system

Abstract

Provide a system. 【Solution means】 An information processing device that automatically generates a learning plan based on the user's skill level and learning goals, A device that provides educational information in an interactive format according to the learning plan, A device that generates a response using natural language processing based on an input from a user, A device that monitors the user's learning progress in real time and provides feedback according to the progress and degree of achievement, A device that translates learning information to support multiple natural languages, A device that provides learning content to support skill improvement in the user's in-home activities, A device that guides the progress of in-home work according to the user's skills, A learning support system including the above.

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, the method 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 digital society, the digital literacy gap has become prominent as a social issue. In particular, it is difficult for the elderly and those unfamiliar with technology to effectively acquire the digital skills necessary in daily life and the workplace. Also, it is difficult to provide a common learning program for users with diverse cultural backgrounds. There is a need to solve this problem and provide an environment in which all users can effectively learn in a way suitable for themselves.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that automatically generates a learning plan based on the user's individual skill level and learning objectives. The system provides educational information in an interactive format and instantly generates appropriate answers to user questions using natural language processing. It also supports continuous improvement of learning by monitoring the user's learning progress in real time and providing feedback according to the progress. Furthermore, by supporting multiple languages ​​and selecting learning materials that take into account the user's cultural background, it is possible to provide an optimal learning experience for diverse users.

[0006] "Skill level" is an indicator that shows the user's current level of technical understanding and operational ability.

[0007] "Learning objectives" refer to specific results or skill goals that users want to achieve through learning.

[0008] A "learning plan" is the overall structure of an educational program based on the user's skill level and learning objectives, and includes the content and sequence of learning.

[0009] "Dialogue-based communication" is a method of communication in which the user and the system exchange information with each other as the process progresses.

[0010] "Educational information" refers to information that includes the knowledge, operating procedures, and explanatory materials necessary for users to progress in their learning.

[0011] "Natural language processing" is a technology that enables computers to understand and process human language.

[0012] "Progress status" refers to the state of the user's learning plan, indicating what they have achieved and the degree of progress they have made so far.

[0013] "Feedback" refers to opinions and suggestions for improvement provided regarding user actions and results.

[0014] "Translation" is a process of converting information expressed in one language into a form understandable in another language.

[0015] "Cultural background" refers to the collection of cultural habits, values, historical background, etc. that users possess.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It 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 Embodiment 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.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the 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.

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

[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 is a digital education system that provides an optimal learning plan based on the user's skill level and learning objectives. The system is designed to enable users to deepen their understanding of digital technologies and effectively acquire the necessary skills.

[0038] The system is primarily composed of three entities: servers, terminals, and users. The roles and processes of each entity are explained below in natural language.

[0039] server

[0040] The server generates a profile from the user's registration information and uses AI technology to automatically create a learning plan tailored to the user. This learning plan includes recommended lesson order and materials. The server also analyzes user questions and inquiries using a natural language processing engine, generating appropriate answers and returning them to the user. It also monitors the user's learning progress in real time and provides corresponding feedback.

[0041] terminal

[0042] The device functions as an interface that displays learning plans and materials sent from the server to the user and facilitates interaction with the user. Specifically, it sends questions entered by the user during learning to the server and displays the answers to the user. Furthermore, the device is designed with a visually appealing and user-friendly UI to provide the user with the optimal learning experience. In addition, it includes a translation function so that educational information can be displayed according to the user's selected language.

[0043] User

[0044] Users are the subjects who learn digital skills through the system. Users first fill out a registration form and set their skill level, language, and learning goals. As they progress through their learning, users can input questions and receive immediate support. Users deepen their understanding and improve their skills by utilizing the feedback provided. For example, if a user wants to learn basic smartphone operations, the server will transfer videos and instructions on basic operations to the user's device based on that request, allowing the user to learn by watching them.

[0045] Thus, the present invention is implemented as a system that provides individual users with a personalized learning experience and supports the efficient acquisition of digital technologies.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users access the digital education system and enter their skill level, learning objectives, preferred language, etc., on the registration screen.

[0049] Step 2:

[0050] The terminal receives user input and sends the registration information to the server.

[0051] Step 3:

[0052] The server generates a user profile based on the registration information it receives and saves it to the database.

[0053] Step 4:

[0054] The server uses AI processing to automatically generate a curriculum tailored to the user's skill level and learning objectives. This includes learning themes, lesson order, and related materials.

[0055] Step 5:

[0056] The server sends the generated curriculum to the terminal.

[0057] Step 6:

[0058] The device displays the curriculum in the user interface and provides the content of the lessons selected by the user.

[0059] Step 7:

[0060] As users progress through their learning, they can input questions or points of confusion through their device.

[0061] Step 8:

[0062] The terminal sends the user's question to the server, which uses a natural language processing engine to analyze the question and generate an appropriate answer.

[0063] Step 9:

[0064] The server sends the generated response to the terminal, and the terminal displays that response to the user.

[0065] Step 10:

[0066] The server monitors the user's progress in real time, checking their learning progress and achievement goals.

[0067] Step 11:

[0068] The server generates feedback based on the progress data it receives and sends it to the terminal.

[0069] Step 12:

[0070] The device provides feedback to the user and prompts them to take the next learning step.

[0071] (Example 1)

[0072] 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."

[0073] In recent years, with the advancement of digital technology, there has been a growing need for users to acquire skills effectively in a short period of time. However, traditional education systems have difficulty providing learning plans tailored to individual users, and creating flexible learning environments that accommodate multiple languages ​​and cultural backgrounds remains a challenge. Therefore, there is a need to provide an optimal learning experience that is tailored to the individual needs of each user.

[0074] 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.

[0075] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for generating an individual profile based on the user's registration information, and means for generating a learning plan using a generative AI model. This enables a personalized learning experience for each user, flexibly accommodating diverse languages ​​and cultural backgrounds, and facilitating efficient skill acquisition.

[0076] A "user" refers to an individual who uses an educational system to engage in learning activities.

[0077] "Skill level" is an indicator that shows the degree of ability a user currently possesses in a particular field or skill.

[0078] "Learning objectives" refer to the specific skills or knowledge acquisition goals that users intend to achieve by using the system.

[0079] A "learning plan" is a set of learning activities and materials proposed to achieve effective and efficient learning based on the user's skill level and learning objectives.

[0080] "Processing means" refers to the function of a system that performs specific calculations or data processing on a server based on user information.

[0081] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0082] "Feedback" refers to advice and evaluation information provided to learners based on their learning progress and results.

[0083] "Translation" refers to the act of converting learning information from one language to another.

[0084] A "profile" is a dataset that represents an individual's characteristics, generated based on a user's registration information and learning history.

[0085] A "generative AI model" refers to an algorithm or system that uses machine learning techniques to generate intelligent, human-like responses to specific inputs.

[0086] This invention is a system that provides personalized learning plans based on the user's skill level and learning objectives. The main components of this system consist of a server, a terminal, and a user.

[0087] server

[0088] The server generates a profile from the user's registration information and uses a generative AI model based on that information to create a learning plan tailored to the user. Specifically, it uses Python and the machine learning library TENSORFLOW® to analyze the user's input information and suggest the most suitable lessons and materials. This system can generate answers to user questions using a natural language processing engine and provide feedback in real time. For example, if a user inputs "I want to learn the basics of Python," the server will select a basic programming course and materials and provide them to the user as a learning plan.

[0089] terminal

[0090] The device visually displays learning plans and materials sent from the server to the user and enables interaction with the user through its interface. It features a visually appealing and user-friendly UI design and includes a translation API to translate learning content into the user's chosen language. Questions entered by the user on the device are immediately sent to the server, and answers are returned.

[0091] User

[0092] Users acquire digital skills through the system. They log in to the system, fill in the required information on the registration form, and set their personal skill level and learning goals to begin learning. During learning, they can enter questions and receive solutions from the server. For example, if a user wants to learn basic smartphone operations, the system will provide basic learning materials and videos tailored to that request and guide the learning process.

[0093] In this way, providing personalized learning experiences to individual users makes it possible to support the efficient acquisition of digital technologies.

[0094] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0095] Step 1: User Registration

[0096] Users log in to the educational system and enter the required information into the registration form. This information includes their name, age, skill level, language of study, and learning goals. The entered information is sent from the terminal to the server. The server receives this information and uses it to generate the subsequent profile.

[0097] Step 2: Profile Generation

[0098] The server generates individual profiles based on the registration information entered by the user. Based on the input (registration information), it organizes and classifies the data to obtain the output (profile). This profile is stored in a database and used to generate learning plans.

[0099] Step 3: Generate a learning plan

[0100] The server utilizes a generative AI model, receiving profile information as input to generate an optimal learning plan. The AI ​​model analyzes the user's skill level and learning goals, identifying the necessary learning materials and lesson order. Batch processing generates the output in the form of a learning plan, which is then sent to the user's device.

[0101] Step 4: Presenting Learning Information

[0102] The device receives the learning plan sent from the server and presents it visually to the user. Through the interface, the user begins learning and uses the learning materials according to the instructions. When the user selects learning content, the system works to display the corresponding learning materials.

[0103] Step 5: Question and Answer Session

[0104] The user enters questions that arise during the learning process into the device. The entered questions are sent to the server via the device. The server uses natural language processing to analyze the questions and generate appropriate answers. The generated answers are sent back to the device and presented to the user.

[0105] Step 6: Provide feedback and monitor learning progress

[0106] The server monitors the user's learning progress in real time. Based on the input of progress status, it performs analysis and generates feedback as needed. This feedback is provided to the user through the terminal to support their learning. The learning plan is also revised as needed.

[0107] (Application Example 1)

[0108] 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."

[0109] In modern households, individual users often face difficulties in receiving adequate support to efficiently and effectively improve their skills. In particular, there is a lack of learning support systems specifically tailored to activities conducted within the home, creating a need for continuous skill development support. Furthermore, the increasing need for personalized learning experiences that take into account individual cultural backgrounds and home environments necessitates addressing these challenges.

[0110] 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.

[0111] In this invention, the server includes an information processing device that automatically generates a learning plan based on the user's skill level and learning objectives, a device that provides learning content to support skill improvement in household activities, and a device that analyzes the user's questions about household tasks and presents appropriate work procedures. This enables the user to receive a personalized learning experience to improve their skills.

[0112] "User skill level" refers to the degree of proficiency in knowledge and skills that a user possesses in a particular field.

[0113] "Learning objectives" refer to the specific results or goals that a user is trying to achieve through learning.

[0114] An "information processing device" is a device that collects and analyzes data and generates information tailored to a specific purpose.

[0115] "Dialogue format" refers to the form of mutual information exchange and communication that takes place between the user and the system.

[0116] "Natural language processing" refers to the technologies and methods that enable computers to understand and use human language.

[0117] A "translation device" is a device that converts information provided in one language into another language.

[0118] "Domestic activities" include various tasks and activities performed within the home, such as cooking and cleaning.

[0119] "Learning content" refers to the system of information and knowledge that users should acquire through learning.

[0120] A "personalized learning experience" refers to a learning process and environment that is customized to the individual user's characteristics and needs.

[0121] The system for realizing this invention is configured primarily around a server, terminals, and users.

[0122] The server primarily handles data processing, generating personal profiles from user registration information. This profile is input into a generation AI model, which automatically generates learning plans tailored to the user's skill level and learning goals. The server uses AI technology to plan optimal learning content and sends it to the terminal in an interactive format. The server utilizes Python®-based machine learning libraries (e.g., TensorFlow), and natural language processing technologies (e.g., spaCy) are used for question analysis and response generation.

[0123] The terminal serves as a user interface and is designed as a home information device. Learning plans and materials are displayed on the terminal in a visually appealing and easy-to-use format, supporting the user's skill development in home activities. It has the ability to receive user input, forward questions to a server, retrieve answers, and display them in real time. It is also designed to display information in multiple natural languages ​​and can utilize external translation software such as Google Translate API.

[0124] Users first register and clarify their skill level and learning goals. They receive a learning plan for household activities and carry out activities accordingly. In particular, for tasks related to everyday household activities such as cooking, they can follow step guides displayed on their device. For example, if a user enters "I want to make pasta," the server will send the most suitable recipe and work procedure to the device based on that information and provide a guide to support the process.

[0125] A concrete example of a prompt message to input into the generating AI model is: "The user has a beginner-level cooking skill level and wants to learn how to make pasta. Generate an optimal learning plan and guide the user through the cooking process." In this way, the system allows users to acquire specific skills and gain an efficient learning experience.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The user performs initial registration using a device. The system collects data such as the user's basic information, current skill level, and learning goals as input. This input data is stored by the server in a database as the user's personal profile.

