system

Abstract

We provide the system. [Solution] A means for generating a customized learning program based on the user's learning progress and goals, A means of calculating and notifying the optimal review timing based on the forgetting curve, A means of analyzing users' learning progress and providing information to maintain feedback and motivation, To streamline financial transactions, a means of managing electronic transactions linked to learning plans, A system that includes means for implementing a reward system to provide compensation based on progress.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in various qualification exams and entrance exams, many examinees lack self-management ability, and there is a problem that they cannot find correct learning strategies and effective review timing, and thus cannot learn efficiently. In addition, there is also a problem that progress is difficult to visualize and it is difficult to maintain motivation.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates customized learning plans for each user based on information about the user's learning. Furthermore, it solves problems by optimizing review timing based on the forgetting curve, monitoring learning progress in real time, and providing feedback and encouraging messages tailored to the user's situation. This system utilizes long-term learning data to provide personalized advice to the user.

[0006] "User learning progress" refers to a collection of data that shows the user's progress and level of achievement in learning.

[0007] A "learning plan" is a schedule or collection of tasks that systematically arranges the learning activities necessary to achieve a specific goal.

[0008] The "forgetting curve" is a mathematical model that illustrates how humans forget learned information over time.

[0009] "Review timing" refers to the optimal time or interval for reviewing what has been learned.

[0010] "Feedback" refers to information provided as a response to or evaluation of a particular action or outcome.

[0011] A "motivational message" is a message of encouragement and motivation that helps learners continue to work towards their goals.

[0012] "Personalized advice" refers to individualized guidance and suggestions provided based on the characteristics and circumstances of each user. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (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.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including 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), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The present invention is an AI-assisted learning platform that supports users who have set learning goals in effectively advancing their learning. Users first input their learning goals, current skill level, and available learning time via a terminal. This information is transmitted to a server and used as basic data for further processing.

[0035] Generating a learning plan

[0036] Based on the user data received, the server generates a learning plan necessary to achieve the user's set goals. This generation utilizes historical databases and machine learning algorithms to enable the most effective task allocation. For example, if a user inputs that they want to improve their score on a business English exam, the server will create a schedule that divides tasks such as vocabulary memorization, listening practice, and mock exams into weekly assignments to help them achieve their target score.

[0037] Calculating the timing of review

[0038] To ensure that learned material is retained for a long period, the server calculates the optimal timing for reviewing each learning item based on the forgetting curve. Specifically, the server analyzes learning data and sends notifications to the user immediately after learning, one day later, and one week later to provide them with opportunities to review.

[0039] Provide feedback

[0040] As users record their progress on their devices, this data is transferred to a server. The server analyzes this data and provides feedback based on their level of achievement and understanding. If progress is stalled, the server generates encouraging messages to motivate the user to continue learning.

[0041] Personalized advice

[0042] Based on the user's learning outcomes and history, the server performs long-term analysis and generates personalized advice based on the insights gained. For example, if a user repeatedly experiences problems in a particular area, the server will provide additional learning resources or rescheduled tasks tailored to that area.

[0043] In this way, the system of the present invention can provide customized learning support for each user, making the process of achieving goals meaningful.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters their learning goals, skill level, and available study time via their device. The device then sends this information to the server.

[0047] Step 2:

[0048] The server analyzes the received user information and generates a customized learning plan based on the user's set learning goals. The server creates this plan based on information from the database and past performance, and sends it to the terminal.

[0049] Step 3:

[0050] The user begins learning using a device and records their progress. The device sends the recorded data to the server in real time. The server analyzes the received data to understand the user's progress.

[0051] Step 4:

[0052] The server calculates the timing for review based on the forgetting curve. This calculation takes into account the nature of the learned material and past review history. The server then notifies the user via their device at the appropriate time to review.

[0053] Step 5:

[0054] The server analyzes the user's progress and generates feedback based on their learning achievements. It also simultaneously generates encouraging messages to maintain motivation for learning. The generated feedback and messages are sent to the device and displayed to the user.

[0055] Step 6:

[0056] The server accumulates long-term learning data from users and generates personalized advice based on that data. Based on the analysis results, the server suggests the next learning steps and notifies the user through their device.

[0057] (Example 1)

[0058] 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."

[0059] In today's educational environment, there is a need to provide effective learning methods tailored to the individual goals and skill levels of each learner. However, creating individualized and optimized learning plans is a laborious process, and learners face challenges in determining the optimal timing for review and receiving feedback on their learning progress.

[0060] 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.

[0061] In this invention, the server includes means for receiving progress, goals, and abilities entered by the user via an information processing device and generating an individualized learning plan; means for creating and presenting an optimal learning plan based on past information and machine learning algorithms; and means for calculating an appropriate review period considering the forgetting curve and notifying the user using an instruction device. This enables personalized and efficient learning support for individual learners, as well as optimal review and feedback.

[0062] An "information processing device" refers to a system for inputting, processing, storing, and outputting data, and is a device that enables two-way communication with the user.

[0063] A "personalized learning plan" is a specific schedule and action plan designed to support a user's customized learning progress, based on their specific goals, skill level, and learning pace.

[0064] The "review period" refers to the optimal time to relearn what you've studied in order to maximize its retention in your memory.

[0065] A "display device" is a device used to display or notify a user of information, and typically provides visual or audio output.

[0066] "Learning progress" is an indicator that shows the progress of a user's learning activities, and includes information such as the degree of achievement and understanding.

[0067] "Feedback" refers to the act of providing evaluations and comments on a user's learning activities and results, and giving information about improvements and next actions.

[0068] A "machine learning algorithm" is a set of techniques that enable the automation of predictions and decision-making by learning patterns from large amounts of data.

[0069] "Personalized learning recommendations" refer to the provision of specific advice and reference materials optimized for individual needs, based on the user's unique learning history and data.

[0070] In this invention, in order to provide an AI-assisted learning platform, a specific system is configured that operates in a manner suitable for the user's environment.

[0071] This system begins with the user using a device to input their learning goals, current abilities, and available study time. The device used by the user is typically a computer or smartphone. The device sends the entered data to a server, which then creates a learning plan based on this data.

[0072] The server utilizes software, including historical training data and machine learning algorithms, to analyze the received information. Specifically, the server uses a database management system and a machine learning platform to generate personalized learning plans tailored to the user's goals and calculate the optimal timing for review. Cloud computing services are often used for this purpose.

[0073] The generated learning plan is then delivered back to the device and presented to the user. For example, a user aiming for a high score on a business English exam could be provided with weekly vocabulary study, listening exercises, and a practice test plan.

[0074] As users progress through their learning, their progress is recorded on their devices. The server receives and analyzes this progress data in real time and provides feedback to the user. Specific feedback includes encouraging messages such as, "Your recent progress is going well. Keep it up!"

[0075] Furthermore, this system provides personalized learning recommendations to users through long-term data analysis. For example, if a user repeatedly struggles with a particular area, it can suggest additional learning materials tailored to that area.

[0076] By utilizing a generative AI model, it can suggest specific learning strategies using prompts such as, "What kind of study plan is needed to raise a specific test score by 200 points in three months?"

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input their learning goals, current abilities, and available study time using their device. This data is sent from the device to the server. The server uses this information as foundational data to create individualized learning plans. Specifically, the user inputs data on the device's interface and clicks the "Send" button to send the data.

[0080] Step 2:

[0081] Based on the received user data, the server generates a learning plan tailored to the user's goals using a historical learning database and machine learning algorithms. The input here is user data, and the output is a personalized learning plan. As part of data processing, the server selects appropriate learning materials according to the user's skill level and incorporates them into the schedule.

[0082] Step 3:

[0083] The server delivers the generated study plan to the terminal. The terminal receives the study plan and displays it to the user. At this time, the plan is displayed with a visual layout. Specifically, the study plan is displayed on the terminal screen as a weekly calendar, and the user proceeds with their studies based on it.

[0084] Step 4:

[0085] The user learns according to a plan and records their progress on their device. The device sends this data to the server. The input is learning progress data, and the server performs data calculations based on this data to quantify the user's achievement and understanding. Specifically, the user presses the progress recording button on their device each time they complete a learning task.

[0086] Step 5:

[0087] The server analyzes the received progress data and generates feedback. This feedback includes an assessment of achievement and advice for future learning. The feedback is sent to the device and displayed to the user. For example, if the user is making good progress, the device might display a message such as, "You're doing great!"

[0088] Step 6:

[0089] The server analyzes long-term learning data and provides personalized advice to the user. This includes suggestions for additional resources in specific areas. The input is accumulated learning data, and the output is customized learning suggestions. Specifically, the server sends appropriate learning material links based on the analysis to the user's device.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern society, there is a demand for efficient and effective learning support tailored to the individual needs of each learner. However, systems that comprehensively support the automatic generation of personalized learning programs based on each individual's learning progress and goals, the setting of appropriate review timings to prevent forgetting, and the provision of financial incentives to enhance learning motivation are extremely limited. In particular, the realization of electronic transactions and reward systems directly linked to learning activities is difficult, and many learners struggle to progress in their learning effectively.

[0093] 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.

[0094] In this invention, the server includes means for generating a customized learning program based on the user's learning progress and goals, means for calculating and notifying the optimal review timing based on the forgetting curve, and means for managing electronic transactions linked to the learning plan in order to streamline financial transactions. This enables the provision of learning support optimized for each user, as well as motivation through the provision of financial incentives linked to learning progress.

[0095] "Learning progress" refers to the current status or degree of progress made towards the learning goals set by the user.

[0096] A "learning program" is a set of educational activities that are customized based on the user's goals and skill level, and designed to facilitate effective learning.

[0097] The "forgetting curve" is a model that quantifies the process by which humans forget information, and it is a concept used to consider the most effective timing for review.

[0098] "Review timing" refers to the optimal time for reviewing learned material to ensure it is retained in long-term memory.

[0099] A "financial transaction" is an economic activity in which a user engages in commercial transactions related to learning and receives payment or compensation for those transactions.

[0100] "Electronic transactions" refer to financial transactions conducted using the internet or digital platforms.

[0101] The term "reward system" refers to the structure of incentives and rewards provided according to the user's learning progress.

[0102] "Motivation" refers to providing users with factors or triggers that encourage them to take action.

[0103] To implement this invention, a system is constructed in which a server plays a central role. The server receives data such as learning goals, skill levels, and learning history entered by the user through a terminal, and generates a customized learning program based on this data. The generated program is optimized by applying machine learning algorithms, taking into account each user's progress and learning efficiency. For example, a machine learning library such as Scikit-learn is used for this purpose.

[0104] Furthermore, the server calculates the optimal review timing based on the forgetting curve and sends notifications. Using tools like Firebase Cloud Messaging, push notifications are sent to the user's device at the appropriate time. This timely review notification serves as a means to promote retention of learned material.

[0105] Furthermore, the server manages electronic transactions linked to learning plans to streamline financial transactions. Purchases of learning content are made via electronic payment services such as Stripe and PayPal, and planned rewards are applied according to the user's learning progress. In addition, Firebase is used to manage the reward system and provide rewards based on the user's learning achievements.

[0106] For example, if a user sets a goal of raising their TOEIC score to 750, the server will design a learning program and recommend a specific online course. At this time, a discount can be offered for purchasing the course to encourage learning. Furthermore, points can be provided as additional rewards upon reaching certain progress levels to maintain learning motivation.

