System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving initial setting information from a user; means for generating an optimal learning plan based on the initial setting information; means for transmitting the generated learning plan to a user terminal; means for recording a learning progress; means for periodically checking a progress status and generating a test; means for analyzing a test result and learning progress data and readjusting the learning plan if necessary; and means for providing a motivation message to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional language learning systems, it is difficult for users to create individually customized plans and flexibly adjust them based on their progress. Maintaining motivation is also a challenge, which hinders users' continuity in learning. Therefore, there is a need for a system that allows users to efficiently and continuously progress in language learning. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving initial setting information from a user and a means for generating an optimal study plan based on the initial setting information. It also includes a means for transmitting the generated study plan to a user terminal, a means for recording study progress, a means for periodically checking the progress and generating tests, and a means for analyzing test results and study progress data and readjusting the study plan as necessary. Furthermore, a means for providing motivational messages to the user is provided to support the user's continued study.

[0006] "User" refers to an individual who uses this system to learn a language.

[0007] "Initial setting information" refers to basic information for generating a learning plan, such as the language the user wants to learn, their learning objectives, available time, and existing skill level.

[0008] A "study plan" is a plan that is generated based on the user's initial setting information and includes daily and weekly study tasks and goals for effectively improving language skills.

[0009] "User terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0010] "Study progress" refers to information that indicates the status and results of a user's completion of planned learning tasks.

[0011] A "test" is an assessment tool provided to evaluate a user's learning progress and measure their understanding and improvement of skills.

[0012] "Test Results" refers to the evaluation data and scores obtained after a user takes a test.

[0013] "Study progress data" refers to information recorded by a user's progress and achievement in completing planned study tasks.

[0014] A "realigned study plan" refers to a new study plan that has been modified to suit the user's needs and circumstances based on test results and learning progress data.

[0015] "Motivational messages" refer to messages of encouragement and advice provided to users to maintain their motivation and interest in continuing their studies. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an 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 process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly adjusts the plan according to the user's progress. Furthermore, the system provides messages of encouragement and advice to maintain the user's motivation.

[0038] Specific examples of the system

[0039] User Preferences

[0040] 1. The user accesses the language learning system and enters information such as the language they wish to learn, their learning goals, the amount of time they have available to learn, and their existing skill level in the initial setup form.

[0041] 2. The terminal sends the entered initial setting information to the server.

[0042] Generate a lesson plan

[0043] 1. The server analyzes the received initial setting information and generates an optimal learning plan tailored to the user's needs. For example, for a user who wants to learn English business communication, the server creates a plan that balances grammar, vocabulary, listening, and speaking skills.

[0044] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[0045] Track your learning and progress

[0046] 1. The user follows the presented study plan and performs daily study tasks.

[0047] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[0048] Checking progress and testing

[0049] 1. The server periodically analyzes the user's progress data and generates tests to check the user's progress. The tests may include listening and speaking exercises.

[0050] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[0051] Re-adjusting your study plan

[0052] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data. For example, if a user has difficulty with listening, it generates a new study plan that includes more listening practice.

[0053] 2. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[0054] Staying motivated

[0055] 1. The device displays encouraging messages and study advice to the user after each day's study. For example, it displays a message such as, "You did a great job today! Let's keep it up next time."

[0056] 2. The server generates customized encouraging messages based on the user's progress and sends them to the device.

[0057] Specific examples

[0058] For example, for a user who has set aside one hour of study time each day to pass a university English audio course, the system works as follows:

[0059] 1. In the initial setup form, the user enters the learning language as English, the goal as passing a university English audio course, and the daily study time as one hour.

[0060] 2. The device sends this information to the server.

[0061] 3. The server analyzes the input information and generates an optimal study plan. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations.

[0062] 4. The server sends this study plan to the user's terminal, and the user proceeds with their studies according to the plan.

[0063] 5. The device records the learning progress and sends the data to the server.

[0064] 6. The server generates periodic tests based on the progress data and sends them to the device.

[0065] 7. The user takes the test and sends the results from the terminal to the server.

[0066] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak at listening.

[0067] 9. The terminal provides the user with a new study plan, and the user continues studying based on it.

[0068] 10. After completing a lesson, the device will display an encouraging message such as "You did a great job today!" to help maintain motivation.

[0069] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[0073] Step 2:

[0074] The terminal transmits the input initial setting information to the server.

[0075] Step 3:

[0076] The server analyzes the received initial setting information and generates an optimal learning plan based on the user's learning goals and available time.

[0077] Step 4:

[0078] The server transmits the generated study plan to the user terminal and presents it to the user.

[0079] Step 5:

[0080] The user follows the presented study plan and performs daily study tasks.

[0081] Step 6:

[0082] The device records the user's progress each time they complete a lesson.

[0083] Step 7:

[0084] The terminal periodically transmits the recorded learning progress data to the server.

[0085] Step 8:

[0086] The server receives the progress data and periodically generates tests to check the user's progress.

[0087] Step 9:

[0088] The server transmits the generated test to the user terminal and provides it to the user.

[0089] Step 10:

[0090] The user takes the test and has the results recorded on the terminal.

[0091] Step 11:

[0092] The terminal transmits the test results to the server.

[0093] Step 12:

[0094] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[0095] Step 13:

[0096] The server generates a new, re-adjusted lesson plan based on the analysis results.

[0097] Step 14:

[0098] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[0099] Step 15:

[0100] The user continues studying based on the new study plan.

[0101] Step 16:

[0102] The device displays encouraging messages and study advice to the user to help maintain motivation.

[0103] Step 17:

[0104] The server generates personalized encouraging messages based on the user's progress and test results and sends them to the device.

[0105] Step 18:

[0106] The terminal displays a customized encouraging message to the user.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] Conventional language learning systems lack the ability to flexibly readjust learning plans according to the user's individual needs and progress. They also lack the ability to generate customized encouraging messages to maintain user motivation. As a result, users have difficulty continuing their learning effectively and do not achieve the expected learning results.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0111] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording progress each time the user completes a study task, means for periodically checking progress and generating tests including listening and speaking, means for analyzing test results and study progress data, analyzing the user's strengths and weaknesses, and readjusting the study plan as needed, means for generating customized encouraging messages based on the user's progress data and transmitting them to the user terminal, means for generating customized messages using a generative AI model, means for using a data analysis tool to analyze the user's progress, and means including a database for storing the study plan and progress data. This enables flexible plan readjustment according to the user's study progress and effective motivation maintenance.

[0112] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, learning time, and existing skill level that the user inputs into the language learning system.

[0113] A "study plan" is a plan that includes specific study tasks and schedules, and is generated based on the user's initial setting information.

[0114] "User terminal" refers to an electronic device used by a user to access the learning system, and includes a personal computer, smartphone, tablet, etc.

[0115] "Progress" is data that indicates the content and progress of what the user has learned according to the study plan.

[0116] "Tests" are exercises or assignments generated by the server to monitor a user's learning progress and assess their skill mastery.

[0117] "Test results" is data that indicates the results and evaluation points of a user when they take a test.

[0118] "Strengths and Weaknesses" are areas where the user's skills are strong and areas that need improvement, analyzed based on test results and learning progress data.

[0119] An "encouraging message" is a message of encouragement or advice that is generated by the server and sent to the user terminal with the aim of motivating the user to continue learning.

[0120] A "generative AI model" is an artificial intelligence model used for natural language generation, data analysis, and other tasks.

[0121] "Data analysis tools" are software and libraries used to analyze users' learning data and understand progress and trends.

[0122] A "database" is a digital storage system for efficiently storing and managing learning plans, progress data, etc.

[0123] This invention relates to a support system for helping users efficiently advance language learning. This system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, it provides messages of encouragement and advice to maintain the user's motivation. Specific embodiments of this system are described below.

[0124] Gathering initial configuration information

[0125] A user accesses the language learning system and enters information into the initial setup form, such as the language they want to learn, their learning goals, the time they have available for studying, their existing skill level, etc. For example, they may enter that their goal is to pass a university English audio course and that they will set aside one hour of study time every day.

[0126] Generate a lesson plan

[0127] The server uses an NLP library such as Apache (registered trademark) OpenNLP to analyze the initial setting information received from the user. Based on the analysis results, it generates a learning plan that best suits the user's needs. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations. The generated learning plan is sent to the device via an HTTP request.

[0128] Track your learning and progress

[0129] The user performs daily learning tasks according to the presented learning plan. For example, 30 minutes of listening material and 30 minutes of speaking practice. Once the learning is completed, the device records the progress and sends the data in JSON format to the server. The progress data is stored in an SQL database (e.g., MySQL®).

[0130] Checking progress and generating tests

[0131] The server periodically analyzes the user's progress using data analysis tools such as the Pandas library. Based on the analysis results, it generates tests to check the user's progress, including listening and speaking. The device provides the generated tests to the user, who then takes the tests online. For example, the test results for answering listening questions are stored in an SQL database.

[0132] Re-adjusting your study plan

[0133] The server analyzes the user's strengths and weaknesses using a machine learning model (for example, the Scikit-learn library) based on the test results and progress data. Based on the analysis results, the server readjusts the learning plan as necessary. For users who are weak at listening, the server generates a new learning plan to improve listening skills and sends it to the device.

[0134] Staying motivated

[0135] The device displays an encouraging message to the user after completing each day's study. For example, it displays a standard message such as, "You did a great job today! Let's keep it up next time." In addition, the server uses a generative AI model (e.g., ChatGPT® by OpenAI®) to generate customized encouraging messages based on the progress data. The generated messages are input to a natural language generation model using prompt sentences.

[0136] Prompt Sentence Examples

[0137] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[0138] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Program processing flow

[0141] Step 1:

[0142] A user accesses the language learning system and enters the language to be learned, learning objectives, available time for learning, and existing skill level in an initial setting form.

[0143] Input: Initial information such as user personal information, learning objectives, etc.

[0144] How it works: A user fills in information on a web form and clicks the "Submit" button.

[0145] Output: User preference information is sent to the system.

[0146] Step 2:

[0147] The terminal transmits the input initial setting information to the server.

[0148] Input: User initial configuration information (JSON format).

[0149] What it does: Sends data to the server via an HTTP POST request.

[0150] Output: The server receives the initialization information.

[0151] Step 3:

[0152] The server analyzes the received initial setting information and generates an optimal learning plan.

[0153] Input: Initial setting information (JSON format).

[0154] How it works: It uses the Apache OpenNLP library to parse the information and generate a learning plan in a Python script.

[0155] Output: A customized learning plan for each user.

[0156] Step 4:

[0157] The server transmits the generated study plan to the user terminal and presents it to the user.

[0158] Input: Learning plan (JSON format).

[0159] Behavior: Sends the lesson plan to the user's device via an HTTP POST request.

[0160] Output: The learning plan is displayed on the user's device.

[0161] Step 5:

[0162] The user follows the presented study plan and performs daily study tasks.

[0163] Input: lesson plan.

[0164] Action: The user follows a study plan and completes the materials and tasks, for example, studying listening material for 30 minutes, followed by 30 minutes of speaking practice.

[0165] Output: Progress of the learning task.

[0166] Step 6:

[0167] The device records the user's progress each time they complete a lesson and sends the data to the server.

[0168] Input: Learning progress data.

[0169] How it works: When you click the button to complete the learning task, the progress is sent to the server in JSON format.

[0170] Output: The server receives the progress data and stores it in a SQL database.

[0171] Step 7:

[0172] The server periodically analyzes the user's progress data and generates tests to check the progress.

[0173] Input: Progress data.

[0174] How it works: It uses the Pandas library to aggregate and analyze progress data and generate tests, including listening and speaking.

[0175] Output: A test question set.

[0176] Step 8:

[0177] The terminal provides the generated test to the user, who then performs the test.

[0178] Input: Test question.

[0179] Action: Take a test online, for example, answer a listening question.

[0180] Output: Test results.

[0181] Step 9:

[0182] The terminal records the results of the test and sends them to the server.

[0183] Input: Test results (JSON format).

[0184] What it does: Sends data to the server via an HTTP POST request.

[0185] Output: The server receives the test results and stores them in a SQL database.

[0186] Step 10:

[0187] Based on test results and progress data, the server analyzes the user's strengths and weaknesses and adjusts their study plan as needed.

[0188] Input: Test results and progress data.

[0189] What it does: Analyzes data using a machine learning model (e.g., Scikit-learn) and creates a new learning plan.

[0190] Output: A re-adjusted lesson plan.

[0191] Step 11:

[0192] The server transmits the readjusted study plan to the user terminal and provides it to the user.

[0193] Input: Realigned learning plan (JSON format).

[0194] Operation: Sends an HTTP POST request to the user's device.

[0195] Output: The new lesson plan is displayed on the user's device.

[0196] Step 12:

[0197] The terminal displays encouraging messages and study advice to the user after completing each day's study.

[0198] Enter: an encouraging message.

[0199] What it does: After a lesson is finished, a message is displayed on the screen. For example, "You did a great job today! Let's keep it up next time."

[0200] Output: The user receives the message.

[0201] Step 13:

[0202] The server generates a customized encouraging message according to the user's progress and transmits it to the terminal.

[0203] Input: User progress data.

[0204] How it works: Using a generative AI model (e.g., OpenAI's ChatGPT), it generates encouraging messages using prompts.

[0205] Output: A customized encouraging message.

[0206] Prompt Sentence Examples

[0207] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] To effectively advance language learning, it is important to flexibly adjust learning plans based on the user's progress and skill level and maintain motivation. However, existing systems do not adequately generate individually customized learning plans or readjust them based on progress, making it difficult for users to progress effectively. Furthermore, they do not provide appropriate messages to maintain motivation, increasing the risk of users giving up. The purpose of this invention is to solve these problems and support users in efficiently advancing their language learning.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording study progress, means for periodically checking progress and generating tests, means for analyzing test results and study progress data and readjusting the study plan as necessary, means for providing motivational messages to the user, means for analyzing the user's strengths and weaknesses based on prompts using a generative AI model, means for providing daily study tasks based on the study plan and collecting and analyzing progress data, and means for providing customized encouraging messages according to the progress data. This enables users to efficiently progress through their individually customized study plans and achieve high learning outcomes while maintaining their motivation.

