[0181] In today's educational environment, there is a problem of insufficient individualized educational support for learners. In particular, the lack of educational support that takes into account learners' emotional states often leads to decreased motivation and increased learning stress. To address these challenges, a system is needed that collects and analyzes not only learner learning data but also emotional data, and provides individually optimized learning support.

[0182] 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.

[0183] In this invention, the server includes information gathering means for collecting learner learning data and emotional data; analysis means for analyzing the learner's progress, understanding, and emotional state based on the learning data and emotional data; and information generation means for generating learning content optimized for the learner and emotionally sensitive interactive feedback based on the analysis results. This makes it possible to provide individualized educational support tailored to each learner's learning progress and to provide emotional care.

[0184] "Information gathering means" refers to a device or method for acquiring learner learning data and emotional data.

[0185] "Analysis means" refers to a device or method that uses collected learning data and sentiment data to evaluate a learner's progress, understanding, and emotional state.

[0186] "Information generation means" refers to a device or method that creates learning content and emotional feedback tailored to the learner based on analyzed data.

[0187] "Information distribution means" refers to a device or method that transfers and displays generated learning content and feedback to the learner's terminal.

[0188] A "response mechanism" is a device or method that provides timely feedback based on the learner's answers and collected sentiment data.

[0189] This invention is a system that provides personalized educational support using learner learning data and emotional data. To achieve this, the system mainly includes a server, terminals, and a user interface.

[0190] The server collects learning data and emotional data from the learner's device. Learning data includes problem answer information, learning time information, and browsing history, while emotional data includes the learner's facial expressions and voice information. For data processing, facial expression analysis is performed using OpenCV, and voice data is converted to text using Google® Speech-to-Text API. The analysis method uses Amazon Comprehend or IBM Watson® Tone Analyzer to evaluate the learner's progress and emotional state. This data analysis provides a detailed understanding of the learner's learning status and emotional state.

[0191] Next, the server uses a generative AI model to generate optimized learning content and emotionally sensitive feedback based on the analysis results, and delivers the information to the learner's device. This allows learners to receive learning support and emotional care tailored to their situation in real time.

[0192] The device has the function of displaying learning content delivered from the server to the learner. It also uses the camera and microphone built into the device to continuously collect the learner's facial expressions and speech, and transmits this data to the server. Interactive feedback is presented to the learner in response to changes in their facial expressions and speech, promoting motivation and improving learning efficiency.

[0193] Based on specific feedback about their learning progress, users can receive suggestions for short breaks or light exercises to refresh their minds. This allows learners to interrupt and resume their studies at appropriate times as part of their self-management.

[0194] For example, if a user is working on a math problem and their expression becomes cloudy, the system might offer a message such as, "Are you stuck right now? Why not try a hint to see it from a different perspective?"

[0195] An example of a prompt message might be, "When an elementary school student is stuck on a math problem, please suggest ways to motivate them." This is then input into the generative AI model, generating appropriate feedback and content that is quickly delivered to the learner.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server receives learner learning data and emotion data from the terminal. It acquires problem answer data, learning time information, facial expression data, and voice data as input. The server aggregates this data and prepares it for the next analysis step.

[0199] Step 2:

[0200] The server analyzes training data and emotion data. For data analysis, it uses OpenCV to analyze facial expression data and the Google Speech-to-Text API to convert speech data into text. The analysis output provides evaluation results regarding the learner's progress, comprehension, and emotional state.

[0201] Step 3:

[0202] The server uses an AI model based on the analysis results to generate learning content optimized for the learner and emotionally sensitive feedback. It uses analysis and evaluation as input and creates customized content and encouraging messages as output.

[0203] Step 4:

[0204] The server delivers the generated learning content and feedback to the device. The device receives this content and displays it to the learner at the appropriate time.

[0205] Step 5:

[0206] The device displays the received learning content to the user while simultaneously continuously collecting the learner's facial expressions and speech using the camera and microphone. This data is transmitted to the server in real time and used for subsequent analyses.

[0207] Step 6:

[0208] Users take action based on feedback from the server. Their actions and responses are also collected as data and provided to the system. This interaction loop allows learners to continuously receive personalized educational support.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] The educational support system of this invention is designed to provide integrated educational support for learners, teachers, and parents. The system consists of a server and multiple terminals.

[0226] The server first receives learning data from the learner's device. This data includes problem answer data, learning time data, and material viewing history data. To analyze this data, the server uses a generative model. Analysis using the generative model allows the server to evaluate the learner's progress and level of understanding, and to identify areas where understanding is low.

[0227] Next, the server generates individually optimized educational content based on the analysis results. This generated content includes supplementary practice exercises, explanatory videos, and additional materials on specific topics. This generated content is then delivered from the server to the learner's device.

[0228] The terminal displays learning content received from the server, making it accessible to students. It also has a function to send the results of students' answers to the server. The answer data is used to generate feedback.

[0229] Users (parents or teachers) can receive learning progress reports provided by the server. This allows parents to effectively support their children's learning at home. Meanwhile, teachers receive automated reports that track homework grading and progress, reducing their workload.

[0230] Thus, the system of the present invention provides individually optimized learning support through a complex configuration that includes data collection and analysis, content generation and distribution, and immediate feedback and reporting. For example, for the area "mathematical factorization problems" which is determined to be a low level of understanding, specially designed practice problems are delivered to the learner, and feedback is provided according to their performance.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The device collects answer data, study time, and viewing history data when learners answer questions, and sends this data to the server.

[0234] Step 2:

[0235] The server receives data sent from the terminal and stores it in a database. This data serves as foundational information used for later analysis.

[0236] Step 3:

[0237] The server analyzes the accumulated data using a generative model to evaluate the learner's progress and understanding. In this process, it identifies areas where understanding is low and topics requiring further study.

[0238] Step 4:

[0239] Based on the analysis results, the server generates educational content optimized for the learner. This content may include additional practice exercises and explanatory videos.

[0240] Step 5:

[0241] The server delivers the generated optimized content to the learner's device. The delivered content is displayed on the learner's device, allowing the learner to continue their learning.

[0242] Step 6:

[0243] The device displays the content delivered to the learner, guiding them to view it and resubmit their answers. This allows the learner to reinforce areas where they lack understanding.

[0244] Step 7:

[0245] The server receives the answer data that the learner has resubmitted and generates feedback based on it. The feedback includes whether the answer is correct or incorrect and areas for improvement.

[0246] Step 8:

[0247] The device presents the generated feedback to the learner in real time. This allows the learner to receive immediate evaluation of their answers.

[0248] Step 9:

[0249] Users (parents or teachers) receive learning progress reports delivered from the server, allowing them to understand the learner's situation. This enables them to provide support for home learning and adjust educational policies accordingly.

[0250] (Example 1)

[0251] 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".

[0252] In today's educational environment, while there is a demand for optimal educational support tailored to each learner's progress and level of understanding, conventional systems face the challenge of efficiently generating and distributing individually optimized educational materials. In particular, there are technical limitations that prevent real-time progress analysis and immediate provision of feedback.

[0253] 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.

[0254] In this invention, the server includes means for collecting learner learning information, means for analyzing the learner's progress and level of understanding based on the learning information, and means for generating educational materials based on the analysis results. This enables individually optimized educational support for learners.

[0255] "Learner learning information" refers to various data related to the learning activities undertaken by learners, and specifically includes answer data, time data, and material viewing history data.

[0256] "Means for analyzing progress and understanding" refers to functions and methods for analyzing learners' progress in their learning activities and their level of understanding of knowledge using collected learning information.

[0257] "Means for generating educational materials" refers to methods and technologies for creating learning materials and resources optimized for individual learners based on their analyzed understanding.

[0258] "Means of distribution to a device" refers to the technology or process of transmitting generated educational materials to a learner's terminal so that the learner can view or use them.

[0259] "Means for generating and immediately presenting responses" refers to methods and systems that generate evaluations and comments based on the answers provided by learners and communicate them to the learners in a timely manner.

[0260] A "generative model" refers to an algorithm or program that uses machine learning or artificial intelligence techniques to analyze collected data and evaluate the performance of learners.

[0261] This invention is an integrated system for realizing individually optimized educational support for each learner. The system mainly consists of a server and terminals, and comprehensively handles everything from collecting and analyzing learning information to generating and distributing educational materials and providing feedback.

[0262] The server receives learning information from the learner's device. This learning information includes detailed data such as answer data, time spent learning, and material viewing history. Based on this data, the server performs analysis using a generative AI model. This analysis utilizes machine learning algorithms to evaluate the learner's progress and level of understanding. The analysis results are used to identify which areas the learner needs to deepen their understanding of.

[0263] Next, the server generates individually optimized educational materials based on the analysis results. The generative AI model used here dynamically generates materials based on prompts. An example of such a prompt is, "Generate supplementary materials to deepen understanding of the following areas." The generated materials include supplementary practice problems, explanatory videos, and related learning resources.

[0264] The terminal receives educational materials distributed from the server and displays them in a way that makes them easily accessible and usable by the learner. When a learner answers a question, the answer is sent from the terminal to the server as feedback. This feedback is used on the server for re-evaluation and enables additional educational support.

[0265] Users, including parents and teachers, can receive learning progress reports provided by the server. These reports visually represent learners' progress using graphs and diagrams, supporting effective learning support and progress management. Parents can streamline support at home, and teachers can streamline lesson planning and assessment.

[0266] For example, if analysis indicates a lack of understanding of "mathematical factorization," the server generates practice problems and video materials specifically for factorization and delivers them to the learner's device. The learner's results from working through these materials are sent to the server, and further optimized educational support is continuously provided.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects learning information from the learner's device. It receives answer data, learning time data, and material viewing history data as input. This data is acquired using a secure communication protocol and stored in a database. This data storage serves as the basis for subsequent analysis processing.

