system

Abstract

We provide the system. [Solution] Means for collecting student learning activity data, A means of analyzing collected learning activity data and evaluating the level of understanding, A means for generating an optimized learning plan for each student based on the analysis results, A means of providing learning materials and practice problems according to the generated learning plan, A means of providing a virtual assistant that answers students' questions in real time, A system that includes a means to automatically generate reports summarizing learning progress and problems, and provide them to instructors and parents.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional education method, it is difficult to provide individualized guidance according to the understanding level of each student, and it is also difficult for parents and teachers to grasp the learning situation of students in real time. Furthermore, individualized education relying on tutors or cram schools is expensive and not available to everyone. Therefore, there is an increasing need for a system that provides low-cost and effective individualized learning support.

Means for Solving the Problems

[0005] This invention is a system that collects and analyzes students' learning activity data in real time to provide each student with an optimized learning plan. It utilizes generative AI technology and features a virtual assistant that provides immediate answers to students' questions, thereby supporting individualized learning. Furthermore, it streamlines follow-up by automatically generating reports summarizing learning progress and problems and providing them to instructors and parents. This makes it possible to realize high-quality, low-cost individualized education on a large scale.

[0006] "Learning activity data" refers to information related to students' learning on online platforms, including answer history and study time.

[0007] "Analysis" is the process of processing collected data to identify specific patterns or trends.

[0008] A "study plan" refers to a combination of learning materials and practice problems customized to the individual needs of each student, and includes a specific schedule to support efficient learning.

[0009] "Generative AI technology" is an artificial intelligence technology that automatically generates appropriate answers and suggestions in real time based on large amounts of data.

[0010] A "virtual assistant" is a software agent that uses AI technology to respond to user questions in real time.

[0011] A "report" is a document that summarizes and visually organizes learning progress and problems, and is intended for use as a reference by instructors and parents. [Brief explanation of the drawing]

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

[0013] 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.

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

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

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

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

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a learning support system that operates on an online education platform. This system was developed to comprehensively support students' learning activities, and its main mechanisms are described below.

[0034] Data collection and analysis

[0035] First, the user accesses the online learning platform and begins learning. The server automatically collects data on the user's learning activity during this process. This data includes information such as answer history, study time, and study frequency.

[0036] The server then analyzes this data in real time. Machine learning algorithms are used for the analysis to evaluate the user's understanding and detect specific weaknesses. The results of the analysis are immediately reflected in the user profile and used to create future learning plans.

[0037] Generating personalized learning plans

[0038] Based on the analysis results, the server generates a personalized learning plan for each user. This learning plan selects and combines the most effective learning materials and practice problems based on the user's current level of understanding.

[0039] For example, if a user has difficulty with differential and integral calculus, the server will select learning materials that progress from basic to advanced levels and display them on the user's dashboard.

[0040] Real-time AI Assistant

[0041] The device features a virtual assistant powered by generative AI technology. This assistant is designed to instantly answer questions and address challenges the user encounters during their learning process.

[0042] For example, if a user is unsure how to solve a particular math problem, they can ask a virtual assistant on their device for help, which will then provide the steps to solve the problem and related knowledge.

[0043] Feedback and report generation

[0044] After a learning session ends, the server aggregates the user's learning data and automatically generates a report summarizing their recent progress and challenges. This report is visually organized, making it easy for instructors and parents to understand the user's learning status.

[0045] Thus, this invention makes it possible to provide effective individualized instruction, which was difficult to achieve with conventional education.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user logs into the online learning platform and accesses a dedicated dashboard. This initiates the user's session and allows them to record their learning activities.

[0049] Step 2:

[0050] Users indicate their intention to begin learning by selecting specific subjects or topics. The selected information is sent to the server, and learning settings are established based on that information.

[0051] Step 3:

[0052] The server monitors the user's learning activity in real time and collects learning activity data such as answer history and study time. This data is recorded in a database for analysis.

[0053] Step 4:

[0054] The server uses machine learning algorithms to analyze the collected learning activity data. This allows it to assess the user's level of understanding and identify specific weaknesses. The results of this analysis are then reflected in the user profile.

[0055] Step 5:

[0056] Based on the analysis results, the server automatically generates a learning plan. This learning plan includes learning materials and practice problems tailored to the user, and is displayed on the user's dashboard by the server.

[0057] Step 6:

[0058] Users progress through their learning according to the provided learning plan. If questions arise during learning, they can ask the virtual assistant on their device to receive immediate answers or hints.

[0059] Step 7:

[0060] Once a learning session ends, the server automatically generates a report summarizing the user's learning progress based on their learning data. This report is visually organized and provided electronically to instructors and parents.

[0061] (Example 1)

[0062] 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."

[0063] With the advancement of online education, there is a growing need to provide personalized education and support to each learner in real time. However, conventional systems have problems in effectively analyzing large amounts of learner data and creating individualized learning plans. Furthermore, it is currently difficult to provide a mechanism to immediately address questions that arise during learning.

[0064] 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.

[0065] In this invention, the server includes means for collecting learner learning activity information, means for analyzing the collected learning activity information and evaluating the learner's level of understanding, and means for generating an optimized educational plan for each learner based on the analysis results. This enables individualized support tailored to each learner's level of understanding and learning progress.

[0066] A "learner" is an individual who participates in educational activities with the aim of acquiring knowledge and skills.

[0067] "Learning activity information" refers to data that records learners' actions and outcomes during the educational process, and includes answer history, study time, and usage of learning materials.

[0068] "Analysis" is the process of processing collected data, finding meaning in it, and deriving patterns and trends.

[0069] "Comprehension level" is an indicator that shows to what extent learners can grasp and apply a particular educational content.

[0070] An "educational plan" is a program that designs the optimal teaching materials and learning steps based on the learner's level of understanding and goals.

[0071] "Educational content" refers to information resources that learners use to acquire knowledge and skills, and includes textbooks, practice problems, videos, and other similar materials.

[0072] A "virtual support device" is an artificial intelligence-based interface that uses computer technology to instantly provide learners with the information and advice they need.

[0073] A "report" is a document that summarizes and presents the results and progress of a learner's learning activities in a visually organized manner.

[0074] "Learning resources" refer to various resources provided to support educational activities, including teaching materials, teacher support, and online tools.

[0075] "Schedule" refers to the time plan for learners to carry out each educational activity, and is also called a timetable or timetable.

[0076] This invention is a system for supporting learners' educational activities on an online education platform. Specific embodiments thereof are described below.

[0077] When users access the online learning platform, they can engage with educational content via their available devices. The server is equipped with a function to first collect user learning activity information, accumulating data such as answer history, study time, and usage of learning materials. This information is immediately transmitted to the server using a data streaming platform such as Apache® Kafka.

[0078] Next, the server preprocesses the collected data using the Python Pandas library and evaluates the learner's understanding using analysis techniques with Scikit-learn. This makes it possible to detect each learner's specific strengths and weaknesses.

[0079] Based on the analysis results, the server generates an optimized learning plan for the learner. This plan includes effective educational content and practice exercises tailored to the individual learner, thanks to a recommendation system utilizing NLP technology. For example, a learner identified as lacking understanding of differential and integral calculus in mathematics will be provided with step-by-step educational content, progressing from basic to advanced levels.

[0080] During the learning process, if a user encounters a question, a virtual support system on the device can be helpful. This virtual support system uses a generative AI model and can instantly generate answers to questions entered by the user. For example, if the user enters "Explain the addition formulas for trigonometric functions," it will provide the theoretical background and specific application examples.

[0081] Furthermore, after a learning session ends, the server aggregates the data and automatically generates a report summarizing learning progress and challenges using visualization tools. This allows users, educators, and parents to understand learning progress at a glance and plan the next learning steps. In this way, the present invention aims to provide an educational experience tailored to the individual needs of learners.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user logs into the online learning platform and begins learning. The server collects learning activity information based on the user's input. Specifically, this data includes the user's answer history, study time, and material usage history. This data is transmitted to the server in real time via a data streaming platform and stored in a database.

[0085] Step 2:

[0086] The server performs data analysis based on the collected learning activity information. The input is pre-processed training data, which is formatted using the Python Pandas library. Next, a machine learning algorithm using Scikit-learn is applied to evaluate the user's level of understanding. The output is an evaluation result showing the strengths and weaknesses of each individual user.

[0087] Step 3:

[0088] The server generates an individualized educational plan based on the analysis results. The input is the evaluation results obtained in step 2, and NLP technology is used to select the optimal educational content. Through this process, it determines which materials the user should use and outputs a list of the best materials to the user's dashboard.

[0089] Step 4:

[0090] As the user progresses through the learning process, they can ask questions to a virtual support system on their device. The input here is a prompt sentence from the user. The generative AI model receives this prompt sentence and generates and provides relevant information and explanations. This output is presented to the user as a solution to a specific problem or as background knowledge.

[0091] Step 5:

[0092] Once a learning session ends, the server aggregates the data and automatically generates a learning progress report. The input is the user's overall learning record, which is then processed using a visualization tool to produce a visually organized report. This makes it easy for both the user and their instructor to understand their learning progress.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern online shopping, users find it difficult to find the best product from a vast amount of product information and choices. Furthermore, the lack of readily available systems to answer product-related questions and concerns leads to delays in purchasing decisions. This situation makes it difficult for users to have a satisfying shopping experience, reducing the efficiency of online shopping.

[0096] 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.

[0097] In this invention, the server includes means for collecting user behavior data, means for analyzing the collected behavior data and evaluating purchasing trends, and means for generating personalized recommendation plans based on the analysis results. This makes it possible to offer personalized product and service suggestions to users and to answer their questions immediately.

[0098] "User" refers to an individual who uses the system to search for or purchase goods or services.

[0099] "Behavioral data" refers to data related to users' online activities, such as purchase history, browsing history, and time spent on a site.

[0100] "Purchasing trends" refer to future purchase predictions and preferences derived from patterns of products and services that users have purchased in the past.

[0101] "Evaluating" refers to analyzing collected data to determine user characteristics and behavioral patterns numerically or qualitatively.

[0102] A "recommendation plan" refers to a specific plan for selecting and proposing products and services that are suitable for the user.

[0103] "Technical support measures" refer to the functions and technologies within a system that enable real-time responses to user inquiries.

[0104] "Purchase history" refers to a record of the goods and services that a user has purchased in the past.

[0105] "User feedback" refers to evaluations, comments, or reviews made by users regarding products or services they have purchased.

[0106] "Automatically generating information" refers to a system generating standardized reports and information based on accumulated data without human intervention.

[0107] "Stakeholders" refers to all parties interested in users' purchasing behavior and trends, including system operators and product providers.

[0108] This system aims to improve users' purchasing experience by providing personalized recommendations for the most suitable products. The server is primarily responsible for collecting and analyzing user behavior data, including browsing history, purchase history, and time spent on the site. The collected data is analyzed using the Python language and machine learning algorithms to evaluate each user's purchasing tendencies.

[0109] The server uses StandardScaler to standardize behavioral data and the KMeans algorithm for clustering. This makes it possible to generate an optimal product recommendation plan for each user. This recommendation plan selects products and services that are suitable for the user and reflects this in future product recommendations.

[0110] As a means of technical support, the terminal utilizes a generative AI model to answer users' questions in real time. This AI model is designed to enable immediate responses to user inquiries and provide rapid feedback.

[0111] For example, if a user is interested in outdoor equipment and has searched for camping-related products many times in the past, the server will generate a recommendation plan based on that data and suggest new products. In this case, if the user asks specific questions about the products, the AI ​​model can immediately provide accurate information.

[0112] An example of a prompt to input into a generating AI model is, "What products would you recommend to this user based on their past purchase data?" This allows for product recommendations based on the user's past behavior data.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects user behavior data. This data includes users' browsing history, purchase history, and time spent on the site. This data is organized for each user and stored in a database. Data collection is performed in real time using web tracking technology.

[0116] Step 2:

[0117] The server uses StandardScaler to standardize the collected behavioral data. The raw input data is scaled based on its mean and standard deviation. This prepares the data for clustering. The output is scaled numerical data.

