system

Abstract

We provide the system. [Solution] Information processing means for receiving learner information and generating an individualized learning plan, A planning means for presenting learning items based on the learning plan, taking into consideration the appropriate timing for relearning; Information analysis means for monitoring learners' learning progress and emotional state, and for providing psychological support, A display means that uses a portable information terminal to display personalized learning information to the user in real time and allows for two-way information exchange, A synchronization mechanism for periodically collecting learner progress data and sending it to a dedicated server, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 current learning support systems, it is difficult to provide a detailed learning plan according to the individual situations of learners. Also, there is a problem that it is impossible to grasp the progress and emotional state of learning in real time and provide appropriate mental support. Furthermore, the timing of review is generally fixed, and it is not possible to review at the optimal timing according to the decline of individual memory, and there is a problem that the environment for learners to learn efficiently is not sufficiently prepared.

Means for Solving the Problems

[0005] This invention provides a data processing means for collecting learner information and generating individualized learning plans. Furthermore, it includes scheduling means for determining the optimal timing for review for each learner based on Ebbinghaus's forgetting curve and presenting learning tasks accordingly. It also includes data analysis means for monitoring learners' learning progress and emotional state in real time and providing mental support as needed. This creates an environment in which learners can progress efficiently and effectively, and also includes means for generating and providing mock exams and practice problems, thereby realizing comprehensive learning support.

[0006] A "learner" is an individual who uses a learning support system and is aiming to achieve a specific goal or qualification.

[0007] "Data processing means" refers to a device or software that has the function of receiving learner input information and generating an individualized learning plan based on that information.

[0008] A "study plan" is a plan that outlines the learning content and schedule necessary for learners to efficiently achieve their goals.

[0009] A "scheduling means" is a device or software that has the function of presenting learning tasks at specific times based on a learning plan.

[0010] "Review timing" refers to periods of time set aside for learners to review the learned material in order to efficiently solidify their memory.

[0011] The Ebbinghaus forgetting curve is a psychological model that illustrates the decline of memory over time, and is a theory used to suggest the optimal interval for review.

[0012] "Data analysis tools" refer to devices or software that process and analyze data to understand learners' learning progress and emotional states, and to provide necessary mental support.

[0013] "Mental support" refers to assistance aimed at reducing the psychological burden and stress that learners face during their studies and maintaining their motivation.

[0014] A "mock exam" is a practice test created based on the format of an actual exam, designed to help learners prepare for their target qualifications or exams.

[0015] "Test generation means" refers to a device or software that has the function of generating mock tests or last-minute preparation questions according to the learner's learning progress. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for providing learners with an individualized learning experience, and it primarily functions using three elements: a server, a terminal, and a user. Each element within the system is responsible for a specific function, and by linking data with each other, it realizes an optimal learning environment for the learner.

[0038] Server operation

[0039] The server receives information about the learner and generates a personalized learning plan based on it. The server's data processing functions analyze the information entered by the user to identify the optimal learning content and sequence. This takes into account the learner's goals, current learning level, and qualification requirements. It also calculates review timing based on the forgetting curve and creates a schedule that presents appropriate learning tasks.

[0040] Furthermore, the server periodically analyzes learners' learning progress data and emotional state, and provides mental support as needed. For example, if a learner is feeling stressed, it sends relaxation tips and encouraging messages. Based on learning results and mock exam scores, the server generates personalized practice problems and provides them to learners via their devices.

[0041] Terminal operation

[0042] The terminal intuitively displays learning plans and assignments delivered from the server to the learner. The user uses the terminal to carry out daily learning based on the plan and record their learning progress. The terminal transmits the user's input data and learning outcomes to the server in real time to support progress monitoring.

[0043] The device also helps learners become familiar with the testing environment by allowing them to take practice tests and review questions. After taking the test, the device displays feedback and provides information indicating which areas the learner should further strengthen.

[0044] User actions

[0045] Users input their learning information via their device and proceed with their studies according to the learning plan provided by the system. Users can learn at their own pace and record and check their progress using their device. In addition, they can check their current level of understanding through mock exams and reinforce areas where they are lacking based on feedback from the server, repeating this cycle to efficiently achieve their goals.

[0046] This invention is a comprehensive system for providing an environment that adapts to learners and promotes sustained learning effectiveness.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users use their devices to input their basic information, learning goals, and current progress. This information includes the name of the qualification they are aiming for, the exam date, and the time and period they can dedicate to studying.

[0050] Step 2:

[0051] The terminal sends user input information to the server. The server analyzes the received information and prepares to generate a learning plan optimized for the user.

[0052] Step 3:

[0053] The server uses Ebbinghaus's forgetting curve as a reference to calculate the optimal timing for review to maximize user retention. This calculation is then incorporated into the learning plan to formulate specific learning steps.

[0054] Step 4:

[0055] The server delivers the formulated learning plan to the terminal. The terminal receives it and presents the user with daily learning assignments and a schedule.

[0056] Step 5:

[0057] Users progress through their daily studies based on the provided learning plan and input their progress and learning outcomes into their device. This includes study time and level of understanding of the material.

[0058] Step 6:

[0059] The device sends the user's learning progress and input data to the server. The server analyzes the progress based on this data and infers the user's emotional state. It generates mental support messages as needed.

[0060] Step 7:

[0061] The server generates individually customized practice tests and last-minute preparation questions, taking into account the user's learning progress. These are then delivered to the terminal.

[0062] Step 8:

[0063] Users take practice tests and study materials received on their devices. After the test, the device sends the results to a server, which then generates feedback.

[0064] Step 9:

[0065] The device presents the user with feedback from the server. The user uses this feedback to incorporate their knowledge into the next learning step.

[0066] Step 10:

[0067] The server periodically reviews the user's learning data, updates the learning plan as needed, and delivers it back to the device. This ensures continuous learning adaptation.

[0068] (Example 1)

[0069] 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."

[0070] In today's diverse learning environment, there is a challenge in providing individualized learning tailored to each learner's needs and progress. Furthermore, maintaining learner motivation while efficiently advancing learning on an optimal schedule is not easy. Additionally, a lack of support that considers learners' emotional states makes it difficult to ensure the sustainability of their learning.

[0071] 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.

[0072] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, time management means for managing learning activities and considering appropriate review times, and analysis means for monitoring the learner's progress and psychological state and providing support. This enables the provision of a learning experience optimized for each individual learner, allowing for efficient learning and maintenance of motivation.

[0073] "Information processing means" refers to a configuration that has the function of analyzing data provided by learners and generating personalized learning plans based on that analysis.

[0074] A "time management system" is a configuration that has the function of identifying the optimal timing for review and adjusting the schedule based on the learner's study plan.

[0075] "Analysis means" refers to a configuration that has the function of analyzing the learner's progress and psychological state and providing necessary support based on that analysis.

[0076] A "test construction method" is a configuration that has the function of generating and providing learners with optimal assessment tests and supplementary assignments.

[0077] A "terminal display means" is a configuration that provides an interface in which the user can input their own information and record and check their learning activities in real time.

[0078] The system is configured as follows in an embodiment for carrying out this invention.

[0079] The system primarily consists of three elements: a server, a terminal, and a user. The server houses a database and a generative AI model, analyzing information from the user to create a personalized learning plan. The scheduling function, which corresponds to the forgetting curve, utilizes a memory retention model, enabling efficient learning. Specifically, the server analyzes input from the user, including learning goals, progress data, and emotional states, and automatically sets up a learning plan and review timing based on the results.

[0080] The terminal provides an interface for users to input information, review their learning progress, and record their progress. The terminal communicates with the server in real time, continuously recording user actions and progress. As a result, the terminal displays updated learning plan feedback from the server and support messages for the learner. This information is displayed in an intuitively understandable format, serving to visualize learning status and clarify the next steps to take.

[0081] Users conduct their daily learning activities through their devices. The information entered is immediately sent to the server and used to improve the overall learning plan. For example, if a user is studying for a programming certification exam, the server can analyze the user's past learning history and provide a customized plan, such as one that focuses on Python.

[0082] (Specific example) Specifically, the server analyzes the user's learning history and, when it senses the need for repetitive learning, sends a "plan to review the previous week's points at the beginning of each week" to the user's device. It also infers the user's emotional state and sends messages to encourage relaxation, making it easier to maintain motivation to learn.

[0083] (Example prompt) "Generate a study plan for a user aiming to obtain a programming certification. Also, consider that the learner is at an intermediate level and focuses on Python, and suggest an appropriate review schedule."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user enters information such as learning goals, current learning level, and past learning history through the device. This input data is then sent to the server by the device. Specifically, the user enters the required information into the form on the device and presses the "Submit" button to transfer the data to the server.

[0087] Step 2:

[0088] The server analyzes data received from the user and generates a personalized learning plan using a generative AI model. The server receives input data and designs the optimal learning content and review schedule based on it. This process generates an optimal curriculum tailored to the learner's learning history and goals. Specifically, the AI ​​model calculates and creates a customized plan based on the provided data.

[0089] Step 3:

[0090] The server sends the generated learning plan to the device. The server transmits the learning plan information to the device via the network. As output, a learning plan optimized for the user is delivered to the device, and the device receives it. Specifically, the server uses a REST API to send the plan data to the device in JSON format.

[0091] Step 4:

[0092] The device displays the received learning plan to the user. The user checks the learning tasks on the device screen and proceeds with their studies accordingly. The device takes the learning plan as input, and the output displayed is the daily learning content and schedule. Specific actions include the user scrolling through the screen to check the tasks.

[0093] Step 5:

[0094] The user performs the learning process and records their progress on their device. User input includes learning progress and feedback, which the device then sends to the server. Output includes learning results and progress, which are saved to the server in real time. Specifically, this includes the user clicking the "Complete" button on their device when they finish a task.

[0095] Step 6:

[0096] The server analyzes collected progress data and user feedback, and generates and sends feedback and support messages to the terminal as needed. The server uses a sentiment analysis algorithm to infer the user's psychological state. Specific outputs include advice and mental support messages for the user. Based on the analysis results, the server sends appropriate information to the terminal.

[0097] Step 7:

[0098] The user takes a practice test on their device. The user inputs answers, which the device then sends to a server. The server analyzes the answers and generates an evaluation result and further learning advice as output. Specifically, the user clicks the "Start Practice Test" button to begin taking the test, and after completion, the device receives and displays the results.

[0099] (Application Example 1)

[0100] 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."

[0101] Traditional learning systems have faced challenges in adequately addressing learners' individual needs and emotional states, as well as lacking effective real-time feedback and psychological support. Furthermore, it has been difficult to set appropriate timings for relearning based on the learner's progress.

[0102] 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.

[0103] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, planning means for presenting learning items based on the learning plan, taking into account appropriate timing for relearning, and information analysis means for monitoring the learner's learning progress and emotional state and providing psychological support. This makes it possible to provide learners with an individualized learning experience and promote efficient and sustainable learning.

[0104] "Learner information" refers to information necessary to personalize learning plans, such as personal data about learners, learning history, goals, and current level of understanding.

[0105] An "individualized learning plan" is a learning schedule and content that is customized based on the learner's specific needs and progress.

[0106] "Information processing means" refers to a device or program in which a server performs calculations or analyses to analyze learner information and generate personalized learning plans.

[0107] A "planning tool" is a method or device that adjusts the timing of presenting learning items and reviewing them based on a learning plan, and provides learners with appropriate tasks.

[0108] "Information analysis means" refers to a device or program for monitoring learners' emotional states and progress, and for providing methods of psychological support when needed.

[0109] A "portable information terminal" is a portable electronic device that users can operate intuitively and that can display learning plans and feedback.

[0110] "Display means" refers to a device or function for visually presenting learning plans, progress, and feedback on a portable information terminal.

[0111] A "synchronization means" is a function or device that periodically sends learner progress data to a server and maintains information between the learner and the server.

[0112] This invention constructs a system in which three elements—a server, a terminal, and a user—interact with each other in order to provide learners with an individualized learning experience.

[0113] The server is equipped with information processing capabilities to generate appropriate learning plans based on information received from learners. Hosted on a cloud platform (e.g., Amazon Web Services or Google Cloud Platform), the server analyzes learner data and creates individually optimized learning plans. Furthermore, the server incorporates information analysis capabilities to monitor learners' progress and emotional states in real time and provide psychological support as needed.

[0114] The terminal is provided to learners as a portable information terminal and is equipped with a display that enables intuitive operation and immediate display of information. This terminal also has a synchronization mechanism that sends learners' progress data to a server, presenting learners with learning plans, progress status, and feedback in real time. As a result, learners can smoothly proceed with their daily learning activities.

[0115] This system allows users to learn at their own pace. For example, learners can use a smartphone app to check their daily study schedule and receive feedback based on their progress. As the exam date approaches, the server analyzes past practice test results and delivers questions focused on areas that need improvement to the device, further supporting the learner's understanding.

