system

Abstract

We provide the system. [Solution] A means for generating an individualized learning plan, Means for tracking and recording learners' progress, An information processing device for presenting learning content based on a generated learning plan, A means of providing rewards according to learning progress, A means of communication for receiving and resolving questions during learning, An educational support system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern educational environment, there is a demand for providing education optimized for each learner's level of understanding and pace. However, in conventional educational facilities, it is difficult to provide learning support according to individual needs due to time and location constraints, resulting in a problem of decreased motivation among learners. The present invention aims to solve such problems and provide an educational experience optimized for learners.

Means for Solving the Problems

[0005] This invention provides an educational support system comprising means for generating an individualized learning plan, means for tracking and recording learner progress, an information processing device for presenting learning content based on the generated learning plan, means for providing rewards according to learning progress, and communication means for receiving and resolving questions during learning. This system allows learners to efficiently learn individualized learning content without being restricted by location or time, and their motivation to learn is enhanced through the reward system. Furthermore, questions arising during learning can be resolved in real time through experts or AI, supporting the learner's understanding.

[0006] An "individualized learning plan" is a learning curriculum that is customized based on each learner's level of understanding, progress, and interests.

[0007] "Means for tracking and recording learner progress" refers to methods that collect data on tasks completed and goals achieved by learners, and provide a function to visualize the progress of their learning.

[0008] "Information processing device for presenting learning content" is a general term for hardware and software used to display and provide learning materials and content to users based on a generated learning plan.

[0009] "Means of providing rewards" refer to functions or methods that provide incentives to learners each time they achieve a set learning goal, thereby improving their motivation to learn.

[0010] "Communication methods" refer to digital communication systems that enable learners to communicate with external experts and AI in real time to resolve questions that arise during their learning process. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] As an embodiment of this invention, the following educational support system can be constructed.

[0033] This system provides personalized learning experiences on an educational platform and consists of three components: servers, terminals, and users.

[0034] First, the user installs the learning app on their device to use the application. The user creates an account and enters basic information such as their grade level, subjects of interest, and learning style, and this information is sent from the device to the server. Based on this information, the server uses generative AI to generate a personalized learning plan and returns it to the user's device. This plan includes specific learning materials, problem sets, videos, and other resources.

[0035] Subsequently, the user selects interactive learning content on their device according to the presented learning plan and begins learning. The device retrieves the necessary data from the server to access the selected content and displays it to the user. During learning, the user's progress and performance are transmitted to the server in real time, and an individual learning history is accumulated. Based on this, the server analyzes the user's progress and reflects it in future learning content.

[0036] Furthermore, by incorporating a reward system to encourage learning, the server provides points or ranking-based badges as rewards each time a user reaches a specific achievement goal, which are then displayed on the device to boost user motivation. For example, if a user continues to study for a certain amount of time each week, they will be awarded a "Continuous Learner Badge," which encourages them to achieve their next learning goal.

[0037] Furthermore, learners can submit questions in real time from their devices via the server if they encounter any difficulties during their learning process. Experts and AI models respond to these questions, providing quick answers to remove obstacles during learning and support smooth progress. For example, if a user finds a particular mathematical concept difficult to understand, they can enter related questions into a form and receive answers in a chat format.

[0038] In this way, a learning environment similar to individualized instruction can be provided without being restricted by time or location. This system allows users to learn at their own pace and receive necessary support quickly.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user installs the learning app on their device and creates a new account. The user enters the necessary personal information, grade level, subjects to study, and preferred learning style, and sends this information from their device to the server.

[0042] Step 2:

[0043] The server receives user input and uses AI to generate a personalized learning plan. The generated plan includes recommended learning materials, video lessons, and interactive exercises. This plan data is then sent from the server to the user's device.

[0044] Step 3:

[0045] The device displays the received learning plan data to the user, and the user selects appropriate content to begin learning. Once the user selects content, the device sends this selection information to the server and retrieves the necessary content data.

[0046] Step 4:

[0047] The user begins learning through the learning content and materials they have selected. The device sends progress data to the server in real time, including the user's activity during learning, answer results, and learning time.

[0048] Step 5:

[0049] The server evaluates and records the user's learning progress based on the received progress data. This accumulates data that can be used to adjust future learning programs, making personalized learning more efficient.

[0050] Step 6:

[0051] Each time a user achieves a set learning goal, the server calculates points or reward badges based on their progress and sends them to the user. The device visually displays the received reward data to maintain user motivation.

[0052] Step 7:

[0053] If a user has questions or doubts during the learning process, they send those questions to the server via their device. Upon receiving the question, the server routes it to an expert or AI to obtain an appropriate answer. Once an answer is generated, the server returns it to the device and presents it to the user, facilitating quick resolution of their questions.

[0054] Step 8:

[0055] Users periodically access the server from their devices to check their past learning history, achievements, and reward information on a dashboard. This allows them to continuously evaluate themselves and receive feedback, which they can then use to plan their next learning session.

[0056] (Example 1)

[0057] 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."

[0058] Traditional educational support systems have faced challenges in adequately providing individualized learning experiences that meet the diverse needs of learners. Furthermore, they lacked features to flexibly modify learning plans according to learners' progress and to easily visualize learning outcomes. In addition, the absence of rewards to enhance learning motivation made maintaining learner motivation a challenge.

[0059] 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.

[0060] In this invention, the server includes means for generating personalized learning plans based on prompt sentences using a generative AI model, means for tracking and recording the learner's progress and behavioral history, and means for providing an interface that visually displays learning outcomes. This enables the provision of flexible and personalized learning experiences that cater to diverse learners. Furthermore, it is possible to increase motivation by providing rewards according to the learner's progress.

[0061] A "generative AI model" is a type of artificial intelligence that automatically generates the optimal solution based on input data and conditions.

[0062] A "prompt message" is the text input to a generative AI model, and it plays a role in determining the direction and content of its output.

[0063] An "individualized learning plan" is an educational program specifically designed to suit the individual learner's abilities and progress.

[0064] "Learning content" refers to a collection of materials and assignments prepared to achieve a specific educational objective.

[0065] "Information processing equipment" is a general term for electronic devices used to receive, process, and display data.

[0066] An "incentive" is a reward or motivating tool provided to encourage a learner's behavior.

[0067] "Communication equipment" is a general term for hardware and software used to send and receive data and information.

[0068] An "interface" refers to a screen or function designed to facilitate interaction between the user and the system.

[0069] This invention is realized by implementing the following educational support system. This system includes three components: user, terminal, and server. The main software used includes a generative AI model.

[0070] The server functions as the core of the educational support system, receiving information provided by users. Users use a terminal to enter data such as their grade level, subjects of interest, and learning style when creating an account. This information is transmitted to the server via the terminal.

[0071] The server inputs the received user information as prompts into the generating AI model, which then generates a personalized learning plan. A specific example of a prompt would be, "Generate learning materials to strengthen English grammar for 9th graders." The generated learning plan would include specific learning materials, problem sets, and video content.

[0072] The terminal receives the learning plan returned from the server and displays it visually to the user. This process requires the terminal as an information processing device. The terminal provides an interface for the user to interact with the learning content.

[0073] Users progress through their learning according to the learning plan presented on their device. Their progress and performance are transmitted to the server in real time. The server analyzes this data and incorporates it into the next learning session to optimize the learning experience.

[0074] Furthermore, the server provides incentives based on learning outcomes to motivate learners. A typical example is displaying a "continuing learner badge" on the device for users who have achieved a certain amount of study time.

[0075] This embodiment of the invention provides an easy way to offer personalized learning plans, enabling users to learn efficiently. Furthermore, the use of a generative AI model enables flexible and responsive educational support.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user installs the learning app on their device and creates an account. The user enters basic information such as their grade level, subjects of interest, and learning style. This information is sent from the device to the server as input data. The specific actions of the device include displaying the input form and sending the data.

[0079] Step 2:

[0080] The server receives user information from the terminal and inputs it into the generating AI model. A prompt message is generated, and based on its content, an individualized learning plan is created. The server analyzes the data and performs data calculations using the generating AI model, outputting it as a learning plan. Specifically, the generated learning plan includes learning materials and problem sets.

[0081] Step 3:

[0082] The server sends the generated learning plan to the user's device. The outputted learning plan is received by the device and displayed visually to the user. Specific actions on the device include notification functions and display via the user interface.

[0083] Step 4:

[0084] Users use their devices to select interactive learning content according to the displayed learning plan and engage in learning. During this process, users access assigned lessons and problems and receive real-time feedback on their devices. Specific device operations include downloading and displaying the selected content.

[0085] Step 5:

[0086] From the terminal, user progress data and grades are sent to the server in real time. The server receives this as input data, tracks the learner's progress, and records it. Specific operations include sending and receiving data, and saving the data.

[0087] Step 6:

[0088] The server uses the user's progress data to perform calculations to optimize the next learning session and updates the learning plan. This updated plan is then sent back to the terminal and presented to the user. The server's specific actions include data analysis and the generation of a new learning plan.

[0089] Step 7:

[0090] Users send questions that arise during learning from their device to the server. The server forwards the received questions to experts or generative AI models to generate answers. The answers are displayed to the user in a chat format on the device, thereby removing obstacles to learning. The specific actions include sending inquiries and receiving answers.

[0091] (Application Example 1)

[0092] 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."

[0093] Traditional learning systems struggle to provide personalized learning guidelines tailored to individual learners' needs, resulting in inefficient learning. Therefore, there is a need for appropriate distribution of educational resources that align with learners' interests and learning styles. Furthermore, there are challenges such as a lack of motivation based on learners' progress and insufficient support for inquiries.

[0094] 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.

[0095] In this invention, the server includes means for generating personalized learning guidelines, means for delivering educational resources tailored to the learner's interests and learning style, and communication means for receiving and resolving inquiries during learning. This makes it possible to provide learners with personalized learning guidelines, thereby enabling efficient and effective learning.

[0096] "Individualized learning guidelines" refer to learning plans that are customized based on each learner's interests, learning style, and progress.

[0097] "Learner progress" refers to data that evaluates and records the level of achievement, engagement, and understanding during learning.

[0098] An "information processing device" refers to a device that uses electronic devices such as computers and servers to process, analyze, and display data.

[0099] "Means of providing rewards" refers to a system that provides motivating rewards, such as points or badges, to learners when they achieve specific goals.

[0100] "Communication means" refers to a system that includes networks and interfaces for sending and receiving information.

[0101] "Means of delivering educational resources" refers to a system for delivering educational content tailored to learners' interests and needs.

[0102] This invention is a system that generates personalized learning guidelines and provides an advanced learning experience based on those guidelines. The system is mainly composed of three entities: a server, a terminal, and a user.

[0103] Program Overview

[0104] The server first generates personalized learning guidelines using a generative AI model based on personal information received from the user—such as grade level, subjects of interest, and learning style. These guidelines include educational resources tailored to the user's interests and learning style, and are delivered to the device at the appropriate time. The server also has the function to track the user's progress and adjust the guidelines as needed.

[0105] Users access learning materials provided by the server through their device. Specific learning content is displayed on the device, allowing users to progress through their studies. Furthermore, if questions arise during learning, users can send inquiries to the server via their device and receive quick answers.

[0106] Hardware and software to be used

[0107] The server is designed to enable centralized data management and advanced computational processing. The software uses programming languages ​​such as Python and libraries for running generative AI models (e.g., TENSORFLOW®, PyTorch). Smartphones and smart glasses are used as terminals, responsible for displaying data and interacting with the user.

[0108] Specific example

[0109] For example, suppose a first-year junior high school student is interested in mathematics. The server generates learning guidelines for this user, including videos and interactive exercises to visually teach the basics of triangles, and delivers them to the user's device.

[0110] Examples of prompts for a generative AI model:

[0111] "Please generate visually-oriented math tutorial content for first-year junior high school students."

[0112] In this way, users can experience a personalized learning environment anywhere, maximizing their learning effectiveness.

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

[0114] Step 1:

[0115] The user installs the learning application on their device and creates an account. During this process, they enter basic information such as their grade level, subjects of interest, and learning style. This information is then sent from the device to the server. The input data is user information (grade level, subjects of interest, learning style), while the output data is user information received by the server.