[0118] Step 3:

[0119] The server performs clustering analysis on standardized data using the KMeans algorithm. The input is scaled data, which is used to classify data points into multiple clusters. The output is the cluster label to which each user belongs. The system statistically evaluates users' purchasing tendencies and groups users with similar characteristics.

[0120] Step 4:

[0121] The server generates a personalized recommendation plan based on the clustering results. This plan includes a list of products and services relevant to a particular user. The input includes cluster labels and associated product data, and the output is a customized list.

[0122] Step 5:

[0123] The device uses a generative AI model to answer users' questions in real time. The input is a natural language question from the user. The AI ​​model analyzes this, searches for appropriate information, and then immediately presents the answer. The output is a text-based answer to the user's question.

[0124] Step 6:

[0125] The server automatically generates information analyzing user satisfaction and areas for improvement in future suggestions, based on each user's purchase history and feedback. This input includes past purchase data and feedback information, and the output is statistical information in report format.

[0126] Step 7:

[0127] Users review product lists and suggestions provided by the server and proceed with their purchases. Post-purchase feedback contributes to improving the accuracy of future recommendation plans. The output of this process is new behavioral data.

[0128] 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.

[0129] This invention combines an emotion engine that recognizes user emotions with an individualized learning system for online education platforms. This system is designed to provide detailed support for students' learning activities, and its main functions are described below.

[0130] Embedding an emotion engine

[0131] The emotion engine in this invention analyzes the user's emotional state in real time and uses that information to adjust learning plans and content. It uses the camera and microphone installed in the device to sense and analyze the user's facial expressions and voice tone. This allows it to measure the user's stress levels and concentration, aiming to improve learning effectiveness.

[0132] Learning adjustments based on user emotions

[0133] Based on the analysis results, the server dynamically adjusts the learning plan according to the user's emotional state. For example, if the emotion engine determines that the user is feeling tired, it may provide low-load tasks or content that promotes relaxation. This not only improves learning efficiency but also helps to enhance the user's overall learning experience.

[0134] Feedback utilizing emotional data

[0135] At the end of a learning session, the server generates a detailed report containing data from the emotion engine. This report details changes in emotions during learning and their impact on learning outcomes, and is provided to instructors and parents. This report provides new perspectives on instruction and can be used for further improvement and support.

[0136] Specific example

[0137] For example, when a user is working on a math problem, the emotion engine detects anxiety from their tone of voice. The device sends this information to the server, which then provides a problem with reduced difficulty to help the user regain their composure. After the learning session is complete, the generated report details how learning was affected during peak anxiety periods.

[0138] By integrating an emotion engine in this way, the present invention enables emotion-based support, which was difficult with conventional personalized learning systems, and allows for more holistic learning support tailored to the individual needs of users.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user logs into the online learning platform and begins learning. At that moment, the device activates its emotion engine and starts real-time analysis of the user's emotional state.

[0142] Step 2:

[0143] During learning, the device's built-in camera and microphone monitor the user's facial expressions and voice. This prepares the device for collecting user emotional data.

[0144] Step 3:

[0145] The device inputs collected emotional data into an emotion engine to analyze the user's emotional state. For example, it can detect joy or anger from the user's facial expressions, and anxiety or restlessness from their voice.

[0146] Step 4:

[0147] The emotion engine analyzes the emotion data and sends it to the server. The server combines this data with learning activity data to understand the user's current state.

[0148] Step 5:

[0149] Based on emotional data, the server dynamically adjusts the learning plan. For example, if it determines that the user is experiencing stress, it may reduce the difficulty of the learning tasks or suggest a break.

[0150] Step 6:

[0151] As the user continues learning, if the emotion engine detects anxiety, the device provides resources to help them calm down (e.g., short relaxation videos).

[0152] Step 7:

[0153] Once a learning session ends, the server automatically generates a report summarizing the user's learning outcomes and emotional changes based on their learning and emotional data. This report is then distributed electronically to instructors and parents.

[0154] (Example 2)

[0155] 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".

[0156] In online education, personalizing the learning experience based on learners' emotions and comprehension levels is a challenging task. Traditional systems provide learning materials based solely on students' progress and grades, lacking emotional considerations and potentially leading to decreased learning efficiency and motivation. Furthermore, while adjustments that reflect learners' real-time emotional states and comprehension levels are needed, there is a lack of effective methods to achieve this.

[0157] 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.

[0158] In this invention, the server includes means for collecting learner progress data, means for analyzing the collected data and evaluating learning comprehension, means for analyzing the learner's emotional state in real time, means for generating a learning plan based on the analysis results and dynamically providing individualized educational materials and practice problems, and means for generating and providing a report integrating emotional changes and learning outcomes to the support provider. This enables the provision of appropriate learning materials that take into account the learner's emotional state and effective learning support based on that.

[0159] A "learner" is an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0160] "Progress data" refers to a series of pieces of information recorded by learners during their learning activities, and is used to measure their level of understanding and learning achievement.

[0161] "Analysis" is the process of computational processing and evaluation to analyze collected data and identify specific patterns or trends.

[0162] "Learning comprehension level" is an indicator that shows how accurately learners understand specific content or tasks.

[0163] A "learning plan" is the design of an individually customized educational process based on the learner's progress and level of understanding.

[0164] "Educational materials" is a general term for content provided to support learners' learning, and includes textbooks, videos, quizzes, and other similar materials.

[0165] "Practice problems" are questions or assignments provided to allow learners to practically confirm and solidify their knowledge and skills.

[0166] "Emotional state" refers to the psychological conditions and moods that learners experience while learning, including concentration, stress, and enjoyment.

[0167] A "report" is a document that summarizes a learner's progress, achievements, and emotional tendencies, and is used for instruction and improvement.

[0168] A "supporter" is an individual or organization that plays a role in assisting the education and growth of learners, and this includes teachers and parents.

[0169] This invention is a system for realizing personalized learning in an online education platform. The system has the function of analyzing the learner's emotional state in real time and dynamically adjusting the learning content based on that analysis.

[0170] The device uses a camera to capture the user's facial expressions and a microphone to analyze their voice tone. This hardware collects the data necessary to run the emotion analysis algorithm. Image processing and speech recognition software are used for emotion analysis, allowing for real-time assessment of the user's concentration level and stress level.

[0171] The server receives sentiment analysis results and learning progress data sent from the terminal and generates an individualized learning plan based on this information. This includes the process of selecting the most suitable educational materials and practice problems from the database and sending them to the user. The server also creates a feedback report tailored to the learner's emotional and comprehension levels and provides it to instructors and parents.

[0172] Users can progress through their learning using materials and reports provided by the system and track their own progress. For example, if a user feels anxious when faced with a math problem, the system analyzes the user's emotions and immediately supports them by presenting a problem with a lower difficulty level.

[0173] An example of a prompt is, "Please tell me about a specific implementation method for a system that combines an emotion engine that recognizes user emotions with an individualized learning system in an online education platform." By using this prompt, the generative AI model can gain insights into how to carry out the invention.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The device activates the camera to detect the user's facial expressions and the microphone to record voice tone. The inputs in this step are camera video and audio data. Image processing software extracts facial features, and voice analysis software analyzes voice tone and speed. As output, data for emotion analysis is generated.

[0177] Step 2:

[0178] The device inputs collected facial feature information and voice data into an emotion analysis algorithm to analyze the user's emotional state in real time. The input for this step is the analysis data generated in step 1. The emotion analysis algorithm evaluates the data using classification and pattern matching to determine the user's emotional state (e.g., focused, relaxed, stressed). The output is a variable indicating the user's emotional state.

[0179] Step 3:

[0180] The server receives emotional state data and learning progress data sent from the terminal. Using this data as input, the server dynamically selects learning content that matches the learner's emotional state. It prepares the content by performing database queries to search for appropriate educational materials and practice problems. As output, a list of optimal educational content is obtained and sent to the user's terminal.

[0181] Step 4:

[0182] The user receives educational content delivered from the server and learns on their device. The input in this step is content data from the server, and the content is based on the learner's situation and emotional state. The user views educational materials and works on practice exercises through their device. As output, the learning progress is updated, and new learning data is generated.

[0183] Step 5:

[0184] Upon completion of a learning session, the server generates a feedback report based on the user's progress data and emotional state history. The input for this step is all data collected during the learning session. Using data analysis techniques, the server comprehensively evaluates learning outcomes and challenges, outputting a detailed report. The final report is provided to the support provider.

[0185] (Application Example 2)

[0186] 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".

[0187] Traditional online education systems have faced challenges in supporting individualized learning that takes into account users' emotions. Specifically, they often fail to provide appropriate content and plans tailored to the learner's mental and physical state, leading to decreased learning efficiency and a lower quality of learning experience. Therefore, there was a need to implement dynamic learning adjustments that take emotional states into account and provide learning support optimized for each individual learner.

[0188] 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.

[0189] In this invention, the server includes means for collecting learner learning behavior data, means for analyzing the collected data and evaluating comprehension, and means for analyzing the learner's emotional state using an emotion analysis engine. This enables real-time analysis of the learner's emotional state and dynamic adjustment of learning content and plans based on emotions.

[0190] "Learning behavior data" refers to all activity information that learners use when using online platforms.

[0191] "Comprehension level" is an indicator that shows how accurately a learner grasps specific learning material and how well they are able to solve problems related to it.

[0192] An "optimized learning plan" is a learning progress plan tailored to the individual needs and learning goals of the learner.

[0193] "Teaching materials and practice exercises" are educational resources provided to learners to acquire specific skills and knowledge.

[0194] A "virtual aid" is a system equipped with artificial intelligence that responds to learners' questions and doubts in real time.

[0195] A "report" is a document that summarizes learning progress and identified challenges, and is provided to educators and parents.

[0196] An "emotion analysis engine" is a technology that analyzes learners' facial expressions and vocal characteristics to evaluate their emotional state.

[0197] "Dynamic adjustment" refers to the process of modifying the content and learning plan provided in response to real-time emotional states and learning progress.

[0198] The system for implementing this invention is configured as follows: The server collects learner learning behavior data and evaluates the level of comprehension based on this data. The collected data is acquired from a terminal equipped with a camera and microphone and analyzed in real time as sentiment data using a face recognition library (e.g., OpenCV) and a speech analysis API (e.g., Google® Cloud Speech-to-Text).

[0199] Based on the results of emotion analysis, the server determines the learner's emotional state and dynamically adjusts the learning plan accordingly. For example, if the emotion engine determines that the learner is feeling stressed, the server can present low-load learning materials and provide content that promotes relaxation.

[0200] Furthermore, after the learning session is complete, the server automatically generates a detailed report including emotional data, which is then provided to educators and parents. This report can describe in detail the changes in emotions during the learning session and their impact on learning outcomes.

[0201] A concrete example is when the emotion engine detects anxiety in a learner's voice while they are studying mathematics. The server immediately uses this information to provide practice problems with a reduced difficulty level, helping the learner regain their composure. Furthermore, the report generated afterward outlines how the learning was affected during the peak of the learner's anxiety.

[0202] An example of a prompt for a generative AI model would be: "If a learner shows high levels of anxiety when tackling difficult problems, what kind of learning content would be effective to provide?"

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device captures the learner's facial expressions and voice through its camera and microphone. This facial and voice data serves as input for emotion analysis. The data is analyzed in real time using the device's facial recognition library and voice analysis API. The output is data that quantifies or classifies the learner's emotional state.

[0206] Step 2:

[0207] The device sends the analyzed emotional data to the server. The server receives this emotional data and evaluates the learner's current emotional state. Based on this evaluation, the server processes the data to determine appropriate learning content. Specifically, it detects changes in stress levels and concentration levels.

[0208] Step 3:

[0209] The server dynamically adjusts the optimal learning plan for the learner based on the evaluation of their emotional state. During this process, it uses a generative AI model to generate prompts and customize the learning content. These prompts offer suggestions regarding the difficulty level of the content. The output consists of the adjusted learning plan and learning materials.

[0210] Step 4:

[0211] The server sends appropriate learning materials and practice exercises to the device based on a tailored learning plan. The device then provides this content to the learner. The learning materials the learner works on are designed to be tailored to their current emotional state and the workload is adjusted accordingly.