[0116] An example of a prompt using a generative AI model is: "Generate an effective learning plan based on the user's desired learning goals and current progress. Also, suggest ways to send encouraging messages if the user is feeling stressed." This prompt serves as a guide for the server to provide flexible and accurate support to learners through the generative AI model.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives learner information. Inputs include learner profile data, learning history, and goals. Based on this data, the server uses information processing tools to analyze the data and generate an optimal learning plan for the learner. The output is a individually optimized learning plan.

[0120] Step 2:

[0121] The terminal receives the learning plan sent from the server and displays it intuitively to the learner. The input is learning plan data from the server. The terminal's display mechanism allows the user to visually confirm the information. As a result, the user can clearly understand how to proceed with their daily learning activities. The output provides the user with a visually organized learning schedule.

[0122] Step 3:

[0123] Users progress through their learning using a device and input their learning progress. Input includes learning outcomes and progress data. The device transmits this data to the server in real time. This is necessary for the server to monitor the learner's progress and collect data. The output is the progress data sent to the server.

[0124] Step 4:

[0125] The server infers the learner's emotional state based on received progress data and provides psychological support if necessary. Inputs include the learner's progress data and data on their past emotional states. The server analyzes the data using information analysis tools, and if it determines that the learner is experiencing stress, it generates an encouraging message and sends it to the terminal. The output is emotional support data, including feedback.

[0126] Step 5:

[0127] The terminal displays feedback from the server to the learner. Inputs include emotional support messages and learning feedback sent from the server. The display allows the user to review this feedback in a timely manner and adjust their learning method as needed. The output is visually presented feedback to the user.

[0128] Step 6:

[0129] The user takes a practice test from a terminal. The input is the practice test questions displayed on the terminal. The user takes the test and enters the results on the terminal. Meanwhile, the terminal sends the entered test results to a server, which analyzes the learner's score and level of understanding. The output is the user's test result data.

[0130] Step 7:

[0131] The server identifies the learner's weaknesses based on the results of a practice test and generates appropriate practice problems. The input is the results of the practice test. The server uses a test generation method to generate practice problems based on the identified weaknesses and sends them back to the terminal. The output is customized practice problems.

[0132] 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.

[0133] This invention provides an individualized learning support system that takes into account the learner's emotional state, and offers a more effective learning environment through the coordinated functioning of three elements: a server, a terminal, and the user. In particular, this system incorporates an emotion engine that can recognize the user's emotions in real time and create an optimal learning environment.

[0134] Server operation

[0135] The server receives basic information and learning objectives entered by the user on the terminal and generates a personalized learning plan. The server's data processing function analyzes the input information and determines the learning content and sequence. During this process, an emotion engine is used to analyze the learner's emotional state and select more appropriate learning content. For example, if the user is anxious, the server suggests learning tasks and methods that are expected to have a relaxing effect.

[0136] The server analyzes progress data based on learner input data and feedback from the emotion engine, and generates mental support and encouragement messages. The server also generates practice tests and study materials tailored to the learner's emotions and provides them to the learner via their device. In this case, if the learner may be experiencing some level of stress, the server prioritizes adjusting the difficulty level and providing content to help them relax.

[0137] Terminal operation

[0138] The device intuitively displays learning plans and analytical data delivered from the server. Users perform daily learning activities and record their progress through the device. Furthermore, it utilizes an emotion engine to recognize the user's emotional state from their input data and transmit it to the server. The device then presents the user with feedback and mental support messages from the server to help maintain learning motivation.

[0139] Furthermore, the device can use an emotion engine to pick up on the user's emotional changes in detail and provide alerts to reduce the burden of learning or notifications to encourage short breaks.

[0140] User actions

[0141] Users input the necessary learning information into their device and proceed with their learning activities according to the learning plan generated by the system. Throughout their daily learning activities, users can receive notifications and support from their device, monitor their emotional state, and adjust it as needed. They can also immediately check their learning progress through mock exams and obtain appropriate improvement measures based on those results.

[0142] This invention provides learners with an optimal learning method and environment that takes their emotional state into consideration, enabling them to learn efficiently while maintaining sustained motivation.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Users use their devices to input learning goals and basic information. This input includes the qualification they are aiming for, the exam date, and an estimated study time.

[0146] Step 2:

[0147] The terminal sends user input information to the server. The server activates the emotion engine and prepares to collect initial data about the user's emotional state.

[0148] Step 3:

[0149] The server generates a personalized learning plan based on user information and data from the emotion engine. Here, the learning content and sequence are adjusted to take the user's emotional state into consideration.

[0150] Step 4:

[0151] The server schedules the generated study plan and practice problems based on the set review timings and delivers them to the terminals.

[0152] Step 5:

[0153] The user checks their device and completes the presented learning tasks. The user's interactions during learning are analyzed by an emotion engine.

[0154] Step 6:

[0155] The device combines the user's learning progress and emotional data and sends it to the server. The server analyzes the acquired data and automatically generates mental support and encouragement messages.

[0156] Step 7:

[0157] The server sends mental support messages, generated based on learning progress and emotional state, to the terminal and displays them to the user. These messages are intended to maintain learning motivation.

[0158] Step 8:

[0159] The device presents the user with practice tests and additional study problems, and provides real-time feedback. It also reassessses the user's emotional state and revises the learning plan as needed.

[0160] Step 9:

[0161] The user takes a practice test and uses the results and feedback to verify the validity of their study plan. The device then sends the relevant data back to the server.

[0162] Step 10:

[0163] The server utilizes the user's latest learning data and sentiment analysis results to update the learning plan as needed and deliver the new plan to the device. By repeating this cycle, learners can continue to learn efficiently under optimal conditions.

[0164] (Example 2)

[0165] 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".

[0166] In recent years, there has been a growing need to address the individual emotional states of learners in learning environments. However, conventional systems provide uniform learning support without considering emotional states, leaving challenges in terms of maintaining learner motivation and reducing stress. Therefore, there is a need for more individualized learning support systems that take learners' emotional states into account.

[0167] 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.

[0168] In this invention, the server includes data processing means, scheduling means, data analysis means, emotion analysis means, and environment adjustment means. This enables the generation of personalized learning plans according to the learner's emotional state and the optimization of the learning environment.

[0169] "Data processing means" refers to a device or method that analyzes learner input information and generates an individualized learning plan.

[0170] A "scheduling means" is a device or method that presents appropriate learning tasks and review timings based on a learning plan.

[0171] "Data analysis means" refers to a device or method for analyzing data necessary to provide mental support while monitoring learners' learning progress and emotional state.

[0172] "Test generation means" refers to a device or method that generates and presents mock tests or practice problems to learners.

[0173] "Emotional analysis means" refers to a device or method that analyzes a user's real-time emotional state and uses that information to adjust the learning environment.

[0174] "Environmental adjustment means" refers to a device or method that dynamically adjusts content and learning conditions in order to reduce learner stress and provide an optimal learning environment.

[0175] This invention provides a system that offers personalized learning support that takes into account the learner's emotional state. The system functions through the cooperation of three parties: a server, a terminal, and a user. Specifically, the server performs data processing and emotion analysis, while the terminal functions as an interface with the user.

[0176] The server uses data processing tools to receive information entered by the user on the terminal. This information includes desired learning content and progress goals. The server also utilizes a generative AI model to create a personalized learning plan tailored to the user. This plan is optimized based on the user's learning history and emotional state analysis results from data analysis tools. For example, if the server detects user anxiety using emotion analysis tools, it will suggest learning content with a relaxing effect.

[0177] The device intuitively displays and notifies the user of learning plans and feedback from the server. It also utilizes sentiment analysis to monitor the user's emotions in real time. This information is sent to the server to further optimize the learning environment. The device can also use environmental adjustment tools to issue alerts prompting short breaks if the user feels overwhelmed.

[0178] Users can learn at their own pace by inputting information into their devices, based on a learning plan provided by the server. The server uses a test generation system to provide users with mock tests and practice questions, giving them opportunities to check their learning progress and understanding. In this process, specific questions such as "What kind of relaxing tasks can you suggest when the user is feeling anxious?" are used as prompts to the generating AI model.

[0179] This embodiment allows learners to continue learning while respecting their own emotional state, providing an environment where they can learn efficiently while maintaining high motivation.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server receives information entered by the user through their terminal. This input includes data such as learning goals, desired progress, and current mindset. Based on this, the server analyzes the information using data processing tools and generates an individualized learning plan. The output here is a learning plan tailored to the user. A generative AI model is used to design the optimal plan by referring to past successful patterns.

[0183] Step 2:

[0184] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time emotion data transmitted from the terminal (e.g., facial expression analysis data and voice tone). The server analyzes this data to determine the user's current emotional state. The output is the emotion analysis result, which provides guidance for adjusting the learning environment.

[0185] Step 3:

[0186] The terminal displays the learning plan and sentiment analysis results received from the server to the user. Input is data from the server, and output is the learning plan and notifications displayed on the screen. The terminal uses environmental adjustment tools to advise the user on necessary adjustments for learning, such as music playback and lighting adjustments. The terminal also directly displays feedback to the user, increasing their motivation for the next learning step.

[0187] Step 4:

[0188] Users progress through their learning according to a learning plan provided by the server using their device. New input data from the user (such as learning progress and feedback) is sent to the server via the device. This allows the server to continuously monitor the user's progress and prepare to suggest new learning steps. The output consists of continuously updated learning plans and feedback.

[0189] Step 5:

[0190] The server uses test generation tools to provide users with practice tests and study materials. The input consists of the user's progress data and past answer history. The server analyzes this data using data analysis tools to identify the user's weaknesses and areas requiring improvement. The output is a customized set of test questions, delivered to the user via a terminal. This allows the user to self-assess their progress and use it to further improve their learning.

[0191] (Application Example 2)

[0192] 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".

[0193] In today's learning environment, providing learning support that adapts to the emotional state of each individual learner is difficult, and in particular, learners struggle to manage their own emotions and motivations when studying at home. This leads to problems such as decreased learning efficiency and difficulty maintaining motivation. Furthermore, existing learning support technologies are insufficient to recognize learners' emotional states in real time and provide adaptive learning adjustments and support.

[0194] 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.

[0195] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan; planning means for presenting learning tasks based on the learning plan, taking into account appropriate review timings; information analysis means for monitoring the learner's learning progress and emotional state and providing mental support; and recognition and suggestion means for recognizing the learner's emotional state in real time and suggesting adjustments to learning content and activities for emotional stabilization accordingly. As a result, learners can proceed with their learning in an environment that is optimal for their emotional state, thereby improving learning efficiency and motivation.

[0196] "Information processing means" refers to devices or systems that receive information provided by learners and generate personalized learning plans based on that information.

[0197] A "plan creation tool" is a device or system that presents learning tasks based on a generated learning plan and determines the appropriate timing for reviewing those tasks.

[0198] "Information analysis tools" refer to devices and systems that monitor learners' learning progress and emotional state, and provide mental support as needed.

[0199] "Recognition and suggestion means" refers to devices or systems that recognize a learner's emotional state in real time and suggest adjustments to learning content or activities for emotional stabilization in accordance with that state.

[0200] In this embodiment of the invention, the system mainly consists of a server, a terminal, and a user. The server, acting as an information processing device, receives the learner's basic information and learning objectives, and generates an individualized learning plan accordingly. The software tools used in this process include a database management system and a plan creation algorithm. The server also employs analysis methods such as natural language processing and image recognition to infer the learner's real-time emotional state. This allows the server to provide appropriate mental support when the learner is in a particular emotional state.

[0201] The terminal is used daily by learners and serves as the user interface. The terminal displays the learning plan sent from the server and accepts learner input. The terminal is equipped with emotion recognition capabilities, analyzing the learner's facial expressions and voice through a webcam and microphone. This allows the terminal to track the learner's emotional state and send feedback to the server.

[0202] The user's role is to follow the learning plan displayed on the device and progress through their daily studies. The user can monitor their emotional state on the device and adjust their learning methods as needed. For example, if they are having difficulty learning, the device may suggest relaxation content or distractions such as games.

[0203] This system uses a specific generative AI model to provide a learning environment based on the user's emotional data and learning history. An example of a prompt might be, "Please suggest music that will help the learner relax."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server receives basic information and learning objectives sent by the user. This includes personal data entered by the learner from their device and their desired subjects to study. The server analyzes this data and generates a personalized learning plan. In generating the learning plan, the server receives assistance from a database management system and constructs the plan based on past learning data and statistical information. Finally, the generated learning plan is sent to the device.

[0207] Step 2:

[0208] The device receives a learning plan from the server and displays it to the user. The user begins learning based on the planned learning tasks. The device provides visual feedback on the current learning progress on the screen based on the user's input information. At this time, it utilizes emotion recognition technology to monitor the user's emotions in real time using data collected from the webcam and microphone.