[0107] As a concrete example of a generative AI model, prompts such as, "Please suggest the optimal study plan to reach my target TOEIC score. My current score is 600, and my target score is 750. I have 10 hours of study time per week," enable personalized learning support for the user.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users send input data, such as learning goals, skill levels, and available study time, to the server via their devices. This input data includes specific goals, such as "aiming for a TOEIC score of 750." This data is sent to the server in JSON format and serves as foundational information necessary for subsequent processing.

[0111] Step 2:

[0112] The server generates a customized learning program using a machine learning model based on the received user data. Specifically, it uses libraries such as Scikit-learn to calculate the optimal learning tasks and schedule for each user. The input for this step is the user's goals and skill level, and the output is the personalized learning program.

[0113] Step 3:

[0114] The server works in conjunction with the learning program to calculate review timing based on the forgetting curve. It uses learning content and forgetting curve data as input to calculate the optimal review schedule. This output is notified to the terminal as a list of review timings.

[0115] Step 4:

[0116] The device pushes a notification to the user via Firebase Cloud Messaging, reminding them when it's time to review. Here, a message is created and delivered to the user based on the review timing calculated in the previous step. This allows the user to review at the appropriate time.

[0117] Step 5:

[0118] The server manages electronic transactions related to the learning program. It efficiently processes purchases using APIs from Stripe and PayPal. The input is the details of the learning content selected by the user, and the output is transaction confirmation information.

[0119] Step 6:

[0120] The server manages a reward system based on learning progress. It sends progress data to the Firebase Database and calculates and awards rewards and points based on that data. The input is learning progress data, and the output is reward information. The server sends this data back to the device, prompting the user to claim their rewards.

[0121] 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.

[0122] The system of this invention provides personalized support to enable users to learn more effectively, and in particular, by combining it with an emotion engine, it enables flexible responses to changes in emotions. First, the user inputs their learning goals and current skill level via a terminal and sends this information to the server. Based on this basic information, the server generates an appropriate learning plan.

[0123] Recognition and analysis of emotions

[0124] The server has an emotion engine implemented to understand the user's emotional state. This emotion engine analyzes emotions in real time through input such as the user's facial expressions, tone of voice, and typing speed. For example, if the emotion engine determines that the user is feeling stressed while learning, it sends that information to the server.

[0125] Adjusting the learning plan

[0126] The server receives data from the emotion engine and dynamically adjusts the learning plan based on the user's emotional state. For example, if the emotion engine detects that the user's concentration is waning, it will provide relaxing content or temporarily reduce the learning task.

[0127] Feedback and content provision

[0128] The server generates appropriate feedback based on the user's emotions. When the user is in a positive emotional state, it provides messages of praise and additional challenges to enhance their sense of accomplishment. If negative emotions are detected, it calms the user through relaxing music and simple games.

[0129] Personalized advice

[0130] The server accumulates the user's past learning and emotional data and provides long-term, personalized advice based on this. This system maximizes long-term learning outcomes by suggesting a learning schedule that takes the user's emotional patterns into account.

[0131] In this way, the system of the present invention can provide flexible learning support while responding to the user's emotions, and offer an efficient process for achieving goals.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user uses a device to input learning goals, skill levels, and initial settings required for emotion recognition. The device then sends this information to the server.

[0135] Step 2:

[0136] The server generates an optimal learning plan to achieve learning objectives based on the received user information. This involves utilizing existing information from the database and machine learning algorithms. The generated plan is sent to the terminal and presented to the user.

[0137] Step 3:

[0138] An emotion engine operates on the device, acquiring real-time data such as the user's facial expressions and voice tone. This allows the system to recognize the user's emotional state. The recognized emotion data is then sent to a server.

[0139] Step 4:

[0140] The server receives data from the emotion engine and adjusts the learning plan based on the user's emotional state. If stress or anxiety is detected, the server provides relaxing content and reduces the load on the plan as needed.

[0141] Step 5:

[0142] As a user progresses through their learning and records their progress on their device, the device sends this information to the server. The server then analyzes the progress data and sentiment data to generate feedback tailored to the user's current learning situation.

[0143] Step 6:

[0144] If the server determines that the user's emotional state is positive, it provides praising feedback and additional challenges to amplify their sense of accomplishment. Conversely, if a negative state is detected, it also offers advice on relaxation techniques and ways to improve motivation.

[0145] Step 7:

[0146] The server accumulates long-term learning and emotional data to provide users with personalized future learning plans and advice. These plans take into account the user's emotional patterns and learning performance, and personalized suggestions are made based on this data.

[0147] (Example 2)

[0148] 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".

[0149] Modern learners are overwhelmed by diverse information and vast amounts of learning materials, making it difficult for them to receive efficient and individually tailored learning support. Furthermore, few systems offer flexible responses to emotional and concentration lapses, making it difficult for learners to continue learning at their own pace. Therefore, there is a need for the development of systems that consider learners' emotional states and provide personalized learning experiences.

[0150] 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.

[0151] In this invention, the server includes means for generating customized learning content based on the user's learning progress and goals, means for analyzing the user's emotional state and dynamically adjusting recommended learning content, and means for generating personalized advice for each user based on long-term learning information. This enables learners to receive an optimal learning experience tailored to their emotional state and learning progress.

[0152] A "user" refers to an individual who uses an information system for learning or gathering information.

[0153] "Learning progress" indicates the current state of achievement of the learning goals set by the user.

[0154] "Learning content" refers to the collective term for questions and learning materials related to the knowledge and skills that the user aims to acquire.

[0155] The "forgetting curve theory" is a theory that describes how human memory is lost over time.

[0156] "Emotional state" refers to the user's current psychological and emotional condition.

[0157] "Personalized advice" refers to individualized learning advice provided based on each user's past learning history and emotional patterns.

[0158] "Generative artificial intelligence" is a type of machine learning technology that has the ability to learn patterns from large amounts of data and generate new data.

[0159] A "learning plan" is a plan of time and content designed to help users efficiently acquire knowledge and skills.

[0160] "Monitoring" refers to the act of continuously observing and recording a user's learning activities and emotional state.

[0161] The system of this invention consists of a user terminal and a central server. The user inputs their learning goals and skill level via the terminal. This data is transmitted from the terminal to the server, which uses generative artificial intelligence to generate a customized learning plan for the user. The user's terminal is also equipped with a camera and microphone, used to analyze the user's facial expressions and voice tone. This allows the system to understand the user's emotional state and transmit it to the server in real time.

[0162] The server implements an emotion engine that dynamically adjusts learning content based on the user's emotional state. This process helps modify the learning plan and provide relaxing content if the user is deemed stressed or lacking concentration. It also has the ability to provide personalized advice based on long-term learning data and emotional patterns. For example, if the system determines that the user is "tired from practicing math," it may suggest listening to relaxation music.

[0163] Examples of prompts include: "What kind of support is effective when you feel tired while learning English?" By inputting such a prompt into the AI ​​model, the system will generate appropriate feedback and suggestions.

[0164] Thus, the system of the present invention provides advanced personalization technology to support the user's learning process and enable them to effectively achieve their goals.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users input their learning goals and skill levels using their device. This data is then sent from the device to the server, allowing the server to receive basic information to understand the user's learning needs.

[0168] Step 2:

[0169] Based on the received learning objectives and skill level data, the server uses a generative AI model to generate an optimal learning plan for the user. The server selects learning materials and designs the learning schedule, then sends this plan to the terminal to notify the user.

[0170] Step 3:

[0171] The user provides facial expressions and voice tone to the server through the camera and microphone built into the device. The device acquires this emotional data in real time and sends it to the server. Through this process, the user's emotional state is collected.

[0172] Step 4:

[0173] The server uses an emotion engine to analyze the received emotional data. The server evaluates the user's emotional state and identifies emotions such as stress or lack of concentration. This allows the server to understand the user's psychological condition.

[0174] Step 5:

[0175] The server dynamically adjusts the learning plan based on the results of sentiment analysis. For example, if the server determines that the user is tired, it will reduce the learning task or provide relaxation content. This change is notified to the user through the device.

[0176] Step 6:

[0177] The server generates feedback and adaptive content regarding the learning process, responding to the user's emotions. It provides additional challenges for positive emotions and relaxing content for negative emotions. This feedback is sent to the user's device.

[0178] Step 7:

[0179] The server accumulates long-term learning and emotional data and analyzes user-specific learning patterns. This prepares the server to improve future learning plans and provide personalized advice.

[0180] (Application Example 2)

[0181] 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".

[0182] A challenge for learners is that they often don't receive appropriate feedback tailored to their individual emotional states. Traditional systems simply provide feedback based on learning outcomes, lacking the flexibility to respond to learners' emotions. As a result, learners may experience stress or loss of motivation, and these issues need to be addressed appropriately.

[0183] 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.

[0184] In this invention, the server includes means for generating an individualized learning plan based on the user's learning progress and objectives, means for calculating and notifying the optimal timing for review based on a forgetting curve, and means for providing auditory and visual stimuli based on the user's emotional state. This enables personalized support that responds to the learner's emotional state.

[0185] A "user" is someone who interacts with the system and provides data on their learning progress and emotional state.

[0186] "Learning progress" refers to information that shows the results and progress a user has achieved so far in the learning process.

[0187] "Purpose" refers to the goals or objectives that users are trying to achieve in their learning activities.

[0188] An "individualized learning plan" is a learning plan and schedule optimized based on the user's specific needs, goals, and learning progress.

[0189] The "forgetting curve" is a theoretical model that shows how information is forgotten over time.

[0190] The "review period" is the optimal time for users to reconfirm the learned material in order to best retain it in their memory.

[0191] "Emotional state" refers to changes in the user's feelings and includes elements such as stress and concentration that affect learning.

[0192] "Auditory and visual stimuli" refer to sensory inputs such as sounds and images used to improve the user's emotional state and enhance learning effectiveness.

[0193] To realize this invention, the system first receives learning goals and current skill levels from the user's terminal. The terminal is equipped with a camera and microphone, and has the ability to capture the user's facial expressions and voice. The server has an emotion engine implemented to analyze this data, analyzing the user's emotional state in real time. Based on the analysis results, the server dynamically generates an individualized learning plan according to the user's progress and emotional state.

[0194] In terms of hardware, this system can run on consumer robots and smart devices owned by the user. In terms of software, it uses the Google® Cloud Natural Language API for sentiment analysis and the Google Speech-to-Text API for real-time text conversion of voice input.

[0195] For example, if the system detects signs of stress from the user's facial expression while they are working on a learning task, the server will play music to help the user relax and adjust the next learning step. Additionally, when the user completes a learning task, a message of praise will be displayed to enhance their sense of accomplishment.

[0196] An example of a prompt message for a generative AI model might be: "The user appears depressed right now. Please suggest ways to improve their mood, especially how to cheer them up with music or words." This prompt message is used by the system to adjust the user's emotional state to a more comfortable level.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The device receives the user's learning goals and current skill level as input and sends it to the server. This information is used by the server as foundational data to generate a personalized learning plan.

[0200] Step 2:

[0201] The device uses its camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server as input. The server processes this data in real time and uses an emotion engine to analyze the user's emotional state. The results of the analysis are used to determine the user's emotional state.

[0202] Step 3:

[0203] The server dynamically adjusts the personalized learning plan based on emotional state data obtained from the emotion engine. Specifically, if the user is experiencing stress, it incorporates content that promotes relaxation into the learning plan.

[0204] Step 4:

[0205] The server monitors the user's learning progress and generates a message that positively evaluates their achievement upon completion of a learning task. This message is sent to the user's device and displayed to them. The aim is to increase user motivation.