[0213] The "means for receiving initial setting information from the user" is a function for obtaining information such as the language of study, purpose of study, time available for study, and existing skill level provided by the user.

[0214] The "means for generating an optimal study plan based on the initial setting information" is a function for creating an effective study schedule according to the individual study needs of the user.

[0215] The "means for transmitting the generated study plan to the user terminal" is a communication function for providing the study plan generated by the server to the user terminal.

[0216] The "means for recording learning progress" is a function that tracks and records the progress of the user as they perform their daily learning tasks.

[0217] The "means for periodically checking progress and generating tests" is a function for periodically creating tests based on the user's learning progress data and checking the progress.

[0218] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to a function that analyzes the user's test results and learning progress data and flexibly reconfigures the learning plan according to the user's needs.

[0219] The "means for providing motivational messages to the user" is a function that displays messages of encouragement and advice to maintain the user's motivation after the user has finished studying.

[0220] "Means for using a generative AI model to analyze a user's strengths and weaknesses based on prompt sentences" refers to a function that utilizes a generative AI model to identify the strengths and weaknesses of a user's skills based on prompt sentences.

[0221] "Means for providing daily learning tasks based on a learning plan and collecting and analyzing progress data" refers to a function that presents daily learning tasks to users and records and analyzes progress data when they are completed.

[0222] The "means for providing an encouraging message customized according to progress data" is a function for generating and providing an encouraging message that is individually customized based on the user's progress data.

[0223] The present invention aims to build a system that provides users with individually customized learning plans and flexibly adjusts the plans according to their progress, with the aim of helping them efficiently advance their language learning. The system operates via communication between the user terminal and a server, and uses a generative AI model to perform a detailed analysis of the user's learning progress.

[0224] Basic configuration

[0225] The system consists of the following main components:

[0226] User device (smartphone, etc.)

[0227] Server (backend system)

[0228] Generative AI Models

[0229] Initial Setup

[0230] 1. The user terminal collects information from the user through an initial setup form, such as the language to be studied, learning objectives, learning time, and existing skill level. This information is sent to the server.

[0231] 2. The server analyzes the initial settings received from the user and generates an optimal learning plan based on that information. This plan balances skills such as grammar, vocabulary, listening, and speaking.

[0232] Implementing the learning plan

[0233] 1. The user device provides the user with daily learning tasks according to the learning plan sent from the server. Each time the user completes a task, their progress is recorded and sent to the server.

[0234] 2. The server periodically checks the user's progress and generates tests as needed. The test results are sent to the user's device and provided to the user.

[0235] Data analysis and reconditioning

[0236] 1. The server periodically analyzes the user's test results and learning progress data, and uses a generative AI model to analyze the user's strengths and weaknesses based on prompts. For example, "If the user's listening skills are lacking, generate a new learning plan to strengthen them."

[0237] 2. The user terminal provides the user with a new, re-adjusted study plan and allows the user to proceed with their studies based on that plan.

[0238] Staying motivated

[0239] 1. The server generates a customized encouraging message based on the user's progress data and sends it to the user's terminal.

[0240] 2. After completing the study, the user device displays a motivational message such as "You did a great job today!" to maintain the user's motivation to study.

[0241] Technology used

[0242] Hardware: User device (smartphone), server (backend system)

[0243] Software: Django (server-side framework), React Native (front-end framework), generative AI model

[0244] Specific examples

[0245] If a user sets aside 30 minutes per day to study English business communication, the system will operate as follows:

[0246] 1. On the user device, the user enters "English," "Business Communication," "30 minutes / day," and "Beginner" in the initial setup form.

[0247] 2. The server generates an optimal learning plan based on the received information, incorporating basic listening and speaking practice in the first week and phrases for meetings and presentations in the second week.

[0248] 3. The user device provides the user with daily learning tasks, records the progress, and sends it to the server.

[0249] 4. The server uses the generative AI model based on progress to generate new learning plans when necessary and provide them to the user.

[0250] 5. After each day's study, the user device displays an encouraging message such as "You did a great job today!"

[0251] Example prompts for generative AI models

[0252] "Generate an English study plan for learning business communication. The user is a beginner and has set aside 30 minutes a day to study."

[0253] With this configuration and operation, users can efficiently progress with language learning and achieve high learning results while maintaining their motivation.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] The user enters information such as the language to learn, learning objectives, learning time, and existing skill level through the initial setup form. This is the input data. The user terminal sends this initial setup information to the server.

[0257] Step 2:

[0258] The server receives and analyzes the initial setup information. Based on the analyzed data, it generates a study plan suited to the user's needs. For example, the server may consider the user's learning goals and available study time and place basic listening and speaking practice in the first week, and phrases for meetings and presentations in the second week. The generated study plan is sent to the user's device as output data.

[0259] Step 3:

[0260] The user terminal presents the study plan received from the server to the user. The user performs daily study tasks according to the study plan. As each study task is completed, progress data is entered into the terminal.

[0261] Step 4:

[0262] Each time a learning task is completed, the user device records progress data, including the time it took to complete the task and the number of correct answers, and transmits the data to the server.

[0263] Step 5:

[0264] The server periodically checks the progress data and generates progress check tests based on the data. These tests are set for each skill, such as listening, speaking, grammar, and vocabulary. The generated tests are sent to the user's device.

[0265] Step 6:

[0266] The user terminal provides the generated test to the user, who takes the test and inputs the result data into the terminal.

[0267] Step 7:

[0268] The user terminal transmits test result data to the server, which analyzes the test result data and learning progress data to identify the user's strengths and weaknesses.

[0269] Step 8:

[0270] The server uses a generative AI model to analyze the user's strengths and weaknesses based on the prompt, and performs data calculations such as "if the user's listening skills are lacking, generate a new learning plan to strengthen them."

[0271] Step 9:

[0272] The server readjusts the lesson plan as needed based on the analysis results, and the new lesson plan is sent back to the user's device.

[0273] Step 10:

[0274] The user terminal provides the user with the readjusted study plan and proceeds with the study based on it.

[0275] Step 11:

[0276] After completing a study session, the user device displays an encouraging message such as "You did a great job today!" to the user, which is expected to help maintain the user's motivation. The encouraging message is customized based on the user's progress data.

[0277] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0278] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, the system provides encouraging and advice messages to maintain the user's motivation, and recognizes the user's emotions to provide more accurate support.

[0279] Specific examples of the system

[0280] User Preferences

[0281] 1. The user accesses the language learning system and enters the necessary information in the initial setup form (language to learn, learning objectives, available time for learning, and existing skill level).

[0282] 2. The terminal sends the entered initial setting information to the server.

[0283] Generate a lesson plan

[0284] 1. The server analyzes the received initial setting information and generates an optimal learning plan based on the user's needs.

[0285] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[0286] Track your learning and progress

[0287] 1. The user follows the presented study plan and performs daily study tasks.

[0288] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[0289] Checking progress and testing

[0290] 1. The server periodically analyzes the user's progress data and generates a test to check the user's progress. For example, the test may include listening and speaking practice.

[0291] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[0292] Re-adjusting your study plan

[0293] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data.

[0294] 2. The server generates a re-adjusted learning plan based on the analysis results.

[0295] 3. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[0296] Use of emotion engine

[0297] 1. The device collects emotional data from the user's facial expressions, voice, etc. and analyzes it using an emotion engine.

[0298] 2. The server recognizes the user's emotional state based on the emotional data obtained from the emotion engine.

[0299] 3. The server adjusts the study plan and motivational messages taking into account the user's emotional state.

[0300] Staying motivated

[0301] 1. The device displays encouraging messages and study advice to the user after completing each day's study.

[0302] 2. The server generates a customized encouraging message based on the user's emotional data and progress and sends it to the device.

[0303] 3. The device displays a customized encouraging message to the user.

[0304] Specific examples

[0305] When a user is learning business English, the following scenarios can be envisaged using the emotion engine:

[0306] 1. The user enters the initial information, setting aside one hour each day to learn business English.

[0307] 2. The device sends this information to the server.

[0308] 3. The server analyzes the configuration information and generates a study plan that allows the user to learn vocabulary and phrases necessary for business situations.

[0309] 4. The server sends the study plan to the user's terminal, and the user proceeds with the study according to the plan.

[0310] 5. The device records the user's learning progress and periodically sends the data to the server.

[0311] 6. The server analyzes the progress data, generates a progress confirmation test, and sends it to the user terminal.

[0312] 7. The user takes the test and sends the results from the device to the server.

[0313] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak in listening.

[0314] 9. The device collects the user's emotional state using facial recognition and voice data and analyzes it using an emotion engine.

[0315] 10. Based on the emotional data, if the user feels frustrated, the server generates an encouraging message such as, "You're doing great! You'll definitely get results."

[0316] 11. The terminal displays the generated message to the user to keep them motivated.

[0317] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing the emotion engine, it can further increase users' motivation and help them achieve their ultimate learning goals.

[0318] The processing flow will be explained below.

[0319] Step 1:

[0320] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[0321] Step 2:

[0322] The terminal transmits the input initial setting information to the server.

[0323] Step 3:

[0324] The server analyzes the received initial setting information and generates an optimal learning plan that meets the user's needs.

[0325] Step 4:

[0326] The server transmits the generated study plan to the user terminal and presents it to the user.

[0327] Step 5:

[0328] The user follows the presented study plan and performs daily study tasks.

[0329] Step 6:

[0330] The device records the user's progress each time they complete a lesson.

[0331] Step 7:

[0332] The terminal periodically transmits the recorded learning progress data to the server.

[0333] Step 8:

[0334] The server receives the progress data and periodically generates tests to check the user's progress.

[0335] Step 9:

[0336] The server transmits the generated test to the user terminal and provides it to the user.

[0337] Step 10:

[0338] The user takes the test and has the results recorded on the terminal.

[0339] Step 11:

[0340] The terminal transmits the test results to the server.

[0341] Step 12:

[0342] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[0343] Step 13:

[0344] The server generates a new, re-adjusted lesson plan based on the analysis results.

[0345] Step 14:

[0346] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[0347] Step 15:

[0348] The user continues studying based on the new study plan.

[0349] Step 16:

[0350] The device displays encouraging messages and study advice to the user to help maintain motivation.

[0351] Step 17:

[0352] The device collects emotion data using facial recognition and voice data from the user.

[0353] Step 18:

[0354] The terminal transmits the collected emotion data to an emotion engine to analyze the user's emotional state.

[0355] Step 19:

[0356] The server recognizes the emotions the user is feeling while studying based on the emotion data obtained from the emotion engine.

[0357] Step 20:

[0358] The server adjusts the study plan and motivational messages to take into account the user's emotional state and generates new customized messages.

[0359] Step 21:

[0360] The server sends a customized encouraging message to the user terminal.

[0361] Step 22:

[0362] The terminal displays the generated encouraging message to the user to maintain motivation for learning.

[0363] Example 2

[0364] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] Conventional language learning systems have the problem that they are unable to accurately grasp the user's learning progress, and that it is difficult to maintain motivation or flexibly respond to individual learning needs. Furthermore, providing learning plans and messages that ignore the user's emotional state can cause users to feel frustrated with learning, making it difficult for them to continue studying.

[0366] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user terminal, means for recording study progress, means for periodically checking the progress status and generating a test, means for analyzing the test results and study progress data and readjusting the study plan as necessary, means for recognizing the user's emotions and providing an adjusted motivational message, means for analyzing the user's facial expressions and voice data to collect emotion data, and means for generating customized messages using a generative AI model. This makes it possible to provide an individually customized study plan and motivational message based on the user's study progress and emotional state.

[0367] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, available learning time, and existing skill level that is input when a user accesses the language learning system.

[0368] A "study plan" is a set of individually customized study schedules and tasks that are generated based on the user's initial setting information.

[0369] A "user terminal" is a device such as a computer or smartphone used by a user, and is a medium for displaying study plans, tests, messages, etc.

[0370] "Study progress" is data that indicates the progress and achievement level of the learning tasks that the user has performed according to the learning plan.

[0371] "Tests" are server-generated tests to assess a user's listening, speaking, or other language skills in order to assess their learning progress.

[0372] "Test results" is data that indicates the scores and evaluations of users when they take a test.

[0373] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions and voice.

[0374] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate content, specifically for generating customized messages.

[0375] "Motivational messages" are messages of encouragement and advice provided to increase the user's motivation to learn and encourage them to continue.

[0376] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized learning plan based on initial setting information input by the user, supports the progress of learning, and provides motivational messages that incorporate the user's emotional state.

[0377] 1. Receive initial setup information and generate a lesson plan

[0378] A user accesses the language learning system and inputs initial information such as the language to be learned, learning objectives, available learning time, existing skill level, etc. The terminal collects this information and sends it to the server.

[0379] The server uses Python data analysis libraries (e.g., Pandas, SciPy) to analyze the received initial configuration information and generate an optimal learning plan based on the user's needs. The generated learning plan includes specific tasks and schedules to achieve the user's learning goals.

[0380] 2. Recording your learning progress

[0381] The user performs daily learning tasks according to the presented learning plan. The device records the user's learning progress and manages the progress data using a database such as MySQL or SQLite. This data is periodically sent to a server to grasp the user's learning status.

[0382] 3. Check your progress and test

[0383] The server analyzes the user's progress data and generates tests to check progress as needed. The tests evaluate skills such as listening and speaking, and are generated using Python data analysis libraries (e.g., Pandas, NumPy).

[0384] 4. Readjust your study plan

[0385] The server analyzes test results and progress data to identify the user's strengths and weaknesses. This is done using machine learning algorithms (e.g., Scikit-learn). Based on the analysis results, the server generates an adjusted study plan and sends it to the user's device. This allows the user to always study based on the optimal study plan.

[0386] 5. Use of Emotion Engine

[0387] The device collects the user's facial expressions and voice data and analyzes it using an emotion recognition engine (e.g., OpenCV, TENSORFLOW (registered trademark)). The server recognizes the user's emotional state based on the emotional data obtained from the emotion recognition engine and reflects this in the content of the study plan and motivational messages.

