system

Abstract

Provide a system. 【Solution means】 Means for authenticating user information, Means for collecting and analyzing the user's learning data, Means for generating a curriculum suitable for the user based on the analysis results, Means for answering questions from the user's terminal in real time, Means for providing the user with multi-mode learning materials, Means for analyzing the user's learning progress and updating the curriculum, Means for generating messages to enhance the user's learning motivation, Means for enabling collaborative learning with other users, Means for displaying the user's learning progress to guardians or instructors, A 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the educational field, the importance of individualized learning according to the understanding level and learning style of each student is increasing. However, in the conventional educational system, it is difficult to provide support suitable for each individual student. In addition, it is necessary to eliminate educational disparities and realize a state where all students can equally enjoy high-quality education. However, at present, there are problems such as insufficient provision of individualized curricula, real-time question answering, and efficient management and analysis of learning progress, making it difficult to continuously enhance students' learning motivation.

Means for Solving the Problems

[0005] This invention is a system that uses generative AI technology to authenticate user information, collect and analyze learning data, and generate personalized curricula. It improves the user's learning experience by answering questions asked by the user via a terminal in real time and providing multi-mode learning materials. Furthermore, it can continuously analyze the user's learning progress and update the curriculum as needed. Learning progress and analysis data are also made visible to parents and instructors, enabling them to assist in instruction. In addition, it can enhance user motivation through collaborative learning functions with other users and digital rewards.

[0006] "User information" refers to data necessary for authentication and personalized learning for users such as students and teachers who access the system.

[0007] "Authentication" is the process of verifying that a user is a legitimate user of the system using their user information.

[0008] "Learning data" refers to information about a user's learning history, progress, style, and level of understanding.

[0009] "Analysis" is the process of using collected training data to understand the user's learning patterns and evaluate individual needs and problems.

[0010] A "curriculum" is a set of educational plans and materials designed to meet the user's learning needs.

[0011] "Real-time question support" is a function that instantly generates and provides answers to questions entered by users.

[0012] "Multi-mode learning materials" refers to educational content provided in various formats, such as text, video, and audio, that users can select from.

[0013] "Learning progress" is an indicator that shows the current level of achievement and understanding of the learning goals set by the user.

[0014] "Collaborative learning" is a learning method in which multiple users can learn from each other and deepen their knowledge together.

[0015] "Digital rewards" are incentives such as badges and points that the system provides based on the user's learning progress.

[0016] "Parents and instructors" are individuals who access the system to support and manage the learning of student users. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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.

Modes for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is an educational system that utilizes generative AI technology to provide personalized learning support to student users. This system is implemented through interaction between a server, terminals, and the user. Its specific operation is described below.

[0039] First, when the device is started up, the user is authenticated through the login screen. The server checks the authentication data sent by the user and retrieves the corresponding user information from the database. If authentication is successful, the server sends the user's past learning data and settings information to the device, and the learning session is ready to begin.

[0040] Next, the system collects user learning data on the server and analyzes their learning style and progress based on that data. This allows it to generate a curriculum optimized for the user and provide it to their device. This curriculum includes content tailored to the user's level of understanding and incorporates learning materials in multiple modes, such as text, video, and audio. Users can immediately begin learning by selecting their preferred learning mode on their device.

[0041] Furthermore, if a user encounters a question during their learning process, they can input it via their device. The server analyzes this question using a generation AI and generates an answer in real time. The answer is sent to the device, and the user can review it immediately.

[0042] In each learning session, the server analyzes the user's learning progress. Based on the progress data, the curriculum is dynamically updated and reflected in the next learning session. This ensures that the user can always continue learning with content appropriate to their level of understanding. The server also generates digital rewards and encouraging messages to motivate the user based on their learning progress and notifies the user's device.

[0043] Furthermore, by using the collaborative learning feature, users can learn while interacting with other students. The server matches users with appropriate learning partners, and collaborative learning sessions are provided through the device. Through such interactions, users can deepen their understanding by collaborating with others.

[0044] Finally, the server also provides learning progress and performance data to the parent / instructor interface. This allows parents and instructors to understand the user's learning progress and provide support and advice as needed.

[0045] In this way, the entire system works together to provide users with a personalized and effective learning experience.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The terminal boots up and displays a login screen to the user. The user enters their ID and password and sends the authentication information to the server via the terminal.

[0049] Step 2:

[0050] The server processes the received authentication information and retrieves the corresponding user information from the database. The server determines whether authentication was successful, and if successful, sends the initial setup information to the terminal.

[0051] Step 3:

[0052] The server collects user learning history and progress data from a database and analyzes the user's learning patterns using a machine learning model. Based on this analysis, an optimized curriculum is generated.

[0053] Step 4:

[0054] The server sends the generated curriculum to the terminal. The terminal displays the received curriculum to the user and prepares to start the learning session.

[0055] Step 5:

[0056] The user enters a question via their device during the learning process. The device sends the question to the server and requests a real-time response.

[0057] Step 6:

[0058] The server uses AI generation to generate answers to the user's questions. The server sends the generated answers to the device, which then displays them to the user.

[0059] Step 7:

[0060] The user selects their preferred learning mode (text, video, or audio) using their device. The server searches for learning materials suitable for the selected mode and sends them to the device. The device then presents these materials to the user.

[0061] Step 8:

[0062] The server periodically receives learning progress data from the terminal and analyzes the current learning status. Based on the analysis results, the curriculum is dynamically updated and the latest version is sent to the terminal.

[0063] Step 9:

[0064] The server generates digital rewards and motivational messages based on the user's learning progress. These are then notified to the device, which displays them to the user.

[0065] Step 10:

[0066] A user requests collaborative learning via their device. The server searches its database for other suitable users and performs the matching process. Collaborative learning session information is sent to both devices, providing an environment where users can learn from each other.

[0067] Step 11:

[0068] The server prepares data for an interface that provides users' learning progress and analytical data to parents and instructors. This data is then sent to the parents' and instructors' devices, enabling support for the user's learning.

[0069] (Example 1)

[0070] 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."

[0071] Traditional education systems have the challenge of not adequately providing individualized learning support for each user. Furthermore, a lack of analysis of learning progress and measures to improve motivation leads to a failure to sustain users' motivation to learn. Additionally, opportunities for collaborative learning among users and the sharing of learning progress are difficult, making it challenging for parents and instructors to provide appropriate support.

[0072] 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.

[0073] In this invention, the server includes means for authenticating user information, means for collecting and analyzing data on the user's knowledge, and means for generating an educational plan suitable for the user based on the analysis results. This makes it possible to provide an optimized learning experience for each individual user and to increase the user's motivation to learn. Furthermore, it facilitates effective collaborative learning with other users and enables parents and instructors to monitor learning progress in real time, thereby providing appropriate support and advice.

[0074] "User information" refers to data necessary to identify an individual user and is used for authentication and providing personalized services.

[0075] "Knowledge-related data" refers to information that shows a user's learning history, current level of understanding, and progress, and is collected and analyzed for learning support purposes.

[0076] An "educational plan" is a curriculum that defines the optimal learning content and methods tailored to the user's learning needs and objectives.

[0077] "Providing answers in real time" means generating and sending responses to user inquiries in a timely manner without delay.

[0078] "Diverse forms of educational materials" refers to learning content provided in different formats, such as text, video, and audio, allowing users to choose the method that is easiest for them to learn from.

[0079] "Learning progress" refers to the user's growth and achievement level during their learning process, and is used to evaluate educational plans and formulate the next learning steps.

[0080] "Communication" refers to informational messages sent to the user, including encouragement for learning and rewards based on progress.

[0081] "Collaborative learning" refers to a learning method in which multiple users work together to learn, deepening their understanding by sharing knowledge with each other.

[0082] "Displaying to supervisors and supporters" means visually showing the user's learning progress to instructors and guardians, which is necessary to provide appropriate feedback and support.

[0083] A "specific algorithm" refers to a series of computational methods and processing procedures used to analyze a user's learning data and generate an individualized learning experience.

[0084] This invention is an educational support system that utilizes generative AI technology and aims to provide users with an individualized learning experience. This system mainly consists of three elements: a server, a terminal, and a user, each of which functions as follows.

[0085] The server authenticates user information and retrieves the user's learning history and individual settings from its database. Authentication software and a database management system (e.g., MySQL®, PostgreSQL) are used for this purpose. The server then collects the user's learning data and analyzes it using data analysis libraries (e.g., Pandas, NumPy). Generative AI models (e.g., general-purpose AI text generation models) are used for analysis to generate an educational plan tailored to the user.

[0086] Once the device receives information from the server, it can present users with educational materials in various formats. These materials include text, videos, and audio, and the device has the ability to select the learning method that is most effective for the user. Users connect to the system using the device and access the learning materials to implement a personalized curriculum.

[0087] Users access educational materials through their devices, and if questions arise during their learning, they input them and send them to the system via their devices. The questions are entered as prompts, for example, "Please explain this new mathematical concept." The server analyzes this input, uses generative AI to calculate answers in real time, and responds to the user via their devices.

[0088] Furthermore, based on the data obtained during learning activities, the server can analyze the user's learning progress and dynamically update the educational plan to reflect it in the next learning session. In addition, rewards and encouraging messages generated according to the user's progress are delivered to the device, providing support to enhance the user's learning motivation.

[0089] In this way, the system is designed to provide a personalized learning experience, enabling users to learn effectively.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The user starts up the device and enters authentication information on the login screen. The entered information, such as username and password, is authentication data and is sent to the server via the device.

[0093] Step 2:

[0094] The server retrieves information about the user by querying the database based on the authentication data it receives. It returns a matching record from the user information held in the database management system and determines whether authentication was successful. An authentication algorithm is applied during this process.

[0095] Step 3:

[0096] If authentication is successful, the server retrieves the user's past learning data and configuration information and sends it to the device. This data concerns the user's learning history and current progress. The retrieved data is converted into a format that can be displayed on the device and presented to the user.

[0097] Step 4:

[0098] The server uses a data analysis library to analyze the user's learning style and progress based on their past learning data. The analyzed data is then used to create a new curriculum using generative AI models. This generates an optimal educational plan for the user and provides it to their device.

[0099] Step 5:

[0100] Users view the curriculum on their device interface and select the assignments and learning modes they wish to study. The selected information is sent from the device to the server, and the corresponding learning materials are downloaded.

[0101] Step 6:

[0102] The device presents the user with downloaded educational materials. These materials include text, videos, and audio, which the user can use to progress through their learning. Each material is displayed in a format appropriate to the learning mode selected by the user.

[0103] Step 7:

[0104] If a user encounters a question during their learning process, they enter it into their device. This question is entered as a prompt, expressed in a format such as "Please explain how to solve this equation." The question is then sent to the server.

[0105] Step 8:

[0106] The server uses a generated AI model to analyze the received prompt and generate an appropriate response. The AI ​​model utilizes relevant knowledge based on the question to create the answer. This response is then forwarded back to the terminal and provided to the user in real time.

[0107] Step 9:

[0108] As learning progresses, the server analyzes user performance data accumulated during the session. It utilizes data analysis libraries to evaluate learning progress. Based on the analysis results, the server updates the educational plan as needed and delivers improved learning materials to the device.

[0109] Step 10:

[0110] The server generates reward and encouragement messages based on the user's progress to promote learning. These messages provide the user with information about their achievements and motivation to move on to the next step. The generated messages are notified to the device and displayed to the user.

[0111] (Application Example 1)

[0112] 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."

[0113] Providing support optimized to the individual needs of learners is crucial in educational settings and at home. However, learners are often constrained by fixed curricula, making flexible learning tailored to their individual questions and progress difficult. Furthermore, limited opportunities for effective interaction with other learners pose challenges to understanding the material and maintaining motivation. Against this backdrop, there is a need to develop systems that provide learners with individualized learning plans, answer questions in real time, and promote opportunities for collaborative learning.

[0114] 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.

[0115] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for facilitating interaction among users by linking multiple educational support devices. This enables the provision of educational plans optimized for individual learners, real-time question answering, and interaction among learners.

[0116] "User information" refers to data related to the personal identification and profile of system users, and is used for authentication and customization.

[0117] "Learning data" refers to various types of information related to a user's learning activities, including learning content, progress, and level of understanding.

[0118] An "educational plan" refers to an optimized learning curriculum generated based on the user's current learning status and needs.

[0119] An "information processing device" refers to a terminal device that users access, and is used to display learning materials and receive input from users.

[0120] "Real-time response methods" refer to functions that provide instant answers to user questions, achieving rapid responses through the use of generative AI.

[0121] "Diverse forms of learning materials" refers to learning information that combines multiple media types, such as text, audio, and video.

[0122] "Educational support devices" refer to equipment and software designed to assist users in their learning, such as displaying learning information and managing progress.

[0123] "Digital rewards" refer to the technical means of providing rewards and encouraging messages based on a user's learning progress and achievements.

[0124] In this invention, interaction between a server, a terminal, and a user takes place to realize an educational support system. The roles of each are described below.

[0125] The server uses an authentication management system to authenticate user information. Personal information entered by the user on the terminal is sent to the server and compared with registered information. If authentication is successful, the server retrieves past learning history and settings information from the user's learning database and sends it to the terminal.

[0126] The terminal functions as an interface with the user, interacting with them through voice input and a touch panel. Based on the learning mode selected by the user on the terminal, information sent from the server is displayed in various forms, including text, audio, and video. The server uses a generative AI model to analyze the user's progress in real time and provide an optimal learning plan. For example, if a user asks, "I don't understand the Pythagorean theorem," it can immediately present relevant videos and text materials.

[0127] Users progress through their learning based on this information. If questions arise during learning, they can input them via their device. The server uses a generation AI to analyze the questions and generate appropriate answers. These answers are then sent to the device for immediate confirmation by the user.