[0270] Step 2:

[0271] The server analyzes progress and comprehension using the collected training information. This process utilizes a generative AI model. The model is fed the collected training information as input, and outputs results that evaluate the learner's comprehension and progress. Data analysis is performed using pattern recognition and statistical methods to identify areas that show particular difficulty in comprehension.

[0272] Step 3:

[0273] The server generates educational materials based on the analysis results. The input includes the learner's weaknesses and required skill areas identified through the analysis. Prompts are passed to a generative AI model, which then creates supplementary materials and practice exercises accordingly. The output is personalized educational materials optimized for the learner. These materials are dynamically adjusted, utilizing natural language processing and content generation algorithms throughout the generation process.

[0274] Step 4:

[0275] The server distributes the generated educational materials to the learner's device. The input is the generated teaching materials. An appropriate communication protocol is used for distribution, and the materials are configured to be displayed on the learner's device. The output is educational content accessible to the learner.

[0276] Step 5:

[0277] The device collects the results of learners' use of learning materials. Input includes the results of questions answered by the learner and their learning history. This data is sent to a server for analysis. Sending this data is important for continuously tracking the learner's progress.

[0278] Step 6:

[0279] The server re-analyzes the learner's answer data. The input is the learner's latest answer data. Through this re-analysis, the newly obtained progress of the learner is evaluated, and the necessary additional educational support is identified. As output, a learner progress report is generated. This report is an important source of information for guardians and teachers.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0282] There is a demand for improving the progress management and individual guidance efficiency of learners in the educational field. However, the conventional system has a problem that real-time learning support and reporting cannot be sufficiently performed. In addition, there is a lack of a mechanism that can flexibly respond to learning in a mobile environment.

[0283] 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.

[0284] In this invention, the server includes an information collection means for collecting the learner's learning data, an analysis device for analyzing the learner's progress and understanding based on the learning data, and a teaching material generation means for generating educational content optimized for the learner based on the analysis result. Thereby, individually optimized educational support for the learner is realized, and continuous learning is possible even during movement.

[0285] The "information collection means" refers to a functional unit that collects the learner's learning data and acquires data including problem-solving answer information, learning time information, and teaching material viewing history information.

[0286] The "analysis device" refers to a functional unit that analyzes the collected learning data and evaluates the learner's progress situation and understanding.

[0287] The "material generation means" is a functional unit that creates educational content optimized for learners based on analysis results, and provides individualized learning materials and supplementary content.

[0288] The "educational material distribution means" is a functional unit that transfers generated educational content to learners' devices and makes it accessible.

[0289] The "response generation means" is a functional unit that creates immediate feedback based on the learner's answer and presents it to the learner.

[0290] "Means of use" refers to a functional component that is compatible with portable devices, enabling learners to continue learning while on the go or in different locations within a smart city environment.

[0291] The system for implementing this invention consists of three main components: a server, a learner's terminal, and a user (teacher / parent).

[0292] The server is equipped with information gathering means, analysis devices, material generation means, and material distribution means. The information gathering means collects problem answer information, study time information, and material viewing history information collected through learners' devices such as smartphones and tablets and stores them on the server. This makes it possible to track learners' learning activities in detail.

[0293] The analysis device uses collected data and a generative AI model to analyze learners' understanding and progress. Based on this analysis, the material generation system creates individually optimized educational content. This content may include video explanations, practice problems tailored to proficiency levels, and additional learning materials.

[0294] The educational material distribution system transmits generated educational content to learners' devices in real time, allowing learners to use the content immediately. Accordingly, the response generation system provides instant feedback based on the answers, supporting the learners' learning.

[0295] The system's purpose is to enable learners to continue their studies wherever they are in a smart city environment, maintaining access to learning through mobile devices such as smartphones and tablets. Users will be responsible for viewing progress reports provided by the server and providing appropriate support to learners.

[0296] As a concrete example, when a middle school student is learning about factorization in mathematics, this system allows them to access practice problems via their smartphone and submit their answers to the server. This answer data is then analyzed on the server, and supplementary materials are provided as needed.

[0297] Examples of prompts for a generative AI model are as follows:

[0298] Please use the following data to evaluate the learner's level of understanding.

[0299] Answer data: 'Question 1': 'Correct', 'Question 2': 'Incorrect'

[0300] Study time: 1 hour

[0301] Please also submit proposals for creating educational content based on the evaluation results.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The terminal collects learner's answer data, study time, and material viewing history, and sends this data to the server. The input is learner activity data, and the output is data packets sent to the server. The terminal provides an interface for data collection and efficiently transmits the data.

[0305] Step 2:

[0306] The server accumulates the received data by means of information collection, and inputs the data into an analysis device. The input is the learning data transmitted from the terminal, and the output is the data organized in an analyzable format. The server manages the data using a database system.

[0307] Step 3:

[0308] The analysis device in the server evaluates the progress and understanding level of the learner using a generated AI model. The input is the organized learning data, and the output is the understanding level evaluation score. In this process, the generated AI model analyzes the data using prompt sentences. Specifically, the model identifies patterns and quantifies the learning situation.

[0309] Step 4:

[0310] The server creates optimized educational content based on the evaluation results using teaching material generation means. The input is the understanding level evaluation score, and the output is educational materials corresponding to the learner. In the generation process, the content selected by AI is produced.

[0311] Step 5:

[0312] The server transmits the generated educational content to the terminal by means of teaching material distribution. The input is the educational materials, and the output is the learning content displayed on the terminal. The server utilizes the network infrastructure to communicate the data promptly.

[0313] Step 6:

[0314] The terminal presents the educational content received from the server to the learner. The input is the data sent from the server, and the output is the information displayed on the user interface. The terminal appropriately displays the visual content and provides a learning experience.

[0315] Step 7:

[0316] Users (teachers or parents) receive feedback from the server based on the learner's progress and provide appropriate learning support. This feedback includes the accuracy rate of answers and advice for solving learning tasks. The input is progress reports from the server, and the output is the user's support actions. Throughout this process, users obtain information through digital devices.

[0317] 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.

[0318] The educational support system according to the present invention provides personalized learning support by collecting and analyzing both learner learning data and emotional data. The system consists of a server, multiple terminals, and an emotional engine.

[0319] The server receives learning data and emotional data transmitted from the learner's device. Learning data includes problem answer data, learning time data, and material viewing history data. Emotional data, on the other hand, is based on the learner's facial expression data and voice data collected via an emotional engine. The server analyzes this data to evaluate not only the learner's progress and understanding, but also their motivation and stress levels during learning.

[0320] Next, the server generates learning content optimized for the learner from the collected and analyzed data. This includes feedback and encouraging messages that take emotional states into consideration, as well as interactive content to maintain motivation. The generated content is then delivered from the server to the learner's device.

[0321] The terminal displays learning content received from the server, making it accessible to students. The terminal also has a facial recognition camera and microphone that transmit the learner's emotional state to an emotion engine. When a learner answers a question, the emotional information is sent back to the server along with the answer data, allowing for real-time monitoring of the learner's state.

[0322] Users can receive feedback that reflects their emotional state. For example, if it is determined that their concentration on learning is declining, they may be presented with interactions suggesting short breaks or recommending simple exercises to relax.

[0323] Thus, the system of the present invention uses data obtained from the emotion engine to realize individually optimized educational support for learners. For example, if it is determined that a learner is experiencing stress when faced with a problem on "equations of motion in physics" that they do not understand well, the system can deliver a temporary interruption to help them relax or an encouragement message to maintain their motivation.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The device records answer data, study time, and viewing history of learning materials when learners answer questions. Furthermore, the device transmits facial expression data and voice data collected through the camera and microphone to the emotion engine.

[0327] Step 2:

[0328] The emotion engine analyzes facial expression and voice data transmitted from the device to estimate the learner's emotional state. The estimated emotional state is evaluated as motivation and stress levels and sent to the server.

[0329] Step 3:

[0330] The server receives learning data from the terminal and emotional data from the emotion engine, and stores them in a database. This provides the foundation for comprehensively understanding the learner's learning progress and emotional state.

[0331] Step 4:

[0332] The server analyzes the accumulated data to evaluate the learner's progress, comprehension, and emotional state. During this process, it identifies areas where comprehension is weak or where stress levels are high, thus hindering learning.

[0333] Step 5:

[0334] Based on the analysis results, the server generates educational content optimized for the learner. This content includes practice exercises and videos to address areas of misunderstanding, as well as emotionally sensitive feedback and motivational messages.

[0335] Step 6:

[0336] The server delivers optimized content to the learner's device. The device then displays this content for the learner to access immediately.

[0337] Step 7:

[0338] The device assists learners in progressing through their studies using the content. Each time a learner answers a question, the device collects the result and sentiment data again and sends it to the server.

[0339] Step 8:

[0340] The server generates feedback using newly received answer data and sentiment data. This feedback includes whether the answer is correct or incorrect, suggestions for improvement, and emotionally-based encouragement and break suggestions.

[0341] Step 9:

[0342] The device presents the generated feedback to the learner in real time. This allows the learner to enhance their learning effectiveness and manage their mental burden appropriately.

[0343] Step 10:

[0344] Users (parents or teachers) can receive regular reports from the server regarding learning progress and emotional state, which can be used to support their child at home and in the classroom.

[0345] (Example 2)

[0346] 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".

[0347] While conventional educational support systems evaluate learners' progress and comprehension based on their learning data, they have struggled to provide individually optimized learning support that takes into account learners' emotional states. Therefore, there is a lack of flexible learning support that takes into account emotional aspects such as stress and decreased motivation that learners experience during their studies.