[0388] 6. Staying motivated

[0389] The device displays encouraging messages and study advice to the user after completing each day's study. The server uses a generative AI model (e.g., a natural language generation algorithm) to generate encouraging messages based on the user's emotional data and progress. The generated messages are sent to the user's device and displayed to the user. This helps to motivate the user and encourage them to continue studying.

[0390] Examples of concrete examples and prompts

[0391] As a concrete example, if a user is learning business English, they would enter the initial settings information as "Business English," "1 hour daily," and "Intermediate level." Based on this information, the server would generate a study plan for learning vocabulary and phrases needed in business situations and send it to the user's device. The user would proceed with their study, and the device would record and send the progress. Based on the progress data, the server would generate a test and provide it to the user. The study plan would be readjusted depending on the test results. The server would also analyze the user's emotional state from their facial expressions and voice, and generate appropriate encouraging messages that would be displayed on the device.

[0392] An example of a prompt to input to a generative AI model is as follows:

[0393] Generate encouraging messages based on the user's learning goals and progress data. For example, if the user is frustrated with listening, the message might be "You're doing great! You'll see results!"

[0394] In this way, this system allows users to progress through language learning efficiently and flexibly, supporting them in continuing their studies and achieving their ultimate goals.

[0395] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0396] Step 1: User enters initial setup information

[0397] A user accesses the language learning system and enters information into an initial setup form, such as the language to learn, learning objectives, available study time, and existing skill level. The entered information becomes the basis for generating a learning plan tailored to the user's learning needs. The terminal collects this initial setup information and sends it to the server. Input is via text input fields and selection boxes, and output is the data sent to the server as initial setup information.

[0398] Step 2: Generate a lesson plan

[0399] The server generates an optimal learning plan based on the received initial setup information. It uses Python data analysis libraries (e.g., Pandas, SciPy) to create a schedule based on learning objectives and available time. Specifically, it analyzes the initial setup information (vocabulary, phrases, skill level, etc.) and assigns learning tasks by time. The output is a customized learning plan, which is sent to the user's device.

[0400] Step 3: Practice and record your progress

[0401] The user performs daily learning tasks according to a learning plan provided by the server. The device records the user's progress each time they complete a study and stores it in a database such as MySQL or SQLite. Specifically, when the user presses a button after completing a task, the time it took to complete the task and the degree of achievement are recorded. The input is the user's operation, and the output is information stored in the database as progress data.

[0402] Step 4: Check progress and generate tests

[0403] The server periodically analyzes the user's progress data and generates tests to check progress. The progress data is aggregated using Python analysis libraries (e.g., Pandas, NumPy) and tests (listening, speaking, etc.) are created according to the user's learning status. The output tests are sent to the user's device and provided to the user. Specifically, a screen for the user to take the test is displayed on the device.

[0404] Step 5: Test results and readjust your study plan

[0405] The user takes the provided test and sends the results from their device to the server. The server uses a machine learning algorithm (e.g., Scikit-learn) to analyze the user's strengths and weaknesses based on the received test results and progress data. Based on the analysis results, the server readjusts the learning plan and sets new learning tasks, if necessary. The output is a readjusted learning plan, which is sent to the user's device. For example, for a user who is weak at listening, a new listening improvement task is added.

[0406] Step 6: Collect and analyze emotion data

[0407] The device collects facial and voice data from the user during and after learning, and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow). This determines the user's emotional state and sends the data to a server. Specifically, the device uses a camera and microphone to capture facial and voice data and recognizes emotions in real time. The input is the user's facial and voice data, and the output is emotional data resulting from the analysis.

[0408] Step 7: Generate and deliver motivational messages

[0409] The server generates a motivational message customized to the user's emotional state based on the emotional data and progress data. For this purpose, it uses a generative AI model (e.g., a natural language generation algorithm). For example, if the user is feeling frustrated, it generates a message saying, "You're doing great! You'll succeed." The generated message is sent to the device and displayed to the user. Specifically, the message appears as a pop-up on the screen. The input is emotional data and progress data, and the output is an encouraging message.

[0410] This detailed step-by-step process allows users to progress through language learning efficiently and flexibly. Furthermore, the use of emotional data further enhances the user's learning experience, helping to maintain motivation.

[0411] (Application example 2)

[0412] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0413] While existing language learning support systems provide functions for recording users' progress and readjusting plans, they lack specific emotional recognition and learning support through virtual interaction. Furthermore, they struggle to maintain users' motivation or provide advice based on their emotional state, and lack flexible, individualized support to maximize learning outcomes. This creates problems that make it difficult for users to continue studying.

[0414] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user device, means for recording study progress, means for collecting and analyzing the user's emotional data, means for generating motivational messages based on the emotional data and providing them to the user, means for recording and analyzing the user's comments and actions in the virtual space, and means for analyzing test results and study progress data and readjusting the study plan as necessary. This enables individualized study support that takes the user's emotional state into consideration, and enables effective language learning through interaction in a virtual environment.

[0415] The "means for receiving initial setting information from a user" refers to the means by which a user accesses the language learning system and inputs information such as the language to be learned, learning objectives, available time for learning, and existing skill level.

[0416] The "means for generating an optimal study plan based on initial setting information" is a means for analyzing the received initial setting information and automatically generating an individual study plan that is most suitable for the user.

[0417] The "means for transmitting the generated study plan to the user device" refers to means for transmitting the generated study plan to the user terminal and presenting it to the user.

[0418] "Means for recording learning progress" refers to means for collecting and recording the progress of users as they proceed with their learning.

[0419] The "means for periodically checking the progress and generating tests" refers to a means for periodically analyzing the user's progress data and generating tests for checking the progress based on that data.

[0420] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to means for detecting the user's strengths and weaknesses based on test results and learning progress data, and for readjusting the learning plan according to the analysis results.

[0421] The "means for collecting and analyzing emotional data" refers to a means for collecting emotional data from the user's facial expressions, voice, etc., and analyzing it.

[0422] The "means for generating a motivational message based on emotional data and providing it to the user" is a means for grasping the emotional state of the user based on the emotional data, and generating a message of encouragement or advice in response to that, and providing it to the user.

[0423] "Means for supporting language learning through interaction with users in a virtual space" refers to means for supporting language learning through dialogue between users and virtual characters in a virtual space such as a virtual store.

[0424] "Means for recording and analyzing user's statements and actions in a virtual space" refers to means for recording and analyzing the statements and actions that a user makes in a virtual space.

[0425] The present invention is a system for efficiently providing specific language learning support in a virtual store environment. This system generates an optimal learning plan based on the user's initial setting information, supports language learning in a virtual space, and has the function of readjusting the learning plan taking into account the user's emotions and progress.

[0426] Hardware and software used

[0427] Hardware: Smartphones, head-mounted displays, smart glasses

[0428] Software: Generative AI model (GPT-4 (registered trademark)), emotion recognition engine (e.g., Microsoft (registered trademark) Azure (registered trademark) Emotion API), database

[0429] System Overview

[0430] 1. Receiving user preferences

[0431] Users input initial setup information via a smartphone, head-mounted display, or smart glasses, including the language to learn, learning goals, available time, and existing skill level.

[0432] 2. Generate a learning plan

[0433] The input initial setting information is sent to the server, which uses a generative AI model (GPT-4) to generate an optimal learning plan and sends it to the user's device.

[0434] 3. Language learning in virtual stores

[0435] Users can learn a language by interacting with virtual characters in a virtual space (such as a virtual cafe or library). The user's comments and actions are recorded and stored as progress data.

[0436] 4. Emotional Data Collection and Analysis

[0437] The user's facial expressions and voice data are analyzed using an emotion recognition engine, which outputs emotion data and sends it to the server.

[0438] 5. Checking progress and generating tests

[0439] The server periodically checks the user's progress data and generates a test for checking the progress. The user takes the test through the terminal and sends the results to the server.

[0440] 6. Readjust your study plan

[0441] The server analyzes the test results and progress data and adjusts the study plan as needed, which is then sent back to the user's device.

[0442] 7. Providing motivational messages

[0443] Based on the user's emotional data, a generative AI model is used to generate personalized motivational messages, which are then displayed on the user's device after training is complete.

[0444] Specific examples

[0445] For example, use prompts such as the following to provide guidance and encouragement to the user:

[0446] "Choose a language to learn." "Choose a time each day to avoid."

[0447] "Hello! Today we're going to learn some new business English vocabulary. Are you ready?"

[0448] "We will give you a short test to check your progress. Please answer the listening questions."

[0449] "You did a great job today! Let's work even harder."

[0450] "You're making great progress!"

[0451] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing emotional data, it is possible to increase motivation and enable sustained learning.

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] The user accesses the language learning system and inputs initial setup information, including the language to be learned, learning goals, available time for learning, and existing skill level. The terminal then transmits this initial setup information to the server. Based on the input initial setup information, the server analyzes the data and generates an optimal learning plan for the user.

[0455] Step 2:

[0456] The server uses a generative AI model (GPT-4) to analyze the user's initial setting information and generate an optimal learning plan. This generated learning plan is customized to the user's learning goals and time. The server then sends the generated learning plan to the user's device. The device then presents this plan to the user, allowing them to begin learning.

[0457] Step 3:

[0458] Users learn a language by interacting with virtual characters in a virtual store. The device records the user's comments and actions and sends them to a server as progress data. Learning in the virtual space is done in an interactive format based on real situations, improving the user's communication skills.

[0459] Step 4:

[0460] The server periodically checks the user's progress and generates a test based on the user's progress. The test may include listening and speaking exercises to assess the user's learning status. The test results are provided to the user via their device and are then sent back to the server.

[0461] Step 5:

[0462] The server uses an emotion recognition engine to collect and analyze emotions from the user's facial expressions and voice data. The emotion data is used to maintain motivation and optimize the user's learning experience. The server readjusts the user's study plan based on the emotion data and generates appropriate encouraging messages.

[0463] Step 6:

[0464] The server readjusts the study plan based on the test results, progress data, and the analysis of the emotional data. The readjusted study plan focuses on the user's weaknesses and areas that need improvement. The server then sends the readjusted study plan to the user's device.

[0465] Step 7:

[0466] The device displays encouraging and advising messages to users after each day's study. These messages are individually customized using a generative AI model and are intended to motivate users. For example, prompts such as "You did a great job today! Keep trying!" and "You're making great progress."

[0467] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0468] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0469] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0470] [Second embodiment]

[0471] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0472] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0473] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0474] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0475] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0476] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0478] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0479] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0480] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0481] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0482] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0483] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly adjusts the plan according to the user's progress. Furthermore, the system provides messages of encouragement and advice to maintain the user's motivation.

[0484] Specific examples of the system

[0485] User Preferences

[0486] 1. The user accesses the language learning system and enters information such as the language they wish to learn, their learning goals, the amount of time they have available to learn, and their existing skill level in the initial setup form.

[0487] 2. The terminal sends the entered initial setting information to the server.

[0488] Generate a lesson plan

[0489] 1. The server analyzes the received initial setting information and generates an optimal learning plan tailored to the user's needs. For example, for a user who wants to learn English business communication, the server creates a plan that balances grammar, vocabulary, listening, and speaking skills.

[0490] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[0491] Track your learning and progress

[0492] 1. The user follows the presented study plan and performs daily study tasks.

[0493] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[0494] Checking progress and testing

[0495] 1. The server periodically analyzes the user's progress data and generates tests to check the user's progress. The tests may include listening and speaking exercises.

[0496] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[0497] Re-adjusting your study plan

[0498] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data. For example, if a user has difficulty with listening, it generates a new study plan that includes more listening practice.

[0499] 2. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[0500] Staying motivated

[0501] 1. The device displays encouraging messages and study advice to the user after each day's study. For example, it displays a message such as, "You did a great job today! Let's keep it up next time."

[0502] 2. The server generates customized encouraging messages based on the user's progress and sends them to the device.

[0503] Specific examples

[0504] For example, for a user who has set aside one hour of study time each day to pass a university English audio course, the system works as follows:

[0505] 1. In the initial setup form, the user enters the learning language as English, the goal as passing a university English audio course, and the daily study time as one hour.

[0506] 2. The device sends this information to the server.

[0507] 3. The server analyzes the input information and generates an optimal study plan. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations.

[0508] 4. The server sends this study plan to the user's terminal, and the user proceeds with their studies according to the plan.

[0509] 5. The device records the learning progress and sends the data to the server.

[0510] 6. The server generates periodic tests based on the progress data and sends them to the device.

[0511] 7. The user takes the test and sends the results from the terminal to the server.

[0512] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak at listening.

[0513] 9. The terminal provides the user with a new study plan, and the user continues studying based on it.

[0514] 10. After completing a lesson, the device will display an encouraging message such as "You did a great job today!" to help maintain motivation.

[0515] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[0519] Step 2:

[0520] The terminal transmits the input initial setting information to the server.

[0521] Step 3:

[0522] The server analyzes the received initial setting information and generates an optimal learning plan based on the user's learning goals and available time.

[0523] Step 4:

[0524] The server transmits the generated study plan to the user terminal and presents it to the user.

[0525] Step 5:

[0526] The user follows the presented study plan and performs daily study tasks.

[0527] Step 6:

[0528] The device records the user's progress each time they complete a lesson.

[0529] Step 7:

[0530] The terminal periodically transmits the recorded learning progress data to the server.

[0531] Step 8:

[0532] The server receives the progress data and periodically generates tests to check the user's progress.

[0533] Step 9:

[0534] The server transmits the generated test to the user terminal and provides it to the user.

[0535] Step 10:

[0536] The user takes the test and has the results recorded on the terminal.

[0537] Step 11:

[0538] The terminal transmits the test results to the server.

[0539] Step 12:

[0540] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[0541] Step 13:

[0542] The server generates a new, re-adjusted lesson plan based on the analysis results.

[0543] Step 14:

[0544] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[0545] Step 15:

[0546] The user continues studying based on the new study plan.