[0128] Furthermore, the server supports collaborative learning with other users by linking multiple educational support devices. This allows learners in different locations to share knowledge with each other and deepen their understanding through discussions and quizzes.

[0129] Example of a prompt:

[0130] Question: "What is the Pythagorean theorem?"

[0131] Prompt: "Please explain the Pythagorean theorem to children, including simple examples."

[0132] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0133] Step 1:

[0134] The user starts up their device and enters personal information via the login screen. The device sends this information to the server. The server performs authentication verification based on the entered user information against the registered information in the database. As a result, a message indicating authentication success or failure is output to the user's device.

[0135] Step 2:

[0136] The server retrieves past learning history and configuration information from the learning database for users who have successfully authenticated. This data is used within the server to generate a user-specific education plan and is then sent to the device. The server analyzes the past history and presents an appropriate plan to the device.

[0137] Step 3:

[0138] When a user selects a learning mode on their device, the device sends this selection information to the server. Based on this information, the server uses a generative AI model to generate an optimal learning plan. This plan includes the user's chosen learning format (text, audio, video) and provides materials optimized for the device.

[0139] Step 4:

[0140] If a user has a question while learning, they can enter it via their device. The server receives the question data and analyzes it using a generative AI model. As a result of the analysis, an appropriate answer is generated and sent to the device in real time. The user reviews this answer and continues learning.

[0141] Step 5:

[0142] The server facilitates matching to promote collaborative learning among users. It communicates with multiple related terminals to initiate interaction between learners. The terminals display details of the collaborative learning sessions in which the user is participating, and the user deepens their understanding through questions and discussions.

[0143] Step 6:

[0144] The server generates digital rewards based on the user's learning progress and achievements. This information is sent to the device as messages to boost learning motivation, and users receive it in real time.

[0145] 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.

[0146] This invention is an educational system that utilizes generative AI technology and an emotion engine to provide users with a more personalized learning experience. The system is realized through the interaction of a server, terminal, and user, and by incorporating an emotion engine, it dynamically adjusts the user's learning motivation and curriculum optimization according to their emotional state.

[0147] When a user begins learning, the device starts up and prompts for login information. The authentication information entered by the user is sent from the device to the server, which verifies this information and retrieves the user's profile and learning history from the database. Following this basic authentication and data setup, the system generates a curriculum tailored to the user's learning style and needs.

[0148] A notable feature of this invention is that the emotion engine is integrated into the device, recognizing the user's emotions in real time. For example, it analyzes the user's mood and whether they are stressed through the camera and microphone. The server receives this emotional information, performs analysis, and adjusts the curriculum as needed to improve the user's learning experience.

[0149] For example, if the emotion engine detects user confusion or stress while a user is working on a difficult task, the server can temporarily switch the curriculum to easier content or slow down the learning pace. Alternatively, it can send a message to the user via the device to encourage relaxation.

[0150] When a user enters a question they are learning into their device, the server instantly analyzes the question using a generating AI and provides an answer. This answer is also customized based on emotion recognition; for example, if a positive emotion is recognized, an encouraging message will be added.

[0151] Furthermore, both progress and sentiment data are analyzed by the server to suggest ideal staging within the user's overall learning plan. Digital rewards and messages designed to boost user motivation are also based on this sentiment data and are designed to maximize their effectiveness.

[0152] In this way, the system leverages an emotion engine to provide a more personalized learning experience that takes user emotions into account, thereby maximizing the effectiveness and efficiency of learning.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The device boots up, and the user enters their ID and password on the login screen. The device sends the user's authentication information to the server.

[0156] Step 2:

[0157] The server verifies the received authentication information and retrieves the user's profile and learning history from the database. If authentication is successful, the server sends the initial setup and past learning data to the device.

[0158] Step 3:

[0159] The device's built-in emotion engine activates and acquires user emotion data through the camera and microphone. For example, it determines the user's emotions in real time based on facial expression analysis and voice tone.

[0160] Step 4:

[0161] The server receives emotional data sent from the emotion engine and analyzes the user's current mental state. Based on this emotional data, the server adjusts the curriculum content and pace.

[0162] Step 5:

[0163] The user inputs a question through their device during the learning process. The device sends the question to the server. The server uses generative AI to analyze the question and generate an answer. The generated answer is customized according to sentiment data and sent back to the device.

[0164] Step 6:

[0165] The server periodically receives learning progress data from the terminal and analyzes it together with sentiment data. Based on the analysis results, it adjusts the next curriculum and sends it to the terminal.

[0166] Step 7:

[0167] Based on the analysis results, the server generates messages and digital rewards to enhance the user's learning motivation. These are then sent to the device at the optimal time, according to the emotional data.

[0168] Step 8:

[0169] If a user wishes to participate in a collaborative learning session, the device sends a request to the server. The server searches for other suitable users and selects the best match, taking sentiment data into consideration. It then sends information to the device so that the selected users can begin a collaborative learning session.

[0170] Step 9:

[0171] The server provides user learning progress and emotional data to the parent / teacher interface. This allows parents and teachers to understand the user's learning situation and emotional state, and provide appropriate support.

[0172] (Example 2)

[0173] 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".

[0174] The current education system has the challenge of not being able to adequately consider the individual needs and emotional states of learners, making it difficult to maximize learning effectiveness and motivation. Furthermore, it is difficult to flexibly adjust the curriculum in real time and provide support tailored to the user's learning progress. Moreover, one-sided learning support that ignores emotional states can increase learner stress and decrease learning efficiency.

[0175] 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.

[0176] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for recognizing the user's emotional state and analyzing that information. This enables dynamic curriculum adjustment to meet individual learning needs. Furthermore, flexible learning support and message delivery based on the user's emotions can improve learning motivation and efficiency.

[0177] "Means of authenticating user information" refers to technologies used to verify the authentication data entered by a user when they access a system, and to confirm that they are a legitimate user.

[0178] "Means for collecting and analyzing user learning data" refers to technologies that collect data on users' learning behavior and progress, and then analyze this data to identify their learning style and needs.

[0179] "Means for generating a curriculum based on analysis results" refers to a technology that uses the results of analyzing learning data to create a curriculum that best suits the user's learning needs.

[0180] "Means for recognizing a user's emotional state and analyzing that information" refers to technologies that detect a user's emotions, analyze that data to understand the user's emotional state, and provide appropriate learning support.

[0181] "Methods for dynamically adjusting the curriculum based on analysis results" refers to technologies that respond to individual learning needs by changing the curriculum content in real time based on an analysis of the user's learning progress and emotional state.

[0182] "A means of providing real-time answers to questions from a user's device" refers to a technology that generates and provides appropriate answers immediately to questions asked by users via their devices.

[0183] "Means of providing users with multi-mode learning materials" refers to technologies that provide users with learning materials in multiple formats, such as text, images, audio, and video, to accommodate diverse learning styles.

[0184] "Methods for analyzing user learning progress and updating the curriculum" refers to technologies that analyze the progress of learning and create or update the curriculum based on the results.

[0185] "Means for generating messages to enhance user learning motivation" refers to technologies that analyze users' emotions and learning progress to create and present messages that increase their motivation.

[0186] "Means that enable collaborative learning with other users" refers to technologies that allow multiple users to engage in collaborative learning activities on a system.

[0187] "Means of displaying a user's learning progress to parents and educators" refers to technologies that organize a user's learning progress information and visually represent it in a way that parents and educators can access.

[0188] This invention is an educational support system that uses neural circuit technology and an emotion analysis engine to provide users with an individualized learning experience. Embodiments of the invention are realized through the cooperation of three parties: the user, the terminal, and the server.

[0189] The terminal receives the user's login information and sends that data to the server for authentication. The server verifies the information received from the client and uses a database system to retrieve the user's profile and past learning history. The database used here is accessed using SQL queries.

[0190] The server uses collected information to apply generative AI technology and generate a curriculum appropriate for the user. The generative AI model used is a deep learning model built in languages ​​such as Python, which creates a new learning plan based on the user's past learning performance.

[0191] The emotion analysis engine built into the device uses image recognition software (e.g., OpenCV) and a speech analysis system to detect the user's emotional state in real time. The recognition results are immediately sent to a server, where analysis is performed to improve the user's learning experience.

[0192] For example, if a user is experiencing stress while working on a particular task, the server can temporarily adjust the curriculum based on emotional data and switch to a less difficult task. It can also send messages to encourage relaxation through the device.

[0193] Furthermore, the server uses neural network models to generate appropriate answers in real time to questions entered by the user on the device. This process is also customized through emotion recognition and may include positive feedback.

[0194] A concrete example of a prompt message would be, "Please tell me how to provide appropriate learning support when a user becomes confused."

[0195] In this way, the invention enables personalized learning that takes user emotions into account, providing an efficient learning experience.

[0196] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0197] Step 1:

[0198] The user enters their login information into the terminal. The entered information (username and password) is sent from the terminal to the server. This causes the server to initiate the user authentication process.

[0199] Step 2:

[0200] The server verifies the received login information against the database and performs authentication. If successful, it retrieves the user's profile data and learning history from the database. Specifically, it uses SQL queries to extract information related to the user. As output of this process, learning history data associated with each individual user is obtained.

[0201] Step 3:

[0202] The server uses a generative AI model based on the acquired data to generate a curriculum optimized for the user. Input data includes the user's progress and past learning performance. The generative AI model analyzes this data to suggest future learning content. The output is individually customized curriculum data.

[0203] Step 4:

[0204] The sentiment analysis engine integrated into the device collects user sentiment data using sensors (camera and microphone). Specifically, it determines the user's emotional state in real time based on image recognition and voice analysis. This sentiment data is processed on the device and sent to the server.

[0205] Step 5:

[0206] The server receives and analyzes emotional data sent from the terminal. Based on the results of this analysis, it dynamically adjusts the already generated curriculum. For example, if the user is experiencing stress, the curriculum is readjusted to make learning tasks easier. The generated, adjusted curriculum data is then sent back to the terminal.

[0207] Step 6:

[0208] When a user enters a question during learning, the device sends this information to the server. The server uses a generative AI model to analyze the question and generate an answer in real time. For example, the prompt might be "How can I provide appropriate learning support when the user becomes confused?" The generated answer is sent back to the device and displayed to the user.

[0209] (Application Example 2)

[0210] 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".

[0211] Modern education systems demand personalized learning experiences tailored to individual learners. However, traditional educational platforms struggle to recognize learners' emotional states in real time and optimize learning content and pace accordingly. Furthermore, they are not adequately providing effective feedback and encouragement based on learners' emotions. As a result, learning efficiency and motivation are hindered.

[0212] 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.

[0213] In this invention, the server includes means for collecting and analyzing user learning data and emotional information, means for generating a curriculum suitable for the user based on the analysis results, and means for optimizing the learning experience based on emotional data. This makes it possible to provide a dynamic and personalized learning experience that takes into account the learner's emotional state.

[0214] "User information" refers to identifying information related to user authentication and profile.

[0215] "Learning data" refers to information about the activities and progress of learners.

[0216] "Emotional information" refers to data that indicates the emotional state obtained through the learner's facial expressions and voice.

[0217] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and generate content.

[0218] "Responding in real time" refers to a process that enables immediate responses to user input.

[0219] "Multi-mode learning materials" refers to educational content in various formats, such as text, audio, images, and videos.

[0220] "Emotional data" refers to information about a learner's emotional state and is used to adapt to the learning process.

[0221] "Digital rewards" refer to rewards and incentives provided online based on the learner's achievement level.

[0222] "Collaborative learning" refers to learning activities carried out by multiple learners working together.

[0223] "Displaying to parents and instructors" means providing parents and instructors with a visual representation of the learner's progress and achievements.

[0224] The system implementing this invention is an educational support platform using emotion recognition technology and a generative AI model. The server, terminal, and user interact with each other to provide a personalized learning experience.

[0225] The server verifies the authentication information sent from the user's device and retrieves the user's profile and learning history from the database. The device incorporates a camera and microphone to collect emotional information in real time through the user's facial expressions and voiceprint. This emotional information is analyzed using Python libraries such as OpenCV and Dlib.

[0226] Based on the analyzed emotion data and training data, the server dynamically generates a learning curriculum optimized for each user using a generative AI model (e.g., OpenAI's GPT-4). Furthermore, appropriate feedback and learning materials are automatically selected according to the learner's situation and provided through the terminal interface.

[0227] For example, if the emotion engine detects that a child is experiencing stress due to a difficult math problem while studying at home, the server can use its AI-generated explanation to create an easy-to-understand explanation and display it on the device. A message of encouragement to help the child relax is also sent.

[0228] An example of a prompt message would be, "Considering this learner's current emotional state, generate a simple task description and an encouraging message to help them relieve stress." This allows users to engage in learning in a less stressful state, which is expected to improve learning effectiveness.

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The terminal starts up and displays a login screen to the user. The user enters their authentication information, which the terminal sends to the server. The server authenticates the user by comparing the entered authentication information with the database. If authentication is successful, the server retrieves the user's profile and past learning history and sends it to the terminal. The input is the authentication information, and the output is the user's profile and learning history.

[0232] Step 2:

[0233] The device uses a camera and microphone to collect the user's facial expressions and voice. Based on the collected data, it analyzes emotional information in real time using the Python libraries OpenCV and Dlib, and sends the results to the server. The input is facial expression and voice data, and the output is the user's emotional information.

[0234] Step 3:

[0235] The server dynamically generates a user-optimized curriculum using a generative AI model based on emotional information and learning history. The generated curriculum is sent to the terminal, which then displays it to the user. The input is emotional information and learning history, and the output is the optimized curriculum.