[0209] Step 3:

[0210] The server receives and analyzes real-time emotional data sent from the terminal. Using an emotional recognition algorithm, it determines, for example, whether the user is lacking concentration or experiencing stress. If a problem is detected, the system uses a generative AI model to adjust the learning content to suit the learner and provide suggestions for emotional stabilization. This information is then sent back to the terminal.

[0211] Step 4:

[0212] Based on feedback received from the server, the device suggests necessary mental support and adjustments to learning methods to the user. For example, if the user is feeling stressed, the device offers options such as light exercise or music for relaxation. It also allows the user to choose from options based on prompts generated using a generative AI model.

[0213] Step 5:

[0214] Users progress through their learning by accepting suggestions from their device and monitoring their own emotional state. By accurately understanding their emotions, they can adjust their learning plan or take breaks as needed. This feedback loop allows users to maintain motivation and learn effectively.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system for providing learners with an individualized learning experience, and it primarily functions using three elements: a server, a terminal, and a user. Each element within the system is responsible for a specific function, and by linking data with each other, it realizes an optimal learning environment for the learner.

[0232] Server operation

[0233] The server receives information about the learner and generates a personalized learning plan based on it. The server's data processing functions analyze the information entered by the user to identify the optimal learning content and sequence. This takes into account the learner's goals, current learning level, and qualification requirements. It also calculates review timing based on the forgetting curve and creates a schedule that presents appropriate learning tasks.

[0234] Furthermore, the server periodically analyzes learners' learning progress data and emotional state, and provides mental support as needed. For example, if a learner is feeling stressed, it sends relaxation tips and encouraging messages. Based on learning results and mock exam scores, the server generates personalized practice problems and provides them to learners via their devices.

[0235] Terminal operation

[0236] The terminal intuitively displays learning plans and assignments delivered from the server to the learner. The user uses the terminal to carry out daily learning based on the plan and record their learning progress. The terminal transmits the user's input data and learning outcomes to the server in real time to support progress monitoring.

[0237] The device also helps learners become familiar with the testing environment by allowing them to take practice tests and review questions. After taking the test, the device displays feedback and provides information indicating which areas the learner should further strengthen.

[0238] User actions

[0239] Users input their learning information via their device and proceed with their studies according to the learning plan provided by the system. Users can learn at their own pace and record and check their progress using their device. In addition, they can check their current level of understanding through mock exams and reinforce areas where they are lacking based on feedback from the server, repeating this cycle to efficiently achieve their goals.

[0240] This invention is a comprehensive system for providing an environment that adapts to learners and promotes sustained learning effectiveness.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] Users use their devices to input their basic information, learning goals, and current progress. This information includes the name of the qualification they are aiming for, the exam date, and the time and period they can dedicate to studying.

[0244] Step 2:

[0245] The terminal sends user input information to the server. The server analyzes the received information and prepares to generate a learning plan optimized for the user.

[0246] Step 3:

[0247] The server uses Ebbinghaus's forgetting curve as a reference to calculate the optimal timing for review to maximize user retention. This calculation is then incorporated into the learning plan to formulate specific learning steps.

[0248] Step 4:

[0249] The server delivers the formulated learning plan to the terminal. The terminal receives it and presents the user with daily learning assignments and a schedule.

[0250] Step 5:

[0251] Users progress through their daily studies based on the provided learning plan and input their progress and learning outcomes into their device. This includes study time and level of understanding of the material.

[0252] Step 6:

[0253] The device sends the user's learning progress and input data to the server. The server analyzes the progress based on this data and infers the user's emotional state. It generates mental support messages as needed.

[0254] Step 7:

[0255] The server generates individually customized practice tests and last-minute preparation questions, taking into account the user's learning progress. These are then delivered to the terminal.

[0256] Step 8:

[0257] Users take practice tests and study materials received on their devices. After the test, the device sends the results to a server, which then generates feedback.

[0258] Step 9:

[0259] The device presents the user with feedback from the server. The user uses this feedback to incorporate their knowledge into the next learning step.

[0260] Step 10:

[0261] The server periodically reviews the user's learning data, updates the learning plan as needed, and delivers it back to the device. This ensures continuous learning adaptation.

[0262] (Example 1)

[0263] 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."

[0264] In today's diverse learning environment, there is a challenge in providing individualized learning tailored to each learner's needs and progress. Furthermore, maintaining learner motivation while efficiently advancing learning on an optimal schedule is not easy. Additionally, a lack of support that considers learners' emotional states makes it difficult to ensure the sustainability of their learning.

[0265] 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.

[0266] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, time management means for managing learning activities and considering appropriate review times, and analysis means for monitoring the learner's progress and psychological state and providing support. This enables the provision of a learning experience optimized for each individual learner, allowing for efficient learning and maintenance of motivation.

[0267] "Information processing means" refers to a configuration that has the function of analyzing data provided by learners and generating personalized learning plans based on that analysis.

[0268] A "time management system" is a configuration that has the function of identifying the optimal timing for review and adjusting the schedule based on the learner's study plan.

[0269] "Analysis means" refers to a configuration that has the function of analyzing the learner's progress and psychological state and providing necessary support based on that analysis.

[0270] A "test construction method" is a configuration that has the function of generating and providing learners with optimal assessment tests and supplementary assignments.

[0271] A "terminal display means" is a configuration that provides an interface in which the user can input their own information and record and check their learning activities in real time.

[0272] The system is configured as follows in an embodiment for carrying out this invention.

[0273] The system primarily consists of three elements: a server, a terminal, and a user. The server houses a database and a generative AI model, analyzing information from the user to create a personalized learning plan. The scheduling function, which corresponds to the forgetting curve, utilizes a memory retention model, enabling efficient learning. Specifically, the server analyzes input from the user, including learning goals, progress data, and emotional states, and automatically sets up a learning plan and review timing based on the results.

[0274] The terminal provides an interface for users to input information, review their learning progress, and record their progress. The terminal communicates with the server in real time, continuously recording user actions and progress. As a result, the terminal displays updated learning plan feedback from the server and support messages for the learner. This information is displayed in an intuitively understandable format, serving to visualize learning status and clarify the next steps to take.

[0275] Users conduct their daily learning activities through their devices. The information entered is immediately sent to the server and used to improve the overall learning plan. For example, if a user is studying for a programming certification exam, the server can analyze the user's past learning history and provide a customized plan, such as one that focuses on Python.

[0276] (Specific example) Specifically, the server analyzes the user's learning history and, when it senses the need for repetitive learning, sends a "plan to review the previous week's points at the beginning of each week" to the user's device. It also infers the user's emotional state and sends messages to encourage relaxation, making it easier to maintain motivation to learn.

[0277] (Example of a prompt sentence) "Please generate a learning plan for the case where the user aims to obtain a programming qualification. Also, considering the situation where the learner focuses on Python at an intermediate level, please propose an appropriate review schedule."

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The user inputs information such as learning goals, current learning level, and past learning history through the terminal. These input data are sent by the terminal to the server. The specific operation here is that the user inputs the necessary information into the form of the terminal and presses the "send" button to transfer the data to the server.

[0281] Step 2:

[0282] The server analyzes the data received from the user and generates an individualized learning plan using the generated AI model. The server receives the input data and designs an optimal learning content and review schedule based on it. In this process, an optimal curriculum according to the learner's learning history and goals is generated as an output. As a specific operation, calculations are made based on the data provided by the AI model to create a customized plan.

[0283] Step 3:

[0284] The server sends the generated learning plan to the terminal. The server sends the information of the learning plan to the terminal via the network. As an output, an optimized learning plan for the user is distributed to the terminal, and the terminal receives it. Specifically, the server utilizes the REST API to send the plan data to the terminal in JSON format.

[0285] Step 4:

[0286] The terminal displays the received learning plan to the user. The user checks the learning tasks on the terminal screen and proceeds with their studies accordingly. The terminal takes in the learning plan as input, and what is displayed as output is the daily learning content and schedule. Specific operations include the user checking tasks while scrolling the screen display.

[0287] Step 5:

[0288] The user conducts learning and records the progress on the terminal. As the user's input, the progress of learning and feedback are input into the terminal, and the terminal sends it to the server. As output, the learning results and progress status are saved on the server in real time. Specifically, it includes the operation of the user clicking the "Completed" button on the terminal when the task is completed.

[0289] Step 6:

[0290] The server analyzes the collected progress data and user feedback, and generates feedback and support messages as needed to send to the terminal. The server uses sentiment analysis algorithms to infer the user's psychological state. Specific outputs include advice to the user and mental support messages. The server performs the operation of sending appropriate information to the terminal based on the analysis results.

[0291] Step 7:

[0292] The user takes a mock exam on the terminal. The user's input is the answer, and the terminal sends the answer to the server. The server analyzes the answer and generates an evaluation result and further learning advice as output. Specifically, the user clicks the mock exam start button to begin the exam, and after completion, the terminal receives and displays the results.

[0293] (Application Example 1)

[0294] 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."

[0295] Traditional learning systems have faced challenges in adequately addressing learners' individual needs and emotional states, as well as lacking effective real-time feedback and psychological support. Furthermore, it has been difficult to set appropriate timings for relearning based on the learner's progress.

[0296] 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.

[0297] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, planning means for presenting learning items based on the learning plan, taking into account appropriate timing for relearning, and information analysis means for monitoring the learner's learning progress and emotional state and providing psychological support. This makes it possible to provide learners with an individualized learning experience and promote efficient and sustainable learning.

[0298] "Learner information" refers to information necessary to personalize learning plans, such as personal data about learners, learning history, goals, and current level of understanding.

[0299] An "individualized learning plan" is a learning schedule and content that is customized based on the learner's specific needs and progress.

[0300] "Information processing means" refers to a device or program in which a server performs calculations or analyses to analyze learner information and generate personalized learning plans.

[0301] A "planning tool" is a method or device that adjusts the timing of presenting learning items and reviewing them based on a learning plan, and provides learners with appropriate tasks.

[0302] The "information analysis means" is a device or program for monitoring the emotional state and progress of learners and providing a method for psychological support when necessary.

[0303] The "portable information terminal" is a portable electronic device that can be intuitively operated by the user and can display learning plans and feedback.

[0304] The "display means" is a device or function for visually presenting learning plans, progress, and feedback on a portable information terminal.

[0305] The "synchronization means" is a function or device for periodically transmitting the progress data of learners to the server and maintaining information between the learners and the server.

[0306] In this invention, in order to provide a personalized learning experience for learners, a system is constructed in which three elements, namely the server, the terminal, and the user, cooperate with each other.

[0307] The server is equipped with information processing means for generating an appropriate learning plan based on the information received from the learners. The server is hosted on a cloud platform (such as Amazon Web Services or Google Cloud Platform), analyzes the data of the learners, and creates an individually optimized learning plan. Furthermore, the server also incorporates information analysis means for monitoring the progress and emotional state of the learners in real time and providing psychological support as needed.

[0308] The terminal is provided to the learners as a portable information terminal and is equipped with display means that enables intuitive operation and immediate display of information. This terminal also has synchronization means for transmitting the progress data of the learners to the server and presenting learning plans, progress status, and feedback to the learners in real time. As a result, the learners can smoothly proceed with their daily learning activities.

[0309] This system allows users to learn at their own pace. For example, learners can use a smartphone app to check their daily study schedule and receive feedback based on their progress. As the exam date approaches, the server analyzes past practice test results and delivers questions focused on areas that need improvement to the device, further supporting the learner's understanding.

[0310] An example of a prompt using a generative AI model is: "Generate an effective learning plan based on the user's desired learning goals and current progress. Also, suggest ways to send encouraging messages if the user is feeling stressed." This prompt serves as a guide for the server to provide flexible and accurate support to learners through the generative AI model.

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The server receives learner information. Inputs include learner profile data, learning history, and goals. Based on this data, the server uses information processing tools to analyze the data and generate an optimal learning plan for the learner. The output is a individually optimized learning plan.

[0314] Step 2:

[0315] The terminal receives the learning plan sent from the server and displays it intuitively to the learner. The input is learning plan data from the server. The terminal's display mechanism allows the user to visually confirm the information. As a result, the user can clearly understand how to proceed with their daily learning activities. The output provides the user with a visually organized learning schedule.

[0316] Step 3:

[0317] Users progress through their learning using a device and input their learning progress. Input includes learning outcomes and progress data. The device transmits this data to the server in real time. This is necessary for the server to monitor the learner's progress and collect data. The output is the progress data sent to the server.

[0318] Step 4:

[0319] The server infers the learner's emotional state based on received progress data and provides psychological support if necessary. Inputs include the learner's progress data and data on their past emotional states. The server analyzes the data using information analysis tools, and if it determines that the learner is experiencing stress, it generates an encouraging message and sends it to the terminal. The output is emotional support data, including feedback.