[0206] Step 5:

[0207] In certain situations, the server uses a generative AI model to generate prompts that improve the user's emotional state. This is intended to include playing context-appropriate music or suggesting appropriate words. These prompts serve as a guide to enhance the user experience.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The present invention is an AI-assisted learning platform that supports users who have set learning goals in effectively advancing their learning. Users first input their learning goals, current skill level, and available learning time via a terminal. This information is transmitted to a server and used as basic data for further processing.

[0225] Generating a learning plan

[0226] Based on the user data received, the server generates a learning plan necessary to achieve the user's set goals. This generation utilizes historical databases and machine learning algorithms to enable the most effective task allocation. For example, if a user inputs that they want to improve their score on a business English exam, the server will create a schedule that divides tasks such as vocabulary memorization, listening practice, and mock exams into weekly assignments to help them achieve their target score.

[0227] Calculating the timing of review

[0228] To ensure that learned material is retained for a long period, the server calculates the optimal timing for reviewing each learning item based on the forgetting curve. Specifically, the server analyzes learning data and sends notifications to the user immediately after learning, one day later, and one week later to provide them with opportunities to review.

[0229] Provide feedback

[0230] As users record their progress on their devices, this data is transferred to a server. The server analyzes this data and provides feedback based on their level of achievement and understanding. If progress is stalled, the server generates encouraging messages to motivate the user to continue learning.

[0231] Personalized advice

[0232] Based on the user's learning outcomes and history, the server performs long-term analysis and generates personalized advice based on the insights gained. For example, if a user repeatedly experiences problems in a particular area, the server will provide additional learning resources or rescheduled tasks tailored to that area.

[0233] In this way, the system of the present invention can provide customized learning support for each user, making the process of achieving goals meaningful.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user enters their learning goals, skill level, and available study time via their device. The device then sends this information to the server.

[0237] Step 2:

[0238] The server analyzes the received user information and generates a customized learning plan based on the user's set learning goals. The server creates this plan based on information from the database and past performance, and sends it to the terminal.

[0239] Step 3:

[0240] The user begins learning using a device and records their progress. The device sends the recorded data to the server in real time. The server analyzes the received data to understand the user's progress.

[0241] Step 4:

[0242] The server calculates the timing for review based on the forgetting curve. This calculation takes into account the nature of the learned material and past review history. The server then notifies the user via their device at the appropriate time to review.

[0243] Step 5:

[0244] The server analyzes the user's progress and generates feedback based on their learning achievements. It also simultaneously generates encouraging messages to maintain motivation for learning. The generated feedback and messages are sent to the device and displayed to the user.

[0245] Step 6:

[0246] The server accumulates long-term learning data from users and generates personalized advice based on that data. Based on the analysis results, the server suggests the next learning steps and notifies the user through their device.

[0247] (Example 1)

[0248] 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."

[0249] In today's educational environment, there is a need to provide effective learning methods tailored to the individual goals and skill levels of each learner. However, creating individualized and optimized learning plans is a laborious process, and learners face challenges in determining the optimal timing for review and receiving feedback on their learning progress.

[0250] 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.

[0251] In this invention, the server includes means for receiving progress, goals, and abilities entered by the user via an information processing device and generating an individualized learning plan; means for creating and presenting an optimal learning plan based on past information and machine learning algorithms; and means for calculating an appropriate review period considering the forgetting curve and notifying the user using an instruction device. This enables personalized and efficient learning support for individual learners, as well as optimal review and feedback.

[0252] An "information processing device" refers to a system for inputting, processing, storing, and outputting data, and is a device that enables two-way communication with the user.

[0253] A "personalized learning plan" is a specific schedule and action plan designed to support a user's customized learning progress, based on their specific goals, skill level, and learning pace.

[0254] The "review period" refers to the optimal time to relearn what you've studied in order to maximize its retention in your memory.

[0255] A "display device" is a device used to display or notify a user of information, and typically provides visual or audio output.

[0256] "Learning progress" is an indicator that shows the progress of a user's learning activities, and includes information such as the degree of achievement and understanding.

[0257] "Feedback" refers to the act of providing evaluations and comments on a user's learning activities and results, and giving information about improvements and next actions.

[0258] A "machine learning algorithm" is a set of techniques that enable the automation of predictions and decision-making by learning patterns from large amounts of data.

[0259] "Personalized learning recommendations" refer to the provision of specific advice and reference materials optimized for individual needs, based on the user's unique learning history and data.

[0260] In this invention, in order to provide an AI-assisted learning platform, a specific system is configured that operates in a manner suitable for the user's environment.

[0261] This system begins with the user using a device to input their learning goals, current abilities, and available study time. The device used by the user is typically a computer or smartphone. The device sends the entered data to a server, which then creates a learning plan based on this data.

[0262] The server utilizes software, including historical training data and machine learning algorithms, to analyze the received information. Specifically, the server uses a database management system and a machine learning platform to generate personalized learning plans tailored to the user's goals and calculate the optimal timing for review. Cloud computing services are often used for this purpose.

[0263] The generated learning plan is then delivered back to the device and presented to the user. For example, a user aiming for a high score on a business English exam could be provided with weekly vocabulary study, listening exercises, and a practice test plan.

[0264] As users progress through their learning, their progress is recorded on their devices. The server receives and analyzes this progress data in real time and provides feedback to the user. Specific feedback includes encouraging messages such as, "Your recent progress is going well. Keep it up!"

[0265] Furthermore, this system provides personalized learning recommendations to users through long-term data analysis. For example, if a user repeatedly struggles with a particular area, it can suggest additional learning materials tailored to that area.

[0266] By utilizing a generative AI model, it can suggest specific learning strategies using prompts such as, "What kind of study plan is needed to raise a specific test score by 200 points in three months?"

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] Users input their learning goals, current abilities, and available study time using their device. This data is sent from the device to the server. The server uses this information as foundational data to create individualized learning plans. Specifically, the user inputs data on the device's interface and clicks the "Send" button to send the data.

[0270] Step 2:

[0271] Based on the received user data, the server generates a learning plan tailored to the user's goals using a historical learning database and machine learning algorithms. The input here is user data, and the output is a personalized learning plan. As part of data processing, the server selects appropriate learning materials according to the user's skill level and incorporates them into the schedule.

[0272] Step 3:

[0273] The server delivers the generated study plan to the terminal. The terminal receives the study plan and displays it to the user. At this time, the plan is displayed with a visual layout. Specifically, the study plan is displayed on the terminal screen as a weekly calendar, and the user proceeds with their studies based on it.

[0274] Step 4:

[0275] The user learns according to a plan and records their progress on their device. The device sends this data to the server. The input is learning progress data, and the server performs data calculations based on this data to quantify the user's achievement and understanding. Specifically, the user presses the progress recording button on their device each time they complete a learning task.

[0276] Step 5:

[0277] The server analyzes the received progress data and generates feedback. This feedback includes an assessment of achievement and advice for future learning. The feedback is sent to the device and displayed to the user. For example, if the user is making good progress, the device might display a message such as, "You're doing great!"

[0278] Step 6:

[0279] The server analyzes long-term learning data and provides personalized advice to the user. This includes suggestions for additional resources in specific areas. The input is accumulated learning data, and the output is customized learning suggestions. Specifically, the server sends appropriate learning material links based on the analysis to the user's device.

[0280] (Application Example 1)

[0281] 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."

[0282] In modern society, there is a demand for efficient and effective learning support tailored to the individual needs of each learner. However, systems that comprehensively support the automatic generation of personalized learning programs based on each individual's learning progress and goals, the setting of appropriate review timings to prevent forgetting, and the provision of financial incentives to enhance learning motivation are extremely limited. In particular, the realization of electronic transactions and reward systems directly linked to learning activities is difficult, and many learners struggle to progress in their learning effectively.

[0283] 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.

[0284] In this invention, the server includes means for generating a customized learning program based on the learning progress and goals of the user, means for calculating and notifying the optimal review timing based on the forgetting curve, and means for managing electronic transactions linked to the learning plan in order to streamline financial transactions. This enables the provision of learning support optimized for each user, and furthermore, motivation can be provided through the supply of financial incentives linked to the learning progress.

[0285] "Learning progress" refers to the current status and degree of progress achieved with respect to the learning goals set by the user.

[0286] "Learning program" is a series of educational activities customized based on the user's goals and skill levels and designed to effectively promote learning.

[0287] "Forgetting curve" is a model that quantifies the process by which humans forget information, and is a concept used to consider the optimal review timing.

[0288] "Review timing" refers to the optimal time point set to fix the learned content in long-term memory.

[0289] "Financial transaction" is an economic activity in which the user conducts business transactions related to learning and obtains the corresponding price or reward.

[0290] "Electronic transaction" refers to a financial transaction conducted using the Internet or a digital platform.

[0291] "Incentive system" represents the structure of incentives and rewards provided according to the learning progress of the user.

[0292] "Motivation" refers to giving factors or triggers that prompt action to the user.

[0293] To implement this invention, a system is constructed in which a server plays a central role. The server receives data such as learning goals, skill levels, and learning history entered by the user through a terminal, and generates a customized learning program based on this data. The generated program is optimized by applying machine learning algorithms, taking into account each user's progress and learning efficiency. For example, a machine learning library such as Scikit-learn is used for this purpose.

[0294] Furthermore, the server calculates the optimal review timing based on the forgetting curve and sends notifications. Using tools like Firebase Cloud Messaging, push notifications are sent to the user's device at the appropriate time. This timely review notification serves as a means to promote retention of learned material.

[0295] Furthermore, the server manages electronic transactions linked to learning plans to streamline financial transactions. Purchases of learning content are made via electronic payment services such as Stripe and PayPal, and planned rewards are applied according to the user's learning progress. In addition, Firebase is used to manage the reward system and provide rewards based on the user's learning achievements.

[0296] For example, if a user sets a goal of raising their TOEIC score to 750, the server will design a learning program and recommend a specific online course. At this time, a discount can be offered for purchasing the course to encourage learning. Furthermore, points can be provided as additional rewards upon reaching certain progress levels to maintain learning motivation.

[0297] As a concrete example of a generative AI model, prompts such as, "Please suggest the optimal study plan to reach my target TOEIC score. My current score is 600, and my target score is 750. I have 10 hours of study time per week," enable personalized learning support for the user.

[0298] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0299] Step 1:

[0300] The user sends input data such as learning goals, skill levels, and available learning time to the server through the terminal. The input data includes specific goals such as "aiming for a TOEIC score of 750". This data is sent to the server in JSON format and serves as the basic information required for subsequent processing.

[0301] Step 2:

[0302] Based on the received user data, the server uses a machine learning model to generate a customized learning program. Specifically, libraries such as Scikit-learn are used to calculate the optimal learning tasks and schedules for each user. The input for this step is the user's goals and skill levels, and the output is an individualized learning program.

[0303] Step 3:

[0304] The server calculates the review timing based on the forgetting curve in cooperation with the learning program. Using the learning content and forgetting curve data as input, the optimal review schedule is calculated. The output result is notified to the terminal as a list of review timings.

[0305] Step 4:

[0306] The terminal pushes a notification of the review timing to the user via Firebase Cloud Messaging. Here, a message based on the review timing calculated in the previous step is created and delivered to the user. This enables the user to conduct reviews at appropriate times.

[0307] Step 5:

[0308] The server manages electronic transactions related to the learning program. It efficiently processes purchases using APIs from Stripe and PayPal. The input is the details of the learning content selected by the user, and the output is transaction confirmation information.