[0348] 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.

[0349] In this invention, the server includes data collection means for collecting learner learning data and emotional data; analysis means for evaluating the learner's progress, understanding, and emotional state based on the learning data and emotional data; and content generation means for generating learning content individually optimized for the learner based on the evaluation results. This makes it possible to consider the learner's emotional state in real time and provide more effective and individually optimized learning support.

[0350] "Data collection methods" refer to processes and devices for efficiently collecting learners' learning data and emotional data.

[0351] "Analysis tools" refer to processes and functions for evaluating learners' progress, comprehension, and emotional state based on collected learning and emotional data.

[0352] "Content generation methods" refer to the processes and tools used to create personalized learning content for learners based on analyzed data.

[0353] "Content distribution means" refers to the process and methods for delivering generated learning content to learners' information devices.

[0354] "Feedback methods" refer to processes and mechanisms for providing appropriate feedback in real time based on learners' answers and sentiment data.

[0355] The educational support system of the present invention aims to improve the learning experience by providing learners with individually optimized learning content. This system comprises a server, terminals, and an emotion engine.

[0356] The server has the primary function of collecting learner learning data and emotional data. Learning data includes problem answer data, learning time data, and material viewing history data, while emotional data includes facial expression data and voice data. The server uses machine learning algorithms and data analysis software to evaluate learners' progress, comprehension, and emotional state using this data. This allows the server to extract important information to improve the quality of education and generate educational content tailored to learners' needs. It also uses generative AI models to create encouraging messages and feedback that are appropriate to the learner's emotional state.

[0357] The terminal acts as the interface with the learner and transfers collected data to the server. The terminal also collects data in real time using a facial recognition camera and microphone, and transmits the learner's emotional state to the emotion engine. When the learner answers a question, the emotional information is sent back to the server along with the answer data, and personalized content based on the results is delivered to the terminal.

[0358] Users receive customized learning content and feedback delivered via their devices, allowing them to progress in their learning. For example, if the system detects a decrease in concentration, it can suggest a break or provide motivational messages.

[0359] For example, when a user is learning "equations of motion in physics," if the system detects the user's stress level, the server will either stream a relaxing video or suggest a deep breathing exercise. In this way, learners can progress through their studies efficiently at their own pace.

[0360] An example of a specific prompt for a generative AI model is, "Provide guidance for generating content that will provide appropriate feedback based on the learner's current emotional state." By using such prompts, the generative AI can specify the most suitable support for each individual learner.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The device collects the learner's problem-solving data, study time data, and learning material viewing history data. Simultaneously, it acquires the learner's facial expression data and voice data using a facial recognition camera and microphone. All of this data is sent to the emotion engine and transferred to the server as input data to understand the learner's emotional state.

[0364] Step 2:

[0365] The server receives learning data and sentiment data sent from the terminal and stores them in a database. Next, it uses machine learning algorithms to analyze this data and evaluate the learner's progress, comprehension, stress level, and motivation status. Based on the input data, it outputs analysis results, including the aforementioned evaluations.

[0366] Step 3:

[0367] The server generates learning content that is individually optimized for each learner based on the analysis results. This generation uses a generative AI model and includes encouraging messages and feedback tailored to the learner's emotional state. The input for content generation is the analysis results, and the output is customized learning content and feedback.

[0368] Step 4:

[0369] The server delivers the generated learning content to the learner's device. At this stage, notifications and scheduling are managed to ensure that the device displays the appropriate content at the optimal time for the learner. The input is the generated content, and the output is the delivery of the content to the learner's device.

[0370] Step 5:

[0371] Users progress through their learning using optimized content delivered to their devices. Based on feedback received during learning, users can check their understanding and motivation in real time. The user's learning activity is monitored again on the device, and the process is repeated, returning to step 1. The inputs are the delivered content and user feedback, and the output is the update of the learning progress.

[0372] (Application Example 2)

[0373] 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."

[0374] In today's educational environment, there is a problem of insufficient individualized educational support for learners. In particular, the lack of educational support that takes into account learners' emotional states often leads to decreased motivation and increased learning stress. To address these challenges, a system is needed that collects and analyzes not only learner learning data but also emotional data, and provides individually optimized learning support.

[0375] 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.

[0376] In this invention, the server includes information gathering means for collecting learner learning data and emotional data; analysis means for analyzing the learner's progress, understanding, and emotional state based on the learning data and emotional data; and information generation means for generating learning content optimized for the learner and emotionally sensitive interactive feedback based on the analysis results. This makes it possible to provide individualized educational support tailored to each learner's learning progress and to provide emotional care.

[0377] "Information gathering means" refers to a device or method for acquiring learner learning data and emotional data.

[0378] "Analysis means" refers to a device or method that uses collected learning data and sentiment data to evaluate a learner's progress, understanding, and emotional state.

[0379] "Information generation means" refers to a device or method that creates learning content and emotional feedback tailored to the learner based on analyzed data.

[0380] "Information distribution means" refers to a device or method that transfers and displays generated learning content and feedback to the learner's terminal.

[0381] A "response mechanism" is a device or method that provides timely feedback based on the learner's answers and collected sentiment data.

[0382] This invention is a system that provides personalized educational support using learner learning data and emotional data. To achieve this, the system mainly includes a server, terminals, and a user interface.

[0383] The server collects learning data and emotional data from the learner's device. Learning data includes problem answer information, learning time information, and browsing history, while emotional data includes the learner's facial expressions and voice information. For data processing, OpenCV is used for facial expression analysis, and the Google Speech-to-Text API is used to convert voice data to text. Amazon Comprehend or IBM Watson Tone Analyzer are used to evaluate the learner's progress and emotional state. This data analysis provides a detailed understanding of the learner's learning status and emotional state.

[0384] Next, the server uses a generative AI model to generate optimized learning content and emotionally sensitive feedback based on the analysis results, and delivers the information to the learner's device. This allows learners to receive learning support and emotional care tailored to their situation in real time.

[0385] The device has the function of displaying learning content delivered from the server to the learner. It also uses the camera and microphone built into the device to continuously collect the learner's facial expressions and speech, and transmits this data to the server. Interactive feedback is presented to the learner in response to changes in their facial expressions and speech, promoting motivation and improving learning efficiency.

[0386] Based on specific feedback about their learning progress, users can receive suggestions for short breaks or light exercises to refresh their minds. This allows learners to interrupt and resume their studies at appropriate times as part of their self-management.

[0387] For example, if a user is working on a math problem and their expression becomes cloudy, the system might offer a message such as, "Are you stuck right now? Why not try a hint to see it from a different perspective?"

[0388] An example of a prompt message might be, "When an elementary school student is stuck on a math problem, please suggest ways to motivate them." This is then input into the generative AI model, generating appropriate feedback and content that is quickly delivered to the learner.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The server receives learner learning data and emotion data from the terminal. It acquires problem answer data, learning time information, facial expression data, and voice data as input. The server aggregates this data and prepares it for the next analysis step.

[0392] Step 2:

[0393] The server analyzes training data and emotion data. For data analysis, it uses OpenCV to analyze facial expression data and the Google Speech-to-Text API to convert speech data into text. The analysis output provides evaluation results regarding the learner's progress, comprehension, and emotional state.

[0394] Step 3:

[0395] The server uses an AI model based on the analysis results to generate learning content optimized for the learner and emotionally sensitive feedback. It uses analysis and evaluation as input and creates customized content and encouraging messages as output.

[0396] Step 4:

[0397] The server delivers the generated learning content and feedback to the device. The device receives this content and displays it to the learner at the appropriate time.

[0398] Step 5:

[0399] The device displays the received learning content to the user while simultaneously continuously collecting the learner's facial expressions and speech using the camera and microphone. This data is transmitted to the server in real time and used for subsequent analyses.

[0400] Step 6:

[0401] Users take action based on feedback from the server. Their actions and responses are also collected as data and provided to the system. This interaction loop allows learners to continuously receive personalized educational support.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] The educational support system of this invention is designed to provide integrated educational support for learners, teachers, and parents. The system consists of a server and multiple terminals.

[0419] The server first receives learning data from the learner's device. This data includes problem answer data, learning time data, and material viewing history data. To analyze this data, the server uses a generative model. Analysis using the generative model allows the server to evaluate the learner's progress and level of understanding, and to identify areas where understanding is low.

[0420] Next, the server generates individually optimized educational content based on the analysis results. This generated content includes supplementary practice exercises, explanatory videos, and additional materials on specific topics. This generated content is then delivered from the server to the learner's device.

[0421] The terminal displays learning content received from the server, making it accessible to students. It also has a function to send the results of students' answers to the server. The answer data is used to generate feedback.

[0422] Users (parents or teachers) can receive learning progress reports provided by the server. This allows parents to effectively support their children's learning at home. Meanwhile, teachers receive automated reports that track homework grading and progress, reducing their workload.

[0423] Thus, the system of the present invention provides individually optimized learning support through a complex configuration that includes data collection and analysis, content generation and distribution, and immediate feedback and reporting. For example, for the area "mathematical factorization problems" which is determined to be a low level of understanding, specially designed practice problems are delivered to the learner, and feedback is provided according to their performance.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The device collects answer data, study time, and viewing history data when learners answer questions, and sends this data to the server.

[0427] Step 2:

[0428] The server receives data sent from the terminal and stores it in a database. This data serves as foundational information used for later analysis.

[0429] Step 3:

[0430] The server analyzes the accumulated data using a generative model to evaluate the learner's progress and understanding. In this process, it identifies areas where understanding is low and topics requiring further study.

[0431] Step 4:

[0432] Based on the analysis results, the server generates educational content optimized for the learner. This content may include additional practice exercises and explanatory videos.