[0547] Step 16:

[0548] The device displays encouraging messages and study advice to the user to help maintain motivation.

[0549] Step 17:

[0550] The server generates personalized encouraging messages based on the user's progress and test results and sends them to the device.

[0551] Step 18:

[0552] The terminal displays a customized encouraging message to the user.

[0553] Example 1

[0554] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] Conventional language learning systems lack the ability to flexibly readjust learning plans according to the user's individual needs and progress. They also lack the ability to generate customized encouraging messages to maintain user motivation. As a result, users have difficulty continuing their learning effectively and do not achieve the expected learning results.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0557] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording progress each time the user completes a study task, means for periodically checking progress and generating tests including listening and speaking, means for analyzing test results and study progress data, analyzing the user's strengths and weaknesses, and readjusting the study plan as needed, means for generating customized encouraging messages based on the user's progress data and transmitting them to the user terminal, means for generating customized messages using a generative AI model, means for using a data analysis tool to analyze the user's progress, and means including a database for storing the study plan and progress data. This enables flexible plan readjustment according to the user's study progress and effective motivation maintenance.

[0558] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, learning time, and existing skill level that the user inputs into the language learning system.

[0559] A "study plan" is a plan that includes specific study tasks and schedules, and is generated based on the user's initial setting information.

[0560] "User terminal" refers to an electronic device used by a user to access the learning system, and includes a personal computer, smartphone, tablet, etc.

[0561] "Progress" is data that indicates the content and progress of what the user has learned according to the study plan.

[0562] "Tests" are exercises or assignments generated by the server to monitor a user's learning progress and assess their skill mastery.

[0563] "Test results" is data that indicates the results and evaluation points of a user when they take a test.

[0564] "Strengths and Weaknesses" are areas where the user's skills are strong and areas that need improvement, analyzed based on test results and learning progress data.

[0565] An "encouraging message" is a message of encouragement or advice that is generated by the server and sent to the user terminal with the aim of motivating the user to continue learning.

[0566] A "generative AI model" is an artificial intelligence model used for natural language generation, data analysis, and other tasks.

[0567] "Data analysis tools" are software and libraries used to analyze users' learning data and understand progress and trends.

[0568] A "database" is a digital storage system for efficiently storing and managing learning plans, progress data, etc.

[0569] This invention relates to a support system for helping users efficiently advance language learning. This system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, it provides messages of encouragement and advice to maintain the user's motivation. Specific embodiments of this system are described below.

[0570] Gathering initial configuration information

[0571] A user accesses the language learning system and enters information into the initial setup form, such as the language they want to learn, their learning goals, the time they have available for studying, their existing skill level, etc. For example, they may enter that their goal is to pass a university English audio course and that they will set aside one hour of study time every day.

[0572] Generate a lesson plan

[0573] The server uses an NLP library such as Apache OpenNLP to analyze the initial setting information received from the user. Based on the analysis results, it generates a learning plan that best suits the user's needs. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations. The generated learning plan is sent to the device via an HTTP request.

[0574] Track your learning and progress

[0575] The user performs daily learning tasks according to the presented learning plan. For example, 30 minutes of listening material and 30 minutes of speaking practice. After completing the learning, the device records the progress and sends the data in JSON format to the server. The progress data is stored in an SQL database (e.g., MySQL).

[0576] Checking progress and generating tests

[0577] The server periodically analyzes the user's progress using data analysis tools such as the Pandas library. Based on the analysis results, it generates tests to check the user's progress, including listening and speaking. The device provides the generated tests to the user, who then takes the tests online. For example, the test results for answering listening questions are stored in an SQL database.

[0578] Re-adjusting your study plan

[0579] The server analyzes the user's strengths and weaknesses using a machine learning model (for example, the Scikit-learn library) based on the test results and progress data. Based on the analysis results, the server readjusts the learning plan as necessary. For users who are weak at listening, the server generates a new learning plan to improve listening skills and sends it to the device.

[0580] Staying motivated

[0581] The device displays an encouraging message to the user after completing each day's study. For example, it displays a standard message such as "You did a great job today! Let's keep it up next time." In addition, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate customized encouraging messages based on progress data. The generated messages are input to a natural language generation model using prompt sentences.

[0582] Prompt Sentence Examples

[0583] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[0584] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[0585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0586] Program processing flow

[0587] Step 1:

[0588] A user accesses the language learning system and enters the language to be learned, learning objectives, available time for learning, and existing skill level in an initial setting form.

[0589] Input: Initial information such as user personal information, learning objectives, etc.

[0590] How it works: A user fills in information on a web form and clicks the "Submit" button.

[0591] Output: User preference information is sent to the system.

[0592] Step 2:

[0593] The terminal transmits the input initial setting information to the server.

[0594] Input: User initial configuration information (JSON format).

[0595] What it does: Sends data to the server via an HTTP POST request.

[0596] Output: The server receives the initialization information.

[0597] Step 3:

[0598] The server analyzes the received initial setting information and generates an optimal learning plan.

[0599] Input: Initial setting information (JSON format).

[0600] How it works: It uses the Apache OpenNLP library to parse the information and generate a learning plan in a Python script.

[0601] Output: A customized learning plan for each user.

[0602] Step 4:

[0603] The server transmits the generated study plan to the user terminal and presents it to the user.

[0604] Input: Learning plan (JSON format).

[0605] Behavior: Sends the lesson plan to the user's device via an HTTP POST request.

[0606] Output: The learning plan is displayed on the user's device.

[0607] Step 5:

[0608] The user follows the presented study plan and performs daily study tasks.

[0609] Input: lesson plan.

[0610] Action: The user follows a study plan and completes the materials and tasks, for example, studying listening material for 30 minutes, followed by 30 minutes of speaking practice.

[0611] Output: Progress of the learning task.

[0612] Step 6:

[0613] The device records the user's progress each time they complete a lesson and sends the data to the server.

[0614] Input: Learning progress data.

[0615] How it works: When you click the button to complete the learning task, the progress is sent to the server in JSON format.

[0616] Output: The server receives the progress data and stores it in a SQL database.

[0617] Step 7:

[0618] The server periodically analyzes the user's progress data and generates tests to check the progress.

[0619] Input: Progress data.

[0620] How it works: It uses the Pandas library to aggregate and analyze progress data and generate tests, including listening and speaking.

[0621] Output: A test question set.

[0622] Step 8:

[0623] The terminal provides the generated test to the user, who then performs the test.

[0624] Input: Test question.

[0625] Action: Take a test online, for example, answer a listening question.

[0626] Output: Test results.

[0627] Step 9:

[0628] The terminal records the results of the test and sends them to the server.

[0629] Input: Test results (JSON format).

[0630] What it does: Sends data to the server via an HTTP POST request.

[0631] Output: The server receives the test results and stores them in a SQL database.

[0632] Step 10:

[0633] Based on test results and progress data, the server analyzes the user's strengths and weaknesses and adjusts their study plan as needed.

[0634] Input: Test results and progress data.

[0635] What it does: Analyzes data using a machine learning model (e.g., Scikit-learn) and creates a new learning plan.

[0636] Output: A re-adjusted lesson plan.

[0637] Step 11:

[0638] The server transmits the readjusted study plan to the user terminal and provides it to the user.

[0639] Input: Realigned learning plan (JSON format).

[0640] Operation: Sends an HTTP POST request to the user's device.

[0641] Output: The new lesson plan is displayed on the user's device.

[0642] Step 12:

[0643] The terminal displays encouraging messages and study advice to the user after completing each day's study.

[0644] Enter: an encouraging message.

[0645] What it does: After a lesson is finished, a message is displayed on the screen. For example, "You did a great job today! Let's keep it up next time."

[0646] Output: The user receives the message.

[0647] Step 13:

[0648] The server generates a customized encouraging message according to the user's progress and transmits it to the terminal.

[0649] Input: User progress data.

[0650] How it works: Using a generative AI model (e.g., OpenAI's ChatGPT), it generates encouraging messages using prompts.

[0651] Output: A customized encouraging message.

[0652] Prompt Sentence Examples

[0653] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[0654] (Application example 1)

[0655] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] To effectively advance language learning, it is important to flexibly adjust learning plans based on the user's progress and skill level and maintain motivation. However, existing systems do not adequately generate individually customized learning plans or readjust them based on progress, making it difficult for users to progress effectively. Furthermore, they do not provide appropriate messages to maintain motivation, increasing the risk of users giving up. The purpose of this invention is to solve these problems and support users in efficiently advancing their language learning.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0658] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording study progress, means for periodically checking progress and generating tests, means for analyzing test results and study progress data and readjusting the study plan as necessary, means for providing motivational messages to the user, means for analyzing the user's strengths and weaknesses based on prompts using a generative AI model, means for providing daily study tasks based on the study plan and collecting and analyzing progress data, and means for providing customized encouraging messages according to the progress data. This enables users to efficiently progress through their individually customized study plans and achieve high learning outcomes while maintaining their motivation.

[0659] The "means for receiving initial setting information from the user" is a function for obtaining information such as the language of study, purpose of study, time available for study, and existing skill level provided by the user.

[0660] The "means for generating an optimal study plan based on the initial setting information" is a function for creating an effective study schedule according to the individual study needs of the user.

[0661] The "means for transmitting the generated study plan to the user terminal" is a communication function for providing the study plan generated by the server to the user terminal.

[0662] The "means for recording learning progress" is a function that tracks and records the progress of the user as they perform their daily learning tasks.

[0663] The "means for periodically checking progress and generating tests" is a function for periodically creating tests based on the user's learning progress data and checking the progress.

[0664] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to a function that analyzes the user's test results and learning progress data and flexibly reconfigures the learning plan according to the user's needs.

[0665] The "means for providing motivational messages to the user" is a function that displays messages of encouragement and advice to maintain the user's motivation after the user has finished studying.

[0666] "Means for using a generative AI model to analyze a user's strengths and weaknesses based on prompt sentences" refers to a function that utilizes a generative AI model to identify the strengths and weaknesses of a user's skills based on prompt sentences.

[0667] "Means for providing daily learning tasks based on a learning plan and collecting and analyzing progress data" refers to a function that presents daily learning tasks to users and records and analyzes progress data when they are completed.

[0668] The "means for providing an encouraging message customized according to progress data" is a function for generating and providing an encouraging message that is individually customized based on the user's progress data.

[0669] The present invention aims to build a system that provides users with individually customized learning plans and flexibly adjusts the plans according to their progress, with the aim of helping them efficiently advance their language learning. The system operates via communication between the user terminal and a server, and uses a generative AI model to perform a detailed analysis of the user's learning progress.

[0670] Basic configuration

[0671] The system consists of the following main components:

[0672] User device (smartphone, etc.)

[0673] Server (backend system)

[0674] Generative AI Models

[0675] Initial Setup

[0676] 1. The user terminal collects information from the user through an initial setup form, such as the language to be studied, learning objectives, learning time, and existing skill level. This information is sent to the server.

[0677] 2. The server analyzes the initial settings received from the user and generates an optimal learning plan based on that information. This plan balances skills such as grammar, vocabulary, listening, and speaking.

[0678] Implementing the learning plan

[0679] 1. The user device provides the user with daily learning tasks according to the learning plan sent from the server. Each time the user completes a task, their progress is recorded and sent to the server.

[0680] 2. The server periodically checks the user's progress and generates tests as needed. The test results are sent to the user's device and provided to the user.

[0681] Data analysis and reconditioning

[0682] 1. The server periodically analyzes the user's test results and learning progress data, and uses a generative AI model to analyze the user's strengths and weaknesses based on prompts. For example, "If the user's listening skills are lacking, generate a new learning plan to strengthen them."

[0683] 2. The user terminal provides the user with a new, re-adjusted study plan and allows the user to proceed with their studies based on that plan.

[0684] Staying motivated

[0685] 1. The server generates a customized encouraging message based on the user's progress data and sends it to the user's terminal.

[0686] 2. After completing the study, the user device displays a motivational message such as "You did a great job today!" to maintain the user's motivation to study.

[0687] Technology used

[0688] Hardware: User device (smartphone), server (backend system)

[0689] Software: Django (server-side framework), React Native (front-end framework), generative AI model

[0690] Specific examples

[0691] If a user sets aside 30 minutes per day to study English business communication, the system will operate as follows:

[0692] 1. On the user device, the user enters "English," "Business Communication," "30 minutes / day," and "Beginner" in the initial setup form.

[0693] 2. The server generates an optimal learning plan based on the received information, incorporating basic listening and speaking practice in the first week and phrases for meetings and presentations in the second week.

[0694] 3. The user device provides the user with daily learning tasks, records the progress, and sends it to the server.

[0695] 4. The server uses the generative AI model based on progress to generate new learning plans when necessary and provide them to the user.

[0696] 5. After each day's study, the user device displays an encouraging message such as "You did a great job today!"

[0697] Example prompts for generative AI models

[0698] "Generate an English study plan for learning business communication. The user is a beginner and has set aside 30 minutes a day to study."

[0699] With this configuration and operation, users can efficiently progress with language learning and achieve high learning results while maintaining their motivation.

[0700] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0701] Step 1:

[0702] The user enters information such as the language to learn, learning objectives, learning time, and existing skill level through the initial setup form. This is the input data. The user terminal sends this initial setup information to the server.

[0703] Step 2:

[0704] The server receives and analyzes the initial setup information. Based on the analyzed data, it generates a study plan suited to the user's needs. For example, the server may consider the user's learning goals and available study time and place basic listening and speaking practice in the first week, and phrases for meetings and presentations in the second week. The generated study plan is sent to the user's device as output data.

[0705] Step 3:

[0706] The user terminal presents the study plan received from the server to the user. The user performs daily study tasks according to the study plan. As each study task is completed, progress data is entered into the terminal.

[0707] Step 4:

[0708] Each time a learning task is completed, the user device records progress data, including the time it took to complete the task and the number of correct answers, and transmits the data to the server.

[0709] Step 5:

[0710] The server periodically checks the progress data and generates progress check tests based on the data. These tests are set for each skill, such as listening, speaking, grammar, and vocabulary. The generated tests are sent to the user's device.