[0116] Step 2:

[0117] The server generates personalized learning guidelines using a generative AI model based on the user information it receives. In this process, the generative AI processes the data considering the user's interests and learning style, and outputs specific educational resources and learning plans. The input data is the user information received by the server, and the output is personalized learning guidelines.

[0118] Step 3:

[0119] The personalized learning plan generated by the server is sent to the terminal. The terminal receives it and presents it to the user. During presentation, each item of the learning plan is visually displayed and accessible. The input data is the personalized learning plan, and the output data is the learning plan displayed to the user.

[0120] Step 4:

[0121] Users access learning content through their devices and begin learning. During learning, the user's progress (e.g., learning time, number of goals achieved) is transmitted to the server in real time. Input data is a record of the user's learning activity, and output data is the user's progress transmitted to the server.

[0122] Step 5:

[0123] The server analyzes the user's progress and determines if adjustments to the learning guidelines are necessary. If so, it generates new learning guidelines and sends them to the terminal as update information. The input data is the user's progress, and the output data is the updated learning guidelines.

[0124] Step 6:

[0125] If a user has a question during the learning process, they can send a query to the server via their device. The server uses a generative AI model to quickly generate an answer and send it back to the user. The input data is the user's question, and the output data is the answer generated by the server's generative AI.

[0126] Step 7:

[0127] When a user achieves a specific learning goal, the server provides points or a ranking-based badge as a reward, which is displayed on the device. The input data is the user's learning progress, and the output data is the reward information displayed to the user.

[0128] These steps enable an effective learning experience tailored to the user's needs.

[0129] 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.

[0130] As a means of implementing this invention, an approach is provided that incorporates emotion recognition functionality into an educational support system. This system integrates a server, terminals, and an emotion engine to personalize and make the user's learning experience more effective.

[0131] First, the user installs the learning app on their device and enters personal information and learning goals during registration. This data is sent to a server via the device, and the server uses a generative AI to create an optimized learning plan, which is then returned to the device. On the device, the user selects the planned learning content and proceeds with their learning based on it.

[0132] The emotion engine is built into the device and uses cameras and sensors to analyze the user's facial expressions, voice, vital signs, etc., to detect their emotional state in real time. The server receives the information from the emotion engine, and the learning plan and content are dynamically adjusted based on the user's emotions. For example, if the user is feeling stressed during learning, the server will suggest relaxation content and interrupt the learning to calm the user down.

[0133] Furthermore, a system is incorporated that provides rewards based on learning progress. This reward system is also adjusted by data from the emotion engine to design incentives that keep users engaged and motivated to learn. For example, if a user completes a task while experiencing positive emotions, the reward is increased.

[0134] On the other hand, if a user has a question during learning, it is sent from the device to the server. The server quickly analyzes this question and returns an answer from experts or AI to the device. This allows users to resolve their questions in real time. Data from the emotion engine allows the responses to questions to be tailored to the user, making it possible to provide a more satisfying learning experience.

[0135] As described above, the integration of the emotion engine and the educational support system makes it possible to create a flexible and personalized learning environment that responds to the emotional state of each individual learner. With this system, users can learn at their own pace while receiving support tailored to their circumstances at any given time, enabling them to achieve optimal learning outcomes.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user installs the learning app on their device and creates a new account. The user enters their personal information, subjects of interest, and learning goals, and sends this information from their device to the server.

[0139] Step 2:

[0140] The server receives user information and uses generative AI to generate a personalized learning plan. The plan includes recommended learning materials, video lessons, and interactive modules, which the server then sends to the device.

[0141] Step 3:

[0142] The terminal displays the learning plan received from the server on the user screen. The user selects learning content of interest from the screen and begins learning.

[0143] Step 4:

[0144] The emotion engine is activated by the device, and it uses the user's camera and microphone to analyze facial expressions and voice data, evaluating the user's emotional state in real time.

[0145] Step 5:

[0146] The device sends the results of the emotion engine's analysis to the server. The server uses this information to adjust the difficulty level and presentation method of the learning content, providing the user with the best possible learning experience. For example, if the server determines that the user is experiencing stress, it may lower the difficulty level or recommend relaxing content.

[0147] Step 6:

[0148] As users progress through the learning process and achieve certain goals, the server calculates rewards based on their progress and emotional data. These rewards (points or badges) are displayed on the device, motivating the user. Bonus rewards may also be applied if the emotional state is positive.

[0149] Step 7:

[0150] During the learning process, users input questions and doubts via their device and send them to the server. The server then prepares quick answers from experts and AI, which are sent back to the user. Because the answers take sentiment data into account, they maximize user understanding.

[0151] Step 8:

[0152] Users can check their daily learning progress, emotional changes, and reward history through a dashboard displayed on their device. This allows them to smoothly proceed with their next learning plan while conducting self-assessments.

[0153] (Example 2)

[0154] 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".

[0155] Conventional educational support systems struggle to provide each learner with an optimal learning plan and to dynamically adjust it according to their progress and emotional state. Furthermore, they lack the technology to enable flexible plan changes based on learners' emotional states and to provide immediate answers to their questions. Therefore, there is a need to provide a comfortable learning environment while optimizing learning effectiveness.

[0156] 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.

[0157] In this invention, the server includes means for generating an individualized plan, means for analyzing emotional states, and means for dynamically adjusting the plan and content based on the analyzed emotional states. This makes it possible to provide an optimal learning environment according to the learner's emotional state and progress, thereby enhancing learning effectiveness.

[0158] "Means for generating personalized plans" refers to a device or method for creating a dedicated learning plan based on the user's individual information and goals.

[0159] "Means for tracking and recording progress" refers to a device or method for monitoring a user's learning progress and recording that information in a database or similar.

[0160] An "information processing device" refers to a computer or related device used to display learning content according to a generated plan.

[0161] "Means of providing rewards" refers to a device or method that provides incentives to users in accordance with their learning progress.

[0162] "Communication means" refers to network technologies and devices used to receive user questions and transmit necessary answers.

[0163] "Means for analyzing emotional states" refers to devices or methods that use cameras or sensors to analyze a user's emotions in real time.

[0164] "Means of dynamic adjustment" refers to a device or method that modifies and optimizes plans and learning content in real time based on analyzed emotional states.

[0165] "Means for adjusting the content of the next session based on behavioral history" refers to a device or method for analyzing past learning data and optimizing the content of the next session.

[0166] "Display means for visualizing and presenting results" refers to devices such as screens or digital interfaces that visually display the user's learning outcomes in an easy-to-understand manner.

[0167] This invention provides an educational support system that dynamically adjusts the learning plan by considering the user's emotional state in order to provide an individualized learning experience. This system utilizes a server, terminals, and an emotion engine to optimize learning effectiveness and create a more satisfying learning environment.

[0168] The server uses a generative AI model to generate personalized learning plans based on personal information and learning goals received from users. These plans are then sent to the user's device via the internet. The server utilizes a high-performance cloud infrastructure to optimize processing power.

[0169] The device is equipped with an interface for displaying learning content that the user can select. During the learning process, the device acquires the user's facial expressions, voice tone, and physical condition data in real time via its camera and sensors, and passes this data to an emotion engine. This emotion engine analyzes the data using an emotion recognition algorithm to determine the user's emotional state. This information is then sent back to the server and used to adjust the learning plan.

[0170] Users can use the system on a daily basis and learn at their own pace. For example, if a user enters a prompt such as "I'm tired, please give me some easy questions," the server can provide easier content based on sentiment data.

[0171] Specific examples of prompt messages include inputs such as "I want music that matches my current mood" or "I want to reinforce my basic math skills."

[0172] This system also features rewards based on learning progress and immediate question support, enabling users to always have an optimal learning experience tailored to their emotional state.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user installs the learning app on their device and enters personal information and learning goals. This information is processed on the device as input data. The device formats this input as digital data and prepares it for transmission to the server.

[0176] Step 2:

[0177] The terminal sends user input data to the server in a predetermined format. The information sent to the server as output data includes the user's profile and learning goals. Upon receiving this data, the server is ready to generate an optimized learning plan based on the user's requests.

[0178] Step 3:

[0179] The server utilizes a generative AI model to analyze the received user data. It processes the user information as input using algorithms to generate a personalized learning plan based on the user's needs. This learning plan is then sent to the terminal as output data.

[0180] Step 4:

[0181] The device displays the learning plan received from the server to the user. This allows the user to begin learning based on the provided plan. Specifically, tasks and content are presented on the device's display, and learning progresses.

[0182] Step 5:

[0183] During learning, the device utilizes its camera and sensors to acquire user facial expressions, voice, and vital signs as input data. This sensor data is analyzed by an emotion engine. The emotion engine uses an emotion analysis algorithm based on the input data to generate output data that clarifies the user's emotional state.

[0184] Step 6:

[0185] The server receives emotional state data sent from the emotion engine as input and uses an algorithm to dynamically adjust the learning plan and content as needed. The adjusted learning plan is then generated as output and sent back to the device. This process optimizes the learning experience in response to the user's stress levels and decreased motivation.

[0186] Step 7:

[0187] If a user has a question during learning, they input that question as a prompt into the terminal. The terminal then sends that prompt as data to the server.

[0188] Step 8:

[0189] The server analyzes the received prompt and utilizes generative AI models and databases to generate the necessary response. After generating the response as output, the server sends the answer to the terminal. This allows the user to obtain the information they want in real time.

[0190] (Application Example 2)

[0191] 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".

[0192] Existing systems have struggled to leverage consumer emotional information to provide personalized recommendations. Furthermore, they are unable to grasp consumers' current emotional states in real time and provide flexible recommendations accordingly, highlighting the need to improve the quality of the consumer experience.

[0193] 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.

[0194] In this invention, the server includes means for analyzing the user's emotional state and dynamically adjusting suggestions, means for adjusting the content for the next interaction based on the consumer's behavioral history, and a dashboard that visualizes and presents the user's results. This enables the provision of product suggestions and reward systems optimized for consumers, resulting in a personalized and meaningful consumer experience.

[0195] An "individualized plan" is an action plan optimized based on the user's characteristics and preferences.

[0196] "User progress" refers to tracking users' activities and achievements over time.

[0197] An "information processing device" is an electrical device used to collect, analyze, and display digital data.

[0198] "Rewards" refer to incentives or benefits provided to users when they complete an action or task.

[0199] "Communication means" refers to technologies and devices for sending and receiving information via a network.

[0200] "Emotional state analysis" is a process of estimating and evaluating emotions based on the user's facial expressions, voice, and other factors.

[0201] "Dynamic adjustment" means changing the content and functions in real time according to the situation.

[0202] "Behavioral history" refers to a collection of recorded information about a user's past actions and choices.

[0203] A "dashboard" refers to an interface that visually displays information, allowing users to intuitively understand the data.

[0204] The system that implements this application incorporates a program to analyze the user's emotional state in real time and provide personalized suggestions. The terminal uses a camera and microphone to capture the user's facial expressions and voice, and analyzes their emotions using software as an emotion engine (for example, a customer service API or a voice emotion recognition API).

[0205] Based on the analysis results, the server uses a generative AI model (e.g., text generation AI) to generate optimal suggestions for the user. This process also considers past behavioral history and progress data, resulting in a more personalized consumer experience. The server then sends the generated suggestions and reward information to the user's device, presenting them visually.

[0206] For example, if a user is using a food delivery app and their facial expression or voice suggests they are tired, the system can offer a suggestion such as, "Would you like a relaxing herbal tea?" Another example of a prompt message is, "User's emotion: Wants to relax. Suggested menu: Please describe the suggestion."

[0207] By using this system, users can receive optimal suggestions tailored to their emotions, thereby improving the quality of their consumer experience.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The device uses a camera and microphone to collect the user's facial expressions and audio data. The input is the user's real-time video and audio. This data is used as input for the emotion analysis described later.

[0211] Step 2:

[0212] The device sends the collected facial and voice data to the emotion engine. The emotion engine analyzes the data and estimates the emotional state, using, for example, a customer service API or a voice emotion recognition API. The output is the estimated result regarding the user's emotions.

[0213] Step 3:

[0214] The server uses an AI model to generate personalized suggestions based on the emotional state received from the emotion engine, taking into account the user's behavioral history and progress data. The inputs are emotional state, behavioral history, and progress data, and the output is the most suitable suggestions for the user.