[0212] Step 5:

[0213] Once a user's learning session ends, the server generates a detailed report based on sentiment data, learning progress, comprehension assessments, and other relevant information. This report summarizes the impact of the learner's emotional fluctuations on their learning. The report is provided to educators and parents to help them further support learning.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention relates to a learning support system that operates on an online education platform. This system was developed to comprehensively support students' learning activities, and its main mechanisms are described below.

[0231] Data collection and analysis

[0232] First, the user accesses the online learning platform and begins learning. The server automatically collects data on the user's learning activity during this process. This data includes information such as answer history, study time, and study frequency.

[0233] The server then analyzes this data in real time. Machine learning algorithms are used for the analysis to evaluate the user's understanding and detect specific weaknesses. The results of the analysis are immediately reflected in the user profile and used to create future learning plans.

[0234] Generating personalized learning plans

[0235] Based on the analysis results, the server generates a personalized learning plan for each user. This learning plan selects and combines the most effective learning materials and practice problems based on the user's current level of understanding.

[0236] For example, if a user has difficulty with differential and integral calculus, the server will select learning materials that progress from basic to advanced levels and display them on the user's dashboard.

[0237] Real-time AI Assistant

[0238] The device features a virtual assistant powered by generative AI technology. This assistant is designed to instantly answer questions and address challenges the user encounters during their learning process.

[0239] For example, if a user is unsure how to solve a particular math problem, they can ask a virtual assistant on their device for help, which will then provide the steps to solve the problem and related knowledge.

[0240] Feedback and report generation

[0241] After a learning session ends, the server aggregates the user's learning data and automatically generates a report summarizing their recent progress and challenges. This report is visually organized, making it easy for instructors and parents to understand the user's learning status.

[0242] Thus, this invention makes it possible to provide effective individualized instruction, which was difficult to achieve with conventional education.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user logs into the online learning platform and accesses a dedicated dashboard. This initiates the user's session and allows them to record their learning activities.

[0246] Step 2:

[0247] Users indicate their intention to begin learning by selecting specific subjects or topics. The selected information is sent to the server, and learning settings are established based on that information.

[0248] Step 3:

[0249] The server monitors the user's learning activity in real time and collects learning activity data such as answer history and study time. This data is recorded in a database for analysis.

[0250] Step 4:

[0251] The server uses machine learning algorithms to analyze the collected learning activity data. This allows it to assess the user's level of understanding and identify specific weaknesses. The results of this analysis are then reflected in the user profile.

[0252] Step 5:

[0253] Based on the analysis results, the server automatically generates a learning plan. This learning plan includes learning materials and practice problems tailored to the user, and is displayed on the user's dashboard by the server.

[0254] Step 6:

[0255] Users progress through their learning according to the provided learning plan. If questions arise during learning, they can ask the virtual assistant on their device to receive immediate answers or hints.

[0256] Step 7:

[0257] Once a learning session ends, the server automatically generates a report summarizing the user's learning progress based on their learning data. This report is visually organized and provided electronically to instructors and parents.

[0258] (Example 1)

[0259] 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."

[0260] With the advancement of online education, there is a growing need to provide personalized education and support to each learner in real time. However, conventional systems have problems in effectively analyzing large amounts of learner data and creating individualized learning plans. Furthermore, it is currently difficult to provide a mechanism to immediately address questions that arise during learning.

[0261] 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.

[0262] In this invention, the server includes means for collecting learner learning activity information, means for analyzing the collected learning activity information and evaluating the learner's level of understanding, and means for generating an optimized educational plan for each learner based on the analysis results. This enables individualized support tailored to each learner's level of understanding and learning progress.

[0263] A "learner" is an individual who participates in educational activities with the aim of acquiring knowledge and skills.

[0264] "Learning activity information" refers to data that records learners' actions and outcomes during the educational process, and includes answer history, study time, and usage of learning materials.

[0265] "Analysis" is the process of processing collected data, finding meaning in it, and deriving patterns and trends.

[0266] "Comprehension level" is an indicator that shows to what extent learners can grasp and apply a particular educational content.

[0267] An "educational plan" is a program that designs the optimal teaching materials and learning steps based on the learner's level of understanding and goals.

[0268] "Educational content" refers to information resources that learners use to acquire knowledge and skills, and includes textbooks, practice problems, videos, and other similar materials.

[0269] A "virtual support device" is an artificial intelligence-based interface that uses computer technology to instantly provide learners with the information and advice they need.

[0270] A "report" is a document that summarizes and presents the results and progress of a learner's learning activities in a visually organized manner.

[0271] "Learning resources" refer to various resources provided to support educational activities, including teaching materials, teacher support, and online tools.

[0272] "Schedule" refers to the time plan for learners to carry out each educational activity, and is also called a timetable or timetable.

[0273] This invention is a system for supporting learners' educational activities on an online education platform. Specific embodiments thereof are described below.

[0274] When users access the online learning platform, they can engage with educational content via their available devices. The server is equipped with a function to first collect user learning activity information, accumulating data such as answer history, study time, and usage of learning materials. This information is immediately transmitted to the server using a data streaming platform such as Apache Kafka.

[0275] Next, the server preprocesses the collected data using the Python Pandas library and evaluates the learner's understanding using analysis techniques with Scikit-learn. This makes it possible to detect each learner's specific strengths and weaknesses.

[0276] Based on the analysis results, the server generates an optimized learning plan for the learner. This plan includes effective educational content and practice exercises tailored to the individual learner, thanks to a recommendation system utilizing NLP technology. For example, a learner identified as lacking understanding of differential and integral calculus in mathematics will be provided with step-by-step educational content, progressing from basic to advanced levels.

[0277] During the learning process, if a user encounters a question, a virtual support system on the device can be helpful. This virtual support system uses a generative AI model and can instantly generate answers to questions entered by the user. For example, if the user enters "Explain the addition formulas for trigonometric functions," it will provide the theoretical background and specific application examples.

[0278] Furthermore, after a learning session ends, the server aggregates the data and automatically generates a report summarizing learning progress and challenges using visualization tools. This allows users, educators, and parents to understand learning progress at a glance and plan the next learning steps. In this way, the present invention aims to provide an educational experience tailored to the individual needs of learners.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user logs in to the online learning platform and starts learning. The server collects learning activity information based on the user's input. Specifically, the data input includes the user's answer history, learning time, and usage history of teaching materials. These data are transmitted to the server in real time via the data streaming platform and stored in the database.

[0282] Step 2:

[0283] The server performs data analysis based on the collected learning activity information. The input is the preprocessed learning data, which is formatted using the Pandas library in Python. Next, a machine learning algorithm using Scikit-learn is applied to evaluate the user's understanding level. The output is the evaluation result indicating the strengths and weaknesses of individual users.

[0284] Step 3:

[0285] The server generates an individualized education plan based on the analysis results. The input is the evaluation result obtained in Step 2, and the optimal educational content is selected using NLP technology. Through this, it is determined which teaching materials the user should use, and an optimal list of teaching materials is output to the user's dashboard.

[0286] Step 4:

[0287] While the user is progressing in learning, the user asks questions to the virtual support device on the terminal. The input here is the prompt sentence by the user. The generative AI model receives this prompt sentence and generates and provides relevant information and explanations. This output is presented to the user as the solution to specific problems and background knowledge.

[0288] Step 5:

[0289] Once a learning session ends, the server aggregates the data and automatically generates a learning progress report. The input is the user's overall learning record, which is then processed using a visualization tool to produce a visually organized report. This makes it easy for both the user and their instructor to understand their learning progress.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] In modern online shopping, users find it difficult to find the best product from a vast amount of product information and choices. Furthermore, the lack of readily available systems to answer product-related questions and concerns leads to delays in purchasing decisions. This situation makes it difficult for users to have a satisfying shopping experience, reducing the efficiency of online shopping.

[0293] 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.

[0294] In this invention, the server includes means for collecting user behavior data, means for analyzing the collected behavior data and evaluating purchasing trends, and means for generating personalized recommendation plans based on the analysis results. This makes it possible to offer personalized product and service suggestions to users and to answer their questions immediately.

[0295] "User" refers to an individual who uses the system to search for or purchase goods or services.

[0296] "Behavioral data" refers to data related to users' online activities, such as purchase history, browsing history, and time spent on a site.

[0297] "Purchasing trends" refer to future purchase predictions and preferences derived from patterns of products and services that users have purchased in the past.

[0298] "Evaluating" refers to analyzing collected data to determine user characteristics and behavioral patterns numerically or qualitatively.

[0299] A "recommendation plan" refers to a specific plan for selecting and proposing products and services that are suitable for the user.

[0300] "Technical support measures" refer to the functions and technologies within a system that enable real-time responses to user inquiries.

[0301] "Purchase history" refers to a record of the goods and services that a user has purchased in the past.

[0302] "User feedback" refers to evaluations, comments, or reviews made by users regarding products or services they have purchased.

[0303] "Automatically generating information" refers to a system generating standardized reports and information based on accumulated data without human intervention.

[0304] "Stakeholders" refers to all parties interested in users' purchasing behavior and trends, including system operators and product providers.

[0305] This system aims to improve users' purchasing experience by providing personalized recommendations for the most suitable products. The server is primarily responsible for collecting and analyzing user behavior data, including browsing history, purchase history, and time spent on the site. The collected data is analyzed using the Python language and machine learning algorithms to evaluate each user's purchasing tendencies.

[0306] On the server, StandardScaler is used for the standardization of behavioral data, and the KMeans algorithm is used for clustering. This enables the generation of an optimal product recommendation plan for each user. This recommendation plan has the role of selecting products and services that suit the user and reflecting them in the next product proposal.

[0307] As a technical support means, the terminal utilizes the generated AI model to answer the real-time questions of the user. This AI model is designed to enable an immediate response to the user's inquiries and obtain rapid feedback.

[0308] For example, if a certain user is interested in outdoor supplies and has searched for camping-related products many times in the past, the server generates a recommendation plan based on that data and makes a proposal for new products. At this time, when the user asks a specific question about the product, the AI model can immediately provide accurate information.

[0309] As an example of the prompt sentence input to the generated AI model, a product recommendation can be made according to the user's past behavioral data in the form of "What are the recommended products for this user based on their past purchase data?"

[0310] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0311] Step 1:

[0312] The server collects the behavioral data of the user. This data includes the user's browsing history, purchase history, and stay time. These data are organized for each user and stored in the database. The data collection is performed in real time using web tracking technology.

[0313] Step 2:

[0314] The server uses StandardScaler to standardize the collected behavioral data. The raw input data is scaled based on its mean and standard deviation. This prepares the data for clustering. The output is scaled numerical data.

[0315] Step 3:

[0316] The server performs clustering analysis on standardized data using the KMeans algorithm. The input is scaled data, which is used to classify data points into multiple clusters. The output is the cluster label to which each user belongs. The system statistically evaluates users' purchasing tendencies and groups users with similar characteristics.

[0317] Step 4:

[0318] The server generates a personalized recommendation plan based on the clustering results. This plan includes a list of products and services relevant to a particular user. The input includes cluster labels and associated product data, and the output is a customized list.

[0319] Step 5:

[0320] The device uses a generative AI model to answer users' questions in real time. The input is a natural language question from the user. The AI ​​model analyzes this, searches for appropriate information, and then immediately presents the answer. The output is a text-based answer to the user's question.

[0321] Step 6:

[0322] The server automatically generates information analyzing user satisfaction and areas for improvement in future suggestions, based on each user's purchase history and feedback. This input includes past purchase data and feedback information, and the output is statistical information in report format.

[0323] Step 7:

[0324] Users review product lists and suggestions provided by the server and proceed with their purchases. Post-purchase feedback contributes to improving the accuracy of future recommendation plans. The output of this process is new behavioral data.

[0325] 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.

[0326] This invention combines an emotion engine that recognizes user emotions with an individualized learning system for online education platforms. This system is designed to provide detailed support for students' learning activities, and its main functions are described below.