[0711] Step 6:

[0712] The user terminal provides the generated test to the user, who takes the test and inputs the result data into the terminal.

[0713] Step 7:

[0714] The user terminal transmits test result data to the server, which analyzes the test result data and learning progress data to identify the user's strengths and weaknesses.

[0715] Step 8:

[0716] The server uses a generative AI model to analyze the user's strengths and weaknesses based on the prompt, and performs data calculations such as "if the user's listening skills are lacking, generate a new learning plan to strengthen them."

[0717] Step 9:

[0718] The server readjusts the lesson plan as needed based on the analysis results, and the new lesson plan is sent back to the user's device.

[0719] Step 10:

[0720] The user terminal provides the user with the readjusted study plan and proceeds with the study based on it.

[0721] Step 11:

[0722] After completing a study session, the user device displays an encouraging message such as "You did a great job today!" to the user, which is expected to help maintain the user's motivation. The encouraging message is customized based on the user's progress data.

[0723] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0724] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, the system provides encouraging and advice messages to maintain the user's motivation, and recognizes the user's emotions to provide more accurate support.

[0725] Specific examples of the system

[0726] User Preferences

[0727] 1. The user accesses the language learning system and enters the necessary information in the initial setup form (language to learn, learning objectives, available time for learning, and existing skill level).

[0728] 2. The terminal sends the entered initial setting information to the server.

[0729] Generate a lesson plan

[0730] 1. The server analyzes the received initial setting information and generates an optimal learning plan based on the user's needs.

[0731] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[0732] Track your learning and progress

[0733] 1. The user follows the presented study plan and performs daily study tasks.

[0734] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[0735] Checking progress and testing

[0736] 1. The server periodically analyzes the user's progress data and generates a test to check the user's progress. For example, the test may include listening and speaking practice.

[0737] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[0738] Re-adjusting your study plan

[0739] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data.

[0740] 2. The server generates a re-adjusted learning plan based on the analysis results.

[0741] 3. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[0742] Use of emotion engine

[0743] 1. The device collects emotional data from the user's facial expressions, voice, etc. and analyzes it using an emotion engine.

[0744] 2. The server recognizes the user's emotional state based on the emotional data obtained from the emotion engine.

[0745] 3. The server adjusts the study plan and motivational messages taking into account the user's emotional state.

[0746] Staying motivated

[0747] 1. The device displays encouraging messages and study advice to the user after completing each day's study.

[0748] 2. The server generates a customized encouraging message based on the user's emotional data and progress and sends it to the device.

[0749] 3. The device displays a customized encouraging message to the user.

[0750] Specific examples

[0751] When a user is learning business English, the following scenarios can be envisaged using the emotion engine:

[0752] 1. The user enters the initial information, setting aside one hour each day to learn business English.

[0753] 2. The device sends this information to the server.

[0754] 3. The server analyzes the configuration information and generates a study plan that allows the user to learn vocabulary and phrases necessary for business situations.

[0755] 4. The server sends the study plan to the user's terminal, and the user proceeds with the study according to the plan.

[0756] 5. The device records the user's learning progress and periodically sends the data to the server.

[0757] 6. The server analyzes the progress data, generates a progress confirmation test, and sends it to the user terminal.

[0758] 7. The user takes the test and sends the results from the device to the server.

[0759] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak in listening.

[0760] 9. The device collects the user's emotional state using facial recognition and voice data and analyzes it using an emotion engine.

[0761] 10. Based on the emotional data, if the user feels frustrated, the server generates an encouraging message such as, "You're doing great! You'll definitely get results."

[0762] 11. The terminal displays the generated message to the user to keep them motivated.

[0763] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing the emotion engine, it can further increase users' motivation and help them achieve their ultimate learning goals.

[0764] The processing flow will be explained below.

[0765] Step 1:

[0766] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[0767] Step 2:

[0768] The terminal transmits the input initial setting information to the server.

[0769] Step 3:

[0770] The server analyzes the received initial setting information and generates an optimal learning plan that meets the user's needs.

[0771] Step 4:

[0772] The server transmits the generated study plan to the user terminal and presents it to the user.

[0773] Step 5:

[0774] The user follows the presented study plan and performs daily study tasks.

[0775] Step 6:

[0776] The device records the user's progress each time they complete a lesson.

[0777] Step 7:

[0778] The terminal periodically transmits the recorded learning progress data to the server.

[0779] Step 8:

[0780] The server receives the progress data and periodically generates tests to check the user's progress.

[0781] Step 9:

[0782] The server transmits the generated test to the user terminal and provides it to the user.

[0783] Step 10:

[0784] The user takes the test and has the results recorded on the terminal.

[0785] Step 11:

[0786] The terminal transmits the test results to the server.

[0787] Step 12:

[0788] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[0789] Step 13:

[0790] The server generates a new, re-adjusted lesson plan based on the analysis results.

[0791] Step 14:

[0792] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[0793] Step 15:

[0794] The user continues studying based on the new study plan.

[0795] Step 16:

[0796] The device displays encouraging messages and study advice to the user to help maintain motivation.

[0797] Step 17:

[0798] The device collects emotion data using facial recognition and voice data from the user.

[0799] Step 18:

[0800] The terminal transmits the collected emotion data to an emotion engine to analyze the user's emotional state.

[0801] Step 19:

[0802] The server recognizes the emotions the user is feeling while studying based on the emotion data obtained from the emotion engine.

[0803] Step 20:

[0804] The server adjusts the study plan and motivational messages to take into account the user's emotional state and generates new customized messages.

[0805] Step 21:

[0806] The server sends a customized encouraging message to the user terminal.

[0807] Step 22:

[0808] The terminal displays the generated encouraging message to the user to maintain motivation for learning.

[0809] Example 2

[0810] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0811] Conventional language learning systems have the problem that they are unable to accurately grasp the user's learning progress, and that it is difficult to maintain motivation or flexibly respond to individual learning needs. Furthermore, providing learning plans and messages that ignore the user's emotional state can cause users to feel frustrated with learning, making it difficult for them to continue studying.

[0812] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user terminal, means for recording study progress, means for periodically checking the progress status and generating a test, means for analyzing the test results and study progress data and readjusting the study plan as necessary, means for recognizing the user's emotions and providing an adjusted motivational message, means for analyzing the user's facial expressions and voice data to collect emotion data, and means for generating customized messages using a generative AI model. This makes it possible to provide an individually customized study plan and motivational message based on the user's study progress and emotional state.

[0813] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, available learning time, and existing skill level that is input when a user accesses the language learning system.

[0814] A "study plan" is a set of individually customized study schedules and tasks that are generated based on the user's initial setting information.

[0815] A "user terminal" is a device such as a computer or smartphone used by a user, and is a medium for displaying study plans, tests, messages, etc.

[0816] "Study progress" is data that indicates the progress and achievement level of the learning tasks that the user has performed according to the learning plan.

[0817] "Tests" are server-generated tests to assess a user's listening, speaking, or other language skills in order to assess their learning progress.

[0818] "Test results" is data that indicates the scores and evaluations of users when they take a test.

[0819] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions and voice.

[0820] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate content, specifically for generating customized messages.

[0821] "Motivational messages" are messages of encouragement and advice provided to increase the user's motivation to learn and encourage them to continue.

[0822] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized learning plan based on initial setting information input by the user, supports the progress of learning, and provides motivational messages that incorporate the user's emotional state.

[0823] 1. Receive initial setup information and generate a lesson plan

[0824] A user accesses the language learning system and inputs initial information such as the language to be learned, learning objectives, available learning time, existing skill level, etc. The terminal collects this information and sends it to the server.

[0825] The server uses Python data analysis libraries (e.g., Pandas, SciPy) to analyze the received initial configuration information and generate an optimal learning plan based on the user's needs. The generated learning plan includes specific tasks and schedules to achieve the user's learning goals.

[0826] 2. Recording your learning progress

[0827] The user performs daily learning tasks according to the presented learning plan. The device records the user's learning progress and manages the progress data using a database such as MySQL or SQLite. This data is periodically sent to a server to grasp the user's learning status.

[0828] 3. Check your progress and test

[0829] The server analyzes the user's progress data and generates tests to check progress as needed. The tests evaluate skills such as listening and speaking, and are generated using Python data analysis libraries (e.g., Pandas, NumPy).

[0830] 4. Readjust your study plan

[0831] The server analyzes test results and progress data to identify the user's strengths and weaknesses. This is done using machine learning algorithms (e.g., Scikit-learn). Based on the analysis results, the server generates an adjusted study plan and sends it to the user's device. This allows the user to always study based on the optimal study plan.

[0832] 5. Use of Emotion Engine

[0833] The device collects the user's facial expression and voice data and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow).The server recognizes the user's emotional state based on the emotional data obtained from the emotion recognition engine and reflects this in the content of the study plan and motivational messages.

[0834] 6. Staying motivated

[0835] The device displays encouraging messages and study advice to the user after completing each day's study. The server uses a generative AI model (e.g., a natural language generation algorithm) to generate encouraging messages based on the user's emotional data and progress. The generated messages are sent to the user's device and displayed to the user. This helps to motivate the user and encourage them to continue studying.

[0836] Examples of concrete examples and prompts

[0837] As a concrete example, if a user is learning business English, they would enter the initial settings information as "Business English," "1 hour daily," and "Intermediate level." Based on this information, the server would generate a study plan for learning vocabulary and phrases needed in business situations and send it to the user's device. The user would proceed with their study, and the device would record and send the progress. Based on the progress data, the server would generate a test and provide it to the user. The study plan would be readjusted depending on the test results. The server would also analyze the user's emotional state from their facial expressions and voice, and generate appropriate encouraging messages that would be displayed on the device.

[0838] An example of a prompt to input to a generative AI model is as follows:

[0839] Generate encouraging messages based on the user's learning goals and progress data. For example, if the user is frustrated with listening, the message might be "You're doing great! You'll see results!"

[0840] In this way, this system allows users to progress through language learning efficiently and flexibly, supporting them in continuing their studies and achieving their ultimate goals.

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1: User enters initial setup information

[0843] A user accesses the language learning system and enters information into an initial setup form, such as the language to learn, learning objectives, available study time, and existing skill level. The entered information becomes the basis for generating a learning plan tailored to the user's learning needs. The terminal collects this initial setup information and sends it to the server. Input is via text input fields and selection boxes, and output is the data sent to the server as initial setup information.

[0844] Step 2: Generate a lesson plan

[0845] The server generates an optimal learning plan based on the received initial setup information. It uses Python data analysis libraries (e.g., Pandas, SciPy) to create a schedule based on learning objectives and available time. Specifically, it analyzes the initial setup information (vocabulary, phrases, skill level, etc.) and assigns learning tasks by time. The output is a customized learning plan, which is sent to the user's device.

[0846] Step 3: Practice and record your progress

[0847] The user performs daily learning tasks according to a learning plan provided by the server. The device records the user's progress each time they complete a study and stores it in a database such as MySQL or SQLite. Specifically, when the user presses a button after completing a task, the time it took to complete the task and the degree of achievement are recorded. The input is the user's operation, and the output is information stored in the database as progress data.

[0848] Step 4: Check progress and generate tests

[0849] The server periodically analyzes the user's progress data and generates tests to check progress. The progress data is aggregated using Python analysis libraries (e.g., Pandas, NumPy) and tests (listening, speaking, etc.) are created according to the user's learning status. The output tests are sent to the user's device and provided to the user. Specifically, a screen for the user to take the test is displayed on the device.

[0850] Step 5: Test results and readjust your study plan

[0851] The user takes the provided test and sends the results from their device to the server. The server uses a machine learning algorithm (e.g., Scikit-learn) to analyze the user's strengths and weaknesses based on the received test results and progress data. Based on the analysis results, the server readjusts the learning plan and sets new learning tasks, if necessary. The output is a readjusted learning plan, which is sent to the user's device. For example, for a user who is weak at listening, a new listening improvement task is added.

[0852] Step 6: Collect and analyze emotion data

[0853] The device collects facial and voice data from the user during and after learning, and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow). This determines the user's emotional state and sends the data to a server. Specifically, the device uses a camera and microphone to capture facial and voice data and recognizes emotions in real time. The input is the user's facial and voice data, and the output is emotional data resulting from the analysis.

[0854] Step 7: Generate and deliver motivational messages

[0855] The server generates a motivational message customized to the user's emotional state based on the emotional data and progress data. For this purpose, it uses a generative AI model (e.g., a natural language generation algorithm). For example, if the user is feeling frustrated, it generates a message saying, "You're doing great! You'll succeed." The generated message is sent to the device and displayed to the user. Specifically, the message appears as a pop-up on the screen. The input is emotional data and progress data, and the output is an encouraging message.

[0856] This detailed step-by-step process allows users to progress through language learning efficiently and flexibly. Furthermore, the use of emotional data further enhances the user's learning experience, helping to maintain motivation.

[0857] (Application example 2)

[0858] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0859] While existing language learning support systems provide functions for recording users' progress and readjusting plans, they lack specific emotional recognition and learning support through virtual interaction. Furthermore, they struggle to maintain users' motivation or provide advice based on their emotional state, and lack flexible, individualized support to maximize learning outcomes. This creates problems that make it difficult for users to continue studying.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user device, means for recording study progress, means for collecting and analyzing the user's emotional data, means for generating motivational messages based on the emotional data and providing them to the user, means for recording and analyzing the user's comments and actions in the virtual space, and means for analyzing test results and study progress data and readjusting the study plan as necessary. This enables individualized study support that takes the user's emotional state into consideration, and enables effective language learning through interaction in a virtual environment.

[0861] The "means for receiving initial setting information from a user" refers to the means by which a user accesses the language learning system and inputs information such as the language to be learned, learning objectives, available time for learning, and existing skill level.

[0862] The "means for generating an optimal study plan based on initial setting information" is a means for analyzing the received initial setting information and automatically generating an individual study plan that is most suitable for the user.

[0863] The "means for transmitting the generated study plan to the user device" refers to means for transmitting the generated study plan to the user terminal and presenting it to the user.