[0236] Step 4:

[0237] The terminal receives a question from the user. The server sends this question to a generation AI model, which uses prompts to generate the optimal answer. The generated answer is sent back to the terminal and displayed to the user. The input is the user's question, and the output is the generated answer.

[0238] Step 5:

[0239] The server analyzes the user's learning progress and emotional data, adjusts the curriculum and learning pace as needed, and generates digital rewards and motivational messages. The terminal notifies the user of these. The input is learning progress and emotional data, and the output is the adjusted curriculum and messages.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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".

[0256] This invention is an educational system that utilizes generative AI technology to provide personalized learning support to student users. This system is implemented through interaction between a server, terminals, and the user. Its specific operation is described below.

[0257] First, when the device is started up, the user is authenticated through the login screen. The server checks the authentication data sent by the user and retrieves the corresponding user information from the database. If authentication is successful, the server sends the user's past learning data and settings information to the device, and the learning session is ready to begin.

[0258] Next, the system collects user learning data on the server and analyzes their learning style and progress based on that data. This allows it to generate a curriculum optimized for the user and provide it to their device. This curriculum includes content tailored to the user's level of understanding and incorporates learning materials in multiple modes, such as text, video, and audio. Users can immediately begin learning by selecting their preferred learning mode on their device.

[0259] Furthermore, if a user encounters a question during their learning process, they can input it via their device. The server analyzes this question using a generation AI and generates an answer in real time. The answer is sent to the device, and the user can review it immediately.

[0260] In each learning session, the server analyzes the user's learning progress. Based on the progress data, the curriculum is dynamically updated and reflected in the next learning session. This ensures that the user can always continue learning with content appropriate to their level of understanding. The server also generates digital rewards and encouraging messages to motivate the user based on their learning progress and notifies the user's device.

[0261] Furthermore, by using the collaborative learning feature, users can learn while interacting with other students. The server matches users with appropriate learning partners, and collaborative learning sessions are provided through the device. Through such interactions, users can deepen their understanding by collaborating with others.

[0262] Finally, the server also provides learning progress and performance data to the parent / instructor interface. This allows parents and instructors to understand the user's learning progress and provide support and advice as needed.

[0263] In this way, the entire system works together to provide users with a personalized and effective learning experience.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The terminal boots up and displays a login screen to the user. The user enters their ID and password and sends the authentication information to the server via the terminal.

[0267] Step 2:

[0268] The server processes the received authentication information and retrieves the corresponding user information from the database. The server determines whether authentication was successful, and if successful, sends the initial setup information to the terminal.

[0269] Step 3:

[0270] The server collects user learning history and progress data from a database and analyzes the user's learning patterns using a machine learning model. Based on this analysis, an optimized curriculum is generated.

[0271] Step 4:

[0272] The server sends the generated curriculum to the terminal. The terminal displays the received curriculum to the user and prepares to start the learning session.

[0273] Step 5:

[0274] The user enters a question via their device during the learning process. The device sends the question to the server and requests a real-time response.

[0275] Step 6:

[0276] The server uses AI generation to generate answers to the user's questions. The server sends the generated answers to the device, which then displays them to the user.

[0277] Step 7:

[0278] The user selects the learning mode (text, video, audio) desired using the terminal. The server searches for learning materials suitable for the selected mode and transmits them to the terminal. The terminal presents this to the user.

[0279] Step 8:

[0280] The server periodically receives learning progress data from the terminal and analyzes the current learning situation. Based on the analysis results, the curriculum is dynamically updated and the latest version is transmitted to the terminal.

[0281] Step 9:

[0282] The server generates digital rewards or messages for motivation improvement according to the user's learning achievement. This is notified to the terminal and the terminal displays it to the user.

[0283] Step 10:

[0284] The user makes a request for collaborative learning via the terminal. The server searches for other compatible users from the database and performs matching. The collaborative learning session information is transmitted to both terminals, providing an environment where users can learn from each other.

[0285] Step 11:

[0286] The server prepares data for the interface to provide the user's learning progress and analysis data to guardians or instructors. This is transmitted to the terminals of guardians or instructors, enabling support for the user's learning.

[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".

[0289] Traditional education systems have the challenge of not adequately providing individualized learning support for each user. Furthermore, a lack of analysis of learning progress and measures to improve motivation leads to a failure to sustain users' motivation to learn. Additionally, opportunities for collaborative learning among users and the sharing of learning progress are difficult, making it challenging for parents and instructors to provide appropriate support.

[0290] 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.

[0291] In this invention, the server includes means for authenticating user information, means for collecting and analyzing data on the user's knowledge, and means for generating an educational plan suitable for the user based on the analysis results. This makes it possible to provide an optimized learning experience for each individual user and to increase the user's motivation to learn. Furthermore, it facilitates effective collaborative learning with other users and enables parents and instructors to monitor learning progress in real time, thereby providing appropriate support and advice.

[0292] "User information" refers to data necessary to identify an individual user and is used for authentication and providing personalized services.

[0293] "Knowledge-related data" refers to information that shows a user's learning history, current level of understanding, and progress, and is collected and analyzed for learning support purposes.

[0294] An "educational plan" is a curriculum that defines the optimal learning content and methods tailored to the user's learning needs and objectives.

[0295] "Providing answers in real time" means generating and sending responses to user inquiries in a timely manner without delay.

[0296] "Diverse forms of educational materials" refers to learning content provided in different formats, such as text, video, and audio, allowing users to choose the method that is easiest for them to learn from.

[0297] "Learning progress" refers to the user's growth and achievement level during their learning process, and is used to evaluate educational plans and formulate the next learning steps.

[0298] "Communication" refers to informational messages sent to the user, including encouragement for learning and rewards based on progress.

[0299] "Collaborative learning" refers to a learning method in which multiple users work together to learn, deepening their understanding by sharing knowledge with each other.

[0300] "Displaying to supervisors and supporters" means visually showing the user's learning progress to instructors and guardians, which is necessary to provide appropriate feedback and support.

[0301] A "specific algorithm" refers to a series of computational methods and processing procedures used to analyze a user's learning data and generate an individualized learning experience.

[0302] This invention is an educational support system that utilizes generative AI technology and aims to provide users with an individualized learning experience. This system mainly consists of three elements: a server, a terminal, and a user, each of which functions as follows.

[0303] The server authenticates user information and retrieves the user's learning history and individual settings from its database. Authentication software and a database management system (e.g., MySQL, PostgreSQL) are used for this purpose. The server then collects the user's learning data and analyzes it using data analysis libraries (e.g., Pandas, NumPy). Generative AI models (e.g., general-purpose AI text generation models) are used for analysis to generate an educational plan tailored to the user.

[0304] Once the terminal receives information from the server, it can present various forms of educational materials to the user. This material includes text, videos, audio, etc., and the terminal has the function of selecting the learning method through which the user can learn most effectively. By connecting to the system using the terminal and accessing the learning materials, an individualized curriculum is implemented.

[0305] The user accesses the educational materials through the terminal. If questions or inquiries arise during learning, the user inputs them and sends them to the system through the terminal. The questions are input as prompt sentences and are expressed in the form of, for example, "Please explain the new mathematical concept." The server analyzes this input, calculates the answer in real time using the generative AI, and returns it to the user via the terminal.

[0306] Furthermore, based on the data obtained during the learning activities, the server can analyze the user's learning progress and dynamically update the educational plan to reflect it in the next learning session. Also, rewards and motivational messages generated according to the user's progress are distributed to the terminal, providing support to enhance the user's learning motivation.

[0307] In this way, the system is designed to provide an individualized learning experience so that the user can effectively proceed with learning.

[0308] The flow of the specific process in Example 1 will be described using FIG. 11.

[0309] Step 1:

[0310] The user activates the terminal and enters authentication information on the login screen. The entered information is authentication data such as the username and password, which is sent to the server through the terminal.

[0311] Step 2:

[0312] The server retrieves information about the user by querying the database based on the authentication data it receives. It returns a matching record from the user information held in the database management system and determines whether authentication was successful. An authentication algorithm is applied during this process.

[0313] Step 3:

[0314] If authentication is successful, the server retrieves the user's past learning data and configuration information and sends it to the device. This data concerns the user's learning history and current progress. The retrieved data is converted into a format that can be displayed on the device and presented to the user.

[0315] Step 4:

[0316] The server uses a data analysis library to analyze the user's learning style and progress based on their past learning data. The analyzed data is then used to create a new curriculum using generative AI models. This generates an optimal educational plan for the user and provides it to their device.

[0317] Step 5:

[0318] Users view the curriculum on their device interface and select the assignments and learning modes they wish to study. The selected information is sent from the device to the server, and the corresponding learning materials are downloaded.

[0319] Step 6:

[0320] The device presents the user with downloaded educational materials. These materials include text, videos, and audio, which the user can use to progress through their learning. Each material is displayed in a format appropriate to the learning mode selected by the user.

[0321] Step 7:

[0322] If a user encounters a question during their learning process, they enter it into their device. This question is entered as a prompt, expressed in a format such as "Please explain how to solve this equation." The question is then sent to the server.

[0323] Step 8:

[0324] The server uses a generated AI model to analyze the received prompt and generate an appropriate response. The AI ​​model utilizes relevant knowledge based on the question to create the answer. This response is then forwarded back to the terminal and provided to the user in real time.

[0325] Step 9:

[0326] As learning progresses, the server analyzes user performance data accumulated during the session. It utilizes data analysis libraries to evaluate learning progress. Based on the analysis results, the server updates the educational plan as needed and delivers improved learning materials to the device.

[0327] Step 10:

[0328] The server generates reward and encouragement messages based on the user's progress to promote learning. These messages provide the user with information about their achievements and motivation to move on to the next step. The generated messages are notified to the device and displayed to the user.

[0329] (Application Example 1)

[0330] 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."

[0331] Providing support optimized to the individual needs of learners is crucial in educational settings and at home. However, learners are often constrained by fixed curricula, making flexible learning tailored to their individual questions and progress difficult. Furthermore, limited opportunities for effective interaction with other learners pose challenges to understanding the material and maintaining motivation. Against this backdrop, there is a need to develop systems that provide learners with individualized learning plans, answer questions in real time, and promote opportunities for collaborative learning.

[0332] 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.

[0333] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for facilitating interaction among users by linking multiple educational support devices. This enables the provision of educational plans optimized for individual learners, real-time question answering, and interaction among learners.

[0334] "User information" refers to data related to the personal identification and profile of system users, and is used for authentication and customization.

[0335] "Learning data" refers to various types of information related to a user's learning activities, including learning content, progress, and level of understanding.

[0336] An "educational plan" refers to an optimized learning curriculum generated based on the user's current learning status and needs.

[0337] An "information processing device" refers to a terminal device that users access, and is used to display learning materials and receive input from users.

[0338] "Real-time response methods" refer to functions that provide instant answers to user questions, achieving rapid responses through the use of generative AI.

[0339] "Diverse forms of learning materials" refers to learning information that combines multiple media types, such as text, audio, and video.

[0340] "Educational support devices" refer to equipment and software designed to assist users in their learning, such as displaying learning information and managing progress.

[0341] "Digital rewards" refer to the technical means of providing rewards and encouraging messages based on a user's learning progress and achievements.

[0342] In this invention, interaction between a server, a terminal, and a user takes place to realize an educational support system. The roles of each are described below.

[0343] The server uses an authentication management system to authenticate user information. Personal information entered by the user on the terminal is sent to the server and compared with registered information. If authentication is successful, the server retrieves past learning history and settings information from the user's learning database and sends it to the terminal.

[0344] The terminal functions as an interface with the user, interacting with them through voice input and a touch panel. Based on the learning mode selected by the user on the terminal, information sent from the server is displayed in various forms, including text, audio, and video. The server uses a generative AI model to analyze the user's progress in real time and provide an optimal learning plan. For example, if a user asks, "I don't understand the Pythagorean theorem," it can immediately present relevant videos and text materials.

[0345] Users progress through their learning based on this information. If questions arise during learning, they can input them via their device. The server uses a generation AI to analyze the questions and generate appropriate answers. These answers are then sent to the device for immediate confirmation by the user.

[0346] Furthermore, the server supports collaborative learning with other users by linking multiple educational support devices. This allows learners in different locations to share knowledge with each other and deepen their understanding through discussions and quizzes.

[0347] Example of a prompt:

[0348] Question: "What is the Pythagorean theorem?"

[0349] Prompt: "Please explain the Pythagorean theorem to children, including simple examples."

[0350] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0351] Step 1:

[0352] The user starts up their device and enters personal information via the login screen. The device sends this information to the server. The server performs authentication verification based on the entered user information against the registered information in the database. As a result, a message indicating authentication success or failure is output to the user's device.

[0353] Step 2:

[0354] The server retrieves past learning history and configuration information from the learning database for users who have successfully authenticated. This data is used within the server to generate a user-specific education plan and is then sent to the device. The server analyzes the past history and presents an appropriate plan to the device.

[0355] Step 3:

[0356] When a user selects a learning mode on their device, the device sends this selection information to the server. Based on this information, the server uses a generative AI model to generate an optimal learning plan. This plan includes the user's chosen learning format (text, audio, video) and provides materials optimized for the device.

[0357] Step 4:

[0358] If a user has a question while learning, they can enter it via their device. The server receives the question data and analyzes it using a generative AI model. As a result of the analysis, an appropriate answer is generated and sent to the device in real time. The user reviews this answer and continues learning.

[0359] Step 5:

[0360] The server facilitates matching to promote collaborative learning among users. It communicates with multiple related terminals to initiate interaction between learners. The terminals display details of the collaborative learning sessions in which the user is participating, and the user deepens their understanding through questions and discussions.

[0361] Step 6:

[0362] The server generates digital rewards based on the user's learning progress and achievements. This information is sent to the device as messages to boost learning motivation, and users receive it in real time.

