system

Abstract

We provide the system. [Solution] A means of generating an educational machine learning model by collecting educational data and training it using natural language processing, A recommendation system that analyzes the user's learning profile to select appropriate educational content, A means of acquiring user learning progress data in real time and adaptively adjusting the learning program based on that data, A means of monitoring the user's level of concentration while learning, using a camera or sensor, A means of providing suggestions to encourage breaks and refocus based on concentration level data, 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, with the aging of the population and the evolution of technology, the demand for lifelong learning is increasing. However, it is difficult for existing educational systems to flexibly meet the needs of individual learners, and as a result, learning efficiency may decrease. In addition, in online education, selecting appropriate courses and maintaining learners' attention are also issues. There is a need to provide a system that solves these problems and enables learners to continue learning more effectively and efficiently.

Means for Solving the Problems

[0005] This invention provides an educational system that utilizes artificial intelligence. Specifically, it includes means for having AI take on the role of an instructor by collecting educational material data and generating an educational machine learning model using natural language processing technology. It also includes means for analyzing the user's learning profile and recommending appropriate educational content. Furthermore, it acquires the user's progress data in real time and adaptively adjusts the learning program to provide an educational experience optimized for each individual's learning pace. In addition, it monitors the user's level of concentration using cameras and sensors and alerts them to the need for breaks based on that data, enabling more focused learning. This realizes a flexible learning environment that meets the individual needs of learners.

[0006] "Educational material data" refers to a collection of materials such as text, images, videos, and audio used for educational purposes.

[0007] "Natural language processing" is a technology that enables computers to understand, generate, and process human language.

[0008] A "machine learning model" is a system based on algorithms that analyze data and recognize patterns, and has the ability to make predictions and decisions about new data.

[0009] A "learning profile" is a collection of information that gathers information about an individual learner's past learning history, interests, and learning goals.

[0010] "Educational content" refers to teaching materials and learning resources designed based on specific educational objectives.

[0011] "Recommendation methods" refer to technologies and algorithms that guide users to the most appropriate options based on their needs and characteristics.

[0012] "Progress data" refers to information that shows how far a learner has progressed in their studies, and includes things like the time taken and the completion status of assignments.

[0013] "Adaptively adjusting" means changing the content of a system or service in accordance with individual needs and conditions.

[0014] "Concentration level" is an indicator of how much attention a learner is paying, and it is measured by facial expressions, eye movements, and other factors.

[0015] An "alert" is a notification or warning sent to draw attention to a specific situation or condition. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. [[ID=二十七]]

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to an educational system that supports lifelong learning by utilizing artificial intelligence and machine learning technologies. The system aims to provide a customized learning experience tailored to each individual user, and is implemented in the following specific form.

[0038] Generation of instructor AI models:

[0039] The server first collects educational data provided by educational institutions and partner scholars. This data includes text, audio clips, and video lectures. The server uses natural language processing techniques with this data to train educational machine learning models. The trained models can convey knowledge to users in a conversational format similar to that of a human instructor. For example, in a "Data Analysis Fundamentals" course, the AI ​​clearly explains terminology definitions and provides examples of analytical methods.

[0040] Automated matching for online courses:

[0041] Users enter their learning profile via their device, sending their areas of interest and goals to the server. The server analyzes this information and generates a list of optimal courses based on past learning history and market trend data. Recommended courses are presented in the user interface, allowing users to select a learning plan that suits their pace. For example, if a user is interested in marketing, customized courses ranging from basic to advanced levels will be suggested.

[0042] Adaptive learning systems:

[0043] The device tracks the user's progress in real time and sends the data to the server. The server evaluates the received data and suggests appropriate content and assignments tailored to each user's learning stage. This adaptive system can accommodate learners with unique strengths and weaknesses, adjusting the curriculum based on their learning speed and comprehension. For example, users struggling with a particular assignment may be provided with additional support materials or practice problems.

[0044] Concentration level monitoring and alerts:

[0045] During learning, the device uses the user's webcam and sensors to measure their level of concentration in real time. This involves using facial recognition technology to analyze expressions and gaze. The server collects this data and automatically sends a notification to the user when it detects a decline in concentration. By displaying a pop-up message recommending a break, the user is encouraged to take action to improve their learning efficiency. This system makes it easier to maintain concentration even during long learning sessions.

[0046] As described above, the system provided is designed to effectively support users' learning habits and create a learning environment that suits individual needs and pace. This aims to improve the efficiency and quality of lifelong learning.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server retrieves educational material data provided by educational institutions and stores it in a database. This includes learning materials in various formats, such as text files, video clips, and audio data.

[0050] Step 2:

[0051] The server executes natural language processing algorithms and trains an instructor AI model using the acquired teaching material data. This process includes phases of data preprocessing, tokenization, and model training.

[0052] Step 3:

[0053] Users enter their personal learning profiles using their devices, clearly indicating their interests and goals. This includes the fields of study they wish to take and their preferred learning pace.

[0054] Step 4:

[0055] The server analyzes the profile data submitted by the user and compares it with the existing course database to recommend the most suitable courses. These recommendations are refined using machine learning and displayed as a personalized list for the user.

[0056] Step 5:

[0057] The device monitors the user's learning progress in real time and periodically sends this data to the server. This includes assignment completion status, accuracy rate, and study time.

[0058] Step 6:

[0059] The server analyzes the received progress data and adjusts the difficulty level or provides additional learning materials according to the user's needs. This optimizes the user's learning experience.

[0060] Step 7:

[0061] The device measures the user's level of concentration using a webcam during learning and sends data on facial expressions and eye movements to a server. This uses an algorithm to quantify attention.

[0062] Step 8:

[0063] The server analyzes concentration data and displays an alert on the device prompting the user to take a break if their attention is waning. This allows the user to continue learning efficiently.

[0064] (Example 1)

[0065] 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."

[0066] In lifelong learning, there is a need to provide learning materials and learning formats that are suitable for individual learners, enabling efficient and sustainable learning. Furthermore, a mechanism is needed to appropriately manage concentration levels during learning and prevent a decline in efficiency during long study sessions. Traditional education systems have struggled to address individual needs, making it difficult for learners to create a learning environment that is optimal for their own learning style.

[0067] 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.

[0068] In this invention, the server includes means for collecting information and generating a learning model using natural language processing; means for analyzing an individual's learning profile and selecting appropriate educational content for recommendation; means for acquiring an individual's learning progress data in real time and adaptively adjusting the learning content based on that data; means for monitoring an individual's level of concentration during learning using a camera or sensing device; and means for issuing instructions to encourage breaks or refocusing based on the level of concentration data. This provides an optimized learning environment for each individual learner, enabling efficient and sustainable learning.

[0069] "Information gathering" is the process of obtaining necessary information from data providers and using it for processing within the system.

[0070] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0071] "Generating a learning model" means using machine learning based on acquired data to build an algorithmic model that is useful for a specific educational purpose.

[0072] A "personal learning profile" is a dataset that includes personal information such as the learner's interests, goals, and history.

[0073] "Recommendation for selecting educational content" is the process of identifying the educational resources and curricula that are best suited to individual learners.

[0074] "Learning progress data" refers to information that shows the tasks a learner has completed and the knowledge they have acquired to date.

[0075] "Adaptive adjustment" means dynamically changing the learning content and pace according to the progress and abilities of individual learners.

[0076] "Recording device or sensing device" refers to data acquisition hardware such as cameras and sensors that record or sense user activity.

[0077] "Monitoring concentration levels" means measuring and evaluating the learner's level of concentration in real time.

[0078] "Instructions to encourage breaks or refocus" refer to providing advice or action suggestions to help learners regain their focus when they lose concentration.

[0079] This invention is an advanced educational system that supports lifelong learning, providing personalized learning experiences by utilizing artificial intelligence and machine learning technologies. Its embodiments are described in detail below.

[0080] The server first collects information. Specifically, it collects educational resources such as text, audio clips, and videos provided by educational institutions and researchers. The server analyzes this data using natural language processing techniques. Specifically, it performs text analysis using Python's natural language processing libraries, NLTK and spaCy. Furthermore, the server generates and trains educational learning models using machine learning frameworks such as TENSORFLOW® and PyTorch. These models have the ability to provide appropriate learning materials to individual learners.

[0081] Users enter their learning profile through their device, which includes PCs and tablets. Users specify their learning goals and areas of interest and send this information to the server. The server analyzes the received information and creates a personal profile of the user. Based on this profile, the server provides personalized learning content.

[0082] The device tracks the user's progress in real time during learning. This data is sent to a server and used to adjust the learning plan adaptively. For example, supplementary materials are automatically provided to users who are falling behind. Furthermore, the user's concentration level is also monitored, and if their concentration wavers, the device prompts the user to take a break or refocus. Concentration levels are evaluated using facial recognition and eye-tracking technologies.

[0083] For example, if a user wants to take a course on "Basic Data Analysis," they can enter a prompt such as "Please ask a question about basic data analysis," and the AI ​​will provide an appropriate answer.

[0084] This system enables users to achieve efficient and personalized learning, and they can expect improved learning outcomes.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects educational resources provided by educational institutions and researchers. These resources include text, audio, and video data. The input data is stored in the server's database. The server converts this data into a specific format for classification and organization.

[0088] Step 2:

[0089] The server analyzes data using natural language processing (NLTK) techniques. Specifically, it uses NLTK and spaCy to analyze text data. The input text data undergoes tokenization, part-of-speech tagging, and grammatical analysis to clarify the information structure. As output, feature information specifically tailored for educational purposes is extracted.

[0090] Step 3:

[0091] The server trains a learning model using a machine learning framework (e.g., TensorFlow, PyTorch). Here, the previously extracted feature information is used as input to build a learning model tailored to the purpose. During the training process, a large amount of data samples are used to iteratively learn, and the output is an enhanced model capable of providing expert knowledge to the user.

[0092] Step 4:

[0093] Users enter their learning profile from their device. The device collects information about their areas of interest and learning goals and sends it to the server. The entered personal information is analyzed on the server, and a profile reflecting the user's learning tendencies and needs is generated.

[0094] Step 5:

[0095] The server selects appropriate educational content based on the generated user profile. The server considers past learning history and market trends to list the most suitable learning resources. As output, a personalized list of courses is displayed on the user's device.

[0096] Step 6:

[0097] The device tracks the user's progress in real time during learning. Specifically, it collects log data related to the user's learning process and records their progress. The entered progress data is sent to the server and used to readjust the learning plan.

[0098] Step 7:

[0099] The server analyzes the user's learning progress data and adjusts the learning plan accordingly. If the user is falling behind in a particular area or task, it provides supplementary materials or additional content. As output, the user's optimized learning stage is presented on their device.

[0100] Step 8:

[0101] The device monitors the user's level of concentration. It uses facial recognition and eye-tracking technologies to measure the user's focus. Based on the input monitoring data, the server detects a decline in concentration and sends instructions to the user to take a break or refocus. This encourages actions to maintain learning efficiency.

[0102] (Application Example 1)

[0103] 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."

[0104] In real-world work environments such as factories, there is a need for efficient means to support the skill development of workers. In particular, a system is needed that can learn and adapt immediately in real time when new technologies or equipment are introduced. Furthermore, it is necessary to improve work efficiency and safety by managing concentration levels during work and incorporating appropriate breaks.

[0105] 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.

[0106] In this invention, the server includes a device that generates an educational machine learning model by collecting educational data and training it using natural language processing; a recommendation device that analyzes the user's learning profile and selects appropriate educational content; and a device that monitors the user's level of concentration while working using an eye-tracking device. This enables factory workers to acquire new skills and optimize their work in real time while maintaining work efficiency.

[0107] "Educational material data" refers to a collection of information such as texts, audio clips, and video lectures used for educational purposes.

[0108] "Natural language processing" is a technology that enables computers to understand, generate, and support human language.

[0109] An "educational machine learning model" is an algorithm trained to efficiently perform specific tasks in the field of education.

[0110] A "user learning profile" is a profile that compiles information such as each user's interests, learning history, and goals.

[0111] A "recommendation system" is a system that selects and provides the most suitable educational content based on an analyzed learning profile.

[0112] An "eye-tracking device" is a technology that tracks the movement of a user's eyes and acquires that information in real time.

[0113] A "concentration level monitoring device" is a device that evaluates the user's concentration level and detects signs of a decline in concentration.

[0114] "Work status" is a term that refers to the current progress or conditions in a particular work environment.

[0115] A "device that provides advice in real time" is a system that suggests the optimal course of action for the user during their activities, according to the situation at hand.

[0116] The system that implements this application primarily operates between a server, smart glasses, and the user. The server generates educational machine learning models using natural language processing techniques based on educational data collected from educational institutions and experts. These models are designed to enable users to quickly acquire new skills and are specifically used to assist users in their work in real time.

[0117] Smart glasses function as both eye-tracking and concentration monitoring devices. These glasses track the user's gaze and transmit the data to a server. The server analyzes the received data and evaluates the user's current level of concentration. If concentration decreases, the server sends a notification to the glasses, recommending a break for the user.

[0118] Users can receive real-time advice through smart glasses while working. This advice is provided by a generated AI model and supports improved work efficiency and the acquisition of new skills.

[0119] As a concrete example, during the operation of newly introduced equipment in a factory, a worker wearing smart glasses can receive advice on operating procedures from an AI-based server. Examples of prompts in this case could be, "Please tell me how to operate the new welding machine," or "Please tell me what can be improved in my current work."