[0129] Step 2:

[0130] The server launches the AI ​​model and inputs the user's profile information into the model. As part of the data processing, the user's skill level and learning goals are analyzed, and an optimal learning plan is automatically generated according to the algorithm. The resulting learning plan includes specific learning content and progress steps.

[0131] Step 3:

[0132] The device receives the learning plan sent from the server and displays it to the user in a visually appealing and user-friendly UI. The learning plan and learning materials are presented as the user's visual and operational interface. Furthermore, the content is laid out in a way that is easy for the user to understand.

[0133] Step 4:

[0134] Users progress through the learning process via their devices. During the learning process, when they enter a question into their device, that question is sent to the server. Based on the sent input data, the server analyzes the question using natural language processing. As a result of the analysis, an accurate answer is generated.

[0135] Step 5:

[0136] The server returns the generated answer to the terminal. The terminal immediately displays the answer to the user, helping to resolve the user's question. As output, the user is provided with the appropriate answer and additional learning support information.

[0137] Step 6:

[0138] The server periodically monitors the user's learning progress. It collects user behavior data and achievement levels, and performs data calculations to generate progress-based feedback. This feedback is provided via the user's device to improve their learning efficiency.

[0139] Step 7:

[0140] The server selects customized learning materials that take into account the user's cultural background and home environment as needed. The output provides learning content and materials tailored to each individual user, personalizing the learning experience.

[0141] 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.

[0142] This invention relates to a system that utilizes an emotion engine to recognize user emotions in a digital education system, thereby personalizing the user's learning experience. This system supports effective learning by dynamically adjusting the learning plan and feedback according to the user's skill level, learning goals, and emotional state.

[0143] A system is primarily composed of three components: servers, terminals, and users.

[0144] server

[0145] The server creates a profile based on the user's registration information and uses AI technology to generate a learning plan tailored to the user. In this process, the emotion engine analyzes the user's emotional state and appropriately adjusts the learning plan and feedback. For example, if the server detects that the user is feeling stressed, it will slow down the learning pace and provide materials of a lower difficulty level. The emotion engine also has the ability to generate encouraging messages tailored to the user in order to promote positive emotional changes.

[0146] terminal

[0147] The terminal is responsible for displaying learning plans and feedback sent from the server on the user interface. It sends user input and questions to the server and presents the analysis results from the emotion engine to the user in real time. The terminal collects emotion data from the user's facial expressions and voice and sends it to the server, providing data to improve the accuracy of the emotion engine.

[0148] User

[0149] Through the digital education system, users learn content tailored to their skill level. Based on data collected by the emotion engine, users can learn at a pace and with materials suited to their individual emotional state. For example, if a user feels anxious while tackling a complex technical problem, the system provides step-by-step instructions and additional information to deepen their understanding. In this way, users receive support that addresses their emotional state, enabling them to learn in a more relaxed state.

[0150] This invention is implemented as a system that promotes the efficient acquisition of digital skills and creates a more comfortable learning environment by providing a customized learning experience that takes user emotions into consideration.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] Users access the digital education system and enter their skill level, learning goals, and preferred language on the registration screen.

[0154] Step 2:

[0155] The device receives user input and sends that information to the server. Simultaneously, the device uses a facial recognition camera and microphone to record the user's facial expressions and voice tone as emotion data and sends it to the server.

[0156] Step 3:

[0157] The server generates a user profile, saves the registration information to the database, and activates the emotion engine to analyze the user's emotional state.

[0158] Step 4:

[0159] The server uses AI technology to generate a personalized learning plan based on the user's skill level, learning goals, and emotional state. If the emotional state is unstable, the difficulty level of the learning content is adjusted.

[0160] Step 5:

[0161] The server transfers the generated learning plan to the terminal.

[0162] Step 6:

[0163] The device presents the user with a learning plan and prepares the user to select a lesson and begin learning.

[0164] Step 7:

[0165] As users progress through their learning process, they can send inquiries to the server via their device if they have questions or points of confusion.

[0166] Step 8:

[0167] The server receives a user inquiry, analyzes it using a natural language processing engine, and generates an appropriate response. The generated response and additional training information are then sent to the device.

[0168] Step 9:

[0169] The device displays responses from the server to the user, supporting their understanding. It also continuously collects and sends sentiment data to the server during the learning process.

[0170] Step 10:

[0171] The server analyzes the user's emotional data in real time, monitoring their learning progress and stress levels. It adjusts the learning content and pace as needed and generates feedback.

[0172] Step 11:

[0173] The server generates feedback and sends it to the device, which then presents that feedback to the user. Encouragement and advice are also provided in response to changes in the user's emotions.

[0174] Step 12:

[0175] Based on user feedback, the learning process is adjusted and continued. Rewarding comments are provided upon achievement to elicit positive user emotions.

[0176] (Example 2)

[0177] 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".

[0178] In today's educational environment, providing flexible learning experiences tailored to the emotional state and skill level of individual learners is challenging. As a result, many learners are unable to continue learning at their own pace without stress, ultimately missing out on effective learning opportunities. Furthermore, providing multilingual support and materials tailored to cultural backgrounds is a difficult challenge to achieve with limited resources.

[0179] 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.

[0180] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for analyzing the user's emotional state and dynamically adjusting the difficulty level of the learning plan and learning materials, and means for providing educational information in an interactive format according to the generated learning plan. This enables individual learners to receive an optimal learning experience tailored to their emotional state, thereby improving the efficiency and comfort of education.

[0181] "User skill level" is an indicator that shows the learner's technical ability and depth of knowledge.

[0182] "Learning objectives" refer to the specific educational outcomes or goals that learners aim to achieve.

[0183] "Generating a learning strategy" refers to the act of formulating an optimal learning plan tailored to the learner's skill level and objectives.

[0184] "Emotional state" refers to the psychological reactions and emotional changes that learners experience during educational activities.

[0185] "Dynamic adjustment" means changing the system's behavior in real time according to the learner's situation and needs.

[0186] "Providing educational information in a dialogue format" means explaining educational content through two-way communication with learners.

[0187] This invention is a system that uses a digital education system to analyze the emotional state of users and personalize their individual learning experiences. This system is composed of three main components: a server, a terminal, and a user.

[0188] server

[0189] The server creates a learning profile based on the user's registration information and generates an individualized learning plan. This process utilizes a generative AI model. Specifically, it uses a machine learning framework to calculate the optimal learning content and pace in real time, tailored to the user's emotional state. The server also uses an emotion analysis engine to analyze the user's voice and facial expressions to identify their emotional state. For example, if a user is stressed, the server slows the learning pace and generates motivational messages. The specific technologies used include common cloud-based AI services.

[0190] terminal

[0191] The device displays learning plans and feedback sent from the server on its user interface. It also collects the user's facial expressions and voice using the camera and microphone and sends that data to the server. The system continuously collects data through the device and tracks the user's emotional state in real time. For example, it accurately understands the user's intentions through question input on the device and provides necessary guidance.

[0192] User

[0193] Users can learn at their own pace using the provided interface. Based on data collected by the emotion engine, a personalized learning plan is presented, allowing users to learn efficiently. For example, when a user is learning a math problem, the system provides step-by-step explanations and sends encouraging messages at points where the user might get stuck.

[0194] Another example of a prompt from the generating AI model is, "What kind of encouraging message should be suggested if the user is feeling anxious while preparing for an exam?" This prompt allows the AI ​​to generate hints to provide appropriate encouragement and support based on the user's emotional state.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] The server receives registration information entered by the user into the system and creates an individualized learning profile based on that information. Specifically, it stores data such as the user's age, interests, skill level, and learning goals in a database. Here, as part of data processing, the information is organized and an initial learning plan is generated using an algorithm. The generated learning plan then builds an educational path on the server that is tailored to the user's individual needs.

[0198] Step 2:

[0199] The device collects facial expressions and audio from the user's camera and microphone, and sends this as emotion data to a server. This process utilizes voice analysis and image processing technologies to prepare the data for analyzing the user's emotional state. The collected data is converted into input, such as text or numerical data, and sent to the server. This process allows for real-time understanding of the user's emotions.

[0200] Step 3:

[0201] The server receives emotion data sent from the terminal and analyzes the user's emotional state using an emotion analysis engine. Based on the input data, a machine learning model is used to determine the emotional state. At this stage, data processing involves labeling the data and identifying emotions using the AI ​​model. The output is derived as a state such as whether the user is cheerful or stressed, and this result is used to adjust the learning plan.

[0202] Step 4:

[0203] The server dynamically adjusts the learning plan and learning materials based on the analysis results. It changes the learning pace and difficulty level of the materials according to the user's emotional state. This process uses a generative AI model to generate new learning steps and encouraging messages, and determines specific follow-up actions based on prompts. The resulting new learning plan is presented to the user via the device.

[0204] Step 5:

[0205] Users progress through their learning according to a learning plan presented on their device. They access learning materials and interact through an interactive interface. When users input questions and answers, this data is sent back to the server, and the learning effectiveness is evaluated in real time. This feedback helps optimize the next learning step.

[0206] (Application Example 2)

[0207] 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".

[0208] Traditional digital education systems often fail to consider the user's emotional state, resulting in the provision of uniform learning materials and progress even when learners experience stress or anxiety. Furthermore, their inability to adapt to individual learners' emotions significantly reduced learning efficiency.

[0209] 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.

[0210] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning objectives, means for analyzing the user's emotional state and dynamically adjusting the learning plan and feedback based on the emotional state, and means for monitoring the user's learning progress in real time and providing feedback according to the progress and achievement level. This enables a personalized learning experience adapted to the emotional state of each individual learner.

[0211] A "user" is an individual or group that uses the system to engage in learning activities.

[0212] "Skill level" is an indicator that shows the degree of knowledge and experience a user currently possesses.

[0213] "Learning objectives" refer to the specific content that a user wants to learn or the results they wish to achieve.

[0214] A "learning plan" is a guideline created based on the user's skill level and learning objectives to guide their progress in learning.

[0215] "Dialogue format" refers to a type of interaction between a user and a system, where questions and answers are exchanged back and forth.

[0216] "Educational information" is a general term for the knowledge, information, and teaching content provided to users.

[0217] "Natural language processing" is a technology used by computers to interpret human language and understand its meaning.

[0218] "Feedback" refers to the evaluations, suggestions, and guidance that a system provides regarding a user's behavior and learning progress.

[0219] "Emotional state" refers to the user's psychological and emotional condition, including emotional factors such as happiness and stress.

[0220] In this invention, in order to realize a personalized education system based on the user's emotional state, three main components are established: a server, a terminal, and a user.

[0221] The server is responsible for generating a learning plan based on the user's registration information and emotional state. This process takes into account the user's skill level and learning goals, and uses a generative AI model to determine appropriate learning content and feedback. For example, if the user is feeling frustrated, the server will adjust the learning pace and provide easier learning materials. It can also dynamically generate encouraging messages using emotion analysis technology.

[0222] The terminal is a device that displays learning plans and feedback from the server through a user interface. It detects the user's facial expressions and voice tone and sends this data to the server for sentiment analysis. The collected sentiment data is also used as input for analyzing the user's real-time emotional state. Specific devices used include smartphones and tablets equipped with cameras and microphones.

[0223] Users benefit from a personalized learning experience provided by this system. The system presents users with appropriate learning materials and plans tailored to their emotional state, allowing them to learn in a more relaxed manner. For example, if a child is learning with a smile, the system provides positive feedback such as, "It looks like you're enjoying learning! Let's move on to the next challenge!"

[0224] Examples of prompts to input into a generative AI model:

[0225] "To improve children's learning experiences, generate feedback that is tailored to their emotional state. Suggest encouraging messages for situations where the child is smiling and their tone of voice is neutral."

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The device collects input data from the user. This input includes the user's facial expressions and voice tone. The device uses a camera and microphone to capture this data and transmits it to a server in digital format.

[0229] Step 2:

[0230] The server inputs the user's facial expression data and voice tone into an emotion analysis engine. This engine uses a generative AI model to analyze the emotional state and identify the user's real-time emotions. The output data consists of tags indicating the user's emotional state, such as "happiness" or "frustration."

[0231] Step 3:

[0232] The server generates an optimal learning plan using emotional states obtained through sentiment analysis and the user's existing profile information. Based on the input data, the AI ​​model searches an existing learning material database to determine content suitable for the user's skill level and learning goals, as well as a learning pace that corresponds to their emotional state. The output is a personalized learning plan.