[0363] 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.

[0364] This invention is an educational system that utilizes generative AI technology and an emotion engine to provide users with a more personalized learning experience. The system is realized through the interaction of a server, terminal, and user, and by incorporating an emotion engine, it dynamically adjusts the user's learning motivation and curriculum optimization according to their emotional state.

[0365] When a user begins learning, the device starts up and prompts for login information. The authentication information entered by the user is sent from the device to the server, which verifies this information and retrieves the user's profile and learning history from the database. Following this basic authentication and data setup, the system generates a curriculum tailored to the user's learning style and needs.

[0366] A notable feature of this invention is that the emotion engine is integrated into the device, recognizing the user's emotions in real time. For example, it analyzes the user's mood and whether they are stressed through the camera and microphone. The server receives this emotional information, performs analysis, and adjusts the curriculum as needed to improve the user's learning experience.

[0367] For example, if the emotion engine detects user confusion or stress while a user is working on a difficult task, the server can temporarily switch the curriculum to easier content or slow down the learning pace. Alternatively, it can send a message to the user via the device to encourage relaxation.

[0368] When a user enters a question they are learning into their device, the server instantly analyzes the question using a generating AI and provides an answer. This answer is also customized based on emotion recognition; for example, if a positive emotion is recognized, an encouraging message will be added.

[0369] Furthermore, both progress and sentiment data are analyzed by the server to suggest ideal staging within the user's overall learning plan. Digital rewards and messages designed to boost user motivation are also based on this sentiment data and are designed to maximize their effectiveness.

[0370] In this way, the system leverages an emotion engine to provide a more personalized learning experience that takes user emotions into account, thereby maximizing the effectiveness and efficiency of learning.

[0371] The following describes the processing flow.

[0372] Step 1:

[0373] The device boots up, and the user enters their ID and password on the login screen. The device sends the user's authentication information to the server.

[0374] Step 2:

[0375] The server verifies the received authentication information and retrieves the user's profile and learning history from the database. If authentication is successful, the server sends the initial setup and past learning data to the device.

[0376] Step 3:

[0377] The device's built-in emotion engine activates and acquires user emotion data through the camera and microphone. For example, it determines the user's emotions in real time based on facial expression analysis and voice tone.

[0378] Step 4:

[0379] The server receives emotional data sent from the emotion engine and analyzes the user's current mental state. Based on this emotional data, the server adjusts the curriculum content and pace.

[0380] Step 5:

[0381] The user inputs a question through their device during the learning process. The device sends the question to the server. The server uses generative AI to analyze the question and generate an answer. The generated answer is customized according to sentiment data and sent back to the device.

[0382] Step 6:

[0383] The server periodically receives learning progress data from the terminal and analyzes it together with sentiment data. Based on the analysis results, it adjusts the next curriculum and sends it to the terminal.

[0384] Step 7:

[0385] Based on the analysis results, the server generates messages and digital rewards to enhance the user's learning motivation. These are then sent to the device at the optimal time, according to the emotional data.

[0386] Step 8:

[0387] If a user wishes to participate in a collaborative learning session, the device sends a request to the server. The server searches for other suitable users and selects the best match, taking sentiment data into consideration. It then sends information to the device so that the selected users can begin a collaborative learning session.

[0388] Step 9:

[0389] The server provides user learning progress and emotional data to the parent / teacher interface. This allows parents and teachers to understand the user's learning situation and emotional state, and provide appropriate support.

[0390] (Example 2)

[0391] 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".

[0392] The current education system has the challenge of not being able to adequately consider the individual needs and emotional states of learners, making it difficult to maximize learning effectiveness and motivation. Furthermore, it is difficult to flexibly adjust the curriculum in real time and provide support tailored to the user's learning progress. Moreover, one-sided learning support that ignores emotional states can increase learner stress and decrease learning efficiency.

[0393] 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.

[0394] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for recognizing the user's emotional state and analyzing that information. This enables dynamic curriculum adjustment to meet individual learning needs. Furthermore, flexible learning support and message delivery based on the user's emotions can improve learning motivation and efficiency.

[0395] "Means of authenticating user information" refers to technologies used to verify the authentication data entered by a user when they access a system, and to confirm that they are a legitimate user.

[0396] "Means for collecting and analyzing user learning data" refers to technologies that collect data on users' learning behavior and progress, and then analyze this data to identify their learning style and needs.

[0397] "Means for generating a curriculum based on analysis results" refers to a technology that uses the results of analyzing learning data to create a curriculum that best suits the user's learning needs.

[0398] "Means for recognizing a user's emotional state and analyzing that information" refers to technologies that detect a user's emotions, analyze that data to understand the user's emotional state, and provide appropriate learning support.

[0399] "Methods for dynamically adjusting the curriculum based on analysis results" refers to technologies that respond to individual learning needs by changing the curriculum content in real time based on an analysis of the user's learning progress and emotional state.

[0400] "A means of providing real-time answers to questions from a user's device" refers to a technology that generates and provides appropriate answers immediately to questions asked by users via their devices.

[0401] "Means of providing users with multi-mode learning materials" refers to technologies that provide users with learning materials in multiple formats, such as text, images, audio, and video, to accommodate diverse learning styles.

[0402] "Methods for analyzing user learning progress and updating the curriculum" refers to technologies that analyze the progress of learning and create or update the curriculum based on the results.

[0403] "Means for generating messages to enhance user learning motivation" refers to technologies that analyze users' emotions and learning progress to create and present messages that increase their motivation.

[0404] "Means that enable collaborative learning with other users" refers to technologies that allow multiple users to engage in collaborative learning activities on a system.

[0405] "Means of displaying a user's learning progress to parents and educators" refers to technologies that organize a user's learning progress information and visually represent it in a way that parents and educators can access.

[0406] This invention is an educational support system that uses neural circuit technology and an emotion analysis engine to provide users with an individualized learning experience. Embodiments of the invention are realized through the cooperation of three parties: the user, the terminal, and the server.

[0407] The terminal receives the user's login information and sends that data to the server for authentication. The server verifies the information received from the client and uses a database system to retrieve the user's profile and past learning history. The database used here is accessed using SQL queries.

[0408] The server uses collected information to apply generative AI technology and generate a curriculum appropriate for the user. The generative AI model used is a deep learning model built in languages ​​such as Python, which creates a new learning plan based on the user's past learning performance.

[0409] The emotion analysis engine built into the device uses image recognition software (e.g., OpenCV) and a speech analysis system to detect the user's emotional state in real time. The recognition results are immediately sent to a server, where analysis is performed to improve the user's learning experience.

[0410] For example, if a user is experiencing stress while working on a particular task, the server can temporarily adjust the curriculum based on emotional data and switch to a less difficult task. It can also send messages to encourage relaxation through the device.

[0411] Furthermore, the server uses neural network models to generate appropriate answers in real time to questions entered by the user on the device. This process is also customized through emotion recognition and may include positive feedback.

[0412] A concrete example of a prompt message would be, "Please tell me how to provide appropriate learning support when a user becomes confused."

[0413] In this way, the invention enables personalized learning that takes user emotions into account, providing an efficient learning experience.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] The user enters their login information into the terminal. The entered information (username and password) is sent from the terminal to the server. This causes the server to initiate the user authentication process.

[0417] Step 2:

[0418] The server verifies the received login information against the database and performs authentication. If successful, it retrieves the user's profile data and learning history from the database. Specifically, it uses SQL queries to extract information related to the user. As output of this process, learning history data associated with each individual user is obtained.

[0419] Step 3:

[0420] The server uses a generative AI model based on the acquired data to generate a curriculum optimized for the user. Input data includes the user's progress and past learning performance. The generative AI model analyzes this data to suggest future learning content. The output is individually customized curriculum data.

[0421] Step 4:

[0422] The sentiment analysis engine integrated into the device collects user sentiment data using sensors (camera and microphone). Specifically, it determines the user's emotional state in real time based on image recognition and voice analysis. This sentiment data is processed on the device and sent to the server.

[0423] Step 5:

[0424] The server receives and analyzes emotional data sent from the terminal. Based on the results of this analysis, it dynamically adjusts the already generated curriculum. For example, if the user is experiencing stress, the curriculum is readjusted to make learning tasks easier. The generated, adjusted curriculum data is then sent back to the terminal.

[0425] Step 6:

[0426] When a user enters a question during learning, the device sends this information to the server. The server uses a generative AI model to analyze the question and generate an answer in real time. For example, the prompt might be "How can I provide appropriate learning support when the user becomes confused?" The generated answer is sent back to the device and displayed to the user.

[0427] (Application Example 2)

[0428] 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".

[0429] Modern education systems demand personalized learning experiences tailored to individual learners. However, traditional educational platforms struggle to recognize learners' emotional states in real time and optimize learning content and pace accordingly. Furthermore, they are not adequately providing effective feedback and encouragement based on learners' emotions. As a result, learning efficiency and motivation are hindered.

[0430] 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.

[0431] In this invention, the server includes means for collecting and analyzing user learning data and emotional information, means for generating a curriculum suitable for the user based on the analysis results, and means for optimizing the learning experience based on emotional data. This makes it possible to provide a dynamic and personalized learning experience that takes into account the learner's emotional state.

[0432] "User information" refers to identifying information related to user authentication and profile.

[0433] "Learning data" refers to information about the activities and progress of learners.

[0434] "Emotional information" refers to data that indicates the emotional state obtained through the learner's facial expressions and voice.

[0435] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and generate content.

[0436] "Responding in real time" refers to a process that enables immediate responses to user input.

[0437] "Multi-mode learning materials" refers to educational content in various formats, such as text, audio, images, and videos.

[0438] "Emotional data" refers to information about a learner's emotional state and is used to adapt to the learning process.

[0439] "Digital rewards" refer to rewards and incentives provided online based on the learner's achievement level.

[0440] "Collaborative learning" refers to learning activities carried out by multiple learners working together.

[0441] "Displaying to parents and instructors" means providing parents and instructors with a visual representation of the learner's progress and achievements.

[0442] The system implementing this invention is an educational support platform using emotion recognition technology and a generative AI model. The server, terminal, and user interact with each other to provide a personalized learning experience.

[0443] The server verifies the authentication information sent from the user's device and retrieves the user's profile and learning history from the database. The device incorporates a camera and microphone to collect emotional information in real time through the user's facial expressions and voiceprint. This emotional information is analyzed using Python libraries such as OpenCV and Dlib.

[0444] Based on the analyzed emotion data and training data, the server dynamically generates a learning curriculum optimized for each user using a generative AI model (e.g., OpenAI's GPT-4). Furthermore, appropriate feedback and learning materials are automatically selected according to the learner's situation and provided through the terminal interface.

[0445] For example, if the emotion engine detects that a child is experiencing stress due to a difficult math problem while studying at home, the server can use its AI-generated explanation to create an easy-to-understand explanation and display it on the device. A message of encouragement to help the child relax is also sent.

[0446] An example of a prompt message would be, "Considering this learner's current emotional state, generate a simple task description and an encouraging message to help them relieve stress." This allows users to engage in learning in a less stressful state, which is expected to improve learning effectiveness.

[0447] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0448] Step 1:

[0449] The terminal starts up and displays a login screen to the user. The user enters their authentication information, which the terminal sends to the server. The server authenticates the user by comparing the entered authentication information with the database. If authentication is successful, the server retrieves the user's profile and past learning history and sends it to the terminal. The input is the authentication information, and the output is the user's profile and learning history.

[0450] Step 2:

[0451] The device uses a camera and microphone to collect the user's facial expressions and voice. Based on the collected data, it analyzes emotional information in real time using the Python libraries OpenCV and Dlib, and sends the results to the server. The input is facial expression and voice data, and the output is the user's emotional information.

[0452] Step 3:

[0453] The server dynamically generates a user-optimized curriculum using a generative AI model based on emotional information and learning history. The generated curriculum is sent to the terminal, which then displays it to the user. The input is emotional information and learning history, and the output is the optimized curriculum.

[0454] Step 4:

[0455] The terminal receives a question from the user. The server sends this question to a generation AI model, which uses prompts to generate the optimal answer. The generated answer is sent back to the terminal and displayed to the user. The input is the user's question, and the output is the generated answer.

[0456] Step 5:

[0457] The server analyzes the user's learning progress and emotional data, adjusts the curriculum and learning pace as needed, and generates digital rewards and motivational messages. The terminal notifies the user of these. The input is learning progress and emotional data, and the output is the adjusted curriculum and messages.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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".

[0474] This invention is an educational system that utilizes generative AI technology to provide personalized learning support to student users. This system is implemented through interaction between a server, terminals, and the user. Its specific operation is described below.

[0475] First, when the device is started up, the user is authenticated through the login screen. The server checks the authentication data sent by the user and retrieves the corresponding user information from the database. If authentication is successful, the server sends the user's past learning data and settings information to the device, and the learning session is ready to begin.

[0476] Next, the system collects user learning data on the server and analyzes their learning style and progress based on that data. This allows it to generate a curriculum optimized for the user and provide it to their device. This curriculum includes content tailored to the user's level of understanding and incorporates learning materials in multiple modes, such as text, video, and audio. Users can immediately begin learning by selecting their preferred learning mode on their device.

[0477] Furthermore, if a user encounters a question during their learning process, they can input it via their device. The server analyzes this question using a generation AI and generates an answer in real time. The answer is sent to the device, and the user can review it immediately.

[0478] In each learning session, the server analyzes the user's learning progress. Based on the progress data, the curriculum is dynamically updated and reflected in the next learning session. This ensures that the user can always continue learning with content appropriate to their level of understanding. The server also generates digital rewards and encouraging messages to motivate the user based on their learning progress and notifies the user's device.