[0433] Step 5:

[0434] The server delivers the generated optimized content to the learner's device. The delivered content is displayed on the learner's device, allowing the learner to continue their learning.

[0435] Step 6:

[0436] The device displays the content delivered to the learner, guiding them to view it and resubmit their answers. This allows the learner to reinforce areas where they lack understanding.

[0437] Step 7:

[0438] The server receives the answer data that the learner has resubmitted and generates feedback based on it. The feedback includes whether the answer is correct or incorrect and areas for improvement.

[0439] Step 8:

[0440] The device presents the generated feedback to the learner in real time. This allows the learner to receive immediate evaluation of their answers.

[0441] Step 9:

[0442] Users (parents or teachers) receive learning progress reports delivered from the server, allowing them to understand the learner's situation. This enables them to provide support for home learning and adjust educational policies accordingly.

[0443] (Example 1)

[0444] 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."

[0445] In today's educational environment, while there is a demand for optimal educational support tailored to each learner's progress and level of understanding, conventional systems face the challenge of efficiently generating and distributing individually optimized educational materials. In particular, there are technical limitations that prevent real-time progress analysis and immediate provision of feedback.

[0446] 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.

[0447] In this invention, the server includes means for collecting learner learning information, means for analyzing the learner's progress and level of understanding based on the learning information, and means for generating educational materials based on the analysis results. This enables individually optimized educational support for learners.

[0448] "Learner learning information" refers to various data related to the learning activities undertaken by learners, and specifically includes answer data, time data, and material viewing history data.

[0449] "Means for analyzing progress and understanding" refers to functions and methods for analyzing learners' progress in their learning activities and their level of understanding of knowledge using collected learning information.

[0450] "Means for generating educational materials" refers to methods and technologies for creating learning materials and resources optimized for individual learners based on their analyzed understanding.

[0451] "Means of distribution to a device" refers to the technology or process of transmitting generated educational materials to a learner's terminal so that the learner can view or use them.

[0452] "Means for generating and immediately presenting responses" refers to methods and systems that generate evaluations and comments based on the answers provided by learners and communicate them to the learners in a timely manner.

[0453] A "generative model" refers to an algorithm or program that uses machine learning or artificial intelligence techniques to analyze collected data and evaluate the performance of learners.

[0454] This invention is an integrated system for realizing individually optimized educational support for each learner. The system mainly consists of a server and terminals, and comprehensively handles everything from collecting and analyzing learning information to generating and distributing educational materials and providing feedback.

[0455] The server receives learning information from the learner's device. This learning information includes detailed data such as answer data, time spent learning, and material viewing history. Based on this data, the server performs analysis using a generative AI model. This analysis utilizes machine learning algorithms to evaluate the learner's progress and level of understanding. The analysis results are used to identify which areas the learner needs to deepen their understanding of.

[0456] Next, the server generates individually optimized educational materials based on the analysis results. The generative AI model used here dynamically generates materials based on prompts. An example of such a prompt is, "Generate supplementary materials to deepen understanding of the following areas." The generated materials include supplementary practice problems, explanatory videos, and related learning resources.

[0457] The terminal receives educational materials distributed from the server and displays them in a way that makes them easily accessible and usable by the learner. When a learner answers a question, the answer is sent from the terminal to the server as feedback. This feedback is used on the server for re-evaluation and enables additional educational support.

[0458] Users, including parents and teachers, can receive learning progress reports provided by the server. These reports visually represent learners' progress using graphs and diagrams, supporting effective learning support and progress management. Parents can streamline support at home, and teachers can streamline lesson planning and assessment.

[0459] For example, if analysis indicates a lack of understanding of "mathematical factorization," the server generates practice problems and video materials specifically for factorization and delivers them to the learner's device. The learner's results from working through these materials are sent to the server, and further optimized educational support is continuously provided.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The server collects learning information from the learner's device. It receives answer data, learning time data, and material viewing history data as input. This data is acquired using a secure communication protocol and stored in a database. This data storage serves as the basis for subsequent analysis processing.

[0463] Step 2:

[0464] The server analyzes progress and comprehension using the collected training information. This process utilizes a generative AI model. The model is fed the collected training information as input, and outputs results that evaluate the learner's comprehension and progress. Data analysis is performed using pattern recognition and statistical methods to identify areas that show particular difficulty in comprehension.

[0465] Step 3:

[0466] The server generates educational materials based on the analysis results. The input includes the learner's weaknesses and required skill areas identified through the analysis. Prompts are passed to a generative AI model, which then creates supplementary materials and practice exercises accordingly. The output is personalized educational materials optimized for the learner. These materials are dynamically adjusted, utilizing natural language processing and content generation algorithms throughout the generation process.

[0467] Step 4:

[0468] The server distributes the generated educational materials to the learner's device. The input is the generated teaching materials. An appropriate communication protocol is used for distribution, and the materials are configured to be displayed on the learner's device. The output is educational content accessible to the learner.

[0469] Step 5:

[0470] The device collects the results of learners' use of learning materials. Input includes the results of questions answered by the learner and their learning history. This data is sent to a server for analysis. Sending this data is important for continuously tracking the learner's progress.

[0471] Step 6:

[0472] The server re-analyzes the learner's response data. The input is the learner's most recent response data. This re-analysis evaluates the newly acquired learner's progress and identifies any further educational support needed. The output is a learner progress report, which is an important source of information for parents and teachers.

[0473] (Application Example 1)

[0474] 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."

[0475] While there is a need for more efficient progress management and individualized instruction in educational settings, conventional systems have limitations in providing adequate real-time learning support and reporting. Furthermore, there is a lack of flexible mechanisms to accommodate learning in mobile environments.

[0476] 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.

[0477] In this invention, the server includes information gathering means for collecting learner learning data, an analysis device for analyzing the learner's progress and level of understanding based on the learning data, and material generation means for generating educational content optimized for the learner based on the analysis results. This enables individually optimized educational support for learners and allows them to continue learning even while on the go.

[0478] "Information gathering means" refers to the functional unit that collects learner learning data, and acquires data including problem answer information, learning time information, and material viewing history information.

[0479] The term "analysis device" refers to a functional unit that analyzes collected learning data and evaluates the learner's progress and level of understanding.

[0480] The "material generation means" is a functional unit that creates educational content optimized for learners based on analysis results, and provides individualized learning materials and supplementary content.

[0481] The "educational material distribution means" is a functional unit that transfers generated educational content to learners' devices and makes it accessible.

[0482] The "response generation means" is a functional unit that creates immediate feedback based on the learner's answer and presents it to the learner.

[0483] "Means of use" refers to a functional component that is compatible with portable devices, enabling learners to continue learning while on the go or in different locations within a smart city environment.

[0484] The system for implementing this invention consists of three main components: a server, a learner's terminal, and a user (teacher / parent).

[0485] The server is equipped with information gathering means, analysis devices, material generation means, and material distribution means. The information gathering means collects problem answer information, study time information, and material viewing history information collected through learners' devices such as smartphones and tablets and stores them on the server. This makes it possible to track learners' learning activities in detail.

[0486] The analysis device uses collected data and a generative AI model to analyze learners' understanding and progress. Based on this analysis, the material generation system creates individually optimized educational content. This content may include video explanations, practice problems tailored to proficiency levels, and additional learning materials.

[0487] The educational material distribution system transmits generated educational content to learners' devices in real time, allowing learners to use the content immediately. Accordingly, the response generation system provides instant feedback based on the answers, supporting the learners' learning.

[0488] The system's purpose is to enable learners to continue their studies wherever they are in a smart city environment, maintaining access to learning through mobile devices such as smartphones and tablets. Users will be responsible for viewing progress reports provided by the server and providing appropriate support to learners.

[0489] As a concrete example, when a middle school student is learning about factorization in mathematics, this system allows them to access practice problems via their smartphone and submit their answers to the server. This answer data is then analyzed on the server, and supplementary materials are provided as needed.

[0490] Examples of prompts for a generative AI model are as follows:

[0491] Please use the following data to evaluate the learner's level of understanding.

[0492] Answer data: 'Question 1': 'Correct', 'Question 2': 'Incorrect'

[0493] Study time: 1 hour

[0494] Please also submit proposals for creating educational content based on the evaluation results.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The terminal collects learner's answer data, study time, and material viewing history, and sends this data to the server. The input is learner activity data, and the output is data packets sent to the server. The terminal provides an interface for data collection and efficiently transmits the data.

[0498] Step 2:

[0499] The server stores the received data using information gathering tools and inputs the data into the analysis device. The input is training data transmitted from the terminal, and the output is data organized in an analyzable format. The server manages the data using a database system.

[0500] Step 3:

[0501] The analysis system on the server uses a generative AI model to evaluate the learner's progress and understanding. The input is organized training data, and the output is an understanding evaluation score. In this process, the generative AI model analyzes the data using prompt sentences. Specifically, the model identifies patterns and quantifies the learning status.

[0502] Step 4:

[0503] The server uses a material generation system to create optimized educational content based on evaluation results. The input is the comprehension evaluation score, and the output is educational material tailored to the learner. The generation process produces content selected by AI.

[0504] Step 5:

[0505] The server transmits the generated educational content to the terminal via a material distribution system. The input is educational materials, and the output is learning content displayed on the terminal. The server utilizes the network infrastructure to communicate data quickly.

[0506] Step 6:

[0507] The terminal presents educational content received from the server to the learner. Input is data sent from the server, and output is information displayed on the user interface. The terminal appropriately displays visual content and provides a learning experience.

[0508] Step 7:

[0509] Users (teachers or parents) receive feedback from the server based on the learner's progress and provide appropriate learning support. This feedback includes the accuracy rate of answers and advice for solving learning tasks. The input is progress reports from the server, and the output is the user's support actions. Throughout this process, users obtain information through digital devices.