[0309] Step 6:

[0310] The server manages a reward system based on learning progress. It sends progress data to the Firebase Database and calculates and awards rewards and points based on that data. The input is learning progress data, and the output is reward information. The server sends this data back to the device, prompting the user to claim their rewards.

[0311] 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.

[0312] The system of this invention provides personalized support to enable users to learn more effectively, and in particular, by combining it with an emotion engine, it enables flexible responses to changes in emotions. First, the user inputs their learning goals and current skill level via a terminal and sends this information to the server. Based on this basic information, the server generates an appropriate learning plan.

[0313] Recognition and analysis of emotions

[0314] The server has an emotion engine implemented to understand the user's emotional state. This emotion engine analyzes emotions in real time through input such as the user's facial expressions, tone of voice, and typing speed. For example, if the emotion engine determines that the user is feeling stressed while learning, it sends that information to the server.

[0315] Adjusting the learning plan

[0316] The server receives data from the emotion engine and dynamically adjusts the learning plan based on the user's emotional state. For example, if the emotion engine detects that the user's concentration is waning, it will provide relaxing content or temporarily reduce the learning task.

[0317] Feedback and content provision

[0318] The server generates appropriate feedback based on the user's emotions. When the user is in a positive emotional state, it provides messages of praise and additional challenges to enhance their sense of accomplishment. If negative emotions are detected, it calms the user through relaxing music and simple games.

[0319] Personalized advice

[0320] The server accumulates the user's past learning and emotional data and provides long-term, personalized advice based on this. This system maximizes long-term learning outcomes by suggesting a learning schedule that takes the user's emotional patterns into account.

[0321] In this way, the system of the present invention can provide flexible learning support while responding to the user's emotions, and offer an efficient process for achieving goals.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The user uses a device to input learning goals, skill levels, and initial settings required for emotion recognition. The device then sends this information to the server.

[0325] Step 2:

[0326] The server generates an optimal learning plan to achieve learning objectives based on the received user information. This involves utilizing existing information from the database and machine learning algorithms. The generated plan is sent to the terminal and presented to the user.

[0327] Step 3:

[0328] An emotion engine operates on the device, acquiring real-time data such as the user's facial expressions and voice tone. This allows the system to recognize the user's emotional state. The recognized emotion data is then sent to a server.

[0329] Step 4:

[0330] The server receives data from the emotion engine and adjusts the learning plan based on the user's emotional state. If stress or anxiety is detected, the server provides relaxing content and reduces the load on the plan as needed.

[0331] Step 5:

[0332] As a user progresses through their learning and records their progress on their device, the device sends this information to the server. The server then analyzes the progress data and sentiment data to generate feedback tailored to the user's current learning situation.

[0333] Step 6:

[0334] If the server determines that the user's emotional state is positive, it provides praising feedback and additional challenges to amplify their sense of accomplishment. Conversely, if a negative state is detected, it also offers advice on relaxation techniques and ways to improve motivation.

[0335] Step 7:

[0336] The server accumulates long-term learning and emotional data to provide users with personalized future learning plans and advice. These plans take into account the user's emotional patterns and learning performance, and personalized suggestions are made based on this data.

[0337] (Example 2)

[0338] 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".

[0339] Modern learners are overwhelmed by diverse information and vast amounts of learning materials, making it difficult for them to receive efficient and individually tailored learning support. Furthermore, few systems offer flexible responses to emotional and concentration lapses, making it difficult for learners to continue learning at their own pace. Therefore, there is a need for the development of systems that consider learners' emotional states and provide personalized learning experiences.

[0340] 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.

[0341] In this invention, the server includes means for generating customized learning content based on the user's learning progress and goals, means for analyzing the user's emotional state and dynamically adjusting recommended learning content, and means for generating personalized advice for each user based on long-term learning information. This enables learners to receive an optimal learning experience tailored to their emotional state and learning progress.

[0342] A "user" refers to an individual who uses an information system for learning or gathering information.

[0343] "Learning progress" indicates the current state of achievement of the learning goals set by the user.

[0344] "Learning content" refers to the collective term for questions and learning materials related to the knowledge and skills that the user aims to acquire.

[0345] The "forgetting curve theory" is a theory that describes how human memory is lost over time.

[0346] "Emotional state" refers to the user's current psychological and emotional condition.

[0347] "Personalized advice" refers to individualized learning advice provided based on each user's past learning history and emotional patterns.

[0348] "Generative artificial intelligence" is a type of machine learning technology that has the ability to learn patterns from large amounts of data and generate new data.

[0349] A "learning plan" is a plan of time and content designed to help users efficiently acquire knowledge and skills.

[0350] "Monitoring" refers to the act of continuously observing and recording a user's learning activities and emotional state.

[0351] The system of this invention consists of a user terminal and a central server. The user inputs their learning goals and skill level via the terminal. This data is transmitted from the terminal to the server, which uses generative artificial intelligence to generate a customized learning plan for the user. The user's terminal is also equipped with a camera and microphone, used to analyze the user's facial expressions and voice tone. This allows the system to understand the user's emotional state and transmit it to the server in real time.

[0352] The server implements an emotion engine that dynamically adjusts learning content based on the user's emotional state. This process helps modify the learning plan and provide relaxing content if the user is deemed stressed or lacking concentration. It also has the ability to provide personalized advice based on long-term learning data and emotional patterns. For example, if the system determines that the user is "tired from practicing math," it may suggest listening to relaxation music.

[0353] Examples of prompts include: "What kind of support is effective when you feel tired while learning English?" By inputting such a prompt into the AI ​​model, the system will generate appropriate feedback and suggestions.

[0354] Thus, the system of the present invention provides advanced personalization technology to support the user's learning process and enable them to effectively achieve their goals.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users input their learning goals and skill levels using their device. This data is then sent from the device to the server, allowing the server to receive basic information to understand the user's learning needs.

[0358] Step 2:

[0359] Based on the received learning objectives and skill level data, the server uses a generative AI model to generate an optimal learning plan for the user. The server selects learning materials and designs the learning schedule, then sends this plan to the terminal to notify the user.

[0360] Step 3:

[0361] The user provides facial expressions and voice tone to the server through the camera and microphone built into the device. The device acquires this emotional data in real time and sends it to the server. Through this process, the user's emotional state is collected.

[0362] Step 4:

[0363] The server uses an emotion engine to analyze the received emotional data. The server evaluates the user's emotional state and identifies emotions such as stress or lack of concentration. This allows the server to understand the user's psychological condition.

[0364] Step 5:

[0365] The server dynamically adjusts the learning plan based on the results of sentiment analysis. For example, if the server determines that the user is tired, it will reduce the learning task or provide relaxation content. This change is notified to the user through the device.

[0366] Step 6:

[0367] The server generates feedback and adaptive content regarding the learning process, responding to the user's emotions. It provides additional challenges for positive emotions and relaxing content for negative emotions. This feedback is sent to the user's device.

[0368] Step 7:

[0369] The server accumulates long-term learning and emotional data and analyzes user-specific learning patterns. This prepares the server to improve future learning plans and provide personalized advice.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0372] A challenge for learners is that they often don't receive appropriate feedback tailored to their individual emotional states. Traditional systems simply provide feedback based on learning outcomes, lacking the flexibility to respond to learners' emotions. As a result, learners may experience stress or loss of motivation, and these issues need to be addressed appropriately.

[0373] 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.

[0374] In this invention, the server includes means for generating an individualized learning plan based on the user's learning progress and objectives, means for calculating and notifying the optimal timing for review based on a forgetting curve, and means for providing auditory and visual stimuli based on the user's emotional state. This enables personalized support that responds to the learner's emotional state.

[0375] A "user" is someone who interacts with the system and provides data on their learning progress and emotional state.

[0376] "Learning progress" refers to information that shows the results and progress a user has achieved so far in the learning process.

[0377] "Purpose" refers to the goals or objectives that users are trying to achieve in their learning activities.

[0378] An "individualized learning plan" is a learning plan and schedule optimized based on the user's specific needs, goals, and learning progress.

[0379] The "forgetting curve" is a theoretical model that shows how information is forgotten over time.

[0380] The "review period" is the optimal time for users to reconfirm the learned material in order to best retain it in their memory.

[0381] "Emotional state" refers to changes in the user's feelings and includes elements such as stress and concentration that affect learning.

[0382] "Auditory and visual stimuli" refer to sensory inputs such as sounds and images used to improve the user's emotional state and enhance learning effectiveness.

[0383] To realize this invention, the system first receives learning goals and current skill levels from the user's terminal. The terminal is equipped with a camera and microphone, and has the ability to capture the user's facial expressions and voice. The server has an emotion engine implemented to analyze this data, analyzing the user's emotional state in real time. Based on the analysis results, the server dynamically generates an individualized learning plan according to the user's progress and emotional state.

[0384] In terms of hardware, this system can run on consumer robots and smart devices owned by users. In terms of software, it uses the Google Cloud Natural Language API for sentiment analysis and the Google Speech-to-Text API for real-time text conversion of voice input.

[0385] For example, if the system detects signs of stress from the user's facial expression while they are working on a learning task, the server will play music to help the user relax and adjust the next learning step. Additionally, when the user completes a learning task, a message of praise will be displayed to enhance their sense of accomplishment.

[0386] An example of a prompt message for a generative AI model might be: "The user appears depressed right now. Please suggest ways to improve their mood, especially how to cheer them up with music or words." This prompt message is used by the system to adjust the user's emotional state to a more comfortable level.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device receives the user's learning goals and current skill level as input and sends it to the server. This information is used by the server as foundational data to generate a personalized learning plan.

[0390] Step 2:

[0391] The device uses its camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server as input. The server processes this data in real time and uses an emotion engine to analyze the user's emotional state. The results of the analysis are used to determine the user's emotional state.

[0392] Step 3:

[0393] The server dynamically adjusts the personalized learning plan based on emotional state data obtained from the emotion engine. Specifically, if the user is experiencing stress, it incorporates content that promotes relaxation into the learning plan.

[0394] Step 4:

[0395] The server monitors the user's learning progress and generates a message that positively evaluates their achievement upon completion of a learning task. This message is sent to the user's device and displayed to them. The aim is to increase user motivation.

[0396] Step 5:

[0397] In certain situations, the server uses a generative AI model to generate prompts that improve the user's emotional state. This is intended to include playing context-appropriate music or suggesting appropriate words. These prompts serve as a guide to enhance the user experience.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The present invention is an AI-assisted learning platform that supports users who have set learning goals in effectively advancing their learning. Users first input their learning goals, current skill level, and available learning time via a terminal. This information is transmitted to a server and used as basic data for further processing.

[0415] Generating a learning plan

[0416] Based on the user data received, the server generates a learning plan necessary to achieve the user's set goals. This generation utilizes historical databases and machine learning algorithms to enable the most effective task allocation. For example, if a user inputs that they want to improve their score on a business English exam, the server will create a schedule that divides tasks such as vocabulary memorization, listening practice, and mock exams into weekly assignments to help them achieve their target score.

[0417] Calculating the timing of review

[0418] To ensure that learned material is retained for a long period, the server calculates the optimal timing for reviewing each learning item based on the forgetting curve. Specifically, the server analyzes learning data and sends notifications to the user immediately after learning, one day later, and one week later to provide them with opportunities to review.

[0419] Provide feedback

[0420] As users record their progress on their devices, this data is transferred to a server. The server analyzes this data and provides feedback based on their level of achievement and understanding. If progress is stalled, the server generates encouraging messages to motivate the user to continue learning.

