system

Abstract

We provide the system. [Solution] A device for collecting learner education data, A device that analyzes collected educational data and identifies areas of weakness, A device that generates an individualized educational plan based on identified areas of weakness, A device for monitoring the progress of the generated educational plan, A device that provides educational support using robots, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an online learning environment where individual guidance is required, there is a problem that it has not been fully realized to efficiently identify the weak fields of each student and provide appropriate learning plans and feedback based on them. For this reason, it is difficult for students to receive appropriate learning support to overcome their own weaknesses, and efficient learning is hindered. Furthermore, since the opportunity to receive real-time guidance is limited, there is also a problem that it is difficult to flexibly adjust the progress of learning.

Means for Solving the Problems

[0005] This invention provides a system that collects students' learning data daily and identifies areas of weakness by analyzing that data. This system includes means for generating individualized learning plans based on the identified areas of weakness and managing their progress. Furthermore, it automatically generates tests corresponding to the areas of weakness and provides feedback based on the results, thereby providing appropriate learning support to students. In addition, it includes means for providing real-time consultations regarding learning, allowing students to receive advice at any time. This enables flexible and effective learning support tailored to individual learning needs.

[0006] "Learning data" refers to information generated by students through their daily learning activities, and includes data such as the subject matter, study time, progress, and test results.

[0007] "Analysis" is the process of analyzing students' learning progress from collected learning data and identifying areas of weakness and areas for improvement based on the obtained data.

[0008] "Areas of weakness" refers to areas identified through analysis where students have a particular lack of understanding or proficiency in their studies.

[0009] A "learning plan" is a plan that includes an individualized learning schedule and content for each student, designed to overcome specific areas of weakness.

[0010] "Progress management" is a management activity that monitors whether students' learning activities are progressing according to the learning plan and adjusts the plan as needed.

[0011] "Automatic test generation" is a process that uses algorithms to automatically create test questions tailored to each student's level of understanding, in order to help them overcome their weak areas.

[0012] "Feedback" refers to the act of providing students with specific advice and instructions for improving their learning, based on data such as test results.

[0013] "Real-time consultations" are a system that allows students to consult online in real time about their learning concerns and questions, and are a means of providing immediate support and advice. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides an online learning support system for efficiently supporting students' learning. Specific embodiments thereof are described below.

[0036] First, users access the system using a terminal and register. During the registration process, they enter basic information such as their name, grade level, and current academic ability. This entered information is sent to the server and stored in the database as a user profile.

[0037] Next, the user enters their daily learning data into the device. This includes study time, subjects studied, test results, and so on. This data is uploaded to the server and stored there.

[0038] The server analyzes the accumulated learning data. Generative AI models are used for the analysis to identify areas where performance is particularly low or areas requiring improvement. Based on these results, the server generates a personalized learning plan for the user. This learning plan includes the content to be learned each day and progress goals, and is provided to the user via their device.

[0039] The server also uses an automated generation algorithm to create quizzes and send them to the user's device. The user takes the quiz and sends the results back to the server. The server analyzes these results and provides the user with feedback on areas for improvement and the next steps in their learning.

[0040] The server also manages progress, monitoring whether the user's learning is progressing according to plan. If any deficiencies or delays occur, the learning plan is dynamically adjusted, and the user is notified via their device.

[0041] Furthermore, this system enables real-time online consultations. Users can individually consult about their studies through their devices, and the server schedules these consultations and coordinates with expert instructors to provide necessary advice.

[0042] For example, if a high school student user has difficulty with English listening comprehension, the server will identify this data and create listening-specific workbooks and study plans. It will administer listening quizzes, provide feedback based on the user's performance, and suggest appropriate guidance to overcome their difficulties. In this way, appropriate learning support tailored to the individual needs of each student is provided.

[0043] The above describes specific embodiments of the present invention. This system allows students to enjoy flexible and effective learning support tailored to their individual learning needs.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user accesses the system using a terminal and begins the registration process. They enter basic information such as their name, grade level, and current academic ability, and submit it to the server. This information is stored in the database as a user profile.

[0047] Step 2:

[0048] Users input their daily learning data into their devices. Information such as study time, subjects studied, and test results is recorded and uploaded to the server. The server stores this data in a database.

[0049] Step 3:

[0050] The server analyzes the accumulated learning data. A generative AI model is applied to identify areas where performance is declining in specific subjects or fields. The results obtained from the analysis are categorized as areas of weakness or areas requiring improvement.

[0051] Step 4:

[0052] The server generates a personalized learning plan based on identified areas of weakness. This learning plan includes daily learning objectives and progress targets. This learning plan is sent to the user's device for review.

[0053] Step 5:

[0054] The server uses an automated generation algorithm to create a quiz and sends the questions to the user's device. The user takes the quiz on their device and sends the results back to the server.

[0055] Step 6:

[0056] The server analyzes the results of the quiz submitted by the user. Based on the results, it provides the user with specific areas for improvement and feedback on the next learning steps. This feedback is displayed on the device.

[0057] Step 7:

[0058] The server continuously monitors the user's learning progress. If learning is not progressing as planned, it dynamically adjusts the learning plan and notifies the user via the terminal.

[0059] Step 8:

[0060] When a user requests a real-time online consultation, they send a notification from their device to the server. The server then schedules the consultation and works with a professional instructor to provide the user with appropriate advice.

[0061] This series of processes allows users to receive efficient and flexible learning support tailored to their individual learning needs.

[0062] (Example 1)

[0063] 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."

[0064] Traditional learning support systems only offer a uniform curriculum, making it difficult to accurately understand each student's academic abilities and areas of weakness, and to provide individualized learning support. Furthermore, they lacked the functionality to continuously monitor student progress and dynamically adjust learning plans as needed, sometimes preventing students from learning effectively.

[0065] 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.

[0066] In this invention, the server includes means for receiving basic student information and storing personal profiles, means for uploading daily learning data, and means for analyzing detailed learning data using a generative AI model to identify areas of academic ability that require improvement. This makes it possible to provide personalized learning support tailored to the characteristics of each student, appropriately manage progress, and improve learning efficiency.

[0067] A "student" is a person who receives education using a learning support system.

[0068] "Basic information" refers to information used to form a student's personal profile, such as name, grade level, and academic ability level.

[0069] A "personal profile" is aggregated data about a student stored on the server and is used to provide personalized services.

[0070] "Learning data" refers to information such as students' daily study time, subjects studied, and test results.

[0071] A "generative AI model" is a type of artificial intelligence that learns from large amounts of data and performs data analysis and prediction.

[0072] "Academic ability area" refers to a specific area related to learning subjects or skills.

[0073] An "individualized learning plan" is a learning plan and schedule tailored to each student's learning data and characteristics.

[0074] An "exam" is a test administered to measure the level of understanding and progress of learned material.

[0075] "Real-time consultations" are immediate learning consultations conducted through direct dialogue between students and instructors.

[0076] This invention is an online learning support system that effectively supports students' learning. Specific embodiments thereof are described below.

[0077] The server serves as the central hub of the learning support system, managing students' basic information and learning data. Users access the system through their terminals and input basic information such as their name, grade level, and academic ability. This information is sent from the terminal to the server, which receives it and stores it in a database as a personal profile.

[0078] Next, the user inputs daily learning data into the device. This data includes study time, subjects studied, and test results. The device uploads this data to the server. The server analyzes the received data using a generating AI model. This AI model is used to identify areas where performance is particularly low or where improvement is needed.

[0079] The server generates a personalized learning plan based on the analysis results. This plan includes the learning content and achievement goals that the student should focus on, and is provided to the user via the terminal. Prompt statements are used to instruct the generating AI model during the learning plan generation process. For example, a prompt such as, "Based on the current learning data, identify the areas where the user needs the most improvement and propose a personalized learning plan," might be used.

[0080] The server also automatically generates tests tailored to the user's academic area and sends them to the terminal. The user takes these tests and sends the results back to the server. The server analyzes the test results and provides feedback. This feedback includes specific areas for improvement, helping the user progress in their studies.

[0081] Furthermore, the server provides a means to enable real-time online consultations, allowing users to consult with expert instructors through their devices. For example, if a high school student struggles with English listening comprehension, the server can create listening-focused workbooks and study plans, and provide guidance to help them overcome their difficulties based on feedback.

[0082] As a result, students can receive effective and flexible learning support tailored to their individual academic abilities and learning styles.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users access the system using a terminal and enter basic information such as their name, grade level, and academic ability. The terminal sends this information to the server. The server analyzes the received data and stores it in a database as a personal profile. In this step, a new profile is output to the database from the basic information of the student that was entered.

[0086] Step 2:

[0087] Users input daily learning data, such as study time, subjects studied, and test results, into their devices. The devices periodically upload this information to a server. The server uses the received learning data to track individual academic progress and stores it in a database. In this step, student learning data is received as input and stored in the database as a learning history.

[0088] Step 3:

[0089] The server analyzes the accumulated training data using a generating AI model. Specifically, it identifies areas where performance is low based on past test results and study time. Using training data as input, it obtains a list of areas where performance is low or areas that need improvement as output. This analysis uses a prompt message that says, "Identify areas that need improvement based on specific study patterns."

[0090] Step 4:

[0091] The server generates an individualized learning plan based on the analysis results obtained by the generated AI model. The learning plan generation process outputs learning content tailored to the areas where each student needs improvement, and this content is provided to the user via their device. This plan includes specific learning content and achievement goals.

[0092] Step 5:

[0093] The server uses an automated generation algorithm to create a test tailored to the identified academic area and sends it to the terminal. The user takes the test and returns the answers to the server via the terminal. The server analyzes the test results and creates feedback including scores and accuracy rates. This information is provided to the user to guide further learning.

[0094] Step 6:

[0095] The server monitors the progress of the learning plan to ensure it is proceeding as planned. It dynamically adjusts the plan as needed and notifies the user's device. If any deficiencies or delays occur, the server outputs a revised learning plan.

[0096] Step 7:

[0097] When a user wishes to seek advice regarding their studies, they request an online consultation using their device. The server manages this and coordinates with expert instructors to provide real-time advice. In this step, effective learning advice is output from the user's consultation input.

[0098] (Application Example 1)

[0099] 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."

[0100] Traditional education systems have struggled to efficiently identify individual learning difficulties faced by learners and provide appropriate learning plans based on those difficulties. Furthermore, a lack of comprehensive educational support, such as real-time educational assistance and progress reports to parents, remains a challenge. Therefore, there is a need to realize educational support using robots to improve the efficiency of learning.

[0101] 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.

[0102] In this invention, the server includes means for collecting learner's educational data, means for analyzing the collected educational data and identifying areas of weakness, means for generating an individualized educational plan based on the identified areas of weakness, means for monitoring the progress of the generated educational plan, and means for providing educational support using a robot. This enables comprehensive support for the individual educational needs of learners.

[0103] "Learner educational data" refers to information that learners record in their daily studies, including study time, subjects, and test results.

[0104] A "weakness" refers to a specific learning area in which a learner performs poorly and needs improvement.

[0105] An "individualized learning plan" is a detailed learning schedule and content that is customized according to each learner's areas of difficulty and learning pace.

[0106] "Means of monitoring progress" refers to a process for regularly evaluating the learner's progress and confirming whether the learning is proceeding according to plan.

[0107] "Means of providing educational support using robots" refers to functions that utilize educational robots to provide learners with interactive learning experiences and present problems using voice.

[0108] "Assessment" refers to tests or quizzes administered to measure learners' learning outcomes.

[0109] A "feedback-providing device" is a system that informs learners of areas for improvement and future learning strategies based on the evaluation results.

[0110] A "device that provides real-time educational consultations" is an online consultation system that enables learners and educators to communicate directly and provide educational guidance.

[0111] A "device that automatically generates progress reports for parents" is a function that collects information on the learning progress and results of students and periodically generates reports for their guardians.

[0112] To implement this invention, a system is constructed to collect learner's educational data and identify areas of weakness. The server has a database to receive data transmitted from learners, and users input their daily learning content and test results via a terminal. The entered data is transmitted to the server via Wi-Fi or a wired network.

[0113] The server has a generative AI model built using the Python language and the TENSORFLOW® library, which analyzes collected educational data. This analysis identifies learners' areas of weakness and generates personalized learning plans based on the results. The learning plans include progress goals and what to learn each day.

[0114] Furthermore, to manage progress, the server continuously monitors the database and tracks the learners' educational progress. It also has the capability to update the educational plan in real time and notify the terminal if any deficiencies or delays occur.

[0115] The robot, equipped with a processor such as a Raspberry Pi, functions as an educational support device within the home. For example, the robot can read out pronunciation practice exercises or report learning progress to parents. This allows learners to overcome their weaknesses through repeated practice.

[0116] As a concrete example, the robot might suggest, "Today, let's practice math, which you're not good at, for 20 minutes," and then, using an AI model, it generates an optimal educational plan using a prompt such as, "What kind of daily learning plan would be effective in improving the student's math performance?"

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users access the system using individual terminals and input learning data. This data includes subjects studied, study time, and test results. This data is transmitted to the server via the network. The input is the user's learning data, and the output is the learning data stored on the server.