[0120] This allows factory workers to continue their work safely and efficiently while immediately learning and adapting to new knowledge.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server collects educational data from educational institutions and professionals. This data includes text, audio clips, and video lectures, and uses natural language processing techniques to generate educational machine learning models. The input data is raw educational data, and the output is a trained machine learning model.

[0124] Step 2:

[0125] When a user begins working through smart glasses, the device collects information about the user's learning profile and current work status. This information is sent to a server and analyzed by an AI model. The input is the learning profile and work status, and the output is optimal work advice and suggestions for educational content.

[0126] Step 3:

[0127] The server uses an AI model to generate real-time advice for the user. This advice is sent to smart glasses and displayed to the user in a timely manner. The input is work status data and analysis results from the model, and the output is specific work instructions and advice provided to the user.

[0128] Step 4:

[0129] An eye-tracking device collects the user's eye-tracking data and sends it to a server. The server uses this data to evaluate the user's level of concentration. The input is eye-tracking data, and the output is the concentration level evaluation result.

[0130] Step 5:

[0131] Based on the concentration level assessment, the server sends a notification to the user recommending a break if their concentration level has decreased. Smart glasses display this notification to the user. The input is the concentration level assessment result, and the output is the notification prompting a break.

[0132] Step 6:

[0133] The user sends a question to the server using a prompt. The server parses this prompt and provides appropriate educational content or a work guide. The input is the prompt from the user, and the output is the AI's response or advice to it.

[0134] 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.

[0135] This invention incorporates emotion recognition technology into conventional online education systems to provide a more adaptive and effective educational experience based on the emotional state of individual learners. The system includes an educational machine learning model, an emotion engine, a concentration monitoring function, and means for integrating these to dynamically adjust the learning program.

[0136] Generating and utilizing machine learning models for educational purposes:

[0137] The server collects educational material data from educational institutions and trains a teacher AI model using natural language processing technology. This model can explain the materials to users and answer questions. This AI model provides real-time explanations of specific examples in courses on the fundamentals of law, as well as Q&A sessions.

[0138] Online course recommendation and progress management:

[0139] Users create a learning profile by entering their learning objectives and areas of interest through their device. The server recommends the most suitable courses based on the user's profile. This recommendation process also takes into account past learning history and market trends. Furthermore, the device monitors learning progress in real time and sends this information to the server, allowing for dynamic adjustments to the curriculum based on progress.

[0140] Adaptive learning support through emotion recognition:

[0141] The server uses an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. This allows the system to suggest more stimulating content if the user is bored, or to change the learning program to a more relaxing one if they are stressed. For example, if the emotion engine determines the user is fatigued, the system will suggest a short break and adjust the pace of the next lesson accordingly.

[0142] Concentration monitoring and refocus alerts:

[0143] The device measures the user's level of concentration while they are learning using facial expression analysis and eye-tracking technology. Based on this concentration data, the server displays an alert prompting the user to refocus or take a break if it determines that their attention is waning. This allows the user to maintain an efficient learning environment.

[0144] In this way, the system evaluates the user's emotions and level of concentration in real time and flexibly adjusts the learning program based on that evaluation, thereby creating an efficient and personalized learning environment. This helps maintain learner motivation and improve learning efficiency.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The server collects educational material data in various formats from educational institutions and stores it in a database. This data includes text, video, and audio clips, which are used to train educational machine learning models.

[0148] Step 2:

[0149] The server uses natural language processing technology to analyze the collected educational material data and implement an educational AI model. This model is optimized to answer user questions and provide explanations of the educational materials.

[0150] Step 3:

[0151] Users use their devices to enter a learning profile that includes their learning objectives and areas of interest. Based on this, a personalized learning plan is created.

[0152] Step 4:

[0153] The server analyzes the user's learning profile, taking into account past learning history and market trends, to recommend the most suitable course. This ensures that the user receives the most appropriate learning resources.

[0154] Step 5:

[0155] The device tracks the user's progress in real time during learning and sends this information to the server. Based on the progress data, the server dynamically adjusts the learning program and suggests the next appropriate learning stage.

[0156] Step 6:

[0157] The server utilizes an emotion engine to analyze the user's emotional state through camera and microphone data. This allows it to assess the level of stress and interest the user is experiencing while learning.

[0158] Step 7:

[0159] The server adaptively modifies the learning content based on the user's emotional state. If it determines that the user is bored, it provides more interactive content; if the user is stressed, it adjusts the difficulty level.

[0160] Step 8:

[0161] The device uses a webcam to measure the user's level of concentration and sends data on facial expressions and eye movements to the server. If the user's concentration level decreases, the server displays an alert on the device prompting them to refocus, supporting them in continuing to learn efficiently.

[0162] (Example 2)

[0163] 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".

[0164] To provide a flexible learning experience in online education that is tailored to each learner's individual state and progress, it is necessary to grasp their emotional state and concentration level in real time and provide an adaptive curriculum based on that information. However, conventional systems have not been able to adequately reflect learners' emotions and concentration levels, and have failed to provide an effective educational experience. Therefore, there is a need for a system that can improve the quality and effectiveness of online education.

[0165] 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.

[0166] In this invention, the server includes means for generating an educational learning model by collecting educational material information and training it using natural language processing; means for recommending appropriate learning content by analyzing the user's learning information; and means for acquiring the user's learning progress in real time and adaptively adjusting the learning plan based on that data. This makes it possible to understand the learner's emotional state and level of concentration and provide a personalized learning experience that reflects this in real time.

[0167] "Educational material information" refers to educational data provided to learners, including in the form of text, images, and videos.

[0168] "Natural language processing" is a technology that enables machines to understand and process human language, and is used in training educational learning models.

[0169] An "educational learning model" is an artificial intelligence model generated using collected educational material information and natural language processing technology, enabling explanations and question-and-answer sessions for learners.

[0170] "Learning information" refers to data provided by learners regarding their learning objectives, areas of interest, and other relevant information, and serves as the foundation for providing personalized educational experiences.

[0171] "Recommendation methods" refer to the process of analyzing a user's learning information and selecting and providing the most suitable learning content.

[0172] "Learning progress" refers to data that shows how far a learner has progressed in their studies, and it can be collected in real time.

[0173] A "learning plan" is the overall structure of a learning curriculum that is dynamically adjusted based on the learner's progress and status.

[0174] "Emotional state" refers to the emotional state a learner exhibits during the learning process, and is evaluated in real time by technology.

[0175] "Concentration level" refers to the degree of a learner's attention during learning, and it can be evaluated using video and audio equipment.

[0176] The system of this invention is designed to provide an individualized learning experience in online education. The system mainly consists of a server, a terminal, and a user interface.

[0177] The server first collects educational material information. This information comes in various formats, including text, images, and videos, and is obtained from online databases or designated folders. Next, the server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. Specifically, it trains an AI model using a natural language processing library so that it can provide answers to user-submitted questions in real time. For example, if a user submits the question, "What are the fundamental laws of physics?", the AI ​​model will respond, "Newton's laws are the fundamental laws of motion." In this case, an example of a prompt might be, "Please answer a basic question about physics."

[0178] The device collects the user's learning information. Through the device, the user inputs their learning objectives and areas of interest, creating learning information. The device sends this information to a server, which then selects and recommends the most suitable learning content. For example, if a user expresses interest in "design," this recommendation system might suggest courses such as "Fundamentals of UI / UX Design" or "How to Use Graphic Design Tools."

[0179] Furthermore, the device monitors the user's learning progress and sends this information to the server in real time. The server analyzes the progress data and dynamically adjusts the learning plan as needed. For example, when the user is ready to move on to the next module, a notification will appear saying, "We recommend you move on to the next module."

[0180] Furthermore, the server utilizes an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. Emotion recognition allows the server to suggest, for example, "You seem tired. Let's take a 5-minute break," if the user appears fatigued. This enables the user to learn more effectively.

[0181] Finally, the device uses video and audio equipment to measure the user's level of concentration while learning. If it determines that the user's concentration is declining, the device displays an alert such as, "Your concentration is waning. Take a short break to refresh yourself," and helps the user regain their focus.

[0182] In this way, the present invention personalizes the user's learning experience and realizes an effective and efficient educational environment.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The server collects educational material information. The input consists of educational data such as text, images, and videos obtained from online databases and designated folders. The server collects this data and converts it into a format usable in the next stage. Specifically, it stores this data in a new database.

[0186] Step 2:

[0187] The server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. The input is the educational material information collected in step 1, and the output is the trained AI model. The server uses a natural language processing library to generate a model that the AI ​​can use to respond to questions. Specifically, it analyzes the educational material data and learns relevant topics.

[0188] Step 3:

[0189] Users input learning information through their device. Users create a learning profile by entering their learning objectives and areas of interest. The input is learning objectives and areas of interest, and the output is the learning profile created on the device. The device sends this data to the server.

[0190] Step 4:

[0191] The server analyzes the user's learning information and recommends appropriate learning content. The input is the learning profile obtained in step 3 and past learning data, and the output is a list of recommended content presented to the user. Here, the server selects the most suitable course based on the user's interests and market trends.

[0192] Step 5:

[0193] The device monitors the user's learning progress and transmits it to the server in real time. Input consists of user operation data and progress data during learning, while output is learning progress information received by the server. The device records chapters studied, video viewing times, and other data.

[0194] Step 6:

[0195] The server analyzes the progress data and dynamically adjusts the learning plan as needed. The input is the learning progress data sent in step 5, and the output is the adjusted learning plan. Based on the analysis results, the server recommends the next learning step or repeated learning.

[0196] Step 7:

[0197] The server uses an emotion engine to collect and analyze emotional data from the user's webcam and microphone. Input is real-time audio and facial expression data, and output is a judgment about the user's emotional state. Specifically, it evaluates the user's stress level and energy level.

[0198] Step 8:

[0199] The terminal measures the user's level of concentration using video and audio equipment. Input is data obtained from facial expression analysis and eye tracking, and output is a concentration level value. Based on this, the terminal displays an alert when concentration decreases.

[0200] In this way, the system sequentially collects and analyzes data to provide a personalized learning experience.

[0201] (Application Example 2)

[0202] 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".

[0203] To achieve efficient operation of factory machinery, it is necessary to monitor the working environment and equipment status in real time and make adaptive adjustments. However, conventional systems have had the problem of difficulty in comprehensively monitoring these elements and responding quickly. As a result, improving operational efficiency and achieving proper equipment maintenance have been difficult.

[0204] 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.

[0205] In this invention, the server includes means for generating an educational machine learning model by collecting educational material information and training it using natural language processing; means for selecting appropriate educational content by analyzing the user's learning profile; and means for analyzing information from observation and sensing devices, evaluating the state of the work equipment and the work environment, and proposing and adjusting the optimal work flow. This enables real-time optimization of the work environment, efficient operation of the equipment, and proper maintenance.

[0206] "Educational material information" refers to materials and data used in learners' educational activities.

[0207] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0208] An "educational machine learning model" is an algorithm trained on educational content to support educational activities.

[0209] A "learning profile" is information that records a learner's learning history, interests, and goals.

[0210] "Recommendation methods" are technologies that select and present the most suitable information and content to learners based on collected data.

[0211] An "observation device" is a piece of equipment used to understand the state of the environment or objects.

[0212] A "sensing device" is a sensor or equipment used to detect changes in the environment.

[0213] "Working equipment" refers to machines and robots used to perform tasks in factories and production lines.

[0214] "Work environment" refers to the place and conditions in which work is performed, and includes elements that affect efficiency and safety.

[0215] The system implementing this invention aims to monitor factory equipment in real time and efficiently manage its operation. The system's processing is described below in natural language.

[0216] The server first collects state data obtained from observation and sensing devices. This state data includes the operating status of the work equipment and changes in the work environment. A camera is used as the observation device, and temperature sensors and vibration sensors are included as sensing devices.

[0217] Next, the server applies a training machine learning model using natural language processing techniques to analyze the collected data. This model is trained to optimize the state and working environment of the equipment and can detect equipment overload and inefficiencies. Based on the analysis results, the server proposes an optimal workflow and issues adjustment instructions to the equipment.

[0218] Furthermore, the server transmits this proposal to the terminal in an executable format and automatically performs the necessary operations. This ensures that the work equipment continues to operate efficiently and that necessary maintenance is performed quickly.

[0219] For example, if the server detects a component jam during the assembly process, it analyzes abnormal heat generation and vibration based on data obtained from observation and sensing devices. As a result, the server instructs a temporary shutdown and arranges for necessary support equipment. It also provides feedback to improve the next work process.

[0220] As an example of how this system works, here are some examples of prompt statements for a generative AI model.

[0221] "Robot State Monitoring Prompt: Using current camera footage and sensor data, analyze the robot's emotional state and propose an optimized workflow."

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The server collects data in real time from observation and sensing devices. Input data includes camera footage from the work equipment, temperature sensor readings, and vibration sensor data. This data is aggregated on the server, initially recording the work environment and equipment status as digital information.

[0225] Step 2:

[0226] The server runs an educational machine learning model to analyze the collected data. The observation and sensing data obtained in Step 1 are used as input. In data processing, an anomaly pattern recognition algorithm is used to detect signs of abnormalities and overload conditions in the work equipment. If no abnormalities are found, the output indicates that normal operation should continue.

[0227] Step 3:

[0228] If an anomaly is detected, the server re-evaluates the equipment status and surrounding environment data to propose the optimal workflow. The input is the output data from step 2. As part of the data calculation, several scenario simulations are performed to generate proposed equipment operation adjustments. The output of this is the improved work procedure and operation adjustment plan.