[0215] Step 4:

[0216] The server sends the generated suggestions to the terminal. The terminal visually displays the suggested content to the user. This includes the specific action of communicating the suggestions generated based on the prompt text to the user.

[0217] Step 5:

[0218] The user reviews the suggestion and selects an action. The user's selection is recorded as the next action history. The input is the user's selection, and the output is the updated action history data.

[0219] Step 6:

[0220] The server calculates reward information related to the user's choice and sends it back to the terminal. The terminal presents the reward information to the user and informs them of the next available reward. This includes actions that increase user motivation.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] As an embodiment of this invention, the following educational support system can be constructed.

[0238] This system provides personalized learning experiences on an educational platform and consists of three components: servers, terminals, and users.

[0239] First, the user installs the learning app on their device to use the application. The user creates an account and enters basic information such as their grade level, subjects of interest, and learning style, and this information is sent from the device to the server. Based on this information, the server uses generative AI to generate a personalized learning plan and returns it to the user's device. This plan includes specific learning materials, problem sets, videos, and other resources.

[0240] Subsequently, the user selects interactive learning content on their device according to the presented learning plan and begins learning. The device retrieves the necessary data from the server to access the selected content and displays it to the user. During learning, the user's progress and performance are transmitted to the server in real time, and an individual learning history is accumulated. Based on this, the server analyzes the user's progress and reflects it in future learning content.

[0241] Furthermore, by incorporating a reward system to encourage learning, the server provides points or ranking-based badges as rewards each time a user reaches a specific achievement goal, which are then displayed on the device to boost user motivation. For example, if a user continues to study for a certain amount of time each week, they will be awarded a "Continuous Learner Badge," which encourages them to achieve their next learning goal.

[0242] Furthermore, learners can submit questions in real time from their devices via the server if they encounter any difficulties during their learning process. Experts and AI models respond to these questions, providing quick answers to remove obstacles during learning and support smooth progress. For example, if a user finds a particular mathematical concept difficult to understand, they can enter related questions into a form and receive answers in a chat format.

[0243] In this way, a learning environment similar to individualized instruction can be provided without being restricted by time or location. This system allows users to learn at their own pace and receive necessary support quickly.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user installs the learning app on their device and creates a new account. The user enters the necessary personal information, grade level, subjects to study, and preferred learning style, and sends this information from their device to the server.

[0247] Step 2:

[0248] The server receives user input and uses AI to generate a personalized learning plan. The generated plan includes recommended learning materials, video lessons, and interactive exercises. This plan data is then sent from the server to the user's device.

[0249] Step 3:

[0250] The device displays the received learning plan data to the user, and the user selects appropriate content to begin learning. Once the user selects content, the device sends this selection information to the server and retrieves the necessary content data.

[0251] Step 4:

[0252] The user begins learning through the learning content and materials they have selected. The device sends progress data to the server in real time, including the user's activity during learning, answer results, and learning time.

[0253] Step 5:

[0254] The server evaluates and records the user's learning progress based on the received progress data. This accumulates data that can be used to adjust future learning programs, making personalized learning more efficient.

[0255] Step 6:

[0256] Each time a user achieves a set learning goal, the server calculates points or reward badges based on their progress and sends them to the user. The device visually displays the received reward data to maintain user motivation.

[0257] Step 7:

[0258] If a user has questions or doubts during the learning process, they send those questions to the server via their device. Upon receiving the question, the server routes it to an expert or AI to obtain an appropriate answer. Once an answer is generated, the server returns it to the device and presents it to the user, facilitating quick resolution of their questions.

[0259] Step 8:

[0260] Users periodically access the server from their devices to check their past learning history, achievements, and reward information on a dashboard. This allows them to continuously evaluate themselves and receive feedback, which they can then use to plan their next learning session.

[0261] (Example 1)

[0262] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] Traditional educational support systems have faced challenges in adequately providing individualized learning experiences that meet the diverse needs of learners. Furthermore, they lacked features to flexibly modify learning plans according to learners' progress and to easily visualize learning outcomes. In addition, the absence of rewards to enhance learning motivation made maintaining learner motivation a challenge.

[0264] 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.

[0265] In this invention, the server includes means for generating personalized learning plans based on prompt sentences using a generative AI model, means for tracking and recording the learner's progress and behavioral history, and means for providing an interface that visually displays learning outcomes. This enables the provision of flexible and personalized learning experiences that cater to diverse learners. Furthermore, it is possible to increase motivation by providing rewards according to the learner's progress.

[0266] A "generative AI model" is a type of artificial intelligence that automatically generates the optimal solution based on input data and conditions.

[0267] A "prompt message" is the text input to a generative AI model, and it plays a role in determining the direction and content of its output.

[0268] An "individualized learning plan" is an educational program specifically designed to suit the individual learner's abilities and progress.

[0269] "Learning content" refers to a collection of materials and assignments prepared to achieve a specific educational objective.

[0270] "Information processing equipment" is a general term for electronic devices used to receive, process, and display data.

[0271] An "incentive" is a reward or motivating tool provided to encourage a learner's behavior.

[0272] "Communication equipment" is a general term for hardware and software used to send and receive data and information.

[0273] An "interface" refers to a screen or function designed to facilitate interaction between the user and the system.

[0274] This invention is realized by implementing the following educational support system. This system includes three components: user, terminal, and server. The main software used includes a generative AI model.

[0275] The server functions as the core of the educational support system, receiving information provided by users. Users use a terminal to enter data such as their grade level, subjects of interest, and learning style when creating an account. This information is transmitted to the server via the terminal.

[0276] The server inputs the received user information as prompts into the generating AI model, which then generates a personalized learning plan. A specific example of a prompt would be, "Generate learning materials to strengthen English grammar for 9th graders." The generated learning plan would include specific learning materials, problem sets, and video content.

[0277] The terminal receives the learning plan returned from the server and displays it visually to the user. This process requires the terminal as an information processing device. The terminal provides an interface for the user to interact with the learning content.

[0278] Users progress through their learning according to the learning plan presented on their device. Their progress and performance are transmitted to the server in real time. The server analyzes this data and incorporates it into the next learning session to optimize the learning experience.

[0279] Furthermore, the server provides incentives based on learning outcomes to motivate learners. A typical example is displaying a "continuing learner badge" on the device for users who have achieved a certain amount of study time.

[0280] This embodiment of the invention provides an easy way to offer personalized learning plans, enabling users to learn efficiently. Furthermore, the use of a generative AI model enables flexible and responsive educational support.

[0281] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0282] Step 1:

[0283] The user installs a learning app on the terminal and creates an account. The user inputs basic information such as grade, subjects of interest, learning style, etc. This information is transmitted from the terminal to the server as input data. Specific operations of the terminal include display of an input form and transmission of data.

[0284] Step 2:

[0285] The server receives the user information from the terminal and inputs it into the generated AI model. A prompt sentence is generated, and based on its content, an individualized learning plan is generated. The server performs data analysis and data calculation by the generated AI model and outputs it as a learning plan. Specific operations include teaching materials and problem sets in the generated learning plan.

[0286] Step 3:

[0287] The server transmits the generated learning plan to the user's terminal. The output learning plan is received by the terminal and visually displayed to the user. Specific operations of the terminal include display by a notification function and user interface.

[0288] Step 4:

[0289] The user uses the terminal to select interactive learning content according to the displayed learning plan and conducts learning. At this time, the user accesses the responsible classes and problems and receives real-time feedback on the terminal. Specific operations of the terminal include downloading and displaying the selected content.

[0290] Step 5:

[0291] From the terminal, user progress data and grades are sent to the server in real time. The server receives this as input data, tracks the learner's progress, and records it. Specific operations include sending and receiving data, and saving the data.

[0292] Step 6:

[0293] The server uses the user's progress data to perform calculations to optimize the next learning session and updates the learning plan. This updated plan is then sent back to the terminal and presented to the user. The server's specific actions include data analysis and the generation of a new learning plan.

[0294] Step 7:

[0295] Users send questions that arise during learning from their device to the server. The server forwards the received questions to experts or generative AI models to generate answers. The answers are displayed to the user in a chat format on the device, thereby removing obstacles to learning. The specific actions include sending inquiries and receiving answers.

[0296] (Application Example 1)

[0297] 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."

[0298] Traditional learning systems struggle to provide personalized learning guidelines tailored to individual learners' needs, resulting in inefficient learning. Therefore, there is a need for appropriate distribution of educational resources that align with learners' interests and learning styles. Furthermore, there are challenges such as a lack of motivation based on learners' progress and insufficient support for inquiries.

[0299] 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.

[0300] In this invention, the server includes means for generating individualized learning guidelines, means for delivering educational resources according to the learner's interests and learning styles, and communication means for receiving and resolving inquiries during learning. As a result, it becomes possible to provide learners with individualized learning guidelines and to achieve efficient and effective learning.

[0301] The "individualized learning guidelines" refer to a learning plan customized based on the interests, learning styles, and progress of each learner.

[0302] The "learner's progress" refers to data obtained by evaluating and recording the degree of achievement, the level of effort, and the level of understanding during learning.

[0303] The "information processing device" refers to a device that processes, analyzes, and displays data using electronic devices such as computers and servers.

[0304] The "means for providing rewards" refers to a mechanism for giving rewards such as points and badges to improve motivation when learners achieve specific goals.

[0305] The "communication means" refers to a mechanism including a network and an interface for transmitting and receiving information.

[0306] The "means for delivering educational resources" refers to a system for delivering educational content according to the interests and needs of learners.

[0307] This invention is a system for generating individualized learning guidelines and providing an advanced learning experience based on those guidelines. The system is mainly composed of three entities: the server, the terminal, and the user.

[0308] Outline of the Program

[0309] The server first generates personalized learning guidelines using a generative AI model based on personal information received from the user—such as grade level, subjects of interest, and learning style. These guidelines include educational resources tailored to the user's interests and learning style, and are delivered to the device at the appropriate time. The server also has the function to track the user's progress and adjust the guidelines as needed.

[0310] Users access learning materials provided by the server through their device. Specific learning content is displayed on the device, allowing users to progress through their studies. Furthermore, if questions arise during learning, users can send inquiries to the server via their device and receive quick answers.

[0311] Hardware and software to be used

[0312] The server is designed to enable centralized data management and advanced computational processing. The software uses programming languages ​​such as Python and libraries for running generative AI models (e.g., TensorFlow, PyTorch). Smartphones and smart glasses are used as terminals, responsible for displaying data and interacting with the user.

[0313] Specific example

[0314] For example, suppose a first-year junior high school student is interested in mathematics. The server generates learning guidelines for this user, including videos and interactive exercises to visually teach the basics of triangles, and delivers them to the user's device.

[0315] Examples of prompts for a generative AI model:

[0316] "Please generate visually-oriented math tutorial content for first-year junior high school students."

[0317] In this way, users can experience a personalized learning environment anywhere, maximizing their learning effectiveness.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The user installs the learning application on their device and creates an account. During this process, they enter basic information such as their grade level, subjects of interest, and learning style. This information is then sent from the device to the server. The input data is user information (grade level, subjects of interest, learning style), while the output data is user information received by the server.

[0321] Step 2:

[0322] The server generates personalized learning guidelines using a generative AI model based on the user information it receives. In this process, the generative AI processes the data considering the user's interests and learning style, and outputs specific educational resources and learning plans. The input data is the user information received by the server, and the output is personalized learning guidelines.

[0323] Step 3:

[0324] The personalized learning plan generated by the server is sent to the terminal. The terminal receives it and presents it to the user. During presentation, each item of the learning plan is visually displayed and accessible. The input data is the personalized learning plan, and the output data is the learning plan displayed to the user.

[0325] Step 4:

[0326] Users access learning content through their devices and begin learning. During learning, the user's progress (e.g., learning time, number of goals achieved) is transmitted to the server in real time. Input data is a record of the user's learning activity, and output data is the user's progress transmitted to the server.

[0327] Step 5:

[0328] The server analyzes the user's progress and determines if adjustments to the learning guidelines are necessary. If so, it generates new learning guidelines and sends them to the terminal as update information. The input data is the user's progress, and the output data is the updated learning guidelines.

[0329] Step 6:

[0330] If a user has a question during the learning process, they can send a query to the server via their device. The server uses a generative AI model to quickly generate an answer and send it back to the user. The input data is the user's question, and the output data is the answer generated by the server's generative AI.