[0120] Step 2:

[0121] The server stores the received learning data in a database. The information stored in the database is used for subsequent data analysis and the creation of educational plans. This enables efficient data management. The input is the learning data sent to the server, and the output is the information stored in the database.

[0122] Step 3:

[0123] The server launches a generative AI model using Python and TensorFlow to analyze the database information. The generative AI model analyzes the data specifically to identify its weaknesses. The input is the training data in the database, and the output is information about the identified weaknesses.

[0124] Step 4:

[0125] The server generates an individualized learning plan based on the analysis results. This plan includes individual learning objectives and daily learning content for each learner, and the server uses an algorithm in the process of generating it. The input is information about areas of weakness, and the output is the individualized learning plan.

[0126] Step 5:

[0127] The server sends the generated lesson plan to the user's terminal. This information is also provided to the educational support robot to assist with instructions and feedback to learners. The input is the lesson plan, and the output is distribution to the user's terminal and the robot device.

[0128] Step 6:

[0129] Educational support robots provide learning guidance to users based on educational plans through voice output and display information. For example, the robot might say, "Let's practice math for 20 minutes." The input is the educational plan delivered to the robot, and the output is the learning instruction given to the user by the robot's actions.

[0130] Step 7:

[0131] Users learn based on educational plans provided through robots and terminals, and record their progress. The progress data is then sent back to the server and analyzed for the next learning cycle. The input is daily learning progress data, and the output is saved data used for the next analysis.

[0132] 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.

[0133] This invention provides an online learning support system that combines an emotion engine that recognizes user emotions with a system designed to enhance individualized learning support for students. This system integrates and analyzes learning data and emotion data to provide effective support in order to improve students' learning experience.

[0134] First, users register with the system via their terminal, entering basic information such as their name, grade level, and academic ability. This information is sent to the server and stored as a user profile. Daily learning data is also entered via the terminal. In addition, user emotional data is collected through voice and video analysis using microphones and cameras, or through user input. Emotional data includes, for example, stress levels and emotional responses during learning.

[0135] The server analyzes the collected training data and emotional data. The training data is analyzed using a generative AI model to identify the user's areas of weakness, while the emotional data is analyzed using an emotional engine to evaluate the user's current learning motivation and stress level.

[0136] The server generates a personalized learning plan. This plan is dynamically adjusted according to the user's academic ability and emotional state, with flexible learning content and schedule design. The plan is sent to the device, and the user uses it for their daily studies.

[0137] Furthermore, the server adjusts the difficulty of quizzes and customizes feedback based on emotional data. For example, if a user is experiencing high stress levels, it provides feedback such as advice on stress reduction and suggestions for learning methods that help manage stress. This feedback can be viewed on the device at any time.

[0138] Progress management is comprehensive, with the server monitoring learning progress and adjusting the plan based on the user's emotional state and learning progress. Users can also request real-time consultations regarding their learning via their device. During these consultations, the server ensures that the guidance reflects the user's emotional state.

[0139] For example, if a middle school student user experiences stress while solving math problems, the system detects this through its emotion engine. Based on this data, the server adjusts the math learning method and provides feedback such as suggestions for a more relaxing environment and mitigation measures. In this way, appropriate and flexible support is provided to improve the quality of the learning experience.

[0140] This format allows users to not only acquire knowledge but also receive comprehensive learning support that takes into account emotional and psychological aspects.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users access the system using their devices and complete initial registration. Users enter basic information, including their name, grade level, and academic ability, and send it to the server via their devices. The server creates a user profile based on the received information and stores it in a database.

[0144] Step 2:

[0145] Users input learning data and emotional data from their device for each learning session. Learning data includes subjects studied, time spent studying, and level of achievement. Emotional data is automatically acquired through facial recognition using the device's camera or voice tone analysis using the microphone, or it can be manually entered by the user.

[0146] Step 3:

[0147] The server analyzes the collected training data and uses a generative AI model to identify areas of weakness. Simultaneously, it uses an emotion engine to analyze emotional data and evaluate the user's stress level and motivation during the learning process.

[0148] Step 4:

[0149] The server generates a personalized learning plan based on identified areas of difficulty and an assessment of emotional data. This learning plan includes appropriate tasks, learning content, and a schedule, adjusted to take emotional state into account. This plan is sent to the terminal for the user to access.

[0150] Step 5:

[0151] The server automatically generates a short quiz, taking into account the user's emotional state. The content and difficulty of the quiz are adjusted according to the user's stress level. The user takes the quiz on their device and sends the results to the server.

[0152] Step 6:

[0153] The server analyzes the quiz results and sentiment data to generate feedback for the user. This feedback includes specific advice on the user's learning progress and areas for emotional improvement. The feedback is displayed on the device and can be reviewed by the user at any time.

[0154] Step 7:

[0155] The server continuously monitors the user's learning progress. If progress is not on schedule or emotional distress is detected, the learning plan is dynamically adjusted, and the user is notified via their device.

[0156] Step 8:

[0157] Users can request a real-time consultation from their device to the server as needed. In response to this request, the server coordinates with a professional instructor and arranges for the consultation to be conducted while taking the user's emotional state into consideration.

[0158] (Example 2)

[0159] 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".

[0160] Conventional online learning support systems typically rely solely on student learning data, making it difficult to consider emotions and mental states. Consequently, factors such as learner stress and decreased motivation are not adequately understood, and individual learning experiences are not fully utilized. This invention aims to overcome these challenges and provide comprehensive learning support that holistically considers the emotional state of learners.

[0161] 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.

[0162] In this invention, the server includes means for collecting basic information about students, means for collecting emotional data through voice / video analysis or user input, and means for comprehensively analyzing students' learning data and emotional data to identify areas of difficulty. This makes it possible to provide dynamic and personalized support that responds to the learner's emotions and learning progress.

[0163] "Student basic information" refers to data such as name, grade level, and academic ability level that is entered into the system to identify learners and personalize the learning process.

[0164] "Emotional data" refers to information, including stress levels and motivation indicators, obtained through audio / video analysis and subjective user input, in order to evaluate the learner's emotional state during learning.

[0165] "Learning data" refers to information that records the learning activities, results, and progress that learners engage in on a daily basis.

[0166] "Integrated analysis" refers to a process that combines students' learning data and emotional data for detailed analysis, comprehensively evaluating learners' areas of difficulty and emotional states.

[0167] A "weakness area" refers to a range of learning content that a learner finds particularly difficult to understand or where there is room for improvement.

[0168] An "individualized learning plan" is a proposal of learning content and schedules that are tailored to each individual learner's academic ability and emotional state, and are optimized for each person.

[0169] "Feedback" refers to evaluations and suggestions for improvement provided based on a learner's progress and emotional state.

[0170] "Real-time instruction" refers to appropriate educational support and advice based on the learner's current situation, provided in a way that learners can access immediately.

[0171] This invention is a system designed to efficiently support student learning, and it integrates functions for collecting and analyzing emotional data. The system operates using a server and terminals in cooperation.

[0172] The user first accesses the learning system through a terminal and enters basic information such as their name, grade level, and academic ability level. The terminal encrypts this information and sends it to the server. The server stores the received basic information in a database.

[0173] Next, users input daily learning data via their device. This learning data includes their learning progress and the content they worked on that day. The device can also collect emotional data through voice and video analysis using its microphone and camera hardware. Furthermore, users can optionally complete emotional questionnaires to report their stress levels and motivation.

[0174] The server analyzes the collected learning data using a generative AI model to identify the learner's areas of weakness. Simultaneously, it analyzes emotional data using an emotion engine to assess learning motivation and stress levels. Based on the analysis results, the server generates an individualized learning plan, dynamically adjusts it, and sends it to the terminal.

[0175] For example, if a middle school student user experiences stress while solving a math problem, the system analyzes this emotional data using an emotion engine. The server then sends feedback to alleviate stress, such as suggesting a relaxing environment, providing the user with the optimal learning experience. Examples of prompts include, "How stressed are you when solving math problems, [username]?" and "Which areas should you focus on in your current learning plan?"

[0176] Through this system, users can not only improve their academic performance but also receive learning support that takes their emotions into consideration.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] Users access the learning system through their device and enter basic information such as their name, grade level, and academic ability. This entered data is encrypted by the device and sent to the server. The server receives this information and stores it in a database as a user profile.

[0180] Step 2:

[0181] Users input daily learning data into their devices. This data includes the day's learning content and progress. This data is sent to the server as foundational data for evaluating the quality of learning. The server stores the received learning data and prepares it for later analysis.

[0182] Step 3:

[0183] The device uses a microphone and camera to collect audio and video, and acquires emotional data. This data includes stress levels and emotional changes during learning. Users also input subjective emotional data on an emotional questionnaire screen and send it to the server via the device.

[0184] Step 4:

[0185] The server analyzes the received training data using a generating AI model. The input for the analysis is the collected training data, and the output identifies the user's areas of weakness. In this process, the user's level of understanding and progress in each field is evaluated, and areas that need improvement are identified.

[0186] Step 5:

[0187] The server uses an emotion engine to analyze emotional data. Inputs include audio and video analysis, as well as emotional information provided by the user. Outputs include evaluations of learning motivation and stress levels. The server integrates this information to understand the user's emotional state.

[0188] Step 6:

[0189] The server generates a personalized learning plan based on identified areas of difficulty and emotional state. This plan is tailored to the user's learning goals and emotional state and sent to the device. The device displays the plan, allowing the user to utilize it in their daily learning.

[0190] Step 7:

[0191] Based on the emotional data it receives, the server provides feedback to the user. For example, if the user is experiencing high stress, the server will notify the device with relaxation techniques and stress reduction advice. This feedback helps to improve the user's learning experience.

[0192] (Application Example 2)

[0193] 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".

[0194] In elderly care settings, providing appropriate care tailored to each individual user's emotional and health condition is a significant burden for caregivers and makes efficient operation difficult. Furthermore, improving the quality of life for users requires a comprehensive understanding of their emotional and health conditions and individualized responses. However, this is difficult to achieve in real time using conventional methods.

[0195] 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.

[0196] In this invention, the server includes means for collecting user learning data and emotional data, means for analyzing the collected data to identify learning patterns and emotional states, and means for generating personalized learning and lifestyle improvement plans based on the identified information. This makes it possible to grasp the user's emotional and health status in real time and provide appropriate care accordingly.

[0197] "Users" are the people who use the system to collect and analyze emotional and learning data.

[0198] "Learning data" refers to data that includes information related to a user's daily activities, habits, and learning.

[0199] "Emotional data" refers to information that indicates the emotional state of a user, and is data obtained from the analysis of audio and video.

[0200] "Analysis" is the process of processing collected data to identify specific patterns or states.

[0201] A "learning pattern" is a specific pattern that indicates a user's behavior, characteristics, and tendencies in learning.

[0202] "Emotional state" refers to the user's psychological or emotional state, including stress levels and feelings of happiness.

[0203] An "individualized learning and lifestyle improvement plan" is a plan designed to optimize learning and lifestyle in accordance with the specific needs of each user, based on their learning patterns and emotional state.

[0204] "Real-time" refers to the temporal immediacy in which data collection, analysis, and responses are performed immediately.

[0205] The system for realizing this invention consists of a terminal used daily by the user and a server operating in the backend. The terminal is equipped with a camera and microphone to collect the user's learning data and emotional data. When the user registers with the system through the terminal and enters basic information, this information is sent to a server in the cloud and stored as the user's profile.

[0206] The server uses Google Cloud's Vision AI to analyze video data from the camera and detect the user's emotions. Audio data is analyzed using IBM Watson® Speech to Text to complementarily assess the emotional state. This data is processed by dedicated analysis software (implemented in Python) equipped with a generative AI model to identify learning patterns and emotional states.

[0207] Based on the identified information, the server uses TensorFlow to generate a learning and lifestyle improvement plan. This plan is dynamically adjusted to each user's needs and sent to the device in real time. Based on this information, users take appropriate care and lifestyle actions. For example, if a user feels stressed after exercise, the server can provide relaxing music or suggest simple exercises to change their mood.

[0208] An example of a prompt to the generating AI model is, "Based on the elderly person's emotional data and activity history, please propose a care plan that promotes relaxation." In this way, the system can provide highly accurate support tailored to the individual needs of the user, thereby improving their quality of life.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The terminal prompts the user for basic information such as their name, age, and medical history, and formats this information into an input format to be sent to the backend server. As output, the user's profile information is saved on the server.

[0212] Step 2:

[0213] The device uses a camera and microphone to collect the user's video and audio in real time and converts this into digital data. It then sends the real-time video and audio data to a server as output.

[0214] Step 3:

[0215] The server uses Google Cloud's Vision AI to analyze video data sent from the device. The input is video data, and the output is emotion data obtained through facial expression analysis.

[0216] Step 4:

[0217] The server uses IBM Watson Speech to Text to convert and analyze audio data into text format. The input is audio data, and the output is text data containing emotion keywords extracted from the audio.

[0218] Step 5:

[0219] The server inputs this emotion data and existing training data into a generating AI model to evaluate the learning patterns and emotional states. The input consists of emotion data and training data, and the output identifies the learning patterns and emotional states for each user.