[0510] 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.

[0511] The educational support system according to the present invention provides personalized learning support by collecting and analyzing both learner learning data and emotional data. The system consists of a server, multiple terminals, and an emotional engine.

[0512] The server receives learning data and emotional data transmitted from the learner's device. Learning data includes problem answer data, learning time data, and material viewing history data. Emotional data, on the other hand, is based on the learner's facial expression data and voice data collected via an emotional engine. The server analyzes this data to evaluate not only the learner's progress and understanding, but also their motivation and stress levels during learning.

[0513] Next, the server generates learning content optimized for the learner from the collected and analyzed data. This includes feedback and encouraging messages that take emotional states into consideration, as well as interactive content to maintain motivation. The generated content is then delivered from the server to the learner's device.

[0514] The terminal displays learning content received from the server, making it accessible to students. The terminal also has a facial recognition camera and microphone that transmit the learner's emotional state to an emotion engine. When a learner answers a question, the emotional information is sent back to the server along with the answer data, allowing for real-time monitoring of the learner's state.

[0515] Users can receive feedback that reflects their emotional state. For example, if it is determined that their concentration on learning is declining, they may be presented with interactions suggesting short breaks or recommending simple exercises to relax.

[0516] Thus, the system of the present invention uses data obtained from the emotion engine to realize individually optimized educational support for learners. For example, if it is determined that a learner is experiencing stress when faced with a problem on "equations of motion in physics" that they do not understand well, the system can deliver a temporary interruption to help them relax or an encouragement message to maintain their motivation.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The device records answer data, study time, and viewing history of learning materials when learners answer questions. Furthermore, the device transmits facial expression data and voice data collected through the camera and microphone to the emotion engine.

[0520] Step 2:

[0521] The emotion engine analyzes facial expression and voice data transmitted from the device to estimate the learner's emotional state. The estimated emotional state is evaluated as motivation and stress levels and sent to the server.

[0522] Step 3:

[0523] The server receives learning data from the terminal and emotional data from the emotion engine, and stores them in a database. This provides the foundation for comprehensively understanding the learner's learning progress and emotional state.

[0524] Step 4:

[0525] The server analyzes the accumulated data to evaluate the learner's progress, comprehension, and emotional state. During this process, it identifies areas where comprehension is weak or where stress levels are high, thus hindering learning.

[0526] Step 5:

[0527] Based on the analysis results, the server generates educational content optimized for the learner. This content includes practice exercises and videos to address areas of misunderstanding, as well as emotionally sensitive feedback and motivational messages.

[0528] Step 6:

[0529] The server delivers optimized content to the learner's device. The device then displays this content for the learner to access immediately.

[0530] Step 7:

[0531] The device assists learners in progressing through their studies using the content. Each time a learner answers a question, the device collects the result and sentiment data again and sends it to the server.

[0532] Step 8:

[0533] The server generates feedback using newly received answer data and sentiment data. This feedback includes whether the answer is correct or incorrect, suggestions for improvement, and emotionally-based encouragement and break suggestions.

[0534] Step 9:

[0535] The device presents the generated feedback to the learner in real time. This allows the learner to enhance their learning effectiveness and manage their mental burden appropriately.

[0536] Step 10:

[0537] Users (parents or teachers) can receive regular reports from the server regarding learning progress and emotional state, which can be used to support their child at home and in the classroom.

[0538] (Example 2)

[0539] 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."

[0540] While conventional educational support systems evaluate learners' progress and comprehension based on their learning data, they have struggled to provide individually optimized learning support that takes into account learners' emotional states. Therefore, there is a lack of flexible learning support that takes into account emotional aspects such as stress and decreased motivation that learners experience during their studies.

[0541] 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.

[0542] In this invention, the server includes data collection means for collecting learner learning data and emotional data; analysis means for evaluating the learner's progress, understanding, and emotional state based on the learning data and emotional data; and content generation means for generating learning content individually optimized for the learner based on the evaluation results. This makes it possible to consider the learner's emotional state in real time and provide more effective and individually optimized learning support.

[0543] "Data collection methods" refer to processes and devices for efficiently collecting learners' learning data and emotional data.

[0544] "Analysis tools" refer to processes and functions for evaluating learners' progress, comprehension, and emotional state based on collected learning and emotional data.

[0545] "Content generation methods" refer to the processes and tools used to create personalized learning content for learners based on analyzed data.

[0546] "Content distribution means" refers to the process and methods for delivering generated learning content to learners' information devices.

[0547] "Feedback methods" refer to processes and mechanisms for providing appropriate feedback in real time based on learners' answers and sentiment data.

[0548] The educational support system of the present invention aims to improve the learning experience by providing learners with individually optimized learning content. This system comprises a server, terminals, and an emotion engine.

[0549] The server has the primary function of collecting learner learning data and emotional data. Learning data includes problem answer data, learning time data, and material viewing history data, while emotional data includes facial expression data and voice data. The server uses machine learning algorithms and data analysis software to evaluate learners' progress, comprehension, and emotional state using this data. This allows the server to extract important information to improve the quality of education and generate educational content tailored to learners' needs. It also uses generative AI models to create encouraging messages and feedback that are appropriate to the learner's emotional state.

[0550] The terminal acts as the interface with the learner and transfers collected data to the server. The terminal also collects data in real time using a facial recognition camera and microphone, and transmits the learner's emotional state to the emotion engine. When the learner answers a question, the emotional information is sent back to the server along with the answer data, and personalized content based on the results is delivered to the terminal.

[0551] Users receive customized learning content and feedback delivered via their devices, allowing them to progress in their learning. For example, if the system detects a decrease in concentration, it can suggest a break or provide motivational messages.

[0552] For example, when a user is learning "equations of motion in physics," if the system detects the user's stress level, the server will either stream a relaxing video or suggest a deep breathing exercise. In this way, learners can progress through their studies efficiently at their own pace.

[0553] An example of a specific prompt for a generative AI model is, "Provide guidance for generating content that will provide appropriate feedback based on the learner's current emotional state." By using such prompts, the generative AI can specify the most suitable support for each individual learner.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The device collects the learner's problem-solving data, study time data, and learning material viewing history data. Simultaneously, it acquires the learner's facial expression data and voice data using a facial recognition camera and microphone. All of this data is sent to the emotion engine and transferred to the server as input data to understand the learner's emotional state.

[0557] Step 2:

[0558] The server receives learning data and sentiment data sent from the terminal and stores them in a database. Next, it uses machine learning algorithms to analyze this data and evaluate the learner's progress, comprehension, stress level, and motivation status. Based on the input data, it outputs analysis results, including the aforementioned evaluations.

[0559] Step 3:

[0560] The server generates learning content that is individually optimized for each learner based on the analysis results. This generation uses a generative AI model and includes encouraging messages and feedback tailored to the learner's emotional state. The input for content generation is the analysis results, and the output is customized learning content and feedback.

[0561] Step 4:

[0562] The server delivers the generated learning content to the learner's device. At this stage, notifications and scheduling are managed to ensure that the device displays the appropriate content at the optimal time for the learner. The input is the generated content, and the output is the delivery of the content to the learner's device.

[0563] Step 5:

[0564] Users progress through their learning using optimized content delivered to their devices. Based on feedback received during learning, users can check their understanding and motivation in real time. The user's learning activity is monitored again on the device, and the process is repeated, returning to step 1. The inputs are the delivered content and user feedback, and the output is the update of the learning progress.

[0565] (Application Example 2)

[0566] 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."

[0567] In today's educational environment, there is a problem of insufficient individualized educational support for learners. In particular, the lack of educational support that takes into account learners' emotional states often leads to decreased motivation and increased learning stress. To address these challenges, a system is needed that collects and analyzes not only learner learning data but also emotional data, and provides individually optimized learning support.

[0568] 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.

[0569] In this invention, the server includes information gathering means for collecting learner learning data and emotional data; analysis means for analyzing the learner's progress, understanding, and emotional state based on the learning data and emotional data; and information generation means for generating learning content optimized for the learner and emotionally sensitive interactive feedback based on the analysis results. This makes it possible to provide individualized educational support tailored to each learner's learning progress and to provide emotional care.

[0570] "Information gathering means" refers to a device or method for acquiring learner learning data and emotional data.

[0571] "Analysis means" refers to a device or method that uses collected learning data and sentiment data to evaluate a learner's progress, understanding, and emotional state.

[0572] "Information generation means" refers to a device or method that creates learning content and emotional feedback tailored to the learner based on analyzed data.

[0573] "Information distribution means" refers to a device or method that transfers and displays generated learning content and feedback to the learner's terminal.

[0574] A "response mechanism" is a device or method that provides timely feedback based on the learner's answers and collected sentiment data.

[0575] This invention is a system that provides personalized educational support using learner learning data and emotional data. To achieve this, the system mainly includes a server, terminals, and a user interface.

[0576] The server collects learning data and emotional data from the learner's device. Learning data includes problem answer information, learning time information, and browsing history, while emotional data includes the learner's facial expressions and voice information. For data processing, OpenCV is used for facial expression analysis, and the Google Speech-to-Text API is used to convert voice data to text. Amazon Comprehend or IBM Watson Tone Analyzer are used to evaluate the learner's progress and emotional state. This data analysis provides a detailed understanding of the learner's learning status and emotional state.

[0577] Next, the server uses a generative AI model to generate optimized learning content and emotionally sensitive feedback based on the analysis results, and delivers the information to the learner's device. This allows learners to receive learning support and emotional care tailored to their situation in real time.