[0479] Furthermore, by using the collaborative learning feature, users can learn while interacting with other students. The server matches users with appropriate learning partners, and collaborative learning sessions are provided through the device. Through such interactions, users can deepen their understanding by collaborating with others.

[0480] Finally, the server also provides learning progress and performance data to the parent / instructor interface. This allows parents and instructors to understand the user's learning progress and provide support and advice as needed.

[0481] In this way, the entire system works together to provide users with a personalized and effective learning experience.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The terminal boots up and displays a login screen to the user. The user enters their ID and password and sends the authentication information to the server via the terminal.

[0485] Step 2:

[0486] The server processes the received authentication information and retrieves the corresponding user information from the database. The server determines whether authentication was successful, and if successful, sends the initial setup information to the terminal.

[0487] Step 3:

[0488] The server collects user learning history and progress data from a database and analyzes the user's learning patterns using a machine learning model. Based on this analysis, an optimized curriculum is generated.

[0489] Step 4:

[0490] The server sends the generated curriculum to the terminal. The terminal displays the received curriculum to the user and prepares to start the learning session.

[0491] Step 5:

[0492] The user enters a question via their device during the learning process. The device sends the question to the server and requests a real-time response.

[0493] Step 6:

[0494] The server uses AI generation to generate answers to the user's questions. The server sends the generated answers to the device, which then displays them to the user.

[0495] Step 7:

[0496] The user selects their preferred learning mode (text, video, or audio) using their device. The server searches for learning materials suitable for the selected mode and sends them to the device. The device then presents these materials to the user.

[0497] Step 8:

[0498] The server periodically receives learning progress data from the terminal and analyzes the current learning status. Based on the analysis results, the curriculum is dynamically updated and the latest version is sent to the terminal.

[0499] Step 9:

[0500] The server generates digital rewards and motivational messages based on the user's learning progress. These are then notified to the device, which displays them to the user.

[0501] Step 10:

[0502] A user requests collaborative learning via their device. The server searches its database for other suitable users and performs the matching process. Collaborative learning session information is sent to both devices, providing an environment where users can learn from each other.

[0503] Step 11:

[0504] The server prepares data for an interface that provides users' learning progress and analytical data to parents and instructors. This data is then sent to the parents' and instructors' devices, enabling support for the user's learning.

[0505] (Example 1)

[0506] 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."

[0507] Traditional education systems have the challenge of not adequately providing individualized learning support for each user. Furthermore, a lack of analysis of learning progress and measures to improve motivation leads to a failure to sustain users' motivation to learn. Additionally, opportunities for collaborative learning among users and the sharing of learning progress are difficult, making it challenging for parents and instructors to provide appropriate support.

[0508] 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.

[0509] In this invention, the server includes means for authenticating user information, means for collecting and analyzing data on the user's knowledge, and means for generating an educational plan suitable for the user based on the analysis results. This makes it possible to provide an optimized learning experience for each individual user and to increase the user's motivation to learn. Furthermore, it facilitates effective collaborative learning with other users and enables parents and instructors to monitor learning progress in real time, thereby providing appropriate support and advice.

[0510] "User information" refers to data necessary to identify an individual user and is used for authentication and providing personalized services.

[0511] "Knowledge-related data" refers to information that shows a user's learning history, current level of understanding, and progress, and is collected and analyzed for learning support purposes.

[0512] An "educational plan" is a curriculum that defines the optimal learning content and methods tailored to the user's learning needs and objectives.

[0513] "Providing answers in real time" means generating and sending responses to user inquiries in a timely manner without delay.

[0514] "Diverse forms of educational materials" refers to learning content provided in different formats, such as text, video, and audio, allowing users to choose the method that is easiest for them to learn from.

[0515] "Learning progress" refers to the user's growth and achievement level during their learning process, and is used to evaluate educational plans and formulate the next learning steps.

[0516] "Communication" refers to informational messages sent to the user, including encouragement for learning and rewards based on progress.

[0517] "Collaborative learning" refers to a learning method in which multiple users work together to learn, deepening their understanding by sharing knowledge with each other.

[0518] "Displaying to supervisors and supporters" means visually showing the user's learning progress to instructors and guardians, which is necessary to provide appropriate feedback and support.

[0519] A "specific algorithm" refers to a series of computational methods and processing procedures used to analyze a user's learning data and generate an individualized learning experience.

[0520] This invention is an educational support system that utilizes generative AI technology and aims to provide users with an individualized learning experience. This system mainly consists of three elements: a server, a terminal, and a user, each of which functions as follows.

[0521] The server authenticates user information and retrieves the user's learning history and individual settings from its database. Authentication software and a database management system (e.g., MySQL, PostgreSQL) are used for this purpose. The server then collects the user's learning data and analyzes it using data analysis libraries (e.g., Pandas, NumPy). Generative AI models (e.g., general-purpose AI text generation models) are used for analysis to generate an educational plan tailored to the user.

[0522] Once the device receives information from the server, it can present users with educational materials in various formats. These materials include text, videos, and audio, and the device has the ability to select the learning method that is most effective for the user. Users connect to the system using the device and access the learning materials to implement a personalized curriculum.

[0523] Users access educational materials through their devices, and if questions arise during their learning, they input them and send them to the system via their devices. The questions are entered as prompts, for example, "Please explain this new mathematical concept." The server analyzes this input, uses generative AI to calculate answers in real time, and responds to the user via their devices.

[0524] Furthermore, based on the data obtained during learning activities, the server can analyze the user's learning progress and dynamically update the educational plan to reflect it in the next learning session. In addition, rewards and encouraging messages generated according to the user's progress are delivered to the device, providing support to enhance the user's learning motivation.

[0525] In this way, the system is designed to provide a personalized learning experience, enabling users to learn effectively.

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

[0527] Step 1:

[0528] The user starts up the device and enters authentication information on the login screen. The entered information, such as username and password, is authentication data and is sent to the server via the device.

[0529] Step 2:

[0530] The server retrieves information about the user by querying the database based on the authentication data it receives. It returns a matching record from the user information held in the database management system and determines whether authentication was successful. An authentication algorithm is applied during this process.

[0531] Step 3:

[0532] If authentication is successful, the server retrieves the user's past learning data and configuration information and sends it to the device. This data concerns the user's learning history and current progress. The retrieved data is converted into a format that can be displayed on the device and presented to the user.

[0533] Step 4:

[0534] The server uses a data analysis library to analyze the user's learning style and progress based on their past learning data. The analyzed data is then used to create a new curriculum using generative AI models. This generates an optimal educational plan for the user and provides it to their device.

[0535] Step 5:

[0536] Users view the curriculum on their device interface and select the assignments and learning modes they wish to study. The selected information is sent from the device to the server, and the corresponding learning materials are downloaded.

[0537] Step 6:

[0538] The device presents the downloaded educational materials to the user. These materials include text, videos, and audio, which the user can use to progress through their learning. Each material is displayed in a format appropriate to the learning mode selected by the user.

[0539] Step 7:

[0540] If a user encounters a question during their learning process, they enter it into their device. This question is entered as a prompt, for example, "Please explain how to solve this equation." The question is then sent to the server.

[0541] Step 8:

[0542] The server uses a generated AI model to analyze the received prompt and generate an appropriate response. The AI ​​model utilizes relevant knowledge based on the question to create the answer. This response is then forwarded back to the terminal and provided to the user in real time.

[0543] Step 9:

[0544] As learning progresses, the server analyzes user performance data accumulated during the session. It utilizes data analysis libraries to evaluate learning progress. Based on the analysis results, the server updates the educational plan as needed and delivers improved learning materials to the device.

[0545] Step 10:

[0546] The server generates reward and encouragement messages based on the user's progress to promote learning. These messages provide the user with information about their achievements and motivation to move on to the next step. The generated messages are notified to the device and displayed to the user.

[0547] (Application Example 1)

[0548] 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."

[0549] Providing support optimized to the individual needs of learners is crucial in educational settings and at home. However, learners are often constrained by fixed curricula, making flexible learning tailored to their individual questions and progress difficult. Furthermore, limited opportunities for effective interaction with other learners pose challenges to understanding the material and maintaining motivation. Against this backdrop, there is a need to develop systems that provide learners with individualized learning plans, answer questions in real time, and promote opportunities for collaborative learning.

[0550] 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.

[0551] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for facilitating interaction among users by linking multiple educational support devices. This enables the provision of educational plans optimized for individual learners, real-time question answering, and interaction among learners.

[0552] "User information" refers to data related to the personal identification and profile of system users, and is used for authentication and customization.

[0553] "Learning data" refers to various types of information related to a user's learning activities, including learning content, progress, and level of understanding.

[0554] An "educational plan" refers to an optimized learning curriculum generated based on the user's current learning status and needs.

[0555] An "information processing device" refers to a terminal device that users access, and is used to display learning materials and receive input from users.

[0556] "Real-time response methods" refer to functions that provide instant answers to user questions, achieving rapid responses through the use of generative AI.

[0557] "Diverse forms of learning materials" refers to learning information that combines multiple media types, such as text, audio, and video.

[0558] "Educational support devices" refer to equipment and software designed to assist users in their learning, such as displaying learning information and managing progress.

[0559] "Digital rewards" refer to the technical means of providing rewards and encouraging messages based on a user's learning progress and achievements.

[0560] In this invention, interaction between a server, a terminal, and a user takes place to realize an educational support system. The roles of each are described below.

[0561] The server uses an authentication management system to authenticate user information. Personal information entered by the user on the terminal is sent to the server and compared with registered information. If authentication is successful, the server retrieves past learning history and settings information from the user's learning database and sends it to the terminal.

[0562] The terminal functions as an interface with the user, interacting with them through voice input and a touch panel. Based on the learning mode selected by the user on the terminal, information sent from the server is displayed in various forms, including text, audio, and video. The server uses a generative AI model to analyze the user's progress in real time and provide an optimal learning plan. For example, if a user asks, "I don't understand the Pythagorean theorem," it can immediately present relevant videos and text materials.

[0563] Users progress through their learning based on this information. If questions arise during learning, they can input them via their device. The server uses a generation AI to analyze the questions and generate appropriate answers. These answers are then sent to the device for immediate confirmation by the user.

[0564] Furthermore, the server supports collaborative learning with other users by linking multiple educational support devices. This allows learners in different locations to share knowledge with each other and deepen their understanding through discussions and quizzes.

[0565] Example of a prompt:

[0566] Question: "What is the Pythagorean theorem?"

[0567] Prompt: "Please explain the Pythagorean theorem to children, including simple examples."

[0568] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0569] Step 1:

[0570] The user starts up their device and enters personal information via the login screen. The device sends this information to the server. The server performs authentication verification based on the entered user information against the registered information in the database. As a result, a message indicating authentication success or failure is output to the user's device.

[0571] Step 2:

[0572] The server retrieves past learning history and configuration information from the learning database for users who have successfully authenticated. This data is used within the server to generate a user-specific education plan and is then sent to the device. The server analyzes the past history and presents an appropriate plan to the device.

[0573] Step 3:

[0574] When a user selects a learning mode on their device, the device sends this selection information to the server. Based on this information, the server uses a generative AI model to generate an optimal learning plan. This plan includes the user's chosen learning format (text, audio, video) and provides materials optimized for the device.

[0575] Step 4:

[0576] If a user has a question while learning, they can enter it via their device. The server receives the question data and analyzes it using a generative AI model. As a result of the analysis, an appropriate answer is generated and sent to the device in real time. The user reviews this answer and continues learning.

[0577] Step 5:

[0578] The server facilitates matching to promote collaborative learning among users. It communicates with multiple related terminals to initiate interaction between learners. The terminals display details of the collaborative learning sessions in which the user is participating, and the user deepens their understanding through questions and discussions.

[0579] Step 6:

[0580] The server generates digital rewards based on the user's learning progress and achievements. This information is sent to the device as messages to boost learning motivation, and users receive it in real time.

[0581] 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.

[0582] This invention is an educational system that utilizes generative AI technology and an emotion engine to provide users with a more personalized learning experience. The system is realized through the interaction of a server, terminal, and user, and by incorporating an emotion engine, it dynamically adjusts the user's learning motivation and curriculum optimization according to their emotional state.

[0583] When a user begins learning, the device starts up and prompts for login information. The authentication information entered by the user is sent from the device to the server, which verifies this information and retrieves the user's profile and learning history from the database. Following this basic authentication and data setup, the system generates a curriculum tailored to the user's learning style and needs.

[0584] A notable feature of this invention is that the emotion engine is integrated into the device, recognizing the user's emotions in real time. For example, it analyzes the user's mood and whether they are stressed through the camera and microphone. The server receives this emotional information, performs analysis, and adjusts the curriculum as needed to improve the user's learning experience.

[0585] For example, if the emotion engine detects user confusion or stress while a user is working on a difficult task, the server can temporarily switch the curriculum to easier content or slow down the learning pace. Alternatively, it can send a message to the user via the device to encourage relaxation.

[0586] When a user enters a question they are learning into their device, the server instantly analyzes the question using a generating AI and provides an answer. This answer is also customized based on emotion recognition; for example, if a positive emotion is recognized, an encouraging message will be added.

[0587] Furthermore, both progress and sentiment data are analyzed by the server to suggest ideal staging within the user's overall learning plan. Digital rewards and messages designed to boost user motivation are also based on this sentiment data and are designed to maximize their effectiveness.

[0588] In this way, the system leverages an emotion engine to provide a more personalized learning experience that takes user emotions into account, thereby maximizing the effectiveness and efficiency of learning.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The device boots up, and the user enters their ID and password on the login screen. The device sends the user's authentication information to the server.

[0592] Step 2:

[0593] The server verifies the received authentication information and retrieves the user's profile and learning history from the database. If authentication is successful, the server sends the initial setup and past learning data to the device.