[0233] Step 4:

[0234] The server composes educational content and feedback in line with the generated learning plan and sends it to the device. This feedback includes encouraging messages tailored to the user's emotional state. The output data consists of text messages and learning instructions displayed on the device.

[0235] Step 5:

[0236] Users review the learning plan and feedback received through their device and proceed with their learning accordingly. Progress data is updated based on user actions. Any new data entered by the user during the learning process is sent back to the server via the device, and the process is repeated.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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".

[0253] This invention is a digital education system that provides an optimal learning plan based on the user's skill level and learning objectives. The system is designed to enable users to deepen their understanding of digital technologies and effectively acquire the necessary skills.

[0254] The system is primarily composed of three entities: servers, terminals, and users. The roles and processes of each entity are explained below in natural language.

[0255] server

[0256] The server generates a profile from the user's registration information and uses AI technology to automatically create a learning plan tailored to the user. This learning plan includes recommended lesson order and materials. The server also analyzes user questions and inquiries using a natural language processing engine, generating appropriate answers and returning them to the user. It also monitors the user's learning progress in real time and provides corresponding feedback.

[0257] terminal

[0258] The device functions as an interface that displays learning plans and materials sent from the server to the user and facilitates interaction with the user. Specifically, it sends questions entered by the user during learning to the server and displays the answers to the user. Furthermore, the device is designed with a visually appealing and user-friendly UI to provide the user with the optimal learning experience. In addition, it includes a translation function so that educational information can be displayed according to the user's selected language.

[0259] User

[0260] Users are the subjects who learn digital skills through the system. Users first fill out a registration form and set their skill level, language, and learning goals. As they progress through their learning, users can input questions and receive immediate support. Users deepen their understanding and improve their skills by utilizing the feedback provided. For example, if a user wants to learn basic smartphone operations, the server will transfer videos and instructions on basic operations to the user's device based on that request, allowing the user to learn by watching them.

[0261] Thus, the present invention is implemented as a system that provides individual users with a personalized learning experience and supports the efficient acquisition of digital technologies.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] Users access the digital education system and enter their skill level, learning objectives, preferred language, etc., on the registration screen.

[0265] Step 2:

[0266] The terminal receives user input and sends the registration information to the server.

[0267] Step 3:

[0268] The server generates a user profile based on the registration information it receives and saves it to the database.

[0269] Step 4:

[0270] The server uses AI processing to automatically generate a curriculum tailored to the user's skill level and learning objectives. This includes learning themes, lesson order, and related materials.

[0271] Step 5:

[0272] The server sends the generated curriculum to the terminal.

[0273] Step 6:

[0274] The device displays the curriculum in the user interface and provides the content of the lessons selected by the user.

[0275] Step 7:

[0276] As users progress through their learning, they can input questions or points of confusion through their device.

[0277] Step 8:

[0278] The terminal sends the user's question to the server, and the server analyzes the question using a natural language processing engine and generates an appropriate answer.

[0279] Step 9:

[0280] The server sends the generated answer to the terminal, and the terminal displays the answer to the user.

[0281] Step 10:

[0282] The server monitors the user's progress in real time and checks the progress of learning and the achievement goals.

[0283] Step 11:

[0284] The server generates feedback based on the obtained progress data and sends it to the terminal.

[0285] Step 12:

[0286] The terminal presents the feedback to the user and prompts the user's next learning step.

[0287] (Example 1)

[0288] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". <0000​​​​​​

[0291] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for generating an individual profile based on the user's registration information, and means for generating a learning plan using a generative AI model. This enables a personalized learning experience for each user, flexibly accommodating diverse languages ​​and cultural backgrounds, and facilitating efficient skill acquisition.

[0292] A "user" refers to an individual who uses an educational system to engage in learning activities.

[0293] "Skill level" is an indicator that shows the degree of ability a user currently possesses in a particular field or skill.

[0294] "Learning objectives" refer to the specific skills or knowledge acquisition goals that users intend to achieve by using the system.

[0295] A "learning plan" is a set of learning activities and materials proposed to achieve effective and efficient learning based on the user's skill level and learning objectives.

[0296] "Processing means" refers to the function of a system that performs specific calculations or data processing on a server based on user information.

[0297] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0298] "Feedback" refers to advice and evaluation information provided to learners based on their learning progress and results.

[0299] "Translation" refers to the act of converting learning information from one language to another.

[0300] A "profile" is a dataset that represents an individual's characteristics, generated based on a user's registration information and learning history.

[0301] The "generative AI model" refers to an algorithm or system that uses machine learning technology to generate intelligent responses like those of humans for specific inputs.

[0302] The present invention is a system that provides an individualized learning plan based on the user's skill level and learning goals. The main components of this system consist of a server, a terminal, and a user.

[0303] Server

[0304] The server generates a profile from the user's registration information and uses the generative AI model based on that information to create a learning plan suitable for the user. Specifically, by using Python and the machine learning library TensorFlow to analyze the user's input information, it proposes optimal lessons and teaching materials. This system can generate answers using a natural language processing engine for the user's questions and provide feedback in real time. For example, when the user inputs "I want to learn the basics of Python", the server selects basic programming courses and teaching materials and provides them to the user as a learning plan.

[0305] Terminal

[0306] The terminal visually displays the learning plan and teaching materials sent from the server to the user and realizes interaction with the user through an interface. This includes a visually appealing and easy-to-operate UI design and has a function to translate the learning content using a translation API to display educational information in the language selected by the user. Questions input by the user on the terminal are immediately sent to the server and an answer is returned.

[0307] User

[0308] Users acquire digital skills through the system. They log in to the system, fill in the required information on the registration form, and set their personal skill level and learning goals to begin learning. During learning, they can enter questions and receive solutions from the server. For example, if a user wants to learn basic smartphone operations, the system will provide basic learning materials and videos tailored to that request and guide the learning process.

[0309] In this way, providing personalized learning experiences to individual users makes it possible to support the efficient acquisition of digital technologies.

[0310] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0311] Step 1: User Registration

[0312] Users log in to the educational system and enter the required information into the registration form. This information includes their name, age, skill level, language of study, and learning goals. The entered information is sent from the terminal to the server. The server receives this information and uses it to generate the subsequent profile.

[0313] Step 2: Profile Generation

[0314] The server generates individual profiles based on the registration information entered by the user. Based on the input (registration information), it organizes and classifies the data to obtain the output (profile). This profile is stored in a database and used to generate learning plans.

[0315] Step 3: Generate a learning plan

[0316] The server utilizes a generative AI model, receiving profile information as input to generate an optimal learning plan. The AI ​​model analyzes the user's skill level and learning goals, identifying the necessary learning materials and lesson order. Batch processing generates the output in the form of a learning plan, which is then sent to the user's device.

[0317] Step 4: Presenting Learning Information

[0318] The device receives the learning plan sent from the server and presents it visually to the user. Through the interface, the user begins learning and uses the learning materials according to the instructions. When the user selects learning content, the system works to display the corresponding learning materials.

[0319] Step 5: Question and Answer Session

[0320] The user enters questions that arise during the learning process into the device. The entered questions are sent to the server via the device. The server analyzes these questions using natural language processing and generates appropriate answers. The generated answers are sent back to the device and presented to the user.

[0321] Step 6: Provide feedback and monitor learning progress

[0322] The server monitors the user's learning progress in real time. Based on the input of progress status, it performs analysis and generates feedback as needed. This feedback is provided to the user through the terminal to support their learning. The learning plan is also revised as needed.

[0323] (Application Example 1)

[0324] 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."

[0325] In modern households, individual users often face difficulties in receiving adequate support to efficiently and effectively improve their skills. In particular, there is a lack of learning support systems specifically tailored to activities conducted within the home, creating a need for continuous skill development support. Furthermore, the increasing need for personalized learning experiences that take into account individual cultural backgrounds and home environments necessitates addressing these challenges.

[0326] 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.

[0327] In this invention, the server includes an information processing device that automatically generates a learning plan based on the user's skill level and learning objectives, a device that provides learning content to support skill improvement in household activities, and a device that analyzes the user's questions about household tasks and presents appropriate work procedures. This enables the user to receive a personalized learning experience to improve their skills.

[0328] "User skill level" refers to the degree of proficiency in knowledge and skills that a user possesses in a particular field.

[0329] "Learning objectives" refer to the specific results or goals that a user is trying to achieve through learning.

[0330] An "information processing device" is a device that collects and analyzes data and generates information tailored to a specific purpose.

[0331] "Dialogue format" refers to the form of mutual information exchange and communication that takes place between the user and the system.

[0332] "Natural language processing" refers to the technologies and methods that enable computers to understand and use human language.

[0333] A "translation device" is a device that converts information provided in one language into another language.

[0334] "Domestic activities" include various tasks and activities performed within the home, such as cooking and cleaning.

[0335] "Learning content" refers to the system of information and knowledge that users should acquire through learning.

[0336] A "personalized learning experience" refers to a learning process and environment that is customized to the individual characteristics and needs of each user.

[0337] The system for realizing this invention is configured primarily around a server, terminals, and users.

[0338] The server primarily handles data processing, generating personal profiles from user registration information. This profile is input into a generation AI model, which automatically generates learning plans tailored to the user's skill level and learning goals. The server uses AI technology to plan optimal learning content and sends it to the terminal in an interactive format. The server utilizes Python-based machine learning libraries (e.g., TensorFlow), and natural language processing technologies (e.g., spaCy) are used for question analysis and response generation.

[0339] The terminal serves as a user interface and is designed as a home information device. Learning plans and materials are displayed on the terminal in a visually appealing and easy-to-use format, supporting the user's skill development in home activities. It has the ability to receive user input, forward questions to a server, retrieve answers, and display them in real time. It is also designed to display information in multiple natural languages ​​and can utilize external translation software such as the Google Translate API.

[0340] Users first register and clarify their skill level and learning goals. They receive a learning plan for household activities and carry out activities accordingly. In particular, for tasks related to everyday household activities such as cooking, they can follow step guides displayed on their device. For example, if a user enters "I want to make pasta," the server will send the most suitable recipe and work procedure to the device based on that information and provide a guide to support the process.

[0341] A concrete example of a prompt message to input into the generating AI model is: "The user has a beginner-level cooking skill level and wants to learn how to make pasta. Generate an optimal learning plan and guide the user through the cooking process." In this way, the system allows users to acquire specific skills and gain an efficient learning experience.

[0342] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0343] Step 1:

[0344] The user performs initial registration using a device. The system collects data such as the user's basic information, current skill level, and learning goals as input. This input data is stored by the server in a database as the user's personal profile.

[0345] Step 2:

[0346] The server launches the AI ​​model and inputs the user's profile information into the model. As part of the data processing, the user's skill level and learning goals are analyzed, and an optimal learning plan is automatically generated according to the algorithm. The resulting learning plan includes specific learning content and progress steps.

[0347] Step 3:

[0348] The device receives the learning plan sent from the server and displays it to the user in a visually appealing and user-friendly UI. The learning plan and learning materials are presented as the user's visual and operational interface. Furthermore, the content is laid out in a way that is easy for the user to understand.

[0349] Step 4:

[0350] Users progress through the learning process via their devices. During the learning process, when they enter a question into their device, that question is sent to the server. Based on the sent input data, the server analyzes the question using natural language processing. As a result of the analysis, an accurate answer is generated.

[0351] Step 5:

[0352] The server returns the generated answer to the terminal. The terminal immediately displays the answer to the user, helping to resolve the user's question. As output, the user is provided with the appropriate answer and additional learning support information.

[0353] Step 6:

[0354] The server periodically monitors the user's learning progress. It collects user behavior data and achievement levels, and performs data calculations to generate progress-based feedback. This feedback is provided via the user's device to improve their learning efficiency.

[0355] Step 7:

[0356] The server selects customized learning materials as needed, taking into account the user's cultural background and home environment. The output provides learning content and materials tailored to each individual user, personalizing the learning experience.

[0357] 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.

[0358] This invention relates to a system that utilizes an emotion engine to recognize user emotions in a digital education system, thereby personalizing the user's learning experience. This system supports effective learning by dynamically adjusting the learning plan and feedback according to the user's skill level, learning goals, and emotional state.

[0359] A system is primarily composed of three components: servers, terminals, and users.