[0229] Step 4:

[0230] The terminal receives suggestions from the server and sends specific instructions to the work device. The input is the adjustment plan obtained in step 3. The terminal generates prompt statements, causing the work device to execute sequential operation instructions and pause commands. The output is the safe shutdown of the device and the implementation of optimal adjustments.

[0231] Step 5:

[0232] The user reviews the adjustment results and improved workflow presented by the system and evaluates whether further manual adjustments are needed. In this step, the user confirms the status of the equipment and provides feedback for the next step. The inputs are the output results and monitoring data from step 4, and the output generates an evaluation report and adjustment feedback.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention relates to an educational system that supports lifelong learning by utilizing artificial intelligence and machine learning technologies. The system aims to provide a customized learning experience tailored to each individual user, and is implemented in the following specific form.

[0250] Generation of instructor AI models:

[0251] The server first collects educational data provided by educational institutions and partner scholars. This data includes text, audio clips, and video lectures. The server uses natural language processing techniques with this data to train educational machine learning models. The trained models can convey knowledge to users in a conversational format similar to that of a human instructor. For example, in a "Data Analysis Fundamentals" course, the AI ​​clearly explains terminology definitions and provides examples of analytical methods.

[0252] Automated matching for online courses:

[0253] Users enter their learning profile via their device, sending their areas of interest and goals to the server. The server analyzes this information and generates a list of optimal courses based on past learning history and market trend data. Recommended courses are presented in the user interface, allowing users to select a learning plan that suits their pace. For example, if a user is interested in marketing, customized courses ranging from basic to advanced levels will be suggested.

[0254] Adaptive learning systems:

[0255] The device tracks the user's progress in real time and sends the data to the server. The server evaluates the received data and suggests appropriate content and assignments tailored to each user's learning stage. This adaptive system can accommodate learners with unique strengths and weaknesses, adjusting the curriculum based on their learning speed and comprehension. For example, users struggling with a particular assignment may be provided with additional support materials or practice problems.

[0256] Concentration level monitoring and alerts:

[0257] During learning, the device uses the user's webcam and sensors to measure their level of concentration in real time. This involves using facial recognition technology to analyze expressions and gaze. The server collects this data and automatically sends a notification to the user when it detects a decline in concentration. By displaying a pop-up message recommending a break, the user is encouraged to take action to improve their learning efficiency. This system makes it easier to maintain concentration even during long learning sessions.

[0258] As described above, the system provided is designed to effectively support users' learning habits and create a learning environment that suits individual needs and pace. This aims to improve the efficiency and quality of lifelong learning.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The server retrieves educational material data provided by educational institutions and stores it in a database. This includes learning materials in various formats, such as text files, video clips, and audio data.

[0262] Step 2:

[0263] The server executes natural language processing algorithms and trains an instructor AI model using the acquired teaching material data. This process includes phases of data preprocessing, tokenization, and model training.

[0264] Step 3:

[0265] Users enter their personal learning profiles using their devices, clearly indicating their interests and goals. This includes the fields of study they wish to take and their preferred learning pace.

[0266] Step 4:

[0267] The server analyzes the profile data submitted by the user and compares it with the existing course database to recommend the most suitable courses. These recommendations are refined using machine learning and displayed as a personalized list for the user.

[0268] Step 5:

[0269] The device monitors the user's learning progress in real time and periodically sends this data to the server. This includes assignment completion status, accuracy rate, and study time.

[0270] Step 6:

[0271] The server analyzes the received progress data and adjusts the difficulty level or provides additional learning materials according to the user's needs. This optimizes the user's learning experience.

[0272] Step 7:

[0273] The device measures the user's level of concentration using a webcam during learning and sends data on facial expressions and eye movements to a server. This uses an algorithm to quantify attention.

[0274] Step 8:

[0275] The server analyzes concentration data and displays an alert on the device prompting the user to take a break if their attention is waning. This allows the user to continue learning efficiently.

[0276] (Example 1)

[0277] 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."

[0278] In lifelong learning, there is a need to provide teaching materials and learning formats suitable for individual learners and enable efficient and sustainable learning. Also, a mechanism is required to appropriately manage the concentration during learning and prevent a decrease in efficiency during long-term learning. In conventional educational systems, it has been difficult to respond to individual needs, and it has been difficult for learners to construct a learning environment optimal for their learning styles.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for collecting information and generating a learning model using natural language processing, means for analyzing an individual's learning profile and selecting appropriate educational content, means for acquiring an individual's learning progress data in real time and adaptively adjusting the learning content based on that data, means for monitoring an individual's concentration during learning using a photographing device or a sensing device, and means for giving instructions to prompt breaks and re-concentration based on the concentration data. Thereby, a learning environment optimized for individual learners is provided, and efficient and sustainable learning becomes possible.

[0281] "Collecting information" is a process of acquiring necessary information from a data provider and using it for processing within the system.

[0282] "Natural language processing" is a technology for a computer to understand, analyze, and generate human language.

[0283] "Generating a learning model" is to construct an algorithm model useful for a specific educational purpose using machine learning based on the acquired data.

[0284] "An individual's learning profile" is a dataset including personal information such as a learner's interests, goals, and history.

[0285] "Recommendation for selecting educational content" is a process of identifying educational resources and curricula most suitable for individual learners.

[0286] The "learning progress data" is information indicating the tasks completed by the learner up to now and the knowledge acquired.

[0287] "Adaptive adjustment" means dynamically changing the learning content and pace according to the progress and capabilities of individual learners.

[0288] The "imaging device or sensing device" refers to data collection hardware such as cameras and sensors, which record or sense the activities of users.

[0289] "Monitoring concentration" means measuring and evaluating the state of the learner's concentration in real time.

[0290] "Instructions to promote breaks and re-concentration" means providing advice and action proposals to restore efficiency when the learner lacks concentration.

[0291] This invention is an advanced educational system that supports lifelong learning and provides individualized learning experiences by leveraging artificial intelligence and machine learning technologies. Its embodiments will be specifically described below.

[0292] The server first collects information. Specifically, it collects educational resources such as texts, audio clips, and videos provided by educational institutions and researchers. The server analyzes these data using natural language processing technology. Specifically, it performs text analysis using NLTK and spaCy, which are natural language processing libraries in Python. Furthermore, the server uses machine learning frameworks such as TensorFlow and PyTorch to generate and train educational learning models. This model has the ability to provide appropriate teaching materials for individual learners.

[0293] Users enter their learning profile through their device, which includes PCs and tablets. Users specify their learning goals and areas of interest and send this information to the server. The server analyzes the received information and creates a personal profile of the user. Based on this profile, the server provides personalized learning content.

[0294] The device tracks the user's progress in real time during learning. This data is sent to a server and used to adjust the learning plan adaptively. For example, supplementary materials are automatically provided to users who are falling behind. Furthermore, the user's concentration level is also monitored, and if their concentration wavers, the device prompts the user to take a break or refocus. Concentration levels are evaluated using facial recognition and eye-tracking technologies.

[0295] For example, if a user wants to take a course on "Basic Data Analysis," they can enter a prompt such as "Please ask a question about basic data analysis," and the AI ​​will provide an appropriate answer.

[0296] This system enables users to achieve efficient and personalized learning, and they can expect improved learning outcomes.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The server collects educational resources provided by educational institutions and researchers. These resources include text, audio, and video data. The input data is stored in the server's database. The server converts this data into a specific format for classification and organization.

[0300] Step 2:

[0301] The server analyzes data using natural language processing technology. Specifically, it performs language analysis on text data using NLTK or spaCy. The input text data is tokenized, part-of-speech tagged, and parsed, and the information structure is clarified. As output, feature information specialized for education is extracted.

[0302] Step 3:

[0303] The server trains a learning model using a machine learning framework (e.g., TensorFlow, PyTorch). Here, using the feature information extracted earlier as input, a learning model tailored to the purpose is constructed. In the training process, a large number of data samples are repeatedly learned, and as output, a strengthened model that can provide specialized knowledge to users is obtained.

[0304] Step 4:

[0305] The user inputs their learning profile from the terminal. The terminal collects information regarding the fields of interest and learning goals, and sends it to the server. The input personal information is analyzed by the server, and a profile reflecting the user's learning trends and needs is generated.

[0306] Step 5:

[0307] The server selects appropriate educational content based on the generated user profile. The server considers past learning histories and market trends and lists up the optimal learning resources. As output, a list of individualized courses is displayed on the user's terminal.

[0308] [[ID=2B]]Step 6:

[0309] The terminal tracks the progress of the learning user in real time. As a specific operation, it collects log data regarding the user's learning process and records the progress. The input progress data is sent to the server and utilized for readjusting the learning plan.

[0310] Step 7:

[0311] The server analyzes the user's learning progress data and adjusts the learning plan accordingly. If the user is falling behind in a particular area or task, it provides supplementary materials or additional content. As output, the user's optimized learning stage is presented on their device.

[0312] Step 8:

[0313] The device monitors the user's level of concentration. It uses facial recognition and eye-tracking technologies to measure the user's focus. Based on the input monitoring data, the server detects a decline in concentration and sends instructions to the user to take a break or refocus. This encourages actions to maintain learning efficiency.

[0314] (Application Example 1)

[0315] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0316] In real-world work environments such as factories, there is a need for efficient means to support the skill development of workers. In particular, a system is needed that can learn and adapt immediately in real time when new technologies or equipment are introduced. Furthermore, it is necessary to improve work efficiency and safety by managing concentration levels during work and incorporating appropriate breaks.

[0317] 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.

[0318] In this invention, the server includes a device that generates an educational machine learning model by collecting educational data and training it using natural language processing; a recommendation device that analyzes the user's learning profile and selects appropriate educational content; and a device that monitors the user's level of concentration while working using an eye-tracking device. This enables factory workers to acquire new skills and optimize their work in real time while maintaining work efficiency.

[0319] "Educational material data" refers to a collection of information such as texts, audio clips, and video lectures used for educational purposes.

[0320] "Natural language processing" is a technology that enables computers to understand, generate, and support human language.

[0321] An "educational machine learning model" is an algorithm trained to efficiently perform specific tasks in the field of education.

[0322] A "user learning profile" is a profile that compiles information such as each user's interests, learning history, and goals.

[0323] A "recommendation system" is a system that selects and provides the most suitable educational content based on an analyzed learning profile.

[0324] An "eye-tracking device" is a technology that tracks the movement of a user's eyes and acquires that information in real time.

[0325] A "concentration level monitoring device" is a device that evaluates the user's concentration level and detects signs of a decline in concentration.

[0326] "Work status" is a term that refers to the current progress or conditions in a particular work environment.

[0327] A "device that provides advice in real time" is a system that suggests the optimal course of action for the user during their activities, according to the situation at hand.

[0328] The system that implements this application primarily operates between a server, smart glasses, and the user. The server generates educational machine learning models using natural language processing techniques based on educational data collected from educational institutions and experts. These models are designed to enable users to quickly acquire new skills and are specifically used to assist users in their work in real time.

[0329] Smart glasses function as both eye-tracking and concentration monitoring devices. These glasses track the user's gaze and transmit the data to a server. The server analyzes the received data and evaluates the user's current level of concentration. If concentration decreases, the server sends a notification to the glasses, recommending a break for the user.

[0330] Users can receive real-time advice through smart glasses while working. This advice is provided by a generated AI model and supports improved work efficiency and the acquisition of new skills.

[0331] As a concrete example, during the operation of newly introduced equipment in a factory, a worker wearing smart glasses can receive advice on operating procedures from an AI-based server. Examples of prompts in this case could be, "Please tell me how to operate the new welding machine," or "Please tell me what can be improved in my current work."

[0332] This allows factory workers to continue their work safely and efficiently while immediately learning and adapting to new knowledge.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The server collects educational data from educational institutions and professionals. This data includes text, audio clips, and video lectures, and uses natural language processing techniques to generate educational machine learning models. The input data is raw educational data, and the output is a trained machine learning model.

[0336] Step 2:

[0337] When a user begins working through smart glasses, the device collects information about the user's learning profile and current work status. This information is sent to a server and analyzed by an AI model. The input is the learning profile and work status, and the output is optimal work advice and suggestions for educational content.

[0338] Step 3:

[0339] The server uses an AI model to generate real-time advice for the user. This advice is sent to smart glasses and displayed to the user in a timely manner. The input is work status data and analysis results from the model, and the output is specific work instructions and advice provided to the user.

[0340] Step 4:

[0341] An eye-tracking device collects the user's eye-tracking data and sends it to a server. The server uses this data to evaluate the user's level of concentration. The input is eye-tracking data, and the output is the concentration level evaluation result.

[0342] Step 5:

[0343] Based on the concentration level assessment, the server sends a notification to the user recommending a break if their concentration level has decreased. Smart glasses display this notification to the user. The input is the concentration level assessment result, and the output is the notification prompting a break.

[0344] Step 6:

[0345] The user sends a question to the server using a prompt. The server parses this prompt and provides appropriate educational content or a work guide. The input is the prompt from the user, and the output is the AI's response or advice to it.

[0346] 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.

[0347] This invention incorporates emotion recognition technology into conventional online education systems to provide a more adaptive and effective educational experience based on the emotional state of individual learners. The system includes an educational machine learning model, an emotion engine, a concentration monitoring function, and means for integrating these to dynamically adjust the learning program.

[0348] Generating and utilizing machine learning models for educational purposes:

[0349] The server collects educational material data from educational institutions and trains a teacher AI model using natural language processing technology. This model can explain the materials to users and answer questions. This AI model provides real-time explanations of specific examples in courses on the fundamentals of law, as well as Q&A sessions.