[0327] Embedding an emotion engine

[0328] The emotion engine in this invention analyzes the user's emotional state in real time and uses that information to adjust learning plans and content. It uses the camera and microphone installed in the device to sense and analyze the user's facial expressions and voice tone. This allows it to measure the user's stress levels and concentration, aiming to improve learning effectiveness.

[0329] Learning adjustments based on user emotions

[0330] Based on the analysis results, the server dynamically adjusts the learning plan according to the user's emotional state. For example, if the emotion engine determines that the user is feeling tired, it may provide low-load tasks or content that promotes relaxation. This not only improves learning efficiency but also helps to enhance the user's overall learning experience.

[0331] Feedback utilizing emotional data

[0332] At the end of a learning session, the server generates a detailed report containing data from the emotion engine. This report details changes in emotions during learning and their impact on learning outcomes, and is provided to instructors and parents. This report provides new perspectives on instruction and can be used for further improvement and support.

[0333] Specific example

[0334] For example, when a user is working on a math problem, the emotion engine detects anxiety from their tone of voice. The device sends this information to the server, which then provides a problem with reduced difficulty to help the user regain their composure. After the learning session is complete, the generated report details how learning was affected during peak anxiety periods.

[0335] By integrating an emotion engine in this way, the present invention enables emotion-based support, which was difficult with conventional personalized learning systems, and allows for more holistic learning support tailored to the individual needs of users.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user logs into the online learning platform and begins learning. At that moment, the device activates its emotion engine and starts real-time analysis of the user's emotional state.

[0339] Step 2:

[0340] During learning, the device's built-in camera and microphone monitor the user's facial expressions and voice. This prepares the device for collecting user emotional data.

[0341] Step 3:

[0342] The device inputs collected emotional data into an emotion engine to analyze the user's emotional state. For example, it can detect joy or anger from the user's facial expressions, and anxiety or restlessness from their voice.

[0343] Step 4:

[0344] The emotion engine analyzes the emotion data and sends it to the server. The server combines this data with learning activity data to understand the user's current state.

[0345] Step 5:

[0346] Based on emotional data, the server dynamically adjusts the learning plan. For example, if it determines that the user is experiencing stress, it may reduce the difficulty of the learning tasks or suggest a break.

[0347] Step 6:

[0348] As the user continues learning, if the emotion engine detects anxiety, the device provides resources to help them calm down (e.g., short relaxation videos).

[0349] Step 7:

[0350] Once a learning session ends, the server automatically generates a report summarizing the user's learning outcomes and emotional changes based on their learning and emotional data. This report is then distributed electronically to instructors and parents.

[0351] (Example 2)

[0352] 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".

[0353] In online education, personalizing the learning experience based on learners' emotions and comprehension levels is a challenging task. Traditional systems provide learning materials based solely on students' progress and grades, lacking emotional considerations and potentially leading to decreased learning efficiency and motivation. Furthermore, while adjustments that reflect learners' real-time emotional states and comprehension levels are needed, there is a lack of effective methods to achieve this.

[0354] 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.

[0355] In this invention, the server includes means for collecting learner progress data, means for analyzing the collected data and evaluating learning comprehension, means for analyzing the learner's emotional state in real time, means for generating a learning plan based on the analysis results and dynamically providing individualized educational materials and practice problems, and means for generating and providing a report integrating emotional changes and learning outcomes to the support provider. This enables the provision of appropriate learning materials that take into account the learner's emotional state and effective learning support based on that.

[0356] A "learner" is an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0357] "Progress data" refers to a series of pieces of information recorded by learners during their learning activities, and is used to measure their level of understanding and learning achievement.

[0358] "Analysis" is the process of computational processing and evaluation to analyze collected data and identify specific patterns or trends.

[0359] "Learning comprehension level" is an indicator that shows how accurately learners understand specific content or tasks.

[0360] A "learning plan" is the design of an individually customized educational process based on the learner's progress and level of understanding.

[0361] "Educational materials" is a general term for content provided to support learners' learning, and includes textbooks, videos, quizzes, and other similar materials.

[0362] "Practice problems" are questions or assignments provided to allow learners to practically confirm and solidify their knowledge and skills.

[0363] "Emotional state" refers to the psychological conditions and moods that learners experience while learning, including concentration, stress, and enjoyment.

[0364] A "report" is a document that summarizes a learner's progress, achievements, and emotional tendencies, and is used for instruction and improvement.

[0365] A "supporter" is an individual or organization that plays a role in assisting the education and growth of learners, and this includes teachers and parents.

[0366] This invention is a system for realizing personalized learning in an online education platform. The system has the function of analyzing the learner's emotional state in real time and dynamically adjusting the learning content based on that analysis.

[0367] The device uses a camera to capture the user's facial expressions and a microphone to analyze their voice tone. This hardware collects the data necessary to run the emotion analysis algorithm. Image processing and speech recognition software are used for emotion analysis, allowing for real-time assessment of the user's concentration level and stress level.

[0368] The server receives sentiment analysis results and learning progress data sent from the terminal and generates an individualized learning plan based on this information. This includes the process of selecting the most suitable educational materials and practice problems from the database and sending them to the user. The server also creates a feedback report tailored to the learner's emotional and comprehension levels and provides it to instructors and parents.

[0369] Users can progress through their learning using materials and reports provided by the system and track their own progress. For example, if a user feels anxious when faced with a math problem, the system analyzes the user's emotions and immediately supports them by presenting a problem with a lower difficulty level.

[0370] An example of a prompt is, "Please tell me about a specific implementation method for a system that combines an emotion engine that recognizes user emotions with an individualized learning system in an online education platform." By using this prompt, the generative AI model can gain insights into how to carry out the invention.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device activates the camera to detect the user's facial expressions and the microphone to record voice tone. The inputs in this step are camera video and audio data. Image processing software extracts facial features, and voice analysis software analyzes voice tone and speed. As output, data for emotion analysis is generated.

[0374] Step 2:

[0375] The device inputs collected facial feature information and voice data into an emotion analysis algorithm to analyze the user's emotional state in real time. The input for this step is the analysis data generated in step 1. The emotion analysis algorithm evaluates the data using classification and pattern matching to determine the user's emotional state (e.g., focused, relaxed, stressed). The output is a variable indicating the user's emotional state.

[0376] Step 3:

[0377] The server receives emotional state data and learning progress data sent from the terminal. Using this data as input, the server dynamically selects learning content that matches the learner's emotional state. It prepares the content by performing database queries to search for appropriate educational materials and practice problems. As output, a list of optimal educational content is obtained and sent to the user's terminal.

[0378] Step 4:

[0379] The user receives educational content delivered from the server and learns on their device. The input in this step is content data from the server, and the content is based on the learner's situation and emotional state. The user views educational materials and works on practice exercises through their device. As output, the learning progress is updated, and new learning data is generated.

[0380] Step 5:

[0381] Upon completion of a learning session, the server generates a feedback report based on the user's progress data and emotional state history. The input for this step is all data collected during the learning session. Using data analysis techniques, the server comprehensively evaluates learning outcomes and challenges, outputting a detailed report. The final report is provided to the support provider.

[0382] (Application Example 2)

[0383] 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."

[0384] Traditional online education systems have faced challenges in supporting individualized learning that takes into account users' emotions. Specifically, they often fail to provide appropriate content and plans tailored to the learner's mental and physical state, leading to decreased learning efficiency and a lower quality of learning experience. Therefore, there was a need to implement dynamic learning adjustments that take emotional states into account and provide learning support optimized for each individual learner.

[0385] 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.

[0386] In this invention, the server includes means for collecting learner learning behavior data, means for analyzing the collected data and evaluating comprehension, and means for analyzing the learner's emotional state using an emotion analysis engine. This enables real-time analysis of the learner's emotional state and dynamic adjustment of learning content and plans based on emotions.

[0387] "Learning behavior data" refers to all activity information that learners use when using online platforms.

[0388] "Comprehension level" is an indicator that shows how accurately a learner grasps specific learning material and how well they are able to solve problems related to it.

[0389] An "optimized learning plan" is a learning progress plan tailored to the individual needs and learning goals of the learner.

[0390] "Teaching materials and practice exercises" are educational resources provided to learners to acquire specific skills and knowledge.

[0391] A "virtual aid" is a system equipped with artificial intelligence that responds to learners' questions and doubts in real time.

[0392] A "report" is a document that summarizes learning progress and identified challenges, and is provided to educators and parents.

[0393] An "emotion analysis engine" is a technology that analyzes learners' facial expressions and vocal characteristics to evaluate their emotional state.

[0394] "Dynamic adjustment" refers to the process of modifying the content and learning plan provided in response to real-time emotional states and learning progress.

[0395] The system for implementing this invention is configured as follows: The server collects learner learning behavior data and evaluates the level of comprehension based on this data. The collected data is acquired from a terminal equipped with a camera and microphone and analyzed in real time as sentiment data using a face recognition library (e.g., OpenCV) or a speech analysis API (e.g., Google Cloud Speech-to-Text).

[0396] Based on the results of emotion analysis, the server determines the learner's emotional state and dynamically adjusts the learning plan accordingly. For example, if the emotion engine determines that the learner is feeling stressed, the server can present low-load learning materials and provide content that promotes relaxation.

[0397] Furthermore, after the learning session is complete, the server automatically generates a detailed report including emotional data, which is then provided to educators and parents. This report can describe in detail the changes in emotions during the learning session and their impact on learning outcomes.

[0398] A concrete example is when the emotion engine detects anxiety in a learner's voice while they are studying mathematics. The server immediately uses this information to provide practice problems with a reduced difficulty level, helping the learner regain their composure. Furthermore, the report generated afterward outlines how the learning was affected during the peak of the learner's anxiety.

[0399] An example of a prompt for a generative AI model would be: "If a learner shows high levels of anxiety when tackling difficult problems, what kind of learning content would be effective to provide?"

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The device captures the learner's facial expressions and voice through its camera and microphone. This facial and voice data serves as input for emotion analysis. The data is analyzed in real time using the device's facial recognition library and voice analysis API. The output is data that quantifies or classifies the learner's emotional state.

[0403] Step 2:

[0404] The device sends the analyzed emotional data to the server. The server receives this emotional data and evaluates the learner's current emotional state. Based on this evaluation, the server processes the data to determine appropriate learning content. Specifically, it detects changes in stress levels and concentration levels.

[0405] Step 3:

[0406] The server dynamically adjusts the optimal learning plan for the learner based on the evaluation of their emotional state. During this process, it uses a generative AI model to generate prompts and customize the learning content. These prompts offer suggestions regarding the difficulty level of the content. The output consists of the adjusted learning plan and learning materials.

[0407] Step 4:

[0408] The server sends appropriate learning materials and practice exercises to the device based on a tailored learning plan. The device then provides this content to the learner. The learning materials the learner works on are designed to be tailored to their current emotional state and the workload is adjusted accordingly.

[0409] Step 5:

[0410] Once a user's learning session ends, the server generates a detailed report based on sentiment data, learning progress, comprehension assessments, and other relevant information. This report summarizes the impact of the learner's emotional fluctuations on their learning. The report is provided to educators and parents to help them further support learning.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention relates to a learning support system that operates on an online education platform. This system was developed to comprehensively support students' learning activities, and its main mechanisms are described below.

[0428] Data collection and analysis

[0429] First, the user accesses the online learning platform and begins learning. The server automatically collects data on the user's learning activity during this process. This data includes information such as answer history, study time, and study frequency.

[0430] The server then analyzes this data in real time. Machine learning algorithms are used for the analysis to evaluate the user's understanding and detect specific weaknesses. The results of the analysis are immediately reflected in the user profile and used to create future learning plans.

[0431] Generating personalized learning plans

[0432] Based on the analysis results, the server generates a personalized learning plan for each user. This learning plan selects and combines the most effective learning materials and practice problems based on the user's current level of understanding.

[0433] For example, if a user has difficulty with differential and integral calculus, the server will select learning materials that progress from basic to advanced levels and display them on the user's dashboard.

[0434] Real-time AI Assistant

[0435] The device features a virtual assistant powered by generative AI technology. This assistant is designed to instantly answer questions and address challenges the user encounters during their learning process.