[0320] Step 5:

[0321] The terminal displays feedback from the server to the learner. Inputs include emotional support messages and learning feedback sent from the server. The display allows the user to review this feedback in a timely manner and adjust their learning method as needed. The output is visually presented feedback to the user.

[0322] Step 6:

[0323] The user takes a practice test from a terminal. The input is the practice test questions displayed on the terminal. The user takes the test and enters the results on the terminal. Meanwhile, the terminal sends the entered test results to a server, which analyzes the learner's score and level of understanding. The output is the user's test result data.

[0324] Step 7:

[0325] The server identifies the learner's weaknesses based on the results of a practice test and generates appropriate practice problems. The input is the results of the practice test. The server uses a test generation method to generate practice problems based on the identified weaknesses and sends them back to the terminal. The output is customized practice problems.

[0326] 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.

[0327] This invention provides an individualized learning support system that takes into account the learner's emotional state, and offers a more effective learning environment through the coordinated functioning of three elements: a server, a terminal, and the user. In particular, this system incorporates an emotion engine that can recognize the user's emotions in real time and create an optimal learning environment.

[0328] Server operation

[0329] The server receives basic information and learning objectives entered by the user on the terminal and generates a personalized learning plan. The server's data processing function analyzes the input information and determines the learning content and sequence. During this process, an emotion engine is used to analyze the learner's emotional state and select more appropriate learning content. For example, if the user is anxious, the server suggests learning tasks and methods that are expected to have a relaxing effect.

[0330] The server analyzes progress data based on learner input data and feedback from the emotion engine, and generates mental support and encouragement messages. The server also generates practice tests and study materials tailored to the learner's emotions and provides them to the learner via their device. In this case, if the learner may be experiencing some level of stress, the server prioritizes adjusting the difficulty level and providing content to help them relax.

[0331] Terminal operation

[0332] The device intuitively displays learning plans and analytical data delivered from the server. Users perform daily learning activities and record their progress through the device. Furthermore, it utilizes an emotion engine to recognize the user's emotional state from their input data and transmit it to the server. The device then presents the user with feedback and mental support messages from the server to help maintain learning motivation.

[0333] Furthermore, the device can use an emotion engine to pick up on the user's emotional changes in detail and provide alerts to reduce the burden of learning or notifications to encourage short breaks.

[0334] User actions

[0335] Users input the necessary learning information into their device and proceed with their learning activities according to the learning plan generated by the system. Throughout their daily learning activities, users can receive notifications and support from their device, monitor their emotional state, and adjust it as needed. They can also immediately check their learning progress through mock exams and obtain appropriate improvement measures based on those results.

[0336] This invention provides learners with an optimal learning method and environment that takes their emotional state into consideration, enabling them to learn efficiently while maintaining sustained motivation.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] Users use their devices to input learning goals and basic information. This input includes the qualification they are aiming for, the exam date, and an estimated study time.

[0340] Step 2:

[0341] The terminal sends user input information to the server. The server activates the emotion engine and prepares to collect initial data about the user's emotional state.

[0342] Step 3:

[0343] The server generates a personalized learning plan based on user information and data from the emotion engine. Here, the learning content and sequence are adjusted to take the user's emotional state into consideration.

[0344] Step 4:

[0345] The server schedules the generated study plan and practice problems based on the set review timings and delivers them to the terminals.

[0346] Step 5:

[0347] The user checks their device and completes the presented learning tasks. The user's interactions during learning are analyzed by an emotion engine.

[0348] Step 6:

[0349] The device combines the user's learning progress and emotional data and sends it to the server. The server analyzes the acquired data and automatically generates mental support and encouragement messages.

[0350] Step 7:

[0351] The server sends mental support messages, generated based on learning progress and emotional state, to the terminal and displays them to the user. These messages are intended to maintain learning motivation.

[0352] Step 8:

[0353] The device presents the user with practice tests and additional study problems, and provides real-time feedback. It also reassessses the user's emotional state and revises the learning plan as needed.

[0354] Step 9:

[0355] The user takes a practice test and uses the results and feedback to verify the validity of their study plan. The device then sends the relevant data back to the server.

[0356] Step 10:

[0357] The server utilizes the user's latest learning data and sentiment analysis results to update the learning plan as needed and deliver the new plan to the device. By repeating this cycle, learners can continue to learn efficiently under optimal conditions.

[0358] (Example 2)

[0359] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0360] In recent years, there has been a growing need to address the individual emotional states of learners in learning environments. However, conventional systems provide uniform learning support without considering emotional states, leaving challenges in terms of maintaining learner motivation and reducing stress. Therefore, there is a need for more individualized learning support systems that take learners' emotional states into account.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0362] In this invention, the server includes data processing means, scheduling means, data analysis means, emotion analysis means, and environment adjustment means. This enables the generation of personalized learning plans according to the learner's emotional state and the optimization of the learning environment.

[0363] "Data processing means" refers to a device or method that analyzes learner input information and generates an individualized learning plan.

[0364] A "scheduling means" is a device or method that presents appropriate learning tasks and review timings based on a learning plan.

[0365] "Data analysis means" refers to a device or method for analyzing data necessary to provide mental support while monitoring learners' learning progress and emotional state.

[0366] "Test generation means" refers to a device or method that generates and presents mock tests or practice problems to learners.

[0367] "Emotional analysis means" refers to a device or method that analyzes a user's real-time emotional state and uses that information to adjust the learning environment.

[0368] "Environmental adjustment means" refers to a device or method that dynamically adjusts content and learning conditions in order to reduce learner stress and provide an optimal learning environment.

[0369] This invention provides a system that offers personalized learning support that takes into account the learner's emotional state. The system functions through the cooperation of three parties: a server, a terminal, and a user. Specifically, the server performs data processing and emotion analysis, while the terminal functions as an interface with the user.

[0370] The server uses data processing tools to receive information entered by the user on the terminal. This information includes desired learning content and progress goals. The server also utilizes a generative AI model to create a personalized learning plan tailored to the user. This plan is optimized based on the user's learning history and emotional state analysis results from data analysis tools. For example, if the server detects user anxiety using emotion analysis tools, it will suggest learning content with a relaxing effect.

[0371] The device intuitively displays and notifies the user of learning plans and feedback from the server. It also utilizes sentiment analysis to monitor the user's emotions in real time. This information is sent to the server to further optimize the learning environment. The device can also use environmental adjustment tools to issue alerts prompting short breaks if the user feels overwhelmed.

[0372] Users can learn at their own pace by inputting information into their devices, based on a learning plan provided by the server. The server uses a test generation system to provide users with mock tests and practice questions, giving them opportunities to check their learning progress and understanding. In this process, specific questions such as "What kind of relaxing tasks can you suggest when the user is feeling anxious?" are used as prompts to the generating AI model.

[0373] This embodiment allows learners to continue learning while respecting their own emotional state, providing an environment where they can learn efficiently while maintaining high motivation.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The server receives information entered by the user through their terminal. This input includes data such as learning goals, desired progress, and current mindset. Based on this, the server analyzes the information using data processing tools and generates an individualized learning plan. The output here is a learning plan tailored to the user. A generative AI model is used to design the optimal plan by referring to past successful patterns.

[0377] Step 2:

[0378] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time emotion data transmitted from the terminal (e.g., facial expression analysis data and voice tone). The server analyzes this data to determine the user's current emotional state. The output is the emotion analysis result, which provides guidance for adjusting the learning environment.

[0379] Step 3:

[0380] The terminal displays the learning plan and sentiment analysis results received from the server to the user. Input is data from the server, and output is the learning plan and notifications displayed on the screen. The terminal uses environmental adjustment tools to advise the user on necessary adjustments for learning, such as music playback and lighting adjustments. The terminal also directly displays feedback to the user, increasing their motivation for the next learning step.

[0381] Step 4:

[0382] Users progress through their learning according to a learning plan provided by the server using their device. New input data from the user (such as learning progress and feedback) is sent to the server via the device. This allows the server to continuously monitor the user's progress and prepare to suggest new learning steps. The output consists of continuously updated learning plans and feedback.

[0383] Step 5:

[0384] The server uses test generation tools to provide users with practice tests and study materials. The input consists of the user's progress data and past answer history. The server analyzes this data using data analysis tools to identify the user's weaknesses and areas requiring improvement. The output is a customized set of test questions, delivered to the user via a terminal. This allows the user to self-assess their progress and use it to further improve their learning.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] In today's learning environment, providing learning support that adapts to the emotional state of each individual learner is difficult, and in particular, learners struggle to manage their own emotions and motivations when studying at home. This leads to problems such as decreased learning efficiency and difficulty maintaining motivation. Furthermore, existing learning support technologies are insufficient to recognize learners' emotional states in real time and provide adaptive learning adjustments and support.

[0388] 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.

[0389] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan; planning means for presenting learning tasks based on the learning plan, taking into account appropriate review timings; information analysis means for monitoring the learner's learning progress and emotional state and providing mental support; and recognition and suggestion means for recognizing the learner's emotional state in real time and suggesting adjustments to learning content and activities for emotional stabilization accordingly. As a result, learners can proceed with their learning in an environment that is optimal for their emotional state, thereby improving learning efficiency and motivation.

[0390] "Information processing means" refers to devices or systems that receive information provided by learners and generate personalized learning plans based on that information.

[0391] A "plan creation tool" is a device or system that presents learning tasks based on a generated learning plan and determines the appropriate timing for reviewing those tasks.

[0392] "Information analysis tools" refer to devices and systems that monitor learners' learning progress and emotional state, and provide mental support as needed.

[0393] "Recognition and suggestion means" refers to devices or systems that recognize a learner's emotional state in real time and suggest adjustments to learning content or activities for emotional stabilization in accordance with that state.

[0394] In this embodiment of the invention, the system mainly consists of a server, a terminal, and a user. The server, acting as an information processing device, receives the learner's basic information and learning objectives, and generates an individualized learning plan accordingly. The software tools used in this process include a database management system and a plan creation algorithm. The server also employs analysis methods such as natural language processing and image recognition to infer the learner's real-time emotional state. This allows the server to provide appropriate mental support when the learner is in a particular emotional state.

[0395] The terminal is used daily by learners and serves as the user interface. The terminal displays the learning plan sent from the server and accepts learner input. The terminal is equipped with emotion recognition capabilities, analyzing the learner's facial expressions and voice through a webcam and microphone. This allows the terminal to track the learner's emotional state and send feedback to the server.

[0396] The user's role is to follow the learning plan displayed on the device and progress through their daily studies. The user can monitor their emotional state on the device and adjust their learning methods as needed. For example, if they are having difficulty learning, the device may suggest relaxation content or distractions such as games.

[0397] This system uses a specific generative AI model to provide a learning environment based on the user's emotional data and learning history. An example of a prompt might be, "Please suggest music that will help the learner relax."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server receives basic information and learning objectives sent by the user. This includes personal data entered by the learner from their device and their desired subjects to study. The server analyzes this data and generates a personalized learning plan. In generating the learning plan, the server receives assistance from a database management system and constructs the plan based on past learning data and statistical information. Finally, the generated learning plan is sent to the device.

[0401] Step 2:

[0402] The device receives a learning plan from the server and displays it to the user. The user begins learning based on the planned learning tasks. The device provides visual feedback on the current learning progress on the screen based on the user's input information. At this time, it utilizes emotion recognition technology to monitor the user's emotions in real time using data collected from the webcam and microphone.

[0403] Step 3:

[0404] The server receives and analyzes real-time emotional data sent from the terminal. Using an emotional recognition algorithm, it determines, for example, whether the user is lacking concentration or experiencing stress. If a problem is detected, the system uses a generative AI model to adjust the learning content to suit the learner and provide suggestions for emotional stabilization. This information is then sent back to the terminal.

[0405] Step 4:

[0406] Based on feedback received from the server, the device suggests necessary mental support and adjustments to learning methods to the user. For example, if the user is feeling stressed, the device offers options such as light exercise or music for relaxation. It also allows the user to choose from options based on prompts generated using a generative AI model.

[0407] Step 5:

[0408] Users progress through their learning by accepting suggestions from their device and monitoring their own emotional state. By accurately understanding their emotions, they can adjust their learning plan or take breaks as needed. This feedback loop allows users to maintain motivation and learn effectively.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention is a system for providing learners with an individualized learning experience, and it primarily functions using three elements: a server, a terminal, and a user. Each element within the system is responsible for a specific function, and by linking data with each other, it realizes an optimal learning environment for the learner.

[0426] Server operation

[0427] The server receives information about the learner and generates a personalized learning plan based on it. The server's data processing functions analyze the information entered by the user to identify the optimal learning content and sequence. This takes into account the learner's goals, current learning level, and qualification requirements. It also calculates review timing based on the forgetting curve and creates a schedule that presents appropriate learning tasks.