[0350] Online course recommendation and progress management:

[0351] Users create a learning profile by entering their learning objectives and areas of interest through their device. The server recommends the most suitable courses based on the user's profile. This recommendation process also takes into account past learning history and market trends. Furthermore, the device monitors learning progress in real time and sends this information to the server, allowing for dynamic adjustments to the curriculum based on progress.

[0352] Adaptive learning support through emotion recognition:

[0353] The server uses an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. This allows the system to suggest more stimulating content if the user is bored, or to change the learning program to a more relaxing one if they are stressed. For example, if the emotion engine determines the user is fatigued, the system will suggest a short break and adjust the pace of the next lesson accordingly.

[0354] Concentration monitoring and refocus alerts:

[0355] The device measures the user's level of concentration while they are learning using facial expression analysis and eye-tracking technology. Based on this concentration data, the server displays an alert prompting the user to refocus or take a break if it determines that their attention is waning. This allows the user to maintain an efficient learning environment.

[0356] In this way, the system evaluates the user's emotions and level of concentration in real time and flexibly adjusts the learning program based on that evaluation, thereby creating an efficient and personalized learning environment. This helps maintain learner motivation and improve learning efficiency.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] The server collects educational material data in various formats from educational institutions and stores it in a database. This data includes text, video, and audio clips, which are used to train educational machine learning models.

[0360] Step 2:

[0361] The server uses natural language processing technology to analyze the collected educational material data and implement an educational AI model. This model is optimized to answer user questions and provide explanations of the educational materials.

[0362] Step 3:

[0363] Users use their devices to enter a learning profile that includes their learning objectives and areas of interest. Based on this, a personalized learning plan is created.

[0364] Step 4:

[0365] The server analyzes the user's learning profile, taking into account past learning history and market trends, to recommend the most suitable course. This ensures that the user receives the most appropriate learning resources.

[0366] Step 5:

[0367] The device tracks the user's progress in real time during learning and sends this information to the server. Based on the progress data, the server dynamically adjusts the learning program and suggests the next appropriate learning stage.

[0368] Step 6:

[0369] The server utilizes an emotion engine to analyze the user's emotional state through camera and microphone data. This allows it to assess the level of stress and interest the user is experiencing while learning.

[0370] Step 7:

[0371] The server adaptively modifies the learning content based on the user's emotional state. If it determines that the user is bored, it provides more interactive content; if the user is stressed, it adjusts the difficulty level.

[0372] Step 8:

[0373] The device uses a webcam to measure the user's level of concentration and sends data on facial expressions and eye movements to the server. If the user's concentration level decreases, the server displays an alert on the device prompting them to refocus, supporting them in continuing to learn efficiently.

[0374] (Example 2)

[0375] 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".

[0376] To provide a flexible learning experience in online education that is tailored to each learner's individual state and progress, it is necessary to grasp their emotional state and concentration level in real time and provide an adaptive curriculum based on that information. However, conventional systems have not been able to adequately reflect learners' emotions and concentration levels, and have failed to provide an effective educational experience. Therefore, there is a need for a system that can improve the quality and effectiveness of online education.

[0377] 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.

[0378] In this invention, the server includes means for generating an educational learning model by collecting educational material information and training it using natural language processing; means for recommending appropriate learning content by analyzing the user's learning information; and means for acquiring the user's learning progress in real time and adaptively adjusting the learning plan based on that data. This makes it possible to understand the learner's emotional state and level of concentration and provide a personalized learning experience that reflects this in real time.

[0379] "Educational material information" refers to educational data provided to learners, including in the form of text, images, and videos.

[0380] "Natural language processing" is a technology that enables machines to understand and process human language, and is used in training educational learning models.

[0381] An "educational learning model" is an artificial intelligence model generated using collected educational material information and natural language processing technology, enabling explanations and question-and-answer sessions for learners.

[0382] "Learning information" refers to data provided by learners regarding their learning objectives, areas of interest, and other relevant information, and serves as the foundation for providing personalized educational experiences.

[0383] "Recommendation methods" refer to the process of analyzing a user's learning information and selecting and providing the most suitable learning content.

[0384] "Learning progress" refers to data that shows how far a learner has progressed in their studies, and it can be collected in real time.

[0385] A "learning plan" is the overall structure of a learning curriculum that is dynamically adjusted based on the learner's progress and status.

[0386] "Emotional state" refers to the emotional state a learner exhibits during the learning process, and is evaluated in real time by technology.

[0387] "Concentration level" refers to the degree of a learner's attention during learning, and it can be evaluated using video and audio equipment.

[0388] The system of this invention is designed to provide an individualized learning experience in online education. The system mainly consists of a server, a terminal, and a user interface.

[0389] The server first collects educational material information. This information comes in various formats, including text, images, and videos, and is obtained from online databases or designated folders. Next, the server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. Specifically, it trains an AI model using a natural language processing library so that it can provide answers to user-submitted questions in real time. For example, if a user submits the question, "What are the fundamental laws of physics?", the AI ​​model will respond, "Newton's laws are the fundamental laws of motion." In this case, an example of a prompt might be, "Please answer a basic question about physics."

[0390] The device collects the user's learning information. Through the device, the user inputs their learning objectives and areas of interest, creating learning information. The device sends this information to a server, which then selects and recommends the most suitable learning content. For example, if a user expresses interest in "design," this recommendation system might suggest courses such as "Fundamentals of UI / UX Design" or "How to Use Graphic Design Tools."

[0391] Furthermore, the device monitors the user's learning progress and sends this information to the server in real time. The server analyzes the progress data and dynamically adjusts the learning plan as needed. For example, when the user is ready to move on to the next module, a notification will appear saying, "We recommend you move on to the next module."

[0392] Furthermore, the server utilizes an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. Emotion recognition allows the server to suggest, for example, "You seem tired. Let's take a 5-minute break," if the user appears fatigued. This enables the user to learn more effectively.

[0393] Finally, the device uses video and audio equipment to measure the user's level of concentration while learning. If it determines that the user's concentration is declining, the device displays an alert such as, "Your concentration is waning. Take a short break to refresh yourself," and helps the user regain their focus.

[0394] In this way, the present invention personalizes the user's learning experience and realizes an effective and efficient educational environment.

[0395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0396] Step 1:

[0397] The server collects educational material information. The input consists of educational data such as text, images, and videos obtained from online databases and designated folders. The server collects this data and converts it into a format usable in the next stage. Specifically, it stores this data in a new database.

[0398] Step 2:

[0399] The server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. The input is the educational material information collected in step 1, and the output is the trained AI model. The server uses a natural language processing library to generate a model that the AI ​​can use to respond to questions. Specifically, it analyzes the educational material data and learns relevant topics.

[0400] Step 3:

[0401] Users input learning information through their device. Users create a learning profile by entering their learning objectives and areas of interest. The input is learning objectives and areas of interest, and the output is the learning profile created on the device. The device sends this data to the server.

[0402] Step 4:

[0403] The server analyzes the user's learning information and recommends appropriate learning content. The input is the learning profile obtained in step 3 and past learning data, and the output is a list of recommended content presented to the user. Here, the server selects the most suitable course based on the user's interests and market trends.

[0404] Step 5:

[0405] The device monitors the user's learning progress and transmits it to the server in real time. Input consists of user operation data and progress data during learning, while output is learning progress information received by the server. The device records chapters studied, video viewing times, and other data.

[0406] Step 6:

[0407] The server analyzes the progress data and dynamically adjusts the learning plan as needed. The input is the learning progress data sent in step 5, and the output is the adjusted learning plan. Based on the analysis results, the server recommends the next learning step or repeated learning.

[0408] Step 7:

[0409] The server uses an emotion engine to collect and analyze emotional data from the user's webcam and microphone. Input is real-time audio and facial expression data, and output is a judgment about the user's emotional state. Specifically, it evaluates the user's stress level and energy level.

[0410] Step 8:

[0411] The terminal measures the user's level of concentration using video and audio equipment. Input is data obtained from facial expression analysis and eye tracking, and output is a concentration level value. Based on this, the terminal displays an alert when concentration decreases.

[0412] In this way, the system sequentially collects and analyzes data to provide a personalized learning experience.

[0413] (Application Example 2)

[0414] 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."

[0415] To achieve efficient operation of factory machinery, it is necessary to monitor the working environment and equipment status in real time and make adaptive adjustments. However, conventional systems have had the problem of difficulty in comprehensively monitoring these elements and responding quickly. As a result, improving operational efficiency and achieving proper equipment maintenance have been difficult.

[0416] 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.

[0417] In this invention, the server includes means for generating an educational machine learning model by collecting educational material information and training it using natural language processing; means for selecting appropriate educational content by analyzing the user's learning profile; and means for analyzing information from observation and sensing devices, evaluating the state of the work equipment and the work environment, and proposing and adjusting the optimal work flow. This enables real-time optimization of the work environment, efficient operation of the equipment, and proper maintenance.

[0418] "Educational material information" refers to materials and data used in learners' educational activities.

[0419] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0420] An "educational machine learning model" is an algorithm trained on educational content to support educational activities.

[0421] A "learning profile" is information that records a learner's learning history, interests, and goals.

[0422] "Recommendation methods" are technologies that select and present the most suitable information and content to learners based on collected data.

[0423] An "observation device" is a piece of equipment used to understand the state of the environment or objects.

[0424] A "sensing device" is a sensor or equipment used to detect changes in the environment.

[0425] "Working equipment" refers to machines and robots used to perform tasks in factories and production lines.

[0426] "Work environment" refers to the place and conditions in which work is performed, and includes elements that affect efficiency and safety.

[0427] The system implementing this invention aims to monitor factory equipment in real time and efficiently manage its operation. The system's processing is described below in natural language.

[0428] The server first collects state data obtained from observation and sensing devices. This state data includes the operating status of the work equipment and changes in the work environment. A camera is used as the observation device, and temperature sensors and vibration sensors are included as sensing devices.

[0429] Next, the server applies a training machine learning model using natural language processing techniques to analyze the collected data. This model is trained to optimize the state and working environment of the equipment and can detect equipment overload and inefficiencies. Based on the analysis results, the server proposes an optimal workflow and issues adjustment instructions to the equipment.

[0430] Furthermore, the server transmits this proposal to the terminal in an executable format and automatically performs the necessary operations. This ensures that the work equipment continues to operate efficiently and that necessary maintenance is performed quickly.

[0431] For example, if the server detects a component jam during the assembly process, it analyzes abnormal heat generation and vibration based on data obtained from observation and sensing devices. As a result, the server instructs a temporary shutdown and arranges for necessary support equipment. It also provides feedback to improve the next work process.

[0432] As an example of how this system works, here are some examples of prompt statements for a generative AI model.

[0433] "Robot State Monitoring Prompt: Using current camera footage and sensor data, analyze the robot's emotional state and propose an optimized workflow."

[0434] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0435] Step 1:

[0436] The server collects data in real time from observation and sensing devices. Input data includes camera footage from the work equipment, temperature sensor readings, and vibration sensor data. This data is aggregated on the server, initially recording the work environment and equipment status as digital information.

[0437] Step 2:

[0438] The server runs an educational machine learning model to analyze the collected data. The observation and sensing data obtained in Step 1 are used as input. In data processing, an anomaly pattern recognition algorithm is used to detect signs of abnormalities and overload conditions in the work equipment. If no abnormalities are found, the output indicates that normal operation should continue.

[0439] Step 3:

[0440] If an anomaly is detected, the server re-evaluates the equipment status and surrounding environment data to propose the optimal workflow. The input is the output data from step 2. As part of the data calculation, several scenario simulations are performed to generate proposed equipment operation adjustments. The output of this is the improved work procedure and operation adjustment plan.

[0441] Step 4:

[0442] The terminal receives suggestions from the server and sends specific instructions to the work device. The input is the adjustment plan obtained in step 3. The terminal generates prompt statements, causing the work device to execute sequential operation instructions and pause commands. The output is the safe shutdown of the device and the implementation of optimal adjustments.

[0443] Step 5:

[0444] The user reviews the adjustment results and improved workflow presented by the system and evaluates whether further manual adjustments are needed. In this step, the user confirms the status of the equipment and provides feedback for the next step. The inputs are the output results and monitoring data from step 4, and the output generates an evaluation report and adjustment feedback.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention relates to an educational system that supports lifelong learning by utilizing artificial intelligence and machine learning technologies. The system aims to provide a customized learning experience tailored to each individual user, and is implemented in the following specific form.

[0462] Generation of instructor AI models:

[0463] The server first collects educational data provided by educational institutions and partner scholars. This data includes text, audio clips, and video lectures. The server uses natural language processing techniques with this data to train educational machine learning models. The trained models can convey knowledge to users in a conversational format similar to that of a human instructor. For example, in a "Data Analysis Fundamentals" course, the AI ​​clearly explains terminology definitions and provides examples of analytical methods.

[0464] Automated matching for online courses:

[0465] Users enter their learning profile via their device, sending their areas of interest and goals to the server. The server analyzes this information and generates a list of optimal courses based on past learning history and market trend data. Recommended courses are presented in the user interface, allowing users to select a learning plan that suits their pace. For example, if a user is interested in marketing, customized courses ranging from basic to advanced levels will be suggested.