[0436] For example, if a user is unsure how to solve a particular math problem, they can ask a virtual assistant on their device for help, which will then provide the steps to solve the problem and related knowledge.

[0437] Feedback and report generation

[0438] After a learning session ends, the server aggregates the user's learning data and automatically generates a report summarizing their recent progress and challenges. This report is visually organized, making it easy for instructors and parents to understand the user's learning status.

[0439] Thus, this invention makes it possible to provide effective individualized instruction, which was difficult to achieve with conventional education.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user logs into the online learning platform and accesses a dedicated dashboard. This initiates the user's session and allows them to record their learning activities.

[0443] Step 2:

[0444] Users indicate their intention to begin learning by selecting specific subjects or topics. The selected information is sent to the server, and learning settings are established based on that information.

[0445] Step 3:

[0446] The server monitors the user's learning activity in real time and collects learning activity data such as answer history and study time. This data is recorded in a database for analysis.

[0447] Step 4:

[0448] The server uses machine learning algorithms to analyze the collected learning activity data. This allows it to assess the user's level of understanding and identify specific weaknesses. The results of this analysis are then reflected in the user profile.

[0449] Step 5:

[0450] Based on the analysis results, the server automatically generates a learning plan. This learning plan includes learning materials and practice problems tailored to the user, and is displayed on the user's dashboard by the server.

[0451] Step 6:

[0452] Users progress through their learning according to the provided learning plan. If questions arise during learning, they can ask the virtual assistant on their device to receive immediate answers or hints.

[0453] Step 7:

[0454] Once a learning session ends, the server automatically generates a report summarizing the user's learning progress based on their learning data. This report is visually organized and provided electronically to instructors and parents.

[0455] (Example 1)

[0456] 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."

[0457] With the advancement of online education, there is a growing need to provide personalized education and support to each learner in real time. However, conventional systems have problems in effectively analyzing large amounts of learner data and creating individualized learning plans. Furthermore, it is currently difficult to provide a mechanism to immediately address questions that arise during learning.

[0458] 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.

[0459] In this invention, the server includes means for collecting learner learning activity information, means for analyzing the collected learning activity information and evaluating the learner's level of understanding, and means for generating an optimized educational plan for each learner based on the analysis results. This enables individualized support tailored to each learner's level of understanding and learning progress.

[0460] A "learner" is an individual who participates in educational activities with the aim of acquiring knowledge and skills.

[0461] "Learning activity information" refers to data that records learners' actions and outcomes during the educational process, and includes answer history, study time, and usage of learning materials.

[0462] "Analysis" is the process of processing collected data, finding meaning in it, and deriving patterns and trends.

[0463] "Comprehension level" is an indicator that shows to what extent learners can grasp and apply a particular educational content.

[0464] An "educational plan" is a program that designs the optimal teaching materials and learning steps based on the learner's level of understanding and goals.

[0465] "Educational content" refers to information resources that learners use to acquire knowledge and skills, and includes textbooks, practice problems, videos, and other similar materials.

[0466] A "virtual support device" is an artificial intelligence-based interface that uses computer technology to instantly provide learners with the information and advice they need.

[0467] A "report" is a document that summarizes and presents the results and progress of a learner's learning activities in a visually organized manner.

[0468] "Learning resources" refer to various resources provided to support educational activities, including teaching materials, teacher support, and online tools.

[0469] "Schedule" refers to the time plan for learners to carry out each educational activity, and is also called a timetable or timetable.

[0470] This invention is a system for supporting learners' educational activities on an online education platform. Specific embodiments thereof are described below.

[0471] When users access the online learning platform, they can engage with educational content via their available devices. The server is equipped with a function to first collect user learning activity information, accumulating data such as answer history, study time, and usage of learning materials. This information is immediately transmitted to the server using a data streaming platform such as Apache Kafka.

[0472] Next, the server preprocesses the collected data using the Python Pandas library and evaluates the learner's understanding using analysis techniques with Scikit-learn. This makes it possible to detect each learner's specific strengths and weaknesses.

[0473] Based on the analysis results, the server generates an optimized learning plan for the learner. This plan includes effective educational content and practice exercises tailored to the individual learner, thanks to a recommendation system utilizing NLP technology. For example, a learner identified as lacking understanding of differential and integral calculus in mathematics will be provided with step-by-step educational content, progressing from basic to advanced levels.

[0474] During the learning process, if a user encounters a question, a virtual support system on the device can be helpful. This virtual support system uses a generative AI model and can instantly generate answers to questions entered by the user. For example, if the user enters "Explain the addition formulas for trigonometric functions," it will provide the theoretical background and specific application examples.

[0475] Furthermore, after a learning session ends, the server aggregates the data and automatically generates a report summarizing learning progress and challenges using visualization tools. This allows users, educators, and parents to understand learning progress at a glance and plan the next learning steps. In this way, the present invention aims to provide an educational experience tailored to the individual needs of learners.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The user logs into the online learning platform and begins learning. The server collects learning activity information based on the user's input. Specifically, this data includes the user's answer history, study time, and material usage history. This data is transmitted to the server in real time via a data streaming platform and stored in a database.

[0479] Step 2:

[0480] The server performs data analysis based on the collected learning activity information. The input is pre-processed training data, which is formatted using the Python Pandas library. Next, a machine learning algorithm using Scikit-learn is applied to evaluate the user's level of understanding. The output is an evaluation result showing the strengths and weaknesses of each individual user.

[0481] Step 3:

[0482] The server generates an individualized educational plan based on the analysis results. The input is the evaluation results obtained in step 2, and NLP technology is used to select the optimal educational content. Through this process, it determines which materials the user should use and outputs a list of the best materials to the user's dashboard.

[0483] Step 4:

[0484] As the user progresses through the learning process, they can ask questions to a virtual support system on their device. The input here is a prompt sentence from the user. The generative AI model receives this prompt sentence and generates and provides relevant information and explanations. This output is presented to the user as a solution to a specific problem or as background knowledge.

[0485] Step 5:

[0486] Once a learning session ends, the server aggregates the data and automatically generates a learning progress report. The input is the user's overall learning record, which is then processed using a visualization tool to produce a visually organized report. This makes it easy for both the user and their instructor to understand their learning progress.

[0487] (Application Example 1)

[0488] 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."

[0489] In modern online shopping, users find it difficult to find the best product from a vast amount of product information and choices. Furthermore, the lack of readily available systems to answer product-related questions and concerns leads to delays in purchasing decisions. This situation makes it difficult for users to have a satisfying shopping experience, reducing the efficiency of online shopping.

[0490] 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.

[0491] In this invention, the server includes means for collecting user behavior data, means for analyzing the collected behavior data and evaluating purchasing trends, and means for generating personalized recommendation plans based on the analysis results. This makes it possible to offer personalized product and service suggestions to users and to answer their questions immediately.

[0492] "User" refers to an individual who uses the system to search for or purchase goods or services.

[0493] "Behavioral data" refers to data related to users' online activities, such as purchase history, browsing history, and time spent on a site.

[0494] "Purchasing trends" refer to future purchase predictions and preferences derived from patterns of products and services that users have purchased in the past.

[0495] "Evaluating" refers to analyzing collected data to determine user characteristics and behavioral patterns numerically or qualitatively.

[0496] A "recommendation plan" refers to a specific plan for selecting and proposing products and services that are suitable for the user.

[0497] "Technical support measures" refer to the functions and technologies within a system that enable real-time responses to user inquiries.

[0498] "Purchase history" refers to a record of the goods and services that a user has purchased in the past.

[0499] "User feedback" refers to evaluations, comments, or reviews made by users regarding products or services they have purchased.

[0500] "Automatically generating information" refers to a system generating standardized reports and information based on accumulated data without human intervention.

[0501] "Stakeholders" refers to all parties interested in users' purchasing behavior and trends, including system operators and product providers.

[0502] This system aims to improve users' purchasing experience by providing personalized recommendations for the most suitable products. The server is primarily responsible for collecting and analyzing user behavior data, including browsing history, purchase history, and time spent on the site. The collected data is analyzed using the Python language and machine learning algorithms to evaluate each user's purchasing tendencies.

[0503] The server uses StandardScaler to standardize behavioral data and the KMeans algorithm for clustering. This makes it possible to generate an optimal product recommendation plan for each user. This recommendation plan selects products and services that are suitable for the user and reflects this in future product recommendations.

[0504] As a means of technical support, the terminal utilizes a generative AI model to answer users' questions in real time. This AI model is designed to enable immediate responses to user inquiries and provide rapid feedback.

[0505] For example, if a user is interested in outdoor equipment and has searched for camping-related products many times in the past, the server will generate a recommendation plan based on that data and suggest new products. In this case, if the user asks specific questions about the products, the AI ​​model can immediately provide accurate information.

[0506] An example of a prompt to input into a generating AI model is, "What products would you recommend to this user based on their past purchase data?" This allows for product recommendations based on the user's past behavior data.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The server collects user behavior data. This data includes users' browsing history, purchase history, and time spent on the site. This data is organized for each user and stored in a database. Data collection is performed in real time using web tracking technology.

[0510] Step 2:

[0511] The server uses StandardScaler to standardize the collected behavioral data. The raw input data is scaled based on its mean and standard deviation. This prepares the data for clustering. The output is scaled numerical data.

[0512] Step 3:

[0513] The server performs clustering analysis on standardized data using the KMeans algorithm. The input is scaled data, which is used to classify data points into multiple clusters. The output is the cluster label to which each user belongs. The system statistically evaluates users' purchasing tendencies and groups users with similar characteristics.

[0514] Step 4:

[0515] The server generates a personalized recommendation plan based on the clustering results. This plan includes a list of products and services relevant to a particular user. The input includes cluster labels and associated product data, and the output is a customized list.

[0516] Step 5:

[0517] The device uses a generative AI model to answer users' questions in real time. The input is a natural language question from the user. The AI ​​model analyzes this, searches for appropriate information, and then immediately presents the answer. The output is a text-based answer to the user's question.

[0518] Step 6:

[0519] The server automatically generates information analyzing user satisfaction and areas for improvement in future suggestions, based on each user's purchase history and feedback. This input includes past purchase data and feedback information, and the output is statistical information in report format.

[0520] Step 7:

[0521] Users review product lists and suggestions provided by the server and proceed with their purchases. Post-purchase feedback contributes to improving the accuracy of future recommendation plans. The output of this process is new behavioral data.

[0522] 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.

[0523] This invention combines an emotion engine that recognizes user emotions with an individualized learning system for online education platforms. This system is designed to provide detailed support for students' learning activities, and its main functions are described below.

[0524] Embedding an emotion engine

[0525] The emotion engine in this invention analyzes the user's emotional state in real time and uses that information to adjust learning plans and content. It uses the camera and microphone installed in the device to sense and analyze the user's facial expressions and voice tone. This allows it to measure the user's stress levels and concentration, aiming to improve learning effectiveness.

[0526] Learning adjustments based on user emotions

[0527] Based on the analysis results, the server dynamically adjusts the learning plan according to the user's emotional state. For example, if the emotion engine determines that the user is feeling tired, it may provide low-load tasks or content that promotes relaxation. This not only improves learning efficiency but also helps to enhance the user's overall learning experience.

[0528] Feedback utilizing emotional data

[0529] At the end of a learning session, the server generates a detailed report containing data from the emotion engine. This report details changes in emotions during learning and their impact on learning outcomes, and is provided to instructors and parents. This report provides new perspectives on instruction and can be used for further improvement and support.

[0530] Specific example

[0531] For example, when a user is working on a math problem, the emotion engine detects anxiety from their tone of voice. The device sends this information to the server, which then provides a problem with reduced difficulty to help the user regain their composure. After the learning session is complete, the generated report details how learning was affected during peak anxiety periods.

[0532] By integrating an emotion engine in this way, the present invention enables emotion-based support, which was difficult with conventional personalized learning systems, and allows for more holistic learning support tailored to the individual needs of users.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] The user logs into the online learning platform and begins learning. At that moment, the device activates its emotion engine and starts real-time analysis of the user's emotional state.

[0536] Step 2:

[0537] During learning, the device's built-in camera and microphone monitor the user's facial expressions and voice. This prepares the device for collecting user emotional data.