[0220] Step 6:

[0221] The server uses TensorFlow to generate personalized learning and life improvement plans based on identified learning patterns and emotional states. It takes learning patterns and emotional states as input and sends the customized plan to the user's device as output.

[0222] Step 7:

[0223] The user takes appropriate lifestyle improvement actions based on the generated plan via their device. As output, they send new tracking data of their daily activities to the server to manage their progress.

[0224] Step 8:

[0225] If necessary, the user requests real-time consultation through their device, and the server uses prompt messages to execute a process of providing appropriate advice through a generated AI model. The input is the consultation content, and the output is a proposal of action based on individual guidance.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention provides an online learning support system for efficiently supporting students' learning. Specific embodiments thereof are described below.

[0243] First, users access the system using a terminal and register. During the registration process, they enter basic information such as their name, grade level, and current academic ability. This entered information is sent to the server and stored in the database as a user profile.

[0244] Next, the user enters their daily learning data into the device. This includes study time, subjects studied, test results, and so on. This data is uploaded to the server and stored there.

[0245] The server analyzes the accumulated learning data. Generative AI models are used for the analysis to identify areas where performance is particularly low or areas requiring improvement. Based on these results, the server generates a personalized learning plan for the user. This learning plan includes the content to be learned each day and progress goals, and is provided to the user via their device.

[0246] The server also uses an automated generation algorithm to create quizzes and send them to the user's device. The user takes the quiz and sends the results back to the server. The server analyzes these results and provides the user with feedback on areas for improvement and the next steps in their learning.

[0247] The server also manages progress, monitoring whether the user's learning is progressing according to plan. If any deficiencies or delays occur, the learning plan is dynamically adjusted, and the user is notified via their device.

[0248] Furthermore, this system enables real-time online consultations. Users can individually consult about their studies through their devices, and the server schedules these consultations and coordinates with expert instructors to provide necessary advice.

[0249] For example, if a high school student user has difficulty with English listening comprehension, the server will identify this data and create listening-specific workbooks and study plans. It will administer listening quizzes, provide feedback based on the user's performance, and suggest appropriate guidance to overcome their difficulties. In this way, appropriate learning support tailored to the individual needs of each student is provided.

[0250] The above describes specific embodiments of the present invention. This system allows students to enjoy flexible and effective learning support tailored to their individual learning needs.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user accesses the system using a terminal and begins the registration process. They enter basic information such as their name, grade level, and current academic ability, and submit it to the server. This information is stored in the database as a user profile.

[0254] Step 2:

[0255] Users input their daily learning data into their devices. Information such as study time, subjects studied, and test results is recorded and uploaded to the server. The server stores this data in a database.

[0256] Step 3:

[0257] The server analyzes the accumulated learning data. A generative AI model is applied to identify areas where performance is declining in specific subjects or fields. The results obtained from the analysis are categorized as areas of weakness or areas requiring improvement.

[0258] Step 4:

[0259] The server generates a personalized learning plan based on identified areas of weakness. This learning plan includes daily learning objectives and progress targets. This learning plan is sent to the user's device for review.

[0260] Step 5:

[0261] The server uses an automated generation algorithm to create a quiz and sends the questions to the user's device. The user takes the quiz on their device and sends the results back to the server.

[0262] Step 6:

[0263] The server analyzes the results of the quiz submitted by the user. Based on the results, it provides the user with specific areas for improvement and feedback on the next learning steps. This feedback is displayed on the device.

[0264] Step 7:

[0265] The server continuously monitors the user's learning progress. If learning is not progressing as planned, it dynamically adjusts the learning plan and notifies the user via the terminal.

[0266] Step 8:

[0267] When a user requests a real-time online consultation, they send a notification from their device to the server. The server then schedules the consultation and works with a professional instructor to provide the user with appropriate advice.

[0268] This series of processes allows users to receive efficient and flexible learning support tailored to their individual learning needs.

[0269] (Example 1)

[0270] 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."

[0271] Traditional learning support systems only offer a uniform curriculum, making it difficult to accurately understand each student's academic abilities and areas of weakness, and to provide individualized learning support. Furthermore, they lacked the functionality to continuously monitor student progress and dynamically adjust learning plans as needed, sometimes preventing students from learning effectively.

[0272] 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.

[0273] In this invention, the server includes means for receiving basic student information and storing personal profiles, means for uploading daily learning data, and means for analyzing detailed learning data using a generative AI model to identify areas of academic ability that require improvement. This makes it possible to provide personalized learning support tailored to the characteristics of each student, appropriately manage progress, and improve learning efficiency.

[0274] A "student" is a person who receives education using a learning support system.

[0275] "Basic information" refers to information used to form a student's personal profile, such as name, grade level, and academic ability level.

[0276] A "personal profile" is aggregated data about a student stored on the server and is used to provide personalized services.

[0277] "Learning data" refers to information such as students' daily study time, subjects studied, and test results.

[0278] A "generative AI model" is a type of artificial intelligence that learns from large amounts of data and performs data analysis and prediction.

[0279] "Academic ability area" refers to a specific area related to learning subjects or skills.

[0280] An "individualized learning plan" is a learning plan and schedule tailored to each student's learning data and characteristics.

[0281] An "exam" is a test administered to measure the level of understanding and progress of learned material.

[0282] "Real-time consultations" are immediate learning consultations conducted through direct dialogue between students and instructors.

[0283] This invention is an online learning support system that effectively supports students' learning. The following describes its specific embodiments.

[0284] The server serves as the center of the learning support system and is responsible for managing students' basic information and learning data. Users access the system through a terminal and input basic information such as name, grade, and academic level. This information is sent from the terminal to the server, which receives it and stores it in a database as a personal profile.

[0285] Next, users input daily learning data into the terminal. This data includes study time, subjects studied, test results, etc. The terminal uploads this data to the server. The server analyzes the received data using a generative AI model. This AI model is used to identify particularly low-performing areas or areas that need improvement.

[0286] The server generates an individualized learning plan based on the analysis results. This plan includes the learning content and achievement goals that students should engage in and is provided to the user via the terminal. In generating the learning plan, a prompt sentence is used to instruct the generative AI model. For example, a prompt such as "Based on the current learning data, identify the areas where the user needs the most improvement and propose an individualized learning plan" is used.

[0287] In addition, the server automatically generates a test according to the academic field and sends it to the terminal. The user takes this test and sends the results to the server. The server analyzes the test results and provides feedback. This feedback includes specific points for improvement and helps with the progress of learning.

[0288] Furthermore, the server provides a means to enable real-time online consultations, allowing users to consult with expert instructors through their devices. For example, if a high school student struggles with English listening comprehension, the server can create listening-focused workbooks and study plans, and provide guidance to help them overcome their difficulties based on feedback.

[0289] As a result, students can receive effective and flexible learning support tailored to their individual academic abilities and learning styles.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] Users access the system using a terminal and enter basic information such as their name, grade level, and academic ability. The terminal sends this information to the server. The server analyzes the received data and stores it in a database as a personal profile. In this step, a new profile is output to the database from the basic information of the student that was entered.

[0293] Step 2:

[0294] Users input daily learning data, such as study time, subjects studied, and test results, into their devices. The devices periodically upload this information to a server. The server uses the received learning data to track individual academic progress and stores it in a database. In this step, student learning data is received as input and stored in the database as a learning history.

[0295] Step 3:

[0296] The server analyzes the accumulated training data using a generating AI model. Specifically, it identifies areas where performance is low based on past test results and study time. Using training data as input, it obtains a list of areas where performance is low or areas that need improvement as output. This analysis uses a prompt message that says, "Identify areas that need improvement based on specific study patterns."

[0297] Step 4:

[0298] The server generates an individualized learning plan based on the analysis results obtained by the generated AI model. The learning plan generation process outputs learning content tailored to the areas where each student needs improvement, and this content is provided to the user via their device. This plan includes specific learning content and achievement goals.

[0299] Step 5:

[0300] The server uses an automated generation algorithm to create a test tailored to the identified academic area and sends it to the terminal. The user takes the test and returns the answers to the server via the terminal. The server analyzes the test results and creates feedback including scores and accuracy rates. This information is provided to the user to guide further learning.

[0301] Step 6:

[0302] The server monitors the progress of the learning plan to ensure it is proceeding as planned. It dynamically adjusts the plan as needed and notifies the user's device. If any deficiencies or delays occur, the server outputs a revised learning plan.

[0303] Step 7:

[0304] When a user wishes to seek advice regarding their studies, they request an online consultation using their device. The server manages this and coordinates with expert instructors to provide real-time advice. In this step, effective learning advice is output from the user's consultation input.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0307] Efficiently identifying the individual educational obstacles of learners and providing appropriate learning plans based on them has been difficult in conventional educational systems. Also, there is an issue that comprehensive educational support, such as real-time educational support and progress reports to parents, is lacking. Therefore, it is required to realize educational support utilizing robots and improve the efficiency of learning.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for collecting educational data of learners, means for analyzing the collected educational data and identifying weak areas, means for generating an individualized educational plan based on the identified weak areas, means for monitoring the progress of the generated educational plan, and means for providing educational support using a robot. Thereby, comprehensive support for the individual educational needs of learners becomes possible.

[0310] "Educational data of learners" is information including the study time, subjects, test results, etc. that learners record in their daily learning.<0000�8>

[0311] "Weak area" is a specific learning area where learners have low evaluations and need improvement in their learning.

[0312] "Individualized educational plan" is a detailed learning schedule and learning content customized according to the weak areas and learning paces of individual learners.

[0313] "Means of monitoring progress" refers to a process for regularly evaluating the learner's progress and confirming whether the learning is proceeding according to plan.

[0314] "Means of providing educational support using robots" refers to functions that utilize educational robots to provide learners with interactive learning experiences and present problems using voice.

[0315] "Assessment" refers to tests or quizzes administered to measure learners' learning outcomes.

[0316] A "feedback-providing device" is a system that informs learners of areas for improvement and future learning strategies based on the evaluation results.

[0317] A "device that provides real-time educational consultations" is an online consultation system that enables learners and educators to communicate directly and provide educational guidance.

[0318] A "device that automatically generates progress reports for parents" is a function that collects information on the learning progress and results of students and periodically generates reports for their guardians.

[0319] To implement this invention, a system is constructed to collect learner's educational data and identify areas of weakness. The server has a database to receive data transmitted from learners, and users input their daily learning content and test results via a terminal. The entered data is transmitted to the server via Wi-Fi or a wired network.

[0320] The server has a generative AI model built using the Python language and TensorFlow library, which analyzes the collected educational data. This analysis identifies areas where learners struggle and generates personalized learning plans based on the results. The learning plans include progress goals and what to learn each day.

[0321] Furthermore, to manage progress, the server continuously monitors the database and tracks the learners' educational progress. It also has the capability to update the educational plan in real time and notify the terminal if any deficiencies or delays occur.

[0322] The robot, equipped with a processor such as a Raspberry Pi, functions as an educational support device within the home. For example, the robot can read out pronunciation practice exercises or report learning progress to parents. This allows learners to overcome their weaknesses through repeated practice.

[0323] As a concrete example, the robot might suggest, "Today, let's practice math, which you're not good at, for 20 minutes," and then, using an AI model, it generates an optimal educational plan using a prompt such as, "What kind of daily learning plan would be effective in improving the student's math performance?"

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] Users access the system using individual terminals and input learning data. This data includes subjects studied, study time, and test results. This data is transmitted to the server via the network. The input is the user's learning data, and the output is the learning data stored on the server.

[0327] Step 2:

[0328] The server stores the received learning data in a database. The information stored in the database is used for subsequent data analysis and the creation of educational plans. This enables efficient data management. The input is the learning data sent to the server, and the output is the information stored in the database.

[0329] Step 3:

[0330] The server launches a generative AI model using Python and TensorFlow to analyze the database information. The generative AI model analyzes the data specifically to identify its weaknesses. The input is the training data in the database, and the output is information about the identified weaknesses.

[0331] Step 4:

[0332] The server generates an individualized learning plan based on the analysis results. This plan includes individual learning objectives and daily learning content for each learner, and the server uses an algorithm in the process of generating it. The input is information about areas of weakness, and the output is the individualized learning plan.

[0333] Step 5:

[0334] The server sends the generated lesson plan to the user's terminal. This information is also provided to the educational support robot to assist with instructions and feedback to learners. The input is the lesson plan, and the output is distribution to the user's terminal and the robot device.

[0335] Step 6:

[0336] Educational support robots provide learning guidance to users based on educational plans through voice output and display information. For example, the robot might say, "Let's practice math for 20 minutes." The input is the educational plan delivered to the robot, and the output is the learning instruction given to the user by the robot's actions.

[0337] Step 7:

[0338] Users learn based on educational plans provided through robots and terminals, and record their progress. The progress data is then sent back to the server and analyzed for the next learning cycle. The input is daily learning progress data, and the output is saved data used for the next analysis.

[0339] 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.

[0340] This invention provides an online learning support system that combines an emotion engine that recognizes user emotions with a system designed to enhance individualized learning support for students. This system integrates and analyzes learning data and emotion data to provide effective support in order to improve students' learning experience.