[0466] Adaptive learning systems:

[0467] The device tracks the user's progress in real time and sends the data to the server. The server evaluates the received data and suggests appropriate content and assignments tailored to each user's learning stage. This adaptive system can accommodate learners with unique strengths and weaknesses, adjusting the curriculum based on their learning speed and comprehension. For example, users struggling with a particular assignment may be provided with additional support materials or practice problems.

[0468] Concentration level monitoring and alerts:

[0469] During learning, the device uses the user's webcam and sensors to measure their level of concentration in real time. This involves using facial recognition technology to analyze expressions and gaze. The server collects this data and automatically sends a notification to the user when it detects a decline in concentration. By displaying a pop-up message recommending a break, the user is encouraged to take action to improve their learning efficiency. This system makes it easier to maintain concentration even during long learning sessions.

[0470] As described above, the system provided is designed to effectively support users' learning habits and create a learning environment that suits individual needs and pace. This aims to improve the efficiency and quality of lifelong learning.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The server retrieves educational material data provided by educational institutions and stores it in a database. This includes learning materials in various formats, such as text files, video clips, and audio data.

[0474] Step 2:

[0475] The server executes natural language processing algorithms and trains an instructor AI model using the acquired teaching material data. This process includes phases of data preprocessing, tokenization, and model training.

[0476] Step 3:

[0477] Users enter their personal learning profiles using their devices, clearly indicating their interests and goals. This includes the fields of study they wish to take and their preferred learning pace.

[0478] Step 4:

[0479] The server analyzes the profile data submitted by the user and compares it with the existing course database to recommend the most suitable courses. These recommendations are refined using machine learning and displayed as a personalized list for the user.

[0480] Step 5:

[0481] The device monitors the user's learning progress in real time and periodically sends this data to the server. This includes assignment completion status, accuracy rate, and study time.

[0482] Step 6:

[0483] The server analyzes the received progress data and adjusts the difficulty level or provides additional learning materials according to the user's needs. This optimizes the user's learning experience.

[0484] Step 7:

[0485] The device measures the user's level of concentration using a webcam during learning and sends data on facial expressions and eye movements to a server. This uses an algorithm to quantify attention.

[0486] Step 8:

[0487] The server analyzes concentration data and displays an alert on the device prompting the user to take a break if their attention is waning. This allows the user to continue learning efficiently.

[0488] (Example 1)

[0489] 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."

[0490] In lifelong learning, there is a need to provide learning materials and learning formats that are suitable for individual learners, enabling efficient and sustainable learning. Furthermore, a mechanism is needed to appropriately manage concentration levels during learning and prevent a decline in efficiency during long study sessions. Traditional education systems have struggled to address individual needs, making it difficult for learners to create a learning environment that is optimal for their own learning style.

[0491] 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.

[0492] In this invention, the server includes means for collecting information and generating a learning model using natural language processing; means for analyzing an individual's learning profile and selecting appropriate educational content for recommendation; means for acquiring an individual's learning progress data in real time and adaptively adjusting the learning content based on that data; means for monitoring an individual's level of concentration during learning using a camera or sensing device; and means for issuing instructions to encourage breaks or refocusing based on the level of concentration data. This provides an optimized learning environment for each individual learner, enabling efficient and sustainable learning.

[0493] "Information gathering" is the process of obtaining necessary information from data providers and using it for processing within the system.

[0494] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0495] "Generating a learning model" means using machine learning based on acquired data to build an algorithmic model that is useful for a specific educational purpose.

[0496] A "personal learning profile" is a dataset that includes personal information such as the learner's interests, goals, and history.

[0497] "Recommendation for selecting educational content" is the process of identifying the educational resources and curricula that are best suited to individual learners.

[0498] "Learning progress data" refers to information that shows the tasks a learner has completed and the knowledge they have acquired to date.

[0499] "Adaptive adjustment" means dynamically changing the learning content and pace according to the progress and abilities of individual learners.

[0500] "Recording device or sensing device" refers to data acquisition hardware such as cameras and sensors that record or sense user activity.

[0501] "Monitoring concentration levels" means measuring and evaluating the learner's level of concentration in real time.

[0502] "Instructions to encourage breaks or refocus" refer to providing advice or action suggestions to help learners regain their focus when they lose concentration.

[0503] This invention is an advanced educational system that supports lifelong learning, providing personalized learning experiences by utilizing artificial intelligence and machine learning technologies. Its embodiments are described in detail below.

[0504] The server first collects information. Specifically, it collects educational resources such as text, audio clips, and videos provided by educational institutions and researchers. The server analyzes this data using natural language processing techniques. Specifically, it performs text analysis using Python's natural language processing libraries, NLTK and spaCy. Furthermore, the server generates and trains educational learning models using machine learning frameworks such as TensorFlow and PyTorch. These models have the ability to provide appropriate learning materials to individual learners.

[0505] Users enter their learning profile through their device, which includes PCs and tablets. Users specify their learning goals and areas of interest and send this information to the server. The server analyzes the received information and creates a personal profile of the user. Based on this profile, the server provides personalized learning content.

[0506] The device tracks the user's progress in real time during learning. This data is sent to a server and used to adjust the learning plan adaptively. For example, supplementary materials are automatically provided to users who are falling behind. Furthermore, the user's concentration level is also monitored, and if their concentration wavers, the device prompts the user to take a break or refocus. Concentration levels are evaluated using facial recognition and eye-tracking technologies.

[0507] For example, if a user wants to take a course on "Basic Data Analysis," they can enter a prompt such as "Please ask a question about basic data analysis," and the AI ​​will provide an appropriate answer.

[0508] This system enables users to achieve efficient and personalized learning, and they can expect improved learning outcomes.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] The server collects educational resources provided by educational institutions and researchers. These resources include text, audio, and video data. The input data is stored in the server's database. The server converts this data into a specific format for classification and organization.

[0512] Step 2:

[0513] The server analyzes data using natural language processing (NLTK) techniques. Specifically, it uses NLTK and spaCy to analyze text data. The input text data undergoes tokenization, part-of-speech tagging, and grammatical analysis to clarify the information structure. As output, feature information specifically tailored for educational purposes is extracted.

[0514] Step 3:

[0515] The server trains a learning model using a machine learning framework (e.g., TensorFlow, PyTorch). Here, the previously extracted feature information is used as input to build a learning model tailored to the purpose. During the training process, a large amount of data samples are used to iteratively learn, and the output is an enhanced model capable of providing expert knowledge to the user.

[0516] Step 4:

[0517] Users enter their learning profile from their device. The device collects information about their areas of interest and learning goals and sends it to the server. The entered personal information is analyzed on the server, and a profile reflecting the user's learning tendencies and needs is generated.

[0518] Step 5:

[0519] The server selects appropriate educational content based on the generated user profile. The server considers past learning history and market trends to list the most suitable learning resources. As output, a personalized list of courses is displayed on the user's device.

[0520] Step 6:

[0521] The device tracks the user's progress in real time during learning. Specifically, it collects log data related to the user's learning process and records their progress. The entered progress data is sent to the server and used to readjust the learning plan.

[0522] Step 7:

[0523] The server analyzes the user's learning progress data and adjusts the learning plan accordingly. If the user is falling behind in a particular area or task, it provides supplementary materials or additional content. As output, the user's optimized learning stage is presented on their device.

[0524] Step 8:

[0525] The device monitors the user's level of concentration. It uses facial recognition and eye-tracking technologies to measure the user's focus. Based on the input monitoring data, the server detects a decline in concentration and sends instructions to the user to take a break or refocus. This encourages actions to maintain learning efficiency.

[0526] (Application Example 1)

[0527] 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."

[0528] In real-world work environments such as factories, there is a need for efficient means to support the skill development of workers. In particular, a system is needed that can learn and adapt immediately in real time when new technologies or equipment are introduced. Furthermore, it is necessary to improve work efficiency and safety by managing concentration levels during work and incorporating appropriate breaks.

[0529] 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.

[0530] In this invention, the server includes a device that generates an educational machine learning model by collecting educational data and training it using natural language processing; a recommendation device that analyzes the user's learning profile and selects appropriate educational content; and a device that monitors the user's level of concentration while working using an eye-tracking device. This enables factory workers to acquire new skills and optimize their work in real time while maintaining work efficiency.

[0531] "Educational material data" refers to a collection of information such as texts, audio clips, and video lectures used for educational purposes.

[0532] "Natural language processing" is a technology that enables computers to understand, generate, and support human language.

[0533] An "educational machine learning model" is an algorithm trained to efficiently perform specific tasks in the field of education.

[0534] A "user learning profile" is a profile that compiles information such as each user's interests, learning history, and goals.

[0535] A "recommendation system" is a system that selects and provides the most suitable educational content based on an analyzed learning profile.

[0536] An "eye-tracking device" is a technology that tracks the movement of a user's eyes and acquires that information in real time.

[0537] A "concentration level monitoring device" is a device that evaluates the user's concentration level and detects signs of a decline in concentration.

[0538] "Work status" is a term that refers to the current progress or conditions in a particular work environment.

[0539] A "device that provides advice in real time" is a system that suggests the optimal course of action for the user during their activities, according to the situation at hand.

[0540] The system that implements this application primarily operates between a server, smart glasses, and the user. The server generates educational machine learning models using natural language processing techniques based on educational data collected from educational institutions and experts. These models are designed to enable users to quickly acquire new skills and are specifically used to assist users in their work in real time.

[0541] Smart glasses function as both eye-tracking and concentration monitoring devices. These glasses track the user's gaze and transmit the data to a server. The server analyzes the received data and evaluates the user's current level of concentration. If concentration decreases, the server sends a notification to the glasses, recommending a break for the user.

[0542] Users can receive real-time advice through smart glasses while working. This advice is provided by a generated AI model and supports improved work efficiency and the acquisition of new skills.

[0543] As a concrete example, during the operation of newly introduced equipment in a factory, a worker wearing smart glasses can receive advice on operating procedures from an AI-based server. Examples of prompts in this case could be, "Please tell me how to operate the new welding machine," or "Please tell me what can be improved in my current work."

[0544] This allows factory workers to continue their work safely and efficiently while immediately learning and adapting to new knowledge.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The server collects educational data from educational institutions and professionals. This data includes text, audio clips, and video lectures, and uses natural language processing techniques to generate educational machine learning models. The input data is raw educational data, and the output is a trained machine learning model.

[0548] Step 2:

[0549] When a user begins working through smart glasses, the device collects information about the user's learning profile and current work status. This information is sent to a server and analyzed by an AI model. The input is the learning profile and work status, and the output is optimal work advice and suggestions for educational content.

[0550] Step 3:

[0551] The server uses an AI model to generate real-time advice for the user. This advice is sent to smart glasses and displayed to the user in a timely manner. The input is work status data and analysis results from the model, and the output is specific work instructions and advice provided to the user.

[0552] Step 4:

[0553] An eye-tracking device collects the user's eye-tracking data and sends it to a server. The server uses this data to evaluate the user's level of concentration. The input is eye-tracking data, and the output is the concentration level evaluation result.

[0554] Step 5:

[0555] Based on the concentration level assessment, the server sends a notification to the user recommending a break if their concentration level has decreased. Smart glasses display this notification to the user. The input is the concentration level assessment result, and the output is the notification prompting a break.

[0556] Step 6:

[0557] The user sends a question to the server using a prompt. The server parses this prompt and provides appropriate educational content or a work guide. The input is the prompt from the user, and the output is the AI's response or advice to it.

[0558] 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.

[0559] This invention incorporates emotion recognition technology into conventional online education systems to provide a more adaptive and effective educational experience based on the emotional state of individual learners. The system includes an educational machine learning model, an emotion engine, a concentration monitoring function, and means for integrating these to dynamically adjust the learning program.

[0560] Generating and utilizing machine learning models for educational purposes:

[0561] The server collects educational material data from educational institutions and trains a teacher AI model using natural language processing technology. This model can explain the materials to users and answer questions. This AI model provides real-time explanations of specific examples in courses on the fundamentals of law, as well as Q&A sessions.

[0562] Online course recommendation and progress management:

[0563] Users create a learning profile by entering their learning objectives and areas of interest through their device. The server recommends the most suitable courses based on the user's profile. This recommendation process also takes into account past learning history and market trends. Furthermore, the device monitors learning progress in real time and sends this information to the server, allowing for dynamic adjustments to the curriculum based on progress.

[0564] Adaptive learning support through emotion recognition:

[0565] The server uses an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. This allows the system to suggest more stimulating content if the user is bored, or to change the learning program to a more relaxing one if they are stressed. For example, if the emotion engine determines the user is fatigued, the system will suggest a short break and adjust the pace of the next lesson accordingly.

[0566] Concentration monitoring and refocus alerts:

[0567] The device measures the user's level of concentration while they are learning using facial expression analysis and eye-tracking technology. Based on this concentration data, the server displays an alert prompting the user to refocus or take a break if it determines that their attention is waning. This allows the user to maintain an efficient learning environment.

[0568] In this way, the system evaluates the user's emotions and level of concentration in real time and flexibly adjusts the learning program based on that evaluation, thereby creating an efficient and personalized learning environment. This helps maintain learner motivation and improve learning efficiency.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The server collects educational material data in various formats from educational institutions and stores it in a database. This data includes text, video, and audio clips, which are used to train educational machine learning models.

[0572] Step 2:

[0573] The server uses natural language processing technology to analyze the collected educational material data and implement an educational AI model. This model is optimized to answer user questions and provide explanations of the educational materials.

[0574] Step 3:

[0575] Users use their devices to enter a learning profile that includes their learning objectives and areas of interest. Based on this, a personalized learning plan is created.