[0538] Step 3:

[0539] The device inputs collected emotional data into an emotion engine to analyze the user's emotional state. For example, it can detect joy or anger from the user's facial expressions, and anxiety or restlessness from their voice.

[0540] Step 4:

[0541] The emotion engine analyzes the emotion data and sends it to the server. The server combines this data with learning activity data to understand the user's current state.

[0542] Step 5:

[0543] Based on emotional data, the server dynamically adjusts the learning plan. For example, if it determines that the user is experiencing stress, it may reduce the difficulty of the learning tasks or suggest a break.

[0544] Step 6:

[0545] As the user continues learning, if the emotion engine detects anxiety, the device provides resources to help them calm down (e.g., short relaxation videos).

[0546] Step 7:

[0547] Once a learning session ends, the server automatically generates a report summarizing the user's learning outcomes and emotional changes based on their learning and emotional data. This report is then distributed electronically to instructors and parents.

[0548] (Example 2)

[0549] 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."

[0550] In online education, personalizing the learning experience based on learners' emotions and comprehension levels is a challenging task. Traditional systems provide learning materials based solely on students' progress and grades, lacking emotional considerations and potentially leading to decreased learning efficiency and motivation. Furthermore, while adjustments that reflect learners' real-time emotional states and comprehension levels are needed, there is a lack of effective methods to achieve this.

[0551] 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.

[0552] In this invention, the server includes means for collecting learner progress data, means for analyzing the collected data and evaluating learning comprehension, means for analyzing the learner's emotional state in real time, means for generating a learning plan based on the analysis results and dynamically providing individualized educational materials and practice problems, and means for generating and providing a report integrating emotional changes and learning outcomes to the support provider. This enables the provision of appropriate learning materials that take into account the learner's emotional state and effective learning support based on that.

[0553] A "learner" is an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0554] "Progress data" refers to a series of pieces of information recorded by learners during their learning activities, and is used to measure their level of understanding and learning achievement.

[0555] "Analysis" is the process of computational processing and evaluation to analyze collected data and identify specific patterns or trends.

[0556] "Learning comprehension level" is an indicator that shows how accurately learners understand specific content or tasks.

[0557] A "learning plan" is the design of an individually customized educational process based on the learner's progress and level of understanding.

[0558] "Educational materials" is a general term for content provided to support learners' learning, and includes textbooks, videos, quizzes, and other similar materials.

[0559] "Practice problems" are questions or assignments provided to allow learners to practically confirm and solidify their knowledge and skills.

[0560] "Emotional state" refers to the psychological conditions and moods that learners experience while learning, including concentration, stress, and enjoyment.

[0561] A "report" is a document that summarizes a learner's progress, achievements, and emotional tendencies, and is used for instruction and improvement.

[0562] A "supporter" is an individual or organization that plays a role in assisting the education and growth of learners, and this includes teachers and parents.

[0563] This invention is a system for realizing personalized learning in an online education platform. The system has the function of analyzing the learner's emotional state in real time and dynamically adjusting the learning content based on that analysis.

[0564] The device uses a camera to capture the user's facial expressions and a microphone to analyze their voice tone. This hardware collects the data necessary to run the emotion analysis algorithm. Image processing and speech recognition software are used for emotion analysis, allowing for real-time assessment of the user's concentration level and stress level.

[0565] The server receives sentiment analysis results and learning progress data sent from the terminal and generates an individualized learning plan based on this information. This includes the process of selecting the most suitable educational materials and practice problems from the database and sending them to the user. The server also creates a feedback report tailored to the learner's emotional and comprehension levels and provides it to instructors and parents.

[0566] Users can progress through their learning using materials and reports provided by the system and track their own progress. For example, if a user feels anxious when faced with a math problem, the system analyzes the user's emotions and immediately supports them by presenting a problem with a lower difficulty level.

[0567] An example of a prompt is, "Please tell me about a specific implementation method for a system that combines an emotion engine that recognizes user emotions with an individualized learning system in an online education platform." By using this prompt, the generative AI model can gain insights into how to carry out the invention.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The device activates the camera to detect the user's facial expressions and the microphone to record voice tone. The inputs in this step are camera video and audio data. Image processing software extracts facial features, and voice analysis software analyzes voice tone and speed. As output, data for emotion analysis is generated.

[0571] Step 2:

[0572] The device inputs collected facial feature information and voice data into an emotion analysis algorithm to analyze the user's emotional state in real time. The input for this step is the analysis data generated in step 1. The emotion analysis algorithm evaluates the data using classification and pattern matching to determine the user's emotional state (e.g., focused, relaxed, stressed). The output is a variable indicating the user's emotional state.

[0573] Step 3:

[0574] The server receives emotional state data and learning progress data sent from the terminal. Using this data as input, the server dynamically selects learning content that matches the learner's emotional state. It prepares the content by performing database queries to search for appropriate educational materials and practice problems. As output, a list of optimal educational content is obtained and sent to the user's terminal.

[0575] Step 4:

[0576] The user receives educational content delivered from the server and learns on their device. The input in this step is content data from the server, and the content is based on the learner's situation and emotional state. The user views educational materials and works on practice exercises through their device. As output, the learning progress is updated, and new learning data is generated.

[0577] Step 5:

[0578] Upon completion of a learning session, the server generates a feedback report based on the user's progress data and emotional state history. The input for this step is all data collected during the learning session. Using data analysis techniques, the server comprehensively evaluates learning outcomes and challenges, outputting a detailed report. The final report is provided to the support provider.

[0579] (Application Example 2)

[0580] 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."

[0581] Traditional online education systems have faced challenges in supporting individualized learning that takes into account users' emotions. Specifically, they often fail to provide appropriate content and plans tailored to the learner's mental and physical state, leading to decreased learning efficiency and a lower quality of learning experience. Therefore, there was a need to implement dynamic learning adjustments that take emotional states into account and provide learning support optimized for each individual learner.

[0582] 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.

[0583] In this invention, the server includes means for collecting learner learning behavior data, means for analyzing the collected data and evaluating comprehension, and means for analyzing the learner's emotional state using an emotion analysis engine. This enables real-time analysis of the learner's emotional state and dynamic adjustment of learning content and plans based on emotions.

[0584] "Learning behavior data" refers to all activity information that learners use when using online platforms.

[0585] "Comprehension level" is an indicator that shows how accurately a learner grasps specific learning material and how well they are able to solve problems related to it.

[0586] An "optimized learning plan" is a learning progress plan tailored to the individual needs and learning goals of the learner.

[0587] "Teaching materials and practice exercises" are educational resources provided to learners to acquire specific skills and knowledge.

[0588] A "virtual aid" is a system equipped with artificial intelligence that responds to learners' questions and doubts in real time.

[0589] A "report" is a document that summarizes learning progress and identified challenges, and is provided to educators and parents.

[0590] An "emotion analysis engine" is a technology that analyzes learners' facial expressions and vocal characteristics to evaluate their emotional state.

[0591] "Dynamic adjustment" refers to the process of modifying the content and learning plan provided in response to real-time emotional states and learning progress.

[0592] The system for implementing this invention is configured as follows: The server collects learner learning behavior data and evaluates the level of comprehension based on this data. The collected data is acquired from a terminal equipped with a camera and microphone and analyzed in real time as sentiment data using a face recognition library (e.g., OpenCV) or a speech analysis API (e.g., Google Cloud Speech-to-Text).

[0593] Based on the results of emotion analysis, the server determines the learner's emotional state and dynamically adjusts the learning plan accordingly. For example, if the emotion engine determines that the learner is feeling stressed, the server can present low-load learning materials and provide content that promotes relaxation.

[0594] Furthermore, after the learning session is complete, the server automatically generates a detailed report including emotional data, which is then provided to educators and parents. This report can describe in detail the changes in emotions during the learning session and their impact on learning outcomes.

[0595] A concrete example is when the emotion engine detects anxiety in a learner's voice while they are studying mathematics. The server immediately uses this information to provide practice problems with a reduced difficulty level, helping the learner regain their composure. Furthermore, the report generated afterward outlines how the learning was affected during the peak of the learner's anxiety.

[0596] An example of a prompt for a generative AI model would be: "If a learner shows high levels of anxiety when tackling difficult problems, what kind of learning content would be effective to provide?"

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The device captures the learner's facial expressions and voice through its camera and microphone. This facial and voice data serves as input for emotion analysis. The data is analyzed in real time using the device's facial recognition library and voice analysis API. The output is data that quantifies or classifies the learner's emotional state.

[0600] Step 2:

[0601] The device sends the analyzed emotional data to the server. The server receives this emotional data and evaluates the learner's current emotional state. Based on this evaluation, the server processes the data to determine appropriate learning content. Specifically, it detects changes in stress levels and concentration levels.

[0602] Step 3:

[0603] The server dynamically adjusts the optimal learning plan for the learner based on the evaluation of their emotional state. During this process, it uses a generative AI model to generate prompts and customize the learning content. These prompts offer suggestions regarding the difficulty level of the content. The output consists of the adjusted learning plan and learning materials.

[0604] Step 4:

[0605] The server sends appropriate learning materials and practice exercises to the device based on a tailored learning plan. The device then provides this content to the learner. The learning materials the learner works on are designed to be tailored to their current emotional state and the workload is adjusted accordingly.

[0606] Step 5:

[0607] Once a user's learning session ends, the server generates a detailed report based on sentiment data, learning progress, comprehension assessments, and other relevant information. This report summarizes the impact of the learner's emotional fluctuations on their learning. The report is provided to educators and parents to help them further support learning.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] This invention relates to a learning support system that operates on an online education platform. This system was developed to comprehensively support students' learning activities, and its main mechanisms are described below.

[0626] Data collection and analysis

[0627] First, the user accesses the online learning platform and begins learning. The server automatically collects data on the user's learning activity during this process. This data includes information such as answer history, study time, and study frequency.

[0628] The server then analyzes this data in real time. Machine learning algorithms are used for the analysis to evaluate the user's understanding and detect specific weaknesses. The results of the analysis are immediately reflected in the user profile and used to create future learning plans.

[0629] Generating personalized learning plans

[0630] Based on the analysis results, the server generates a personalized learning plan for each user. This learning plan selects and combines the most effective learning materials and practice problems based on the user's current level of understanding.

[0631] For example, if a user has difficulty with differential and integral calculus, the server will select learning materials that progress from basic to advanced levels and display them on the user's dashboard.

[0632] Real-time AI Assistant

[0633] The device features a virtual assistant powered by generative AI technology. This assistant is designed to instantly answer questions and address challenges the user encounters during their learning process.

[0634] For example, if a user is unsure how to solve a particular math problem, they can ask a virtual assistant on their device for help, which will then provide the steps to solve the problem and related knowledge.

[0635] Feedback and report generation

[0636] After a learning session ends, the server aggregates the user's learning data and automatically generates a report summarizing their recent progress and challenges. This report is visually organized, making it easy for instructors and parents to understand the user's learning status.

[0637] Thus, this invention makes it possible to provide effective individualized instruction, which was difficult to achieve with conventional education.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user logs into the online learning platform and accesses a dedicated dashboard. This initiates the user's session and allows them to record their learning activities.

[0641] Step 2:

[0642] Users indicate their intention to begin learning by selecting specific subjects or topics. The selected information is sent to the server, and learning settings are established based on that information.

[0643] Step 3:

[0644] The server monitors the user's learning activity in real time and collects learning activity data such as answer history and study time. This data is recorded in a database for analysis.

[0645] Step 4:

[0646] The server uses machine learning algorithms to analyze the collected learning activity data. This allows it to assess the user's level of understanding and identify specific weaknesses. The results of this analysis are then reflected in the user profile.

[0647] Step 5:

[0648] Based on the analysis results, the server automatically generates a learning plan. This learning plan includes learning materials and practice problems tailored to the user, and is displayed on the user's dashboard by the server.

[0649] Step 6:

[0650] Users progress through their learning according to the provided learning plan. If questions arise during learning, they can ask the virtual assistant on their device to receive immediate answers or hints.