[0428] Furthermore, the server periodically analyzes learners' learning progress data and emotional state, and provides mental support as needed. For example, if a learner is feeling stressed, it sends relaxation tips and encouraging messages. Based on learning results and mock exam scores, the server generates personalized practice problems and provides them to learners via their devices.

[0429] Terminal operation

[0430] The terminal intuitively displays learning plans and assignments delivered from the server to the learner. The user uses the terminal to carry out daily learning based on the plan and record their learning progress. The terminal transmits the user's input data and learning outcomes to the server in real time to support progress monitoring.

[0431] The device also helps learners become familiar with the testing environment by allowing them to take practice tests and review questions. After taking the test, the device displays feedback and provides information indicating which areas the learner should further strengthen.

[0432] User actions

[0433] Users input their learning information via their device and proceed with their studies according to the learning plan provided by the system. Users can learn at their own pace and record and check their progress using their device. In addition, they can check their current level of understanding through mock exams and reinforce areas where they are lacking based on feedback from the server, repeating this cycle to efficiently achieve their goals.

[0434] This invention is a comprehensive system for providing an environment that adapts to learners and promotes sustained learning effectiveness.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] Users use their devices to input their basic information, learning goals, and current progress. This information includes the name of the qualification they are aiming for, the exam date, and the time and period they can dedicate to studying.

[0438] Step 2:

[0439] The terminal sends user input information to the server. The server analyzes the received information and prepares to generate a learning plan optimized for the user.

[0440] Step 3:

[0441] The server uses Ebbinghaus's forgetting curve as a reference to calculate the optimal timing for review to maximize user retention. This calculation is then incorporated into the learning plan to formulate specific learning steps.

[0442] Step 4:

[0443] The server delivers the formulated learning plan to the terminal. The terminal receives it and presents the user with daily learning assignments and a schedule.

[0444] Step 5:

[0445] Users progress through their daily studies based on the provided learning plan and input their progress and learning outcomes into their device. This includes study time and level of understanding of the material.

[0446] Step 6:

[0447] The device sends the user's learning progress and input data to the server. The server analyzes the progress based on this data and infers the user's emotional state. It generates mental support messages as needed.

[0448] Step 7:

[0449] The server generates individually customized practice tests and last-minute preparation questions, taking into account the user's learning progress. These are then delivered to the terminal.

[0450] Step 8:

[0451] Users take practice tests and study materials received on their devices. After the test, the device sends the results to a server, which then generates feedback.

[0452] Step 9:

[0453] The device presents the user with feedback from the server. The user uses this feedback to incorporate their knowledge into the next learning step.

[0454] Step 10:

[0455] The server periodically reviews the user's learning data, updates the learning plan as needed, and delivers it back to the device. This ensures continuous learning adaptation.

[0456] (Example 1)

[0457] 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."

[0458] In today's diverse learning environment, there is a challenge in providing individualized learning tailored to each learner's needs and progress. Furthermore, maintaining learner motivation while efficiently advancing learning on an optimal schedule is not easy. Additionally, a lack of support that considers learners' emotional states makes it difficult to ensure the sustainability of their learning.

[0459] 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.

[0460] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, time management means for managing learning activities and considering appropriate review times, and analysis means for monitoring the learner's progress and psychological state and providing support. This enables the provision of a learning experience optimized for each individual learner, allowing for efficient learning and maintenance of motivation.

[0461] "Information processing means" refers to a configuration that has the function of analyzing data provided by learners and generating personalized learning plans based on that analysis.

[0462] A "time management system" is a configuration that has the function of identifying the optimal timing for review and adjusting the schedule based on the learner's study plan.

[0463] "Analysis means" refers to a configuration that has the function of analyzing the learner's progress and psychological state and providing necessary support based on that analysis.

[0464] A "test construction method" is a configuration that has the function of generating and providing learners with optimal assessment tests and supplementary assignments.

[0465] A "terminal display means" is a configuration that provides an interface in which the user can input their own information and record and check their learning activities in real time.

[0466] The system is configured as follows in an embodiment for carrying out this invention.

[0467] The system primarily consists of three elements: a server, a terminal, and a user. The server houses a database and a generative AI model, analyzing information from the user to create a personalized learning plan. The scheduling function, which corresponds to the forgetting curve, utilizes a memory retention model, enabling efficient learning. Specifically, the server analyzes input from the user, including learning goals, progress data, and emotional states, and automatically sets up a learning plan and review timing based on the results.

[0468] The terminal provides an interface for users to input information, review their learning progress, and record their progress. The terminal communicates with the server in real time, continuously recording user actions and progress. As a result, the terminal displays updated learning plan feedback from the server and support messages for the learner. This information is displayed in an intuitively understandable format, serving to visualize learning status and clarify the next steps to take.

[0469] Users conduct their daily learning activities through their devices. The information entered is immediately sent to the server and used to improve the overall learning plan. For example, if a user is studying for a programming certification exam, the server can analyze the user's past learning history and provide a customized plan, such as one that focuses on Python.

[0470] (Specific example) Specifically, the server analyzes the user's learning history and, when it senses the need for repetitive learning, sends a "plan to review the previous week's points at the beginning of each week" to the user's device. It also infers the user's emotional state and sends messages to encourage relaxation, making it easier to maintain motivation to learn.

[0471] (Example prompt) "Generate a study plan for a user aiming to obtain a programming certification. Also, consider that the learner is at an intermediate level and focuses on Python, and suggest an appropriate review schedule."

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user enters information such as learning goals, current learning level, and past learning history through the device. This input data is then sent to the server by the device. Specifically, the user enters the required information into the form on the device and presses the "Submit" button to transfer the data to the server.

[0475] Step 2:

[0476] The server analyzes data received from the user and generates a personalized learning plan using a generative AI model. The server receives input data and designs the optimal learning content and review schedule based on it. This process generates an optimal curriculum tailored to the learner's learning history and goals. Specifically, the AI ​​model calculates and creates a customized plan based on the provided data.

[0477] Step 3:

[0478] The server sends the generated learning plan to the device. The server transmits the learning plan information to the device via the network. As output, a learning plan optimized for the user is delivered to the device, and the device receives it. Specifically, the server uses a REST API to send the plan data to the device in JSON format.

[0479] Step 4:

[0480] The device displays the received learning plan to the user. The user checks the learning tasks on the device screen and proceeds with their studies accordingly. The device takes the learning plan as input, and the output displayed is the daily learning content and schedule. Specific actions include the user scrolling through the screen to check the tasks.

[0481] Step 5:

[0482] The user performs the learning process and records their progress on their device. User input includes learning progress and feedback, which the device then sends to the server. Output includes learning results and progress, which are saved to the server in real time. Specifically, this includes the user clicking the "Complete" button on their device when they finish a task.

[0483] Step 6:

[0484] The server analyzes collected progress data and user feedback, and generates and sends feedback and support messages to the terminal as needed. The server uses a sentiment analysis algorithm to infer the user's psychological state. Specific outputs include advice and mental support messages for the user. Based on the analysis results, the server sends appropriate information to the terminal.

[0485] Step 7:

[0486] The user takes a practice test on their device. The user inputs answers, which the device then sends to a server. The server analyzes the answers and generates an evaluation result and further learning advice as output. Specifically, the user clicks the "Start Practice Test" button to begin taking the test, and after completion, the device receives and displays the results.

[0487] (Application Example 1)

[0488] 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."

[0489] Traditional learning systems have faced challenges in adequately addressing learners' individual needs and emotional states, as well as lacking effective real-time feedback and psychological support. Furthermore, it has been difficult to set appropriate timings for relearning based on the learner's progress.

[0490] 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.

[0491] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, planning means for presenting learning items based on the learning plan, taking into account appropriate timing for relearning, and information analysis means for monitoring the learner's learning progress and emotional state and providing psychological support. This makes it possible to provide learners with an individualized learning experience and promote efficient and sustainable learning.

[0492] "Learner information" refers to information necessary to personalize learning plans, such as personal data about learners, learning history, goals, and current level of understanding.

[0493] An "individualized learning plan" is a learning schedule and content that is customized based on the learner's specific needs and progress.

[0494] "Information processing means" refers to a device or program in which a server performs calculations or analyses to analyze learner information and generate personalized learning plans.

[0495] A "planning tool" is a method or device that adjusts the timing of presenting learning items and reviewing them based on a learning plan, and provides learners with appropriate tasks.

[0496] "Information analysis means" refers to a device or program for monitoring learners' emotional states and progress, and for providing methods of psychological support when needed.

[0497] A "portable information terminal" is a portable electronic device that users can operate intuitively and that can display learning plans and feedback.

[0498] "Display means" refers to a device or function for visually presenting learning plans, progress, and feedback on a portable information terminal.

[0499] A "synchronization means" is a function or device that periodically sends learner progress data to a server and maintains information between the learner and the server.

[0500] This invention constructs a system in which three elements—a server, a terminal, and a user—interact with each other in order to provide learners with an individualized learning experience.

[0501] The server is equipped with information processing capabilities to generate appropriate learning plans based on information received from learners. Hosted on a cloud platform (e.g., Amazon Web Services or Google Cloud Platform), the server analyzes learner data and creates individually optimized learning plans. Furthermore, the server incorporates information analysis capabilities to monitor learners' progress and emotional states in real time and provide psychological support as needed.

[0502] The terminal is provided to learners as a portable information terminal and is equipped with a display that enables intuitive operation and immediate display of information. This terminal also has a synchronization mechanism that sends learners' progress data to a server, presenting learners with learning plans, progress status, and feedback in real time. As a result, learners can smoothly proceed with their daily learning activities.

[0503] This system allows users to learn at their own pace. For example, learners can use a smartphone app to check their daily study schedule and receive feedback based on their progress. As the exam date approaches, the server analyzes past practice test results and delivers questions focused on areas that need improvement to the device, further supporting the learner's understanding.

[0504] An example of a prompt using a generative AI model is: "Generate an effective learning plan based on the user's desired learning goals and current progress. Also, suggest ways to send encouraging messages if the user is feeling stressed." This prompt serves as a guide for the server to provide flexible and accurate support to learners through the generative AI model.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server receives learner information. Inputs include learner profile data, learning history, and goals. Based on this data, the server uses information processing tools to analyze the data and generate an optimal learning plan for the learner. The output is a individually optimized learning plan.

[0508] Step 2:

[0509] The terminal receives the learning plan sent from the server and displays it intuitively to the learner. The input is learning plan data from the server. The terminal's display mechanism allows the user to visually confirm the information. As a result, the user can clearly understand how to proceed with their daily learning activities. The output provides the user with a visually organized learning schedule.

[0510] Step 3:

[0511] Users progress through their learning using a device and input their learning progress. Input includes learning outcomes and progress data. The device transmits this data to the server in real time. This is necessary for the server to monitor the learner's progress and collect data. The output is the progress data sent to the server.

[0512] Step 4:

[0513] The server infers the learner's emotional state based on received progress data and provides psychological support if necessary. Inputs include the learner's progress data and data on their past emotional states. The server analyzes the data using information analysis tools, and if it determines that the learner is experiencing stress, it generates an encouraging message and sends it to the terminal. The output is emotional support data, including feedback.

[0514] Step 5:

[0515] The terminal displays feedback from the server to the learner. Inputs include emotional support messages and learning feedback sent from the server. The display allows the user to review this feedback in a timely manner and adjust their learning method as needed. The output is visually presented feedback to the user.

[0516] Step 6:

[0517] The user takes a practice test from a terminal. The input is the practice test questions displayed on the terminal. The user takes the test and enters the results on the terminal. Meanwhile, the terminal sends the entered test results to a server, which analyzes the learner's score and level of understanding. The output is the user's test result data.

[0518] Step 7:

[0519] The server identifies the learner's weaknesses based on the results of a practice test and generates appropriate practice problems. The input is the results of the practice test. The server uses a test generation method to generate practice problems based on the identified weaknesses and sends them back to the terminal. The output is customized practice problems.

[0520] 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.

[0521] This invention provides an individualized learning support system that takes into account the learner's emotional state, and offers a more effective learning environment through the coordinated functioning of three elements: a server, a terminal, and the user. In particular, this system incorporates an emotion engine that can recognize the user's emotions in real time and create an optimal learning environment.

[0522] Server operation

[0523] The server receives basic information and learning objectives entered by the user on the terminal and generates a personalized learning plan. The server's data processing function analyzes the input information and determines the learning content and sequence. During this process, an emotion engine is used to analyze the learner's emotional state and select more appropriate learning content. For example, if the user is anxious, the server suggests learning tasks and methods that are expected to have a relaxing effect.

[0524] The server analyzes progress data based on learner input data and feedback from the emotion engine, and generates mental support and encouragement messages. The server also generates practice tests and study materials tailored to the learner's emotions and provides them to the learner via their device. In this case, if the learner may be experiencing some level of stress, the server prioritizes adjusting the difficulty level and providing content to help them relax.