[0421] Personalized advice

[0422] Based on the user's learning outcomes and history, the server performs long-term analysis and generates personalized advice based on the insights gained. For example, if a user repeatedly experiences problems in a particular area, the server will provide additional learning resources or rescheduled tasks tailored to that area.

[0423] In this way, the system of the present invention can provide customized learning support for each user, making the process of achieving goals meaningful.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The user enters their learning goals, skill level, and available study time via their device. The device then sends this information to the server.

[0427] Step 2:

[0428] The server analyzes the received user information and generates a customized learning plan based on the user's set learning goals. The server creates this plan based on information from the database and past performance, and sends it to the terminal.

[0429] Step 3:

[0430] The user begins learning using a device and records their progress. The device sends the recorded data to the server in real time. The server analyzes the received data to understand the user's progress.

[0431] Step 4:

[0432] The server calculates the timing for review based on the forgetting curve. This calculation takes into account the nature of the learned material and past review history. The server then notifies the user via their device at the appropriate time to review.

[0433] Step 5:

[0434] The server analyzes the user's progress and generates feedback based on their learning achievements. It also simultaneously generates encouraging messages to maintain motivation for learning. The generated feedback and messages are sent to the device and displayed to the user.

[0435] Step 6:

[0436] The server accumulates long-term learning data from users and generates personalized advice based on that data. Based on the analysis results, the server suggests the next learning steps and notifies the user through their device.

[0437] (Example 1)

[0438] 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."

[0439] In today's educational environment, there is a need to provide effective learning methods tailored to the individual goals and skill levels of each learner. However, creating individualized and optimized learning plans is a laborious process, and learners face challenges in determining the optimal timing for review and receiving feedback on their learning progress.

[0440] 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.

[0441] In this invention, the server includes means for receiving progress, goals, and abilities entered by the user via an information processing device and generating an individualized learning plan; means for creating and presenting an optimal learning plan based on past information and machine learning algorithms; and means for calculating an appropriate review period considering the forgetting curve and notifying the user using an instruction device. This enables personalized and efficient learning support for individual learners, as well as optimal review and feedback.

[0442] An "information processing device" refers to a system for inputting, processing, storing, and outputting data, and is a device that enables two-way communication with the user.

[0443] A "personalized learning plan" is a specific schedule and action plan designed to support a user's customized learning progress, based on their specific goals, skill level, and learning pace.

[0444] The "review period" refers to the optimal time to relearn what you've studied in order to maximize its retention in your memory.

[0445] A "display device" is a device used to display or notify a user of information, and typically provides visual or audio output.

[0446] "Learning progress" is an indicator that shows the progress of a user's learning activities, and includes information such as the degree of achievement and understanding.

[0447] "Feedback" refers to the act of providing evaluations and comments on a user's learning activities and results, and giving information about improvements and next actions.

[0448] A "machine learning algorithm" is a set of techniques that enable the automation of predictions and decision-making by learning patterns from large amounts of data.

[0449] "Personalized learning recommendations" refer to the provision of specific advice and reference materials optimized for individual needs, based on the user's unique learning history and data.

[0450] In this invention, in order to provide an AI-assisted learning platform, a specific system is configured that operates in a manner suitable for the user's environment.

[0451] This system begins with the user using a device to input their learning goals, current abilities, and available study time. The device used by the user is typically a computer or smartphone. The device sends the entered data to a server, which then creates a learning plan based on this data.

[0452] The server utilizes software, including historical training data and machine learning algorithms, to analyze the received information. Specifically, the server uses a database management system and a machine learning platform to generate personalized learning plans tailored to the user's goals and calculate the optimal timing for review. Cloud computing services are often used for this purpose.

[0453] The generated learning plan is then delivered back to the device and presented to the user. For example, a user aiming for a high score on a business English exam could be provided with weekly vocabulary study, listening exercises, and a practice test plan.

[0454] As users progress through their learning, their progress is recorded on their devices. The server receives and analyzes this progress data in real time and provides feedback to the user. Specific feedback includes encouraging messages such as, "Your recent progress is going well. Keep it up!"

[0455] Furthermore, this system provides personalized learning recommendations to users through long-term data analysis. For example, if a user repeatedly struggles with a particular area, it can suggest additional learning materials tailored to that area.

[0456] By utilizing a generative AI model, it can suggest specific learning strategies using prompts such as, "What kind of study plan is needed to raise a specific test score by 200 points in three months?"

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] Users input their learning goals, current abilities, and available study time using their device. This data is sent from the device to the server. The server uses this information as foundational data to create individualized learning plans. Specifically, the user inputs data on the device's interface and clicks the "Send" button to send the data.

[0460] Step 2:

[0461] Based on the received user data, the server generates a learning plan tailored to the user's goals using a historical learning database and machine learning algorithms. The input here is user data, and the output is a personalized learning plan. As part of data processing, the server selects appropriate learning materials according to the user's skill level and incorporates them into the schedule.

[0462] Step 3:

[0463] The server delivers the generated study plan to the terminal. The terminal receives the study plan and displays it to the user. At this time, the plan is displayed with a visual layout. Specifically, the study plan is displayed on the terminal screen as a weekly calendar, and the user proceeds with their studies based on it.

[0464] Step 4:

[0465] The user learns according to a plan and records their progress on their device. The device sends this data to the server. The input is learning progress data, and the server performs data calculations based on this data to quantify the user's achievement and understanding. Specifically, the user presses the progress recording button on their device each time they complete a learning task.

[0466] Step 5:

[0467] The server analyzes the received progress data and generates feedback. This feedback includes an assessment of achievement and advice for future learning. The feedback is sent to the device and displayed to the user. For example, if the user is making good progress, the device might display a message such as, "You're doing great!"

[0468] Step 6:

[0469] The server analyzes long-term learning data and provides personalized advice to the user. This includes suggestions for additional resources in specific areas. The input is accumulated learning data, and the output is customized learning suggestions. Specifically, the server sends appropriate learning material links based on the analysis to the user's device.

[0470] (Application Example 1)

[0471] 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."

[0472] In modern society, there is a demand for efficient and effective learning support tailored to the individual needs of each learner. However, systems that comprehensively support the automatic generation of personalized learning programs based on each individual's learning progress and goals, the setting of appropriate review timings to prevent forgetting, and the provision of financial incentives to enhance learning motivation are extremely limited. In particular, the realization of electronic transactions and reward systems directly linked to learning activities is difficult, and many learners struggle to progress in their learning effectively.

[0473] 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.

[0474] In this invention, the server includes means for generating a customized learning program based on the user's learning progress and goals, means for calculating and notifying the optimal review timing based on the forgetting curve, and means for managing electronic transactions linked to the learning plan in order to streamline financial transactions. This enables the provision of learning support optimized for each user, as well as motivation through the provision of financial incentives linked to learning progress.

[0475] "Learning progress" refers to the current status or degree of progress made towards the learning goals set by the user.

[0476] A "learning program" is a set of educational activities that are customized based on the user's goals and skill level, and designed to facilitate effective learning.

[0477] The "forgetting curve" is a model that quantifies the process by which humans forget information, and it is a concept used to consider the most effective timing for review.

[0478] "Review timing" refers to the optimal time for reviewing learned material to ensure it is retained in long-term memory.

[0479] A "financial transaction" is an economic activity in which a user engages in commercial transactions related to learning and receives payment or compensation for those transactions.

[0480] "Electronic transactions" refer to financial transactions conducted using the internet or digital platforms.

[0481] The term "reward system" refers to the structure of incentives and rewards provided according to the user's learning progress.

[0482] "Motivation" refers to providing users with factors or triggers that encourage them to take action.

[0483] To implement this invention, a system is constructed in which a server plays a central role. The server receives data such as learning goals, skill levels, and learning history entered by the user through a terminal, and generates a customized learning program based on this data. The generated program is optimized by applying machine learning algorithms, taking into account each user's progress and learning efficiency. For example, a machine learning library such as Scikit-learn is used for this purpose.

[0484] Furthermore, the server calculates the optimal review timing based on the forgetting curve and sends notifications. Using tools like Firebase Cloud Messaging, push notifications are sent to the user's device at the appropriate time. This timely review notification serves as a means to promote retention of learned material.

[0485] Furthermore, the server manages electronic transactions linked to learning plans to streamline financial transactions. Purchases of learning content are made via electronic payment services such as Stripe and PayPal, and planned rewards are applied according to the user's learning progress. In addition, Firebase is used to manage the reward system and provide rewards based on the user's learning achievements.

[0486] For example, if a user sets a goal of raising their TOEIC score to 750, the server will design a learning program and recommend a specific online course. At this time, a discount can be offered for purchasing the course to encourage learning. Furthermore, points can be provided as additional rewards upon reaching certain progress levels to maintain learning motivation.

[0487] As a concrete example of a generative AI model, prompts such as, "Please suggest the optimal study plan to reach my target TOEIC score. My current score is 600, and my target score is 750. I have 10 hours of study time per week," enable personalized learning support for the user.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] Users send input data, such as learning goals, skill levels, and available study time, to the server via their devices. This input data includes specific goals, such as "aiming for a TOEIC score of 750." This data is sent to the server in JSON format and serves as foundational information necessary for subsequent processing.

[0491] Step 2:

[0492] The server generates a customized learning program using a machine learning model based on the received user data. Specifically, it uses libraries such as Scikit-learn to calculate the optimal learning tasks and schedule for each user. The input for this step is the user's goals and skill level, and the output is the personalized learning program.

[0493] Step 3:

[0494] The server works in conjunction with the learning program to calculate review timing based on the forgetting curve. It uses learning content and forgetting curve data as input to calculate the optimal review schedule. This output is notified to the terminal as a list of review timings.

[0495] Step 4:

[0496] The device pushes a notification to the user via Firebase Cloud Messaging, reminding them when it's time to review. Here, a message is created and delivered to the user based on the review timing calculated in the previous step. This allows the user to review at the appropriate time.

[0497] Step 5:

[0498] The server manages electronic transactions related to the learning program. It efficiently processes purchases using APIs from Stripe and PayPal. The input is the details of the learning content selected by the user, and the output is transaction confirmation information.

[0499] Step 6:

[0500] The server manages a reward system based on learning progress. It sends progress data to the Firebase Database and calculates and awards rewards and points based on that data. The input is learning progress data, and the output is reward information. The server sends this data back to the device, prompting the user to claim their rewards.

[0501] 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.

[0502] The system of this invention provides personalized support to enable users to learn more effectively, and in particular, by combining it with an emotion engine, it enables flexible responses to changes in emotions. First, the user inputs their learning goals and current skill level via a terminal and sends this information to the server. Based on this basic information, the server generates an appropriate learning plan.

[0503] Recognition and analysis of emotions

[0504] The server has an emotion engine implemented to understand the user's emotional state. This emotion engine analyzes emotions in real time through input such as the user's facial expressions, tone of voice, and typing speed. For example, if the emotion engine determines that the user is feeling stressed while learning, it sends that information to the server.

[0505] Adjusting the learning plan

[0506] The server receives data from the emotion engine and dynamically adjusts the learning plan based on the user's emotional state. For example, if the emotion engine detects that the user's concentration is waning, it will provide relaxing content or temporarily reduce the learning task.

[0507] Feedback and content provision

[0508] The server generates appropriate feedback based on the user's emotions. When the user is in a positive emotional state, it provides messages of praise and additional challenges to enhance their sense of accomplishment. If negative emotions are detected, it calms the user through relaxing music and simple games.