[0341] First, users register with the system via their terminal, entering basic information such as their name, grade level, and academic ability. This information is sent to the server and stored as a user profile. Daily learning data is also entered via the terminal. In addition, user emotional data is collected through voice and video analysis using microphones and cameras, or through user input. Emotional data includes, for example, stress levels and emotional responses during learning.

[0342] The server analyzes the collected training data and emotional data. The training data is analyzed using a generative AI model to identify the user's areas of weakness, while the emotional data is analyzed using an emotional engine to evaluate the user's current learning motivation and stress level.

[0343] The server generates a personalized learning plan. This plan is dynamically adjusted according to the user's academic ability and emotional state, with flexible learning content and schedule design. The plan is sent to the device, and the user uses it for their daily studies.

[0344] Furthermore, the server adjusts the difficulty of quizzes and customizes feedback based on emotional data. For example, if a user is experiencing high stress levels, it provides feedback such as advice on stress reduction and suggestions for learning methods that help manage stress. This feedback can be viewed on the device at any time.

[0345] Progress management is comprehensive, with the server monitoring learning progress and adjusting the plan based on the user's emotional state and learning progress. Users can also request real-time consultations regarding their learning via their device. During these consultations, the server ensures that the guidance reflects the user's emotional state.

[0346] For example, if a middle school student user experiences stress while solving math problems, the system detects this through its emotion engine. Based on this data, the server adjusts the math learning method and provides feedback such as suggestions for a more relaxing environment and mitigation measures. In this way, appropriate and flexible support is provided to improve the quality of the learning experience.

[0347] This format allows users to not only acquire knowledge but also receive comprehensive learning support that takes into account emotional and psychological aspects.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] Users access the system using their devices and complete initial registration. Users enter basic information, including their name, grade level, and academic ability, and send it to the server via their devices. The server creates a user profile based on the received information and stores it in a database.

[0351] Step 2:

[0352] Users input learning data and emotional data from their device for each learning session. Learning data includes subjects studied, time spent studying, and level of achievement. Emotional data is automatically acquired through facial recognition using the device's camera or voice tone analysis using the microphone, or it can be manually entered by the user.

[0353] Step 3:

[0354] The server analyzes the collected training data and uses a generative AI model to identify areas of weakness. Simultaneously, it uses an emotion engine to analyze emotional data and evaluate the user's stress level and motivation during the learning process.

[0355] Step 4:

[0356] The server generates a personalized learning plan based on identified areas of difficulty and an assessment of emotional data. This learning plan includes appropriate tasks, learning content, and a schedule, adjusted to take emotional state into account. This plan is sent to the terminal for the user to access.

[0357] Step 5:

[0358] The server automatically generates a short quiz, taking into account the user's emotional state. The content and difficulty of the quiz are adjusted according to the user's stress level. The user takes the quiz on their device and sends the results to the server.

[0359] Step 6:

[0360] The server analyzes the quiz results and sentiment data to generate feedback for the user. This feedback includes specific advice on the user's learning progress and areas for emotional improvement. The feedback is displayed on the device and can be reviewed by the user at any time.

[0361] Step 7:

[0362] The server continuously monitors the user's learning progress. If progress is not on schedule or emotional distress is detected, the learning plan is dynamically adjusted, and the user is notified via their device.

[0363] Step 8:

[0364] Users can request a real-time consultation from their device to the server as needed. In response to this request, the server coordinates with a professional instructor and arranges for the consultation to be conducted while taking the user's emotional state into consideration.

[0365] (Example 2)

[0366] 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".

[0367] Conventional online learning support systems typically rely solely on student learning data, making it difficult to consider emotions and mental states. Consequently, factors such as learner stress and decreased motivation are not adequately understood, and individual learning experiences are not fully utilized. This invention aims to overcome these challenges and provide comprehensive learning support that holistically considers the emotional state of learners.

[0368] 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.

[0369] In this invention, the server includes means for collecting basic information about students, means for collecting emotional data through voice / video analysis or user input, and means for comprehensively analyzing students' learning data and emotional data to identify areas of difficulty. This makes it possible to provide dynamic and personalized support that responds to the learner's emotions and learning progress.

[0370] "Student basic information" refers to data such as name, grade level, and academic ability level that is entered into the system to identify learners and personalize the learning process.

[0371] "Emotional data" refers to information, including stress levels and motivation indicators, obtained through audio / video analysis and subjective user input, in order to evaluate the learner's emotional state during learning.

[0372] "Learning data" refers to information that records the learning activities, results, and progress that learners engage in on a daily basis.

[0373] "Integrated analysis" refers to a process that combines students' learning data and emotional data for detailed analysis, comprehensively evaluating learners' areas of difficulty and emotional states.

[0374] A "weakness area" refers to a range of learning content that a learner finds particularly difficult to understand or where there is room for improvement.

[0375] An "individualized learning plan" is a proposal of learning content and schedules that are tailored to each individual learner's academic ability and emotional state, and are optimized for each person.

[0376] "Feedback" refers to evaluations and suggestions for improvement provided based on a learner's progress and emotional state.

[0377] "Real-time instruction" refers to appropriate educational support and advice based on the learner's current situation, provided in a way that learners can access immediately.

[0378] This invention is a system designed to efficiently support student learning, and it integrates functions for collecting and analyzing emotional data. The system operates using a server and terminals in cooperation.

[0379] The user first accesses the learning system through a terminal and enters basic information such as their name, grade level, and academic ability level. The terminal encrypts this information and sends it to the server. The server stores the received basic information in a database.

[0380] Next, users input daily learning data via their device. This learning data includes their learning progress and the content they worked on that day. The device can also collect emotional data through voice and video analysis using its microphone and camera hardware. Furthermore, users can optionally complete emotional questionnaires to report their stress levels and motivation.

[0381] The server analyzes the collected learning data using a generative AI model to identify the learner's areas of weakness. Simultaneously, it analyzes emotional data using an emotion engine to assess learning motivation and stress levels. Based on the analysis results, the server generates an individualized learning plan, dynamically adjusts it, and sends it to the terminal.

[0382] For example, if a middle school student user experiences stress while solving a math problem, the system analyzes this emotional data using an emotion engine. The server then sends feedback to alleviate stress, such as suggesting a relaxing environment, providing the user with the optimal learning experience. Examples of prompts include, "How stressed are you when solving math problems, [username]?" and "Which areas should you focus on in your current learning plan?"

[0383] Through this system, users can not only improve their academic performance but also receive learning support that takes their emotions into consideration.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] Users access the learning system through their device and enter basic information such as their name, grade level, and academic ability. This entered data is encrypted by the device and sent to the server. The server receives this information and stores it in a database as a user profile.

[0387] Step 2:

[0388] Users input daily learning data into their devices. This data includes the day's learning content and progress. This data is sent to the server as foundational data for evaluating the quality of learning. The server stores the received learning data and prepares it for later analysis.

[0389] Step 3:

[0390] The device uses a microphone and camera to collect audio and video, and acquires emotional data. This data includes stress levels and emotional changes during learning. Users also input subjective emotional data on an emotional questionnaire screen and send it to the server via the device.

[0391] Step 4:

[0392] The server analyzes the received training data using a generating AI model. The input for the analysis is the collected training data, and the output identifies the user's areas of weakness. In this process, the user's level of understanding and progress in each field is evaluated, and areas that need improvement are identified.

[0393] Step 5:

[0394] The server uses an emotion engine to analyze emotional data. Inputs include audio and video analysis, as well as emotional information provided by the user. Outputs include evaluations of learning motivation and stress levels. The server integrates this information to understand the user's emotional state.

[0395] Step 6:

[0396] The server generates a personalized learning plan based on identified areas of difficulty and emotional state. This plan is tailored to the user's learning goals and emotional state and sent to the device. The device displays the plan, allowing the user to utilize it in their daily learning.

[0397] Step 7:

[0398] Based on the emotional data it receives, the server provides feedback to the user. For example, if the user is experiencing high stress, the server will notify the device with relaxation techniques and stress reduction advice. This feedback helps to improve the user's learning experience.

[0399] (Application Example 2)

[0400] 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."

[0401] In elderly care settings, providing appropriate care tailored to each individual user's emotional and health condition is a significant burden for caregivers and makes efficient operation difficult. Furthermore, improving the quality of life for users requires a comprehensive understanding of their emotional and health conditions and individualized responses. However, this is difficult to achieve in real time using conventional methods.

[0402] 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.

[0403] In this invention, the server includes means for collecting user learning data and emotional data, means for analyzing the collected data to identify learning patterns and emotional states, and means for generating personalized learning and lifestyle improvement plans based on the identified information. This makes it possible to grasp the user's emotional and health status in real time and provide appropriate care accordingly.

[0404] "Users" are the people who use the system to collect and analyze emotional and learning data.

[0405] "Learning data" refers to data that includes information related to a user's daily activities, habits, and learning.

[0406] "Emotional data" refers to information that indicates the emotional state of a user, and is data obtained from the analysis of audio and video.

[0407] "Analysis" is the process of processing collected data to identify specific patterns or states.

[0408] A "learning pattern" is a specific pattern that indicates a user's behavior, characteristics, and tendencies in learning.

[0409] "Emotional state" refers to the user's psychological or emotional state, including stress levels and feelings of happiness.

[0410] An "individualized learning and lifestyle improvement plan" is a plan designed to optimize learning and lifestyle in accordance with the specific needs of each user, based on their learning patterns and emotional state.

[0411] "Real-time" refers to the temporal immediacy in which data collection, analysis, and responses are performed immediately.

[0412] The system for realizing this invention consists of a terminal used daily by the user and a server operating in the backend. The terminal is equipped with a camera and microphone to collect the user's learning data and emotional data. When the user registers with the system through the terminal and enters basic information, this information is sent to a server in the cloud and stored as the user's profile.

[0413] The server uses Google Cloud's Vision AI to analyze video data from the camera and detect the user's emotions. Audio data is also analyzed using IBM Watson Speech to Text to complementarily assess the emotional state. This data is processed by dedicated analysis software (implemented in Python) equipped with a generative AI model to identify learning patterns and emotional states.

[0414] Based on the identified information, the server uses TensorFlow to generate a learning and lifestyle improvement plan. This plan is dynamically adjusted to each user's needs and sent to the device in real time. Based on this information, users take appropriate care and lifestyle actions. For example, if a user feels stressed after exercise, the server can provide relaxing music or suggest simple exercises to change their mood.

[0415] An example of a prompt to the generating AI model is, "Based on the elderly person's emotional data and activity history, please propose a care plan that promotes relaxation." In this way, the system can provide highly accurate support tailored to the individual needs of the user, thereby improving their quality of life.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The terminal prompts the user for basic information such as their name, age, and medical history, and formats this information into an input format to be sent to the backend server. As output, the user's profile information is saved on the server.

[0419] Step 2:

[0420] The device uses a camera and microphone to collect the user's video and audio in real time and converts this into digital data. It then sends the real-time video and audio data to a server as output.

[0421] Step 3:

[0422] The server uses Google Cloud's Vision AI to analyze video data sent from the device. The input is video data, and the output is emotion data obtained through facial expression analysis.

[0423] Step 4:

[0424] The server uses IBM Watson Speech to Text to convert and analyze audio data into text format. The input is audio data, and the output is text data containing emotion keywords extracted from the audio.

[0425] Step 5:

[0426] The server inputs this emotion data and existing training data into a generating AI model to evaluate the learning patterns and emotional states. The input consists of emotion data and training data, and the output identifies the learning patterns and emotional states for each user.

[0427] Step 6:

[0428] The server uses TensorFlow to generate personalized learning and life improvement plans based on identified learning patterns and emotional states. It takes learning patterns and emotional states as input and sends the customized plan to the user's device as output.

[0429] Step 7:

[0430] The user takes appropriate lifestyle improvement actions based on the generated plan via their device. As output, they send new tracking data of their daily activities to the server to manage their progress.

[0431] Step 8:

[0432] If necessary, the user requests real-time consultation through their device, and the server uses prompt messages to execute a process of providing appropriate advice through a generated AI model. The input is the consultation content, and the output is a proposal of action based on individual guidance.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention provides an online learning support system for efficiently supporting students' learning. Specific embodiments thereof are described below.

[0450] First, users access the system using a terminal and register. During the registration process, they enter basic information such as their name, grade level, and current academic ability. This entered information is sent to the server and stored in the database as a user profile.

[0451] Next, the user enters their daily learning data into the device. This includes study time, subjects studied, test results, and so on. This data is uploaded to the server and stored there.

[0452] The server analyzes the accumulated learning data. Generative AI models are used for the analysis to identify areas where performance is particularly low or areas requiring improvement. Based on these results, the server generates a personalized learning plan for the user. This learning plan includes the content to be learned each day and progress goals, and is provided to the user via their device.

[0453] The server also uses an automated generation algorithm to create quizzes and send them to the user's device. The user takes the quiz and sends the results back to the server. The server analyzes these results and provides the user with feedback on areas for improvement and the next steps in their learning.

[0454] The server also manages progress, monitoring whether the user's learning is progressing according to plan. If any deficiencies or delays occur, the learning plan is dynamically adjusted, and the user is notified via their device.