[0331] Step 7:

[0332] When a user achieves a specific learning goal, the server provides points or a ranking-based badge as a reward, which is displayed on the device. The input data is the user's learning progress, and the output data is the reward information displayed to the user.

[0333] These steps enable an effective learning experience tailored to the user's needs.

[0334] 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.

[0335] As a means of implementing this invention, an approach is provided that incorporates emotion recognition functionality into an educational support system. This system integrates a server, terminals, and an emotion engine to personalize and make the user's learning experience more effective.

[0336] First, the user installs the learning app on their device and enters personal information and learning goals during registration. This data is sent to a server via the device, and the server uses a generative AI to create an optimized learning plan, which is then returned to the device. On the device, the user selects the planned learning content and proceeds with their learning based on it.

[0337] The emotion engine is built into the device and uses cameras and sensors to analyze the user's facial expressions, voice, vital signs, etc., to detect their emotional state in real time. The server receives the information from the emotion engine, and the learning plan and content are dynamically adjusted based on the user's emotions. For example, if the user is feeling stressed during learning, the server will suggest relaxation content and interrupt the learning to calm the user down.

[0338] Furthermore, a system is incorporated that provides rewards based on learning progress. This reward system is also adjusted by data from the emotion engine to design incentives that keep users engaged and motivated to learn. For example, if a user completes a task while experiencing positive emotions, the reward is increased.

[0339] On the other hand, if a user has a question during learning, it is sent from the device to the server. The server quickly analyzes this question and returns an answer from experts or AI to the device. This allows users to resolve their questions in real time. Data from the emotion engine allows the responses to questions to be tailored to the user, making it possible to provide a more satisfying learning experience.

[0340] As described above, the integration of the emotion engine and the educational support system makes it possible to create a flexible and personalized learning environment that responds to the emotional state of each individual learner. With this system, users can learn at their own pace while receiving support tailored to their circumstances at any given time, enabling them to achieve optimal learning outcomes.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user installs the learning app on their device and creates a new account. The user enters their personal information, subjects of interest, and learning goals, and sends this information from their device to the server.

[0344] Step 2:

[0345] The server receives user information and uses generative AI to generate a personalized learning plan. The plan includes recommended learning materials, video lessons, and interactive modules, which the server then sends to the device.

[0346] Step 3:

[0347] The terminal displays the learning plan received from the server on the user screen. The user selects learning content of interest from the screen and begins learning.

[0348] Step 4:

[0349] The emotion engine is activated by the device, and it uses the user's camera and microphone to analyze facial expressions and voice data, evaluating the user's emotional state in real time.

[0350] Step 5:

[0351] The device sends the results of the emotion engine's analysis to the server. The server uses this information to adjust the difficulty level and presentation method of the learning content, providing the user with the best possible learning experience. For example, if the server determines that the user is experiencing stress, it may lower the difficulty level or recommend relaxing content.

[0352] Step 6:

[0353] As users progress through the learning process and achieve certain goals, the server calculates rewards based on their progress and emotional data. These rewards (points or badges) are displayed on the device, motivating the user. Bonus rewards may also be applied if the emotional state is positive.

[0354] Step 7:

[0355] During the learning process, users input questions and doubts via their device and send them to the server. The server then prepares quick answers from experts and AI, which are sent back to the user. Because the answers take sentiment data into account, they maximize user understanding.

[0356] Step 8:

[0357] Users can check their daily learning progress, emotional changes, and reward history through a dashboard displayed on their device. This allows them to smoothly proceed with their next learning plan while conducting self-assessments.

[0358] (Example 2)

[0359] 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".

[0360] Conventional educational support systems struggle to provide each learner with an optimal learning plan and to dynamically adjust it according to their progress and emotional state. Furthermore, they lack the technology to enable flexible plan changes based on learners' emotional states and to provide immediate answers to their questions. Therefore, there is a need to provide a comfortable learning environment while optimizing learning effectiveness.

[0361] 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.

[0362] In this invention, the server includes means for generating an individualized plan, means for analyzing emotional states, and means for dynamically adjusting the plan and content based on the analyzed emotional states. This makes it possible to provide an optimal learning environment according to the learner's emotional state and progress, thereby enhancing learning effectiveness.

[0363] "Means for generating personalized plans" refers to a device or method for creating a dedicated learning plan based on the user's individual information and goals.

[0364] "Means for tracking and recording progress" refers to a device or method for monitoring a user's learning progress and recording that information in a database or similar.

[0365] An "information processing device" refers to a computer or related device used to display learning content according to a generated plan.

[0366] "Means of providing rewards" refers to a device or method that provides incentives to users in accordance with their learning progress.

[0367] "Communication means" refers to network technologies and devices used to receive user questions and transmit necessary answers.

[0368] "Means for analyzing emotional states" refers to devices or methods that use cameras or sensors to analyze a user's emotions in real time.

[0369] "Means of dynamic adjustment" refers to a device or method that modifies and optimizes plans and learning content in real time based on analyzed emotional states.

[0370] "Means for adjusting the content of the next session based on behavioral history" refers to a device or method for analyzing past learning data and optimizing the content of the next session.

[0371] "Display means for visualizing and presenting results" refers to devices such as screens or digital interfaces that visually display the user's learning outcomes in an easy-to-understand manner.

[0372] This invention provides an educational support system that dynamically adjusts the learning plan by considering the user's emotional state in order to provide an individualized learning experience. This system utilizes a server, terminals, and an emotion engine to optimize learning effectiveness and create a more satisfying learning environment.

[0373] The server uses a generative AI model to generate personalized learning plans based on personal information and learning goals received from users. These plans are then sent to the user's device via the internet. The server utilizes a high-performance cloud infrastructure to optimize processing power.

[0374] The device is equipped with an interface for displaying learning content that the user can select. During the learning process, the device acquires the user's facial expressions, voice tone, and physical condition data in real time via its camera and sensors, and passes this data to an emotion engine. This emotion engine analyzes the data using an emotion recognition algorithm to determine the user's emotional state. This information is then sent back to the server and used to adjust the learning plan.

[0375] Users can use the system on a daily basis and learn at their own pace. For example, if a user enters a prompt such as "I'm tired, please give me some easy questions," the server can provide easier content based on sentiment data.

[0376] Specific examples of prompt messages include inputs such as "I want music that matches my current mood" or "I want to reinforce my basic math skills."

[0377] This system also features rewards based on learning progress and immediate question support, enabling users to always have an optimal learning experience tailored to their emotional state.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The user installs the learning app on their device and enters personal information and learning goals. This information is processed on the device as input data. The device formats this input as digital data and prepares it for transmission to the server.

[0381] Step 2:

[0382] The terminal sends user input data to the server in a predetermined format. The information sent to the server as output data includes the user's profile and learning goals. Upon receiving this data, the server is ready to generate an optimized learning plan based on the user's requests.

[0383] Step 3:

[0384] The server utilizes a generative AI model to analyze the received user data. It processes the user information as input using algorithms to generate a personalized learning plan based on the user's needs. This learning plan is then sent to the terminal as output data.

[0385] Step 4:

[0386] The device displays the learning plan received from the server to the user. This allows the user to begin learning based on the provided plan. Specifically, tasks and content are presented on the device's display, and learning progresses.

[0387] Step 5:

[0388] During learning, the device utilizes its camera and sensors to acquire user facial expressions, voice, and vital signs as input data. This sensor data is analyzed by an emotion engine. The emotion engine uses an emotion analysis algorithm based on the input data to generate output data that clarifies the user's emotional state.

[0389] Step 6:

[0390] The server receives emotional state data sent from the emotion engine as input and uses an algorithm to dynamically adjust the learning plan and content as needed. The adjusted learning plan is then generated as output and sent back to the device. This process optimizes the learning experience in response to the user's stress levels and decreased motivation.

[0391] Step 7:

[0392] If a user has a question during learning, they input that question as a prompt into the terminal. The terminal then sends that prompt as data to the server.

[0393] Step 8:

[0394] The server analyzes the received prompt and utilizes generative AI models and databases to generate the necessary response. After generating the response as output, the server sends the answer to the terminal. This allows the user to obtain the information they want in real time.

[0395] (Application Example 2)

[0396] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0397] Existing systems have struggled to leverage consumer emotional information to provide personalized recommendations. Furthermore, they are unable to grasp consumers' current emotional states in real time and provide flexible recommendations accordingly, highlighting the need to improve the quality of the consumer experience.

[0398] 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.

[0399] In this invention, the server includes means for analyzing the user's emotional state and dynamically adjusting suggestions, means for adjusting the content for the next interaction based on the consumer's behavioral history, and a dashboard that visualizes and presents the user's results. This enables the provision of product suggestions and reward systems optimized for consumers, resulting in a personalized and meaningful consumer experience.

[0400] An "individualized plan" is an action plan optimized based on the user's characteristics and preferences.

[0401] "User progress" refers to tracking users' activities and achievements over time.

[0402] An "information processing device" is an electrical device used to collect, analyze, and display digital data.

[0403] "Rewards" refer to incentives or benefits provided to users when they complete an action or task.

[0404] "Communication means" refers to technologies and devices for sending and receiving information via a network.

[0405] "Emotional state analysis" is a process of estimating and evaluating emotions based on the user's facial expressions, voice, and other factors.

[0406] "Dynamic adjustment" means changing the content and functions in real time according to the situation.

[0407] "Behavioral history" refers to a collection of recorded information about a user's past actions and choices.

[0408] A "dashboard" refers to an interface that visually displays information, allowing users to intuitively understand the data.

[0409] The system that implements this application incorporates a program to analyze the user's emotional state in real time and provide personalized suggestions. The terminal uses a camera and microphone to capture the user's facial expressions and voice, and analyzes their emotions using software as an emotion engine (for example, a customer service API or a voice emotion recognition API).

[0410] Based on the analysis results, the server uses a generative AI model (e.g., text generation AI) to generate optimal suggestions for the user. This process also considers past behavioral history and progress data, resulting in a more personalized consumer experience. The server then sends the generated suggestions and reward information to the user's device, presenting them visually.

[0411] For example, if a user is using a food delivery app and their facial expression or voice suggests they are tired, the system can offer a suggestion such as, "Would you like a relaxing herbal tea?" Another example of a prompt message is, "User's emotion: Wants to relax. Suggested menu: Please describe the suggestion."

[0412] By using this system, users can receive optimal suggestions tailored to their emotions, thereby improving the quality of their consumer experience.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The device uses a camera and microphone to collect the user's facial expressions and audio data. The input is the user's real-time video and audio. This data is used as input for the emotion analysis described later.

[0416] Step 2:

[0417] The device sends the collected facial and voice data to the emotion engine. The emotion engine analyzes the data and estimates the emotional state, using, for example, a customer service API or a voice emotion recognition API. The output is the estimated result regarding the user's emotions.

[0418] Step 3:

[0419] The server uses an AI model to generate personalized suggestions based on the emotional state received from the emotion engine, taking into account the user's behavioral history and progress data. The inputs are emotional state, behavioral history, and progress data, and the output is the most suitable suggestions for the user.

[0420] Step 4:

[0421] The server sends the generated suggestions to the terminal. The terminal visually displays the suggested content to the user. This includes the specific action of communicating the suggestions generated based on the prompt text to the user.

[0422] Step 5:

[0423] The user reviews the suggestion and selects an action. The user's selection is recorded as the next action history. The input is the user's selection, and the output is the updated action history data.

[0424] Step 6:

[0425] The server calculates reward information related to the user's choice and sends it back to the terminal. The terminal presents the reward information to the user and informs them of the next available reward. This includes actions that increase user motivation.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] As an embodiment of this invention, the following educational support system can be constructed.

[0443] This system provides personalized learning experiences on an educational platform and consists of three components: servers, terminals, and users.

[0444] First, the user installs the learning app on their device to use the application. The user creates an account and enters basic information such as their grade level, subjects of interest, and learning style, and this information is sent from the device to the server. Based on this information, the server uses generative AI to generate a personalized learning plan and returns it to the user's device. This plan includes specific learning materials, problem sets, videos, and other resources.

[0445] Subsequently, the user selects interactive learning content on their device according to the presented learning plan and begins learning. The device retrieves the necessary data from the server to access the selected content and displays it to the user. During learning, the user's progress and performance are transmitted to the server in real time, and an individual learning history is accumulated. Based on this, the server analyzes the user's progress and reflects it in future learning content.