[0578] The device has the function of displaying learning content delivered from the server to the learner. It also uses the camera and microphone built into the device to continuously collect the learner's facial expressions and speech, and transmits this data to the server. Interactive feedback is presented to the learner in response to changes in their facial expressions and speech, promoting motivation and improving learning efficiency.

[0579] Based on specific feedback about their learning progress, users can receive suggestions for short breaks or light exercises to refresh their minds. This allows learners to interrupt and resume their studies at appropriate times as part of their self-management.

[0580] For example, if a user is working on a math problem and their expression becomes cloudy, the system might offer a message such as, "Are you stuck right now? Why not try a hint to see it from a different perspective?"

[0581] An example of a prompt message might be, "When an elementary school student is stuck on a math problem, please suggest ways to motivate them." This is then input into the generative AI model, generating appropriate feedback and content that is quickly delivered to the learner.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The server receives learner learning data and emotion data from the terminal. It acquires problem answer data, learning time information, facial expression data, and voice data as input. The server aggregates this data and prepares it for the next analysis step.

[0585] Step 2:

[0586] The server analyzes training data and emotion data. For data analysis, it uses OpenCV to analyze facial expression data and the Google Speech-to-Text API to convert speech data into text. The analysis output provides evaluation results regarding the learner's progress, comprehension, and emotional state.

[0587] Step 3:

[0588] The server uses an AI model based on the analysis results to generate learning content optimized for the learner and emotionally sensitive feedback. It uses analysis and evaluation as input and creates customized content and encouraging messages as output.

[0589] Step 4:

[0590] The server delivers the generated learning content and feedback to the device. The device receives this content and displays it to the learner at the appropriate time.

[0591] Step 5:

[0592] The device displays the received learning content to the user while simultaneously continuously collecting the learner's facial expressions and speech using the camera and microphone. This data is transmitted to the server in real time and used for subsequent analyses.

[0593] Step 6:

[0594] Users take action based on feedback from the server. Their actions and responses are also collected as data and provided to the system. This interaction loop allows learners to continuously receive personalized educational support.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] The educational support system of this invention is designed to provide integrated educational support for learners, teachers, and parents. The system consists of a server and multiple terminals.

[0613] The server first receives learning data from the learner's device. This data includes problem answer data, learning time data, and material viewing history data. To analyze this data, the server uses a generative model. Analysis using the generative model allows the server to evaluate the learner's progress and level of understanding, and to identify areas where understanding is low.

[0614] Next, the server generates individually optimized educational content based on the analysis results. This generated content includes supplementary practice exercises, explanatory videos, and additional materials on specific topics. This generated content is then delivered from the server to the learner's device.

[0615] The terminal displays learning content received from the server, making it accessible to students. It also has a function to send the results of students' answers to the server. The answer data is used to generate feedback.

[0616] Users (parents or teachers) can receive learning progress reports provided by the server. This allows parents to effectively support their children's learning at home. Meanwhile, teachers receive automated reports that track homework grading and progress, reducing their workload.

[0617] Thus, the system of the present invention provides individually optimized learning support through a complex configuration that includes data collection and analysis, content generation and distribution, and immediate feedback and reporting. For example, for the area "mathematical factorization problems" which is determined to be a low level of understanding, specially designed practice problems are delivered to the learner, and feedback is provided according to their performance.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The device collects answer data, study time, and viewing history data when learners answer questions, and sends this data to the server.

[0621] Step 2:

[0622] The server receives data sent from the terminal and stores it in a database. This data serves as foundational information used for later analysis.

[0623] Step 3:

[0624] The server analyzes the accumulated data using a generative model to evaluate the learner's progress and understanding. In this process, it identifies areas where understanding is low and topics requiring further study.

[0625] Step 4:

[0626] Based on the analysis results, the server generates educational content optimized for the learner. This content may include additional practice exercises and explanatory videos.

[0627] Step 5:

[0628] The server delivers the generated optimized content to the learner's device. The delivered content is displayed on the learner's device, allowing the learner to continue their learning.

[0629] Step 6:

[0630] The device displays the content delivered to the learner, guiding them to view it and resubmit their answers. This allows the learner to reinforce areas where they lack understanding.

[0631] Step 7:

[0632] The server receives the answer data that the learner has resubmitted and generates feedback based on it. The feedback includes whether the answer is correct or incorrect and areas for improvement.

[0633] Step 8:

[0634] The device presents the generated feedback to the learner in real time. This allows the learner to receive immediate evaluation of their answers.

[0635] Step 9:

[0636] Users (parents or teachers) receive learning progress reports delivered from the server, allowing them to understand the learner's situation. This enables them to provide support for home learning and adjust educational policies accordingly.

[0637] (Example 1)

[0638] 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".

[0639] In today's educational environment, while there is a demand for optimal educational support tailored to each learner's progress and level of understanding, conventional systems face the challenge of efficiently generating and distributing individually optimized educational materials. In particular, there are technical limitations that prevent real-time progress analysis and immediate provision of feedback.

[0640] 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.

[0641] In this invention, the server includes means for collecting learner learning information, means for analyzing the learner's progress and level of understanding based on the learning information, and means for generating educational materials based on the analysis results. This enables individually optimized educational support for learners.

[0642] "Learner learning information" refers to various data related to the learning activities undertaken by learners, and specifically includes answer data, time data, and material viewing history data.

[0643] "Means for analyzing progress and understanding" refers to functions and methods for analyzing learners' progress in their learning activities and their level of understanding of knowledge using collected learning information.

[0644] "Means for generating educational materials" refers to methods and technologies for creating learning materials and resources optimized for individual learners based on their analyzed understanding.

[0645] "Means of distribution to a device" refers to the technology or process of transmitting generated educational materials to a learner's terminal so that the learner can view or use them.

[0646] "Means for generating and immediately presenting responses" refers to methods and systems that generate evaluations and comments based on the answers provided by learners and communicate them to the learners in a timely manner.

[0647] A "generative model" refers to an algorithm or program that uses machine learning or artificial intelligence techniques to analyze collected data and evaluate the performance of learners.

[0648] This invention is an integrated system for realizing individually optimized educational support for each learner. The system mainly consists of a server and terminals, and comprehensively handles everything from collecting and analyzing learning information to generating and distributing educational materials and providing feedback.

[0649] The server receives learning information from the learner's device. This learning information includes detailed data such as answer data, time spent learning, and material viewing history. Based on this data, the server performs analysis using a generative AI model. This analysis utilizes machine learning algorithms to evaluate the learner's progress and level of understanding. The analysis results are used to identify which areas the learner needs to deepen their understanding of.

[0650] Next, the server generates individually optimized educational materials based on the analysis results. The generative AI model used here dynamically generates materials based on prompts. An example of such a prompt is, "Generate supplementary materials to deepen understanding of the following areas." The generated materials include supplementary practice problems, explanatory videos, and related learning resources.

[0651] The terminal receives educational materials distributed from the server and displays them in a way that makes them easily accessible and usable by the learner. When a learner answers a question, the answer is sent from the terminal to the server as feedback. This feedback is used on the server for re-evaluation and enables additional educational support.

[0652] Users, including parents and teachers, can receive learning progress reports provided by the server. These reports visually represent learners' progress using graphs and diagrams, supporting effective learning support and progress management. Parents can streamline support at home, and teachers can streamline lesson planning and assessment.

[0653] For example, if analysis indicates a lack of understanding of "mathematical factorization," the server generates practice problems and video materials specifically for factorization and delivers them to the learner's device. The learner's results from working through these materials are sent to the server, and further optimized educational support is continuously provided.

[0654] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0655] Step 1:

[0656] The server collects learning information from the learner's device. It receives answer data, learning time data, and material viewing history data as input. This data is acquired using a secure communication protocol and stored in a database. This data storage serves as the basis for subsequent analysis processing.

[0657] Step 2:

[0658] The server analyzes progress and comprehension using the collected training information. This process utilizes a generative AI model. The model is fed the collected training information as input, and outputs results that evaluate the learner's comprehension and progress. Data analysis is performed using pattern recognition and statistical methods to identify areas that show particular difficulty in comprehension.

[0659] Step 3:

[0660] The server generates educational materials based on the analysis results. The input includes the learner's weaknesses and required skill areas identified through the analysis. Prompts are passed to a generative AI model, which then creates supplementary materials and practice exercises accordingly. The output is personalized educational materials optimized for the learner. These materials are dynamically adjusted, utilizing natural language processing and content generation algorithms throughout the generation process.

[0661] Step 4:

[0662] The server distributes the generated educational materials to the learner's device. The input is the generated teaching materials. An appropriate communication protocol is used for distribution, and the materials are configured to be displayed on the learner's device. The output is educational content accessible to the learner.

[0663] Step 5:

[0664] The device collects the results of learners' use of learning materials. Input includes the results of questions answered by the learner and their learning history. This data is sent to a server for analysis. Sending this data is important for continuously tracking the learner's progress.

[0665] Step 6:

[0666] The server re-analyzes the learner's response data. The input is the learner's most recent response data. This re-analysis evaluates the newly acquired learner's progress and identifies any further educational support needed. The output is a learner progress report, which is an important source of information for parents and teachers.

[0667] (Application Example 1)

[0668] 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".

[0669] While there is a need for more efficient progress management and individualized instruction in educational settings, conventional systems have limitations in providing adequate real-time learning support and reporting. Furthermore, there is a lack of flexible mechanisms to accommodate learning in mobile environments.

[0670] 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.

[0671] In this invention, the server includes information gathering means for collecting learner learning data, an analysis device for analyzing the learner's progress and level of understanding based on the learning data, and material generation means for generating educational content optimized for the learner based on the analysis results. This enables individually optimized educational support for learners and allows them to continue learning even while on the go.