[0576] Step 4:

[0577] The server analyzes the user's learning profile, taking into account past learning history and market trends, to recommend the most suitable course. This ensures that the user receives the most appropriate learning resources.

[0578] Step 5:

[0579] The device tracks the user's progress in real time during learning and sends this information to the server. Based on the progress data, the server dynamically adjusts the learning program and suggests the next appropriate learning stage.

[0580] Step 6:

[0581] The server utilizes an emotion engine to analyze the user's emotional state through camera and microphone data. This allows it to assess the level of stress and interest the user is experiencing while learning.

[0582] Step 7:

[0583] The server adaptively modifies the learning content based on the user's emotional state. If it determines that the user is bored, it provides more interactive content; if the user is stressed, it adjusts the difficulty level.

[0584] Step 8:

[0585] The device uses a webcam to measure the user's level of concentration and sends data on facial expressions and eye movements to the server. If the user's concentration level decreases, the server displays an alert on the device prompting them to refocus, supporting them in continuing to learn efficiently.

[0586] (Example 2)

[0587] 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."

[0588] To provide a flexible learning experience in online education that is tailored to each learner's individual state and progress, it is necessary to grasp their emotional state and concentration level in real time and provide an adaptive curriculum based on that information. However, conventional systems have not been able to adequately reflect learners' emotions and concentration levels, and have failed to provide an effective educational experience. Therefore, there is a need for a system that can improve the quality and effectiveness of online education.

[0589] 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.

[0590] In this invention, the server includes means for generating an educational learning model by collecting educational material information and training it using natural language processing; means for recommending appropriate learning content by analyzing the user's learning information; and means for acquiring the user's learning progress in real time and adaptively adjusting the learning plan based on that data. This makes it possible to understand the learner's emotional state and level of concentration and provide a personalized learning experience that reflects this in real time.

[0591] "Educational material information" refers to educational data provided to learners, including in the form of text, images, and videos.

[0592] "Natural language processing" is a technology that enables machines to understand and process human language, and is used in training educational learning models.

[0593] An "educational learning model" is an artificial intelligence model generated using collected educational material information and natural language processing technology, enabling explanations and question-and-answer sessions for learners.

[0594] "Learning information" refers to data provided by learners regarding their learning objectives, areas of interest, and other relevant information, and serves as the foundation for providing personalized educational experiences.

[0595] "Recommendation methods" refer to the process of analyzing a user's learning information and selecting and providing the most suitable learning content.

[0596] "Learning progress" refers to data that shows how far a learner has progressed in their studies, and it can be collected in real time.

[0597] A "learning plan" is the overall structure of a learning curriculum that is dynamically adjusted based on the learner's progress and status.

[0598] "Emotional state" refers to the emotional state a learner exhibits during the learning process, and is evaluated in real time by technology.

[0599] "Concentration level" refers to the degree of a learner's attention during learning, and it can be evaluated using video and audio equipment.

[0600] The system of this invention is designed to provide an individualized learning experience in online education. The system mainly consists of a server, a terminal, and a user interface.

[0601] The server first collects educational material information. This information comes in various formats, including text, images, and videos, and is obtained from online databases or designated folders. Next, the server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. Specifically, it trains an AI model using a natural language processing library so that it can provide answers to user-submitted questions in real time. For example, if a user submits the question, "What are the fundamental laws of physics?", the AI ​​model will respond, "Newton's laws are the fundamental laws of motion." In this case, an example of a prompt might be, "Please answer a basic question about physics."

[0602] The device collects the user's learning information. Through the device, the user inputs their learning objectives and areas of interest, creating learning information. The device sends this information to a server, which then selects and recommends the most suitable learning content. For example, if a user expresses interest in "design," this recommendation system might suggest courses such as "Fundamentals of UI / UX Design" or "How to Use Graphic Design Tools."

[0603] Furthermore, the device monitors the user's learning progress and sends this information to the server in real time. The server analyzes the progress data and dynamically adjusts the learning plan as needed. For example, when the user is ready to move on to the next module, a notification will appear saying, "We recommend you move on to the next module."

[0604] Furthermore, the server utilizes an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. Emotion recognition allows the server to suggest, for example, "You seem tired. Let's take a 5-minute break," if the user appears fatigued. This enables the user to learn more effectively.

[0605] Finally, the device uses video and audio equipment to measure the user's level of concentration while learning. If it determines that the user's concentration is declining, the device displays an alert such as, "Your concentration is waning. Take a short break to refresh yourself," and helps the user regain their focus.

[0606] In this way, the present invention personalizes the user's learning experience and realizes an effective and efficient educational environment.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] The server collects educational material information. The input consists of educational data such as text, images, and videos obtained from online databases and designated folders. The server collects this data and converts it into a format usable in the next stage. Specifically, it stores this data in a new database.

[0610] Step 2:

[0611] The server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. The input is the educational material information collected in step 1, and the output is the trained AI model. The server uses a natural language processing library to generate a model that the AI ​​can use to respond to questions. Specifically, it analyzes the educational material data and learns relevant topics.

[0612] Step 3:

[0613] Users input learning information through their device. Users create a learning profile by entering their learning objectives and areas of interest. The input is learning objectives and areas of interest, and the output is the learning profile created on the device. The device sends this data to the server.

[0614] Step 4:

[0615] The server analyzes the user's learning information and recommends appropriate learning content. The input is the learning profile obtained in step 3 and past learning data, and the output is a list of recommended content presented to the user. Here, the server selects the most suitable course based on the user's interests and market trends.

[0616] Step 5:

[0617] The device monitors the user's learning progress and transmits it to the server in real time. Input consists of user operation data and progress data during learning, while output is learning progress information received by the server. The device records chapters studied, video viewing times, and other data.

[0618] Step 6:

[0619] The server analyzes the progress data and dynamically adjusts the learning plan as needed. The input is the learning progress data sent in step 5, and the output is the adjusted learning plan. Based on the analysis results, the server recommends the next learning step or repeated learning.

[0620] Step 7:

[0621] The server uses an emotion engine to collect and analyze emotional data from the user's webcam and microphone. Input is real-time audio and facial expression data, and output is a judgment about the user's emotional state. Specifically, it evaluates the user's stress level and energy level.

[0622] Step 8:

[0623] The terminal measures the user's level of concentration using video and audio equipment. Input is data obtained from facial expression analysis and eye tracking, and output is a concentration level value. Based on this, the terminal displays an alert when concentration decreases.

[0624] In this way, the system sequentially collects and analyzes data to provide a personalized learning experience.

[0625] (Application Example 2)

[0626] 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."

[0627] To achieve efficient operation of factory machinery, it is necessary to monitor the working environment and equipment status in real time and make adaptive adjustments. However, conventional systems have had the problem of difficulty in comprehensively monitoring these elements and responding quickly. As a result, improving operational efficiency and achieving proper equipment maintenance have been difficult.

[0628] 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.

[0629] In this invention, the server includes means for generating an educational machine learning model by collecting educational material information and training it using natural language processing; means for selecting appropriate educational content by analyzing the user's learning profile; and means for analyzing information from observation and sensing devices, evaluating the state of the work equipment and the work environment, and proposing and adjusting the optimal work flow. This enables real-time optimization of the work environment, efficient operation of the equipment, and proper maintenance.

[0630] "Educational material information" refers to materials and data used in learners' educational activities.

[0631] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0632] An "educational machine learning model" is an algorithm trained on educational content to support educational activities.

[0633] A "learning profile" is information that records a learner's learning history, interests, and goals.

[0634] "Recommendation methods" are technologies that select and present the most suitable information and content to learners based on collected data.

[0635] An "observation device" is a piece of equipment used to understand the state of the environment or objects.

[0636] A "sensing device" is a sensor or equipment used to detect changes in the environment.

[0637] "Working equipment" refers to machines and robots used to perform tasks in factories and production lines.

[0638] "Work environment" refers to the place and conditions in which work is performed, and includes elements that affect efficiency and safety.

[0639] The system implementing this invention aims to monitor factory equipment in real time and efficiently manage its operation. The system's processing is described below in natural language.

[0640] The server first collects state data obtained from observation and sensing devices. This state data includes the operating status of the work equipment and changes in the work environment. A camera is used as the observation device, and temperature sensors and vibration sensors are included as sensing devices.

[0641] Next, the server applies a training machine learning model using natural language processing techniques to analyze the collected data. This model is trained to optimize the state and working environment of the equipment and can detect equipment overload and inefficiencies. Based on the analysis results, the server proposes an optimal workflow and issues adjustment instructions to the equipment.

[0642] Furthermore, the server transmits this proposal to the terminal in an executable format and automatically performs the necessary operations. This ensures that the work equipment continues to operate efficiently and that necessary maintenance is performed quickly.

[0643] For example, if the server detects a component jam during the assembly process, it analyzes abnormal heat generation and vibration based on data obtained from observation and sensing devices. As a result, the server instructs a temporary shutdown and arranges for necessary support equipment. It also provides feedback to improve the next work process.

[0644] As an example of how this system works, here are some examples of prompt statements for a generative AI model.

[0645] "Robot State Monitoring Prompt: Using current camera footage and sensor data, analyze the robot's emotional state and propose an optimized workflow."

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The server collects data in real time from observation and sensing devices. Input data includes camera footage from the work equipment, temperature sensor readings, and vibration sensor data. This data is aggregated on the server, initially recording the work environment and equipment status as digital information.

[0649] Step 2:

[0650] The server runs an educational machine learning model to analyze the collected data. The observation and sensing data obtained in Step 1 are used as input. In data processing, an anomaly pattern recognition algorithm is used to detect signs of abnormalities and overload conditions in the work equipment. If no abnormalities are found, the output indicates that normal operation should continue.

[0651] Step 3:

[0652] If an anomaly is detected, the server re-evaluates the equipment status and surrounding environment data to propose the optimal workflow. The input is the output data from step 2. As part of the data calculation, several scenario simulations are performed to generate proposed equipment operation adjustments. The output of this is the improved work procedure and operation adjustment plan.

[0653] Step 4:

[0654] The terminal receives suggestions from the server and sends specific instructions to the work device. The input is the adjustment plan obtained in step 3. The terminal generates prompt statements, causing the work device to execute sequential operation instructions and pause commands. The output is the safe shutdown of the device and the implementation of optimal adjustments.

[0655] Step 5:

[0656] The user reviews the adjustment results and improved workflow presented by the system and evaluates whether further manual adjustments are needed. In this step, the user confirms the status of the equipment and provides feedback for the next step. The inputs are the output results and monitoring data from step 4, and the output generates an evaluation report and adjustment feedback.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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).

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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".

[0674] This invention relates to an educational system that supports lifelong learning by utilizing artificial intelligence and machine learning technologies. The system aims to provide a customized learning experience tailored to each individual user, and is implemented in the following specific form.

[0675] Generation of instructor AI models:

[0676] The server first collects educational data provided by educational institutions and partner scholars. This data includes text, audio clips, and video lectures. The server uses natural language processing techniques with this data to train educational machine learning models. The trained models can convey knowledge to users in a conversational format similar to that of a human instructor. For example, in a "Data Analysis Fundamentals" course, the AI ​​clearly explains terminology definitions and provides examples of analytical methods.

[0677] Automated matching for online courses:

[0678] Users enter their learning profile via their device, sending their areas of interest and goals to the server. The server analyzes this information and generates a list of optimal courses based on past learning history and market trend data. Recommended courses are presented in the user interface, allowing users to select a learning plan that suits their pace. For example, if a user is interested in marketing, customized courses ranging from basic to advanced levels will be suggested.

[0679] Adaptive learning systems:

[0680] The device tracks the user's progress in real time and sends the data to the server. The server evaluates the received data and suggests appropriate content and assignments tailored to each user's learning stage. This adaptive system can accommodate learners with unique strengths and weaknesses, adjusting the curriculum based on their learning speed and comprehension. For example, users struggling with a particular assignment may be provided with additional support materials or practice problems.

[0681] Concentration level monitoring and alerts:

[0682] During learning, the device uses the user's webcam and sensors to measure their level of concentration in real time. This involves using facial recognition technology to analyze expressions and gaze. The server collects this data and automatically sends a notification to the user when it detects a decline in concentration. By displaying a pop-up message recommending a break, the user is encouraged to take action to improve their learning efficiency. This system makes it easier to maintain concentration even during long learning sessions.

[0683] As described above, the system provided is designed to effectively support users' learning habits and create a learning environment that suits individual needs and pace. This aims to improve the efficiency and quality of lifelong learning.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The server retrieves educational material data provided by educational institutions and stores it in a database. This includes learning materials in various formats, such as text files, video clips, and audio data.

[0687] Step 2:

[0688] The server executes natural language processing algorithms and trains an instructor AI model using the acquired teaching material data. This process includes phases of data preprocessing, tokenization, and model training.

[0689] Step 3:

[0690] Users enter their personal learning profiles using their devices, clearly indicating their interests and goals. This includes the fields of study they wish to take and their preferred learning pace.

[0691] Step 4:

[0692] The server analyzes the profile data submitted by the user and compares it with the existing course database to recommend the most suitable courses. These recommendations are refined using machine learning and displayed as a personalized list for the user.

[0693] Step 5:

[0694] The device monitors the user's learning progress in real time and periodically sends this data to the server. This includes assignment completion status, accuracy rate, and study time.

[0695] Step 6:

[0696] The server analyzes the received progress data and adjusts the difficulty level or provides additional learning materials according to the user's needs. This optimizes the user's learning experience.