[0651] Step 7:

[0652] Once a learning session ends, the server automatically generates a report summarizing the user's learning progress based on their learning data. This report is visually organized and provided electronically to instructors and parents.

[0653] (Example 1)

[0654] 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".

[0655] With the advancement of online education, there is a growing need to provide personalized education and support to each learner in real time. However, conventional systems have problems in effectively analyzing large amounts of learner data and creating individualized learning plans. Furthermore, it is currently difficult to provide a mechanism to immediately address questions that arise during learning.

[0656] 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.

[0657] In this invention, the server includes means for collecting learner learning activity information, means for analyzing the collected learning activity information and evaluating the learner's level of understanding, and means for generating an optimized educational plan for each learner based on the analysis results. This enables individualized support tailored to each learner's level of understanding and learning progress.

[0658] A "learner" is an individual who participates in educational activities with the aim of acquiring knowledge and skills.

[0659] "Learning activity information" refers to data that records learners' actions and outcomes during the educational process, and includes answer history, study time, and usage of learning materials.

[0660] "Analysis" is the process of processing collected data, finding meaning in it, and deriving patterns and trends.

[0661] "Comprehension level" is an indicator that shows to what extent learners can grasp and apply a particular educational content.

[0662] An "educational plan" is a program that designs the optimal teaching materials and learning steps based on the learner's level of understanding and goals.

[0663] "Educational content" refers to information resources that learners use to acquire knowledge and skills, and includes textbooks, practice problems, videos, and other similar materials.

[0664] A "virtual support device" is an artificial intelligence-based interface that uses computer technology to instantly provide learners with the information and advice they need.

[0665] A "report" is a document that summarizes and presents the results and progress of a learner's learning activities in a visually organized manner.

[0666] "Learning resources" refer to various resources provided to support educational activities, including teaching materials, teacher support, and online tools.

[0667] "Schedule" refers to the time plan for learners to carry out each educational activity, and is also called a timetable or timetable.

[0668] This invention is a system for supporting learners' educational activities on an online education platform. Specific embodiments thereof are described below.

[0669] When users access the online learning platform, they can engage with educational content via their available devices. The server is equipped with a function to first collect user learning activity information, accumulating data such as answer history, study time, and usage of learning materials. This information is immediately transmitted to the server using a data streaming platform such as Apache Kafka.

[0670] Next, the server preprocesses the collected data using the Python Pandas library and evaluates the learner's understanding using analysis techniques with Scikit-learn. This makes it possible to detect each learner's specific strengths and weaknesses.

[0671] Based on the analysis results, the server generates an optimized learning plan for the learner. This plan includes effective educational content and practice exercises tailored to the individual learner, thanks to a recommendation system utilizing NLP technology. For example, a learner identified as lacking understanding of differential and integral calculus in mathematics will be provided with step-by-step educational content, progressing from basic to advanced levels.

[0672] During the learning process, if a user encounters a question, a virtual support system on the device can be helpful. This virtual support system uses a generative AI model and can instantly generate answers to questions entered by the user. For example, if the user enters "Explain the addition formulas for trigonometric functions," it will provide the theoretical background and specific application examples.

[0673] Furthermore, after a learning session ends, the server aggregates the data and automatically generates a report summarizing learning progress and challenges using visualization tools. This allows users, educators, and parents to understand learning progress at a glance and plan the next learning steps. In this way, the present invention aims to provide an educational experience tailored to the individual needs of learners.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The user logs into the online learning platform and begins learning. The server collects learning activity information based on the user's input. Specifically, this data includes the user's answer history, study time, and material usage history. This data is transmitted to the server in real time via a data streaming platform and stored in a database.

[0677] Step 2:

[0678] The server performs data analysis based on the collected learning activity information. The input is pre-processed training data, which is formatted using the Python Pandas library. Next, a machine learning algorithm using Scikit-learn is applied to evaluate the user's level of understanding. The output is an evaluation result showing the strengths and weaknesses of each individual user.

[0679] Step 3:

[0680] The server generates an individualized educational plan based on the analysis results. The input is the evaluation results obtained in step 2, and NLP technology is used to select the optimal educational content. Through this process, it determines which materials the user should use and outputs a list of the best materials to the user's dashboard.

[0681] Step 4:

[0682] As the user progresses through the learning process, they can ask questions to a virtual support system on their device. The input here is a prompt sentence from the user. The generative AI model receives this prompt sentence and generates and provides relevant information and explanations. This output is presented to the user as a solution to a specific problem or as background knowledge.

[0683] Step 5:

[0684] Once a learning session ends, the server aggregates the data and automatically generates a learning progress report. The input is the user's overall learning record, which is then processed using a visualization tool to produce a visually organized report. This makes it easy for both the user and their instructor to understand their learning progress.

[0685] (Application Example 1)

[0686] 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".

[0687] In modern online shopping, users find it difficult to find the best product from a vast amount of product information and choices. Furthermore, the lack of readily available systems to answer product-related questions and concerns leads to delays in purchasing decisions. This situation makes it difficult for users to have a satisfying shopping experience, reducing the efficiency of online shopping.

[0688] 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.

[0689] In this invention, the server includes means for collecting user behavior data, means for analyzing the collected behavior data and evaluating purchasing trends, and means for generating personalized recommendation plans based on the analysis results. This makes it possible to offer personalized product and service suggestions to users and to answer their questions immediately.

[0690] "User" refers to an individual who uses the system to search for or purchase goods or services.

[0691] "Behavioral data" refers to data related to users' online activities, such as purchase history, browsing history, and time spent on a site.

[0692] "Purchasing trends" refer to future purchase predictions and preferences derived from patterns of products and services that users have purchased in the past.

[0693] "Evaluating" refers to analyzing collected data to determine user characteristics and behavioral patterns numerically or qualitatively.

[0694] A "recommendation plan" refers to a specific plan for selecting and proposing products and services that are suitable for the user.

[0695] "Technical support measures" refer to the functions and technologies within a system that enable real-time responses to user inquiries.

[0696] "Purchase history" refers to a record of the goods and services that a user has purchased in the past.

[0697] "User feedback" refers to evaluations, comments, or reviews made by users regarding products or services they have purchased.

[0698] "Automatically generating information" refers to a system generating standardized reports and information based on accumulated data without human intervention.

[0699] "Stakeholders" refers to all parties interested in users' purchasing behavior and trends, including system operators and product providers.

[0700] This system aims to improve users' purchasing experience by providing personalized recommendations for the most suitable products. The server is primarily responsible for collecting and analyzing user behavior data, including browsing history, purchase history, and time spent on the site. The collected data is analyzed using the Python language and machine learning algorithms to evaluate each user's purchasing tendencies.

[0701] The server uses StandardScaler to standardize behavioral data and the KMeans algorithm for clustering. This makes it possible to generate an optimal product recommendation plan for each user. This recommendation plan selects products and services that are suitable for the user and reflects this in future product recommendations.

[0702] As a means of technical support, the terminal utilizes a generative AI model to answer users' questions in real time. This AI model is designed to enable immediate responses to user inquiries and provide rapid feedback.

[0703] For example, if a user is interested in outdoor equipment and has searched for camping-related products many times in the past, the server will generate a recommendation plan based on that data and suggest new products. In this case, if the user asks specific questions about the products, the AI ​​model can immediately provide accurate information.

[0704] An example of a prompt to input into a generating AI model is, "What products would you recommend to this user based on their past purchase data?" This allows for product recommendations based on the user's past behavior data.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The server collects user behavior data. This data includes users' browsing history, purchase history, and time spent on the site. This data is organized for each user and stored in a database. Data collection is performed in real time using web tracking technology.

[0708] Step 2:

[0709] The server uses StandardScaler to standardize the collected behavioral data. The raw input data is scaled based on its mean and standard deviation. This prepares the data for clustering. The output is scaled numerical data.

[0710] Step 3:

[0711] The server performs clustering analysis on standardized data using the KMeans algorithm. The input is scaled data, which is used to classify data points into multiple clusters. The output is the cluster label to which each user belongs. The system statistically evaluates users' purchasing tendencies and groups users with similar characteristics.

[0712] Step 4:

[0713] The server generates a personalized recommendation plan based on the clustering results. This plan includes a list of products and services relevant to a particular user. The input includes cluster labels and associated product data, and the output is a customized list.

[0714] Step 5:

[0715] The device uses a generative AI model to answer users' questions in real time. The input is a natural language question from the user. The AI ​​model analyzes this, searches for appropriate information, and then immediately presents the answer. The output is a text-based answer to the user's question.

[0716] Step 6:

[0717] The server automatically generates information analyzing user satisfaction and areas for improvement in future suggestions, based on each user's purchase history and feedback. This input includes past purchase data and feedback information, and the output is statistical information in report format.

[0718] Step 7:

[0719] Users review product lists and suggestions provided by the server and proceed with their purchases. Post-purchase feedback contributes to improving the accuracy of future recommendation plans. The output of this process is new behavioral data.

[0720] 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.

[0721] This invention combines an emotion engine that recognizes user emotions with an individualized learning system for online education platforms. This system is designed to provide detailed support for students' learning activities, and its main functions are described below.

[0722] Embedding an emotion engine

[0723] The emotion engine in this invention analyzes the user's emotional state in real time and uses that information to adjust learning plans and content. It uses the camera and microphone installed in the device to sense and analyze the user's facial expressions and voice tone. This allows it to measure the user's stress levels and concentration, aiming to improve learning effectiveness.

[0724] Learning adjustments based on user emotions

[0725] Based on the analysis results, the server dynamically adjusts the learning plan according to the user's emotional state. For example, if the emotion engine determines that the user is feeling tired, it may provide low-load tasks or content that promotes relaxation. This not only improves learning efficiency but also helps to enhance the user's overall learning experience.

[0726] Feedback utilizing emotional data

[0727] At the end of a learning session, the server generates a detailed report containing data from the emotion engine. This report details changes in emotions during learning and their impact on learning outcomes, and is provided to instructors and parents. This report provides new perspectives on instruction and can be used for further improvement and support.

[0728] Specific example

[0729] For example, when a user is working on a math problem, the emotion engine detects anxiety from their tone of voice. The device sends this information to the server, which then provides a problem with reduced difficulty to help the user regain their composure. After the learning session is complete, the generated report details how learning was affected during peak anxiety periods.

[0730] By integrating an emotion engine in this way, the present invention enables emotion-based support, which was difficult with conventional personalized learning systems, and allows for more holistic learning support tailored to the individual needs of users.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The user logs into the online learning platform and begins learning. At that moment, the device activates its emotion engine and starts real-time analysis of the user's emotional state.

[0734] Step 2:

[0735] During learning, the device's built-in camera and microphone monitor the user's facial expressions and voice. This prepares the device for collecting user emotional data.

[0736] Step 3:

[0737] The device inputs collected emotional data into an emotion engine to analyze the user's emotional state. For example, it can detect joy or anger from the user's facial expressions, and anxiety or restlessness from their voice.

[0738] Step 4:

[0739] The emotion engine analyzes the emotion data and sends it to the server. The server combines this data with learning activity data to understand the user's current state.

[0740] Step 5:

[0741] Based on emotional data, the server dynamically adjusts the learning plan. For example, if it determines that the user is experiencing stress, it may reduce the difficulty of the learning tasks or suggest a break.

[0742] Step 6:

[0743] As the user continues learning, if the emotion engine detects anxiety, the device provides resources to help them calm down (e.g., short relaxation videos).

[0744] Step 7:

[0745] Once a learning session ends, the server automatically generates a report summarizing the user's learning outcomes and emotional changes based on their learning and emotional data. This report is then distributed electronically to instructors and parents.

[0746] (Example 2)

[0747] 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".

[0748] In online education, personalizing the learning experience based on learners' emotions and comprehension levels is a challenging task. Traditional systems provide learning materials based solely on students' progress and grades, lacking emotional considerations and potentially leading to decreased learning efficiency and motivation. Furthermore, while adjustments that reflect learners' real-time emotional states and comprehension levels are needed, there is a lack of effective methods to achieve this.

[0749] 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.