[0455] Furthermore, this system enables real-time online consultations. Users can individually consult about their studies through their devices, and the server schedules these consultations and coordinates with expert instructors to provide necessary advice.

[0456] For example, if a high school student user has difficulty with English listening comprehension, the server will identify this data and create listening-specific workbooks and study plans. It will administer listening quizzes, provide feedback based on the user's performance, and suggest appropriate guidance to overcome their difficulties. In this way, appropriate learning support tailored to the individual needs of each student is provided.

[0457] The above describes specific embodiments of the present invention. This system allows students to enjoy flexible and effective learning support tailored to their individual learning needs.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The user accesses the system using a terminal and begins the registration process. They enter basic information such as their name, grade level, and current academic ability, and submit it to the server. This information is stored in the database as a user profile.

[0461] Step 2:

[0462] Users input their daily learning data into their devices. Information such as study time, subjects studied, and test results is recorded and uploaded to the server. The server stores this data in a database.

[0463] Step 3:

[0464] The server analyzes the accumulated learning data. A generative AI model is applied to identify areas where performance is declining in specific subjects or fields. The results obtained from the analysis are categorized as areas of weakness or areas requiring improvement.

[0465] Step 4:

[0466] The server generates a personalized learning plan based on identified areas of weakness. This learning plan includes daily learning objectives and progress targets. This learning plan is sent to the user's device for review.

[0467] Step 5:

[0468] The server uses an automated generation algorithm to create a quiz and sends the questions to the user's device. The user takes the quiz on their device and sends the results back to the server.

[0469] Step 6:

[0470] The server analyzes the results of the quiz submitted by the user. Based on the results, it provides the user with specific areas for improvement and feedback on the next learning steps. This feedback is displayed on the device.

[0471] Step 7:

[0472] The server continuously monitors the user's learning progress. If learning is not progressing as planned, it dynamically adjusts the learning plan and notifies the user via the terminal.

[0473] Step 8:

[0474] When a user requests a real-time online consultation, they send a notification from their device to the server. The server then schedules the consultation and works with a professional instructor to provide the user with appropriate advice.

[0475] This series of processes allows users to receive efficient and flexible learning support tailored to their individual learning needs.

[0476] (Example 1)

[0477] 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."

[0478] Traditional learning support systems only offer a uniform curriculum, making it difficult to accurately understand each student's academic abilities and areas of weakness, and to provide individualized learning support. Furthermore, they lacked the functionality to continuously monitor student progress and dynamically adjust learning plans as needed, sometimes preventing students from learning effectively.

[0479] 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.

[0480] In this invention, the server includes means for receiving basic student information and storing personal profiles, means for uploading daily learning data, and means for analyzing detailed learning data using a generative AI model to identify areas of academic ability that require improvement. This makes it possible to provide personalized learning support tailored to the characteristics of each student, appropriately manage progress, and improve learning efficiency.

[0481] A "student" is a person who receives education using a learning support system.

[0482] "Basic information" refers to information used to form a student's personal profile, such as name, grade level, and academic ability level.

[0483] A "personal profile" is aggregated data about a student stored on the server and is used to provide personalized services.

[0484] "Learning data" refers to information such as students' daily study time, subjects studied, and test results.

[0485] A "generative AI model" is a type of artificial intelligence that learns from large amounts of data and performs data analysis and prediction.

[0486] "Academic ability area" refers to a specific area related to learning subjects or skills.

[0487] An "individualized learning plan" is a learning plan and schedule tailored to each student's learning data and characteristics.

[0488] An "exam" is a test administered to measure the level of understanding and progress of learned material.

[0489] "Real-time consultations" are immediate learning consultations conducted through direct dialogue between students and instructors.

[0490] This invention is an online learning support system that effectively supports students' learning. Specific embodiments thereof are described below.

[0491] The server serves as the central hub of the learning support system, managing students' basic information and learning data. Users access the system through their terminals and input basic information such as their name, grade level, and academic ability. This information is sent from the terminal to the server, which receives it and stores it in a database as a personal profile.

[0492] Next, the user inputs daily learning data into the device. This data includes study time, subjects studied, and test results. The device uploads this data to the server. The server analyzes the received data using a generating AI model. This AI model is used to identify areas where performance is particularly low or where improvement is needed.

[0493] The server generates a personalized learning plan based on the analysis results. This plan includes the learning content and achievement goals that the student should focus on, and is provided to the user via the terminal. Prompt statements are used to instruct the generating AI model during the learning plan generation process. For example, a prompt such as, "Based on the current learning data, identify the areas where the user needs the most improvement and propose a personalized learning plan," might be used.

[0494] The server also automatically generates tests tailored to the user's academic area and sends them to the terminal. The user takes these tests and sends the results back to the server. The server analyzes the test results and provides feedback. This feedback includes specific areas for improvement, helping the user progress in their studies.

[0495] Furthermore, the server provides a means to enable real-time online consultations, allowing users to consult with expert instructors through their devices. For example, if a high school student struggles with English listening comprehension, the server can create listening-focused workbooks and study plans, and provide guidance to help them overcome their difficulties based on feedback.

[0496] As a result, students can receive effective and flexible learning support tailored to their individual academic abilities and learning styles.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] Users access the system using a terminal and enter basic information such as their name, grade level, and academic ability. The terminal sends this information to the server. The server analyzes the received data and stores it in a database as a personal profile. In this step, a new profile is output to the database from the basic information of the student that was entered.

[0500] Step 2:

[0501] Users input daily learning data, such as study time, subjects studied, and test results, into their devices. The devices periodically upload this information to a server. The server uses the received learning data to track individual academic progress and stores it in a database. In this step, student learning data is received as input and stored in the database as a learning history.

[0502] Step 3:

[0503] The server analyzes the accumulated training data using a generating AI model. Specifically, it identifies areas where performance is low based on past test results and study time. Using training data as input, it obtains a list of areas where performance is low or areas that need improvement as output. This analysis uses a prompt message that says, "Identify areas that need improvement based on specific study patterns."

[0504] Step 4:

[0505] The server generates an individualized learning plan based on the analysis results obtained by the generated AI model. The learning plan generation process outputs learning content tailored to the areas where each student needs improvement, and this content is provided to the user via their device. This plan includes specific learning content and achievement goals.

[0506] Step 5:

[0507] The server uses an automated generation algorithm to create a test tailored to the identified academic area and sends it to the terminal. The user takes the test and returns the answers to the server via the terminal. The server analyzes the test results and creates feedback including scores and accuracy rates. This information is provided to the user to guide further learning.

[0508] Step 6:

[0509] The server monitors the progress of the learning plan to ensure it is proceeding as planned. It dynamically adjusts the plan as needed and notifies the user's device. If any deficiencies or delays occur, the server outputs a revised learning plan.

[0510] Step 7:

[0511] When a user wishes to seek advice regarding their studies, they request an online consultation using their device. The server manages this and coordinates with expert instructors to provide real-time advice. In this step, effective learning advice is output from the user's consultation input.

[0512] (Application Example 1)

[0513] 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."

[0514] Traditional education systems have struggled to efficiently identify individual learning difficulties faced by learners and provide appropriate learning plans based on those difficulties. Furthermore, a lack of comprehensive educational support, such as real-time educational assistance and progress reports to parents, remains a challenge. Therefore, there is a need to realize educational support using robots to improve the efficiency of learning.

[0515] 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.

[0516] In this invention, the server includes means for collecting learner's educational data, means for analyzing the collected educational data and identifying areas of weakness, means for generating an individualized educational plan based on the identified areas of weakness, means for monitoring the progress of the generated educational plan, and means for providing educational support using a robot. This enables comprehensive support for the individual educational needs of learners.

[0517] "Learner educational data" refers to information that learners record in their daily studies, including study time, subjects, and test results.

[0518] A "weakness" refers to a specific learning area in which a learner performs poorly and needs improvement.

[0519] An "individualized learning plan" is a detailed learning schedule and content that is customized according to each learner's areas of difficulty and learning pace.

[0520] "Means of monitoring progress" refers to a process for regularly evaluating the learner's progress and confirming whether the learning is proceeding according to plan.

[0521] "Means of providing educational support using robots" refers to functions that utilize educational robots to provide learners with interactive learning experiences and present problems using voice.

[0522] "Assessment" refers to tests or quizzes administered to measure learners' learning outcomes.

[0523] A "feedback-providing device" is a system that informs learners of areas for improvement and future learning strategies based on the evaluation results.

[0524] A "device that provides real-time educational consultations" is an online consultation system that enables learners and educators to communicate directly and provide educational guidance.

[0525] A "device that automatically generates progress reports for parents" is a function that collects information on the learning progress and results of students and periodically generates reports for their guardians.

[0526] To implement this invention, a system is constructed to collect learner's educational data and identify areas of weakness. The server has a database to receive data transmitted from learners, and users input their daily learning content and test results via a terminal. The entered data is transmitted to the server via Wi-Fi or a wired network.

[0527] The server has a generative AI model built using the Python language and TensorFlow library, which analyzes the collected educational data. This analysis identifies areas where learners struggle and generates personalized learning plans based on the results. The learning plans include progress goals and what to learn each day.

[0528] Furthermore, to manage progress, the server continuously monitors the database and tracks the learners' educational progress. It also has the capability to update the educational plan in real time and notify the terminal if any deficiencies or delays occur.

[0529] The robot, equipped with a processor such as a Raspberry Pi, functions as an educational support device within the home. For example, the robot can read out pronunciation practice exercises or report learning progress to parents. This allows learners to overcome their weaknesses through repeated practice.

[0530] As a concrete example, the robot might suggest, "Today, let's practice math, which you're not good at, for 20 minutes," and then, using an AI model, it generates an optimal educational plan using a prompt such as, "What kind of daily learning plan would be effective in improving the student's math performance?"

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] Users access the system using individual terminals and input learning data. This data includes subjects studied, study time, and test results. This data is transmitted to the server via the network. The input is the user's learning data, and the output is the learning data stored on the server.

[0534] Step 2:

[0535] The server stores the received learning data in a database. The information stored in the database is used for subsequent data analysis and the creation of educational plans. This enables efficient data management. The input is the learning data sent to the server, and the output is the information stored in the database.

[0536] Step 3:

[0537] The server launches a generative AI model using Python and TensorFlow to analyze the database information. The generative AI model analyzes the data specifically to identify its weaknesses. The input is the training data in the database, and the output is information about the identified weaknesses.

[0538] Step 4:

[0539] The server generates an individualized learning plan based on the analysis results. This plan includes individual learning objectives and daily learning content for each learner, and the server uses an algorithm in the process of generating it. The input is information about areas of weakness, and the output is the individualized learning plan.

[0540] Step 5:

[0541] The server sends the generated lesson plan to the user's terminal. This information is also provided to the educational support robot to assist with instructions and feedback to learners. The input is the lesson plan, and the output is distribution to the user's terminal and the robot device.

[0542] Step 6:

[0543] Educational support robots provide learning guidance to users based on educational plans through voice output and display information. For example, the robot might say, "Let's practice math for 20 minutes." The input is the educational plan delivered to the robot, and the output is the learning instruction given to the user by the robot's actions.

[0544] Step 7:

[0545] Users learn based on educational plans provided through robots and terminals, and record their progress. The progress data is then sent back to the server and analyzed for the next learning cycle. The input is daily learning progress data, and the output is saved data used for the next analysis.

[0546] 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.

[0547] This invention provides an online learning support system that combines an emotion engine that recognizes user emotions with a system designed to enhance individualized learning support for students. This system integrates and analyzes learning data and emotion data to provide effective support in order to improve students' learning experience.

[0548] First, users register with the system via their terminal, entering basic information such as their name, grade level, and academic ability. This information is sent to the server and stored as a user profile. Daily learning data is also entered via the terminal. In addition, user emotional data is collected through voice and video analysis using microphones and cameras, or through user input. Emotional data includes, for example, stress levels and emotional responses during learning.

[0549] The server analyzes the collected training data and emotional data. The training data is analyzed using a generative AI model to identify the user's areas of weakness, while the emotional data is analyzed using an emotional engine to evaluate the user's current learning motivation and stress level.

[0550] The server generates a personalized learning plan. This plan is dynamically adjusted according to the user's academic ability and emotional state, with flexible learning content and schedule design. The plan is sent to the device, and the user uses it for their daily studies.

[0551] Furthermore, the server adjusts the difficulty of quizzes and customizes feedback based on emotional data. For example, if a user is experiencing high stress levels, it provides feedback such as advice on stress reduction and suggestions for learning methods that help manage stress. This feedback can be viewed on the device at any time.

[0552] Progress management is comprehensive, with the server monitoring learning progress and adjusting the plan based on the user's emotional state and learning progress. Users can also request real-time consultations regarding their learning via their device. During these consultations, the server ensures that the guidance reflects the user's emotional state.

[0553] For example, if a middle school student user experiences stress while solving math problems, the system detects this through its emotion engine. Based on this data, the server adjusts the math learning method and provides feedback such as suggestions for a more relaxing environment and mitigation measures. In this way, appropriate and flexible support is provided to improve the quality of the learning experience.