[0672] "Information gathering means" refers to the functional unit that collects learner learning data, and acquires data including problem answer information, learning time information, and material viewing history information.

[0673] The term "analysis device" refers to a functional unit that analyzes collected learning data and evaluates the learner's progress and level of understanding.

[0674] The "material generation means" is a functional unit that creates educational content optimized for learners based on analysis results, and provides individualized learning materials and supplementary content.

[0675] The "educational material distribution means" is a functional unit that transfers generated educational content to learners' devices and makes it accessible.

[0676] The "response generation means" is a functional unit that creates immediate feedback based on the learner's answer and presents it to the learner.

[0677] "Means of use" refers to a functional component that is compatible with portable devices, enabling learners to continue learning while on the go or in different locations within a smart city environment.

[0678] The system for implementing this invention consists of three main components: a server, a learner's terminal, and a user (teacher / parent).

[0679] The server is equipped with information gathering means, analysis devices, material generation means, and material distribution means. The information gathering means collects problem answer information, study time information, and material viewing history information collected through learners' devices such as smartphones and tablets and stores them on the server. This makes it possible to track learners' learning activities in detail.

[0680] The analysis device uses collected data and a generative AI model to analyze learners' understanding and progress. Based on this analysis, the material generation system creates individually optimized educational content. This content may include video explanations, practice problems tailored to proficiency levels, and additional learning materials.

[0681] The educational material distribution system transmits generated educational content to learners' devices in real time, allowing learners to use the content immediately. Accordingly, the response generation system provides instant feedback based on the answers, supporting the learners' learning.

[0682] The system's purpose is to enable learners to continue their studies wherever they are in a smart city environment, maintaining access to learning through mobile devices such as smartphones and tablets. Users will be responsible for viewing progress reports provided by the server and providing appropriate support to learners.

[0683] As a concrete example, when a middle school student is learning about factorization in mathematics, this system allows them to access practice problems via their smartphone and submit their answers to the server. This answer data is then analyzed on the server, and supplementary materials are provided as needed.

[0684] Examples of prompts for a generative AI model are as follows:

[0685] Please use the following data to evaluate the learner's level of understanding.

[0686] Answer data: 'Question 1': 'Correct', 'Question 2': 'Incorrect'

[0687] Study time: 1 hour

[0688] Please also submit proposals for creating educational content based on the evaluation results.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The terminal collects learner's answer data, study time, and material viewing history, and sends this data to the server. The input is learner activity data, and the output is data packets sent to the server. The terminal provides an interface for data collection and efficiently transmits the data.

[0692] Step 2:

[0693] The server stores the received data using information gathering tools and inputs the data into the analysis device. The input is training data transmitted from the terminal, and the output is data organized in an analyzable format. The server manages the data using a database system.

[0694] Step 3:

[0695] The analysis system on the server uses a generative AI model to evaluate the learner's progress and understanding. The input is organized training data, and the output is an understanding evaluation score. In this process, the generative AI model analyzes the data using prompt sentences. Specifically, the model identifies patterns and quantifies the learning status.

[0696] Step 4:

[0697] The server uses a material generation system to create optimized educational content based on evaluation results. The input is the comprehension evaluation score, and the output is educational material tailored to the learner. The generation process produces content selected by AI.

[0698] Step 5:

[0699] The server transmits the generated educational content to the terminal via a material distribution system. The input is educational materials, and the output is learning content displayed on the terminal. The server utilizes the network infrastructure to communicate data quickly.

[0700] Step 6:

[0701] The terminal presents educational content received from the server to the learner. Input is data sent from the server, and output is information displayed on the user interface. The terminal appropriately displays visual content and provides a learning experience.

[0702] Step 7:

[0703] Users (teachers or parents) receive feedback from the server based on the learner's progress and provide appropriate learning support. This feedback includes the accuracy rate of answers and advice for solving learning tasks. The input is progress reports from the server, and the output is the user's support actions. Throughout this process, users obtain information through digital devices.

[0704] 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.

[0705] The educational support system according to the present invention provides personalized learning support by collecting and analyzing both learner learning data and emotional data. The system consists of a server, multiple terminals, and an emotional engine.

[0706] The server receives learning data and emotional data transmitted from the learner's device. Learning data includes problem answer data, learning time data, and material viewing history data. Emotional data, on the other hand, is based on the learner's facial expression data and voice data collected via an emotional engine. The server analyzes this data to evaluate not only the learner's progress and understanding, but also their motivation and stress levels during learning.

[0707] Next, the server generates learning content optimized for the learner from the collected and analyzed data. This includes feedback and encouraging messages that take emotional states into consideration, as well as interactive content to maintain motivation. The generated content is then delivered from the server to the learner's device.

[0708] The terminal displays learning content received from the server, making it accessible to students. The terminal also has a facial recognition camera and microphone that transmit the learner's emotional state to an emotion engine. When a learner answers a question, the emotional information is sent back to the server along with the answer data, allowing for real-time monitoring of the learner's state.

[0709] Users can receive feedback that reflects their emotional state. For example, if it is determined that their concentration on learning is declining, they may be presented with interactions suggesting short breaks or recommending simple exercises to relax.

[0710] Thus, the system of the present invention uses data obtained from the emotion engine to realize individually optimized educational support for learners. For example, if it is determined that a learner is experiencing stress when faced with a problem on "equations of motion in physics" that they do not understand well, the system can deliver a temporary interruption to help them relax or an encouragement message to maintain their motivation.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The device records answer data, study time, and viewing history of learning materials when learners answer questions. Furthermore, the device transmits facial expression data and voice data collected through the camera and microphone to the emotion engine.

[0714] Step 2:

[0715] The emotion engine analyzes facial expression and voice data transmitted from the device to estimate the learner's emotional state. The estimated emotional state is evaluated as motivation and stress levels and sent to the server.

[0716] Step 3:

[0717] The server receives learning data from the terminal and emotional data from the emotion engine, and stores them in a database. This provides the foundation for comprehensively understanding the learner's learning progress and emotional state.

[0718] Step 4:

[0719] The server analyzes the accumulated data to evaluate the learner's progress, comprehension, and emotional state. During this process, it identifies areas where comprehension is weak or where stress levels are high, thus hindering learning.

[0720] Step 5:

[0721] Based on the analysis results, the server generates educational content optimized for the learner. This content includes practice exercises and videos to address areas of misunderstanding, as well as emotionally sensitive feedback and motivational messages.

[0722] Step 6:

[0723] The server delivers optimized content to the learner's device. The device then displays this content for the learner to access immediately.

[0724] Step 7:

[0725] The device assists learners in progressing through their studies using the content. Each time a learner answers a question, the device collects the result and sentiment data again and sends it to the server.

[0726] Step 8:

[0727] The server generates feedback using newly received answer data and sentiment data. This feedback includes whether the answer is correct or incorrect, suggestions for improvement, and emotionally-based encouragement and break suggestions.

[0728] Step 9:

[0729] The device presents the generated feedback to the learner in real time. This allows the learner to enhance their learning effectiveness and manage their mental burden appropriately.

[0730] Step 10:

[0731] Users (parents or teachers) can receive regular reports from the server regarding learning progress and emotional state, which can be used to support their child at home and in the classroom.

[0732] (Example 2)

[0733] 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".

[0734] While conventional educational support systems evaluate learners' progress and comprehension based on their learning data, they have struggled to provide individually optimized learning support that takes into account learners' emotional states. Therefore, there is a lack of flexible learning support that takes into account emotional aspects such as stress and decreased motivation that learners experience during their studies.

[0735] 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.

[0736] In this invention, the server includes data collection means for collecting learner learning data and emotional data; analysis means for evaluating the learner's progress, understanding, and emotional state based on the learning data and emotional data; and content generation means for generating learning content individually optimized for the learner based on the evaluation results. This makes it possible to consider the learner's emotional state in real time and provide more effective and individually optimized learning support.

[0737] "Data collection methods" refer to processes and devices for efficiently collecting learners' learning data and emotional data.

[0738] "Analysis tools" refer to processes and functions for evaluating learners' progress, comprehension, and emotional state based on collected learning and emotional data.

[0739] "Content generation methods" refer to the processes and tools used to create personalized learning content for learners based on analyzed data.

[0740] "Content distribution means" refers to the process and methods for delivering generated learning content to learners' information devices.

[0741] "Feedback methods" refer to processes and mechanisms for providing appropriate feedback in real time based on learners' answers and sentiment data.

[0742] The educational support system of the present invention aims to improve the learning experience by providing learners with individually optimized learning content. This system comprises a server, terminals, and an emotion engine.

[0743] The server has the primary function of collecting learner learning data and emotional data. Learning data includes problem answer data, learning time data, and material viewing history data, while emotional data includes facial expression data and voice data. The server uses machine learning algorithms and data analysis software to evaluate learners' progress, comprehension, and emotional state using this data. This allows the server to extract important information to improve the quality of education and generate educational content tailored to learners' needs. It also uses generative AI models to create encouraging messages and feedback that are appropriate to the learner's emotional state.

[0744] The terminal acts as the interface with the learner and transfers collected data to the server. The terminal also collects data in real time using a facial recognition camera and microphone, and transmits the learner's emotional state to the emotion engine. When the learner answers a question, the emotional information is sent back to the server along with the answer data, and personalized content based on the results is delivered to the terminal.

[0745] Users receive customized learning content and feedback delivered via their devices, allowing them to progress in their learning. For example, if the system detects a decrease in concentration, it can suggest a break or provide motivational messages.

[0746] For example, when a user is learning "equations of motion in physics," if the system detects the user's stress level, the server will either stream a relaxing video or suggest a deep breathing exercise. In this way, learners can progress through their studies efficiently at their own pace.