[0750] In this invention, the server includes means for collecting learner progress data, means for analyzing the collected data and evaluating learning comprehension, means for analyzing the learner's emotional state in real time, means for generating a learning plan based on the analysis results and dynamically providing individualized educational materials and practice problems, and means for generating and providing a report integrating emotional changes and learning outcomes to the support provider. This enables the provision of appropriate learning materials that take into account the learner's emotional state and effective learning support based on that.

[0751] A "learner" is an individual who seeks to acquire knowledge and skills through the use of an educational system.

[0752] "Progress data" refers to a series of pieces of information recorded by learners during their learning activities, and is used to measure their level of understanding and learning achievement.

[0753] "Analysis" is the process of computational processing and evaluation to analyze collected data and identify specific patterns or trends.

[0754] "Learning comprehension level" is an indicator that shows how accurately learners understand specific content or tasks.

[0755] A "learning plan" is the design of an individually customized educational process based on the learner's progress and level of understanding.

[0756] "Educational materials" is a general term for content provided to support learners' learning, and includes textbooks, videos, quizzes, and other similar materials.

[0757] "Practice problems" are questions or assignments provided to allow learners to practically confirm and solidify their knowledge and skills.

[0758] "Emotional state" refers to the psychological conditions and moods that learners experience while learning, including concentration, stress, and enjoyment.

[0759] A "report" is a document that summarizes a learner's progress, achievements, and emotional tendencies, and is used for instruction and improvement.

[0760] A "supporter" is an individual or organization that plays a role in assisting the education and growth of learners, and this includes teachers and parents.

[0761] This invention is a system for realizing personalized learning in an online education platform. The system has the function of analyzing the learner's emotional state in real time and dynamically adjusting the learning content based on that analysis.

[0762] The device uses a camera to capture the user's facial expressions and a microphone to analyze their voice tone. This hardware collects the data necessary to run the emotion analysis algorithm. Image processing and speech recognition software are used for emotion analysis, allowing for real-time assessment of the user's concentration level and stress level.

[0763] The server receives sentiment analysis results and learning progress data sent from the terminal and generates an individualized learning plan based on this information. This includes the process of selecting the most suitable educational materials and practice problems from the database and sending them to the user. The server also creates a feedback report tailored to the learner's emotional and comprehension levels and provides it to instructors and parents.

[0764] Users can progress through their learning using materials and reports provided by the system and track their own progress. For example, if a user feels anxious when faced with a math problem, the system analyzes the user's emotions and immediately supports them by presenting a problem with a lower difficulty level.

[0765] An example of a prompt is, "Please tell me about a specific implementation method for a system that combines an emotion engine that recognizes user emotions with an individualized learning system in an online education platform." By using this prompt, the generative AI model can gain insights into how to carry out the invention.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The device activates the camera to detect the user's facial expressions and the microphone to record voice tone. The inputs in this step are camera video and audio data. Image processing software extracts facial features, and voice analysis software analyzes voice tone and speed. As output, data for emotion analysis is generated.

[0769] Step 2:

[0770] The device inputs collected facial feature information and voice data into an emotion analysis algorithm to analyze the user's emotional state in real time. The input for this step is the analysis data generated in step 1. The emotion analysis algorithm evaluates the data using classification and pattern matching to determine the user's emotional state (e.g., focused, relaxed, stressed). The output is a variable indicating the user's emotional state.

[0771] Step 3:

[0772] The server receives emotional state data and learning progress data sent from the terminal. Using this data as input, the server dynamically selects learning content that matches the learner's emotional state. It prepares the content by performing database queries to search for appropriate educational materials and practice problems. As output, a list of optimal educational content is obtained and sent to the user's terminal.

[0773] Step 4:

[0774] The user receives educational content delivered from the server and learns on their device. The input in this step is content data from the server, and the content is based on the learner's situation and emotional state. The user views educational materials and works on practice exercises through their device. As output, the learning progress is updated, and new learning data is generated.

[0775] Step 5:

[0776] Upon completion of a learning session, the server generates a feedback report based on the user's progress data and emotional state history. The input for this step is all data collected during the learning session. Using data analysis techniques, the server comprehensively evaluates learning outcomes and challenges, outputting a detailed report. The final report is provided to the support provider.

[0777] (Application Example 2)

[0778] 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".

[0779] Traditional online education systems have faced challenges in supporting individualized learning that takes into account users' emotions. Specifically, they often fail to provide appropriate content and plans tailored to the learner's mental and physical state, leading to decreased learning efficiency and a lower quality of learning experience. Therefore, there was a need to implement dynamic learning adjustments that take emotional states into account and provide learning support optimized for each individual learner.

[0780] 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.

[0781] In this invention, the server includes means for collecting learner learning behavior data, means for analyzing the collected data and evaluating comprehension, and means for analyzing the learner's emotional state using an emotion analysis engine. This enables real-time analysis of the learner's emotional state and dynamic adjustment of learning content and plans based on emotions.

[0782] "Learning behavior data" refers to all activity information that learners use when using online platforms.

[0783] "Comprehension level" is an indicator that shows how accurately a learner grasps specific learning material and how well they are able to solve problems related to it.

[0784] An "optimized learning plan" is a learning progress plan tailored to the individual needs and learning goals of the learner.

[0785] "Teaching materials and practice exercises" are educational resources provided to learners to acquire specific skills and knowledge.

[0786] A "virtual aid" is a system equipped with artificial intelligence that responds to learners' questions and doubts in real time.

[0787] A "report" is a document that summarizes learning progress and identified challenges, and is provided to educators and parents.

[0788] An "emotion analysis engine" is a technology that analyzes learners' facial expressions and vocal characteristics to evaluate their emotional state.

[0789] "Dynamic adjustment" refers to the process of modifying the content and learning plan provided in response to real-time emotional states and learning progress.

[0790] The system for implementing this invention is configured as follows: The server collects learner learning behavior data and evaluates the level of comprehension based on this data. The collected data is acquired from a terminal equipped with a camera and microphone and analyzed in real time as sentiment data using a face recognition library (e.g., OpenCV) or a speech analysis API (e.g., Google Cloud Speech-to-Text).

[0791] Based on the results of emotion analysis, the server determines the learner's emotional state and dynamically adjusts the learning plan accordingly. For example, if the emotion engine determines that the learner is feeling stressed, the server can present low-load learning materials and provide content that promotes relaxation.

[0792] Furthermore, after the learning session is complete, the server automatically generates a detailed report including emotional data, which is then provided to educators and parents. This report can describe in detail the changes in emotions during the learning session and their impact on learning outcomes.

[0793] A concrete example is when the emotion engine detects anxiety in a learner's voice while they are studying mathematics. The server immediately uses this information to provide practice problems with a reduced difficulty level, helping the learner regain their composure. Furthermore, the report generated afterward outlines how the learning was affected during the peak of the learner's anxiety.

[0794] An example of a prompt for a generative AI model would be: "If a learner shows high levels of anxiety when tackling difficult problems, what kind of learning content would be effective to provide?"

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The device captures the learner's facial expressions and voice through its camera and microphone. This facial and voice data serves as input for emotion analysis. The data is analyzed in real time using the device's facial recognition library and voice analysis API. The output is data that quantifies or classifies the learner's emotional state.

[0798] Step 2:

[0799] The device sends the analyzed emotional data to the server. The server receives this emotional data and evaluates the learner's current emotional state. Based on this evaluation, the server processes the data to determine appropriate learning content. Specifically, it detects changes in stress levels and concentration levels.

[0800] Step 3:

[0801] The server dynamically adjusts the optimal learning plan for the learner based on the evaluation of their emotional state. During this process, it uses a generative AI model to generate prompts and customize the learning content. These prompts offer suggestions regarding the difficulty level of the content. The output consists of the adjusted learning plan and learning materials.

[0802] Step 4:

[0803] The server sends appropriate learning materials and practice exercises to the device based on a tailored learning plan. The device then provides this content to the learner. The learning materials the learner works on are designed to be tailored to their current emotional state and the workload is adjusted accordingly.

[0804] Step 5:

[0805] Once a user's learning session ends, the server generates a detailed report based on sentiment data, learning progress, comprehension assessments, and other relevant information. This report summarizes the impact of the learner's emotional fluctuations on their learning. The report is provided to educators and parents to help them further support learning.

[0806] 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.

[0807] 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.

[0808] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] Means for collecting student learning activity data,

[0830] A means of analyzing collected learning activity data and evaluating the level of understanding,

[0831] A means for generating an optimized learning plan for each student based on the analysis results,

[0832] A means of providing learning materials and practice problems according to the generated learning plan,

[0833] A means of providing a virtual assistant that answers students' questions in real time,

[0834] A system that includes a means to automatically generate reports summarizing learning progress and problems, and provide them to instructors and parents.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for determining the user's strengths and weaknesses through real-time data analysis and selecting the optimal learning resources.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for automatically adjusting the schedule based on the user's learning history.

[0839] "Example 1"

[0840] (Claim 1)

[0841] Means for collecting learners' learning activity information,

[0842] A means of analyzing collected learning activity information and evaluating learners' level of understanding,

[0843] A means for generating an optimized educational plan for each learner based on the analysis results,

[0844] A means of providing educational content and practice tasks according to the generated educational plan,

[0845] A means of providing a virtual support device that answers learners' questions in real time,

[0846] A system that includes a means of automatically generating reports summarizing learning progress and problems, and providing them to educators and parents.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for determining the learner's strengths and weaknesses through real-time information analysis and selecting the optimal learning resources.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising means for automatically adjusting the schedule based on the learner's learning history.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] Means for collecting user behavior data,

[0854] A means of analyzing collected behavioral data and evaluating purchasing trends,

[0855] A means for generating a recommendation plan optimized for each user based on the analysis results,

[0856] A means of providing goods and services according to the generated recommendation plan,

[0857] A technical support tool that answers users' questions in real time,

[0858] A system that includes means for automatically generating and providing information summarizing purchase history and usage feedback to relevant parties.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for determining the user's purchasing trends through real-time data analysis and selecting the optimal product resources.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for automatically adjusting the recommended plan based on the user's behavioral history.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means of collecting learner progress data,

[0866] A means of analyzing collected progress data and evaluating the level of learning comprehension,

[0867] A means for generating a customized learning plan for each learner based on the analysis results,

[0868] A means of providing educational materials and practice problems individually according to the generated learning plan,

[0869] A means of analyzing the user's emotional state,

[0870] A means of dynamically adjusting learning content based on analyzed emotional states,

[0871] A system that includes a means of automatically generating and providing to supporters a detailed report that integrates emotional changes and results during learning.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for identifying learners' strengths and weaknesses through real-time data analysis and selecting the most suitable educational resources.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for automatically adjusting the learning schedule based on the learner's learning history.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] Means for collecting learner learning behavior data,

[0879] A means of analyzing collected learning behavior data and evaluating the level of understanding,

[0880] A means for generating an optimized learning plan for each learner based on the analysis results,

[0881] A means of providing learning materials and practice problems according to the generated learning plan,

[0882] A means of providing virtual aids that answer learners' questions in real time,

[0883] A means of automatically generating reports summarizing learning progress and problems, and providing them to educators and parents,

[0884] In an individualized learning system, a means for analyzing the learner's emotional state using an emotion analysis engine and dynamically adjusting the learning content based on that emotional state,

[0885] A system that includes means for optimizing learners' learning experiences using real-time emotional data.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for determining the strengths and weaknesses of users through real-time data analysis and selecting the optimal learning resources.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising means for automatically adjusting the plan based on the user's learning history and providing content based on their emotional state. [Explanation of Symbols]

[0890] 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. Means for collecting student learning activity data, A means of analyzing collected learning activity data and evaluating the level of understanding, A means for generating an optimized learning plan for each student based on the analysis results, A means of providing learning materials and practice problems according to the generated learning plan, A means of providing a virtual assistant that answers students' questions in real time, A system that includes a means to automatically generate reports summarizing learning progress and problems, and provide them to instructors and parents.

2. The system according to claim 1, further comprising means for determining the user's strengths and weaknesses through real-time data analysis and selecting the optimal learning resources.

3. The system according to claim 1, further comprising means for automatically adjusting the schedule based on the user's learning history.

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