[0554] This format allows users to not only acquire knowledge but also receive comprehensive learning support that takes into account emotional and psychological aspects.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users access the system using their devices and complete initial registration. Users enter basic information, including their name, grade level, and academic ability, and send it to the server via their devices. The server creates a user profile based on the received information and stores it in a database.

[0558] Step 2:

[0559] Users input learning data and emotional data from their device for each learning session. Learning data includes subjects studied, time spent studying, and level of achievement. Emotional data is automatically acquired through facial recognition using the device's camera or voice tone analysis using the microphone, or it can be manually entered by the user.

[0560] Step 3:

[0561] The server analyzes the collected training data and uses a generative AI model to identify areas of weakness. Simultaneously, it uses an emotion engine to analyze emotional data and evaluate the user's stress level and motivation during the learning process.

[0562] Step 4:

[0563] The server generates a personalized learning plan based on identified areas of difficulty and an assessment of emotional data. This learning plan includes appropriate tasks, learning content, and a schedule, adjusted to take emotional state into account. This plan is sent to the terminal for the user to access.

[0564] Step 5:

[0565] The server automatically generates a short quiz, taking into account the user's emotional state. The content and difficulty of the quiz are adjusted according to the user's stress level. The user takes the quiz on their device and sends the results to the server.

[0566] Step 6:

[0567] The server analyzes the quiz results and sentiment data to generate feedback for the user. This feedback includes specific advice on the user's learning progress and areas for emotional improvement. The feedback is displayed on the device and can be reviewed by the user at any time.

[0568] Step 7:

[0569] The server continuously monitors the user's learning progress. If progress is not on schedule or emotional distress is detected, the learning plan is dynamically adjusted, and the user is notified via their device.

[0570] Step 8:

[0571] Users can request a real-time consultation from their device to the server as needed. In response to this request, the server coordinates with a professional instructor and arranges for the consultation to be conducted while taking the user's emotional state into consideration.

[0572] (Example 2)

[0573] 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."

[0574] Conventional online learning support systems typically rely solely on student learning data, making it difficult to consider emotions and mental states. Consequently, factors such as learner stress and decreased motivation are not adequately understood, and individual learning experiences are not fully utilized. This invention aims to overcome these challenges and provide comprehensive learning support that holistically considers the emotional state of learners.

[0575] 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.

[0576] In this invention, the server includes means for collecting basic information about students, means for collecting emotional data through voice / video analysis or user input, and means for comprehensively analyzing students' learning data and emotional data to identify areas of difficulty. This makes it possible to provide dynamic and personalized support that responds to the learner's emotions and learning progress.

[0577] "Student basic information" refers to data such as name, grade level, and academic ability level that is entered into the system to identify learners and personalize the learning process.

[0578] "Emotional data" refers to information, including stress levels and motivation indicators, obtained through audio / video analysis and subjective user input, in order to evaluate the learner's emotional state during learning.

[0579] "Learning data" refers to information that records the learning activities, results, and progress that learners engage in on a daily basis.

[0580] "Integrated analysis" refers to a process that combines students' learning data and emotional data for detailed analysis, comprehensively evaluating learners' areas of difficulty and emotional states.

[0581] A "weakness area" refers to a range of learning content that a learner finds particularly difficult to understand or where there is room for improvement.

[0582] An "individualized learning plan" is a proposal of learning content and schedules that are tailored to each individual learner's academic ability and emotional state, and are optimized for each person.

[0583] "Feedback" refers to evaluations and suggestions for improvement provided based on a learner's progress and emotional state.

[0584] "Real-time instruction" refers to appropriate educational support and advice based on the learner's current situation, provided in a way that learners can access immediately.

[0585] This invention is a system designed to efficiently support student learning, and it integrates functions for collecting and analyzing emotional data. The system operates using a server and terminals in cooperation.

[0586] The user first accesses the learning system through a terminal and enters basic information such as their name, grade level, and academic ability level. The terminal encrypts this information and sends it to the server. The server stores the received basic information in a database.

[0587] Next, users input daily learning data via their device. This learning data includes their learning progress and the content they worked on that day. The device can also collect emotional data through voice and video analysis using its microphone and camera hardware. Furthermore, users can optionally complete emotional questionnaires to report their stress levels and motivation.

[0588] The server analyzes the collected learning data using a generative AI model to identify the learner's areas of weakness. Simultaneously, it analyzes emotional data using an emotion engine to assess learning motivation and stress levels. Based on the analysis results, the server generates an individualized learning plan, dynamically adjusts it, and sends it to the terminal.

[0589] For example, if a middle school student user experiences stress while solving a math problem, the system analyzes this emotional data using an emotion engine. The server then sends feedback to alleviate stress, such as suggesting a relaxing environment, providing the user with the optimal learning experience. Examples of prompts include, "How stressed are you when solving math problems, [username]?" and "Which areas should you focus on in your current learning plan?"

[0590] Through this system, users can not only improve their academic performance but also receive learning support that takes their emotions into consideration.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] Users access the learning system through their device and enter basic information such as their name, grade level, and academic ability. This entered data is encrypted by the device and sent to the server. The server receives this information and stores it in a database as a user profile.

[0594] Step 2:

[0595] Users input daily learning data into their devices. This data includes the day's learning content and progress. This data is sent to the server as foundational data for evaluating the quality of learning. The server stores the received learning data and prepares it for later analysis.

[0596] Step 3:

[0597] The device uses a microphone and camera to collect audio and video, and acquires emotional data. This data includes stress levels and emotional changes during learning. Users also input subjective emotional data on an emotional questionnaire screen and send it to the server via the device.

[0598] Step 4:

[0599] The server analyzes the received training data using a generating AI model. The input for the analysis is the collected training data, and the output identifies the user's areas of weakness. In this process, the user's level of understanding and progress in each field is evaluated, and areas that need improvement are identified.

[0600] Step 5:

[0601] The server uses an emotion engine to analyze emotional data. Inputs include audio and video analysis, as well as emotional information provided by the user. Outputs include evaluations of learning motivation and stress levels. The server integrates this information to understand the user's emotional state.

[0602] Step 6:

[0603] The server generates a personalized learning plan based on identified areas of difficulty and emotional state. This plan is tailored to the user's learning goals and emotional state and sent to the device. The device displays the plan, allowing the user to utilize it in their daily learning.

[0604] Step 7:

[0605] Based on the emotional data it receives, the server provides feedback to the user. For example, if the user is experiencing high stress, the server will notify the device with relaxation techniques and stress reduction advice. This feedback helps to improve the user's learning experience.

[0606] (Application Example 2)

[0607] 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."

[0608] In elderly care settings, providing appropriate care tailored to each individual user's emotional and health condition is a significant burden for caregivers and makes efficient operation difficult. Furthermore, improving the quality of life for users requires a comprehensive understanding of their emotional and health conditions and individualized responses. However, this is difficult to achieve in real time using conventional methods.

[0609] 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.

[0610] In this invention, the server includes means for collecting user learning data and emotional data, means for analyzing the collected data to identify learning patterns and emotional states, and means for generating personalized learning and lifestyle improvement plans based on the identified information. This makes it possible to grasp the user's emotional and health status in real time and provide appropriate care accordingly.

[0611] "Users" are the people who use the system to collect and analyze emotional and learning data.

[0612] "Learning data" refers to data that includes information related to a user's daily activities, habits, and learning.

[0613] "Emotional data" refers to information that indicates the emotional state of a user, and is data obtained from the analysis of audio and video.

[0614] "Analysis" is the process of processing collected data to identify specific patterns or states.

[0615] A "learning pattern" is a specific pattern that indicates a user's behavior, characteristics, and tendencies in learning.

[0616] "Emotional state" refers to the user's psychological or emotional state, including stress levels and feelings of happiness.

[0617] An "individualized learning and lifestyle improvement plan" is a plan designed to optimize learning and lifestyle in accordance with the specific needs of each user, based on their learning patterns and emotional state.

[0618] "Real-time" refers to the temporal immediacy in which data collection, analysis, and responses are performed immediately.

[0619] The system for realizing this invention consists of a terminal used daily by the user and a server operating in the backend. The terminal is equipped with a camera and microphone to collect the user's learning data and emotional data. When the user registers with the system through the terminal and enters basic information, this information is sent to a server in the cloud and stored as the user's profile.

[0620] The server uses Google Cloud's Vision AI to analyze video data from the camera and detect the user's emotions. Audio data is also analyzed using IBM Watson Speech to Text to complementarily assess the emotional state. This data is processed by dedicated analysis software (implemented in Python) equipped with a generative AI model to identify learning patterns and emotional states.

[0621] Based on the identified information, the server uses TensorFlow to generate a learning and lifestyle improvement plan. This plan is dynamically adjusted to each user's needs and sent to the device in real time. Based on this information, users take appropriate care and lifestyle actions. For example, if a user feels stressed after exercise, the server can provide relaxing music or suggest simple exercises to change their mood.

[0622] An example of a prompt to the generating AI model is, "Based on the elderly person's emotional data and activity history, please propose a care plan that promotes relaxation." In this way, the system can provide highly accurate support tailored to the individual needs of the user, thereby improving their quality of life.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The terminal prompts the user for basic information such as their name, age, and medical history, and formats this information into an input format to be sent to the backend server. As output, the user's profile information is saved on the server.

[0626] Step 2:

[0627] The device uses a camera and microphone to collect the user's video and audio in real time and converts this into digital data. It then sends the real-time video and audio data to a server as output.

[0628] Step 3:

[0629] The server uses Google Cloud's Vision AI to analyze video data sent from the device. The input is video data, and the output is emotion data obtained through facial expression analysis.

[0630] Step 4:

[0631] The server uses IBM Watson Speech to Text to convert and analyze audio data into text format. The input is audio data, and the output is text data containing emotion keywords extracted from the audio.

[0632] Step 5:

[0633] The server inputs this emotion data and existing training data into a generating AI model to evaluate the learning patterns and emotional states. The input consists of emotion data and training data, and the output identifies the learning patterns and emotional states for each user.

[0634] Step 6:

[0635] The server uses TensorFlow to generate personalized learning and life improvement plans based on identified learning patterns and emotional states. It takes learning patterns and emotional states as input and sends the customized plan to the user's device as output.

[0636] Step 7:

[0637] The user takes appropriate lifestyle improvement actions based on the generated plan via their device. As output, they send new tracking data of their daily activities to the server to manage their progress.

[0638] Step 8:

[0639] If necessary, the user requests real-time consultation through their device, and the server uses prompt messages to execute a process of providing appropriate advice through a generated AI model. The input is the consultation content, and the output is a proposal of action based on individual guidance.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention provides an online learning support system for efficiently supporting students' learning. Specific embodiments thereof are described below.

[0658] First, users access the system using a terminal and register. During the registration process, they enter basic information such as their name, grade level, and current academic ability. This entered information is sent to the server and stored in the database as a user profile.

[0659] Next, the user enters their daily learning data into the device. This includes study time, subjects studied, test results, and so on. This data is uploaded to the server and stored there.

[0660] The server analyzes the accumulated learning data. Generative AI models are used for the analysis to identify areas where performance is particularly low or areas requiring improvement. Based on these results, the server generates a personalized learning plan for the user. This learning plan includes the content to be learned each day and progress goals, and is provided to the user via their device.

[0661] The server also uses an automated generation algorithm to create quizzes and send them to the user's device. The user takes the quiz and sends the results back to the server. The server analyzes these results and provides the user with feedback on areas for improvement and the next steps in their learning.

[0662] The server also manages progress, monitoring whether the user's learning is progressing according to plan. If any deficiencies or delays occur, the learning plan is dynamically adjusted, and the user is notified via their device.

[0663] Furthermore, this system enables real-time online consultations. Users can individually consult about their studies through their devices, and the server schedules these consultations and coordinates with expert instructors to provide necessary advice.

[0664] For example, if a high school student user has difficulty with English listening comprehension, the server will identify this data and create listening-specific workbooks and study plans. It will administer listening quizzes, provide feedback based on the user's performance, and suggest appropriate guidance to overcome their difficulties. In this way, appropriate learning support tailored to the individual needs of each student is provided.

[0665] The above describes specific embodiments of the present invention. This system allows students to enjoy flexible and effective learning support tailored to their individual learning needs.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The user accesses the system using a terminal and begins the registration process. They enter basic information such as their name, grade level, and current academic ability, and submit it to the server. This information is stored in the database as a user profile.

[0669] Step 2:

[0670] Users input their daily learning data into their devices. Information such as study time, subjects studied, and test results is recorded and uploaded to the server. The server stores this data in a database.

[0671] Step 3:

[0672] The server analyzes the accumulated learning data. A generative AI model is applied to identify areas where performance is declining in specific subjects or fields. The results obtained from the analysis are categorized as areas of weakness or areas requiring improvement.

[0673] Step 4:

[0674] The server generates a personalized learning plan based on identified areas of weakness. This learning plan includes daily learning objectives and progress targets. This learning plan is sent to the user's device for review.

[0675] Step 5:

[0676] The server uses an automated generation algorithm to create a quiz and sends the questions to the user's device. The user takes the quiz on their device and sends the results back to the server.