[0594] Step 3:

[0595] The device's built-in emotion engine activates and acquires user emotion data through the camera and microphone. For example, it determines the user's emotions in real time based on facial expression analysis and voice tone.

[0596] Step 4:

[0597] The server receives emotional data sent from the emotion engine and analyzes the user's current mental state. Based on this emotional data, the server adjusts the curriculum content and pace.

[0598] Step 5:

[0599] The user inputs a question through their device during the learning process. The device sends the question to the server. The server uses generative AI to analyze the question and generate an answer. The generated answer is customized according to sentiment data and sent back to the device.

[0600] Step 6:

[0601] The server periodically receives learning progress data from the terminal and analyzes it together with sentiment data. Based on the analysis results, it adjusts the next curriculum and sends it to the terminal.

[0602] Step 7:

[0603] Based on the analysis results, the server generates messages and digital rewards to enhance the user's learning motivation. These are then sent to the device at the optimal time, according to the emotional data.

[0604] Step 8:

[0605] If a user wishes to participate in a collaborative learning session, the device sends a request to the server. The server searches for other suitable users and selects the best match, taking sentiment data into consideration. It then sends information to the device so that the selected users can begin a collaborative learning session.

[0606] Step 9:

[0607] The server provides user learning progress and emotional data to the parent / teacher interface. This allows parents and teachers to understand the user's learning situation and emotional state, and provide appropriate support.

[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] The current education system has the challenge of not being able to adequately consider the individual needs and emotional states of learners, making it difficult to maximize learning effectiveness and motivation. Furthermore, it is difficult to flexibly adjust the curriculum in real time and provide support tailored to the user's learning progress. Moreover, one-sided learning support that ignores emotional states can increase learner stress and decrease learning efficiency.

[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 authenticating user information, means for collecting and analyzing user learning data, and means for recognizing the user's emotional state and analyzing that information. This enables dynamic curriculum adjustment to meet individual learning needs. Furthermore, flexible learning support and message delivery based on the user's emotions can improve learning motivation and efficiency.

[0613] "Means of authenticating user information" refers to technologies used to verify the authentication data entered by a user when they access a system, and to confirm that they are a legitimate user.

[0614] "Means for collecting and analyzing user learning data" refers to technologies that collect data on users' learning behavior and progress, and then analyze this data to identify their learning style and needs.

[0615] "Means for generating a curriculum based on analysis results" refers to a technology that uses the results of analyzing learning data to create a curriculum that best suits the user's learning needs.

[0616] "Means for recognizing a user's emotional state and analyzing that information" refers to technologies that detect a user's emotions, analyze that data to understand the user's emotional state, and provide appropriate learning support.

[0617] "Methods for dynamically adjusting the curriculum based on analysis results" refers to technologies that respond to individual learning needs by changing the curriculum content in real time based on an analysis of the user's learning progress and emotional state.

[0618] "A means of providing real-time answers to questions from a user's device" refers to a technology that generates and provides appropriate answers immediately to questions asked by users via their devices.

[0619] "Means of providing users with multi-mode learning materials" refers to technologies that provide users with learning materials in multiple formats, such as text, images, audio, and video, to accommodate diverse learning styles.

[0620] "Methods for analyzing user learning progress and updating the curriculum" refers to technologies that analyze the progress of learning and create or update the curriculum based on the results.

[0621] "Means for generating messages to enhance user learning motivation" refers to technologies that analyze users' emotions and learning progress to create and present messages that increase their motivation.

[0622] "Means that enable collaborative learning with other users" refers to technologies that allow multiple users to engage in collaborative learning activities on a system.

[0623] "Means of displaying a user's learning progress to parents and educators" refers to technologies that organize a user's learning progress information and visually represent it in a way that parents and educators can access.

[0624] This invention is an educational support system that uses neural circuit technology and an emotion analysis engine to provide users with an individualized learning experience. Embodiments of the invention are realized through the cooperation of three parties: the user, the terminal, and the server.

[0625] The terminal receives the user's login information and sends that data to the server for authentication. The server verifies the information received from the client and uses a database system to retrieve the user's profile and past learning history. The database used here is accessed using SQL queries.

[0626] The server uses collected information to apply generative AI technology and generate a curriculum appropriate for the user. The generative AI model used is a deep learning model built in languages ​​such as Python, which creates a new learning plan based on the user's past learning performance.

[0627] The emotion analysis engine built into the device uses image recognition software (e.g., OpenCV) and a speech analysis system to detect the user's emotional state in real time. The recognition results are immediately sent to a server, where analysis is performed to improve the user's learning experience.

[0628] For example, if a user is experiencing stress while working on a particular task, the server can temporarily adjust the curriculum based on emotional data and switch to a less difficult task. It can also send messages to encourage relaxation through the device.

[0629] Furthermore, the server uses neural network models to generate appropriate answers in real time to questions entered by the user on the device. This process is also customized through emotion recognition and may include positive feedback.

[0630] A concrete example of a prompt message would be, "Please tell me how to provide appropriate learning support when a user becomes confused."

[0631] In this way, the invention enables personalized learning that takes user emotions into account, providing an efficient learning experience.

[0632] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0633] Step 1:

[0634] The user enters their login information into the terminal. The entered information (username and password) is sent from the terminal to the server. This causes the server to initiate the user authentication process.

[0635] Step 2:

[0636] The server verifies the received login information against the database and performs authentication. If successful, it retrieves the user's profile data and learning history from the database. Specifically, it uses SQL queries to extract information related to the user. As output of this process, learning history data associated with each individual user is obtained.

[0637] Step 3:

[0638] The server uses a generative AI model based on the acquired data to generate a curriculum optimized for the user. Input data includes the user's progress and past learning performance. The generative AI model analyzes this data to suggest future learning content. The output is individually customized curriculum data.

[0639] Step 4:

[0640] The sentiment analysis engine integrated into the device collects user sentiment data using sensors (camera and microphone). Specifically, it determines the user's emotional state in real time based on image recognition and voice analysis. This sentiment data is processed on the device and sent to the server.

[0641] Step 5:

[0642] The server receives and analyzes emotional data sent from the terminal. Based on the results of this analysis, it dynamically adjusts the already generated curriculum. For example, if the user is experiencing stress, the curriculum is readjusted to make learning tasks easier. The generated, adjusted curriculum data is then sent back to the terminal.

[0643] Step 6:

[0644] When a user enters a question during learning, the device sends this information to the server. The server uses a generative AI model to analyze the question and generate an answer in real time. For example, the prompt might be "How can I provide appropriate learning support when the user becomes confused?" The generated answer is sent back to the device and displayed to the user.

[0645] (Application Example 2)

[0646] 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."

[0647] Modern education systems demand personalized learning experiences tailored to individual learners. However, traditional educational platforms struggle to recognize learners' emotional states in real time and optimize learning content and pace accordingly. Furthermore, they are not adequately providing effective feedback and encouragement based on learners' emotions. As a result, learning efficiency and motivation are hindered.

[0648] 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.

[0649] In this invention, the server includes means for collecting and analyzing user learning data and emotional information, means for generating a curriculum suitable for the user based on the analysis results, and means for optimizing the learning experience based on emotional data. This makes it possible to provide a dynamic and personalized learning experience that takes into account the learner's emotional state.

[0650] "User information" refers to identifying information related to user authentication and profile.

[0651] "Learning data" refers to information about the activities and progress of learners.

[0652] "Emotional information" refers to data that indicates the emotional state obtained through the learner's facial expressions and voice.

[0653] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and generate content.

[0654] "Responding in real time" refers to a process that enables immediate responses to user input.

[0655] "Multi-mode learning materials" refers to educational content in various formats, such as text, audio, images, and videos.

[0656] "Emotional data" refers to information about a learner's emotional state and is used to adapt to the learning process.

[0657] "Digital rewards" refer to rewards and incentives provided online based on the learner's achievement level.

[0658] "Collaborative learning" refers to learning activities carried out by multiple learners working together.

[0659] "Displaying to parents and instructors" means providing parents and instructors with a visual representation of the learner's progress and achievements.

[0660] The system implementing this invention is an educational support platform using emotion recognition technology and a generative AI model. The server, terminal, and user interact with each other to provide a personalized learning experience.

[0661] The server verifies the authentication information sent from the user's device and retrieves the user's profile and learning history from the database. The device incorporates a camera and microphone to collect emotional information in real time through the user's facial expressions and voiceprint. This emotional information is analyzed using Python libraries such as OpenCV and Dlib.

[0662] Based on the analyzed emotion data and training data, the server dynamically generates a learning curriculum optimized for each user using a generative AI model (e.g., OpenAI's GPT-4). Furthermore, appropriate feedback and learning materials are automatically selected according to the learner's situation and provided through the terminal interface.

[0663] For example, if the emotion engine detects that a child is experiencing stress due to a difficult math problem while studying at home, the server can use its AI-generated explanation to create an easy-to-understand explanation and display it on the device. A message of encouragement to help the child relax is also sent.

[0664] An example of a prompt message would be, "Considering this learner's current emotional state, generate a simple task description and an encouraging message to help them relieve stress." This allows users to engage in learning in a less stressful state, which is expected to improve learning effectiveness.

[0665] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0666] Step 1:

[0667] The terminal starts up and displays a login screen to the user. The user enters their authentication information, which the terminal sends to the server. The server authenticates the user by comparing the entered authentication information with the database. If authentication is successful, the server retrieves the user's profile and past learning history and sends it to the terminal. The input is the authentication information, and the output is the user's profile and learning history.

[0668] Step 2:

[0669] The device uses a camera and microphone to collect the user's facial expressions and voice. Based on the collected data, it analyzes emotional information in real time using the Python libraries OpenCV and Dlib, and sends the results to the server. The input is facial expression and voice data, and the output is the user's emotional information.

[0670] Step 3:

[0671] The server dynamically generates a user-optimized curriculum using a generative AI model based on emotional information and learning history. The generated curriculum is sent to the terminal, which then displays it to the user. The input is emotional information and learning history, and the output is the optimized curriculum.

[0672] Step 4:

[0673] The terminal receives a question from the user. The server sends this question to a generation AI model, which uses prompts to generate the optimal answer. The generated answer is sent back to the terminal and displayed to the user. The input is the user's question, and the output is the generated answer.

[0674] Step 5:

[0675] The server analyzes the user's learning progress and emotional data, adjusts the curriculum and learning pace as needed, and generates digital rewards and motivational messages. The terminal notifies the user of these. The input is learning progress and emotional data, and the output is the adjusted curriculum and messages.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] [Fourth Embodiment]

[0680] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0681] 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.

[0682] 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).

[0683] 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.

[0684] 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.

[0685] 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).

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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".

[0693] This invention is an educational system that utilizes generative AI technology to provide personalized learning support to student users. This system is implemented through interaction between a server, terminals, and the user. Its specific operation is described below.

[0694] First, when the device is started up, the user is authenticated through the login screen. The server checks the authentication data sent by the user and retrieves the corresponding user information from the database. If authentication is successful, the server sends the user's past learning data and settings information to the device, and the learning session is ready to begin.

[0695] Next, the system collects user learning data on the server and analyzes their learning style and progress based on that data. This allows it to generate a curriculum optimized for the user and provide it to their device. This curriculum includes content tailored to the user's level of understanding and incorporates learning materials in multiple modes, such as text, video, and audio. Users can immediately begin learning by selecting their preferred learning mode on their device.

[0696] Furthermore, if a user encounters a question during their learning process, they can input it via their device. The server analyzes this question using a generation AI and generates an answer in real time. The answer is sent to the device, and the user can review it immediately.

[0697] In each learning session, the server analyzes the user's learning progress. Based on the progress data, the curriculum is dynamically updated and reflected in the next learning session. This ensures that the user can always continue learning with content appropriate to their level of understanding. The server also generates digital rewards and encouraging messages to motivate the user based on their learning progress and notifies the user's device.

[0698] Furthermore, by using the collaborative learning feature, users can learn while interacting with other students. The server matches users with appropriate learning partners, and collaborative learning sessions are provided through the device. Through such interactions, users can deepen their understanding by collaborating with others.

[0699] Finally, the server also provides learning progress and performance data to the parent / instructor interface. This allows parents and instructors to understand the user's learning progress and provide support and advice as needed.

[0700] In this way, the entire system works together to provide users with a personalized and effective learning experience.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The terminal boots up and displays a login screen to the user. The user enters their ID and password and sends the authentication information to the server via the terminal.

[0704] Step 2:

[0705] The server processes the received authentication information and retrieves the corresponding user information from the database. The server determines whether authentication was successful, and if successful, sends the initial setup information to the terminal.

[0706] Step 3:

[0707] The server collects user learning history and progress data from a database and analyzes the user's learning patterns using a machine learning model. Based on this analysis, an optimized curriculum is generated.

[0708] Step 4:

[0709] The server sends the generated curriculum to the terminal. The terminal displays the received curriculum to the user and prepares to start the learning session.

[0710] Step 5:

[0711] The user enters a question via their device during the learning process. The device sends the question to the server and requests a real-time response.

[0712] Step 6:

[0713] The server uses AI generation to generate answers to the user's questions. The server sends the generated answers to the device, which then displays them to the user.

[0714] Step 7:

[0715] The user selects their preferred learning mode (text, video, or audio) using their device. The server searches for learning materials suitable for the selected mode and sends them to the device. The device then presents these materials to the user.

[0716] Step 8:

[0717] The server periodically receives learning progress data from the terminal and analyzes the current learning status. Based on the analysis results, the curriculum is dynamically updated and the latest version is sent to the terminal.

[0718] Step 9:

[0719] The server generates digital rewards and motivational messages based on the user's learning progress. These are then notified to the device, which displays them to the user.