[0360] server

[0361] The server creates a profile based on the user's registration information and uses AI technology to generate a learning plan tailored to the user. In this process, the emotion engine analyzes the user's emotional state and appropriately adjusts the learning plan and feedback. For example, if the server detects that the user is feeling stressed, it will slow down the learning pace and provide materials of a lower difficulty level. The emotion engine also has the ability to generate encouraging messages tailored to the user in order to promote positive emotional changes.

[0362] terminal

[0363] The terminal is responsible for displaying learning plans and feedback sent from the server on the user interface. It sends user input and questions to the server and presents the analysis results from the emotion engine to the user in real time. The terminal collects emotion data from the user's facial expressions and voice and sends it to the server, providing data to improve the accuracy of the emotion engine.

[0364] User

[0365] Through the digital education system, users learn content tailored to their skill level. Based on data collected by the emotion engine, users can learn at a pace and with materials suited to their individual emotional state. For example, if a user feels anxious while tackling a complex technical problem, the system provides step-by-step instructions and additional information to deepen their understanding. In this way, users receive support that addresses their emotional state, enabling them to learn in a more relaxed state.

[0366] This invention is implemented as a system that promotes the efficient acquisition of digital skills and creates a more comfortable learning environment by providing a customized learning experience that takes user emotions into consideration.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] Users access the digital education system and enter their skill level, learning goals, and preferred language on the registration screen.

[0370] Step 2:

[0371] The device receives user input and sends that information to the server. Simultaneously, the device uses a facial recognition camera and microphone to record the user's facial expressions and voice tone as emotion data and sends it to the server.

[0372] Step 3:

[0373] The server generates a user profile, saves the registration information to the database, and activates the emotion engine to analyze the user's emotional state.

[0374] Step 4:

[0375] The server uses AI technology to generate a personalized learning plan based on the user's skill level, learning goals, and emotional state. If the emotional state is unstable, the difficulty level of the learning content is adjusted.

[0376] Step 5:

[0377] The server transfers the generated learning plan to the terminal.

[0378] Step 6:

[0379] The device presents the user with a learning plan and prepares the user to select a lesson and begin learning.

[0380] Step 7:

[0381] As users progress through their learning process, they can contact the server via their device if they have questions or points of confusion.

[0382] Step 8:

[0383] The server receives a user inquiry, analyzes it using a natural language processing engine, and generates an appropriate response. The generated response and additional training information are then sent to the device.

[0384] Step 9:

[0385] The device displays responses from the server to the user, supporting their understanding. It also continuously collects and sends sentiment data to the server during the learning process.

[0386] Step 10:

[0387] The server analyzes the user's emotional data in real time, monitoring their learning progress and stress levels. It adjusts the learning content and pace as needed and generates feedback.

[0388] Step 11:

[0389] The server generates feedback and sends it to the device, which then presents that feedback to the user. Encouragement and advice are also provided in response to changes in the user's emotions.

[0390] Step 12:

[0391] Based on user feedback, the learning process is adjusted and continued. Rewarding comments are provided upon achievement to elicit positive user emotions.

[0392] (Example 2)

[0393] 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".

[0394] In today's educational environment, providing flexible learning experiences tailored to the emotional state and skill level of individual learners is challenging. As a result, many learners are unable to continue learning at their own pace without stress, ultimately missing out on effective learning opportunities. Furthermore, providing multilingual support and materials tailored to cultural backgrounds is a difficult challenge to achieve with limited resources.

[0395] 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.

[0396] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for analyzing the user's emotional state and dynamically adjusting the difficulty level of the learning plan and learning materials, and means for providing educational information in an interactive format according to the generated learning plan. This enables individual learners to receive an optimal learning experience tailored to their emotional state, thereby improving the efficiency and comfort of education.

[0397] "User skill level" is an indicator that shows the learner's technical ability and depth of knowledge.

[0398] "Learning objectives" refer to the specific educational outcomes or goals that learners aim to achieve.

[0399] "Generating a learning strategy" refers to the act of formulating an optimal learning plan tailored to the learner's skill level and objectives.

[0400] "Emotional state" refers to the psychological reactions and emotional changes that learners experience during educational activities.

[0401] "Dynamic adjustment" means changing the system's behavior in real time according to the learner's situation and needs.

[0402] "Providing educational information in a dialogue format" means explaining educational content through two-way communication with learners.

[0403] This invention is a system that uses a digital education system to analyze the emotional state of users and personalize their individual learning experiences. This system is composed of three main components: a server, a terminal, and a user.

[0404] server

[0405] The server creates a learning profile based on the user's registration information and generates an individualized learning plan. This process utilizes a generative AI model. Specifically, it uses a machine learning framework to calculate the optimal learning content and pace in real time, tailored to the user's emotional state. The server also uses an emotion analysis engine to analyze the user's voice and facial expressions to identify their emotional state. For example, if a user is stressed, the server slows the learning pace and generates motivational messages. The specific technologies used include common cloud-based AI services.

[0406] terminal

[0407] The device displays learning plans and feedback sent from the server on its user interface. It also collects the user's facial expressions and voice using the camera and microphone and sends that data to the server. The system continuously collects data through the device and tracks the user's emotional state in real time. For example, it accurately understands the user's intentions through question input on the device and provides necessary guidance.

[0408] User

[0409] Users can learn at their own pace using the provided interface. Based on data collected by the emotion engine, a personalized learning plan is presented, allowing users to learn efficiently. For example, when a user is learning a math problem, the system provides step-by-step explanations and sends encouraging messages at points where the user might get stuck.

[0410] Another example of a prompt from the generating AI model is, "What kind of encouraging message should be suggested if the user is feeling anxious while preparing for an exam?" This prompt allows the AI ​​to generate hints to provide appropriate encouragement and support based on the user's emotional state.

[0411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0412] Step 1:

[0413] The server receives registration information entered by the user into the system and creates an individualized learning profile based on that information. Specifically, it stores data such as the user's age, interests, skill level, and learning goals in a database. Here, as part of data processing, the information is organized and an initial learning plan is generated using an algorithm. The generated learning plan then builds an educational path on the server that is tailored to the user's individual needs.

[0414] Step 2:

[0415] The device collects facial expressions and audio from the user's camera and microphone, and sends this as emotion data to a server. This process utilizes voice analysis and image processing technologies to prepare the data for analyzing the user's emotional state. The collected data is converted into input, such as text or numerical data, and sent to the server. This process allows for real-time understanding of the user's emotions.

[0416] Step 3:

[0417] The server receives emotion data sent from the terminal and analyzes the user's emotional state using an emotion analysis engine. Based on the input data, a machine learning model is used to determine the emotional state. At this stage, data processing involves labeling the data and identifying emotions using the AI ​​model. The output is derived as a state such as whether the user is cheerful or stressed, and this result is used to adjust the learning plan.

[0418] Step 4:

[0419] The server dynamically adjusts the learning plan and learning materials based on the analysis results. It changes the learning pace and difficulty level of the materials according to the user's emotional state. This process uses a generative AI model to generate new learning steps and encouraging messages, and determines specific follow-up actions based on prompts. The resulting new learning plan is presented to the user via the device.

[0420] Step 5:

[0421] Users progress through their learning according to a learning plan presented on their device. They access learning materials and interact through an interactive interface. When users input questions and answers, this data is sent back to the server, and the learning effectiveness is evaluated in real time. This feedback helps optimize the next learning step.

[0422] (Application Example 2)

[0423] 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 as the "terminal".

[0424] Traditional digital education systems often fail to consider the user's emotional state, resulting in the provision of uniform learning materials and progress even when learners experience stress or anxiety. Furthermore, their inability to adapt to individual learners' emotions significantly reduced learning efficiency.

[0425] 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.

[0426] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning objectives, means for analyzing the user's emotional state and dynamically adjusting the learning plan and feedback based on the emotional state, and means for monitoring the user's learning progress in real time and providing feedback according to the progress and achievement level. This enables a personalized learning experience adapted to the emotional state of each individual learner.

[0427] A "user" is an individual or group that uses the system to engage in learning activities.

[0428] "Skill level" is an indicator that shows the degree of knowledge and experience a user currently possesses.

[0429] "Learning objectives" refer to the specific content that a user wants to learn or the results they wish to achieve.

[0430] A "learning plan" is a guideline created based on the user's skill level and learning objectives to guide their progress in learning.

[0431] "Dialogue format" refers to a type of interaction between a user and a system, where questions and answers are exchanged back and forth.

[0432] "Educational information" is a general term for the knowledge, information, and teaching content provided to users.

[0433] "Natural language processing" is a technology used by computers to interpret human language and understand its meaning.

[0434] "Feedback" refers to the evaluations, suggestions, and guidance that a system provides regarding a user's behavior and learning progress.

[0435] "Emotional state" refers to the user's psychological and emotional condition, including emotional factors such as happiness and stress.

[0436] In this invention, in order to realize a personalized education system based on the user's emotional state, three main components are established: a server, a terminal, and a user.

[0437] The server is responsible for generating a learning plan based on the user's registration information and emotional state. This process takes into account the user's skill level and learning goals, and uses a generative AI model to determine appropriate learning content and feedback. For example, if the user is feeling frustrated, the server will adjust the learning pace and provide easier learning materials. It can also dynamically generate encouraging messages using emotion analysis technology.

[0438] The terminal is a device that displays learning plans and feedback from the server through a user interface. It detects the user's facial expressions and voice tone and sends this data to the server for sentiment analysis. The collected sentiment data is also used as input for analyzing the user's real-time emotional state. Specific devices used include smartphones and tablets equipped with cameras and microphones.

[0439] Users benefit from a personalized learning experience provided by this system. The system presents users with appropriate learning materials and plans tailored to their emotional state, allowing them to learn in a more relaxed manner. For example, if a child is learning with a smile, the system provides positive feedback such as, "It looks like you're enjoying learning! Let's move on to the next challenge!"

[0440] Examples of prompts to input into a generative AI model:

[0441] "To improve children's learning experiences, generate feedback that is tailored to their emotional state. Suggest encouraging messages for situations where the child is smiling and their tone of voice is neutral."

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] The device collects input data from the user. This input includes the user's facial expressions and voice tone. The device uses a camera and microphone to capture this data and transmits it to a server in digital format.

[0445] Step 2:

[0446] The server inputs the user's facial expression data and voice tone into an emotion analysis engine. This engine uses a generative AI model to analyze the emotional state and identify the user's real-time emotions. The output data consists of tags indicating the user's emotional state, such as "happiness" or "frustration."

[0447] Step 3:

[0448] The server generates an optimal learning plan using emotional states obtained through sentiment analysis and the user's existing profile information. Based on the input data, the AI ​​model searches an existing learning material database to determine content suitable for the user's skill level and learning goals, as well as a learning pace that corresponds to their emotional state. The output is a personalized learning plan.

[0449] Step 4:

[0450] The server composes educational content and feedback in line with the generated learning plan and sends it to the device. This feedback includes encouraging messages tailored to the user's emotional state. The output data consists of text messages and learning instructions displayed on the device.

[0451] Step 5:

[0452] Users review the learning plan and feedback received through their device and proceed with their learning accordingly. Progress data is updated based on user actions. Any new data entered by the user during the learning process is sent back to the server via the device, and the process is repeated.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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".

[0469] This invention is a digital education system that provides an optimal learning plan based on the user's skill level and learning objectives. The system is designed to enable users to deepen their understanding of digital technologies and effectively acquire the necessary skills.

[0470] The system is primarily composed of three entities: servers, terminals, and users. The roles and processes of each entity are explained below in natural language.

[0471] server

[0472] The server generates a profile from the user's registration information and uses AI technology to automatically create a learning plan tailored to the user. This learning plan includes recommended lesson order and materials. The server also analyzes user questions and inquiries using a natural language processing engine, generating appropriate answers and returning them to the user. It also monitors the user's learning progress in real time and provides corresponding feedback.

[0473] terminal

[0474] The device functions as an interface that displays learning plans and materials sent from the server to the user and facilitates interaction with the user. Specifically, it sends questions entered by the user during learning to the server and displays the answers to the user. Furthermore, the device is designed with a visually appealing and user-friendly UI to provide the user with the optimal learning experience. In addition, it includes a translation function so that educational information can be displayed according to the user's selected language.

[0475] User

[0476] Users are the subjects who learn digital skills through the system. Users first fill out a registration form and set their skill level, language, and learning goals. As they progress through their learning, users can input questions and receive immediate support. Users deepen their understanding and improve their skills by utilizing the feedback provided. For example, if a user wants to learn basic smartphone operations, the server will transfer videos and instructions on basic operations to the user's device based on that request, allowing the user to learn by watching them.