[0677] Step 6:

[0678] The server analyzes the results of the quiz submitted by the user. Based on the results, it provides the user with specific areas for improvement and feedback on the next learning steps. This feedback is displayed on the device.

[0679] Step 7:

[0680] The server continuously monitors the user's learning progress. If learning is not progressing as planned, it dynamically adjusts the learning plan and notifies the user via the terminal.

[0681] Step 8:

[0682] When a user requests a real-time online consultation, they send a notification from their device to the server. The server then schedules the consultation and works with a professional instructor to provide the user with appropriate advice.

[0683] This series of processes allows users to receive efficient and flexible learning support tailored to their individual learning needs.

[0684] (Example 1)

[0685] 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".

[0686] Traditional learning support systems only offer a uniform curriculum, making it difficult to accurately understand each student's academic abilities and areas of weakness, and to provide individualized learning support. Furthermore, they lacked the functionality to continuously monitor student progress and dynamically adjust learning plans as needed, sometimes preventing students from learning effectively.

[0687] 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.

[0688] In this invention, the server includes means for receiving basic student information and storing personal profiles, means for uploading daily learning data, and means for analyzing detailed learning data using a generative AI model to identify areas of academic ability that require improvement. This makes it possible to provide personalized learning support tailored to the characteristics of each student, appropriately manage progress, and improve learning efficiency.

[0689] A "student" is a person who receives education using a learning support system.

[0690] "Basic information" refers to information used to form a student's personal profile, such as name, grade level, and academic ability level.

[0691] A "personal profile" is aggregated data about a student stored on the server and is used to provide personalized services.

[0692] "Learning data" refers to information such as students' daily study time, subjects studied, and test results.

[0693] A "generative AI model" is a type of artificial intelligence that learns from large amounts of data and performs data analysis and prediction.

[0694] "Academic ability area" refers to a specific area related to learning subjects or skills.

[0695] An "individualized learning plan" is a learning plan and schedule tailored to each student's learning data and characteristics.

[0696] An "exam" is a test administered to measure the level of understanding and progress of learned material.

[0697] "Real-time consultations" are immediate learning consultations conducted through direct dialogue between students and instructors.

[0698] This invention is an online learning support system that effectively supports students' learning. Specific embodiments thereof are described below.

[0699] The server serves as the central hub of the learning support system, managing students' basic information and learning data. Users access the system through their terminals and input basic information such as their name, grade level, and academic ability. This information is sent from the terminal to the server, which receives it and stores it in a database as a personal profile.

[0700] Next, the user inputs daily learning data into the device. This data includes study time, subjects studied, and test results. The device uploads this data to the server. The server analyzes the received data using a generating AI model. This AI model is used to identify areas where performance is particularly low or where improvement is needed.

[0701] The server generates a personalized learning plan based on the analysis results. This plan includes the learning content and achievement goals that the student should focus on, and is provided to the user via the terminal. Prompt statements are used to instruct the generating AI model during the learning plan generation process. For example, a prompt such as, "Based on the current learning data, identify the areas where the user needs the most improvement and propose a personalized learning plan," might be used.

[0702] The server also automatically generates tests tailored to the user's academic area and sends them to the terminal. The user takes these tests and sends the results back to the server. The server analyzes the test results and provides feedback. This feedback includes specific areas for improvement, helping the user progress in their studies.

[0703] Furthermore, the server provides a means to enable real-time online consultations, allowing users to consult with expert instructors through their devices. For example, if a high school student struggles with English listening comprehension, the server can create listening-focused workbooks and study plans, and provide guidance to help them overcome their difficulties based on feedback.

[0704] As a result, students can receive effective and flexible learning support tailored to their individual academic abilities and learning styles.

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] Users access the system using a terminal and enter basic information such as their name, grade level, and academic ability. The terminal sends this information to the server. The server analyzes the received data and stores it in a database as a personal profile. In this step, a new profile is output to the database from the basic information of the student that was entered.

[0708] Step 2:

[0709] Users input daily learning data, such as study time, subjects studied, and test results, into their devices. The devices periodically upload this information to a server. The server uses the received learning data to track individual academic progress and stores it in a database. In this step, student learning data is received as input and stored in the database as a learning history.

[0710] Step 3:

[0711] The server analyzes the accumulated training data using a generating AI model. Specifically, it identifies areas where performance is low based on past test results and study time. Using training data as input, it obtains a list of areas where performance is low or areas that need improvement as output. This analysis uses a prompt message that says, "Identify areas that need improvement based on specific study patterns."

[0712] Step 4:

[0713] The server generates an individualized learning plan based on the analysis results obtained by the generated AI model. The learning plan generation process outputs learning content tailored to the areas where each student needs improvement, and this content is provided to the user via their device. This plan includes specific learning content and achievement goals.

[0714] Step 5:

[0715] The server uses an automated generation algorithm to create a test tailored to the identified academic area and sends it to the terminal. The user takes the test and returns the answers to the server via the terminal. The server analyzes the test results and creates feedback including scores and accuracy rates. This information is provided to the user to guide further learning.

[0716] Step 6:

[0717] The server monitors the progress of the learning plan to ensure it is proceeding as planned. It dynamically adjusts the plan as needed and notifies the user's device. If any deficiencies or delays occur, the server outputs a revised learning plan.

[0718] Step 7:

[0719] When a user wishes to seek advice regarding their studies, they request an online consultation using their device. The server manages this and coordinates with expert instructors to provide real-time advice. In this step, effective learning advice is output from the user's consultation input.

[0720] (Application Example 1)

[0721] 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".

[0722] Traditional education systems have struggled to efficiently identify individual learning difficulties faced by learners and provide appropriate learning plans based on those difficulties. Furthermore, a lack of comprehensive educational support, such as real-time educational assistance and progress reports to parents, remains a challenge. Therefore, there is a need to realize educational support using robots to improve the efficiency of learning.

[0723] 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.

[0724] In this invention, the server includes means for collecting learner's educational data, means for analyzing the collected educational data and identifying areas of weakness, means for generating an individualized educational plan based on the identified areas of weakness, means for monitoring the progress of the generated educational plan, and means for providing educational support using a robot. This enables comprehensive support for the individual educational needs of learners.

[0725] "Learner educational data" refers to information that learners record in their daily studies, including study time, subjects, and test results.

[0726] A "weakness" refers to a specific learning area in which a learner performs poorly and needs improvement.

[0727] An "individualized learning plan" is a detailed learning schedule and content that is customized according to each learner's areas of difficulty and learning pace.

[0728] "Means of monitoring progress" refers to a process for regularly evaluating the learner's progress and confirming whether the learning is proceeding according to plan.

[0729] "Means of providing educational support using robots" refers to functions that utilize educational robots to provide learners with interactive learning experiences and present problems using voice.

[0730] "Assessment" refers to tests or quizzes administered to measure learners' learning outcomes.

[0731] A "feedback-providing device" is a system that informs learners of areas for improvement and future learning strategies based on the evaluation results.

[0732] A "device that provides real-time educational consultations" is an online consultation system that enables learners and educators to communicate directly and provide educational guidance.

[0733] A "device that automatically generates progress reports for parents" is a function that collects information on the learning progress and results of students and periodically generates reports for their guardians.

[0734] To implement this invention, a system is constructed to collect learner's educational data and identify areas of weakness. The server has a database to receive data transmitted from learners, and users input their daily learning content and test results via a terminal. The entered data is transmitted to the server via Wi-Fi or a wired network.

[0735] The server has a generative AI model built using the Python language and TensorFlow library, which analyzes the collected educational data. This analysis identifies areas where learners struggle and generates personalized learning plans based on the results. The learning plans include progress goals and what to learn each day.

[0736] Furthermore, to manage progress, the server continuously monitors the database and tracks the learners' educational progress. It also has the capability to update the educational plan in real time and notify the terminal if any deficiencies or delays occur.

[0737] The robot, equipped with a processor such as a Raspberry Pi, functions as an educational support device within the home. For example, the robot can read out pronunciation practice exercises or report learning progress to parents. This allows learners to overcome their weaknesses through repeated practice.

[0738] As a concrete example, the robot might suggest, "Today, let's practice math, which you're not good at, for 20 minutes," and then, using an AI model, it generates an optimal educational plan using a prompt such as, "What kind of daily learning plan would be effective in improving the student's math performance?"

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] Users access the system using individual terminals and input learning data. This data includes subjects studied, study time, and test results. This data is transmitted to the server via the network. The input is the user's learning data, and the output is the learning data stored on the server.

[0742] Step 2:

[0743] The server stores the received learning data in a database. The information stored in the database is used for subsequent data analysis and the creation of educational plans. This enables efficient data management. The input is the learning data sent to the server, and the output is the information stored in the database.

[0744] Step 3:

[0745] The server launches a generative AI model using Python and TensorFlow to analyze the database information. The generative AI model analyzes the data specifically to identify its weaknesses. The input is the training data in the database, and the output is information about the identified weaknesses.

[0746] Step 4:

[0747] The server generates an individualized learning plan based on the analysis results. This plan includes individual learning objectives and daily learning content for each learner, and the server uses an algorithm in the process of generating it. The input is information about areas of weakness, and the output is the individualized learning plan.

[0748] Step 5:

[0749] The server sends the generated lesson plan to the user's terminal. This information is also provided to the educational support robot to assist with instructions and feedback to learners. The input is the lesson plan, and the output is distribution to the user's terminal and the robot device.

[0750] Step 6:

[0751] Educational support robots provide learning guidance to users based on educational plans through voice output and display information. For example, the robot might say, "Let's practice math for 20 minutes." The input is the educational plan delivered to the robot, and the output is the learning instruction given to the user by the robot's actions.

[0752] Step 7:

[0753] Users learn based on educational plans provided through robots and terminals, and record their progress. The progress data is then sent back to the server and analyzed for the next learning cycle. The input is daily learning progress data, and the output is saved data used for the next analysis.

[0754] 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.

[0755] This invention provides an online learning support system that combines an emotion engine that recognizes user emotions with a system designed to enhance individualized learning support for students. This system integrates and analyzes learning data and emotion data to provide effective support in order to improve students' learning experience.

[0756] First, users register with the system via their terminal, entering basic information such as their name, grade level, and academic ability. This information is sent to the server and stored as a user profile. Daily learning data is also entered via the terminal. In addition, user emotional data is collected through voice and video analysis using microphones and cameras, or through user input. Emotional data includes, for example, stress levels and emotional responses during learning.

[0757] The server analyzes the collected training data and emotional data. The training data is analyzed using a generative AI model to identify the user's areas of weakness, while the emotional data is analyzed using an emotional engine to evaluate the user's current learning motivation and stress level.

[0758] The server generates a personalized learning plan. This plan is dynamically adjusted according to the user's academic ability and emotional state, with flexible learning content and schedule design. The plan is sent to the device, and the user uses it for their daily studies.

[0759] Furthermore, the server adjusts the difficulty of quizzes and customizes feedback based on emotional data. For example, if a user is experiencing high stress levels, it provides feedback such as advice on stress reduction and suggestions for learning methods that help manage stress. This feedback can be viewed on the device at any time.

[0760] Progress management is comprehensive, with the server monitoring learning progress and adjusting the plan based on the user's emotional state and learning progress. Users can also request real-time consultations regarding their learning via their device. During these consultations, the server ensures that the guidance reflects the user's emotional state.

[0761] For example, if a middle school student user experiences stress while solving math problems, the system detects this through its emotion engine. Based on this data, the server adjusts the math learning method and provides feedback such as suggestions for a more relaxing environment and mitigation measures. In this way, appropriate and flexible support is provided to improve the quality of the learning experience.

[0762] This format allows users to not only acquire knowledge but also receive comprehensive learning support that takes into account emotional and psychological aspects.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] Users access the system using their devices and complete initial registration. Users enter basic information, including their name, grade level, and academic ability, and send it to the server via their devices. The server creates a user profile based on the received information and stores it in a database.

[0766] Step 2:

[0767] Users input learning data and emotional data from their device for each learning session. Learning data includes subjects studied, time spent studying, and level of achievement. Emotional data is automatically acquired through facial recognition using the device's camera or voice tone analysis using the microphone, or it can be manually entered by the user.

[0768] Step 3:

[0769] The server analyzes the collected training data and uses a generative AI model to identify areas of weakness. Simultaneously, it uses an emotion engine to analyze emotional data and evaluate the user's stress level and motivation during the learning process.

[0770] Step 4:

[0771] The server generates a personalized learning plan based on identified areas of difficulty and an assessment of emotional data. This learning plan includes appropriate tasks, learning content, and a schedule, adjusted to take emotional state into account. This plan is sent to the terminal for the user to access.

[0772] Step 5:

[0773] The server automatically generates a short quiz, taking into account the user's emotional state. The content and difficulty of the quiz are adjusted according to the user's stress level. The user takes the quiz on their device and sends the results to the server.

[0774] Step 6:

[0775] The server analyzes the quiz results and sentiment data to generate feedback for the user. This feedback includes specific advice on the user's learning progress and areas for emotional improvement. The feedback is displayed on the device and can be reviewed by the user at any time.