[0720] Step 10:

[0721] A user requests collaborative learning via their device. The server searches its database for other suitable users and performs the matching process. Collaborative learning session information is sent to both devices, providing an environment where users can learn from each other.

[0722] Step 11:

[0723] The server prepares data for an interface that provides users' learning progress and analytical data to parents and instructors. This data is then sent to the parents' and instructors' devices, enabling support for the user's learning.

[0724] (Example 1)

[0725] 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".

[0726] Traditional education systems have the challenge of not adequately providing individualized learning support for each user. Furthermore, a lack of analysis of learning progress and measures to improve motivation leads to a failure to sustain users' motivation to learn. Additionally, opportunities for collaborative learning among users and the sharing of learning progress are difficult, making it challenging for parents and instructors to provide appropriate support.

[0727] 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.

[0728] In this invention, the server includes means for authenticating user information, means for collecting and analyzing data on the user's knowledge, and means for generating an educational plan suitable for the user based on the analysis results. This makes it possible to provide an optimized learning experience for each individual user and to increase the user's motivation to learn. Furthermore, it facilitates effective collaborative learning with other users and enables parents and instructors to monitor learning progress in real time, thereby providing appropriate support and advice.

[0729] "User information" refers to data necessary to identify an individual user and is used for authentication and providing personalized services.

[0730] "Knowledge-related data" refers to information that shows a user's learning history, current level of understanding, and progress, and is collected and analyzed for learning support purposes.

[0731] An "educational plan" is a curriculum that defines the optimal learning content and methods tailored to the user's learning needs and objectives.

[0732] "Providing answers in real time" means generating and sending responses to user inquiries in a timely manner without delay.

[0733] "Diverse forms of educational materials" refers to learning content provided in different formats, such as text, video, and audio, allowing users to choose the method that is easiest for them to learn from.

[0734] "Learning progress" refers to the user's growth and achievement level during their learning process, and is used to evaluate educational plans and formulate the next learning steps.

[0735] "Communication" refers to informational messages sent to the user, including encouragement for learning and rewards based on progress.

[0736] "Collaborative learning" refers to a learning method in which multiple users work together to learn, deepening their understanding by sharing knowledge with each other.

[0737] "Displaying to supervisors and supporters" means visually showing the user's learning progress to instructors and guardians, which is necessary to provide appropriate feedback and support.

[0738] A "specific algorithm" refers to a series of computational methods and processing procedures used to analyze a user's learning data and generate an individualized learning experience.

[0739] This invention is an educational support system that utilizes generative AI technology and aims to provide users with an individualized learning experience. This system mainly consists of three elements: a server, a terminal, and a user, each of which functions as follows.

[0740] The server authenticates user information and retrieves the user's learning history and individual settings from its database. Authentication software and a database management system (e.g., MySQL, PostgreSQL) are used for this purpose. The server then collects the user's learning data and analyzes it using data analysis libraries (e.g., Pandas, NumPy). Generative AI models (e.g., general-purpose AI text generation models) are used for analysis to generate an educational plan tailored to the user.

[0741] Once the device receives information from the server, it can present users with educational materials in various formats. These materials include text, videos, and audio, and the device has the ability to select the learning method that is most effective for the user. Users connect to the system using the device and access the learning materials to implement a personalized curriculum.

[0742] Users access educational materials through their devices, and if questions arise during their learning, they input them and send them to the system via their devices. The questions are entered as prompts, for example, "Please explain this new mathematical concept." The server analyzes this input, uses generative AI to calculate answers in real time, and responds to the user via their devices.

[0743] Furthermore, based on the data obtained during learning activities, the server can analyze the user's learning progress and dynamically update the educational plan to reflect it in the next learning session. In addition, rewards and encouraging messages generated according to the user's progress are delivered to the device, providing support to enhance the user's learning motivation.

[0744] In this way, the system is designed to provide a personalized learning experience, enabling users to learn effectively.

[0745] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0746] Step 1:

[0747] The user starts up the device and enters authentication information on the login screen. The entered information, such as username and password, is authentication data and is sent to the server via the device.

[0748] Step 2:

[0749] The server retrieves information about the user by querying the database based on the authentication data it receives. It returns a matching record from the user information held in the database management system and determines whether authentication was successful. An authentication algorithm is applied during this process.

[0750] Step 3:

[0751] If authentication is successful, the server retrieves the user's past learning data and configuration information and sends it to the device. This data concerns the user's learning history and current progress. The retrieved data is converted into a format that can be displayed on the device and presented to the user.

[0752] Step 4:

[0753] The server uses a data analysis library to analyze the user's learning style and progress based on their past learning data. The analyzed data is then used to create a new curriculum using generative AI models. This generates an optimal educational plan for the user and provides it to their device.

[0754] Step 5:

[0755] Users view the curriculum on their device interface and select the assignments and learning modes they wish to study. The selected information is sent from the device to the server, and the corresponding learning materials are downloaded.

[0756] Step 6:

[0757] The device presents the downloaded educational materials to the user. These materials include text, videos, and audio, which the user can use to progress through their learning. Each material is displayed in a format appropriate to the learning mode selected by the user.

[0758] Step 7:

[0759] If a user encounters a question during their learning process, they enter it into their device. This question is entered as a prompt, for example, "Please explain how to solve this equation." The question is then sent to the server.

[0760] Step 8:

[0761] The server uses a generated AI model to analyze the received prompt and generate an appropriate response. The AI ​​model utilizes relevant knowledge based on the question to create the answer. This response is then forwarded back to the terminal and provided to the user in real time.

[0762] Step 9:

[0763] As learning progresses, the server analyzes user performance data accumulated during the session. It utilizes data analysis libraries to evaluate learning progress. Based on the analysis results, the server updates the educational plan as needed and delivers improved learning materials to the device.

[0764] Step 10:

[0765] The server generates reward and encouragement messages based on the user's progress to promote learning. These messages provide the user with information about their achievements and motivation to move on to the next step. The generated messages are notified to the device and displayed to the user.

[0766] (Application Example 1)

[0767] 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".

[0768] Providing support optimized to the individual needs of learners is crucial in educational settings and at home. However, learners are often constrained by fixed curricula, making flexible learning tailored to their individual questions and progress difficult. Furthermore, limited opportunities for effective interaction with other learners pose challenges to understanding the material and maintaining motivation. Against this backdrop, there is a need to develop systems that provide learners with individualized learning plans, answer questions in real time, and promote opportunities for collaborative learning.

[0769] 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.

[0770] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for facilitating interaction among users by linking multiple educational support devices. This enables the provision of educational plans optimized for individual learners, real-time question answering, and interaction among learners.

[0771] "User information" refers to data related to the personal identification and profile of system users, and is used for authentication and customization.

[0772] "Learning data" refers to various types of information related to a user's learning activities, including learning content, progress, and level of understanding.

[0773] An "educational plan" refers to an optimized learning curriculum generated based on the user's current learning status and needs.

[0774] An "information processing device" refers to a terminal device that users access, and is used to display learning materials and receive input from users.

[0775] "Real-time response methods" refer to functions that provide instant answers to user questions, achieving rapid responses through the use of generative AI.

[0776] "Diverse forms of learning materials" refers to learning information that combines multiple media types, such as text, audio, and video.

[0777] "Educational support devices" refer to equipment and software designed to assist users in their learning, such as displaying learning information and managing progress.

[0778] "Digital rewards" refer to the technical means of providing rewards and encouraging messages based on a user's learning progress and achievements.

[0779] In this invention, interaction between a server, a terminal, and a user takes place to realize an educational support system. The roles of each are described below.

[0780] The server uses an authentication management system to authenticate user information. Personal information entered by the user on the terminal is sent to the server and compared with registered information. If authentication is successful, the server retrieves past learning history and settings information from the user's learning database and sends it to the terminal.

[0781] The terminal functions as an interface with the user, interacting with them through voice input and a touch panel. Based on the learning mode selected by the user on the terminal, information sent from the server is displayed in various forms, including text, audio, and video. The server uses a generative AI model to analyze the user's progress in real time and provide an optimal learning plan. For example, if a user asks, "I don't understand the Pythagorean theorem," it can immediately present relevant videos and text materials.

[0782] Users progress through their learning based on this information. If questions arise during learning, they can input them via their device. The server uses a generation AI to analyze the questions and generate appropriate answers. These answers are then sent to the device for immediate confirmation by the user.

[0783] Furthermore, the server supports collaborative learning with other users by linking multiple educational support devices. This allows learners in different locations to share knowledge with each other and deepen their understanding through discussions and quizzes.

[0784] Example of a prompt:

[0785] Question: "What is the Pythagorean theorem?"

[0786] Prompt: "Please explain the Pythagorean theorem to children, including simple examples."

[0787] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0788] Step 1:

[0789] The user starts up their device and enters personal information via the login screen. The device sends this information to the server. The server performs authentication verification based on the entered user information against the registered information in the database. As a result, a message indicating authentication success or failure is output to the user's device.

[0790] Step 2:

[0791] The server retrieves past learning history and configuration information from the learning database for users who have successfully authenticated. This data is used within the server to generate a user-specific education plan and is then sent to the device. The server analyzes the past history and presents an appropriate plan to the device.

[0792] Step 3:

[0793] When a user selects a learning mode on their device, the device sends this selection information to the server. Based on this information, the server uses a generative AI model to generate an optimal learning plan. This plan includes the user's chosen learning format (text, audio, or video) and provides materials optimized for the device.

[0794] Step 4:

[0795] If a user has a question while learning, they can enter it via their device. The server receives the question data and analyzes it using a generative AI model. As a result of the analysis, an appropriate answer is generated and sent to the device in real time. The user reviews this answer and continues learning.

[0796] Step 5:

[0797] The server facilitates matching to promote collaborative learning among users. It communicates with multiple related terminals to initiate interaction between learners. The terminals display details of the collaborative learning sessions in which the user is participating, and the user deepens their understanding through questions and discussions.

[0798] Step 6:

[0799] The server generates digital rewards based on the user's learning progress and achievements. This information is sent to the device as messages to enhance learning motivation, and users receive it in real time.

[0800] 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.

[0801] This invention is an educational system that utilizes generative AI technology and an emotion engine to provide users with a more personalized learning experience. The system is realized through the interaction of a server, terminal, and user, and by incorporating an emotion engine, it dynamically adjusts the user's learning motivation and curriculum optimization according to their emotional state.

[0802] When a user begins learning, the device starts up and prompts for login information. The authentication information entered by the user is sent from the device to the server, which verifies this information and retrieves the user's profile and learning history from the database. Following this basic authentication and data setup, the system generates a curriculum tailored to the user's learning style and needs.

[0803] A notable feature of this invention is that the emotion engine is integrated into the device, recognizing the user's emotions in real time. For example, it analyzes the user's mood and whether they are stressed through the camera and microphone. The server receives this emotional information, performs analysis, and adjusts the curriculum as needed to improve the user's learning experience.

[0804] For example, if the emotion engine detects user confusion or stress while a user is working on a difficult task, the server can temporarily switch the curriculum to easier content or slow down the learning pace. Alternatively, it can send a message to the user via the device to encourage relaxation.

[0805] When a user enters a question they are learning into their device, the server instantly analyzes the question using a generating AI and provides an answer. This answer is also customized based on emotion recognition; for example, if a positive emotion is recognized, an encouraging message will be added.

[0806] Furthermore, both progress and sentiment data are analyzed by the server to suggest ideal staging within the user's overall learning plan. Digital rewards and messages designed to boost user motivation are also based on this sentiment data and are designed to maximize their effectiveness.

[0807] In this way, the system leverages an emotion engine to provide a more personalized learning experience that takes user emotions into account, thereby maximizing the effectiveness and efficiency of learning.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The device boots up, and the user enters their ID and password on the login screen. The device sends the user's authentication information to the server.

[0811] Step 2:

[0812] The server verifies the received authentication information and retrieves the user's profile and learning history from the database. If authentication is successful, the server sends the initial setup and past learning data to the device.

[0813] Step 3:

[0814] The device's built-in emotion engine activates and acquires user emotion data through the camera and microphone. For example, it determines the user's emotions in real time based on facial expression analysis and voice tone.

[0815] Step 4:

[0816] The server receives emotional data sent from the emotion engine and analyzes the user's current mental state. Based on this emotional data, the server adjusts the curriculum content and pace.

[0817] Step 5:

[0818] The user inputs a question through their device during the learning process. The device sends the question to the server. The server uses generative AI to analyze the question and generate an answer. The generated answer is customized according to sentiment data and sent back to the device.

[0819] Step 6:

[0820] The server periodically receives learning progress data from the terminal and analyzes it together with sentiment data. Based on the analysis results, it adjusts the next curriculum and sends it to the terminal.

[0821] Step 7:

[0822] Based on the analysis results, the server generates messages and digital rewards to enhance the user's learning motivation. These are then sent to the device at the optimal time, according to the emotional data.

[0823] Step 8:

[0824] If a user wishes to participate in a collaborative learning session, the device sends a request to the server. The server searches for other suitable users and selects the best match, taking sentiment data into consideration. It then sends information to the device so that the selected users can begin a collaborative learning session.

[0825] Step 9:

[0826] The server provides user learning progress and emotional data to the parent / teacher interface. This allows parents and teachers to understand the user's learning situation and emotional state, and provide appropriate support.

[0827] (Example 2)

[0828] 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".

[0829] The current education system has the challenge of not being able to adequately consider the individual needs and emotional states of learners, making it difficult to maximize learning effectiveness and motivation. Furthermore, it is difficult to flexibly adjust the curriculum in real time and provide support tailored to the user's learning progress. Moreover, one-sided learning support that ignores emotional states can increase learner stress and decrease learning efficiency.

[0830] 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.