[0446] Furthermore, by incorporating a reward system to encourage learning, the server provides points or ranking-based badges as rewards each time a user reaches a specific achievement goal, which are then displayed on the device to boost user motivation. For example, if a user continues to study for a certain amount of time each week, they will be awarded a "Continuous Learner Badge," which encourages them to achieve their next learning goal.

[0447] Furthermore, learners can submit questions in real time from their devices via the server if they encounter any difficulties during their learning process. Experts and AI models respond to these questions, providing quick answers to remove obstacles during learning and support smooth progress. For example, if a user finds a particular mathematical concept difficult to understand, they can enter related questions into a form and receive answers in a chat format.

[0448] In this way, a learning environment similar to individualized instruction can be provided without being restricted by time or location. This system allows users to learn at their own pace and receive necessary support quickly.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The user installs the learning app on their device and creates a new account. The user enters the necessary personal information, grade level, subjects to study, and preferred learning style, and sends this information from their device to the server.

[0452] Step 2:

[0453] The server receives user input and uses AI to generate a personalized learning plan. The generated plan includes recommended learning materials, video lessons, and interactive exercises. This plan data is then sent from the server to the user's device.

[0454] Step 3:

[0455] The device displays the received learning plan data to the user, and the user selects appropriate content to begin learning. Once the user selects content, the device sends this selection information to the server and retrieves the necessary content data.

[0456] Step 4:

[0457] The user begins learning through the learning content and materials they have selected. The device sends progress data to the server in real time, including the user's activity during learning, answer results, and learning time.

[0458] Step 5:

[0459] The server evaluates and records the user's learning progress based on the received progress data. This accumulates data that can be used to adjust future learning programs, making personalized learning more efficient.

[0460] Step 6:

[0461] Each time a user achieves a set learning goal, the server calculates points or reward badges based on their progress and sends them to the user. The device visually displays the received reward data to maintain user motivation.

[0462] Step 7:

[0463] If a user has questions or doubts during the learning process, they send those questions to the server via their device. Upon receiving the question, the server routes it to an expert or AI to obtain an appropriate answer. Once an answer is generated, the server returns it to the device and presents it to the user, facilitating quick resolution of their questions.

[0464] Step 8:

[0465] Users periodically access the server from their devices to check their past learning history, achievements, and reward information on a dashboard. This allows them to continuously evaluate themselves and receive feedback, which they can then use to plan their next learning session.

[0466] (Example 1)

[0467] 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."

[0468] Traditional educational support systems have faced challenges in adequately providing individualized learning experiences that meet the diverse needs of learners. Furthermore, they lacked features to flexibly modify learning plans according to learners' progress and to easily visualize learning outcomes. In addition, the absence of rewards to enhance learning motivation made maintaining learner motivation a challenge.

[0469] 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.

[0470] In this invention, the server includes means for generating personalized learning plans based on prompt sentences using a generative AI model, means for tracking and recording the learner's progress and behavioral history, and means for providing an interface that visually displays learning outcomes. This enables the provision of flexible and personalized learning experiences that cater to diverse learners. Furthermore, it is possible to increase motivation by providing rewards according to the learner's progress.

[0471] A "generative AI model" is a type of artificial intelligence that automatically generates the optimal solution based on input data and conditions.

[0472] A "prompt message" is the text input to a generative AI model, and it plays a role in determining the direction and content of its output.

[0473] An "individualized learning plan" is an educational program specifically designed to suit the individual learner's abilities and progress.

[0474] "Learning content" refers to a collection of materials and assignments prepared to achieve a specific educational objective.

[0475] "Information processing equipment" is a general term for electronic devices used to receive, process, and display data.

[0476] An "incentive" is a reward or motivating tool provided to encourage a learner's behavior.

[0477] "Communication equipment" is a general term for hardware and software used to send and receive data and information.

[0478] An "interface" refers to a screen or function designed to facilitate interaction between the user and the system.

[0479] This invention is realized by implementing the following educational support system. This system includes three components: user, terminal, and server. The main software used includes a generative AI model.

[0480] The server functions as the core of the educational support system, receiving information provided by users. Users use a terminal to enter data such as their grade level, subjects of interest, and learning style when creating an account. This information is transmitted to the server via the terminal.

[0481] The server inputs the received user information as prompts into the generating AI model, which then generates a personalized learning plan. A specific example of a prompt would be, "Generate learning materials to strengthen English grammar for 9th graders." The generated learning plan would include specific learning materials, problem sets, and video content.

[0482] The terminal receives the learning plan returned from the server and displays it visually to the user. This process requires the terminal as an information processing device. The terminal provides an interface for the user to interact with the learning content.

[0483] Users progress through their learning according to the learning plan presented on their device. Their progress and performance are transmitted to the server in real time. The server analyzes this data and incorporates it into the next learning session to optimize the learning experience.

[0484] Furthermore, the server provides incentives based on learning outcomes to motivate learners. A typical example is displaying a "continuing learner badge" on the device for users who have achieved a certain amount of study time.

[0485] This embodiment of the invention provides an easy way to offer personalized learning plans, enabling users to learn efficiently. Furthermore, the use of a generative AI model enables flexible and responsive educational support.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user installs the learning app on their device and creates an account. The user enters basic information such as their grade level, subjects of interest, and learning style. This information is sent from the device to the server as input data. The specific actions of the device include displaying the input form and sending the data.

[0489] Step 2:

[0490] The server receives user information from the terminal and inputs it into the generating AI model. A prompt message is generated, and based on its content, an individualized learning plan is created. The server analyzes the data and performs data calculations using the generating AI model, outputting it as a learning plan. Specifically, the generated learning plan includes learning materials and problem sets.

[0491] Step 3:

[0492] The server sends the generated learning plan to the user's device. The outputted learning plan is received by the device and displayed visually to the user. Specific actions on the device include notification functions and display via the user interface.

[0493] Step 4:

[0494] Users use their devices to select interactive learning content according to the displayed learning plan and engage in learning. During this process, users access assigned lessons and problems and receive real-time feedback on their devices. Specific device operations include downloading and displaying the selected content.

[0495] Step 5:

[0496] From the terminal, user progress data and grades are sent to the server in real time. The server receives this as input data, tracks the learner's progress, and records it. Specific operations include sending and receiving data, and saving the data.

[0497] Step 6:

[0498] The server uses the user's progress data to perform calculations to optimize the next learning session and updates the learning plan. This updated plan is then sent back to the terminal and presented to the user. The server's specific actions include data analysis and the generation of a new learning plan.

[0499] Step 7:

[0500] Users send questions that arise during learning from their device to the server. The server forwards the received questions to experts or generative AI models to generate answers. The answers are displayed to the user in a chat format on the device, thereby removing obstacles to learning. The specific actions include sending inquiries and receiving answers.

[0501] (Application Example 1)

[0502] 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."

[0503] Traditional learning systems struggle to provide personalized learning guidelines tailored to individual learners' needs, resulting in inefficient learning. Therefore, there is a need for appropriate distribution of educational resources that align with learners' interests and learning styles. Furthermore, there are challenges such as a lack of motivation based on learners' progress and insufficient support for inquiries.

[0504] 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.

[0505] In this invention, the server includes means for generating personalized learning guidelines, means for delivering educational resources tailored to the learner's interests and learning style, and communication means for receiving and resolving inquiries during learning. This makes it possible to provide learners with personalized learning guidelines, thereby enabling efficient and effective learning.

[0506] "Individualized learning guidelines" refer to learning plans that are customized based on each learner's interests, learning style, and progress.

[0507] "Learner progress" refers to data that evaluates and records the level of achievement, engagement, and understanding during learning.

[0508] An "information processing device" refers to a device that uses electronic devices such as computers and servers to process, analyze, and display data.

[0509] "Means of providing rewards" refers to a system that provides motivating rewards, such as points or badges, to learners when they achieve specific goals.

[0510] "Communication means" refers to a system that includes networks and interfaces for sending and receiving information.

[0511] "Means of delivering educational resources" refers to a system for delivering educational content tailored to learners' interests and needs.

[0512] This invention is a system that generates personalized learning guidelines and provides an advanced learning experience based on those guidelines. The system is mainly composed of three entities: a server, a terminal, and a user.

[0513] Program Overview

[0514] The server first generates personalized learning guidelines using a generative AI model based on personal information received from the user—such as grade level, subjects of interest, and learning style. These guidelines include educational resources tailored to the user's interests and learning style, and are delivered to the device at the appropriate time. The server also has the function to track the user's progress and adjust the guidelines as needed.

[0515] Users access learning materials provided by the server through their device. Specific learning content is displayed on the device, allowing users to progress through their studies. Furthermore, if questions arise during learning, users can send inquiries to the server via their device and receive quick answers.

[0516] Hardware and software to be used

[0517] The server is designed to enable centralized data management and advanced computational processing. The software uses programming languages ​​such as Python and libraries for running generative AI models (e.g., TensorFlow, PyTorch). Smartphones and smart glasses are used as terminals, responsible for displaying data and interacting with the user.

[0518] Specific example

[0519] For example, suppose a first-year junior high school student is interested in mathematics. The server generates learning guidelines for this user, including videos and interactive exercises to visually teach the basics of triangles, and delivers them to the user's device.

[0520] Examples of prompts for a generative AI model:

[0521] "Please generate visually-oriented math tutorial content for first-year junior high school students."

[0522] In this way, users can experience a personalized learning environment anywhere, maximizing their learning effectiveness.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The user installs the learning application on their device and creates an account. During this process, they enter basic information such as their grade level, subjects of interest, and learning style. This information is then sent from the device to the server. The input data is user information (grade level, subjects of interest, learning style), while the output data is user information received by the server.

[0526] Step 2:

[0527] The server generates personalized learning guidelines using a generative AI model based on the user information it receives. In this process, the generative AI processes the data considering the user's interests and learning style, and outputs specific educational resources and learning plans. The input data is the user information received by the server, and the output is personalized learning guidelines.

[0528] Step 3:

[0529] The personalized learning plan generated by the server is sent to the terminal. The terminal receives it and presents it to the user. During presentation, each item of the learning plan is visually displayed and accessible. The input data is the personalized learning plan, and the output data is the learning plan displayed to the user.

[0530] Step 4:

[0531] Users access learning content through their devices and begin learning. During learning, the user's progress (e.g., learning time, number of goals achieved) is transmitted to the server in real time. Input data is a record of the user's learning activity, and output data is the user's progress transmitted to the server.

[0532] Step 5:

[0533] The server analyzes the user's progress and determines if adjustments to the learning guidelines are necessary. If so, it generates new learning guidelines and sends them to the terminal as update information. The input data is the user's progress, and the output data is the updated learning guidelines.

[0534] Step 6:

[0535] If a user has a question during the learning process, they can send a query to the server via their device. The server uses a generative AI model to quickly generate an answer and send it back to the user. The input data is the user's question, and the output data is the answer generated by the server's generative AI.

[0536] Step 7:

[0537] When a user achieves a specific learning goal, the server provides points or a ranking-based badge as a reward, which is displayed on the device. The input data is the user's learning progress, and the output data is the reward information displayed to the user.

[0538] These steps enable an effective learning experience tailored to the user's needs.

[0539] 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.

[0540] As a means of implementing this invention, an approach is provided that incorporates emotion recognition functionality into an educational support system. This system integrates a server, terminals, and an emotion engine to personalize and make the user's learning experience more effective.

[0541] First, the user installs the learning app on their device and enters personal information and learning goals during registration. This data is sent to a server via the device, and the server uses a generative AI to create an optimized learning plan, which is then returned to the device. On the device, the user selects the planned learning content and proceeds with their learning based on it.

[0542] The emotion engine is built into the device and uses cameras and sensors to analyze the user's facial expressions, voice, vital signs, etc., to detect their emotional state in real time. The server receives the information from the emotion engine, and the learning plan and content are dynamically adjusted based on the user's emotions. For example, if the user is feeling stressed during learning, the server will suggest relaxation content and interrupt the learning to calm the user down.

[0543] Furthermore, a system is incorporated that provides rewards based on learning progress. This reward system is also adjusted by data from the emotion engine to design incentives that keep users engaged and motivated to learn. For example, if a user completes a task while experiencing positive emotions, the reward is increased.