[0747] An example of a specific prompt for a generative AI model is, "Provide guidance for generating content that will provide appropriate feedback based on the learner's current emotional state." By using such prompts, the generative AI can specify the most suitable support for each individual learner.

[0748] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0749] Step 1:

[0750] The device collects the learner's problem-solving data, study time data, and learning material viewing history data. Simultaneously, it acquires the learner's facial expression data and voice data using a facial recognition camera and microphone. All of this data is sent to the emotion engine and transferred to the server as input data to understand the learner's emotional state.

[0751] Step 2:

[0752] The server receives learning data and sentiment data sent from the terminal and stores them in a database. Next, it uses machine learning algorithms to analyze this data and evaluate the learner's progress, comprehension, stress level, and motivation status. Based on the input data, it outputs analysis results, including the aforementioned evaluations.

[0753] Step 3:

[0754] The server generates learning content that is individually optimized for each learner based on the analysis results. This generation uses a generative AI model and includes encouraging messages and feedback tailored to the learner's emotional state. The input for content generation is the analysis results, and the output is customized learning content and feedback.

[0755] Step 4:

[0756] The server delivers the generated learning content to the learner's device. At this stage, notifications and scheduling are managed to ensure that the device displays the appropriate content at the optimal time for the learner. The input is the generated content, and the output is the delivery of the content to the learner's device.

[0757] Step 5:

[0758] Users progress through their learning using optimized content delivered to their devices. Based on feedback received during learning, users can check their understanding and motivation in real time. The user's learning activity is monitored again on the device, and the process is repeated, returning to step 1. The inputs are the delivered content and user feedback, and the output is the update of the learning progress.

[0759] (Application Example 2)

[0760] 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".

[0761] In today's educational environment, there is a problem of insufficient individualized educational support for learners. In particular, the lack of educational support that takes into account learners' emotional states often leads to decreased motivation and increased learning stress. To address these challenges, a system is needed that collects and analyzes not only learner learning data but also emotional data, and provides individually optimized learning support.

[0762] 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.

[0763] In this invention, the server includes information gathering means for collecting learner learning data and emotional data; analysis means for analyzing the learner's progress, understanding, and emotional state based on the learning data and emotional data; and information generation means for generating learning content optimized for the learner and emotionally sensitive interactive feedback based on the analysis results. This makes it possible to provide individualized educational support tailored to each learner's learning progress and to provide emotional care.

[0764] "Information gathering means" refers to a device or method for acquiring learner learning data and emotional data.

[0765] "Analysis means" refers to a device or method that uses collected learning data and sentiment data to evaluate a learner's progress, understanding, and emotional state.

[0766] "Information generation means" refers to a device or method that creates learning content and emotional feedback tailored to the learner based on analyzed data.

[0767] "Information distribution means" refers to a device or method that transfers and displays generated learning content and feedback to the learner's terminal.

[0768] A "response mechanism" is a device or method that provides timely feedback based on the learner's answers and collected sentiment data.

[0769] This invention is a system that provides personalized educational support using learner learning data and emotional data. To achieve this, the system mainly includes a server, terminals, and a user interface.

[0770] The server collects learning data and emotional data from the learner's device. Learning data includes problem answer information, learning time information, and browsing history, while emotional data includes the learner's facial expressions and voice information. For data processing, OpenCV is used for facial expression analysis, and the Google Speech-to-Text API is used to convert voice data to text. Amazon Comprehend or IBM Watson Tone Analyzer are used to evaluate the learner's progress and emotional state. This data analysis provides a detailed understanding of the learner's learning status and emotional state.

[0771] Next, the server uses a generative AI model to generate optimized learning content and emotionally sensitive feedback based on the analysis results, and delivers the information to the learner's device. This allows learners to receive learning support and emotional care tailored to their situation in real time.

[0772] The device has the function of displaying learning content delivered from the server to the learner. It also uses the camera and microphone built into the device to continuously collect the learner's facial expressions and speech, and transmits this data to the server. Interactive feedback is presented to the learner in response to changes in their facial expressions and speech, promoting motivation and improving learning efficiency.

[0773] Based on specific feedback about their learning progress, users can receive suggestions for short breaks or light exercises to refresh their minds. This allows learners to interrupt and resume their studies at appropriate times as part of their self-management.

[0774] For example, if a user is working on a math problem and their expression becomes cloudy, the system might offer a message such as, "Are you stuck right now? Why not try a hint to see it from a different perspective?"

[0775] An example of a prompt message might be, "When an elementary school student is stuck on a math problem, please suggest ways to motivate them." This is then input into the generative AI model, generating appropriate feedback and content that is quickly delivered to the learner.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The server receives learner learning data and emotion data from the terminal. It acquires problem answer data, learning time information, facial expression data, and voice data as input. The server aggregates this data and prepares it for the next analysis step.

[0779] Step 2:

[0780] The server analyzes training data and emotion data. For data analysis, it uses OpenCV to analyze facial expression data and the Google Speech-to-Text API to convert speech data into text. The analysis output provides evaluation results regarding the learner's progress, comprehension, and emotional state.

[0781] Step 3:

[0782] The server uses an AI model based on the analysis results to generate learning content optimized for the learner and emotionally sensitive feedback. It uses analysis and evaluation as input and creates customized content and encouraging messages as output.

[0783] Step 4:

[0784] The server delivers the generated learning content and feedback to the device. The device receives this content and displays it to the learner at the appropriate time.

[0785] Step 5:

[0786] The device displays the received learning content to the user while simultaneously continuously collecting the learner's facial expressions and speech using the camera and microphone. This data is transmitted to the server in real time and used for subsequent analyses.

[0787] Step 6:

[0788] Users take action based on feedback from the server. Their actions and responses are also collected as data and provided to the system. This interaction loop allows learners to continuously receive personalized educational support.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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."

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] A data collection method for collecting learner learning data,

[0813] An analytical means for analyzing the learner's progress and level of understanding based on the aforementioned learning data,

[0814] A content generation means that generates learning content optimized for learners based on the aforementioned analysis results,

[0815] A content distribution means for distributing the optimized learning content to the learner's device,

[0816] A feedback mechanism that generates and presents feedback in real time based on the learner's answers,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the data collection means is configured to record learner's problem answer data, study time data, and material viewing history data.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the analysis means is configured to determine the learner's level of understanding using a generative model.

[0822] "Example 1"

[0823] (Claim 1)

[0824] Means for collecting learners' learning information,

[0825] A means for analyzing the learner's progress and level of understanding based on the aforementioned learning information,

[0826] A means for generating educational materials optimized for learners based on the aforementioned analysis results,

[0827] A means for distributing the optimized educational materials to the learner's device,

[0828] A means of generating and immediately presenting responses based on learners' answers,

[0829] A system that includes means for coordinating the distribution of generated educational materials.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the collection means is configured to record learner's answer data, time data, and material viewing history data.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the analysis means is configured to evaluate the learner's level of understanding using a generative model.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Information gathering means for collecting learner learning data,

[0837] An analysis device that analyzes the learner's progress and level of understanding based on the aforementioned learning data,

[0838] A means for generating educational materials that generates educational content optimized for learners based on the aforementioned analysis results,

[0839] A means for delivering the optimized educational content to learners' devices,

[0840] A response generation means that generates and provides feedback based on the learner's answers in real time,

[0841] In a smart city environment, a means of use involves using compatible portable devices that allow learning even while on the go,

[0842] An educational support system that includes this.

[0843] (Claim 2)

[0844] The educational support system according to claim 1, wherein the information gathering means is configured to record learner's problem answer information, study time information, and material viewing history information.

[0845] (Claim 3)

[0846] The educational support system according to claim 1, wherein the analysis device is configured to evaluate the learner's level of understanding using a generative model.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] A data collection method for collecting learner learning data and emotional data,

[0850] An analysis means for evaluating the learner's progress, understanding, and emotional state based on the aforementioned learning data and emotional data,

[0851] A content generation means that generates learning content individually optimized for learners based on the evaluation results,

[0852] The optimized learning content and a content distribution means that delivers feedback and encouraging messages tailored to the learner's emotional state to the learner's information terminal,

[0853] A feedback system that generates and presents feedback in real time based on learners' answers and sentiment data,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the data collection means is configured to record learner's problem answer data, study time data, material viewing history data, facial expression data, and voice data.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the analysis means is configured to determine the learner's level of understanding and emotional state using a generative model.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] Information gathering methods for collecting learner learning data and emotional data,

[0862] An analysis means for analyzing the learner's progress, understanding, and emotional state based on the aforementioned learning data and emotional data,

[0863] Information generation means that generates learning content optimized for the learner and interactive feedback that takes emotions into consideration, based on the aforementioned analysis results,

[0864] Information distribution means for delivering the optimized learning content and feedback to the learner's device,

[0865] A response system that generates and presents feedback based on the learner's answers and emotions in real time,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the information gathering means is configured to record learner's problem answer data, study time data, material viewing history data, and facial expression data or voice data.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the analysis means is configured to determine the learner's level of understanding and emotional state using generation technology. [Explanation of symbols]

[0871] 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. Information gathering means for collecting learner learning data, An analysis device that analyzes the learner's progress and level of understanding based on the aforementioned learning data, A means for generating educational materials that generates educational content optimized for learners based on the aforementioned analysis results, A means for delivering the optimized educational content to learners' devices, A response generation means that generates and provides feedback based on the learner's answers in real time, In a smart city environment, a means of use involves using compatible portable devices that allow learning even while on the go, An educational support system that includes this.

2. The educational support system according to claim 1, wherein the information gathering means is configured to record learner's problem answer information, study time information, and material viewing history information.

3. The educational support system according to claim 1, wherein the analysis device is configured to evaluate the learner's level of understanding using a generative model.

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