[0831] In this invention, the server includes means for authenticating user information, means for collecting and analyzing user learning data, and means for recognizing the user's emotional state and analyzing that information. This enables dynamic curriculum adjustment to meet individual learning needs. Furthermore, flexible learning support and message delivery based on the user's emotions can improve learning motivation and efficiency.

[0832] "Means of authenticating user information" refers to technologies used to verify the authentication data entered by a user when they access a system, and to confirm that they are a legitimate user.

[0833] "Means for collecting and analyzing user learning data" refers to technologies that collect data on users' learning behavior and progress, and then analyze this data to identify their learning style and needs.

[0834] "Means for generating a curriculum based on analysis results" refers to a technology that uses the results of analyzing learning data to create a curriculum that best suits the user's learning needs.

[0835] "Means for recognizing a user's emotional state and analyzing that information" refers to technologies that detect a user's emotions, analyze that data to understand the user's emotional state, and provide appropriate learning support.

[0836] "Methods for dynamically adjusting the curriculum based on analysis results" refers to technologies that respond to individual learning needs by changing the curriculum content in real time based on an analysis of the user's learning progress and emotional state.

[0837] "A means of providing real-time answers to questions from a user's device" refers to a technology that generates and provides appropriate answers immediately to questions asked by users via their devices.

[0838] "Means of providing users with multi-mode learning materials" refers to technologies that provide users with learning materials in multiple formats, such as text, images, audio, and video, to accommodate diverse learning styles.

[0839] "Methods for analyzing user learning progress and updating the curriculum" refers to technologies that analyze the progress of learning and create or update the curriculum based on the results.

[0840] "Means for generating messages to enhance user learning motivation" refers to technologies that analyze users' emotions and learning progress to create and present messages that increase their motivation.

[0841] "Means that enable collaborative learning with other users" refers to technologies that allow multiple users to engage in collaborative learning activities on a system.

[0842] "Means of displaying a user's learning progress to parents and educators" refers to technologies that organize a user's learning progress information and visually represent it in a way that parents and educators can access.

[0843] This invention is an educational support system that uses neural circuit technology and an emotion analysis engine to provide users with an individualized learning experience. Embodiments of the invention are realized through the cooperation of three parties: the user, the terminal, and the server.

[0844] The terminal receives the user's login information and sends that data to the server for authentication. The server verifies the information received from the client and uses a database system to retrieve the user's profile and past learning history. The database used here is accessed using SQL queries.

[0845] The server uses collected information to apply generative AI technology and generate a curriculum appropriate for the user. The generative AI model used is a deep learning model built in languages ​​such as Python, which creates a new learning plan based on the user's past learning performance.

[0846] The emotion analysis engine built into the device uses image recognition software (e.g., OpenCV) and a speech analysis system to detect the user's emotional state in real time. The recognition results are immediately sent to a server, where analysis is performed to improve the user's learning experience.

[0847] For example, if a user is experiencing stress while working on a particular task, the server can temporarily adjust the curriculum based on emotional data and switch to a less difficult task. It can also send messages to encourage relaxation through the device.

[0848] Furthermore, the server uses neural network models to generate appropriate answers in real time to questions entered by the user on the device. This process is also customized through emotion recognition and may include positive feedback.

[0849] A concrete example of a prompt message would be, "Please tell me how to provide appropriate learning support when a user becomes confused."

[0850] In this way, the invention enables personalized learning that takes user emotions into account, providing an efficient learning experience.

[0851] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0852] Step 1:

[0853] The user enters their login information into the terminal. The entered information (username and password) is sent from the terminal to the server. This causes the server to initiate the user authentication process.

[0854] Step 2:

[0855] The server verifies the received login information against the database and performs authentication. If successful, it retrieves the user's profile data and learning history from the database. Specifically, it uses SQL queries to extract information related to the user. As output of this process, learning history data associated with each individual user is obtained.

[0856] Step 3:

[0857] The server uses a generative AI model based on the acquired data to generate a curriculum optimized for the user. Input data includes the user's progress and past learning performance. The generative AI model analyzes this data to suggest future learning content. The output is individually customized curriculum data.

[0858] Step 4:

[0859] The sentiment analysis engine integrated into the device collects user sentiment data using sensors (camera and microphone). Specifically, it determines the user's emotional state in real time based on image recognition and voice analysis. This sentiment data is processed on the device and sent to the server.

[0860] Step 5:

[0861] The server receives and analyzes emotional data sent from the terminal. Based on the results of this analysis, it dynamically adjusts the already generated curriculum. For example, if the user is experiencing stress, the curriculum is readjusted to make learning tasks easier. The generated, adjusted curriculum data is then sent back to the terminal.

[0862] Step 6:

[0863] When a user enters a question during learning, the device sends this information to the server. The server uses a generative AI model to analyze the question and generate an answer in real time. For example, the prompt might be "How can I provide appropriate learning support when the user becomes confused?" The generated answer is sent back to the device and displayed to the user.

[0864] (Application Example 2)

[0865] 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".

[0866] Modern education systems demand personalized learning experiences tailored to individual learners. However, traditional educational platforms struggle to recognize learners' emotional states in real time and optimize learning content and pace accordingly. Furthermore, they are not adequately providing effective feedback and encouragement based on learners' emotions. As a result, learning efficiency and motivation are hindered.

[0867] 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.

[0868] In this invention, the server includes means for collecting and analyzing user learning data and emotional information, means for generating a curriculum suitable for the user based on the analysis results, and means for optimizing the learning experience based on emotional data. This makes it possible to provide a dynamic and personalized learning experience that takes into account the learner's emotional state.

[0869] "User information" refers to identifying information related to user authentication and profile.

[0870] "Learning data" refers to information about the activities and progress of learners.

[0871] "Emotional information" refers to data that indicates the emotional state obtained through the learner's facial expressions and voice.

[0872] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and generate content.

[0873] "Responding in real time" refers to a process that enables immediate responses to user input.

[0874] "Multi-mode learning materials" refers to educational content in various formats, such as text, audio, images, and videos.

[0875] "Emotional data" refers to information about a learner's emotional state and is used to adapt to the learning process.

[0876] "Digital rewards" refer to rewards and incentives provided online based on the learner's achievement level.

[0877] "Collaborative learning" refers to learning activities carried out by multiple learners working together.

[0878] "Displaying to parents and instructors" means providing parents and instructors with a visual representation of the learner's progress and achievements.

[0879] The system implementing this invention is an educational support platform using emotion recognition technology and a generative AI model. The server, terminal, and user interact with each other to provide a personalized learning experience.

[0880] The server verifies the authentication information sent from the user's device and retrieves the user's profile and learning history from the database. The device incorporates a camera and microphone to collect emotional information in real time through the user's facial expressions and voiceprint. This emotional information is analyzed using Python libraries such as OpenCV and Dlib.

[0881] Based on the analyzed emotion data and training data, the server dynamically generates a learning curriculum optimized for each user using a generative AI model (e.g., OpenAI's GPT-4). Furthermore, appropriate feedback and learning materials are automatically selected according to the learner's situation and provided through the terminal interface.

[0882] For example, if the emotion engine detects that a child is experiencing stress due to a difficult math problem while studying at home, the server can use its AI-generated explanation to create an easy-to-understand explanation and display it on the device. A message of encouragement to help the child relax is also sent.

[0883] An example of a prompt message would be, "Considering this learner's current emotional state, generate a simple task description and an encouraging message to help them relieve stress." This allows users to engage in learning in a less stressful state, which is expected to improve learning effectiveness.

[0884] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0885] Step 1:

[0886] The terminal starts up and displays a login screen to the user. The user enters their authentication information, which the terminal sends to the server. The server authenticates the user by comparing the entered authentication information with the database. If authentication is successful, the server retrieves the user's profile and past learning history and sends it to the terminal. The input is the authentication information, and the output is the user's profile and learning history.

[0887] Step 2:

[0888] The device uses a camera and microphone to collect the user's facial expressions and voice. Based on the collected data, it analyzes emotional information in real time using the Python libraries OpenCV and Dlib, and sends the results to the server. The input is facial expression and voice data, and the output is the user's emotional information.

[0889] Step 3:

[0890] The server dynamically generates a user-optimized curriculum using a generative AI model based on emotional information and learning history. The generated curriculum is sent to the terminal, which then displays it to the user. The input is emotional information and learning history, and the output is the optimized curriculum.

[0891] Step 4:

[0892] The terminal receives a question from the user. The server sends this question to a generation AI model, which uses prompts to generate the optimal answer. The generated answer is sent back to the terminal and displayed to the user. The input is the user's question, and the output is the generated answer.

[0893] Step 5:

[0894] The server analyzes the user's learning progress and emotional data, adjusts the curriculum and learning pace as needed, and generates digital rewards and motivational messages. The terminal notifies the user of these. The input is learning progress and emotional data, and the output is the adjusted curriculum and messages.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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."

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] The following is further disclosed regarding the embodiments described above.

[0917] (Claim 1)

[0918] Means for authenticating user information,

[0919] A means of collecting and analyzing user learning data,

[0920] A means for generating a curriculum suitable for the user based on the analysis results,

[0921] A means of providing real-time answers to questions from the user's device,

[0922] A means of providing users with multi-mode learning materials,

[0923] A means of analyzing user learning progress and updating the curriculum,

[0924] A means of generating messages to enhance the user's motivation to learn,

[0925] A means to enable collaborative learning with other users,

[0926] A means of displaying the user's learning progress to parents and instructors,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, which analyzes the user's learning patterns using a machine learning model.

[0930] (Claim 3)

[0931] The system according to claim 1, which generates digital rewards based on the user's progress.

[0932] "Example 1"

[0933] (Claim 1)

[0934] Means for authenticating user information,

[0935] A means of collecting and analyzing data related to user knowledge,

[0936] A means for generating an educational plan suitable for the user based on the analysis results,

[0937] A means of providing real-time answers to questions from users' communication devices,

[0938] A means of providing users with diverse forms of educational materials,

[0939] A means of analyzing the user's learning progress and updating the educational plan,

[0940] A means for generating communications to enhance the user's motivation to learn,

[0941] A means to enable collaborative learning with other users,

[0942] A means of displaying the user's learning progress to supervisors and supporters,

[0943] A means of providing users with a personalized learning experience using a specific algorithm,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, which analyzes the user's learning style using data analysis technology.

[0947] (Claim 3)

[0948] The system according to claim 1, which generates rewards for users based on their progress.

[0949] "Application Example 1"

[0950] (Claim 1)

[0951] Means for authenticating user information,

[0952] A means of collecting and analyzing user learning data,

[0953] A means for generating an educational plan suitable for the user based on the analysis results,

[0954] A means of responding in real time to questions from the user's information processing device,

[0955] A means of providing users with diverse forms of learning materials,

[0956] A means of analyzing the user's learning progress and updating the educational plan,

[0957] A means of generating messages to enhance the user's motivation to learn,

[0958] A means to enable collaborative learning with other users,

[0959] A means of displaying the user's learning progress to parents and instructors,

[0960] A means of promoting interaction among users by linking multiple educational support devices,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, which analyzes the user's learning pattern using an inference algorithm.

[0964] (Claim 3)

[0965] The system according to claim 1, which generates digital rewards based on the user's progress.

[0966] "Example 2 of combining an emotion engine"

[0967] (Claim 1)

[0968] Means for authenticating user information,

[0969] A means of collecting and analyzing user learning data,

[0970] A means for generating a curriculum suitable for the user based on the analysis results,

[0971] A means of recognizing the user's emotional state and analyzing that information,

[0972] A means of dynamically adjusting the curriculum based on the analysis results,

[0973] A means of providing real-time answers to questions from the user's device,

[0974] A means of providing users with multi-mode learning materials,

[0975] A means of analyzing user learning progress and updating the curriculum,

[0976] A means of generating messages to enhance the user's motivation to learn,

[0977] A means to enable collaborative learning with other users,

[0978] A means of displaying the user's learning progress to parents and instructors,

[0979] A system that includes this.

[0980] (Claim 2)

[0981] The system according to claim 1, which analyzes the user's learning patterns using a machine learning model.

[0982] (Claim 3)

[0983] The system according to claim 1, which generates digital rewards based on the user's progress.

[0984] "Application example 2 when combining with an emotional engine"

[0985] (Claim 1)

[0986] Means for authenticating user information,

[0987] A means for collecting and analyzing user learning data and sentiment information,

[0988] A means for generating a curriculum suitable for the user based on the analysis results,

[0989] A means of providing real-time answers to questions from the user's device using a generative AI model,

[0990] A means of providing users with multi-mode learning materials,

[0991] A means of dynamically updating the curriculum by analyzing user learning progress and emotional data,

[0992] A means of generating messages to enhance the user's motivation to learn,

[0993] A means to enable collaborative learning with other users,

[0994] A means of displaying the user's learning progress to parents and instructors,

[0995] A means of optimizing the learning experience based on emotional data,

[0996] A system that includes this.

[0997] (Claim 2)

[0998] The system according to claim 1, which analyzes the user's learning patterns and emotional state using a machine learning model.

[0999] (Claim 3)

[1000] The system according to claim 1, which generates digital rewards based on user progress and sentiment data. [Explanation of symbols]

[1001] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for authenticating user information, A means of collecting and analyzing user learning data, A means for generating a curriculum suitable for the user based on the analysis results, A means of providing real-time answers to questions from the user's device, A means of providing users with multi-mode learning materials, A means of analyzing user learning progress and updating the curriculum, A means of generating messages to enhance the user's motivation to learn, A means to enable collaborative learning with other users, A means of displaying the user's learning progress to parents and instructors, A system that includes this.

2. The system according to claim 1, which analyzes the user's learning patterns using a machine learning model.

3. The system according to claim 1, which generates digital rewards based on the user's progress.

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