[0697] Step 7:

[0698] The device measures the user's level of concentration using a webcam during learning and sends data on facial expressions and eye movements to a server. This uses an algorithm to quantify attention.

[0699] Step 8:

[0700] The server analyzes concentration data and displays an alert on the device prompting the user to take a break if their attention is waning. This allows the user to continue learning efficiently.

[0701] (Example 1)

[0702] 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".

[0703] In lifelong learning, there is a need to provide learning materials and learning formats that are suitable for individual learners, enabling efficient and sustainable learning. Furthermore, a mechanism is needed to appropriately manage concentration levels during learning and prevent a decline in efficiency during long study sessions. Traditional education systems have struggled to address individual needs, making it difficult for learners to create a learning environment that is optimal for their own learning style.

[0704] 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.

[0705] In this invention, the server includes means for collecting information and generating a learning model using natural language processing; means for analyzing an individual's learning profile and selecting appropriate educational content for recommendation; means for acquiring an individual's learning progress data in real time and adaptively adjusting the learning content based on that data; means for monitoring an individual's level of concentration during learning using a camera or sensing device; and means for issuing instructions to encourage breaks or refocusing based on the level of concentration data. This provides an optimized learning environment for each individual learner, enabling efficient and sustainable learning.

[0706] "Information gathering" is the process of obtaining necessary information from data providers and using it for processing within the system.

[0707] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0708] "Generating a learning model" means using machine learning based on acquired data to build an algorithmic model that is useful for a specific educational purpose.

[0709] A "personal learning profile" is a dataset that includes personal information such as the learner's interests, goals, and history.

[0710] "Recommendation for selecting educational content" is the process of identifying the educational resources and curricula that are best suited to individual learners.

[0711] "Learning progress data" refers to information that shows the tasks a learner has completed and the knowledge they have acquired to date.

[0712] "Adaptive adjustment" means dynamically changing the learning content and pace according to the progress and abilities of individual learners.

[0713] "Recording device or sensing device" refers to data acquisition hardware such as cameras and sensors that record or sense user activity.

[0714] "Monitoring concentration levels" means measuring and evaluating the learner's level of concentration in real time.

[0715] "Instructions to encourage breaks or refocus" refer to providing advice or action suggestions to help learners regain their focus when they lose concentration.

[0716] This invention is an advanced educational system that supports lifelong learning, providing personalized learning experiences by utilizing artificial intelligence and machine learning technologies. Its embodiments are described in detail below.

[0717] The server first collects information. Specifically, it collects educational resources such as text, audio clips, and videos provided by educational institutions and researchers. The server analyzes this data using natural language processing techniques. Specifically, it performs text analysis using Python's natural language processing libraries, NLTK and spaCy. Furthermore, the server generates and trains educational learning models using machine learning frameworks such as TensorFlow and PyTorch. These models have the ability to provide appropriate learning materials to individual learners.

[0718] Users enter their learning profile through their device, which includes PCs and tablets. Users specify their learning goals and areas of interest and send this information to the server. The server analyzes the received information and creates a personal profile of the user. Based on this profile, the server provides personalized learning content.

[0719] The device tracks the user's progress in real time during learning. This data is sent to a server and used to adjust the learning plan adaptively. For example, supplementary materials are automatically provided to users who are falling behind. Furthermore, the user's concentration level is also monitored, and if their concentration wavers, the device prompts the user to take a break or refocus. Concentration levels are evaluated using facial recognition and eye-tracking technologies.

[0720] For example, if a user wants to take a course on "Basic Data Analysis," they can enter a prompt such as "Please ask a question about basic data analysis," and the AI ​​will provide an appropriate answer.

[0721] This system enables users to achieve efficient and personalized learning, and they can expect improved learning outcomes.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] The server collects educational resources provided by educational institutions and researchers. These resources include text, audio, and video data. The input data is stored in the server's database. The server converts this data into a specific format for classification and organization.

[0725] Step 2:

[0726] The server analyzes data using natural language processing (NLTK) techniques. Specifically, it uses NLTK and spaCy to analyze text data. The input text data undergoes tokenization, part-of-speech tagging, and grammatical analysis to clarify the information structure. As output, feature information specifically tailored for educational purposes is extracted.

[0727] Step 3:

[0728] The server trains a learning model using a machine learning framework (e.g., TensorFlow, PyTorch). Here, the previously extracted feature information is used as input to build a learning model tailored to the purpose. During the training process, a large amount of data samples are used to iteratively learn, and the output is an enhanced model capable of providing expert knowledge to the user.

[0729] Step 4:

[0730] Users enter their learning profile from their device. The device collects information about their areas of interest and learning goals and sends it to the server. The entered personal information is analyzed on the server, and a profile reflecting the user's learning tendencies and needs is generated.

[0731] Step 5:

[0732] The server selects appropriate educational content based on the generated user profile. The server considers past learning history and market trends to list the most suitable learning resources. As output, a personalized list of courses is displayed on the user's device.

[0733] Step 6:

[0734] The device tracks the user's progress in real time during learning. Specifically, it collects log data related to the user's learning process and records their progress. The entered progress data is sent to the server and used to readjust the learning plan.

[0735] Step 7:

[0736] The server analyzes the user's learning progress data and adjusts the learning plan accordingly. If the user is falling behind in a particular area or task, it provides supplementary materials or additional content. As output, the user's optimized learning stage is presented on their device.

[0737] Step 8:

[0738] The device monitors the user's level of concentration. It uses facial recognition and eye-tracking technologies to measure the user's focus. Based on the input monitoring data, the server detects a decline in concentration and sends instructions to the user to take a break or refocus. This encourages actions to maintain learning efficiency.

[0739] (Application Example 1)

[0740] 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".

[0741] In real-world work environments such as factories, there is a need for efficient means to support the skill development of workers. In particular, a system is needed that can learn and adapt immediately in real time when new technologies or equipment are introduced. Furthermore, it is necessary to improve work efficiency and safety by managing concentration levels during work and incorporating appropriate breaks.

[0742] 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.

[0743] In this invention, the server includes a device that generates an educational machine learning model by collecting educational data and training it using natural language processing; a recommendation device that analyzes the user's learning profile and selects appropriate educational content; and a device that monitors the user's level of concentration while working using an eye-tracking device. This enables factory workers to acquire new skills and optimize their work in real time while maintaining work efficiency.

[0744] "Educational material data" refers to a collection of information such as texts, audio clips, and video lectures used for educational purposes.

[0745] "Natural language processing" is a technology that enables computers to understand, generate, and support human language.

[0746] An "educational machine learning model" is an algorithm trained to efficiently perform specific tasks in the field of education.

[0747] A "user learning profile" is a profile that compiles information such as each user's interests, learning history, and goals.

[0748] A "recommendation system" is a system that selects and provides the most suitable educational content based on an analyzed learning profile.

[0749] An "eye-tracking device" is a technology that tracks the movement of a user's eyes and acquires that information in real time.

[0750] A "concentration level monitoring device" is a device that evaluates the user's concentration level and detects signs of a decline in concentration.

[0751] "Work status" is a term that refers to the current progress or conditions in a particular work environment.

[0752] A "device that provides advice in real time" is a system that suggests the optimal course of action for the user during their activities, according to the situation at hand.

[0753] The system that implements this application primarily operates between a server, smart glasses, and the user. The server generates educational machine learning models using natural language processing techniques based on educational data collected from educational institutions and experts. These models are designed to enable users to quickly acquire new skills and are specifically used to assist users in their work in real time.

[0754] Smart glasses function as both eye-tracking and concentration monitoring devices. These glasses track the user's gaze and transmit the data to a server. The server analyzes the received data and evaluates the user's current level of concentration. If concentration decreases, the server sends a notification to the glasses, recommending a break for the user.

[0755] Users can receive real-time advice through smart glasses while working. This advice is provided by a generated AI model and supports improved work efficiency and the acquisition of new skills.

[0756] As a concrete example, during the operation of newly introduced equipment in a factory, a worker wearing smart glasses can receive advice on operating procedures from an AI-based server. Examples of prompts in this case could be, "Please tell me how to operate the new welding machine," or "Please tell me what can be improved in my current work."

[0757] This allows factory workers to continue their work safely and efficiently while immediately learning and adapting to new knowledge.

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] The server collects educational data from educational institutions and professionals. This data includes text, audio clips, and video lectures, and uses natural language processing techniques to generate educational machine learning models. The input data is raw educational data, and the output is a trained machine learning model.

[0761] Step 2:

[0762] When a user begins working through smart glasses, the device collects information about the user's learning profile and current work status. This information is sent to a server and analyzed by an AI model. The input is the learning profile and work status, and the output is optimal work advice and suggestions for educational content.

[0763] Step 3:

[0764] The server uses an AI model to generate real-time advice for the user. This advice is sent to smart glasses and displayed to the user in a timely manner. The input is work status data and analysis results from the model, and the output is specific work instructions and advice provided to the user.

[0765] Step 4:

[0766] An eye-tracking device collects the user's eye-tracking data and sends it to a server. The server uses this data to evaluate the user's level of concentration. The input is eye-tracking data, and the output is the concentration level evaluation result.

[0767] Step 5:

[0768] Based on the concentration level assessment, the server sends a notification to the user recommending a break if their concentration level has decreased. Smart glasses display this notification to the user. The input is the concentration level assessment result, and the output is the notification prompting a break.

[0769] Step 6:

[0770] The user sends a question to the server using a prompt. The server parses this prompt and provides appropriate educational content or a work guide. The input is the prompt from the user, and the output is the AI's response or advice to it.

[0771] 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.

[0772] This invention incorporates emotion recognition technology into conventional online education systems to provide a more adaptive and effective educational experience based on the emotional state of individual learners. The system includes an educational machine learning model, an emotion engine, a concentration monitoring function, and means for integrating these to dynamically adjust the learning program.

[0773] Generating and utilizing machine learning models for educational purposes:

[0774] The server collects educational material data from educational institutions and trains a teacher AI model using natural language processing technology. This model can explain the materials to users and answer questions. This AI model provides real-time explanations of specific examples in courses on the fundamentals of law, as well as Q&A sessions.

[0775] Online course recommendation and progress management:

[0776] Users create a learning profile by entering their learning objectives and areas of interest through their device. The server recommends the most suitable courses based on the user's profile. This recommendation process also takes into account past learning history and market trends. Furthermore, the device monitors learning progress in real time and sends this information to the server, allowing for dynamic adjustments to the curriculum based on progress.

[0777] Adaptive learning support through emotion recognition:

[0778] The server uses an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. This allows the system to suggest more stimulating content if the user is bored, or to change the learning program to a more relaxing one if they are stressed. For example, if the emotion engine determines the user is fatigued, the system will suggest a short break and adjust the pace of the next lesson accordingly.

[0779] Concentration monitoring and refocus alerts:

[0780] The device measures the user's level of concentration while they are learning using facial expression analysis and eye-tracking technology. Based on this concentration data, the server displays an alert prompting the user to refocus or take a break if it determines that their attention is waning. This allows the user to maintain an efficient learning environment.

[0781] In this way, the system evaluates the user's emotions and level of concentration in real time and flexibly adjusts the learning program based on that evaluation, thereby creating an efficient and personalized learning environment. This helps maintain learner motivation and improve learning efficiency.

[0782] The following describes the processing flow.

[0783] Step 1:

[0784] The server collects educational material data in various formats from educational institutions and stores it in a database. This data includes text, video, and audio clips, which are used to train educational machine learning models.

[0785] Step 2:

[0786] The server uses natural language processing technology to analyze the collected educational material data and implement an educational AI model. This model is optimized to answer user questions and provide explanations of the educational materials.

[0787] Step 3:

[0788] Users use their devices to enter a learning profile that includes their learning objectives and areas of interest. Based on this, a personalized learning plan is created.

[0789] Step 4:

[0790] The server analyzes the user's learning profile, taking into account past learning history and market trends, to recommend the most suitable course. This ensures that the user receives the most appropriate learning resources.

[0791] Step 5:

[0792] The device tracks the user's progress in real time during learning and sends this information to the server. Based on the progress data, the server dynamically adjusts the learning program and suggests the next appropriate learning stage.

[0793] Step 6:

[0794] The server utilizes an emotion engine to analyze the user's emotional state through camera and microphone data. This allows it to assess the level of stress and interest the user is experiencing while learning.

[0795] Step 7:

[0796] The server adaptively modifies the learning content based on the user's emotional state. If it determines that the user is bored, it provides more interactive content; if the user is stressed, it adjusts the difficulty level.

[0797] Step 8:

[0798] The device uses a webcam to measure the user's level of concentration and sends data on facial expressions and eye movements to the server. If the user's concentration level decreases, the server displays an alert on the device prompting them to refocus, supporting them in continuing to learn efficiently.

[0799] (Example 2)

[0800] 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".

[0801] To provide a flexible learning experience in online education that is tailored to each learner's individual state and progress, it is necessary to grasp their emotional state and concentration level in real time and provide an adaptive curriculum based on that information. However, conventional systems have not been able to adequately reflect learners' emotions and concentration levels, and have failed to provide an effective educational experience. Therefore, there is a need for a system that can improve the quality and effectiveness of online education.

[0802] 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.

[0803] In this invention, the server includes means for generating an educational learning model by collecting educational material information and training it using natural language processing; means for recommending appropriate learning content by analyzing the user's learning information; and means for acquiring the user's learning progress in real time and adaptively adjusting the learning plan based on that data. This makes it possible to understand the learner's emotional state and level of concentration and provide a personalized learning experience that reflects this in real time.