[0477] Thus, the present invention is implemented as a system that provides individual users with a personalized learning experience and supports the efficient acquisition of digital technologies.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] Users access the digital education system and enter their skill level, learning objectives, preferred language, etc., on the registration screen.

[0481] Step 2:

[0482] The terminal receives user input and sends the registration information to the server.

[0483] Step 3:

[0484] The server generates a user profile based on the registration information it receives and saves it to the database.

[0485] Step 4:

[0486] The server uses AI processing to automatically generate a curriculum tailored to the user's skill level and learning objectives. This includes learning themes, lesson order, and related materials.

[0487] Step 5:

[0488] The server sends the generated curriculum to the terminal.

[0489] Step 6:

[0490] The device displays the curriculum in the user interface and provides the content of the lessons selected by the user.

[0491] Step 7:

[0492] As users progress through their learning, they can input questions or points of confusion through their device.

[0493] Step 8:

[0494] The terminal sends the user's question to the server, which uses a natural language processing engine to analyze the question and generate an appropriate answer.

[0495] Step 9:

[0496] The server sends the generated response to the terminal, and the terminal displays that response to the user.

[0497] Step 10:

[0498] The server monitors the user's progress in real time, checking their learning progress and achievement goals.

[0499] Step 11:

[0500] The server generates feedback based on the progress data it receives and sends it to the terminal.

[0501] Step 12:

[0502] The device provides feedback to the user and prompts them to take the next learning step.

[0503] (Example 1)

[0504] 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."

[0505] In recent years, with the advancement of digital technology, there has been a growing need for users to acquire skills effectively in a short period of time. However, traditional education systems have difficulty providing learning plans tailored to individual users, and creating flexible learning environments that accommodate multiple languages ​​and cultural backgrounds remains a challenge. Therefore, there is a need to provide an optimal learning experience that is tailored to the individual needs of each user.

[0506] 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.

[0507] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for generating an individual profile based on the user's registration information, and means for generating a learning plan using a generative AI model. This enables a personalized learning experience for each user, flexibly accommodating diverse languages ​​and cultural backgrounds, and facilitating efficient skill acquisition.

[0508] A "user" refers to an individual who uses an educational system to engage in learning activities.

[0509] "Skill level" is an indicator that shows the degree of ability a user currently possesses in a particular field or skill.

[0510] "Learning objectives" refer to the specific skills or knowledge acquisition goals that users intend to achieve by using the system.

[0511] A "learning plan" is a set of learning activities and materials proposed to achieve effective and efficient learning based on the user's skill level and learning objectives.

[0512] "Processing means" refers to the function of a system that performs specific calculations or data processing on a server based on user information.

[0513] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0514] "Feedback" refers to advice and evaluation information provided to learners based on their learning progress and results.

[0515] "Translation" refers to the act of converting learning information from one language to another.

[0516] A "profile" is a dataset that represents an individual's characteristics, generated based on a user's registration information and learning history.

[0517] A "generative AI model" refers to an algorithm or system that uses machine learning techniques to generate intelligent, human-like responses to specific inputs.

[0518] This invention is a system that provides personalized learning plans based on the user's skill level and learning objectives. The main components of this system consist of a server, a terminal, and a user.

[0519] server

[0520] The server generates a profile from the user's registration information and uses a generative AI model based on that information to create a learning plan tailored to the user. Specifically, it uses Python and the machine learning library TensorFlow to analyze the user's input information and suggest the most suitable lessons and materials. This system can generate answers to user questions using a natural language processing engine and provide feedback in real time. For example, if a user inputs "I want to learn the basics of Python," the server will select basic programming courses and materials and provide them to the user as a learning plan.

[0521] terminal

[0522] The device visually displays learning plans and materials sent from the server to the user and enables interaction with the user through its interface. It features a visually appealing and user-friendly UI design and includes a translation API to translate learning content into the user's chosen language. Questions entered by the user on the device are immediately sent to the server, and answers are returned.

[0523] User

[0524] Users acquire digital skills through the system. They log in to the system, fill in the required information on the registration form, and set their personal skill level and learning goals to begin learning. During learning, they can enter questions and receive solutions from the server. For example, if a user wants to learn basic smartphone operations, the system will provide basic learning materials and videos tailored to that request and guide the learning process.

[0525] In this way, providing personalized learning experiences to individual users makes it possible to support the efficient acquisition of digital technologies.

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1: User Registration

[0528] Users log in to the educational system and enter the required information into the registration form. This information includes their name, age, skill level, language of study, and learning goals. The entered information is sent from the terminal to the server. The server receives this information and uses it to generate the subsequent profile.

[0529] Step 2: Profile Generation

[0530] The server generates individual profiles based on the registration information entered by the user. Based on the input (registration information), it organizes and classifies the data to obtain the output (profile). This profile is stored in a database and used to generate learning plans.

[0531] Step 3: Generate a learning plan

[0532] The server utilizes a generative AI model, receiving profile information as input to generate an optimal learning plan. The AI ​​model analyzes the user's skill level and learning goals, identifying the necessary learning materials and lesson order. Batch processing generates the output in the form of a learning plan, which is then sent to the user's device.

[0533] Step 4: Presenting Learning Information

[0534] The device receives the learning plan sent from the server and presents it visually to the user. Through the interface, the user begins learning and uses the learning materials according to the instructions. When the user selects learning content, the system works to display the corresponding learning materials.

[0535] Step 5: Question and Answer Session

[0536] The user enters questions that arise during the learning process into the device. The entered questions are sent to the server via the device. The server uses natural language processing to analyze the questions and generate appropriate answers. The generated answers are sent back to the device and presented to the user.

[0537] Step 6: Provide feedback and monitor learning progress

[0538] The server monitors the user's learning progress in real time. Based on the input of progress status, it performs analysis and generates feedback as needed. This feedback is provided to the user through the terminal to support their learning. The learning plan is also revised as needed.

[0539] (Application Example 1)

[0540] 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."

[0541] In modern households, individual users often face difficulties in receiving adequate support to efficiently and effectively improve their skills. In particular, there is a lack of learning support systems specifically tailored to activities conducted within the home, creating a need for continuous skill development support. Furthermore, the increasing need for personalized learning experiences that take into account individual cultural backgrounds and home environments necessitates addressing these challenges.

[0542] 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.

[0543] In this invention, the server includes an information processing device that automatically generates a learning plan based on the user's skill level and learning objectives, a device that provides learning content to support skill improvement in household activities, and a device that analyzes the user's questions about household tasks and presents appropriate work procedures. This enables the user to receive a personalized learning experience to improve their skills.

[0544] "User skill level" refers to the degree of proficiency in knowledge and skills that a user possesses in a particular field.

[0545] "Learning objectives" refer to the specific results or goals that a user is trying to achieve through learning.

[0546] An "information processing device" is a device that collects and analyzes data and generates information tailored to a specific purpose.

[0547] "Dialogue format" refers to the form of mutual information exchange and communication that takes place between the user and the system.

[0548] "Natural language processing" refers to the technologies and methods that enable computers to understand and use human language.

[0549] A "translation device" is a device that converts information provided in one language into another language.

[0550] "Domestic activities" include various tasks and activities performed within the home, such as cooking and cleaning.

[0551] "Learning content" refers to the system of information and knowledge that users should acquire through learning.

[0552] A "personalized learning experience" refers to a learning process and environment that is customized to the individual user's characteristics and needs.

[0553] The system for realizing this invention is configured primarily around a server, terminals, and users.

[0554] The server primarily handles data processing, generating personal profiles from user registration information. This profile is input into a generation AI model, which automatically generates learning plans tailored to the user's skill level and learning goals. The server uses AI technology to plan optimal learning content and sends it to the terminal in an interactive format. The server utilizes Python-based machine learning libraries (e.g., TensorFlow), and natural language processing technologies (e.g., spaCy) are used for question analysis and response generation.

[0555] The terminal serves as a user interface and is designed as a home information device. Learning plans and materials are displayed on the terminal in a visually appealing and easy-to-use format, supporting the user's skill development in home activities. It has the ability to receive user input, forward questions to a server, retrieve answers, and display them in real time. It is also designed to display information in multiple natural languages ​​and can utilize external translation software such as the Google Translate API.

[0556] Users first register and clarify their skill level and learning goals. They receive a learning plan for household activities and carry out activities accordingly. In particular, for tasks related to everyday household activities such as cooking, they can follow step guides displayed on their device. For example, if a user enters "I want to make pasta," the server will send the most suitable recipe and work procedure to the device based on that information and provide a guide to support the process.

[0557] A concrete example of a prompt message to input into the generating AI model is: "The user has a beginner-level cooking skill level and wants to learn how to make pasta. Generate an optimal learning plan and guide the user through the cooking process." In this way, the system allows users to acquire specific skills and gain an efficient learning experience.

[0558] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0559] Step 1:

[0560] The user performs initial registration using a device. The system collects data such as the user's basic information, current skill level, and learning goals as input. This input data is stored by the server in a database as the user's personal profile.

[0561] Step 2:

[0562] The server launches the AI ​​model and inputs the user's profile information into the model. As part of the data processing, the user's skill level and learning goals are analyzed, and an optimal learning plan is automatically generated according to the algorithm. The resulting learning plan includes specific learning content and progress steps.

[0563] Step 3:

[0564] The device receives the learning plan sent from the server and displays it to the user in a visually appealing and user-friendly UI. The learning plan and learning materials are presented as the user's visual and operational interface. Furthermore, the content is laid out in a way that is easy for the user to understand.

[0565] Step 4:

[0566] Users progress through the learning process via their devices. During the learning process, when they enter a question into their device, that question is sent to the server. Based on the sent input data, the server analyzes the question using natural language processing. As a result of the analysis, an accurate answer is generated.

[0567] Step 5:

[0568] The server returns the generated answer to the terminal. The terminal immediately displays the answer to the user, helping to resolve the user's question. As output, the user is provided with the appropriate answer and additional learning support information.

[0569] Step 6:

[0570] The server periodically monitors the user's learning progress. It collects user behavior data and achievement levels, and performs data calculations to generate progress-based feedback. This feedback is provided via the user's device to improve their learning efficiency.

[0571] Step 7:

[0572] The server selects customized learning materials that take into account the user's cultural background and home environment as needed. The output provides learning content and materials tailored to each individual user, personalizing the learning experience.

[0573] 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.

[0574] This invention relates to a system that utilizes an emotion engine to recognize user emotions in a digital education system, thereby personalizing the user's learning experience. This system supports effective learning by dynamically adjusting the learning plan and feedback according to the user's skill level, learning goals, and emotional state.

[0575] A system is primarily composed of three components: servers, terminals, and users.

[0576] server

[0577] The server creates a profile based on the user's registration information and uses AI technology to generate a learning plan tailored to the user. In this process, the emotion engine analyzes the user's emotional state and appropriately adjusts the learning plan and feedback. For example, if the server detects that the user is feeling stressed, it will slow down the learning pace and provide materials of a lower difficulty level. The emotion engine also has the ability to generate encouraging messages tailored to the user in order to promote positive emotional changes.

[0578] terminal

[0579] The terminal is responsible for displaying learning plans and feedback sent from the server on the user interface. It sends user input and questions to the server and presents the analysis results from the emotion engine to the user in real time. The terminal collects emotion data from the user's facial expressions and voice and sends it to the server, providing data to improve the accuracy of the emotion engine.

[0580] User

[0581] Through the digital education system, users learn content tailored to their skill level. Based on data collected by the emotion engine, users can learn at a pace and with materials suited to their individual emotional state. For example, if a user feels anxious while tackling a complex technical problem, the system provides step-by-step instructions and additional information to deepen their understanding. In this way, users receive support that addresses their emotional state, enabling them to learn in a more relaxed state.

[0582] This invention is implemented as a system that promotes the efficient acquisition of digital skills and creates a more comfortable learning environment by providing a customized learning experience that takes user emotions into consideration.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] Users access the digital education system and enter their skill level, learning goals, and preferred language on the registration screen.

[0586] Step 2:

[0587] The device receives user input and sends that information to the server. Simultaneously, the device uses a facial recognition camera and microphone to record the user's facial expressions and voice tone as emotion data and sends it to the server.

[0588] Step 3:

[0589] The server generates a user profile, saves the registration information to the database, and activates the emotion engine to analyze the user's emotional state.

[0590] Step 4:

[0591] The server uses AI technology to generate a personalized learning plan based on the user's skill level, learning goals, and emotional state. If the emotional state is unstable, the difficulty level of the learning content is adjusted.