[0509] Personalized advice

[0510] The server accumulates the user's past learning and emotional data and provides long-term, personalized advice based on this. This system maximizes long-term learning outcomes by suggesting a learning schedule that takes the user's emotional patterns into account.

[0511] In this way, the system of the present invention can provide flexible learning support while responding to the user's emotions, and offer an efficient process for achieving goals.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user uses a device to input learning goals, skill levels, and initial settings required for emotion recognition. The device then sends this information to the server.

[0515] Step 2:

[0516] The server generates an optimal learning plan to achieve learning objectives based on the received user information. This involves utilizing existing information from the database and machine learning algorithms. The generated plan is sent to the terminal and presented to the user.

[0517] Step 3:

[0518] An emotion engine operates on the device, acquiring real-time data such as the user's facial expressions and voice tone. This allows the system to recognize the user's emotional state. The recognized emotion data is then sent to a server.

[0519] Step 4:

[0520] The server receives data from the emotion engine and adjusts the learning plan based on the user's emotional state. If stress or anxiety is detected, the server provides relaxing content and reduces the load on the plan as needed.

[0521] Step 5:

[0522] As a user progresses through their learning and records their progress on their device, the device sends this information to the server. The server then analyzes the progress data and sentiment data to generate feedback tailored to the user's current learning situation.

[0523] Step 6:

[0524] If the server determines that the user's emotional state is positive, it provides praising feedback and additional challenges to amplify their sense of accomplishment. Conversely, if a negative state is detected, it also offers advice on relaxation techniques and ways to improve motivation.

[0525] Step 7:

[0526] The server accumulates long-term learning and emotional data to provide users with personalized future learning plans and advice. These plans take into account the user's emotional patterns and learning performance, and personalized suggestions are made based on this data.

[0527] (Example 2)

[0528] 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."

[0529] Modern learners are overwhelmed by diverse information and vast amounts of learning materials, making it difficult for them to receive efficient and individually tailored learning support. Furthermore, few systems offer flexible responses to emotional and concentration lapses, making it difficult for learners to continue learning at their own pace. Therefore, there is a need for the development of systems that consider learners' emotional states and provide personalized learning experiences.

[0530] 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.

[0531] In this invention, the server includes means for generating customized learning content based on the user's learning progress and goals, means for analyzing the user's emotional state and dynamically adjusting recommended learning content, and means for generating personalized advice for each user based on long-term learning information. This enables learners to receive an optimal learning experience tailored to their emotional state and learning progress.

[0532] A "user" refers to an individual who uses an information system for learning or gathering information.

[0533] "Learning progress" indicates the current state of achievement of the learning goals set by the user.

[0534] "Learning content" refers to the collective term for questions and learning materials related to the knowledge and skills that the user aims to acquire.

[0535] The "forgetting curve theory" is a theory that describes how human memory is lost over time.

[0536] "Emotional state" refers to the user's current psychological and emotional condition.

[0537] "Personalized advice" refers to individualized learning advice provided based on each user's past learning history and emotional patterns.

[0538] "Generative artificial intelligence" is a type of machine learning technology that has the ability to learn patterns from large amounts of data and generate new data.

[0539] A "learning plan" is a plan of time and content designed to help users efficiently acquire knowledge and skills.

[0540] "Monitoring" refers to the act of continuously observing and recording a user's learning activities and emotional state.

[0541] The system of this invention consists of a user terminal and a central server. The user inputs their learning goals and skill level via the terminal. This data is transmitted from the terminal to the server, which uses generative artificial intelligence to generate a customized learning plan for the user. The user's terminal is also equipped with a camera and microphone, used to analyze the user's facial expressions and voice tone. This allows the system to understand the user's emotional state and transmit it to the server in real time.

[0542] The server implements an emotion engine that dynamically adjusts learning content based on the user's emotional state. This process helps modify the learning plan and provide relaxing content if the user is deemed stressed or lacking concentration. It also has the ability to provide personalized advice based on long-term learning data and emotional patterns. For example, if the system determines that the user is "tired from practicing math," it may suggest listening to relaxation music.

[0543] Examples of prompts include: "What kind of support is effective when you feel tired while learning English?" By inputting such a prompt into the AI ​​model, the system will generate appropriate feedback and suggestions.

[0544] Thus, the system of the present invention provides advanced personalization technology to support the user's learning process and enable them to effectively achieve their goals.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Users input their learning goals and skill levels using their device. This data is then sent from the device to the server, allowing the server to receive basic information to understand the user's learning needs.

[0548] Step 2:

[0549] Based on the received learning objectives and skill level data, the server uses a generative AI model to generate an optimal learning plan for the user. The server selects learning materials and designs the learning schedule, then sends this plan to the terminal to notify the user.

[0550] Step 3:

[0551] The user provides facial expressions and voice tone to the server through the camera and microphone built into the device. The device acquires this emotional data in real time and sends it to the server. Through this process, the user's emotional state is collected.

[0552] Step 4:

[0553] The server uses an emotion engine to analyze the received emotional data. The server evaluates the user's emotional state and identifies emotions such as stress or lack of concentration. This allows the server to understand the user's psychological condition.

[0554] Step 5:

[0555] The server dynamically adjusts the learning plan based on the results of sentiment analysis. For example, if the server determines that the user is tired, it will reduce the learning task or provide relaxation content. This change is notified to the user through the device.

[0556] Step 6:

[0557] The server generates feedback and adaptive content regarding the learning process, responding to the user's emotions. It provides additional challenges for positive emotions and relaxing content for negative emotions. This feedback is sent to the user's device.

[0558] Step 7:

[0559] The server accumulates long-term learning and emotional data and analyzes user-specific learning patterns. This prepares the server to improve future learning plans and provide personalized advice.

[0560] (Application Example 2)

[0561] 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."

[0562] A challenge for learners is that they often don't receive appropriate feedback tailored to their individual emotional states. Traditional systems simply provide feedback based on learning outcomes, lacking the flexibility to respond to learners' emotions. As a result, learners may experience stress or loss of motivation, and these issues need to be addressed appropriately.

[0563] 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.

[0564] In this invention, the server includes means for generating an individualized learning plan based on the user's learning progress and objectives, means for calculating and notifying the optimal timing for review based on a forgetting curve, and means for providing auditory and visual stimuli based on the user's emotional state. This enables personalized support that responds to the learner's emotional state.

[0565] A "user" is someone who interacts with the system and provides data on their learning progress and emotional state.

[0566] "Learning progress" refers to information that shows the results and progress a user has achieved so far in the learning process.

[0567] "Purpose" refers to the goals or objectives that users are trying to achieve in their learning activities.

[0568] An "individualized learning plan" is a learning plan and schedule optimized based on the user's specific needs, goals, and learning progress.

[0569] The "forgetting curve" is a theoretical model that shows how information is forgotten over time.

[0570] The "review period" is the optimal time for users to reconfirm the learned material in order to best retain it in their memory.

[0571] "Emotional state" refers to changes in the user's feelings and includes elements such as stress and concentration that affect learning.

[0572] "Auditory and visual stimuli" refer to sensory inputs such as sounds and images used to improve the user's emotional state and enhance learning effectiveness.

[0573] To realize this invention, the system first receives learning goals and current skill levels from the user's terminal. The terminal is equipped with a camera and microphone, and has the ability to capture the user's facial expressions and voice. The server has an emotion engine implemented to analyze this data, analyzing the user's emotional state in real time. Based on the analysis results, the server dynamically generates an individualized learning plan according to the user's progress and emotional state.

[0574] In terms of hardware, this system can run on consumer robots and smart devices owned by users. In terms of software, it uses the Google Cloud Natural Language API for sentiment analysis and the Google Speech-to-Text API for real-time text conversion of voice input.

[0575] For example, if the system detects signs of stress from the user's facial expression while they are working on a learning task, the server will play music to help the user relax and adjust the next learning step. Additionally, when the user completes a learning task, a message of praise will be displayed to enhance their sense of accomplishment.

[0576] An example of a prompt message for a generative AI model might be: "The user appears depressed right now. Please suggest ways to improve their mood, especially how to cheer them up with music or words." This prompt message is used by the system to adjust the user's emotional state to a more comfortable level.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The device receives the user's learning goals and current skill level as input and sends it to the server. This information is used by the server as foundational data to generate a personalized learning plan.

[0580] Step 2:

[0581] The device uses its camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server as input. The server processes this data in real time and uses an emotion engine to analyze the user's emotional state. The results of the analysis are used to determine the user's emotional state.

[0582] Step 3:

[0583] The server dynamically adjusts the personalized learning plan based on emotional state data obtained from the emotion engine. Specifically, if the user is experiencing stress, it incorporates content that promotes relaxation into the learning plan.

[0584] Step 4:

[0585] The server monitors the user's learning progress and generates a message that positively evaluates their achievement upon completion of a learning task. This message is sent to the user's device and displayed to them. The aim is to increase user motivation.

[0586] Step 5:

[0587] In certain situations, the server uses a generative AI model to generate prompts that improve the user's emotional state. This is intended to include playing context-appropriate music or suggesting appropriate words. These prompts serve as a guide to enhance the user experience.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] The present invention is an AI-assisted learning platform that supports users who have set learning goals in effectively advancing their learning. Users first input their learning goals, current skill level, and available learning time via a terminal. This information is transmitted to a server and used as basic data for further processing.

[0606] Generating a learning plan

[0607] Based on the user data received, the server generates a learning plan necessary to achieve the user's set goals. This generation utilizes historical databases and machine learning algorithms to enable the most effective task allocation. For example, if a user inputs that they want to improve their score on a business English exam, the server will create a schedule that divides tasks such as vocabulary memorization, listening practice, and mock exams into weekly assignments to help them achieve their target score.

[0608] Calculating the timing of review

[0609] To ensure that learned material is retained for a long period, the server calculates the optimal timing for reviewing each learning item based on the forgetting curve. Specifically, the server analyzes learning data and sends notifications to the user immediately after learning, one day later, and one week later to provide them with opportunities to review.

[0610] Provide feedback

[0611] As users record their progress on their devices, this data is transferred to a server. The server analyzes this data and provides feedback based on their level of achievement and understanding. If progress is stalled, the server generates encouraging messages to motivate the user to continue learning.

[0612] Personalized advice

[0613] Based on the user's learning outcomes and history, the server performs long-term analysis and generates personalized advice based on the insights gained. For example, if a user repeatedly experiences problems in a particular area, the server will provide additional learning resources or rescheduled tasks tailored to that area.

[0614] In this way, the system of the present invention can provide customized learning support for each user, making the process of achieving goals meaningful.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The user enters their learning goals, skill level, and available study time via their device. The device then sends this information to the server.

[0618] Step 2:

[0619] The server analyzes the received user information and generates a customized learning plan based on the user's set learning goals. The server creates this plan based on information from the database and past performance, and sends it to the terminal.

[0620] Step 3:

[0621] The user begins learning using a device and records their progress. The device sends the recorded data to the server in real time. The server analyzes the received data to understand the user's progress.

[0622] Step 4:

[0623] The server calculates the timing for review based on the forgetting curve. This calculation takes into account the nature of the learned material and past review history. The server then notifies the user via their device at the appropriate time to review.

[0624] Step 5:

[0625] The server analyzes the user's progress and generates feedback based on their learning achievements. It also simultaneously generates encouraging messages to maintain motivation for learning. The generated feedback and messages are sent to the device and displayed to the user.

[0626] Step 6:

[0627] The server accumulates long-term learning data from users and generates personalized advice based on that data. Based on the analysis results, the server suggests the next learning steps and notifies the user through their device.