[0804] "Educational material information" refers to educational data provided to learners, including in the form of text, images, and videos.

[0805] "Natural language processing" is a technology that enables machines to understand and process human language, and is used in training educational learning models.

[0806] An "educational learning model" is an artificial intelligence model generated using collected educational material information and natural language processing technology, enabling explanations and question-and-answer sessions for learners.

[0807] "Learning information" refers to data provided by learners regarding their learning objectives, areas of interest, and other relevant information, and serves as the foundation for providing personalized educational experiences.

[0808] "Recommendation methods" refer to the process of analyzing a user's learning information and selecting and providing the most suitable learning content.

[0809] "Learning progress" refers to data that shows how far a learner has progressed in their studies, and it can be collected in real time.

[0810] A "learning plan" is the overall structure of a learning curriculum that is dynamically adjusted based on the learner's progress and status.

[0811] "Emotional state" refers to the emotional state a learner exhibits during the learning process, and is evaluated in real time by technology.

[0812] "Concentration level" refers to the degree of a learner's attention during learning, and it can be evaluated using video and audio equipment.

[0813] The system of this invention is designed to provide an individualized learning experience in online education. The system mainly consists of a server, a terminal, and a user interface.

[0814] The server first collects educational material information. This information comes in various formats, including text, images, and videos, and is obtained from online databases or designated folders. Next, the server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. Specifically, it trains an AI model using a natural language processing library so that it can provide answers to user-submitted questions in real time. For example, if a user submits the question, "What are the fundamental laws of physics?", the AI ​​model will respond, "Newton's laws are the fundamental laws of motion." In this case, an example of a prompt might be, "Please answer a basic question about physics."

[0815] The device collects the user's learning information. Through the device, the user inputs their learning objectives and areas of interest, creating learning information. The device sends this information to a server, which then selects and recommends the most suitable learning content. For example, if a user expresses interest in "design," this recommendation system might suggest courses such as "Fundamentals of UI / UX Design" or "How to Use Graphic Design Tools."

[0816] Furthermore, the device monitors the user's learning progress and sends this information to the server in real time. The server analyzes the progress data and dynamically adjusts the learning plan as needed. For example, when the user is ready to move on to the next module, a notification will appear saying, "We recommend you move on to the next module."

[0817] Furthermore, the server utilizes an emotion engine to analyze data from the user's webcam and microphone to understand their emotional state during learning. Emotion recognition allows the server to suggest, for example, "You seem tired. Let's take a 5-minute break," if the user appears fatigued. This enables the user to learn more effectively.

[0818] Finally, the device uses video and audio equipment to measure the user's level of concentration while learning. If it determines that the user's concentration is declining, the device displays an alert such as, "Your concentration is waning. Take a short break to refresh yourself," and helps the user regain their focus.

[0819] In this way, the present invention personalizes the user's learning experience and realizes an effective and efficient educational environment.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] The server collects educational material information. The input consists of educational data such as text, images, and videos obtained from online databases and designated folders. The server collects this data and converts it into a format usable in the next stage. Specifically, it stores this data in a new database.

[0823] Step 2:

[0824] The server uses the collected educational material information to generate an educational learning model utilizing natural language processing technology. The input is the educational material information collected in step 1, and the output is the trained AI model. The server uses a natural language processing library to generate a model that the AI ​​can use to respond to questions. Specifically, it analyzes the educational material data and learns relevant topics.

[0825] Step 3:

[0826] Users input learning information through their device. Users create a learning profile by entering their learning objectives and areas of interest. The input is learning objectives and areas of interest, and the output is the learning profile created on the device. The device sends this data to the server.

[0827] Step 4:

[0828] The server analyzes the user's learning information and recommends appropriate learning content. The input is the learning profile obtained in step 3 and past learning data, and the output is a list of recommended content presented to the user. Here, the server selects the most suitable course based on the user's interests and market trends.

[0829] Step 5:

[0830] The device monitors the user's learning progress and transmits it to the server in real time. Input consists of user operation data and progress data during learning, while output is learning progress information received by the server. The device records chapters studied, video viewing times, and other data.

[0831] Step 6:

[0832] The server analyzes the progress data and dynamically adjusts the learning plan as needed. The input is the learning progress data sent in step 5, and the output is the adjusted learning plan. Based on the analysis results, the server recommends the next learning step or repeated learning.

[0833] Step 7:

[0834] The server uses an emotion engine to collect and analyze emotional data from the user's webcam and microphone. Input is real-time audio and facial expression data, and output is a judgment about the user's emotional state. Specifically, it evaluates the user's stress level and energy level.

[0835] Step 8:

[0836] The terminal measures the user's level of concentration using video and audio equipment. Input is data obtained from facial expression analysis and eye tracking, and output is a concentration level value. Based on this, the terminal displays an alert when concentration decreases.

[0837] In this way, the system sequentially collects and analyzes data to provide a personalized learning experience.

[0838] (Application Example 2)

[0839] 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".

[0840] To achieve efficient operation of factory machinery, it is necessary to monitor the working environment and equipment status in real time and make adaptive adjustments. However, conventional systems have had the problem of difficulty in comprehensively monitoring these elements and responding quickly. As a result, improving operational efficiency and achieving proper equipment maintenance have been difficult.

[0841] 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.

[0842] In this invention, the server includes means for generating an educational machine learning model by collecting educational material information and training it using natural language processing; means for selecting appropriate educational content by analyzing the user's learning profile; and means for analyzing information from observation and sensing devices, evaluating the state of the work equipment and the work environment, and proposing and adjusting the optimal work flow. This enables real-time optimization of the work environment, efficient operation of the equipment, and proper maintenance.

[0843] "Educational material information" refers to materials and data used in learners' educational activities.

[0844] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0845] An "educational machine learning model" is an algorithm trained on educational content to support educational activities.

[0846] A "learning profile" is information that records a learner's learning history, interests, and goals.

[0847] "Recommendation methods" are technologies that select and present the most suitable information and content to learners based on collected data.

[0848] An "observation device" is a piece of equipment used to understand the state of the environment or objects.

[0849] A "sensing device" is a sensor or equipment used to detect changes in the environment.

[0850] "Working equipment" refers to machines and robots used to perform tasks in factories and production lines.

[0851] "Work environment" refers to the place and conditions in which work is performed, and includes elements that affect efficiency and safety.

[0852] The system implementing this invention aims to monitor factory equipment in real time and efficiently manage its operation. The system's processing is described below in natural language.

[0853] The server first collects state data obtained from observation and sensing devices. This state data includes the operating status of the work equipment and changes in the work environment. A camera is used as the observation device, and temperature sensors and vibration sensors are included as sensing devices.

[0854] Next, the server applies a training machine learning model using natural language processing techniques to analyze the collected data. This model is trained to optimize the state and working environment of the equipment and can detect equipment overload and inefficiencies. Based on the analysis results, the server proposes an optimal workflow and issues adjustment instructions to the equipment.

[0855] Furthermore, the server transmits this proposal to the terminal in an executable format and automatically performs the necessary operations. This ensures that the work equipment continues to operate efficiently and that necessary maintenance is performed quickly.

[0856] For example, if the server detects a component jam during the assembly process, it analyzes abnormal heat generation and vibration based on data obtained from observation and sensing devices. As a result, the server instructs a temporary shutdown and arranges for necessary support equipment. It also provides feedback to improve the next work process.

[0857] As an example of how this system works, here are some examples of prompt statements for a generative AI model.

[0858] "Robot State Monitoring Prompt: Using current camera footage and sensor data, analyze the robot's emotional state and propose an optimized workflow."

[0859] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0860] Step 1:

[0861] The server collects data in real time from observation and sensing devices. Input data includes camera footage from the work equipment, temperature sensor readings, and vibration sensor data. This data is aggregated on the server, initially recording the work environment and equipment status as digital information.

[0862] Step 2:

[0863] The server runs an educational machine learning model to analyze the collected data. The observation and sensing data obtained in Step 1 are used as input. In data processing, an anomaly pattern recognition algorithm is used to detect signs of abnormalities and overload conditions in the work equipment. If no abnormalities are found, the output indicates that normal operation should continue.

[0864] Step 3:

[0865] If an anomaly is detected, the server re-evaluates the equipment status and surrounding environment data to propose the optimal workflow. The input is the output data from step 2. As part of the data calculation, several scenario simulations are performed to generate proposed equipment operation adjustments. The output of this is the improved work procedure and operation adjustment plan.

[0866] Step 4:

[0867] The terminal receives suggestions from the server and sends specific instructions to the work device. The input is the adjustment plan obtained in step 3. The terminal generates prompt statements, causing the work device to execute sequential operation instructions and pause commands. The output is the safe shutdown of the device and the implementation of optimal adjustments.

[0868] Step 5:

[0869] The user reviews the adjustment results and improved workflow presented by the system and evaluates whether further manual adjustments are needed. In this step, the user confirms the status of the equipment and provides feedback for the next step. The inputs are the output results and monitoring data from step 4, and the output generates an evaluation report and adjustment feedback.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] A means of generating an educational machine learning model by collecting educational data and training it using natural language processing,

[0894] A recommendation system that analyzes the user's learning profile to select appropriate educational content,

[0895] A means of acquiring user learning progress data in real time and adaptively adjusting the learning program based on that data,

[0896] A means of monitoring the user's level of concentration while learning, using a camera or sensor,

[0897] A means of providing suggestions to encourage breaks and refocus based on concentration level data,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, characterized in that the recommendation method also takes into account the user's past learning history and market trends.

[0901] (Claim 3)

[0902] The system according to claim 1, characterized in that the concentration level monitoring means evaluates the level of concentration by analyzing the user's facial expressions and gaze.

[0903] "Example 1"

[0904] (Claim 1)

[0905] A means for collecting information and generating a learning model using natural language processing,

[0906] A recommendation system that analyzes an individual's learning profile to select appropriate educational content,

[0907] A means of acquiring individual learning progress data in real time and adaptively adjusting the learning content based on that data,

[0908] A means of monitoring an individual's level of concentration during learning using a camera or sensing device,

[0909] A means of giving instructions to encourage breaks or refocus based on concentration level data,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, characterized in that the recommendation method also takes into account an individual's past learning history and market trends.

[0913] (Claim 3)

[0914] The system according to claim 1, characterized in that the concentration level monitoring means evaluates the level of concentration by analyzing an individual's facial expressions and gaze.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A device that generates an educational machine learning model by collecting educational data and training it using natural language processing,

[0918] A recommendation system that analyzes the user's learning profile and selects appropriate educational content,

[0919] A device that acquires user learning progress data in real time and adaptively adjusts the learning plan based on that data,

[0920] A device that uses eye-tracking technology to monitor the user's level of concentration while working,

[0921] A device that provides notifications to encourage breaks or refocus based on concentration level data,

[0922] A device that provides advice in real time based on the work status,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, characterized in that the recommendation device takes into account the user's past learning history, market trends, and work status.

[0926] (Claim 3)

[0927] The system according to claim 1, characterized in that the concentration monitoring device evaluates the user's level of concentration by analyzing their gaze and supports the improvement of work efficiency.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] A means for generating an educational learning model by collecting educational material information and training it using natural language processing,

[0931] A recommendation system that analyzes users' learning information to select appropriate learning content,

[0932] A means of acquiring the user's learning progress in real time and adaptively adjusting the learning plan based on that data,

[0933] A means of analyzing the user's emotional state using emotion recognition technology and dynamically adjusting the learning experience,

[0934] A means for measuring the level of concentration of a user during learning, using video or audio equipment,

[0935] A means of providing notifications to encourage breaks or refocusing based on concentration level information,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, characterized in that the recommendation method also takes into account the user's past learning history and market trends.

[0939] (Claim 3)

[0940] The system according to claim 1, characterized in that the concentration level measurement means evaluates the level of concentration by analyzing changes in the user's facial expressions and eye tracking.

[0941] "Application example 2 when combining with an emotional engine"

[0942] (Claim 1)

[0943] A means of generating an educational machine learning model by collecting educational material information and training it using natural language processing,

[0944] A recommendation system that analyzes the user's learning profile to select appropriate educational content,

[0945] A means of acquiring user learning progress data in real time and adaptively adjusting the learning program based on that data,

[0946] A means for monitoring the user's level of concentration while learning, using an observation device or sensing device,

[0947] A means of providing suggestions to encourage breaks and refocus based on concentration level data,

[0948] A means of analyzing information from observation and sensing devices, evaluating the status of work equipment and the work environment, and proposing and adjusting the optimal work flow,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, characterized in that, in the recommendation method, it takes into account the user's past learning history and market trends, as well as the historical information of the work equipment and changes in the surrounding environment.

[0952] (Claim 3)

[0953] The system according to claim 1, characterized in that the concentration level monitoring means evaluates the level of concentration by analyzing the facial expressions, gaze, or movements of the user or work device. [Explanation of Symbols]

[0954] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of generating an educational machine learning model by collecting educational data and training it using natural language processing, A recommendation system that analyzes the user's learning profile to select appropriate educational content, A means of acquiring user learning progress data in real time and adaptively adjusting the learning program based on that data, A means of monitoring the user's level of concentration while learning, using a camera or sensor, A means of providing suggestions to encourage breaks and refocus based on concentration level data, A system that includes this.

2. The system according to claim 1, characterized in that the recommendation method also takes into account the user's past learning history and market trends.

3. The system according to claim 1, characterized in that the concentration level monitoring means evaluates the level of concentration by analyzing the user's facial expressions and gaze.

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