[0864] "Means for recording learning progress" refers to means for collecting and recording the progress of users as they proceed with their learning.

[0865] The "means for periodically checking the progress and generating tests" refers to a means for periodically analyzing the user's progress data and generating tests for checking the progress based on that data.

[0866] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to means for detecting the user's strengths and weaknesses based on test results and learning progress data, and for readjusting the learning plan according to the analysis results.

[0867] The "means for collecting and analyzing emotional data" refers to a means for collecting emotional data from the user's facial expressions, voice, etc., and analyzing it.

[0868] The "means for generating a motivational message based on emotional data and providing it to the user" is a means for grasping the emotional state of the user based on the emotional data, and generating a message of encouragement or advice in response to that, and providing it to the user.

[0869] "Means for supporting language learning through interaction with users in a virtual space" refers to means for supporting language learning through dialogue between users and virtual characters in a virtual space such as a virtual store.

[0870] "Means for recording and analyzing user's statements and actions in a virtual space" refers to means for recording and analyzing the statements and actions that a user makes in a virtual space.

[0871] The present invention is a system for efficiently providing specific language learning support in a virtual store environment. This system generates an optimal learning plan based on the user's initial setting information, supports language learning in a virtual space, and has the function of readjusting the learning plan taking into account the user's emotions and progress.

[0872] Hardware and software used

[0873] Hardware: Smartphones, head-mounted displays, smart glasses

[0874] Software: Generative AI model (GPT-4), emotion recognition engine (e.g., Microsoft Azure's Emotion API), database

[0875] System Overview

[0876] 1. Receiving user preferences

[0877] Users input initial setup information via a smartphone, head-mounted display, or smart glasses, including the language to learn, learning goals, available time, and existing skill level.

[0878] 2. Generate a learning plan

[0879] The input initial setting information is sent to the server, which uses a generative AI model (GPT-4) to generate an optimal learning plan and sends it to the user's device.

[0880] 3. Language learning in virtual stores

[0881] Users can learn a language by interacting with virtual characters in a virtual space (such as a virtual cafe or library). The user's comments and actions are recorded and stored as progress data.

[0882] 4. Emotional Data Collection and Analysis

[0883] The user's facial expressions and voice data are analyzed using an emotion recognition engine, which outputs emotion data and sends it to the server.

[0884] 5. Checking progress and generating tests

[0885] The server periodically checks the user's progress data and generates a test for checking the progress. The user takes the test through the terminal and sends the results to the server.

[0886] 6. Readjust your study plan

[0887] The server analyzes the test results and progress data and adjusts the study plan as needed, which is then sent back to the user's device.

[0888] 7. Providing motivational messages

[0889] Based on the user's emotional data, a generative AI model is used to generate personalized motivational messages, which are then displayed on the user's device after training is complete.

[0890] Specific examples

[0891] For example, use prompts such as the following to provide guidance and encouragement to the user:

[0892] "Choose a language to learn." "Choose a time each day to avoid."

[0893] "Hello! Today we're going to learn some new business English vocabulary. Are you ready?"

[0894] "We will give you a short test to check your progress. Please answer the listening questions."

[0895] "You did a great job today! Let's work even harder."

[0896] "You're making great progress!"

[0897] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing emotional data, it is possible to increase motivation and enable sustained learning.

[0898] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0899] Step 1:

[0900] The user accesses the language learning system and inputs initial setup information, including the language to be learned, learning goals, available time for learning, and existing skill level. The terminal then transmits this initial setup information to the server. Based on the input initial setup information, the server analyzes the data and generates an optimal learning plan for the user.

[0901] Step 2:

[0902] The server uses a generative AI model (GPT-4) to analyze the user's initial setting information and generate an optimal learning plan. This generated learning plan is customized to the user's learning goals and time. The server then sends the generated learning plan to the user's device. The device then presents this plan to the user, allowing them to begin learning.

[0903] Step 3:

[0904] Users learn a language by interacting with virtual characters in a virtual store. The device records the user's comments and actions and sends them to a server as progress data. Learning in the virtual space is done in an interactive format based on real situations, improving the user's communication skills.

[0905] Step 4:

[0906] The server periodically checks the user's progress and generates a test based on the user's progress. The test may include listening and speaking exercises to assess the user's learning status. The test results are provided to the user via their device and are then sent back to the server.

[0907] Step 5:

[0908] The server uses an emotion recognition engine to collect and analyze emotions from the user's facial expressions and voice data. The emotion data is used to maintain motivation and optimize the user's learning experience. The server readjusts the user's study plan based on the emotion data and generates appropriate encouraging messages.

[0909] Step 6:

[0910] The server readjusts the study plan based on the test results, progress data, and the analysis of the emotional data. The readjusted study plan focuses on the user's weaknesses and areas that need improvement. The server then sends the readjusted study plan to the user's device.

[0911] Step 7:

[0912] The device displays encouraging and advising messages to users after each day's study. These messages are individually customized using a generative AI model and are intended to motivate users. For example, prompts such as "You did a great job today! Keep trying!" and "You're making great progress."

[0913] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0914] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0915] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0916] [Third embodiment]

[0917] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0918] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0919] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0920] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0921] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0922] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0923] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0924] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0925] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0926] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0927] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0928] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0929] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly adjusts the plan according to the user's progress. Furthermore, the system provides messages of encouragement and advice to maintain the user's motivation.

[0930] Specific examples of the system

[0931] User Preferences

[0932] 1. The user accesses the language learning system and enters information such as the language they wish to learn, their learning goals, the amount of time they have available to learn, and their existing skill level in the initial setup form.

[0933] 2. The terminal sends the entered initial setting information to the server.

[0934] Generate a lesson plan

[0935] 1. The server analyzes the received initial setting information and generates an optimal learning plan tailored to the user's needs. For example, for a user who wants to learn English business communication, the server creates a plan that balances grammar, vocabulary, listening, and speaking skills.

[0936] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[0937] Track your learning and progress

[0938] 1. The user follows the presented study plan and performs daily study tasks.

[0939] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[0940] Checking progress and testing

[0941] 1. The server periodically analyzes the user's progress data and generates tests to check the user's progress. The tests may include listening and speaking exercises.

[0942] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[0943] Re-adjusting your study plan

[0944] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data. For example, if a user has difficulty with listening, it generates a new study plan that includes more listening practice.

[0945] 2. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[0946] Staying motivated

[0947] 1. The device displays encouraging messages and study advice to the user after each day's study. For example, it displays a message such as, "You did a great job today! Let's keep it up next time."

[0948] 2. The server generates customized encouraging messages based on the user's progress and sends them to the device.

[0949] Specific examples

[0950] For example, for a user who has set aside one hour of study time each day to pass a university English audio course, the system works as follows:

[0951] 1. In the initial setup form, the user enters the learning language as English, the goal as passing a university English audio course, and the daily study time as one hour.

[0952] 2. The device sends this information to the server.

[0953] 3. The server analyzes the input information and generates an optimal study plan. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations.

[0954] 4. The server sends this study plan to the user's terminal, and the user proceeds with their studies according to the plan.

[0955] 5. The device records the learning progress and sends the data to the server.

[0956] 6. The server generates periodic tests based on the progress data and sends them to the device.

[0957] 7. The user takes the test and sends the results from the terminal to the server.

[0958] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak at listening.

[0959] 9. The terminal provides the user with a new study plan, and the user continues studying based on it.

[0960] 10. After completing a lesson, the device will display an encouraging message such as "You did a great job today!" to help maintain motivation.

[0961] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[0965] Step 2:

[0966] The terminal transmits the input initial setting information to the server.

[0967] Step 3:

[0968] The server analyzes the received initial setting information and generates an optimal learning plan based on the user's learning goals and available time.

[0969] Step 4:

[0970] The server transmits the generated study plan to the user terminal and presents it to the user.

[0971] Step 5:

[0972] The user follows the presented study plan and performs daily study tasks.

[0973] Step 6:

[0974] The device records the user's progress each time they complete a lesson.

[0975] Step 7:

[0976] The terminal periodically transmits the recorded learning progress data to the server.

[0977] Step 8:

[0978] The server receives the progress data and periodically generates tests to check the user's progress.

[0979] Step 9:

[0980] The server transmits the generated test to the user terminal and provides it to the user.

[0981] Step 10:

[0982] The user takes the test and has the results recorded on the terminal.

[0983] Step 11:

[0984] The terminal transmits the test results to the server.

[0985] Step 12:

[0986] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[0987] Step 13:

[0988] The server generates a new, re-adjusted lesson plan based on the analysis results.

[0989] Step 14:

[0990] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[0991] Step 15:

[0992] The user continues studying based on the new study plan.

[0993] Step 16:

[0994] The device displays encouraging messages and study advice to the user to help maintain motivation.

[0995] Step 17:

[0996] The server generates personalized encouraging messages based on the user's progress and test results and sends them to the device.

[0997] Step 18:

[0998] The terminal displays a customized encouraging message to the user.

[0999] Example 1

[1000] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] Conventional language learning systems lack the ability to flexibly readjust learning plans according to the user's individual needs and progress. They also lack the ability to generate customized encouraging messages to maintain user motivation. As a result, users have difficulty continuing their learning effectively and do not achieve the expected learning results.

[1002] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1003] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording progress each time the user completes a study task, means for periodically checking progress and generating tests including listening and speaking, means for analyzing test results and study progress data, analyzing the user's strengths and weaknesses, and readjusting the study plan as needed, means for generating customized encouraging messages based on the user's progress data and transmitting them to the user terminal, means for generating customized messages using a generative AI model, means for using a data analysis tool to analyze the user's progress, and means including a database for storing the study plan and progress data. This enables flexible plan readjustment according to the user's study progress and effective motivation maintenance.

[1004] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, learning time, and existing skill level that the user inputs into the language learning system.

[1005] A "study plan" is a plan that includes specific study tasks and schedules, and is generated based on the user's initial setting information.

[1006] "User terminal" refers to an electronic device used by a user to access the learning system, and includes a personal computer, smartphone, tablet, etc.

[1007] "Progress" is data that indicates the content and progress of what the user has learned according to the study plan.

[1008] "Tests" are exercises or assignments generated by the server to monitor a user's learning progress and assess their skill mastery.

[1009] "Test results" is data that indicates the results and evaluation points of a user when they take a test.

[1010] "Strengths and Weaknesses" are areas where the user's skills are strong and areas that need improvement, analyzed based on test results and learning progress data.

[1011] An "encouraging message" is a message of encouragement or advice that is generated by the server and sent to the user terminal with the aim of motivating the user to continue learning.

[1012] A "generative AI model" is an artificial intelligence model used for natural language generation, data analysis, and other tasks.

[1013] "Data analysis tools" are software and libraries used to analyze users' learning data and understand progress and trends.

[1014] A "database" is a digital storage system for efficiently storing and managing learning plans, progress data, etc.

[1015] This invention relates to a support system for helping users efficiently advance language learning. This system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, it provides messages of encouragement and advice to maintain the user's motivation. Specific embodiments of this system are described below.

[1016] Gathering initial configuration information

[1017] A user accesses the language learning system and enters information into the initial setup form, such as the language they want to learn, their learning goals, the time they have available for studying, their existing skill level, etc. For example, they may enter that their goal is to pass a university English audio course and that they will set aside one hour of study time every day.

[1018] Generate a lesson plan

[1019] The server uses an NLP library such as Apache OpenNLP to analyze the initial setting information received from the user. Based on the analysis results, it generates a learning plan that best suits the user's needs. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations. The generated learning plan is sent to the device via an HTTP request.

[1020] Track your learning and progress

[1021] The user performs daily learning tasks according to the presented learning plan. For example, 30 minutes of listening material and 30 minutes of speaking practice. After completing the learning, the device records the progress and sends the data in JSON format to the server. The progress data is stored in an SQL database (e.g., MySQL).

[1022] Checking progress and generating tests

[1023] The server periodically analyzes the user's progress using data analysis tools such as the Pandas library. Based on the analysis results, it generates tests to check the user's progress, including listening and speaking. The device provides the generated tests to the user, who then takes the tests online. For example, the test results for answering listening questions are stored in an SQL database.

[1024] Re-adjusting your study plan

[1025] The server analyzes the user's strengths and weaknesses using a machine learning model (for example, the Scikit-learn library) based on the test results and progress data. Based on the analysis results, the server readjusts the learning plan as necessary. For users who are weak at listening, the server generates a new learning plan to improve listening skills and sends it to the device.

[1026] Staying motivated

[1027] The device displays an encouraging message to the user after completing each day's study. For example, it displays a standard message such as "You did a great job today! Let's keep it up next time." In addition, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate customized encouraging messages based on progress data. The generated messages are input to a natural language generation model using prompt sentences.

[1028] Prompt Sentence Examples

[1029] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[1030] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[1031] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1032] Program processing flow

[1033] Step 1:

[1034] A user accesses the language learning system and enters the language to be learned, learning objectives, available time for learning, and existing skill level in an initial setting form.

[1035] Input: Initial information such as user personal information, learning objectives, etc.

[1036] How it works: A user fills in information on a web form and clicks the "Submit" button.

[1037] Output: User preference information is sent to the system.

[1038] Step 2:

[1039] The terminal transmits the input initial setting information to the server.

[1040] Input: User initial configuration information (JSON format).

[1041] What it does: Sends data to the server via an HTTP POST request.

[1042] Output: The server receives the initialization information.

[1043] Step 3:

[1044] The server analyzes the received initial setting information and generates an optimal learning plan.

[1045] Input: Initial setting information (JSON format).

[1046] How it works: It uses the Apache OpenNLP library to parse the information and generate a learning plan in a Python script.

[1047] Output: A customized learning plan for each user.

[1048] Step 4:

[1049] The server transmits the generated study plan to the user terminal and presents it to the user.

[1050] Input: Learning plan (JSON format).

[1051] Behavior: Sends the lesson plan to the user's device via an HTTP POST request.

[1052] Output: The learning plan is displayed on the user's device.

[1053] Step 5:

[1054] The user follows the presented study plan and performs daily study tasks.