[0544] On the other hand, if a user has a question during learning, it is sent from the device to the server. The server quickly analyzes this question and returns an answer from experts or AI to the device. This allows users to resolve their questions in real time. Data from the emotion engine allows the responses to questions to be tailored to the user, making it possible to provide a more satisfying learning experience.

[0545] As described above, the integration of the emotion engine and the educational support system makes it possible to create a flexible and personalized learning environment that responds to the emotional state of each individual learner. With this system, users can learn at their own pace while receiving support tailored to their circumstances at any given time, enabling them to achieve optimal learning outcomes.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] The user installs the learning app on their device and creates a new account. The user enters their personal information, subjects of interest, and learning goals, and sends this information from their device to the server.

[0549] Step 2:

[0550] The server receives user information and uses generative AI to generate a personalized learning plan. The plan includes recommended learning materials, video lessons, and interactive modules, which the server then sends to the device.

[0551] Step 3:

[0552] The terminal displays the learning plan received from the server on the user screen. The user selects learning content of interest from the screen and begins learning.

[0553] Step 4:

[0554] The emotion engine is activated by the device, and it uses the user's camera and microphone to analyze facial expressions and voice data, evaluating the user's emotional state in real time.

[0555] Step 5:

[0556] The device sends the results of the emotion engine's analysis to the server. The server uses this information to adjust the difficulty level and presentation method of the learning content, providing the user with the best possible learning experience. For example, if the server determines that the user is experiencing stress, it may lower the difficulty level or recommend relaxing content.

[0557] Step 6:

[0558] As users progress through the learning process and achieve certain goals, the server calculates rewards based on their progress and emotional data. These rewards (points or badges) are displayed on the device, motivating the user. Bonus rewards may also be applied if the emotional state is positive.

[0559] Step 7:

[0560] During the learning process, users input questions and doubts via their device and send them to the server. The server then prepares quick answers from experts and AI, which are sent back to the user. Because the answers take sentiment data into account, they maximize user understanding.

[0561] Step 8:

[0562] Users can check their daily learning progress, emotional changes, and reward history through a dashboard displayed on their device. This allows them to smoothly proceed with their next learning plan while conducting self-assessments.

[0563] (Example 2)

[0564] 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."

[0565] Conventional educational support systems struggle to provide each learner with an optimal learning plan and to dynamically adjust it according to their progress and emotional state. Furthermore, they lack the technology to enable flexible plan changes based on learners' emotional states and to provide immediate answers to their questions. Therefore, there is a need to provide a comfortable learning environment while optimizing learning effectiveness.

[0566] 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.

[0567] In this invention, the server includes means for generating an individualized plan, means for analyzing emotional states, and means for dynamically adjusting the plan and content based on the analyzed emotional states. This makes it possible to provide an optimal learning environment according to the learner's emotional state and progress, thereby enhancing learning effectiveness.

[0568] "Means for generating personalized plans" refers to a device or method for creating a dedicated learning plan based on the user's individual information and goals.

[0569] "Means for tracking and recording progress" refers to a device or method for monitoring a user's learning progress and recording that information in a database or similar.

[0570] An "information processing device" refers to a computer or related device used to display learning content according to a generated plan.

[0571] "Means of providing rewards" refers to a device or method that provides incentives to users in accordance with their learning progress.

[0572] "Communication means" refers to network technologies and devices used to receive user questions and transmit necessary answers.

[0573] "Means for analyzing emotional states" refers to devices or methods that use cameras or sensors to analyze a user's emotions in real time.

[0574] "Means of dynamic adjustment" refers to a device or method that modifies and optimizes plans and learning content in real time based on analyzed emotional states.

[0575] "Means for adjusting the content of the next session based on behavioral history" refers to a device or method for analyzing past learning data and optimizing the content of the next session.

[0576] "Display means for visualizing and presenting results" refers to devices such as screens or digital interfaces that visually display the user's learning outcomes in an easy-to-understand manner.

[0577] This invention provides an educational support system that dynamically adjusts the learning plan by considering the user's emotional state in order to provide an individualized learning experience. This system utilizes a server, terminals, and an emotion engine to optimize learning effectiveness and create a more satisfying learning environment.

[0578] The server uses a generative AI model to generate personalized learning plans based on personal information and learning goals received from users. These plans are then sent to the user's device via the internet. The server utilizes a high-performance cloud infrastructure to optimize processing power.

[0579] The device is equipped with an interface for displaying learning content that the user can select. During the learning process, the device acquires the user's facial expressions, voice tone, and physical condition data in real time via its camera and sensors, and passes this data to an emotion engine. This emotion engine analyzes the data using an emotion recognition algorithm to determine the user's emotional state. This information is then sent back to the server and used to adjust the learning plan.

[0580] Users can use the system on a daily basis and learn at their own pace. For example, if a user enters a prompt such as "I'm tired, please give me some easy questions," the server can provide easier content based on sentiment data.

[0581] Specific examples of prompt messages include inputs such as "I want music that matches my current mood" or "I want to reinforce my basic math skills."

[0582] This system also features rewards based on learning progress and immediate question support, enabling users to always have an optimal learning experience tailored to their emotional state.

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] The user installs the learning app on their device and enters personal information and learning goals. This information is processed on the device as input data. The device formats this input as digital data and prepares it for transmission to the server.

[0586] Step 2:

[0587] The terminal sends user input data to the server in a predetermined format. The information sent to the server as output data includes the user's profile and learning goals. Upon receiving this data, the server is ready to generate an optimized learning plan based on the user's requests.

[0588] Step 3:

[0589] The server utilizes a generative AI model to analyze the received user data. It processes the user information as input using algorithms to generate a personalized learning plan based on the user's needs. This learning plan is then sent to the terminal as output data.

[0590] Step 4:

[0591] The device displays the learning plan received from the server to the user. This allows the user to begin learning based on the provided plan. Specifically, tasks and content are presented on the device's display, and learning progresses.

[0592] Step 5:

[0593] During learning, the device utilizes its camera and sensors to acquire user facial expressions, voice, and vital signs as input data. This sensor data is analyzed by an emotion engine. The emotion engine uses an emotion analysis algorithm based on the input data to generate output data that clarifies the user's emotional state.

[0594] Step 6:

[0595] The server receives emotional state data sent from the emotion engine as input and uses an algorithm to dynamically adjust the learning plan and content as needed. The adjusted learning plan is then generated as output and sent back to the device. This process optimizes the learning experience in response to the user's stress levels and decreased motivation.

[0596] Step 7:

[0597] If a user has a question during learning, they input that question as a prompt into the terminal. The terminal then sends that prompt as data to the server.

[0598] Step 8:

[0599] The server analyzes the received prompt and utilizes generative AI models and databases to generate the necessary response. After generating the response as output, the server sends the answer to the terminal. This allows the user to obtain the information they want in real time.

[0600] (Application Example 2)

[0601] 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."

[0602] Existing systems have struggled to leverage consumer emotional information to provide personalized recommendations. Furthermore, they are unable to grasp consumers' current emotional states in real time and provide flexible recommendations accordingly, highlighting the need to improve the quality of the consumer experience.

[0603] 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.

[0604] In this invention, the server includes means for analyzing the user's emotional state and dynamically adjusting suggestions, means for adjusting the content for the next interaction based on the consumer's behavioral history, and a dashboard that visualizes and presents the user's results. This enables the provision of product suggestions and reward systems optimized for consumers, resulting in a personalized and meaningful consumer experience.

[0605] An "individualized plan" is an action plan optimized based on the user's characteristics and preferences.

[0606] "User progress" refers to tracking users' activities and achievements over time.

[0607] An "information processing device" is an electrical device used to collect, analyze, and display digital data.

[0608] "Rewards" refer to incentives or benefits provided to users when they complete an action or task.

[0609] "Communication means" refers to technologies and devices for sending and receiving information via a network.

[0610] "Emotional state analysis" is a process of estimating and evaluating emotions based on the user's facial expressions, voice, and other factors.

[0611] "Dynamic adjustment" means changing the content and functions in real time according to the situation.

[0612] "Behavioral history" refers to a collection of recorded information about a user's past actions and choices.

[0613] A "dashboard" refers to an interface that visually displays information, allowing users to intuitively understand the data.

[0614] The system that implements this application incorporates a program to analyze the user's emotional state in real time and provide personalized suggestions. The terminal uses a camera and microphone to capture the user's facial expressions and voice, and analyzes their emotions using software as an emotion engine (for example, a customer service API or a voice emotion recognition API).

[0615] Based on the analysis results, the server uses a generative AI model (e.g., text generation AI) to generate optimal suggestions for the user. This process also considers past behavioral history and progress data, resulting in a more personalized consumer experience. The server then sends the generated suggestions and reward information to the user's device, presenting them visually.

[0616] For example, if a user is using a food delivery app and their facial expression or voice suggests they are tired, the system can offer a suggestion such as, "Would you like a relaxing herbal tea?" Another example of a prompt message is, "User's emotion: Wants to relax. Suggested menu: Please describe the suggestion."

[0617] By using this system, users can receive optimal suggestions tailored to their emotions, thereby improving the quality of their consumer experience.

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The device uses a camera and microphone to collect the user's facial expressions and audio data. The input is the user's real-time video and audio. This data is used as input for the emotion analysis described later.

[0621] Step 2:

[0622] The device sends the collected facial and voice data to the emotion engine. The emotion engine analyzes the data and estimates the emotional state, for example, using a customer service API or a voice emotion recognition API. The output is the estimated result regarding the user's emotions.

[0623] Step 3:

[0624] The server uses an AI model to generate personalized suggestions based on the emotional state received from the emotion engine, taking into account the user's behavioral history and progress data. The inputs are emotional state, behavioral history, and progress data, and the output is the most suitable suggestions for the user.

[0625] Step 4:

[0626] The server sends the generated suggestions to the terminal. The terminal visually displays the suggested content to the user. This includes the specific action of communicating the suggestions generated based on the prompt text to the user.

[0627] Step 5:

[0628] The user reviews the suggestion and selects an action. The user's selection is recorded as the next action history. The input is the user's selection, and the output is the updated action history data.

[0629] Step 6:

[0630] The server calculates reward information related to the user's choice and sends it back to the terminal. The terminal presents the reward information to the user and informs them of the next available reward. This includes actions that increase user motivation.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] [Fourth Embodiment]

[0635] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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".

[0648] As an embodiment of this invention, the following educational support system can be constructed.

[0649] This system provides personalized learning experiences on an educational platform and consists of three components: servers, terminals, and users.

[0650] First, the user installs the learning app on their device to use the application. The user creates an account and enters basic information such as their grade level, subjects of interest, and learning style, and this information is sent from the device to the server. Based on this information, the server uses generative AI to generate a personalized learning plan and returns it to the user's device. This plan includes specific learning materials, problem sets, videos, and other resources.

[0651] Subsequently, the user selects interactive learning content on their device according to the presented learning plan and begins learning. The device retrieves the necessary data from the server to access the selected content and displays it to the user. During learning, the user's progress and performance are transmitted to the server in real time, and an individual learning history is accumulated. Based on this, the server analyzes the user's progress and reflects it in future learning content.

[0652] Furthermore, by incorporating a reward system to encourage learning, the server provides points or ranking-based badges as rewards each time a user reaches a specific achievement goal, which are then displayed on the device to boost user motivation. For example, if a user continues to study for a certain amount of time each week, they will be awarded a "Continuous Learner Badge," which encourages them to achieve their next learning goal.

[0653] Furthermore, learners can submit questions in real time from their devices via the server if they encounter any difficulties during their learning process. Experts and AI models respond to these questions, providing quick answers to remove obstacles during learning and support smooth progress. For example, if a user finds a particular mathematical concept difficult to understand, they can enter related questions into a form and receive answers in a chat format.

[0654] In this way, a learning environment similar to individualized instruction can be provided without being restricted by time or location. This system allows users to learn at their own pace and receive necessary support quickly.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The user installs the learning app on their device and creates a new account. The user enters the necessary personal information, grade level, subjects to study, and preferred learning style, and sends this information from their device to the server.

[0658] Step 2:

[0659] The server receives user input and uses AI to generate a personalized learning plan. The generated plan includes recommended learning materials, video lessons, and interactive exercises. This plan data is then sent from the server to the user's device.