[0592] Step 5:

[0593] The server transfers the generated learning plan to the terminal.

[0594] Step 6:

[0595] The device presents the user with a learning plan and prepares the user to select a lesson and begin learning.

[0596] Step 7:

[0597] As users progress through their learning process, they can send inquiries to the server via their device if they have questions or points of confusion.

[0598] Step 8:

[0599] The server receives a user inquiry, analyzes it using a natural language processing engine, and generates an appropriate response. The generated response and additional training information are then sent to the device.

[0600] Step 9:

[0601] The device displays responses from the server to the user, supporting their understanding. It also continuously collects and sends sentiment data to the server during the learning process.

[0602] Step 10:

[0603] The server analyzes the user's emotional data in real time, monitoring their learning progress and stress levels. It adjusts the learning content and pace as needed and generates feedback.

[0604] Step 11:

[0605] The server generates feedback and sends it to the device, which then presents that feedback to the user. Encouragement and advice are also provided in response to changes in the user's emotions.

[0606] Step 12:

[0607] Based on user feedback, the learning process is adjusted and continued. Rewarding comments are provided upon achievement to elicit positive user emotions.

[0608] (Example 2)

[0609] 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."

[0610] In today's educational environment, providing flexible learning experiences tailored to the emotional state and skill level of individual learners is challenging. As a result, many learners are unable to continue learning at their own pace without stress, ultimately missing out on effective learning opportunities. Furthermore, providing multilingual support and materials tailored to cultural backgrounds is a difficult challenge to achieve with limited resources.

[0611] 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.

[0612] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for analyzing the user's emotional state and dynamically adjusting the difficulty level of the learning plan and learning materials, and means for providing educational information in an interactive format according to the generated learning plan. This enables individual learners to receive an optimal learning experience tailored to their emotional state, thereby improving the efficiency and comfort of education.

[0613] "User skill level" is an indicator that shows the learner's technical ability and depth of knowledge.

[0614] "Learning objectives" refer to the specific educational outcomes or goals that learners aim to achieve.

[0615] "Generating a learning strategy" refers to the act of formulating an optimal learning plan tailored to the learner's skill level and objectives.

[0616] "Emotional state" refers to the psychological reactions and emotional changes that learners experience during educational activities.

[0617] "Dynamic adjustment" means changing the system's behavior in real time according to the learner's situation and needs.

[0618] "Providing educational information in a dialogue format" means explaining educational content through two-way communication with learners.

[0619] This invention is a system that uses a digital education system to analyze the emotional state of users and personalize their individual learning experiences. This system is composed of three main components: a server, a terminal, and a user.

[0620] server

[0621] The server creates a learning profile based on the user's registration information and generates an individualized learning plan. This process utilizes a generative AI model. Specifically, it uses a machine learning framework to calculate the optimal learning content and pace in real time, tailored to the user's emotional state. The server also uses an emotion analysis engine to analyze the user's voice and facial expressions to identify their emotional state. For example, if a user is stressed, the server slows the learning pace and generates motivational messages. The specific technologies used include common cloud-based AI services.

[0622] terminal

[0623] The device displays learning plans and feedback sent from the server on its user interface. It also collects the user's facial expressions and voice using the camera and microphone and sends that data to the server. The system continuously collects data through the device and tracks the user's emotional state in real time. For example, it accurately understands the user's intentions through question input on the device and provides necessary guidance.

[0624] User

[0625] Users can learn at their own pace using the provided interface. Based on data collected by the emotion engine, a personalized learning plan is presented, allowing users to learn efficiently. For example, when a user is learning a math problem, the system provides step-by-step explanations and sends encouraging messages at points where the user might get stuck.

[0626] Another example of a prompt from the generating AI model is, "What kind of encouraging message should be suggested if the user is feeling anxious while preparing for an exam?" This prompt allows the AI ​​to generate hints to provide appropriate encouragement and support based on the user's emotional state.

[0627] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0628] Step 1:

[0629] The server receives registration information entered by the user into the system and creates an individualized learning profile based on that information. Specifically, it stores data such as the user's age, interests, skill level, and learning goals in a database. Here, as part of data processing, the information is organized and an initial learning plan is generated using an algorithm. The generated learning plan then builds an educational path on the server that is tailored to the user's individual needs.

[0630] Step 2:

[0631] The device collects facial expressions and audio from the user's camera and microphone, and sends this as emotion data to a server. This process utilizes voice analysis and image processing technologies to prepare the data for analyzing the user's emotional state. The collected data is converted into input, such as text or numerical data, and sent to the server. This process allows for real-time understanding of the user's emotions.

[0632] Step 3:

[0633] The server receives emotion data sent from the terminal and analyzes the user's emotional state using an emotion analysis engine. Based on the input data, a machine learning model is used to determine the emotional state. At this stage, data processing involves labeling the data and identifying emotions using the AI ​​model. The output is derived as a state such as whether the user is cheerful or stressed, and this result is used to adjust the learning plan.

[0634] Step 4:

[0635] The server dynamically adjusts the learning plan and learning materials based on the analysis results. It changes the learning pace and difficulty level of the materials according to the user's emotional state. This process uses a generative AI model to generate new learning steps and encouraging messages, and determines specific follow-up actions based on prompts. The resulting new learning plan is presented to the user via the device.

[0636] Step 5:

[0637] Users progress through their learning according to a learning plan presented on their device. They access learning materials and interact through an interactive interface. When users input questions and answers, this data is sent back to the server, and the learning effectiveness is evaluated in real time. This feedback helps optimize the next learning step.

[0638] (Application Example 2)

[0639] 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."

[0640] Traditional digital education systems often fail to consider the user's emotional state, resulting in the provision of uniform learning materials and progress even when learners experience stress or anxiety. Furthermore, their inability to adapt to individual learners' emotions significantly reduced learning efficiency.

[0641] 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.

[0642] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning objectives, means for analyzing the user's emotional state and dynamically adjusting the learning plan and feedback based on the emotional state, and means for monitoring the user's learning progress in real time and providing feedback according to the progress and achievement level. This enables a personalized learning experience adapted to the emotional state of each individual learner.

[0643] A "user" is an individual or group that uses the system to engage in learning activities.

[0644] "Skill level" is an indicator that shows the degree of knowledge and experience a user currently possesses.

[0645] "Learning objectives" refer to the specific content that a user wants to learn or the results they wish to achieve.

[0646] A "learning plan" is a guideline created based on the user's skill level and learning objectives to guide their progress in learning.

[0647] "Dialogue format" refers to a type of interaction between a user and a system, where questions and answers are exchanged back and forth.

[0648] "Educational information" is a general term for the knowledge, information, and teaching content provided to users.

[0649] "Natural language processing" is a technology used by computers to interpret human language and understand its meaning.

[0650] "Feedback" refers to the evaluations, suggestions, and guidance that a system provides regarding a user's behavior and learning progress.

[0651] "Emotional state" refers to the user's psychological and emotional condition, including emotional factors such as happiness and stress.

[0652] In this invention, in order to realize a personalized education system based on the user's emotional state, three main components are established: a server, a terminal, and a user.

[0653] The server is responsible for generating a learning plan based on the user's registration information and emotional state. This process takes into account the user's skill level and learning goals, and uses a generative AI model to determine appropriate learning content and feedback. For example, if the user is feeling frustrated, the server will adjust the learning pace and provide easier learning materials. It can also dynamically generate encouraging messages using emotion analysis technology.

[0654] The terminal is a device that displays learning plans and feedback from the server through a user interface. It detects the user's facial expressions and voice tone and sends this data to the server for sentiment analysis. The collected sentiment data is also used as input for analyzing the user's real-time emotional state. Specific devices used include smartphones and tablets equipped with cameras and microphones.

[0655] Users benefit from a personalized learning experience provided by this system. The system presents users with appropriate learning materials and plans tailored to their emotional state, allowing them to learn in a more relaxed manner. For example, if a child is learning with a smile, the system provides positive feedback such as, "It looks like you're enjoying learning! Let's move on to the next challenge!"

[0656] Examples of prompts to input into a generative AI model:

[0657] "To improve children's learning experiences, generate feedback that is tailored to their emotional state. Suggest encouraging messages for situations where the child is smiling and their tone of voice is neutral."

[0658] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0659] Step 1:

[0660] The device collects input data from the user. This input includes the user's facial expressions and voice tone. The device uses a camera and microphone to capture this data and transmits it to a server in digital format.

[0661] Step 2:

[0662] The server inputs the user's facial expression data and voice tone into an emotion analysis engine. This engine uses a generative AI model to analyze the emotional state and identify the user's real-time emotions. The output data consists of tags indicating the user's emotional state, such as "happiness" or "frustration."

[0663] Step 3:

[0664] The server generates an optimal learning plan using emotional states obtained through sentiment analysis and the user's existing profile information. Based on the input data, the AI ​​model searches an existing learning material database to determine content suitable for the user's skill level and learning goals, as well as a learning pace that corresponds to their emotional state. The output is a personalized learning plan.

[0665] Step 4:

[0666] The server composes educational content and feedback in line with the generated learning plan and sends it to the device. This feedback includes encouraging messages tailored to the user's emotional state. The output data consists of text messages and learning instructions displayed on the device.

[0667] Step 5:

[0668] Users review the learning plan and feedback received through their device and proceed with their learning accordingly. Progress data is updated based on user actions. Any new data entered by the user during the learning process is sent back to the server via the device, and the process is repeated.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] [Fourth Embodiment]

[0673] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0674] 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.

[0675] 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).

[0676] 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.

[0677] 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.

[0678] 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).

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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".

[0686] This invention is a digital education system that provides an optimal learning plan based on the user's skill level and learning objectives. The system is designed to enable users to deepen their understanding of digital technologies and effectively acquire the necessary skills.

[0687] The system is primarily composed of three entities: servers, terminals, and users. The roles and processes of each entity are explained below in natural language.

[0688] server

[0689] The server generates a profile from the user's registration information and uses AI technology to automatically create a learning plan tailored to the user. This learning plan includes recommended lesson order and materials. The server also analyzes user questions and inquiries using a natural language processing engine, generating appropriate answers and returning them to the user. It also monitors the user's learning progress in real time and provides corresponding feedback.

[0690] terminal

[0691] The device functions as an interface that displays learning plans and materials sent from the server to the user and facilitates interaction with the user. Specifically, it sends questions entered by the user during learning to the server and displays the answers to the user. Furthermore, the device is designed with a visually appealing and user-friendly UI to provide the user with the optimal learning experience. In addition, it includes a translation function so that educational information can be displayed according to the user's selected language.

[0692] User

[0693] Users are the subjects who learn digital skills through the system. Users first fill out a registration form and set their skill level, language, and learning goals. As they progress through their learning, users can input questions and receive immediate support. Users deepen their understanding and improve their skills by utilizing the feedback provided. For example, if a user wants to learn basic smartphone operations, the server will transfer videos and instructions on basic operations to the user's device based on that request, allowing the user to learn by watching them.

[0694] Thus, the present invention is implemented as a system that provides individual users with a personalized learning experience and supports the efficient acquisition of digital technologies.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] Users access the digital education system and enter their skill level, learning objectives, preferred language, etc., on the registration screen.

[0698] Step 2:

[0699] The terminal receives user input and sends the registration information to the server.

[0700] Step 3:

[0701] The server generates a user profile based on the registration information it receives and saves it to the database.

[0702] Step 4:

[0703] The server uses AI processing to automatically generate a curriculum tailored to the user's skill level and learning objectives. This includes learning themes, lesson order, and related materials.

[0704] Step 5:

[0705] The server sends the generated curriculum to the terminal.

[0706] Step 6:

[0707] The device displays the curriculum in the user interface and provides the content of the lessons selected by the user.

[0708] Step 7:

[0709] As users progress through their learning, they can input questions or points of confusion through their device.

[0710] Step 8:

[0711] The terminal sends the user's question to the server, which uses a natural language processing engine to analyze the question and generate an appropriate answer.

[0712] Step 9:

[0713] The server sends the generated response to the terminal, and the terminal displays that response to the user.

[0714] Step 10:

[0715] The server monitors the user's progress in real time, checking their learning progress and achievement goals.

[0716] Step 11:

[0717] The server generates feedback based on the progress data it receives and sends it to the terminal.

[0718] Step 12:

[0719] The device provides feedback to the user and prompts them to take the next learning step.

[0720] (Example 1)

[0721] 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".