[0525] Terminal operation

[0526] The device intuitively displays learning plans and analytical data delivered from the server. Users perform daily learning activities and record their progress through the device. Furthermore, it utilizes an emotion engine to recognize the user's emotional state from their input data and transmit it to the server. The device then presents the user with feedback and mental support messages from the server to help maintain learning motivation.

[0527] Furthermore, the device can use an emotion engine to pick up on the user's emotional changes in detail and provide alerts to reduce the burden of learning or notifications to encourage short breaks.

[0528] User actions

[0529] Users input the necessary learning information into their device and proceed with their learning activities according to the learning plan generated by the system. Throughout their daily learning activities, users can receive notifications and support from their device, monitor their emotional state, and adjust it as needed. They can also immediately check their learning progress through mock exams and obtain appropriate improvement measures based on those results.

[0530] This invention provides learners with an optimal learning method and environment that takes their emotional state into consideration, enabling them to learn efficiently while maintaining sustained motivation.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] Users use their devices to input learning goals and basic information. This input includes the qualification they are aiming for, the exam date, and an estimated study time.

[0534] Step 2:

[0535] The terminal sends user input information to the server. The server activates the emotion engine and prepares to collect initial data about the user's emotional state.

[0536] Step 3:

[0537] The server generates a personalized learning plan based on user information and data from the emotion engine. Here, the learning content and sequence are adjusted to take the user's emotional state into consideration.

[0538] Step 4:

[0539] The server schedules the generated study plan and practice problems based on the set review timings and delivers them to the terminals.

[0540] Step 5:

[0541] The user checks their device and completes the presented learning tasks. The user's interactions during learning are analyzed by an emotion engine.

[0542] Step 6:

[0543] The device combines the user's learning progress and emotional data and sends it to the server. The server analyzes the acquired data and automatically generates mental support and encouragement messages.

[0544] Step 7:

[0545] The server sends mental support messages, generated based on learning progress and emotional state, to the terminal and displays them to the user. These messages are intended to maintain learning motivation.

[0546] Step 8:

[0547] The device presents the user with practice tests and additional study problems, and provides real-time feedback. It also reassessses the user's emotional state and revises the learning plan as needed.

[0548] Step 9:

[0549] The user takes a practice test and uses the results and feedback to verify the validity of their study plan. The device then sends the relevant data back to the server.

[0550] Step 10:

[0551] The server utilizes the user's latest learning data and sentiment analysis results to update the learning plan as needed and deliver the new plan to the device. By repeating this cycle, learners can continue to learn efficiently under optimal conditions.

[0552] (Example 2)

[0553] 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."

[0554] In recent years, there has been a growing need to address the individual emotional states of learners in learning environments. However, conventional systems provide uniform learning support without considering emotional states, leaving challenges in terms of maintaining learner motivation and reducing stress. Therefore, there is a need for more individualized learning support systems that take learners' emotional states into account.

[0555] 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.

[0556] In this invention, the server includes data processing means, scheduling means, data analysis means, emotion analysis means, and environment adjustment means. This enables the generation of personalized learning plans according to the learner's emotional state and the optimization of the learning environment.

[0557] "Data processing means" refers to a device or method that analyzes learner input information and generates an individualized learning plan.

[0558] A "scheduling means" is a device or method that presents appropriate learning tasks and review timings based on a learning plan.

[0559] "Data analysis means" refers to a device or method for analyzing data necessary to provide mental support while monitoring learners' learning progress and emotional state.

[0560] "Test generation means" refers to a device or method that generates and presents mock tests or practice problems to learners.

[0561] "Emotional analysis means" refers to a device or method that analyzes a user's real-time emotional state and uses that information to adjust the learning environment.

[0562] "Environmental adjustment means" refers to a device or method that dynamically adjusts content and learning conditions in order to reduce learner stress and provide an optimal learning environment.

[0563] This invention provides a system that offers personalized learning support that takes into account the learner's emotional state. The system functions through the cooperation of three parties: a server, a terminal, and a user. Specifically, the server performs data processing and emotion analysis, while the terminal functions as an interface with the user.

[0564] The server uses data processing tools to receive information entered by the user on the terminal. This information includes desired learning content and progress goals. The server also utilizes a generative AI model to create a personalized learning plan tailored to the user. This plan is optimized based on the user's learning history and emotional state analysis results from data analysis tools. For example, if the server detects user anxiety using emotion analysis tools, it will suggest learning content with a relaxing effect.

[0565] The device intuitively displays and notifies the user of learning plans and feedback from the server. It also utilizes sentiment analysis to monitor the user's emotions in real time. This information is sent to the server to further optimize the learning environment. The device can also use environmental adjustment tools to issue alerts prompting short breaks if the user feels overwhelmed.

[0566] Users can learn at their own pace by inputting information into their devices, based on a learning plan provided by the server. The server uses a test generation system to provide users with mock tests and practice questions, giving them opportunities to check their learning progress and understanding. In this process, specific questions such as "What kind of relaxing tasks can you suggest when the user is feeling anxious?" are used as prompts to the generating AI model.

[0567] This embodiment allows learners to continue learning while respecting their own emotional state, providing an environment where they can learn efficiently while maintaining high motivation.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The server receives information entered by the user through their terminal. This input includes data such as learning goals, desired progress, and current mindset. Based on this, the server analyzes the information using data processing tools and generates an individualized learning plan. The output here is a learning plan tailored to the user. A generative AI model is used to design the optimal plan by referring to past successful patterns.

[0571] Step 2:

[0572] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time emotion data transmitted from the terminal (e.g., facial expression analysis data and voice tone). The server analyzes this data to determine the user's current emotional state. The output is the emotion analysis result, which provides guidance for adjusting the learning environment.

[0573] Step 3:

[0574] The terminal displays the learning plan and sentiment analysis results received from the server to the user. Input is data from the server, and output is the learning plan and notifications displayed on the screen. The terminal uses environmental adjustment tools to advise the user on necessary adjustments for learning, such as music playback and lighting adjustments. The terminal also directly displays feedback to the user, increasing their motivation for the next learning step.

[0575] Step 4:

[0576] Users progress through their learning according to a learning plan provided by the server using their device. New input data from the user (such as learning progress and feedback) is sent to the server via the device. This allows the server to continuously monitor the user's progress and prepare to suggest new learning steps. The output consists of continuously updated learning plans and feedback.

[0577] Step 5:

[0578] The server uses test generation tools to provide users with practice tests and study materials. The input consists of the user's progress data and past answer history. The server analyzes this data using data analysis tools to identify the user's weaknesses and areas requiring improvement. The output is a customized set of test questions, delivered to the user via a terminal. This allows the user to self-assess their progress and use it to further improve their learning.

[0579] (Application Example 2)

[0580] 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."

[0581] In today's learning environment, providing learning support that adapts to the emotional state of each individual learner is difficult, and in particular, learners struggle to manage their own emotions and motivations when studying at home. This leads to problems such as decreased learning efficiency and difficulty maintaining motivation. Furthermore, existing learning support technologies are insufficient to recognize learners' emotional states in real time and provide adaptive learning adjustments and support.

[0582] 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.

[0583] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan; planning means for presenting learning tasks based on the learning plan, taking into account appropriate review timings; information analysis means for monitoring the learner's learning progress and emotional state and providing mental support; and recognition and suggestion means for recognizing the learner's emotional state in real time and suggesting adjustments to learning content and activities for emotional stabilization accordingly. As a result, learners can proceed with their learning in an environment that is optimal for their emotional state, thereby improving learning efficiency and motivation.

[0584] "Information processing means" refers to devices or systems that receive information provided by learners and generate personalized learning plans based on that information.

[0585] A "plan creation tool" is a device or system that presents learning tasks based on a generated learning plan and determines the appropriate timing for reviewing those tasks.

[0586] "Information analysis tools" refer to devices and systems that monitor learners' learning progress and emotional state, and provide mental support as needed.

[0587] "Recognition and suggestion means" refers to devices or systems that recognize a learner's emotional state in real time and suggest adjustments to learning content or activities for emotional stabilization in accordance with that state.

[0588] In this embodiment of the invention, the system mainly consists of a server, a terminal, and a user. The server, acting as an information processing device, receives the learner's basic information and learning objectives, and generates an individualized learning plan accordingly. The software tools used in this process include a database management system and a plan creation algorithm. The server also employs analysis methods such as natural language processing and image recognition to infer the learner's real-time emotional state. This allows the server to provide appropriate mental support when the learner is in a particular emotional state.

[0589] The terminal is used daily by learners and serves as the user interface. The terminal displays the learning plan sent from the server and accepts learner input. The terminal is equipped with emotion recognition capabilities, analyzing the learner's facial expressions and voice through a webcam and microphone. This allows the terminal to track the learner's emotional state and send feedback to the server.

[0590] The user's role is to follow the learning plan displayed on the device and progress through their daily studies. The user can monitor their emotional state on the device and adjust their learning methods as needed. For example, if they are having difficulty learning, the device may suggest relaxation content or distractions such as games.

[0591] This system uses a specific generative AI model to provide a learning environment based on the user's emotional data and learning history. An example of a prompt might be, "Please suggest music that will help the learner relax."

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The server receives basic information and learning objectives sent by the user. This includes personal data entered by the learner from their device and their desired subjects to study. The server analyzes this data and generates a personalized learning plan. In generating the learning plan, the server receives assistance from a database management system and constructs the plan based on past learning data and statistical information. Finally, the generated learning plan is sent to the device.

[0595] Step 2:

[0596] The device receives a learning plan from the server and displays it to the user. The user begins learning based on the planned learning tasks. The device provides visual feedback on the current learning progress on the screen based on the user's input information. At this time, it utilizes emotion recognition technology to monitor the user's emotions in real time using data collected from the webcam and microphone.

[0597] Step 3:

[0598] The server receives and analyzes real-time emotional data sent from the terminal. Using an emotional recognition algorithm, it determines, for example, whether the user is lacking concentration or experiencing stress. If a problem is detected, the system uses a generative AI model to adjust the learning content to suit the learner and provide suggestions for emotional stabilization. This information is then sent back to the terminal.

[0599] Step 4:

[0600] Based on feedback received from the server, the device suggests necessary mental support and adjustments to learning methods to the user. For example, if the user is feeling stressed, the device offers options such as light exercise or music for relaxation. It also allows the user to choose from options based on prompts generated using a generative AI model.

[0601] Step 5:

[0602] Users progress through their learning by accepting suggestions from their device and monitoring their own emotional state. By accurately understanding their emotions, they can adjust their learning plan or take breaks as needed. This feedback loop allows users to maintain motivation and learn effectively.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention is a system for providing learners with an individualized learning experience, and it primarily functions using three elements: a server, a terminal, and a user. Each element within the system is responsible for a specific function, and by linking data with each other, it realizes an optimal learning environment for the learner.

[0621] Server operation

[0622] The server receives information about the learner and generates a personalized learning plan based on it. The server's data processing functions analyze the information entered by the user to identify the optimal learning content and sequence. This takes into account the learner's goals, current learning level, and qualification requirements. It also calculates review timing based on the forgetting curve and creates a schedule that presents appropriate learning tasks.

[0623] Furthermore, the server periodically analyzes learners' learning progress data and emotional state, and provides mental support as needed. For example, if a learner is feeling stressed, it sends relaxation tips and encouraging messages. Based on learning results and mock exam scores, the server generates personalized practice problems and provides them to learners via their devices.

[0624] Terminal operation

[0625] The terminal intuitively displays learning plans and assignments delivered from the server to the learner. The user uses the terminal to carry out daily learning based on the plan and record their learning progress. The terminal transmits the user's input data and learning outcomes to the server in real time to support progress monitoring.

[0626] The device also helps learners become familiar with the testing environment by allowing them to take practice tests and review questions. After taking the test, the device displays feedback and provides information indicating which areas the learner should further strengthen.

[0627] User actions

[0628] Users input their learning information via their device and proceed with their studies according to the learning plan provided by the system. Users can learn at their own pace and record and check their progress using their device. In addition, they can check their current level of understanding through mock exams and reinforce areas where they are lacking based on feedback from the server, repeating this cycle to efficiently achieve their goals.

[0629] This invention is a comprehensive system for providing an environment that adapts to learners and promotes sustained learning effectiveness.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] Users use their devices to input their basic information, learning goals, and current progress. This information includes the name of the qualification they are aiming for, the exam date, and the time and period they can dedicate to studying.

[0633] Step 2:

[0634] The terminal sends user input information to the server. The server analyzes the received information and prepares to generate a learning plan optimized for the user.

[0635] Step 3:

[0636] The server uses Ebbinghaus's forgetting curve as a reference to calculate the optimal timing for review to maximize user retention. This calculation is then incorporated into the learning plan to formulate specific learning steps.