[1055] Input: lesson plan.

[1056] Action: The user follows a study plan and completes the materials and tasks, for example, studying listening material for 30 minutes, followed by 30 minutes of speaking practice.

[1057] Output: Progress of the learning task.

[1058] Step 6:

[1059] The device records the user's progress each time they complete a lesson and sends the data to the server.

[1060] Input: Learning progress data.

[1061] How it works: When you click the button to complete the learning task, the progress is sent to the server in JSON format.

[1062] Output: The server receives the progress data and stores it in a SQL database.

[1063] Step 7:

[1064] The server periodically analyzes the user's progress data and generates tests to check the progress.

[1065] Input: Progress data.

[1066] How it works: It uses the Pandas library to aggregate and analyze progress data and generate tests, including listening and speaking.

[1067] Output: A test question set.

[1068] Step 8:

[1069] The terminal provides the generated test to the user, who then performs the test.

[1070] Input: Test question.

[1071] Action: Take a test online, for example, answer a listening question.

[1072] Output: Test results.

[1073] Step 9:

[1074] The terminal records the results of the test and sends them to the server.

[1075] Input: Test results (JSON format).

[1076] What it does: Sends data to the server via an HTTP POST request.

[1077] Output: The server receives the test results and stores them in a SQL database.

[1078] Step 10:

[1079] Based on test results and progress data, the server analyzes the user's strengths and weaknesses and adjusts their study plan as needed.

[1080] Input: Test results and progress data.

[1081] What it does: Analyzes data using a machine learning model (e.g., Scikit-learn) and creates a new learning plan.

[1082] Output: A re-adjusted lesson plan.

[1083] Step 11:

[1084] The server transmits the readjusted study plan to the user terminal and provides it to the user.

[1085] Input: Realigned learning plan (JSON format).

[1086] Operation: Sends an HTTP POST request to the user's device.

[1087] Output: The new lesson plan is displayed on the user's device.

[1088] Step 12:

[1089] The terminal displays encouraging messages and study advice to the user after completing each day's study.

[1090] Enter: an encouraging message.

[1091] What it does: After a lesson is finished, a message is displayed on the screen. For example, "You did a great job today! Let's keep it up next time."

[1092] Output: The user receives the message.

[1093] Step 13:

[1094] The server generates a customized encouraging message according to the user's progress and transmits it to the terminal.

[1095] Input: User progress data.

[1096] How it works: Using a generative AI model (e.g., OpenAI's ChatGPT), it generates encouraging messages using prompts.

[1097] Output: A customized encouraging message.

[1098] Prompt Sentence Examples

[1099] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[1100] (Application example 1)

[1101] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1102] To effectively advance language learning, it is important to flexibly adjust learning plans based on the user's progress and skill level and maintain motivation. However, existing systems do not adequately generate individually customized learning plans or readjust them based on progress, making it difficult for users to progress effectively. Furthermore, they do not provide appropriate messages to maintain motivation, increasing the risk of users giving up. The purpose of this invention is to solve these problems and support users in efficiently advancing their language learning.

[1103] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1104] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording study progress, means for periodically checking progress and generating tests, means for analyzing test results and study progress data and readjusting the study plan as necessary, means for providing motivational messages to the user, means for analyzing the user's strengths and weaknesses based on prompts using a generative AI model, means for providing daily study tasks based on the study plan and collecting and analyzing progress data, and means for providing customized encouraging messages according to the progress data. This enables users to efficiently progress through their individually customized study plans and achieve high learning outcomes while maintaining their motivation.

[1105] The "means for receiving initial setting information from the user" is a function for obtaining information such as the language of study, purpose of study, time available for study, and existing skill level provided by the user.

[1106] The "means for generating an optimal study plan based on the initial setting information" is a function for creating an effective study schedule according to the individual study needs of the user.

[1107] The "means for transmitting the generated study plan to the user terminal" is a communication function for providing the study plan generated by the server to the user terminal.

[1108] The "means for recording learning progress" is a function that tracks and records the progress of the user as they perform their daily learning tasks.

[1109] The "means for periodically checking progress and generating tests" is a function for periodically creating tests based on the user's learning progress data and checking the progress.

[1110] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to a function that analyzes the user's test results and learning progress data and flexibly reconfigures the learning plan according to the user's needs.

[1111] The "means for providing motivational messages to the user" is a function that displays messages of encouragement and advice to maintain the user's motivation after the user has finished studying.

[1112] "Means for using a generative AI model to analyze a user's strengths and weaknesses based on prompt sentences" refers to a function that utilizes a generative AI model to identify the strengths and weaknesses of a user's skills based on prompt sentences.

[1113] "Means for providing daily learning tasks based on a learning plan and collecting and analyzing progress data" refers to a function that presents daily learning tasks to users and records and analyzes progress data when they are completed.

[1114] The "means for providing an encouraging message customized according to progress data" is a function for generating and providing an encouraging message that is individually customized based on the user's progress data.

[1115] The present invention aims to build a system that provides users with individually customized learning plans and flexibly adjusts the plans according to their progress, with the aim of helping them efficiently advance their language learning. The system operates via communication between the user terminal and a server, and uses a generative AI model to perform a detailed analysis of the user's learning progress.

[1116] Basic configuration

[1117] The system consists of the following main components:

[1118] User device (smartphone, etc.)

[1119] Server (backend system)

[1120] Generative AI Models

[1121] Initial Setup

[1122] 1. The user terminal collects information from the user through an initial setup form, such as the language to be studied, learning objectives, learning time, and existing skill level. This information is sent to the server.

[1123] 2. The server analyzes the initial settings received from the user and generates an optimal learning plan based on that information. This plan balances skills such as grammar, vocabulary, listening, and speaking.

[1124] Implementing the learning plan

[1125] 1. The user device provides the user with daily learning tasks according to the learning plan sent from the server. Each time the user completes a task, their progress is recorded and sent to the server.

[1126] 2. The server periodically checks the user's progress and generates tests as needed. The test results are sent to the user's device and provided to the user.

[1127] Data analysis and reconditioning

[1128] 1. The server periodically analyzes the user's test results and learning progress data, and uses a generative AI model to analyze the user's strengths and weaknesses based on prompts. For example, "If the user's listening skills are lacking, generate a new learning plan to strengthen them."

[1129] 2. The user terminal provides the user with a new, re-adjusted study plan and allows the user to proceed with their studies based on that plan.

[1130] Staying motivated

[1131] 1. The server generates a customized encouraging message based on the user's progress data and sends it to the user's terminal.

[1132] 2. After completing the study, the user device displays a motivational message such as "You did a great job today!" to maintain the user's motivation to study.

[1133] Technology used

[1134] Hardware: User device (smartphone), server (backend system)

[1135] Software: Django (server-side framework), React Native (front-end framework), generative AI model

[1136] Specific examples

[1137] If a user sets aside 30 minutes per day to study English business communication, the system will operate as follows:

[1138] 1. On the user device, the user enters "English," "Business Communication," "30 minutes / day," and "Beginner" in the initial setup form.

[1139] 2. The server generates an optimal learning plan based on the received information, incorporating basic listening and speaking practice in the first week and phrases for meetings and presentations in the second week.

[1140] 3. The user device provides the user with daily learning tasks, records the progress, and sends it to the server.

[1141] 4. The server uses the generative AI model based on progress to generate new learning plans when necessary and provide them to the user.

[1142] 5. After each day's study, the user device displays an encouraging message such as "You did a great job today!"

[1143] Example prompts for generative AI models

[1144] "Generate an English study plan for learning business communication. The user is a beginner and has set aside 30 minutes a day to study."

[1145] With this configuration and operation, users can efficiently progress with language learning and achieve high learning results while maintaining their motivation.

[1146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1147] Step 1:

[1148] The user enters information such as the language to learn, learning objectives, learning time, and existing skill level through the initial setup form. This is the input data. The user terminal sends this initial setup information to the server.

[1149] Step 2:

[1150] The server receives and analyzes the initial setup information. Based on the analyzed data, it generates a study plan suited to the user's needs. For example, the server may consider the user's learning goals and available study time and place basic listening and speaking practice in the first week, and phrases for meetings and presentations in the second week. The generated study plan is sent to the user's device as output data.

[1151] Step 3:

[1152] The user terminal presents the study plan received from the server to the user. The user performs daily study tasks according to the study plan. As each study task is completed, progress data is entered into the terminal.

[1153] Step 4:

[1154] Each time a learning task is completed, the user device records progress data, including the time it took to complete the task and the number of correct answers, and transmits the data to the server.

[1155] Step 5:

[1156] The server periodically checks the progress data and generates progress check tests based on the data. These tests are set for each skill, such as listening, speaking, grammar, and vocabulary. The generated tests are sent to the user's device.

[1157] Step 6:

[1158] The user terminal provides the generated test to the user, who takes the test and inputs the result data into the terminal.

[1159] Step 7:

[1160] The user terminal transmits test result data to the server, which analyzes the test result data and learning progress data to identify the user's strengths and weaknesses.

[1161] Step 8:

[1162] The server uses a generative AI model to analyze the user's strengths and weaknesses based on the prompt, and performs data calculations such as "if the user's listening skills are lacking, generate a new learning plan to strengthen them."

[1163] Step 9:

[1164] The server readjusts the lesson plan as needed based on the analysis results, and the new lesson plan is sent back to the user's device.

[1165] Step 10:

[1166] The user terminal provides the user with the readjusted study plan and proceeds with the study based on it.

[1167] Step 11:

[1168] After completing a study session, the user device displays an encouraging message such as "You did a great job today!" to the user, which is expected to help maintain the user's motivation. The encouraging message is customized based on the user's progress data.

[1169] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1170] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, the system provides encouraging and advice messages to maintain the user's motivation, and recognizes the user's emotions to provide more accurate support.

[1171] Specific examples of the system

[1172] User Preferences

[1173] 1. The user accesses the language learning system and enters the necessary information in the initial setup form (language to learn, learning objectives, available time for learning, and existing skill level).

[1174] 2. The terminal sends the entered initial setting information to the server.

[1175] Generate a lesson plan

[1176] 1. The server analyzes the received initial setting information and generates an optimal learning plan based on the user's needs.

[1177] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[1178] Track your learning and progress

[1179] 1. The user follows the presented study plan and performs daily study tasks.

[1180] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[1181] Checking progress and testing

[1182] 1. The server periodically analyzes the user's progress data and generates a test to check the user's progress. For example, the test may include listening and speaking practice.

[1183] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[1184] Re-adjusting your study plan

[1185] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data.

[1186] 2. The server generates a re-adjusted learning plan based on the analysis results.

[1187] 3. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[1188] Use of emotion engine

[1189] 1. The device collects emotional data from the user's facial expressions, voice, etc. and analyzes it using an emotion engine.

[1190] 2. The server recognizes the user's emotional state based on the emotional data obtained from the emotion engine.

[1191] 3. The server adjusts the study plan and motivational messages taking into account the user's emotional state.

[1192] Staying motivated

[1193] 1. The device displays encouraging messages and study advice to the user after completing each day's study.

[1194] 2. The server generates a customized encouraging message based on the user's emotional data and progress and sends it to the device.

[1195] 3. The device displays a customized encouraging message to the user.

[1196] Specific examples

[1197] When a user is learning business English, the following scenarios can be envisaged using the emotion engine:

[1198] 1. The user enters the initial information, setting aside one hour each day to learn business English.

[1199] 2. The device sends this information to the server.

[1200] 3. The server analyzes the configuration information and generates a study plan that allows the user to learn vocabulary and phrases necessary for business situations.

[1201] 4. The server sends the study plan to the user's terminal, and the user proceeds with the study according to the plan.

[1202] 5. The device records the user's learning progress and periodically sends the data to the server.

[1203] 6. The server analyzes the progress data, generates a progress confirmation test, and sends it to the user terminal.

[1204] 7. The user takes the test and sends the results from the device to the server.

[1205] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak in listening.

[1206] 9. The device collects the user's emotional state using facial recognition and voice data and analyzes it using an emotion engine.

[1207] 10. Based on the emotional data, if the user feels frustrated, the server generates an encouraging message such as, "You're doing great! You'll definitely get results."

[1208] 11. The terminal displays the generated message to the user to keep them motivated.

[1209] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing the emotion engine, it can further increase users' motivation and help them achieve their ultimate learning goals.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[1213] Step 2:

[1214] The terminal transmits the input initial setting information to the server.

[1215] Step 3:

[1216] The server analyzes the received initial setting information and generates an optimal learning plan that meets the user's needs.

[1217] Step 4:

[1218] The server transmits the generated study plan to the user terminal and presents it to the user.

[1219] Step 5:

[1220] The user follows the presented study plan and performs daily study tasks.

[1221] Step 6:

[1222] The device records the user's progress each time they complete a lesson.

[1223] Step 7:

[1224] The terminal periodically transmits the recorded learning progress data to the server.

[1225] Step 8:

[1226] The server receives the progress data and periodically generates tests to check the user's progress.

[1227] Step 9:

[1228] The server transmits the generated test to the user terminal and provides it to the user.

[1229] Step 10:

[1230] The user takes the test and has the results recorded on the terminal.

[1231] Step 11:

[1232] The terminal transmits the test results to the server.

[1233] Step 12:

[1234] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[1235] Step 13:

[1236] The server generates a new, re-adjusted lesson plan based on the analysis results.

[1237] Step 14:

[1238] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[1239] Step 15:

[1240] The user continues studying based on the new study plan.

[1241] Step 16:

[1242] The device displays encouraging messages and study advice to the user to help maintain motivation.

[1243] Step 17:

[1244] The device collects emotion data using facial recognition and voice data from the user.

[1245] Step 18:

[1246] The terminal transmits the collected emotion data to an emotion engine to analyze the user's emotional state.

[1247] Step 19:

[1248] The server recognizes the emotions the user is feeling while studying based on the emotion data obtained from the emotion engine.

[1249] Step 20:

[1250] The server adjusts the study plan and motivational messages to take into account the user's emotional state and generates new customized messages.