[0722] In recent years, with the advancement of digital technology, there has been a growing need for users to acquire skills effectively in a short period of time. However, traditional education systems have difficulty providing learning plans tailored to individual users, and creating flexible learning environments that accommodate multiple languages ​​and cultural backgrounds remains a challenge. Therefore, there is a need to provide an optimal learning experience that is tailored to the individual needs of each user.

[0723] 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.

[0724] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for generating an individual profile based on the user's registration information, and means for generating a learning plan using a generative AI model. This enables a personalized learning experience for each user, flexibly accommodating diverse languages ​​and cultural backgrounds, and facilitating efficient skill acquisition.

[0725] A "user" refers to an individual who uses an educational system to engage in learning activities.

[0726] "Skill level" is an indicator that shows the degree of ability a user currently possesses in a particular field or skill.

[0727] "Learning objectives" refer to the specific skills or knowledge acquisition goals that users intend to achieve by using the system.

[0728] A "learning plan" is a set of learning activities and materials proposed to achieve effective and efficient learning based on the user's skill level and learning objectives.

[0729] "Processing means" refers to the function of a system that performs specific calculations or data processing on a server based on user information.

[0730] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0731] "Feedback" refers to advice and evaluation information provided to learners based on their learning progress and results.

[0732] "Translation" refers to the act of converting learning information from one language to another.

[0733] A "profile" is a dataset that represents an individual's characteristics, generated based on a user's registration information and learning history.

[0734] A "generative AI model" refers to an algorithm or system that uses machine learning techniques to generate intelligent, human-like responses to specific inputs.

[0735] This invention is a system that provides personalized learning plans based on the user's skill level and learning objectives. The main components of this system consist of a server, a terminal, and a user.

[0736] server

[0737] The server generates a profile from the user's registration information and uses a generative AI model based on that information to create a learning plan tailored to the user. Specifically, it uses Python and the machine learning library TensorFlow to analyze the user's input information and suggest the most suitable lessons and materials. This system can generate answers to user questions using a natural language processing engine and provide feedback in real time. For example, if a user inputs "I want to learn the basics of Python," the server will select basic programming courses and materials and provide them to the user as a learning plan.

[0738] terminal

[0739] The device visually displays learning plans and materials sent from the server to the user and enables interaction with the user through its interface. It features a visually appealing and user-friendly UI design and includes a translation API to translate learning content into the user's chosen language. Questions entered by the user on the device are immediately sent to the server, and answers are returned.

[0740] User

[0741] Users acquire digital skills through the system. They log in to the system, fill in the required information on the registration form, and set their personal skill level and learning goals to begin learning. During learning, they can enter questions and receive solutions from the server. For example, if a user wants to learn basic smartphone operations, the system will provide basic learning materials and videos tailored to that request and guide the learning process.

[0742] In this way, providing personalized learning experiences to individual users makes it possible to support the efficient acquisition of digital technologies.

[0743] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0744] Step 1: User Registration

[0745] Users log in to the educational system and enter the required information into the registration form. This information includes their name, age, skill level, language of study, and learning goals. The entered information is sent from the terminal to the server. The server receives this information and uses it to generate the subsequent profile.

[0746] Step 2: Profile Generation

[0747] The server generates individual profiles based on the registration information entered by the user. Based on the input (registration information), it organizes and classifies the data to obtain the output (profile). This profile is stored in a database and used to generate learning plans.

[0748] Step 3: Generate a learning plan

[0749] The server utilizes a generative AI model, receiving profile information as input to generate an optimal learning plan. The AI ​​model analyzes the user's skill level and learning goals, identifying the necessary learning materials and lesson order. Batch processing generates the output in the form of a learning plan, which is then sent to the user's device.

[0750] Step 4: Presenting Learning Information

[0751] The device receives the learning plan sent from the server and presents it visually to the user. Through the interface, the user begins learning and uses the learning materials according to the instructions. When the user selects learning content, the system works to display the corresponding learning materials.

[0752] Step 5: Question and Answer Session

[0753] The user enters questions that arise during the learning process into the device. The entered questions are sent to the server via the device. The server uses natural language processing to analyze the questions and generate appropriate answers. The generated answers are sent back to the device and presented to the user.

[0754] Step 6: Provide feedback and monitor learning progress

[0755] The server monitors the user's learning progress in real time. Based on the input of progress status, it performs analysis and generates feedback as needed. This feedback is provided to the user through the terminal to support their learning. The learning plan is also revised as needed.

[0756] (Application Example 1)

[0757] 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".

[0758] In modern households, individual users often face difficulties in receiving adequate support to efficiently and effectively improve their skills. In particular, there is a lack of learning support systems specifically tailored to activities conducted within the home, creating a need for continuous skill development support. Furthermore, the increasing need for personalized learning experiences that take into account individual cultural backgrounds and home environments necessitates addressing these challenges.

[0759] 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.

[0760] In this invention, the server includes an information processing device that automatically generates a learning plan based on the user's skill level and learning objectives, a device that provides learning content to support skill improvement in household activities, and a device that analyzes the user's questions about household tasks and presents appropriate work procedures. This enables the user to receive a personalized learning experience to improve their skills.

[0761] "User skill level" refers to the degree of proficiency in knowledge and skills that a user possesses in a particular field.

[0762] "Learning objectives" refer to the specific results or goals that a user is trying to achieve through learning.

[0763] An "information processing device" is a device that collects and analyzes data and generates information tailored to a specific purpose.

[0764] "Dialogue format" refers to the form of mutual information exchange and communication that takes place between the user and the system.

[0765] "Natural language processing" refers to the technologies and methods that enable computers to understand and use human language.

[0766] A "translation device" is a device that converts information provided in one language into another language.

[0767] "Domestic activities" include various tasks and activities performed within the home, such as cooking and cleaning.

[0768] "Learning content" refers to the system of information and knowledge that users should acquire through learning.

[0769] A "personalized learning experience" refers to a learning process and environment that is customized to the individual user's characteristics and needs.

[0770] The system for realizing this invention is configured primarily around a server, terminals, and users.

[0771] The server primarily handles data processing, generating personal profiles from user registration information. This profile is input into a generation AI model, which automatically generates learning plans tailored to the user's skill level and learning goals. The server uses AI technology to plan optimal learning content and sends it to the terminal in an interactive format. The server utilizes Python-based machine learning libraries (e.g., TensorFlow), and natural language processing technologies (e.g., spaCy) are used for question analysis and response generation.

[0772] The terminal serves as a user interface and is designed as a home information device. Learning plans and materials are displayed on the terminal in a visually appealing and easy-to-use format, supporting the user's skill development in home activities. It has the ability to receive user input, forward questions to a server, retrieve answers, and display them in real time. It is also designed to display information in multiple natural languages ​​and can utilize external translation software such as the Google Translate API.

[0773] Users first register and clarify their skill level and learning goals. They receive a learning plan for household activities and carry out activities accordingly. In particular, for tasks related to everyday household activities such as cooking, they can follow step guides displayed on their device. For example, if a user enters "I want to make pasta," the server will send the most suitable recipe and work procedure to the device based on that information and provide a guide to support the process.

[0774] A concrete example of a prompt message to input into the generating AI model is: "The user has a beginner-level cooking skill level and wants to learn how to make pasta. Generate an optimal learning plan and guide the user through the cooking process." In this way, the system allows users to acquire specific skills and gain an efficient learning experience.

[0775] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0776] Step 1:

[0777] The user performs initial registration using a device. The system collects data such as the user's basic information, current skill level, and learning goals as input. This input data is stored by the server in a database as the user's personal profile.

[0778] Step 2:

[0779] The server launches the AI ​​model and inputs the user's profile information into the model. As part of the data processing, the user's skill level and learning goals are analyzed, and an optimal learning plan is automatically generated according to the algorithm. The resulting learning plan includes specific learning content and progress steps.

[0780] Step 3:

[0781] The device receives the learning plan sent from the server and displays it to the user in a visually appealing and user-friendly UI. The learning plan and learning materials are presented as the user's visual and operational interface. Furthermore, the content is laid out in a way that is easy for the user to understand.

[0782] Step 4:

[0783] Users progress through the learning process via their devices. During the learning process, when they enter a question into their device, that question is sent to the server. Based on the sent input data, the server analyzes the question using natural language processing. As a result of the analysis, an accurate answer is generated.

[0784] Step 5:

[0785] The server returns the generated answer to the terminal. The terminal immediately displays the answer to the user, helping to resolve the user's question. As output, the user is provided with the appropriate answer and additional learning support information.

[0786] Step 6:

[0787] The server periodically monitors the user's learning progress. It collects user behavior data and achievement levels, and performs data calculations to generate progress-based feedback. This feedback is provided via the user's device to improve their learning efficiency.

[0788] Step 7:

[0789] The server selects customized learning materials that take into account the user's cultural background and home environment as needed. The output provides learning content and materials tailored to each individual user, personalizing the learning experience.

[0790] 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.

[0791] This invention relates to a system that utilizes an emotion engine to recognize user emotions in a digital education system, thereby personalizing the user's learning experience. This system supports effective learning by dynamically adjusting the learning plan and feedback according to the user's skill level, learning goals, and emotional state.

[0792] A system is primarily composed of three components: servers, terminals, and users.

[0793] server

[0794] The server creates a profile based on the user's registration information and uses AI technology to generate a learning plan tailored to the user. In this process, the emotion engine analyzes the user's emotional state and appropriately adjusts the learning plan and feedback. For example, if the server detects that the user is feeling stressed, it will slow down the learning pace and provide materials of a lower difficulty level. The emotion engine also has the ability to generate encouraging messages tailored to the user in order to promote positive emotional changes.

[0795] terminal

[0796] The terminal is responsible for displaying learning plans and feedback sent from the server on the user interface. It sends user input and questions to the server and presents the analysis results from the emotion engine to the user in real time. The terminal collects emotion data from the user's facial expressions and voice and sends it to the server, providing data to improve the accuracy of the emotion engine.

[0797] User

[0798] Through the digital education system, users learn content tailored to their skill level. Based on data collected by the emotion engine, users can learn at a pace and with materials suited to their individual emotional state. For example, if a user feels anxious while tackling a complex technical problem, the system provides step-by-step instructions and additional information to deepen their understanding. In this way, users receive support that addresses their emotional state, enabling them to learn in a more relaxed state.

[0799] This invention is implemented as a system that promotes the efficient acquisition of digital skills and creates a more comfortable learning environment by providing a customized learning experience that takes user emotions into consideration.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] Users access the digital education system and enter their skill level, learning goals, and preferred language on the registration screen.

[0803] Step 2:

[0804] The device receives user input and sends that information to the server. Simultaneously, the device uses a facial recognition camera and microphone to record the user's facial expressions and voice tone as emotion data and sends it to the server.

[0805] Step 3:

[0806] The server generates a user profile, saves the registration information to the database, and activates the emotion engine to analyze the user's emotional state.

[0807] Step 4:

[0808] The server uses AI technology to generate a personalized learning plan based on the user's skill level, learning goals, and emotional state. If the emotional state is unstable, the difficulty level of the learning content is adjusted.

[0809] Step 5:

[0810] The server transfers the generated learning plan to the terminal.

[0811] Step 6:

[0812] The device presents the user with a learning plan and prepares the user to select a lesson and begin learning.

[0813] Step 7:

[0814] As users progress through their learning process, they can send inquiries to the server via their device if they have questions or points of confusion.

[0815] Step 8:

[0816] The server receives a user inquiry, analyzes it using a natural language processing engine, and generates an appropriate response. The generated response and additional training information are then sent to the device.

[0817] Step 9:

[0818] The device displays responses from the server to the user, supporting their understanding. It also continuously collects and sends sentiment data to the server during the learning process.

[0819] Step 10:

[0820] The server analyzes the user's emotional data in real time, monitoring their learning progress and stress levels. It adjusts the learning content and pace as needed and generates feedback.

[0821] Step 11:

[0822] The server generates feedback and sends it to the device, which then presents that feedback to the user. Encouragement and advice are also provided in response to changes in the user's emotions.

[0823] Step 12:

[0824] Based on user feedback, the learning process is adjusted and continued. Rewarding comments are provided upon achievement to elicit positive user emotions.

[0825] (Example 2)

[0826] 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".

[0827] In today's educational environment, providing flexible learning experiences tailored to the emotional state and skill level of individual learners is challenging. As a result, many learners are unable to continue learning at their own pace without stress, ultimately missing out on effective learning opportunities. Furthermore, providing multilingual support and materials tailored to cultural backgrounds is a difficult challenge to achieve with limited resources.

[0828] 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.