[0628] (Example 1)

[0629] 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".

[0630] In today's educational environment, there is a need to provide effective learning methods tailored to the individual goals and skill levels of each learner. However, creating individualized and optimized learning plans is a laborious process, and learners face challenges in determining the optimal timing for review and receiving feedback on their learning progress.

[0631] 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.

[0632] In this invention, the server includes means for receiving progress, goals, and abilities entered by the user via an information processing device and generating an individualized learning plan; means for creating and presenting an optimal learning plan based on past information and machine learning algorithms; and means for calculating an appropriate review period considering the forgetting curve and notifying the user using an instruction device. This enables personalized and efficient learning support for individual learners, as well as optimal review and feedback.

[0633] An "information processing device" refers to a system for inputting, processing, storing, and outputting data, and is a device that enables two-way communication with the user.

[0634] A "personalized learning plan" is a specific schedule and action plan designed to support a user's customized learning progress, based on their specific goals, skill level, and learning pace.

[0635] The "review period" refers to the optimal time to relearn what you've studied in order to maximize its retention in your memory.

[0636] A "display device" is a device used to display or notify a user of information, and typically provides visual or audio output.

[0637] "Learning progress" is an indicator that shows the progress of a user's learning activities, and includes information such as the degree of achievement and understanding.

[0638] "Feedback" refers to the act of providing evaluations and comments on a user's learning activities and results, and giving information about improvements and next actions.

[0639] A "machine learning algorithm" is a set of techniques that enable the automation of predictions and decision-making by learning patterns from large amounts of data.

[0640] "Personalized learning recommendations" refer to the provision of specific advice and reference materials optimized for individual needs, based on the user's unique learning history and data.

[0641] In this invention, in order to provide an AI-assisted learning platform, a specific system is configured that operates in a manner suitable for the user's environment.

[0642] This system begins with the user using a device to input their learning goals, current abilities, and available study time. The device used by the user is typically a computer or smartphone. The device sends the entered data to a server, which then creates a learning plan based on this data.

[0643] The server utilizes software, including historical training data and machine learning algorithms, to analyze the received information. Specifically, the server uses a database management system and a machine learning platform to generate personalized learning plans tailored to the user's goals and calculate the optimal timing for review. Cloud computing services are often used for this purpose.

[0644] The generated learning plan is then delivered back to the device and presented to the user. For example, a user aiming for a high score on a business English exam could be provided with weekly vocabulary study, listening exercises, and a practice test plan.

[0645] As users progress through their learning, their progress is recorded on their devices. The server receives and analyzes this progress data in real time and provides feedback to the user. Specific feedback includes encouraging messages such as, "Your recent progress is going well. Keep it up!"

[0646] Furthermore, this system provides personalized learning recommendations to users through long-term data analysis. For example, if a user repeatedly struggles with a particular area, it can suggest additional learning materials tailored to that area.

[0647] By utilizing a generative AI model, it can suggest specific learning strategies using prompts such as, "What kind of study plan is needed to raise a specific test score by 200 points in three months?"

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] Users input their learning goals, current abilities, and available study time using their device. This data is sent from the device to the server. The server uses this information as foundational data to create individualized learning plans. Specifically, the user inputs data on the device's interface and clicks the "Send" button to send the data.

[0651] Step 2:

[0652] Based on the received user data, the server generates a learning plan tailored to the user's goals using a historical learning database and machine learning algorithms. The input here is user data, and the output is a personalized learning plan. As part of data processing, the server selects appropriate learning materials according to the user's skill level and incorporates them into the schedule.

[0653] Step 3:

[0654] The server delivers the generated study plan to the terminal. The terminal receives the study plan and displays it to the user. At this time, the plan is displayed with a visual layout. Specifically, the study plan is displayed on the terminal screen as a weekly calendar, and the user proceeds with their studies based on it.

[0655] Step 4:

[0656] The user learns according to a plan and records their progress on their device. The device sends this data to the server. The input is learning progress data, and the server performs data calculations based on this data to quantify the user's achievement and understanding. Specifically, the user presses the progress recording button on their device each time they complete a learning task.

[0657] Step 5:

[0658] The server analyzes the received progress data and generates feedback. This feedback includes an assessment of achievement and advice for future learning. The feedback is sent to the device and displayed to the user. For example, if the user is making good progress, the device might display a message such as, "You're doing great!"

[0659] Step 6:

[0660] The server analyzes long-term learning data and provides personalized advice to the user. This includes suggestions for additional resources in specific areas. The input is accumulated learning data, and the output is customized learning suggestions. Specifically, the server sends appropriate learning material links based on the analysis to the user's device.

[0661] (Application Example 1)

[0662] 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".

[0663] In modern society, there is a demand for efficient and effective learning support tailored to the individual needs of each learner. However, systems that comprehensively support the automatic generation of personalized learning programs based on each individual's learning progress and goals, the setting of appropriate review timings to prevent forgetting, and the provision of financial incentives to enhance learning motivation are extremely limited. In particular, the realization of electronic transactions and reward systems directly linked to learning activities is difficult, and many learners struggle to progress in their learning effectively.

[0664] 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.

[0665] In this invention, the server includes means for generating a customized learning program based on the user's learning progress and goals, means for calculating and notifying the optimal review timing based on the forgetting curve, and means for managing electronic transactions linked to the learning plan in order to streamline financial transactions. This enables the provision of learning support optimized for each user, as well as motivation through the provision of financial incentives linked to learning progress.

[0666] "Learning progress" refers to the current status or degree of progress made towards the learning goals set by the user.

[0667] A "learning program" is a set of educational activities that are customized based on the user's goals and skill level, and designed to facilitate effective learning.

[0668] The "forgetting curve" is a model that quantifies the process by which humans forget information, and it is a concept used to consider the most effective timing for review.

[0669] "Review timing" refers to the optimal time for reviewing learned material to ensure it is retained in long-term memory.

[0670] A "financial transaction" is an economic activity in which a user engages in commercial transactions related to learning and receives payment or compensation for those transactions.

[0671] "Electronic transactions" refer to financial transactions conducted using the internet or digital platforms.

[0672] The term "reward system" refers to the structure of incentives and rewards provided according to the user's learning progress.

[0673] "Motivation" refers to providing users with factors or triggers that encourage them to take action.

[0674] To implement this invention, a system is constructed in which a server plays a central role. The server receives data such as learning goals, skill levels, and learning history entered by the user through a terminal, and generates a customized learning program based on this data. The generated program is optimized by applying machine learning algorithms, taking into account each user's progress and learning efficiency. For example, a machine learning library such as Scikit-learn is used for this purpose.

[0675] Furthermore, the server calculates the optimal review timing based on the forgetting curve and sends notifications. Using tools like Firebase Cloud Messaging, push notifications are sent to the user's device at the appropriate time. This timely review notification serves as a means to promote retention of learned material.

[0676] Furthermore, the server manages electronic transactions linked to learning plans to streamline financial transactions. Purchases of learning content are made via electronic payment services such as Stripe and PayPal, and planned rewards are applied according to the user's learning progress. In addition, Firebase is used to manage the reward system and provide rewards based on the user's learning achievements.

[0677] For example, if a user sets a goal of raising their TOEIC score to 750, the server will design a learning program and recommend a specific online course. At this time, a discount can be offered for purchasing the course to encourage learning. Furthermore, points can be provided as additional rewards upon reaching certain progress levels to maintain learning motivation.

[0678] As a concrete example of a generative AI model, prompts such as, "Please suggest the optimal study plan to reach my target TOEIC score. My current score is 600, and my target score is 750. I have 10 hours of study time per week," enable personalized learning support for the user.

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] Users send input data, such as learning goals, skill levels, and available study time, to the server via their devices. This input data includes specific goals, such as "aiming for a TOEIC score of 750." This data is sent to the server in JSON format and serves as foundational information necessary for subsequent processing.

[0682] Step 2:

[0683] The server generates a customized learning program using a machine learning model based on the received user data. Specifically, it uses libraries such as Scikit-learn to calculate the optimal learning tasks and schedule for each user. The input for this step is the user's goals and skill level, and the output is the personalized learning program.

[0684] Step 3:

[0685] The server works in conjunction with the learning program to calculate review timing based on the forgetting curve. It uses learning content and forgetting curve data as input to calculate the optimal review schedule. This output is notified to the terminal as a list of review timings.

[0686] Step 4:

[0687] The device pushes a notification to the user via Firebase Cloud Messaging, reminding them when it's time to review. Here, a message is created and delivered to the user based on the review timing calculated in the previous step. This allows the user to review at the appropriate time.

[0688] Step 5:

[0689] The server manages electronic transactions related to the learning program. It efficiently processes purchases using APIs from Stripe and PayPal. The input is the details of the learning content selected by the user, and the output is transaction confirmation information.

[0690] Step 6:

[0691] The server manages a reward system based on learning progress. It sends progress data to the Firebase Database and calculates and awards rewards and points based on that data. The input is learning progress data, and the output is reward information. The server sends this data back to the device, prompting the user to claim their rewards.

[0692] 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.

[0693] The system of this invention provides personalized support to enable users to learn more effectively, and in particular, by combining it with an emotion engine, it enables flexible responses to changes in emotions. First, the user inputs their learning goals and current skill level via a terminal and sends this information to the server. Based on this basic information, the server generates an appropriate learning plan.

[0694] Recognition and analysis of emotions

[0695] The server has an emotion engine implemented to understand the user's emotional state. This emotion engine analyzes emotions in real time through input such as the user's facial expressions, tone of voice, and typing speed. For example, if the emotion engine determines that the user is feeling stressed while learning, it sends that information to the server.

[0696] Adjusting the learning plan

[0697] The server receives data from the emotion engine and dynamically adjusts the learning plan based on the user's emotional state. For example, if the emotion engine detects that the user's concentration is waning, it will provide relaxing content or temporarily reduce the learning task.

[0698] Feedback and content provision

[0699] The server generates appropriate feedback based on the user's emotions. When the user is in a positive emotional state, it provides messages of praise and additional challenges to enhance their sense of accomplishment. If negative emotions are detected, it calms the user through relaxing music and simple games.

[0700] Personalized advice

[0701] The server accumulates the user's past learning and emotional data and provides long-term, personalized advice based on this. This system maximizes long-term learning outcomes by suggesting a learning schedule that takes the user's emotional patterns into account.

[0702] In this way, the system of the present invention can provide flexible learning support while responding to the user's emotions, and offer an efficient process for achieving goals.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user uses a device to input learning goals, skill levels, and initial settings required for emotion recognition. The device then sends this information to the server.

[0706] Step 2:

[0707] The server generates an optimal learning plan to achieve learning objectives based on the received user information. This involves utilizing existing information from the database and machine learning algorithms. The generated plan is sent to the terminal and presented to the user.

[0708] Step 3:

[0709] An emotion engine operates on the device, acquiring real-time data such as the user's facial expressions and voice tone. This allows the system to recognize the user's emotional state. The recognized emotion data is then sent to a server.

[0710] Step 4:

[0711] The server receives data from the emotion engine and adjusts the learning plan based on the user's emotional state. If stress or anxiety is detected, the server provides relaxing content and reduces the load on the plan as needed.

[0712] Step 5:

[0713] As a user progresses through their learning and records their progress on their device, the device sends this information to the server. The server then analyzes the progress data and sentiment data to generate feedback tailored to the user's current learning situation.

[0714] Step 6:

[0715] If the server determines that the user's emotional state is positive, it provides praising feedback and additional challenges to amplify their sense of accomplishment. Conversely, if a negative state is detected, it also offers advice on relaxation techniques and ways to improve motivation.