[0660] Step 3:

[0661] The device displays the received learning plan data to the user, and the user selects appropriate content to begin learning. Once the user selects content, the device sends this selection information to the server and retrieves the necessary content data.

[0662] Step 4:

[0663] The user begins learning through the learning content and materials they have selected. The device sends progress data, such as the user's activity, answers, and learning time, to the server in real time.

[0664] Step 5:

[0665] The server evaluates and records the user's learning progress based on the received progress data. This accumulates data that can be used to adjust future learning programs, making personalized learning more efficient.

[0666] Step 6:

[0667] Each time a user achieves a set learning goal, the server calculates points or reward badges based on their progress and sends them to the user. The device visually displays the received reward data to maintain user motivation.

[0668] Step 7:

[0669] If a user has questions or doubts during the learning process, they send those questions to the server via their device. Upon receiving the question, the server routes it to an expert or AI to obtain an appropriate answer. Once an answer is generated, the server returns it to the device and presents it to the user, facilitating quick resolution of their questions.

[0670] Step 8:

[0671] Users periodically access the server from their devices to check their past learning history, achievements, and reward information on a dashboard. This allows them to continuously evaluate themselves and receive feedback, which they can then use to inform their next learning plan.

[0672] (Example 1)

[0673] 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".

[0674] Traditional educational support systems have faced challenges in adequately providing individualized learning experiences that meet the diverse needs of learners. Furthermore, they lacked features to flexibly modify learning plans according to learners' progress and to easily visualize learning outcomes. In addition, the lack of rewards to enhance learning motivation made maintaining learner motivation a challenge.

[0675] 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.

[0676] In this invention, the server includes means for generating personalized learning plans based on prompt sentences using a generative AI model, means for tracking and recording the learner's progress and behavioral history, and means for providing an interface that visually displays learning outcomes. This enables the provision of flexible and personalized learning experiences that cater to diverse learners. Furthermore, it is possible to increase motivation by providing rewards according to the learner's progress.

[0677] A "generative AI model" is a type of artificial intelligence that automatically generates the optimal solution based on input data and conditions.

[0678] A "prompt message" is the text input to a generative AI model, and it plays a role in determining the direction and content of its output.

[0679] An "individualized learning plan" is an educational program specifically designed to suit the individual learner's abilities and progress.

[0680] "Learning content" refers to a collection of materials and assignments prepared to achieve a specific educational objective.

[0681] "Information processing equipment" is a general term for electronic devices used to receive, process, and display data.

[0682] An "incentive" is a reward or motivating tool provided to encourage a learner's behavior.

[0683] "Communication equipment" is a general term for hardware and software used to send and receive data and information.

[0684] An "interface" refers to a screen or function designed to facilitate interaction between the user and the system.

[0685] This invention is realized by implementing the following educational support system. This system includes three components: user, terminal, and server. The main software used includes a generative AI model.

[0686] The server functions as the core of the educational support system, receiving information provided by users. Users use a terminal to enter data such as their grade level, subjects of interest, and learning style when creating an account. This information is transmitted to the server via the terminal.

[0687] The server inputs the received user information as prompts into the generating AI model, which then generates a personalized learning plan. A specific example of a prompt would be, "Generate learning materials to strengthen English grammar for 9th graders." The generated learning plan would include specific learning materials, problem sets, and video content.

[0688] The terminal receives the learning plan returned from the server and displays it visually to the user. This process requires the terminal as an information processing device. The terminal provides an interface for the user to interact with the learning content.

[0689] Users progress through their learning according to the learning plan presented on their device. Their progress and performance are transmitted to the server in real time. The server analyzes this data and incorporates it into the next learning session to optimize the learning experience.

[0690] Furthermore, the server provides incentives based on learning outcomes to motivate learners. A typical example is displaying a "continuing learner badge" on the device for users who have achieved a certain amount of study time.

[0691] This embodiment of the invention provides an easy way to offer personalized learning plans, enabling users to learn efficiently. Furthermore, the use of a generative AI model enables flexible and responsive educational support.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user installs the learning app on their device and creates an account. The user enters basic information such as their grade level, subjects of interest, and learning style. This information is sent from the device to the server as input data. The specific actions of the device include displaying the input form and sending the data.

[0695] Step 2:

[0696] The server receives user information from the terminal and inputs it into the generating AI model. A prompt message is generated, and based on its content, an individualized learning plan is created. The server analyzes the data and performs data calculations using the generating AI model, outputting it as a learning plan. Specifically, the generated learning plan includes learning materials and problem sets.

[0697] Step 3:

[0698] The server sends the generated learning plan to the user's device. The outputted learning plan is received by the device and displayed visually to the user. Specific actions on the device include notification functions and display via the user interface.

[0699] Step 4:

[0700] Users use their devices to select interactive learning content according to the displayed learning plan and engage in learning. During this process, users access assigned lessons and problems and receive real-time feedback on their devices. Specific device operations include downloading and displaying the selected content.

[0701] Step 5:

[0702] From the terminal, user progress data and grades are sent to the server in real time. The server receives this as input data, tracks the learner's progress, and records it. Specific operations include sending and receiving data, and saving the data.

[0703] Step 6:

[0704] The server uses the user's progress data to perform calculations to optimize the next learning session and updates the learning plan. This updated plan is then sent back to the terminal and presented to the user. The server's specific actions include data analysis and the generation of a new learning plan.

[0705] Step 7:

[0706] Users send questions that arise during learning from their device to the server. The server forwards the received questions to experts or generative AI models to generate answers. The answers are displayed to the user in a chat format on the device, thereby removing obstacles to learning. The specific actions include sending inquiries and receiving answers.

[0707] (Application Example 1)

[0708] 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".

[0709] Traditional learning systems struggle to provide personalized learning guidelines tailored to individual learners' needs, resulting in inefficient learning. Therefore, there is a need for appropriate distribution of educational resources that align with learners' interests and learning styles. Furthermore, there are challenges such as a lack of motivation based on learners' progress and insufficient support for inquiries.

[0710] 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.

[0711] In this invention, the server includes means for generating personalized learning guidelines, means for delivering educational resources tailored to the learner's interests and learning style, and communication means for receiving and resolving inquiries during learning. This makes it possible to provide learners with personalized learning guidelines, thereby enabling efficient and effective learning.

[0712] "Individualized learning guidelines" refer to learning plans that are customized based on each learner's interests, learning style, and progress.

[0713] "Learner progress" refers to data that evaluates and records the level of achievement, engagement, and understanding during learning.

[0714] An "information processing device" refers to a device that uses electronic devices such as computers and servers to process, analyze, and display data.

[0715] "Means of providing rewards" refers to a system that provides motivating rewards, such as points or badges, to learners when they achieve specific goals.

[0716] "Communication means" refers to a system that includes networks and interfaces for sending and receiving information.

[0717] "Means of delivering educational resources" refers to a system for delivering educational content tailored to learners' interests and needs.

[0718] This invention is a system that generates personalized learning guidelines and provides an advanced learning experience based on those guidelines. The system is mainly composed of three entities: a server, a terminal, and a user.

[0719] Program Overview

[0720] The server first generates personalized learning guidelines using a generative AI model based on personal information received from the user—such as grade level, subjects of interest, and learning style. These guidelines include educational resources tailored to the user's interests and learning style, and are delivered to the device at the appropriate time. The server also has the function to track the user's progress and adjust the guidelines as needed.

[0721] Users access learning materials provided by the server through their device. Specific learning content is displayed on the device, allowing users to progress through their studies. Furthermore, if questions arise during learning, users can send inquiries to the server via their device and receive quick answers.

[0722] Hardware and software to be used

[0723] The server is designed to enable centralized data management and advanced computational processing. The software uses programming languages ​​such as Python and libraries for running generative AI models (e.g., TensorFlow, PyTorch). Smartphones and smart glasses are used as terminals, responsible for displaying data and interacting with the user.

[0724] Specific example

[0725] For example, suppose a first-year junior high school student is interested in mathematics. The server generates learning guidelines for this user, including videos and interactive exercises to visually teach the basics of triangles, and delivers them to the user's device.

[0726] Examples of prompts for a generative AI model:

[0727] "Please generate visually-oriented math tutorial content for first-year junior high school students."

[0728] In this way, users can experience a personalized learning environment anywhere, maximizing their learning effectiveness.

[0729] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0730] Step 1:

[0731] The user installs the learning application on their device and creates an account. During this process, they enter basic information such as their grade level, subjects of interest, and learning style. This information is then sent from the device to the server. The input data is user information (grade level, subjects of interest, learning style), while the output data is user information received by the server.

[0732] Step 2:

[0733] The server generates personalized learning guidelines using a generative AI model based on the user information it receives. In this process, the generative AI processes the data considering the user's interests and learning style, and outputs specific educational resources and learning plans. The input data is the user information received by the server, and the output is personalized learning guidelines.

[0734] Step 3:

[0735] The personalized learning plan generated by the server is sent to the terminal. The terminal receives it and presents it to the user. During presentation, each item of the learning plan is visually displayed and accessible. The input data is the personalized learning plan, and the output data is the learning plan displayed to the user.

[0736] Step 4:

[0737] Users access learning content through their devices and begin learning. During learning, the user's progress (e.g., learning time, number of goals achieved) is transmitted to the server in real time. Input data is a record of the user's learning activity, and output data is the user's progress transmitted to the server.

[0738] Step 5:

[0739] The server analyzes the user's progress and determines if adjustments to the learning guidelines are necessary. If so, it generates new learning guidelines and sends them to the terminal as update information. The input data is the user's progress, and the output data is the updated learning guidelines.

[0740] Step 6:

[0741] If a user has a question during the learning process, they can send a query to the server via their device. The server uses a generative AI model to quickly generate an answer and send it back to the user. The input data is the user's question, and the output data is the answer generated by the server's generative AI.

[0742] Step 7:

[0743] When a user achieves a specific learning goal, the server provides points or a ranking-based badge as a reward, which is displayed on the device. The input data is the user's learning progress, and the output data is the reward information displayed to the user.

[0744] These steps enable an effective learning experience tailored to the user's needs.

[0745] 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.

[0746] As a means of implementing this invention, an approach is provided that incorporates emotion recognition functionality into an educational support system. This system integrates a server, terminals, and an emotion engine to personalize and make the user's learning experience more effective.

[0747] First, the user installs the learning app on their device and enters personal information and learning goals during registration. This data is sent to a server via the device, and the server uses a generative AI to create an optimized learning plan, which is then returned to the device. On the device, the user selects the planned learning content and proceeds with their learning based on it.

[0748] The emotion engine is built into the device and uses cameras and sensors to analyze the user's facial expressions, voice, vital signs, etc., to detect their emotional state in real time. The server receives the information from the emotion engine, and the learning plan and content are dynamically adjusted based on the user's emotions. For example, if the user is feeling stressed during learning, the server will suggest relaxation content and interrupt the learning to calm the user down.

[0749] Furthermore, a system is incorporated that provides rewards based on learning progress. This reward system is also adjusted by data from the emotion engine to design incentives that keep users engaged and motivated to learn. For example, if a user completes a task while experiencing positive emotions, the reward is increased.

[0750] On the other hand, if a user has a question during learning, it is sent from the device to the server. The server quickly analyzes this question and returns an answer from experts or AI to the device. This allows users to resolve their questions in real time. Data from the emotion engine allows the responses to questions to be tailored to the user, making it possible to provide a more satisfying learning experience.

[0751] As described above, the integration of the emotion engine and the educational support system makes it possible to create a flexible and personalized learning environment that responds to the emotional state of each individual learner. With this system, users can learn at their own pace while receiving support tailored to their circumstances at any given time, enabling them to achieve optimal learning outcomes.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] The user installs the learning app on their device and creates a new account. The user enters their personal information, subjects of interest, and learning goals, and sends this information from their device to the server.

[0755] Step 2:

[0756] The server receives user information and uses generative AI to generate a personalized learning plan. The plan includes recommended learning materials, video lessons, and interactive modules, which the server then sends to the device.

[0757] Step 3:

[0758] The terminal displays the learning plan received from the server on the user screen. The user selects learning content of interest from the screen and begins learning.

[0759] Step 4:

[0760] The emotion engine is activated by the device, and it uses the user's camera and microphone to analyze facial expressions and voice data, evaluating the user's emotional state in real time.