[0637] Step 4:

[0638] The server delivers the formulated learning plan to the terminal. The terminal receives it and presents the user with daily learning assignments and a schedule.

[0639] Step 5:

[0640] Users progress through their daily studies based on the provided learning plan and input their progress and learning outcomes into their device. This includes study time and level of understanding of the material.

[0641] Step 6:

[0642] The device sends the user's learning progress and input data to the server. The server analyzes the progress based on this data and infers the user's emotional state. It generates mental support messages as needed.

[0643] Step 7:

[0644] The server generates individually customized practice tests and last-minute preparation questions, taking into account the user's learning progress. These are then delivered to the terminal.

[0645] Step 8:

[0646] Users take practice tests and study materials received on their devices. After the test, the device sends the results to a server, which then generates feedback.

[0647] Step 9:

[0648] The device presents the user with feedback from the server. The user uses this feedback to incorporate their knowledge into the next learning step.

[0649] Step 10:

[0650] The server periodically reviews the user's learning data, updates the learning plan as needed, and delivers it back to the device. This ensures continuous learning adaptation.

[0651] (Example 1)

[0652] 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".

[0653] In today's diverse learning environment, there is a challenge in providing individualized learning tailored to each learner's needs and progress. Furthermore, maintaining learner motivation while efficiently advancing learning on an optimal schedule is not easy. Additionally, a lack of support that considers learners' emotional states makes it difficult to ensure the sustainability of their learning.

[0654] 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.

[0655] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, time management means for managing learning activities and considering appropriate review times, and analysis means for monitoring the learner's progress and psychological state and providing support. This enables the provision of a learning experience optimized for each individual learner, allowing for efficient learning and maintenance of motivation.

[0656] "Information processing means" refers to a configuration that has the function of analyzing data provided by learners and generating personalized learning plans based on that analysis.

[0657] A "time management system" is a configuration that has the function of identifying the optimal timing for review and adjusting the schedule based on the learner's study plan.

[0658] "Analysis means" refers to a configuration that has the function of analyzing the learner's progress and psychological state and providing necessary support based on that analysis.

[0659] A "test construction method" is a configuration that has the function of generating and providing learners with optimal assessment tests and supplementary assignments.

[0660] A "terminal display means" is a configuration that provides an interface in which the user can input their own information and record and check their learning activities in real time.

[0661] The system is configured as follows in an embodiment for carrying out this invention.

[0662] The system primarily consists of three elements: a server, a terminal, and a user. The server houses a database and a generative AI model, analyzing information from the user to create a personalized learning plan. The scheduling function, which corresponds to the forgetting curve, utilizes a memory retention model, enabling efficient learning. Specifically, the server analyzes input from the user, including learning goals, progress data, and emotional states, and automatically sets up a learning plan and review timing based on the results.

[0663] The terminal provides an interface for users to input information, review their learning progress, and record their progress. The terminal communicates with the server in real time, continuously recording user actions and progress. As a result, the terminal displays updated learning plan feedback from the server and support messages for the learner. This information is displayed in an intuitively understandable format, serving to visualize learning status and clarify the next steps to take.

[0664] Users conduct their daily learning activities through their devices. The information entered is immediately sent to the server and used to improve the overall learning plan. For example, if a user is studying for a programming certification exam, the server can analyze the user's past learning history and provide a customized plan, such as one that focuses on Python.

[0665] (Specific example) Specifically, the server analyzes the user's learning history and, when it senses the need for repetitive learning, sends a "plan to review the previous week's points at the beginning of each week" to the user's device. It also infers the user's emotional state and sends messages to encourage relaxation, making it easier to maintain motivation to learn.

[0666] (Example prompt) "Generate a study plan for a user aiming to obtain a programming certification. Also, consider that the learner is at an intermediate level and focuses on Python, and suggest an appropriate review schedule."

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The user enters information such as learning goals, current learning level, and past learning history through the device. This input data is then sent to the server by the device. Specifically, the user enters the required information into the form on the device and presses the "Submit" button to transfer the data to the server.

[0670] Step 2:

[0671] The server analyzes data received from the user and generates a personalized learning plan using a generative AI model. The server receives input data and designs the optimal learning content and review schedule based on it. This process generates an optimal curriculum tailored to the learner's learning history and goals. Specifically, the AI ​​model calculates and creates a customized plan based on the provided data.

[0672] Step 3:

[0673] The server sends the generated learning plan to the device. The server transmits the learning plan information to the device via the network. As output, a learning plan optimized for the user is delivered to the device, and the device receives it. Specifically, the server uses a REST API to send the plan data to the device in JSON format.

[0674] Step 4:

[0675] The device displays the received learning plan to the user. The user checks the learning tasks on the device screen and proceeds with their studies accordingly. The device takes the learning plan as input, and the output displayed is the daily learning content and schedule. Specific actions include the user scrolling through the screen to check the tasks.

[0676] Step 5:

[0677] The user performs the learning process and records their progress on their device. User input includes learning progress and feedback, which the device then sends to the server. Output includes learning results and progress, which are saved to the server in real time. Specifically, this includes the user clicking the "Complete" button on their device when they finish a task.

[0678] Step 6:

[0679] The server analyzes collected progress data and user feedback, and generates and sends feedback and support messages to the terminal as needed. The server uses a sentiment analysis algorithm to infer the user's psychological state. Specific outputs include advice and mental support messages for the user. Based on the analysis results, the server sends appropriate information to the terminal.

[0680] Step 7:

[0681] The user takes a practice test on their device. The user inputs answers, which the device then sends to a server. The server analyzes the answers and generates an evaluation result and further learning advice as output. Specifically, the user clicks the "Start Practice Test" button to begin taking the test, and after completion, the device receives and displays the results.

[0682] (Application Example 1)

[0683] 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".

[0684] Traditional learning systems have faced challenges in adequately addressing learners' individual needs and emotional states, as well as lacking effective real-time feedback and psychological support. Furthermore, it has been difficult to set appropriate timings for relearning based on the learner's progress.

[0685] 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.

[0686] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan, planning means for presenting learning items based on the learning plan, taking into account appropriate timing for relearning, and information analysis means for monitoring the learner's learning progress and emotional state and providing psychological support. This makes it possible to provide learners with an individualized learning experience and promote efficient and sustainable learning.

[0687] "Learner information" refers to information necessary to personalize learning plans, such as personal data about learners, learning history, goals, and current level of understanding.

[0688] An "individualized learning plan" is a learning schedule and content that is customized based on the learner's specific needs and progress.

[0689] "Information processing means" refers to a device or program in which a server performs calculations or analyses to analyze learner information and generate personalized learning plans.

[0690] A "planning tool" is a method or device that adjusts the timing of presenting learning items and reviewing them based on a learning plan, and provides learners with appropriate tasks.

[0691] "Information analysis means" refers to a device or program for monitoring learners' emotional states and progress, and for providing methods of psychological support when needed.

[0692] A "portable information terminal" is a portable electronic device that users can operate intuitively and that can display learning plans and feedback.

[0693] "Display means" refers to a device or function for visually presenting learning plans, progress, and feedback on a portable information terminal.

[0694] A "synchronization means" is a function or device that periodically sends learner progress data to a server and maintains information between the learner and the server.

[0695] This invention constructs a system in which three elements—a server, a terminal, and a user—interact with each other in order to provide learners with an individualized learning experience.

[0696] The server is equipped with information processing capabilities to generate appropriate learning plans based on information received from learners. Hosted on a cloud platform (e.g., Amazon Web Services or Google Cloud Platform), the server analyzes learner data and creates individually optimized learning plans. Furthermore, the server incorporates information analysis capabilities to monitor learners' progress and emotional states in real time and provide psychological support as needed.

[0697] The terminal is provided to learners as a portable information terminal and is equipped with a display that enables intuitive operation and immediate display of information. This terminal also has a synchronization mechanism that sends learners' progress data to a server, presenting learners with learning plans, progress status, and feedback in real time. As a result, learners can smoothly proceed with their daily learning activities.

[0698] This system allows users to learn at their own pace. For example, learners can use a smartphone app to check their daily study schedule and receive feedback based on their progress. As the exam date approaches, the server analyzes past practice test results and delivers questions focused on areas that need improvement to the device, further supporting the learner's understanding.

[0699] An example of a prompt using a generative AI model is: "Generate an effective learning plan based on the user's desired learning goals and current progress. Also, suggest ways to send encouraging messages if the user is feeling stressed." This prompt serves as a guide for the server to provide flexible and accurate support to learners through the generative AI model.

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The server receives learner information. Inputs include learner profile data, learning history, and goals. Based on this data, the server uses information processing tools to analyze the data and generate an optimal learning plan for the learner. The output is a individually optimized learning plan.

[0703] Step 2:

[0704] The terminal receives the learning plan sent from the server and displays it intuitively to the learner. The input is learning plan data from the server. The terminal's display mechanism allows the user to visually confirm the information. As a result, the user can clearly understand how to proceed with their daily learning activities. The output provides the user with a visually organized learning schedule.

[0705] Step 3:

[0706] Users progress through their learning using a device and input their learning progress. Input includes learning outcomes and progress data. The device transmits this data to the server in real time. This is necessary for the server to monitor the learner's progress and collect data. The output is the progress data sent to the server.

[0707] Step 4:

[0708] The server infers the learner's emotional state based on received progress data and provides psychological support if necessary. Inputs include the learner's progress data and data on their past emotional states. The server analyzes the data using information analysis tools, and if it determines that the learner is experiencing stress, it generates an encouraging message and sends it to the terminal. The output is emotional support data, including feedback.

[0709] Step 5:

[0710] The terminal displays feedback from the server to the learner. Inputs include emotional support messages and learning feedback sent from the server. The display allows the user to review this feedback in a timely manner and adjust their learning method as needed. The output is visually presented feedback to the user.

[0711] Step 6:

[0712] The user takes a practice test from a terminal. The input is the practice test questions displayed on the terminal. The user takes the test and enters the results on the terminal. Meanwhile, the terminal sends the entered test results to a server, which analyzes the learner's score and level of understanding. The output is the user's test result data.

[0713] Step 7:

[0714] The server identifies the learner's weaknesses based on the results of a practice test and generates appropriate practice problems. The input is the results of the practice test. The server uses a test generation method to generate practice problems based on the identified weaknesses and sends them back to the terminal. The output is customized practice problems.

[0715] 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.

[0716] This invention provides an individualized learning support system that takes into account the learner's emotional state, and offers a more effective learning environment through the coordinated functioning of three elements: a server, a terminal, and the user. In particular, this system incorporates an emotion engine that can recognize the user's emotions in real time and create an optimal learning environment.

[0717] Server operation

[0718] The server receives basic information and learning objectives entered by the user on the terminal and generates a personalized learning plan. The server's data processing function analyzes the input information and determines the learning content and sequence. During this process, an emotion engine is used to analyze the learner's emotional state and select more appropriate learning content. For example, if the user is anxious, the server suggests learning tasks and methods that are expected to have a relaxing effect.

[0719] The server analyzes progress data based on learner input data and feedback from the emotion engine, and generates mental support and encouragement messages. The server also generates practice tests and study materials tailored to the learner's emotions and provides them to the learner via their device. In this case, if the learner may be experiencing some level of stress, the server prioritizes adjusting the difficulty level and providing content to help them relax.

[0720] Terminal operation

[0721] The device intuitively displays learning plans and analytical data delivered from the server. Users perform daily learning activities and record their progress through the device. Furthermore, it utilizes an emotion engine to recognize the user's emotional state from their input data and transmit it to the server. The device then presents the user with feedback and mental support messages from the server to help maintain learning motivation.

[0722] Furthermore, the device can use an emotion engine to pick up on the user's emotional changes in detail and provide alerts to reduce the burden of learning or notifications to encourage short breaks.

[0723] User actions

[0724] Users input the necessary learning information into their device and proceed with their learning activities according to the learning plan generated by the system. Throughout their daily learning activities, users can receive notifications and support from their device, monitor their emotional state, and adjust it as needed. They can also immediately check their learning progress through mock exams and obtain appropriate improvement measures based on those results.

[0725] This invention provides learners with an optimal learning method and environment that takes their emotional state into consideration, enabling them to learn efficiently while maintaining sustained motivation.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] Users use their devices to input learning goals and basic information. This input includes the qualification they are aiming for, the exam date, and an estimated study time.

[0729] Step 2:

[0730] The terminal sends user input information to the server. The server activates the emotion engine and prepares to collect initial data about the user's emotional state.

[0731] Step 3:

[0732] The server generates a personalized learning plan based on user information and data from the emotion engine. Here, the learning content and sequence are adjusted to take the user's emotional state into consideration.

[0733] Step 4:

[0734] The server schedules the generated study plan and practice problems based on the set review timings and delivers them to the terminals.