[0829] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning goals, means for analyzing the user's emotional state and dynamically adjusting the difficulty level of the learning plan and learning materials, and means for providing educational information in an interactive format according to the generated learning plan. This enables individual learners to receive an optimal learning experience tailored to their emotional state, thereby improving the efficiency and comfort of education.

[0830] "User skill level" is an indicator that shows the learner's technical ability and depth of knowledge.

[0831] "Learning objectives" refer to the specific educational outcomes or goals that learners aim to achieve.

[0832] "Generating a learning strategy" refers to the act of formulating an optimal learning plan tailored to the learner's skill level and objectives.

[0833] "Emotional state" refers to the psychological reactions and emotional changes that learners experience during educational activities.

[0834] "Dynamic adjustment" means changing the system's behavior in real time according to the learner's situation and needs.

[0835] "Providing educational information in a dialogue format" means explaining educational content through two-way communication with learners.

[0836] This invention is a system that uses a digital education system to analyze the emotional state of users and personalize their individual learning experiences. This system is composed of three main components: a server, a terminal, and a user.

[0837] server

[0838] The server creates a learning profile based on the user's registration information and generates an individualized learning plan. This process utilizes a generative AI model. Specifically, it uses a machine learning framework to calculate the optimal learning content and pace in real time, tailored to the user's emotional state. The server also uses an emotion analysis engine to analyze the user's voice and facial expressions to identify their emotional state. For example, if a user is stressed, the server slows the learning pace and generates motivational messages. The specific technologies used include common cloud-based AI services.

[0839] terminal

[0840] The device displays learning plans and feedback sent from the server on its user interface. It also collects the user's facial expressions and voice using the camera and microphone and sends that data to the server. The system continuously collects data through the device and tracks the user's emotional state in real time. For example, it accurately understands the user's intentions through question input on the device and provides necessary guidance.

[0841] User

[0842] Users can learn at their own pace using the provided interface. Based on data collected by the emotion engine, a personalized learning plan is presented, allowing users to learn efficiently. For example, when a user is learning a math problem, the system provides step-by-step explanations and sends encouraging messages at points where the user might get stuck.

[0843] Another example of a prompt from the generating AI model is, "What kind of encouraging message should be suggested if the user is feeling anxious while preparing for an exam?" This prompt allows the AI ​​to generate hints to provide appropriate encouragement and support based on the user's emotional state.

[0844] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0845] Step 1:

[0846] The server receives registration information entered by the user into the system and creates an individualized learning profile based on that information. Specifically, it stores data such as the user's age, interests, skill level, and learning goals in a database. Here, as part of data processing, the information is organized and an initial learning plan is generated using an algorithm. The generated learning plan then builds an educational path on the server that is tailored to the user's individual needs.

[0847] Step 2:

[0848] The device collects facial expressions and audio from the user's camera and microphone, and sends this as emotion data to a server. This process utilizes voice analysis and image processing technologies to prepare the data for analyzing the user's emotional state. The collected data is converted into input, such as text or numerical data, and sent to the server. This process allows for real-time understanding of the user's emotions.

[0849] Step 3:

[0850] The server receives emotion data sent from the terminal and analyzes the user's emotional state using an emotion analysis engine. Based on the input data, a machine learning model is used to determine the emotional state. At this stage, data processing involves labeling the data and identifying emotions using the AI ​​model. The output is derived as a state such as whether the user is cheerful or stressed, and this result is used to adjust the learning plan.

[0851] Step 4:

[0852] The server dynamically adjusts the learning plan and learning materials based on the analysis results. It changes the learning pace and difficulty level of the materials according to the user's emotional state. This process uses a generative AI model to generate new learning steps and encouraging messages, and determines specific follow-up actions based on prompts. The resulting new learning plan is presented to the user via the device.

[0853] Step 5:

[0854] Users progress through their learning according to a learning plan presented on their device. They access learning materials and interact through an interactive interface. When users input questions and answers, this data is sent back to the server, and the learning effectiveness is evaluated in real time. This feedback helps optimize the next learning step.

[0855] (Application Example 2)

[0856] 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".

[0857] Traditional digital education systems often fail to consider the user's emotional state, resulting in the provision of uniform learning materials and progress even when learners experience stress or anxiety. Furthermore, their inability to adapt to individual learners' emotions significantly reduced learning efficiency.

[0858] 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.

[0859] In this invention, the server includes means for automatically generating a learning plan based on the user's skill level and learning objectives, means for analyzing the user's emotional state and dynamically adjusting the learning plan and feedback based on the emotional state, and means for monitoring the user's learning progress in real time and providing feedback according to the progress and achievement level. This enables a personalized learning experience adapted to the emotional state of each individual learner.

[0860] A "user" is an individual or group that uses the system to engage in learning activities.

[0861] "Skill level" is an indicator that shows the degree of knowledge and experience a user currently possesses.

[0862] "Learning objectives" refer to the specific content that a user wants to learn or the results they wish to achieve.

[0863] A "learning plan" is a guideline created based on the user's skill level and learning objectives to guide their progress in learning.

[0864] "Dialogue format" refers to a type of interaction between a user and a system, where questions and answers are exchanged back and forth.

[0865] "Educational information" is a general term for the knowledge, information, and teaching content provided to users.

[0866] "Natural language processing" is a technology used by computers to interpret human language and understand its meaning.

[0867] "Feedback" refers to the evaluations, suggestions, and guidance that a system provides regarding a user's behavior and learning progress.

[0868] "Emotional state" refers to the user's psychological and emotional condition, including emotional factors such as happiness and stress.

[0869] In this invention, in order to realize a personalized education system based on the user's emotional state, three main components are established: a server, a terminal, and a user.

[0870] The server is responsible for generating a learning plan based on the user's registration information and emotional state. This process takes into account the user's skill level and learning goals, and uses a generative AI model to determine appropriate learning content and feedback. For example, if the user is feeling frustrated, the server will adjust the learning pace and provide easier learning materials. It can also dynamically generate encouraging messages using emotion analysis technology.

[0871] The terminal is a device that displays learning plans and feedback from the server through a user interface. It detects the user's facial expressions and voice tone and sends this data to the server for sentiment analysis. The collected sentiment data is also used as input for analyzing the user's real-time emotional state. Specific devices used include smartphones and tablets equipped with cameras and microphones.

[0872] Users benefit from a personalized learning experience provided by this system. The system presents users with appropriate learning materials and plans tailored to their emotional state, allowing them to learn in a more relaxed manner. For example, if a child is learning with a smile, the system provides positive feedback such as, "It looks like you're enjoying learning! Let's move on to the next challenge!"

[0873] Examples of prompts to input into a generative AI model:

[0874] "To improve children's learning experiences, generate feedback that is tailored to their emotional state. Suggest encouraging messages for situations where the child is smiling and their tone of voice is neutral."

[0875] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0876] Step 1:

[0877] The device collects input data from the user. This input includes the user's facial expressions and voice tone. The device uses a camera and microphone to capture this data and transmits it to a server in digital format.

[0878] Step 2:

[0879] The server inputs the user's facial expression data and voice tone into an emotion analysis engine. This engine uses a generative AI model to analyze the emotional state and identify the user's real-time emotions. The output data consists of tags indicating the user's emotional state, such as "happiness" or "frustration."

[0880] Step 3:

[0881] The server generates an optimal learning plan using emotional states obtained through sentiment analysis and the user's existing profile information. Based on the input data, the AI ​​model searches an existing learning material database to determine content suitable for the user's skill level and learning goals, as well as a learning pace that corresponds to their emotional state. The output is a personalized learning plan.

[0882] Step 4:

[0883] The server composes educational content and feedback in line with the generated learning plan and sends it to the device. This feedback includes encouraging messages tailored to the user's emotional state. The output data consists of text messages and learning instructions displayed on the device.

[0884] Step 5:

[0885] Users review the learning plan and feedback received through their device and proceed with their learning accordingly. Progress data is updated based on user actions. Any new data entered by the user during the learning process is sent back to the server via the device, and the process is repeated.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A processing means that automatically generates a learning plan based on the user's skill level and learning objectives,

[0910] A means of providing educational information in an interactive format in accordance with the aforementioned learning plan,

[0911] A means for generating a response using natural language processing based on user input,

[0912] A means of monitoring users' learning progress in real time and providing feedback according to their progress and achievement level,

[0913] To support multiple languages, a means of translating learning information,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, comprising means for analyzing a user's questions regarding the operation of a digital device and for providing appropriate operating procedures.

[0917] (Claim 3)

[0918] The system according to claim 1, comprising means for selecting learning materials according to the user's cultural background.

[0919] "Example 1"

[0920] (Claim 1)

[0921] A processing means that automatically generates a learning plan based on the user's skill level and learning objectives,

[0922] A means of providing educational information in an interactive format in accordance with the aforementioned learning plan,

[0923] A means for generating a response using natural language processing based on user input,

[0924] A means of monitoring users' learning progress in real time and providing feedback according to their progress and achievement level,

[0925] To support multiple languages, a means of translating learning information,

[0926] A means of generating individual profiles based on user registration information,

[0927] A means of generating a learning plan using a generative AI model,

[0928] A means of continuously revising the study plan,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, comprising means for analyzing a user's question regarding the operation of an information processing device and for presenting appropriate operating procedures.

[0932] (Claim 3)

[0933] The system according to claim 1, comprising means for selecting learning materials according to the user's cultural background.

[0934] "Application Example 1"

[0935] (Claim 1)

[0936] An information processing device that automatically generates a learning plan based on the user's skill level and learning objectives,

[0937] A device that provides educational information in an interactive format according to the aforementioned learning plan,

[0938] A device that generates a response using natural language processing based on user input,

[0939] A device that monitors the user's learning progress in real time and provides feedback according to the progress and level of achievement,

[0940] To support multiple natural languages, a device for translating learning information,

[0941] A device that provides learning content to support the improvement of users' skills in household activities,

[0942] A device that guides the progress of household tasks according to the user's skill level,

[0943] A learning support system that includes this.

[0944] (Claim 2)

[0945] The learning support system according to claim 1, comprising a device that analyzes questions from the user regarding household tasks and presents appropriate work procedures.

[0946] (Claim 3)

[0947] The learning support system according to claim 1, comprising a device for selecting learning materials according to the user's cultural background and home environment.

[0948] "Example 2 of combining an emotion engine"

[0949] (Claim 1)

[0950] A device that automatically generates a learning plan based on the user's skill level and learning goals,

[0951] A device that analyzes the user's emotional state and dynamically adjusts the learning approach and difficulty level of the learning materials,

[0952] A device that provides educational information in an interactive format according to the generated learning plan,

[0953] A device that generates a response using natural language processing based on user input,

[0954] A device that monitors the user's learning progress in real time and provides feedback according to their progress and achievement level,

[0955] A device that translates educational information to support multiple languages,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, comprising a device that analyzes user questions regarding operating equipment and provides appropriate operating procedures.

[0959] (Claim 3)

[0960] The system according to claim 1, comprising a device for selecting learning materials according to the user's cultural background.

[0961] "Application example 2 when combining with an emotional engine"

[0962] (Claim 1)

[0963] A means for automatically generating a learning plan based on the user's skill level and learning objectives,

[0964] A means of providing educational information in an interactive format in accordance with the aforementioned learning plan,

[0965] A means for generating a response using natural language processing based on user input,

[0966] A means of monitoring users' learning progress in real time and providing feedback according to their progress and achievement level,

[0967] To support multiple languages, a means of translating learning information,

[0968] A means of analyzing the user's emotional state and dynamically adjusting learning plans and feedback based on that emotional state,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, comprising means for analyzing a user's questions regarding the operation of a digital device and for providing appropriate operating procedures.

[0972] (Claim 3)

[0973] The system according to claim 1, comprising means for selecting learning materials according to the user's cultural background. [Explanation of Symbols]

[0974] 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. An information processing device that automatically generates a learning plan based on the user's skill level and learning objectives, A device that provides educational information in an interactive format according to the aforementioned learning plan, A device that generates a response using natural language processing based on user input, A device that monitors the user's learning progress in real time and provides feedback according to the progress and level of achievement, To support multiple natural languages, a device for translating learning information, A device that provides learning content to support the improvement of users' skills in household activities, A device that guides the progress of household tasks according to the user's skill level, A learning support system that includes this.

2. The learning support system according to claim 1, comprising a device that analyzes the user's questions regarding household tasks and presents appropriate work procedures.

3. The learning support system according to claim 1, comprising a device for selecting learning materials according to the user's cultural background and home environment.

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