[0776] Step 7:

[0777] The server continuously monitors the user's learning progress. If progress is not on schedule or emotional distress is detected, the learning plan is dynamically adjusted, and the user is notified via their device.

[0778] Step 8:

[0779] Users can request a real-time consultation from their device to the server as needed. In response to this request, the server coordinates with a professional instructor and arranges for the consultation to be conducted while taking the user's emotional state into consideration.

[0780] (Example 2)

[0781] 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".

[0782] Conventional online learning support systems typically rely solely on student learning data, making it difficult to consider emotions and mental states. Consequently, factors such as learner stress and decreased motivation are not adequately understood, and individual learning experiences are not fully utilized. This invention aims to overcome these challenges and provide comprehensive learning support that holistically considers the emotional state of learners.

[0783] 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.

[0784] In this invention, the server includes means for collecting basic information about students, means for collecting emotional data through voice / video analysis or user input, and means for comprehensively analyzing students' learning data and emotional data to identify areas of difficulty. This makes it possible to provide dynamic and personalized support that responds to the learner's emotions and learning progress.

[0785] "Student basic information" refers to data such as name, grade level, and academic ability level that is entered into the system to identify learners and personalize the learning process.

[0786] "Emotional data" refers to information, including stress levels and motivation indicators, obtained through audio / video analysis and subjective user input, in order to evaluate the learner's emotional state during learning.

[0787] "Learning data" refers to information that records the learning activities, results, and progress that learners engage in on a daily basis.

[0788] "Integrated analysis" refers to a process that combines students' learning data and emotional data for detailed analysis, comprehensively evaluating learners' areas of difficulty and emotional states.

[0789] A "weakness area" refers to a range of learning content that a learner finds particularly difficult to understand or where there is room for improvement.

[0790] An "individualized learning plan" is a proposal of learning content and schedules that are tailored to each individual learner's academic ability and emotional state, and are optimized for each person.

[0791] "Feedback" refers to evaluations and suggestions for improvement provided based on a learner's progress and emotional state.

[0792] "Real-time instruction" refers to appropriate educational support and advice based on the learner's current situation, provided in a way that learners can access immediately.

[0793] This invention is a system designed to efficiently support student learning, and it integrates functions for collecting and analyzing emotional data. The system operates using a server and terminals in cooperation.

[0794] The user first accesses the learning system through a terminal and enters basic information such as their name, grade level, and academic ability level. The terminal encrypts this information and sends it to the server. The server stores the received basic information in a database.

[0795] Next, users input daily learning data via their device. This learning data includes their learning progress and the content they worked on that day. The device can also collect emotional data through voice and video analysis using its microphone and camera hardware. Furthermore, users can optionally complete emotional questionnaires to report their stress levels and motivation.

[0796] The server analyzes the collected learning data using a generative AI model to identify the learner's areas of weakness. Simultaneously, it analyzes emotional data using an emotion engine to assess learning motivation and stress levels. Based on the analysis results, the server generates an individualized learning plan, dynamically adjusts it, and sends it to the terminal.

[0797] For example, if a middle school student user experiences stress while solving a math problem, the system analyzes this emotional data using an emotion engine. The server then sends feedback to alleviate stress, such as suggesting a relaxing environment, providing the user with the optimal learning experience. Examples of prompts include, "How stressed are you when solving math problems, [username]?" and "Which areas should you focus on in your current learning plan?"

[0798] Through this system, users can not only improve their academic performance but also receive learning support that takes their emotions into consideration.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] Users access the learning system through their device and enter basic information such as their name, grade level, and academic ability. This entered data is encrypted by the device and sent to the server. The server receives this information and stores it in a database as a user profile.

[0802] Step 2:

[0803] Users input daily learning data into their devices. This data includes the day's learning content and progress. This data is sent to the server as foundational data for evaluating the quality of learning. The server stores the received learning data and prepares it for later analysis.

[0804] Step 3:

[0805] The device uses a microphone and camera to collect audio and video, and acquires emotional data. This data includes stress levels and emotional changes during learning. Users also input subjective emotional data on an emotional questionnaire screen and send it to the server via the device.

[0806] Step 4:

[0807] The server analyzes the received training data using a generating AI model. The input for the analysis is the collected training data, and the output identifies the user's areas of weakness. In this process, the user's level of understanding and progress in each field is evaluated, and areas that need improvement are identified.

[0808] Step 5:

[0809] The server uses an emotion engine to analyze emotional data. Inputs include audio and video analysis, as well as emotional information provided by the user. Outputs include evaluations of learning motivation and stress levels. The server integrates this information to understand the user's emotional state.

[0810] Step 6:

[0811] The server generates a personalized learning plan based on identified areas of difficulty and emotional state. This plan is tailored to the user's learning goals and emotional state and sent to the device. The device displays the plan, allowing the user to utilize it in their daily learning.

[0812] Step 7:

[0813] Based on the emotional data it receives, the server provides feedback to the user. For example, if the user is experiencing high stress, the server will notify the device with relaxation techniques and stress reduction advice. This feedback helps to improve the user's learning experience.

[0814] (Application Example 2)

[0815] 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".

[0816] In elderly care settings, providing appropriate care tailored to each individual user's emotional and health condition is a significant burden for caregivers and makes efficient operation difficult. Furthermore, improving the quality of life for users requires a comprehensive understanding of their emotional and health conditions and individualized responses. However, this is difficult to achieve in real time using conventional methods.

[0817] 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.

[0818] In this invention, the server includes means for collecting user learning data and emotional data, means for analyzing the collected data to identify learning patterns and emotional states, and means for generating personalized learning and lifestyle improvement plans based on the identified information. This makes it possible to grasp the user's emotional and health status in real time and provide appropriate care accordingly.

[0819] "Users" are the people who use the system to collect and analyze emotional and learning data.

[0820] "Learning data" refers to data that includes information related to a user's daily activities, habits, and learning.

[0821] "Emotional data" refers to information that indicates the emotional state of a user, and is data obtained from the analysis of audio and video.

[0822] "Analysis" is the process of processing collected data to identify specific patterns or states.

[0823] A "learning pattern" is a specific pattern that indicates a user's behavior, characteristics, and tendencies in learning.

[0824] "Emotional state" refers to the user's psychological or emotional state, including stress levels and feelings of happiness.

[0825] An "individualized learning and lifestyle improvement plan" is a plan designed to optimize learning and lifestyle in accordance with the specific needs of each user, based on their learning patterns and emotional state.

[0826] "Real-time" refers to the temporal immediacy in which data collection, analysis, and responses are performed immediately.

[0827] The system for realizing this invention consists of a terminal used daily by the user and a server operating in the backend. The terminal is equipped with a camera and microphone to collect the user's learning data and emotional data. When the user registers with the system through the terminal and enters basic information, this information is sent to a server in the cloud and stored as the user's profile.

[0828] The server uses Google Cloud's Vision AI to analyze video data from the camera and detect the user's emotions. Audio data is also analyzed using IBM Watson Speech to Text to complementarily assess the emotional state. This data is processed by dedicated analysis software (implemented in Python) equipped with a generative AI model to identify learning patterns and emotional states.

[0829] Based on the identified information, the server uses TensorFlow to generate a learning and lifestyle improvement plan. This plan is dynamically adjusted to each user's needs and sent to the device in real time. Based on this information, users take appropriate care and lifestyle actions. For example, if a user feels stressed after exercise, the server can provide relaxing music or suggest simple exercises to change their mood.

[0830] An example of a prompt to the generating AI model is, "Based on the elderly person's emotional data and activity history, please propose a care plan that promotes relaxation." In this way, the system can provide highly accurate support tailored to the individual needs of the user, thereby improving their quality of life.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] The terminal prompts the user for basic information such as their name, age, and medical history, and formats this information into an input format to be sent to the backend server. As output, the user's profile information is saved on the server.

[0834] Step 2:

[0835] The device uses a camera and microphone to collect the user's video and audio in real time and converts this into digital data. It then sends the real-time video and audio data to a server as output.

[0836] Step 3:

[0837] The server uses Google Cloud's Vision AI to analyze video data sent from the device. The input is video data, and the output is emotion data obtained through facial expression analysis.

[0838] Step 4:

[0839] The server uses IBM Watson Speech to Text to convert and analyze audio data into text format. The input is audio data, and the output is text data containing emotion keywords extracted from the audio.

[0840] Step 5:

[0841] The server inputs this emotion data and existing training data into a generating AI model to evaluate the learning patterns and emotional states. The input consists of emotion data and training data, and the output identifies the learning patterns and emotional states for each user.

[0842] Step 6:

[0843] The server uses TensorFlow to generate personalized learning and life improvement plans based on identified learning patterns and emotional states. It takes learning patterns and emotional states as input and sends the customized plan to the user's device as output.

[0844] Step 7:

[0845] The user takes appropriate lifestyle improvement actions based on the generated plan via their device. As output, they send new tracking data of their daily activities to the server to manage their progress.

[0846] Step 8:

[0847] If necessary, the user requests real-time consultation through their device, and the server uses prompt messages to execute a process of providing appropriate advice through a generated AI model. The input is the consultation content, and the output is a proposal of action based on individual guidance.

[0848] 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.

[0849] 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.

[0850] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] Means for collecting student learning data,

[0872] A means for analyzing the collected learning data and identifying areas of weakness,

[0873] A means for generating an individualized learning plan based on identified areas of weakness,

[0874] A means for managing the progress of the generated learning plan,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] A method for automatically generating tests that address areas of weakness,

[0878] A means for analyzing the results of the generated tests and providing feedback,

[0879] The system according to claim 1, including the following:

[0880] (Claim 3)

[0881] The system according to claim 1, comprising means for providing real-time consultations regarding learning.

[0882] "Example 1"

[0883] (Claim 1)

[0884] A means of receiving basic student information and saving personal profiles,

[0885] A means of uploading daily learning data,

[0886] A method for analyzing detailed learning data using a generative AI model to identify areas of academic ability that need improvement,

[0887] A means for generating an individualized learning plan based on identified academic areas,

[0888] A means for sending the generated learning plan to a terminal and managing its progress,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] A means for automatically generating tests corresponding to identified academic areas and sending them to a terminal,

[0892] A means of analyzing the results of the generated tests and providing specific feedback,

[0893] The system according to claim 1, including the following:

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for scheduling learning-related meetings with expert instructors in real time.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] A device for collecting learner education data,

[0899] A device that analyzes collected educational data and identifies areas of weakness,

[0900] A device that generates an individualized educational plan based on identified areas of weakness,

[0901] A device for monitoring the progress of the generated educational plan,

[0902] A device that provides educational support using robots,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] A device that automatically generates assessments corresponding to areas of weakness,

[0906] A device that analyzes the generated evaluation results and provides feedback,

[0907] A device that performs pronunciation checks and reads out problems via an educational support robot,

[0908] The system according to claim 1, including the following:

[0909] (Claim 3)

[0910] A device that provides real-time consultations regarding education,

[0911] The system according to claim 1, comprising a device for automatically generating progress reports for parents.

[0912] "Example 2 of combining an emotion engine"

[0913] (Claim 1)

[0914] Means of collecting basic information about students,

[0915] A means for storing the collected basic information in a database,

[0916] A means of collecting emotional data through audio / video analysis or user input,

[0917] A means for analyzing the collected emotional data and evaluating learning motivation and stress levels,

[0918] A method for identifying areas of weakness by comprehensively analyzing students' learning data and emotional data,

[0919] A means for generating an individualized learning plan based on identified areas of weakness and emotional states,

[0920] A means for dynamically adjusting the generated learning plan and managing its progress,

[0921] A means of adjusting the difficulty of a test based on emotional data,

[0922] A means of providing feedback that responds to emotions,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, which automatically generates tests corresponding to areas of weakness and provides feedback that takes into account the user's emotional state.

[0926] (Claim 3)

[0927] The system according to claim 1, which provides real-time instruction or consultations tailored based on emotional data and learning progress.

[0928] "Application example 2 when combining with an emotional engine"

[0929] (Claim 1)

[0930] A means of collecting user learning data and emotional data,

[0931] A means for analyzing the collected data and identifying learning patterns and emotional states,

[0932] A means for generating personalized learning and lifestyle improvement plans based on identified information,

[0933] Means for managing the progress of the generated plan and dynamically adjusting it according to emotional state,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] A means for automatically generating assessment activities that correspond to learning and emotional states,

[0937] A means for analyzing the results of the generated evaluation activities and providing customized feedback,

[0938] The system according to claim 1, including the following:

[0939] (Claim 3)

[0940] The system according to claim 1, comprising means for providing real-time consultation regarding learning and living conditions. [Explanation of symbols]

[0941] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for collecting learner education data, A device that analyzes collected educational data and identifies areas of weakness, A device that generates an individualized educational plan based on identified areas of weakness, A device for monitoring the progress of the generated educational plan, A device that provides educational support using robots, A system that includes this.

2. A device that automatically generates assessments corresponding to areas of weakness, A device that analyzes the generated evaluation results and provides feedback, A device that performs pronunciation checks and reads out problems via an educational support robot, The system according to claim 1, including the following:

3. A device that provides real-time consultations regarding education, The system according to claim 1, comprising a device for automatically generating progress reports for parents.

Images