[0735] Step 5:

[0736] The user checks their device and completes the presented learning tasks. The user's interactions during learning are analyzed by an emotion engine.

[0737] Step 6:

[0738] The device combines the user's learning progress and emotional data and sends it to the server. The server analyzes the acquired data and automatically generates mental support and encouragement messages.

[0739] Step 7:

[0740] The server sends mental support messages, generated based on learning progress and emotional state, to the terminal and displays them to the user. These messages are intended to maintain learning motivation.

[0741] Step 8:

[0742] The device presents the user with practice tests and additional study problems, and provides real-time feedback. It also reassessses the user's emotional state and revises the learning plan as needed.

[0743] Step 9:

[0744] The user takes a practice test and uses the results and feedback to verify the validity of their study plan. The device then sends the relevant data back to the server.

[0745] Step 10:

[0746] The server utilizes the user's latest learning data and sentiment analysis results to update the learning plan as needed and deliver the new plan to the device. By repeating this cycle, learners can continue to learn efficiently under optimal conditions.

[0747] (Example 2)

[0748] 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".

[0749] In recent years, there has been a growing need to address the individual emotional states of learners in learning environments. However, conventional systems provide uniform learning support without considering emotional states, leaving challenges in terms of maintaining learner motivation and reducing stress. Therefore, there is a need for more individualized learning support systems that take learners' emotional states into account.

[0750] 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.

[0751] In this invention, the server includes data processing means, scheduling means, data analysis means, emotion analysis means, and environment adjustment means. This enables the generation of personalized learning plans according to the learner's emotional state and the optimization of the learning environment.

[0752] "Data processing means" refers to a device or method that analyzes learner input information and generates an individualized learning plan.

[0753] A "scheduling means" is a device or method that presents appropriate learning tasks and review timings based on a learning plan.

[0754] "Data analysis means" refers to a device or method for analyzing data necessary to provide mental support while monitoring learners' learning progress and emotional state.

[0755] "Test generation means" refers to a device or method that generates and presents mock tests or practice problems to learners.

[0756] "Emotional analysis means" refers to a device or method that analyzes a user's real-time emotional state and uses that information to adjust the learning environment.

[0757] "Environmental adjustment means" refers to a device or method that dynamically adjusts content and learning conditions in order to reduce learner stress and provide an optimal learning environment.

[0758] This invention provides a system that offers personalized learning support that takes into account the learner's emotional state. The system functions through the cooperation of three parties: a server, a terminal, and a user. Specifically, the server performs data processing and emotion analysis, while the terminal functions as an interface with the user.

[0759] The server uses data processing tools to receive information entered by the user on the terminal. This information includes desired learning content and progress goals. The server also utilizes a generative AI model to create a personalized learning plan tailored to the user. This plan is optimized based on the user's learning history and emotional state analysis results from data analysis tools. For example, if the server detects user anxiety using emotion analysis tools, it will suggest learning content with a relaxing effect.

[0760] The device intuitively displays and notifies the user of learning plans and feedback from the server. It also utilizes sentiment analysis to monitor the user's emotions in real time. This information is sent to the server to further optimize the learning environment. The device can also use environmental adjustment tools to issue alerts prompting short breaks if the user feels overwhelmed.

[0761] Users can learn at their own pace by inputting information into their devices, based on a learning plan provided by the server. The server uses a test generation system to provide users with mock tests and practice questions, giving them opportunities to check their learning progress and understanding. In this process, specific questions such as "What kind of relaxing tasks can you suggest when the user is feeling anxious?" are used as prompts to the generating AI model.

[0762] This embodiment allows learners to continue learning while respecting their own emotional state, providing an environment where they can learn efficiently while maintaining high motivation.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The server receives information entered by the user through their terminal. This input includes data such as learning goals, desired progress, and current mindset. Based on this, the server analyzes the information using data processing tools and generates an individualized learning plan. The output here is a learning plan tailored to the user. A generative AI model is used to design the optimal plan by referring to past successful patterns.

[0766] Step 2:

[0767] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time emotion data transmitted from the terminal (e.g., facial expression analysis data and voice tone). The server analyzes this data to determine the user's current emotional state. The output is the emotion analysis result, which provides guidance for adjusting the learning environment.

[0768] Step 3:

[0769] The terminal displays the learning plan and sentiment analysis results received from the server to the user. Input is data from the server, and output is the learning plan and notifications displayed on the screen. The terminal uses environmental adjustment tools to advise the user on necessary adjustments for learning, such as music playback and lighting adjustments. The terminal also directly displays feedback to the user, increasing their motivation for the next learning step.

[0770] Step 4:

[0771] Users progress through their learning according to a learning plan provided by the server using their device. New input data from the user (such as learning progress and feedback) is sent to the server via the device. This allows the server to continuously monitor the user's progress and prepare to suggest new learning steps. The output consists of continuously updated learning plans and feedback.

[0772] Step 5:

[0773] The server uses test generation tools to provide users with practice tests and study materials. The input consists of the user's progress data and past answer history. The server analyzes this data using data analysis tools to identify the user's weaknesses and areas requiring improvement. The output is a customized set of test questions, delivered to the user via a terminal. This allows the user to self-assess their progress and use it to further improve their learning.

[0774] (Application Example 2)

[0775] 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".

[0776] In today's learning environment, providing learning support that adapts to the emotional state of each individual learner is difficult, and in particular, learners struggle to manage their own emotions and motivations when studying at home. This leads to problems such as decreased learning efficiency and difficulty maintaining motivation. Furthermore, existing learning support technologies are insufficient to recognize learners' emotional states in real time and provide adaptive learning adjustments and support.

[0777] 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.

[0778] In this invention, the server includes information processing means for receiving learner information and generating an individualized learning plan; planning means for presenting learning tasks based on the learning plan, taking into account appropriate review timings; information analysis means for monitoring the learner's learning progress and emotional state and providing mental support; and recognition and suggestion means for recognizing the learner's emotional state in real time and suggesting adjustments to learning content and activities for emotional stabilization accordingly. As a result, learners can proceed with their learning in an environment that is optimal for their emotional state, thereby improving learning efficiency and motivation.

[0779] "Information processing means" refers to devices or systems that receive information provided by learners and generate personalized learning plans based on that information.

[0780] A "plan creation tool" is a device or system that presents learning tasks based on a generated learning plan and determines the appropriate timing for reviewing those tasks.

[0781] "Information analysis tools" refer to devices and systems that monitor learners' learning progress and emotional state, and provide mental support as needed.

[0782] "Recognition and suggestion means" refers to devices or systems that recognize a learner's emotional state in real time and suggest adjustments to learning content or activities for emotional stabilization in accordance with that state.

[0783] In this embodiment of the invention, the system mainly consists of a server, a terminal, and a user. The server, acting as an information processing device, receives the learner's basic information and learning objectives, and generates an individualized learning plan accordingly. The software tools used in this process include a database management system and a plan creation algorithm. The server also employs analysis methods such as natural language processing and image recognition to infer the learner's real-time emotional state. This allows the server to provide appropriate mental support when the learner is in a particular emotional state.

[0784] The terminal is used daily by learners and serves as the user interface. The terminal displays the learning plan sent from the server and accepts learner input. The terminal is equipped with emotion recognition capabilities, analyzing the learner's facial expressions and voice through a webcam and microphone. This allows the terminal to track the learner's emotional state and send feedback to the server.

[0785] The user's role is to follow the learning plan displayed on the device and progress through their daily studies. The user can monitor their emotional state on the device and adjust their learning methods as needed. For example, if they are having difficulty learning, the device may suggest relaxation content or distractions such as games.

[0786] This system uses a specific generative AI model to provide a learning environment based on the user's emotional data and learning history. An example of a prompt might be, "Please suggest music that will help the learner relax."

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The server receives basic information and learning objectives sent by the user. This includes personal data entered by the learner from their device and their desired subjects to study. The server analyzes this data and generates a personalized learning plan. In generating the learning plan, the server receives assistance from a database management system and constructs the plan based on past learning data and statistical information. Finally, the generated learning plan is sent to the device.

[0790] Step 2:

[0791] The device receives a learning plan from the server and displays it to the user. The user begins learning based on the planned learning tasks. The device provides visual feedback on the current learning progress on the screen based on the user's input information. At this time, it utilizes emotion recognition technology to monitor the user's emotions in real time using data collected from the webcam and microphone.

[0792] Step 3:

[0793] The server receives and analyzes real-time emotional data sent from the terminal. Using an emotional recognition algorithm, it determines, for example, whether the user is lacking concentration or experiencing stress. If a problem is detected, the system uses a generative AI model to adjust the learning content to suit the learner and provide suggestions for emotional stabilization. This information is then sent back to the terminal.

[0794] Step 4:

[0795] Based on feedback received from the server, the device suggests necessary mental support and adjustments to learning methods to the user. For example, if the user is feeling stressed, the device offers options such as light exercise or music for relaxation. It also allows the user to choose from options based on prompts generated using a generative AI model.

[0796] Step 5:

[0797] Users progress through their learning by accepting suggestions from their device and monitoring their own emotional state. By accurately understanding their emotions, they can adjust their learning plan or take breaks as needed. This feedback loop allows users to maintain motivation and learn effectively.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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 as being incorporated by reference.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] A data processing means for receiving learner information and generating an individualized learning plan,

[0822] A scheduling means for presenting learning tasks based on the learning plan, taking into account appropriate review timings,

[0823] A data analysis tool for monitoring learners' learning progress and emotional state, and for providing mental support,

[0824] A test generation means that provides learners with mock tests and practice questions,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising an analysis means that uses input data during learning to infer the emotional state of the learner.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the timing of the review is calculated based on Ebbinghaus's forgetting curve.

[0830] "Example 1"

[0831] (Claim 1)

[0832] Information processing means for receiving learner information and generating an individualized learning plan,

[0833] A time management system for managing learning activities based on the learning plan and considering appropriate review periods,

[0834] Analytical means for monitoring learners' progress and psychological state and providing support,

[0835] A means for constructing an examination that provides learners with assessment tests and supplementary assignments,

[0836] A terminal display means for users to input their own information and record their learning activities,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, comprising an analysis means that uses input information during learning in order to infer the psychological state of the learner.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the timing of the review is calculated based on a memory retention model.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] Information processing means for receiving learner information and generating an individualized learning plan,

[0845] A planning means for presenting learning items based on the learning plan, taking into consideration the appropriate timing for relearning;

[0846] Information analysis means for monitoring learners' learning progress and emotional state, and for providing psychological support,

[0847] A display means that uses a portable information terminal to display personalized learning information to the user in real time and allows for two-way information exchange,

[0848] A synchronization mechanism for periodically collecting learner progress data and sending it to a dedicated server,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising an analysis means that uses input information during learning to infer the learner's emotional state and, if necessary, send an encouraging message to the terminal.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the timing of the relearning is calculated based on the pattern of forgetting the recognized information.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A data processing means for receiving learner information and generating an individualized learning plan,

[0857] A scheduling means for presenting learning tasks based on the learning plan, taking into account appropriate review timings,

[0858] A data analysis tool for monitoring learners' learning progress and emotional state, and for providing mental support,

[0859] A test generation means that provides learners with mock tests and practice questions,

[0860] A means of analyzing a user's emotional state in real time and proposing the optimal learning environment,

[0861] Environmental adjustment measures for providing content optimized to reduce learner stress,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising an analysis means that uses input data during learning to infer the emotional state of the learner.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the timing of the review is calculated based on Ebbinghaus's forgetting curve.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] Information processing means for receiving learner information and generating an individualized learning plan,

[0870] A means for creating a plan to present learning tasks based on the learning plan, taking into account appropriate review periods,

[0871] Information analysis tools for monitoring learners' learning progress and emotional state, and for providing mental support,

[0872] A test generation means that provides learners with mock tests and practice questions,

[0873] A means for recognizing and suggesting learning content and activities for emotional stabilization in real time, and for recognizing the learner's emotional state accordingly.

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising a device for inferring the emotional state of a learner and providing learning support at home based on that inference.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the timing of the review is calculated based on Ebbinghaus's forgetting curve and adjusted according to emotional fluctuations. [Explanation of Symbols]

[0879] 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. Information processing means for receiving learner information and generating an individualized learning plan, A planning means for presenting learning items based on the learning plan, taking into consideration the appropriate timing for relearning; Information analysis means for monitoring learners' learning progress and emotional state, and for providing psychological support, A display means that uses a portable information terminal to display personalized learning information to the user in real time and allows for two-way information exchange, A synchronization mechanism for periodically collecting learner progress data and sending it to a dedicated server, A system that includes this.

2. The system according to claim 1, further comprising an analysis means that uses input information during learning to infer the learner's emotional state and, if necessary, send an encouraging message to the terminal.

3. The system according to claim 1, wherein the timing of the relearning is calculated based on the pattern of forgetting the recognized information.

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