[0761] Step 5:

[0762] The device sends the results of the emotion engine's analysis to the server. The server uses this information to adjust the difficulty level and presentation method of the learning content, providing the user with the best possible learning experience. For example, if the server determines that the user is experiencing stress, it may lower the difficulty level or recommend relaxing content.

[0763] Step 6:

[0764] As users progress through the learning process and achieve certain goals, the server calculates rewards based on their progress and emotional data. These rewards (points or badges) are displayed on the device, motivating the user. Bonus rewards may also be applied if the emotional state is positive.

[0765] Step 7:

[0766] During the learning process, users input questions and doubts via their device and send them to the server. The server then prepares quick answers from experts and AI, which are sent back to the user. Because the answers take sentiment data into account, they maximize user understanding.

[0767] Step 8:

[0768] Users can check their daily learning progress, emotional changes, and reward history through a dashboard displayed on their device. This allows them to smoothly proceed with their next learning plan while conducting self-assessments.

[0769] (Example 2)

[0770] 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".

[0771] Conventional educational support systems struggle to provide each learner with an optimal learning plan and to dynamically adjust it according to their progress and emotional state. Furthermore, they lack the technology to enable flexible plan changes based on learners' emotional states and to provide immediate answers to their questions. Therefore, there is a need to provide a comfortable learning environment while optimizing learning effectiveness.

[0772] 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.

[0773] In this invention, the server includes means for generating an individualized plan, means for analyzing emotional states, and means for dynamically adjusting the plan and content based on the analyzed emotional states. This makes it possible to provide an optimal learning environment according to the learner's emotional state and progress, thereby enhancing learning effectiveness.

[0774] "Means for generating personalized plans" refers to a device or method for creating a dedicated learning plan based on the user's individual information and goals.

[0775] "Means for tracking and recording progress" refers to a device or method for monitoring a user's learning progress and recording that information in a database or similar.

[0776] An "information processing device" refers to a computer or related device used to display learning content according to a generated plan.

[0777] "Means of providing rewards" refers to a device or method that provides incentives to users in accordance with their learning progress.

[0778] "Communication means" refers to network technologies and devices used to receive user questions and transmit necessary answers.

[0779] "Means for analyzing emotional states" refers to devices or methods that use cameras or sensors to analyze a user's emotions in real time.

[0780] "Means of dynamic adjustment" refers to a device or method that modifies and optimizes plans and learning content in real time based on analyzed emotional states.

[0781] "Means for adjusting the content of the next session based on behavioral history" refers to a device or method for analyzing past learning data and optimizing the content of the next session.

[0782] "Display means for visualizing and presenting results" refers to devices such as screens or digital interfaces that visually display the user's learning outcomes in an easy-to-understand manner.

[0783] This invention provides an educational support system that dynamically adjusts the learning plan by considering the user's emotional state in order to provide an individualized learning experience. This system utilizes a server, terminals, and an emotion engine to optimize learning effectiveness and create a more satisfying learning environment.

[0784] The server uses a generative AI model to generate personalized learning plans based on personal information and learning goals received from users. These plans are then sent to the user's device via the internet. The server utilizes a high-performance cloud infrastructure to optimize processing power.

[0785] The device is equipped with an interface for displaying learning content that the user can select. During the learning process, the device acquires the user's facial expressions, voice tone, and physical condition data in real time via its camera and sensors, and passes this data to an emotion engine. This emotion engine analyzes the data using an emotion recognition algorithm to determine the user's emotional state. This information is then sent back to the server and used to adjust the learning plan.

[0786] Users can use the system on a daily basis and learn at their own pace. For example, if a user enters a prompt such as "I'm tired, please give me some easy questions," the server can provide easier content based on sentiment data.

[0787] Specific examples of prompt messages include inputs such as "I want music that matches my current mood" or "I want to reinforce my basic math skills."

[0788] This system also features rewards based on learning progress and immediate question support, enabling users to always have an optimal learning experience tailored to their emotional state.

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The user installs the learning app on their device and enters personal information and learning goals. This information is processed on the device as input data. The device formats this input as digital data and prepares it for transmission to the server.

[0792] Step 2:

[0793] The terminal sends user input data to the server in a predetermined format. The information sent to the server as output data includes the user's profile and learning goals. Upon receiving this data, the server is ready to generate an optimized learning plan based on the user's requests.

[0794] Step 3:

[0795] The server utilizes a generative AI model to analyze the received user data. It processes the user information as input using algorithms to generate a personalized learning plan based on the user's needs. This learning plan is then sent to the terminal as output data.

[0796] Step 4:

[0797] The device displays the learning plan received from the server to the user. This allows the user to begin learning based on the provided plan. Specifically, tasks and content are presented on the device's display, and learning progresses.

[0798] Step 5:

[0799] During learning, the device utilizes its camera and sensors to acquire user facial expressions, voice, and vital signs as input data. This sensor data is analyzed by an emotion engine. The emotion engine uses an emotion analysis algorithm based on the input data to generate output data that clarifies the user's emotional state.

[0800] Step 6:

[0801] The server receives emotional state data sent from the emotion engine as input and uses an algorithm to dynamically adjust the learning plan and content as needed. The adjusted learning plan is then generated as output and sent back to the device. This process optimizes the learning experience in response to the user's stress levels and decreased motivation.

[0802] Step 7:

[0803] If a user has a question during learning, they input that question as a prompt into the terminal. The terminal then sends that prompt as data to the server.

[0804] Step 8:

[0805] The server analyzes the received prompt and utilizes generative AI models and databases to generate the necessary response. After generating the response as output, the server sends the answer to the terminal. This allows the user to obtain the information they want in real time.

[0806] (Application Example 2)

[0807] 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".

[0808] Existing systems have struggled to leverage consumer emotional information to provide personalized recommendations. Furthermore, they are unable to grasp consumers' current emotional states in real time and provide flexible recommendations accordingly, highlighting the need to improve the quality of the consumer experience.

[0809] 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.

[0810] In this invention, the server includes means for analyzing the user's emotional state and dynamically adjusting suggestions, means for adjusting the content for the next interaction based on the consumer's behavioral history, and a dashboard that visualizes and presents the user's results. This enables the provision of product suggestions and reward systems optimized for consumers, resulting in a personalized and meaningful consumer experience.

[0811] An "individualized plan" is an action plan optimized based on the user's characteristics and preferences.

[0812] "User progress" refers to tracking users' activities and achievements over time.

[0813] An "information processing device" is an electrical device used to collect, analyze, and display digital data.

[0814] "Rewards" refer to incentives or benefits provided to users when they complete an action or task.

[0815] "Communication means" refers to technologies and devices for sending and receiving information via a network.

[0816] "Emotional state analysis" is a process of estimating and evaluating emotions based on the user's facial expressions, voice, and other factors.

[0817] "Dynamic adjustment" means changing the content and functions in real time according to the situation.

[0818] "Behavioral history" refers to a collection of recorded information about a user's past actions and choices.

[0819] A "dashboard" refers to an interface that visually displays information, allowing users to intuitively understand the data.

[0820] The system that implements this application incorporates a program to analyze the user's emotional state in real time and provide personalized suggestions. The terminal uses a camera and microphone to capture the user's facial expressions and voice, and analyzes their emotions using software as an emotion engine (for example, a customer service API or a voice emotion recognition API).

[0821] Based on the analysis results, the server uses a generative AI model (e.g., text generation AI) to generate optimal suggestions for the user. This process also considers past behavioral history and progress data, resulting in a more personalized consumer experience. The server then sends the generated suggestions and reward information to the user's device, presenting them visually.

[0822] For example, if a user is using a food delivery app and their facial expression or voice suggests they are tired, the system can offer a suggestion such as, "Would you like a relaxing herbal tea?" Another example of a prompt message is, "User's emotion: Wants to relax. Suggested menu: Please describe the suggestion."

[0823] By using this system, users can receive optimal suggestions tailored to their emotions, thereby improving the quality of their consumer experience.

[0824] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0825] Step 1:

[0826] The device uses a camera and microphone to collect the user's facial expressions and audio data. The input is the user's real-time video and audio. This data is used as input for the emotion analysis described later.

[0827] Step 2:

[0828] The device sends the collected facial and voice data to the emotion engine. The emotion engine analyzes the data and estimates the emotional state, using, for example, a customer service API or a voice emotion recognition API. The output is the estimated result regarding the user's emotions.

[0829] Step 3:

[0830] The server uses an AI model to generate personalized suggestions based on the emotional state received from the emotion engine, taking into account the user's behavioral history and progress data. The inputs are emotional state, behavioral history, and progress data, and the output is the most suitable suggestions for the user.

[0831] Step 4:

[0832] The server sends the generated suggestions to the terminal. The terminal visually displays the suggested content to the user. This includes the specific action of communicating the suggestions generated based on the prompt text to the user.

[0833] Step 5:

[0834] The user reviews the suggestion and selects an action. The user's selection is recorded as the next action history. The input is the user's selection, and the output is the updated action history data.

[0835] Step 6:

[0836] The server calculates reward information related to the user's choice and sends it back to the terminal. The terminal presents the reward information to the user and informs them of the next available reward. This includes actions that increase user motivation.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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."

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] The following is further disclosed regarding the embodiments described above.

[0859] (Claim 1)

[0860] A means for generating an individualized learning plan,

[0861] Means for tracking and recording learners' progress,

[0862] An information processing device for presenting learning content based on a generated learning plan,

[0863] A means of providing rewards according to learning progress,

[0864] A means of communication for receiving and resolving questions during learning,

[0865] An educational support system that includes this.

[0866] (Claim 2)

[0867] The educational support system according to claim 1, comprising means for adjusting the content of the next learning session based on the learner's behavioral history.

[0868] (Claim 3)

[0869] The educational support system according to claim 1, comprising a dashboard that visualizes and presents learning outcomes to learners.

[0870] "Example 1"

[0871] (Claim 1)

[0872] A means for generating an individualized learning plan based on prompt sentences using a generative AI model,

[0873] Means for tracking and recording learners' progress,

[0874] Information processing equipment for providing learning content based on a generated learning plan,

[0875] A means of providing rewards as incentives according to learning progress,

[0876] A communication device for receiving and resolving questions and doubts during learning,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising means for optimizing the content of the next learning session based on the learner's past learning behavior.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising an interface that visually displays learning outcomes.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] A means of generating individualized learning guidelines,

[0885] Means for tracking and recording learners' progress,

[0886] An information processing device for presenting learning items based on generated learning guidelines,

[0887] A means of providing rewards according to learning progress,

[0888] A means of communication for receiving and resolving inquiries during learning,

[0889] A means of delivering educational resources tailored to learners' interests and learning styles,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for adjusting the next learning items based on the learner's behavior history.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising a display device for visualizing and presenting learning outcomes to learners.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] Means for generating individualized plans,

[0898] Means for tracking and recording user progress,

[0899] An information processing device for presenting content based on the generated plan,

[0900] A means of providing rewards according to progress,

[0901] A means of communication for receiving and resolving questions,

[0902] A means of analyzing emotional states,

[0903] A means of dynamically adjusting plans and content based on analyzed emotional states,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, comprising means for adjusting the content of the next session based on the user's behavior history.

[0907] (Claim 3)

[0908] The system according to claim 1, including a display means for visualizing and presenting results to the user.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] Means for generating individualized plans,

[0912] Means for tracking and recording user progress,

[0913] An information processing device for presenting content based on the generated plan,

[0914] A means of providing rewards according to progress,

[0915] A means of communication to receive and resolve questions during use,

[0916] A means of dynamically adjusting suggestions by analyzing the user's emotional state,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising means for adjusting the content of the next session based on the user's behavior history.

[0920] (Claim 3)

[0921] The system according to claim 1, including a dashboard that visualizes and presents the user's results. [Explanation of Symbols]

[0922] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating an individualized learning plan, Means for tracking and recording learners' progress, An information processing device for presenting learning content based on a generated learning plan, A means of providing rewards according to learning progress, A means of communication for receiving and resolving questions during learning, An educational support system that includes this.

2. The educational support system according to claim 1, comprising means for adjusting the content of the next learning session based on the learner's behavioral history.

3. The educational support system according to claim 1, comprising a dashboard that visualizes and presents learning outcomes to learners.

Images