[0716] Step 7:

[0717] The server accumulates long-term learning and emotional data to provide users with personalized future learning plans and advice. These plans take into account the user's emotional patterns and learning performance, and personalized suggestions are made based on this data.

[0718] (Example 2)

[0719] 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".

[0720] Modern learners are overwhelmed by diverse information and vast amounts of learning materials, making it difficult for them to receive efficient and individually tailored learning support. Furthermore, few systems offer flexible responses to emotional and concentration lapses, making it difficult for learners to continue learning at their own pace. Therefore, there is a need for the development of systems that consider learners' emotional states and provide personalized learning experiences.

[0721] 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.

[0722] In this invention, the server includes means for generating customized learning content based on the user's learning progress and goals, means for analyzing the user's emotional state and dynamically adjusting recommended learning content, and means for generating personalized advice for each user based on long-term learning information. This enables learners to receive an optimal learning experience tailored to their emotional state and learning progress.

[0723] A "user" refers to an individual who uses an information system for learning or gathering information.

[0724] "Learning progress" indicates the current state of achievement of the learning goals set by the user.

[0725] "Learning content" refers to the collective term for questions and learning materials related to the knowledge and skills that the user aims to acquire.

[0726] The "forgetting curve theory" is a theory that describes how human memory is lost over time.

[0727] "Emotional state" refers to the user's current psychological and emotional condition.

[0728] "Personalized advice" refers to individualized learning advice provided based on each user's past learning history and emotional patterns.

[0729] "Generative artificial intelligence" is a type of machine learning technology that has the ability to learn patterns from large amounts of data and generate new data.

[0730] A "learning plan" is a plan of time and content designed to help users efficiently acquire knowledge and skills.

[0731] "Monitoring" refers to the act of continuously observing and recording a user's learning activities and emotional state.

[0732] The system of this invention consists of a user terminal and a central server. The user inputs their learning goals and skill level via the terminal. This data is transmitted from the terminal to the server, which uses generative artificial intelligence to generate a customized learning plan for the user. The user's terminal is also equipped with a camera and microphone, used to analyze the user's facial expressions and voice tone. This allows the system to understand the user's emotional state and transmit it to the server in real time.

[0733] The server implements an emotion engine that dynamically adjusts learning content based on the user's emotional state. This process helps modify the learning plan and provide relaxing content if the user is deemed stressed or lacking concentration. It also has the ability to provide personalized advice based on long-term learning data and emotional patterns. For example, if the system determines that the user is "tired from practicing math," it may suggest listening to relaxation music.

[0734] Examples of prompts include: "What kind of support is effective when you feel tired while learning English?" By inputting such a prompt into the AI ​​model, the system will generate appropriate feedback and suggestions.

[0735] Thus, the system of the present invention provides advanced personalization technology to support the user's learning process and enable them to effectively achieve their goals.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] Users input their learning goals and skill levels using their device. This data is then sent from the device to the server, allowing the server to receive basic information to understand the user's learning needs.

[0739] Step 2:

[0740] Based on the received learning objectives and skill level data, the server uses a generative AI model to generate an optimal learning plan for the user. The server selects learning materials and designs the learning schedule, then sends this plan to the terminal to notify the user.

[0741] Step 3:

[0742] The user provides facial expressions and voice tone to the server through the camera and microphone built into the device. The device acquires this emotional data in real time and sends it to the server. Through this process, the user's emotional state is collected.

[0743] Step 4:

[0744] The server uses an emotion engine to analyze the received emotional data. The server evaluates the user's emotional state and identifies emotions such as stress or lack of concentration. This allows the server to understand the user's psychological condition.

[0745] Step 5:

[0746] The server dynamically adjusts the learning plan based on the results of sentiment analysis. For example, if the server determines that the user is tired, it will reduce the learning task or provide relaxation content. This change is notified to the user through the device.

[0747] Step 6:

[0748] The server generates feedback and adaptive content regarding the learning process, responding to the user's emotions. It provides additional challenges for positive emotions and relaxing content for negative emotions. This feedback is sent to the user's device.

[0749] Step 7:

[0750] The server accumulates long-term learning and emotional data and analyzes user-specific learning patterns. This prepares the server to improve future learning plans and provide personalized advice.

[0751] (Application Example 2)

[0752] 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".

[0753] A challenge for learners is that they often don't receive appropriate feedback tailored to their individual emotional states. Traditional systems simply provide feedback based on learning outcomes, lacking the flexibility to respond to learners' emotions. As a result, learners may experience stress or loss of motivation, and these issues need to be addressed appropriately.

[0754] 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.

[0755] In this invention, the server includes means for generating an individualized learning plan based on the user's learning progress and objectives, means for calculating and notifying the optimal timing for review based on a forgetting curve, and means for providing auditory and visual stimuli based on the user's emotional state. This enables personalized support that responds to the learner's emotional state.

[0756] A "user" is someone who interacts with the system and provides data on their learning progress and emotional state.

[0757] "Learning progress" refers to information that shows the results and progress a user has achieved so far in the learning process.

[0758] "Purpose" refers to the goals or objectives that users are trying to achieve in their learning activities.

[0759] An "individualized learning plan" is a learning plan and schedule optimized based on the user's specific needs, goals, and learning progress.

[0760] The "forgetting curve" is a theoretical model that shows how information is forgotten over time.

[0761] The "review period" is the optimal time for users to reconfirm the learned material in order to best retain it in their memory.

[0762] "Emotional state" refers to changes in the user's feelings and includes elements such as stress and concentration that affect learning.

[0763] "Auditory and visual stimuli" refer to sensory inputs such as sounds and images used to improve the user's emotional state and enhance learning effectiveness.

[0764] To realize this invention, the system first receives learning goals and current skill levels from the user's terminal. The terminal is equipped with a camera and microphone, and has the ability to capture the user's facial expressions and voice. The server has an emotion engine implemented to analyze this data, analyzing the user's emotional state in real time. Based on the analysis results, the server dynamically generates an individualized learning plan according to the user's progress and emotional state.

[0765] In terms of hardware, this system can run on consumer robots and smart devices owned by users. In terms of software, it uses the Google Cloud Natural Language API for sentiment analysis and the Google Speech-to-Text API for real-time text conversion of voice input.

[0766] For example, if the system detects signs of stress from the user's facial expression while they are working on a learning task, the server will play music to help the user relax and adjust the next learning step. Additionally, when the user completes a learning task, a message of praise will be displayed to enhance their sense of accomplishment.

[0767] An example of a prompt message for a generative AI model might be: "The user appears depressed right now. Please suggest ways to improve their mood, especially how to cheer them up with music or words." This prompt message is used by the system to adjust the user's emotional state to a more comfortable level.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] The device receives the user's learning goals and current skill level as input and sends it to the server. This information is used by the server as foundational data to generate a personalized learning plan.

[0771] Step 2:

[0772] The device uses its camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server as input. The server processes this data in real time and uses an emotion engine to analyze the user's emotional state. The results of the analysis are used to determine the user's emotional state.

[0773] Step 3:

[0774] The server dynamically adjusts the personalized learning plan based on emotional state data obtained from the emotion engine. Specifically, if the user is experiencing stress, it incorporates content that promotes relaxation into the learning plan.

[0775] Step 4:

[0776] The server monitors the user's learning progress and generates a message that positively evaluates their achievement upon completion of a learning task. This message is sent to the user's device and displayed to them. The aim is to increase user motivation.

[0777] Step 5:

[0778] In certain situations, the server uses a generative AI model to generate prompts that improve the user's emotional state. This is intended to include playing context-appropriate music or suggesting appropriate words. These prompts serve as a guide to enhance the user experience.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means of generating a customized learning plan based on the user's learning progress and goals,

[0803] A means of calculating and notifying the optimal review timing based on the forgetting curve,

[0804] A means of analyzing users' learning progress and providing feedback and messages to maintain motivation,

[0805] A system that includes means for generating personalized advice for each user based on long-term learning data.

[0806] (Claim 2)

[0807] The system according to claim 1, which receives goals and skill levels entered by a user and generates a learning plan based on them.

[0808] (Claim 3)

[0809] The system according to claim 1, which monitors the user's progress in real time and presents the next learning task based on that progress.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means for receiving progress, goals, and abilities entered by the user via an information processing device, and generating an individualized learning plan,

[0813] A means of creating and presenting an optimal learning plan based on past information and machine learning algorithms,

[0814] A means of calculating an appropriate review period considering the forgetting curve and notifying the user using an indicator device,

[0815] A means of analyzing progress information obtained from users and providing feedback and messages for continued support tailored to the user's proficiency and understanding,

[0816] A system that includes means to analyze accumulated learning information over the long term and generate learning recommendations optimized for each individual user.

[0817] (Claim 2)

[0818] The system according to claim 1, which acquires user goal and ability level data via an information device and generates a learning plan based on this data.

[0819] (Claim 3)

[0820] The system according to claim 1, which monitors the user's progress in real time and presents the optimal next learning item accordingly.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means for generating a customized learning program based on the user's learning progress and goals,

[0824] A means of calculating and notifying the optimal review timing based on the forgetting curve,

[0825] A means of analyzing users' learning progress and providing information to maintain feedback and motivation,

[0826] To streamline financial transactions, a means of managing electronic transactions linked to learning plans,

[0827] A system that includes means for implementing a reward system to provide compensation based on progress.

[0828] (Claim 2)

[0829] The system according to claim 1, which receives goals and skill levels entered by a user and generates a learning program based on them.

[0830] (Claim 3)

[0831] The system according to claim 1, which monitors the user's progress in real time and presents the next learning task based on that progress.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] A means for generating customized learning content based on the user's learning progress and goals,

[0835] A means of calculating and notifying the optimal review timing based on the forgetting curve theory,

[0836] A means to analyze the user's emotional state and dynamically adjust recommended learning content,

[0837] A means of providing relaxing music and simple activities according to the user's emotional state,

[0838] A system that includes means for generating personalized advice for each user based on long-term learning information.

[0839] (Claim 2)

[0840] The system according to claim 1, which receives goals and skill levels transmitted by a user and constructs a learning plan using generative artificial intelligence.

[0841] (Claim 3)

[0842] The system according to claim 1, which monitors the user's progress and emotional data in real time and presents the next learning task accordingly.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for generating an individualized learning plan based on the user's learning progress and objectives,

[0846] A means of calculating and notifying the optimal review time based on the forgetting curve,

[0847] A means of analyzing users' learning progress and providing information to maintain evaluation and motivation,

[0848] A means of generating personalized advice for each user based on long-term learning information,

[0849] A system that includes means of providing auditory and visual stimuli based on the user's emotional state.

[0850] (Claim 2)

[0851] The system according to claim 1, which receives goals and skill levels entered by a user and generates a learning plan based on them.

[0852] (Claim 3)

[0853] The system according to claim 1, which monitors the user's progress in real time and presents the next learning task based on that progress. [Explanation of symbols]

[0854] 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 a customized learning program based on the user's learning progress and goals, A means of calculating and notifying the optimal review timing based on the forgetting curve, A means of analyzing users' learning progress and providing information to maintain feedback and motivation, To streamline financial transactions, a means of managing electronic transactions linked to learning plans, A system that includes means for implementing a reward system to provide compensation based on progress.

2. The system according to claim 1, which receives goals and skill levels entered by a user and generates a learning program based on them.

3. The system according to claim 1, which monitors the user's progress in real time and presents the next learning task based on that progress.

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