[1251] Step 21:

[1252] The server sends a customized encouraging message to the user terminal.

[1253] Step 22:

[1254] The terminal displays the generated encouraging message to the user to maintain motivation for learning.

[1255] Example 2

[1256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1257] Conventional language learning systems have the problem that they are unable to accurately grasp the user's learning progress, and that it is difficult to maintain motivation or flexibly respond to individual learning needs. Furthermore, providing learning plans and messages that ignore the user's emotional state can cause users to feel frustrated with learning, making it difficult for them to continue studying.

[1258] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user terminal, means for recording study progress, means for periodically checking the progress status and generating a test, means for analyzing the test results and study progress data and readjusting the study plan as necessary, means for recognizing the user's emotions and providing an adjusted motivational message, means for analyzing the user's facial expressions and voice data to collect emotion data, and means for generating customized messages using a generative AI model. This makes it possible to provide an individually customized study plan and motivational message based on the user's study progress and emotional state.

[1259] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, available learning time, and existing skill level that is input when a user accesses the language learning system.

[1260] A "study plan" is a set of individually customized study schedules and tasks that are generated based on the user's initial setting information.

[1261] A "user terminal" is a device such as a computer or smartphone used by a user, and is a medium for displaying study plans, tests, messages, etc.

[1262] "Study progress" is data that indicates the progress and achievement level of the learning tasks that the user has performed according to the learning plan.

[1263] "Tests" are server-generated tests to assess a user's listening, speaking, or other language skills in order to assess their learning progress.

[1264] "Test results" is data that indicates the scores and evaluations of users when they take a test.

[1265] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions and voice.

[1266] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate content, specifically for generating customized messages.

[1267] "Motivational messages" are messages of encouragement and advice provided to increase the user's motivation to learn and encourage them to continue.

[1268] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized learning plan based on initial setting information input by the user, supports the progress of learning, and provides motivational messages that incorporate the user's emotional state.

[1269] 1. Receive initial setup information and generate a lesson plan

[1270] A user accesses the language learning system and inputs initial information such as the language to be learned, learning objectives, available learning time, existing skill level, etc. The terminal collects this information and sends it to the server.

[1271] The server uses Python data analysis libraries (e.g., Pandas, SciPy) to analyze the received initial configuration information and generate an optimal learning plan based on the user's needs. The generated learning plan includes specific tasks and schedules to achieve the user's learning goals.

[1272] 2. Recording your learning progress

[1273] The user performs daily learning tasks according to the presented learning plan. The device records the user's learning progress and manages the progress data using a database such as MySQL or SQLite. This data is periodically sent to a server to grasp the user's learning status.

[1274] 3. Check your progress and test

[1275] The server analyzes the user's progress data and generates tests to check progress as needed. The tests evaluate skills such as listening and speaking, and are generated using Python data analysis libraries (e.g., Pandas, NumPy).

[1276] 4. Readjust your study plan

[1277] The server analyzes test results and progress data to identify the user's strengths and weaknesses. This is done using machine learning algorithms (e.g., Scikit-learn). Based on the analysis results, the server generates an adjusted study plan and sends it to the user's device. This allows the user to always study based on the optimal study plan.

[1278] 5. Use of Emotion Engine

[1279] The device collects the user's facial expression and voice data and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow).The server recognizes the user's emotional state based on the emotional data obtained from the emotion recognition engine and reflects this in the content of the study plan and motivational messages.

[1280] 6. Staying motivated

[1281] The device displays encouraging messages and study advice to the user after completing each day's study. The server uses a generative AI model (e.g., a natural language generation algorithm) to generate encouraging messages based on the user's emotional data and progress. The generated messages are sent to the user's device and displayed to the user. This helps to motivate the user and encourage them to continue studying.

[1282] Examples of concrete examples and prompts

[1283] As a concrete example, if a user is learning business English, they would enter the initial settings information as "Business English," "1 hour daily," and "Intermediate level." Based on this information, the server would generate a study plan for learning vocabulary and phrases needed in business situations and send it to the user's device. The user would proceed with their study, and the device would record and send the progress. Based on the progress data, the server would generate a test and provide it to the user. The study plan would be readjusted depending on the test results. The server would also analyze the user's emotional state from their facial expressions and voice, and generate appropriate encouraging messages that would be displayed on the device.

[1284] An example of a prompt to input to a generative AI model is as follows:

[1285] Generate encouraging messages based on the user's learning goals and progress data. For example, if the user is frustrated with listening, the message might be "You're doing great! You'll see results!"

[1286] In this way, this system allows users to progress through language learning efficiently and flexibly, supporting them in continuing their studies and achieving their ultimate goals.

[1287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1288] Step 1: User enters initial setup information

[1289] A user accesses the language learning system and enters information into an initial setup form, such as the language to learn, learning objectives, available study time, and existing skill level. The entered information becomes the basis for generating a learning plan tailored to the user's learning needs. The terminal collects this initial setup information and sends it to the server. Input is via text input fields and selection boxes, and output is the data sent to the server as initial setup information.

[1290] Step 2: Generate a lesson plan

[1291] The server generates an optimal learning plan based on the received initial setup information. It uses Python data analysis libraries (e.g., Pandas, SciPy) to create a schedule based on learning objectives and available time. Specifically, it analyzes the initial setup information (vocabulary, phrases, skill level, etc.) and assigns learning tasks by time. The output is a customized learning plan, which is sent to the user's device.

[1292] Step 3: Practice and record your progress

[1293] The user performs daily learning tasks according to a learning plan provided by the server. The device records the user's progress each time they complete a study and stores it in a database such as MySQL or SQLite. Specifically, when the user presses a button after completing a task, the time it took to complete the task and the degree of achievement are recorded. The input is the user's operation, and the output is information stored in the database as progress data.

[1294] Step 4: Check progress and generate tests

[1295] The server periodically analyzes the user's progress data and generates tests to check progress. The progress data is aggregated using Python analysis libraries (e.g., Pandas, NumPy) and tests (listening, speaking, etc.) are created according to the user's learning status. The output tests are sent to the user's device and provided to the user. Specifically, a screen for the user to take the test is displayed on the device.

[1296] Step 5: Test results and readjust your study plan

[1297] The user takes the provided test and sends the results from their device to the server. The server uses a machine learning algorithm (e.g., Scikit-learn) to analyze the user's strengths and weaknesses based on the received test results and progress data. Based on the analysis results, the server readjusts the learning plan and sets new learning tasks, if necessary. The output is a readjusted learning plan, which is sent to the user's device. For example, for a user who is weak at listening, a new listening improvement task is added.

[1298] Step 6: Collect and analyze emotion data

[1299] The device collects facial and voice data from the user during and after learning, and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow). This determines the user's emotional state and sends the data to a server. Specifically, the device uses a camera and microphone to capture facial and voice data and recognizes emotions in real time. The input is the user's facial and voice data, and the output is emotional data resulting from the analysis.

[1300] Step 7: Generate and deliver motivational messages

[1301] The server generates a motivational message customized to the user's emotional state based on the emotional data and progress data. For this purpose, it uses a generative AI model (e.g., a natural language generation algorithm). For example, if the user is feeling frustrated, it generates a message saying, "You're doing great! You'll succeed." The generated message is sent to the device and displayed to the user. Specifically, the message appears as a pop-up on the screen. The input is emotional data and progress data, and the output is an encouraging message.

[1302] This detailed step-by-step process allows users to progress through language learning efficiently and flexibly. Furthermore, the use of emotional data further enhances the user's learning experience, helping to maintain motivation.

[1303] (Application example 2)

[1304] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1305] While existing language learning support systems provide functions for recording users' progress and readjusting plans, they lack specific emotional recognition and learning support through virtual interaction. Furthermore, they struggle to maintain users' motivation or provide advice based on their emotional state, and lack flexible, individualized support to maximize learning outcomes. This creates problems that make it difficult for users to continue studying.

[1306] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user device, means for recording study progress, means for collecting and analyzing the user's emotional data, means for generating motivational messages based on the emotional data and providing them to the user, means for recording and analyzing the user's comments and actions in the virtual space, and means for analyzing test results and study progress data and readjusting the study plan as necessary. This enables individualized study support that takes the user's emotional state into consideration, and enables effective language learning through interaction in a virtual environment.

[1307] The "means for receiving initial setting information from a user" refers to the means by which a user accesses the language learning system and inputs information such as the language to be learned, learning objectives, available time for learning, and existing skill level.

[1308] The "means for generating an optimal study plan based on initial setting information" is a means for analyzing the received initial setting information and automatically generating an individual study plan that is most suitable for the user.

[1309] The "means for transmitting the generated study plan to the user device" refers to means for transmitting the generated study plan to the user terminal and presenting it to the user.

[1310] "Means for recording learning progress" refers to means for collecting and recording the progress of users as they proceed with their learning.

[1311] The "means for periodically checking the progress and generating tests" refers to a means for periodically analyzing the user's progress data and generating tests for checking the progress based on that data.

[1312] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to means for detecting the user's strengths and weaknesses based on test results and learning progress data, and for readjusting the learning plan according to the analysis results.

[1313] The "means for collecting and analyzing emotional data" refers to a means for collecting emotional data from the user's facial expressions, voice, etc., and analyzing it.

[1314] The "means for generating a motivational message based on emotional data and providing it to the user" is a means for grasping the emotional state of the user based on the emotional data, and generating a message of encouragement or advice in response to that, and providing it to the user.

[1315] "Means for supporting language learning through interaction with users in a virtual space" refers to means for supporting language learning through dialogue between users and virtual characters in a virtual space such as a virtual store.

[1316] "Means for recording and analyzing user's statements and actions in a virtual space" refers to means for recording and analyzing the statements and actions that a user makes in a virtual space.

[1317] The present invention is a system for efficiently providing specific language learning support in a virtual store environment. This system generates an optimal learning plan based on the user's initial setting information, supports language learning in a virtual space, and has the function of readjusting the learning plan taking into account the user's emotions and progress.

[1318] Hardware and software used

[1319] Hardware: Smartphones, head-mounted displays, smart glasses

[1320] Software: Generative AI model (GPT-4), emotion recognition engine (e.g., Microsoft Azure's Emotion API), database

[1321] System Overview

[1322] 1. Receiving user preferences

[1323] Users input initial setup information via a smartphone, head-mounted display, or smart glasses, including the language to learn, learning goals, available time, and existing skill level.

[1324] 2. Generate a learning plan

[1325] The input initial setting information is sent to the server, which uses a generative AI model (GPT-4) to generate an optimal learning plan and sends it to the user's device.

[1326] 3. Language learning in virtual stores

[1327] Users can learn a language by interacting with virtual characters in a virtual space (such as a virtual cafe or library). The user's comments and actions are recorded and stored as progress data.

[1328] 4. Emotional Data Collection and Analysis

[1329] The user's facial expressions and voice data are analyzed using an emotion recognition engine, which outputs emotion data and sends it to the server.

[1330] 5. Checking progress and generating tests

[1331] The server periodically checks the user's progress data and generates a test for checking the progress. The user takes the test through the terminal and sends the results to the server.

[1332] 6. Readjust your study plan

[1333] The server analyzes the test results and progress data and adjusts the study plan as needed, which is then sent back to the user's device.

[1334] 7. Providing motivational messages

[1335] Based on the user's emotional data, a generative AI model is used to generate personalized motivational messages, which are then displayed on the user's device after training is complete.

[1336] Specific examples

[1337] For example, use prompts such as the following to provide guidance and encouragement to the user:

[1338] "Choose a language to learn." "Choose a time each day to avoid."

[1339] "Hello! Today we're going to learn some new business English vocabulary. Are you ready?"

[1340] "We will give you a short test to check your progress. Please answer the listening questions."

[1341] "You did a great job today! Let's work even harder."

[1342] "You're making great progress!"

[1343] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing emotional data, it is possible to increase motivation and enable sustained learning.

[1344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1345] Step 1:

[1346] The user accesses the language learning system and inputs initial setup information, including the language to be learned, learning goals, available time for learning, and existing skill level. The terminal then transmits this initial setup information to the server. Based on the input initial setup information, the server analyzes the data and generates an optimal learning plan for the user.

[1347] Step 2:

[1348] The server uses a generative AI model (GPT-4) to analyze the user's initial setting information and generate an optimal learning plan. This generated learning plan is customized to the user's learning goals and time. The server then sends the generated learning plan to the user's device. The device then presents this plan to the user, allowing them to begin learning.

[1349] Step 3:

[1350] Users learn a language by interacting with virtual characters in a virtual store. The device records the user's comments and actions and sends them to a server as progress data. Learning in the virtual space is done in an interactive format based on real situations, improving the user's communication skills.

[1351] Step 4:

[1352] The server periodically checks the user's progress and generates a test based on the user's progress. The test may include listening and speaking exercises to assess the user's learning status. The test results are provided to the user via their device and are then sent back to the server.

[1353] Step 5:

[1354] The server uses an emotion recognition engine to collect and analyze emotions from the user's facial expressions and voice data. The emotion data is used to maintain motivation and optimize the user's learning experience. The server readjusts the user's study plan based on the emotion data and generates appropriate encouraging messages.

[1355] Step 6:

[1356] The server readjusts the study plan based on the test results, progress data, and the analysis of the emotional data. The readjusted study plan focuses on the user's weaknesses and areas that need improvement. The server then sends the readjusted study plan to the user's device.

[1357] Step 7:

[1358] The device displays encouraging and advising messages to users after each day's study. These messages are individually customized using a generative AI model and are intended to motivate users. For example, prompts such as "You did a great job today! Keep trying!" and "You're making great progress."

[1359] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1360] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1361] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1362] [Fourth embodiment]

[1363] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1364] 7, a 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.

[1365] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1366] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1367] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1368] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1369] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1370] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1371] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1372] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1373] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1374] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1375] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1376] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly adjusts the plan according to the user's progress. Furthermore, the system provides messages of encouragement and advice to maintain the user's motivation.

[1377] Specific examples of the system

[1378] User Preferences

[1379] 1. The user accesses the language learning system and enters information such as the language they wish to learn, their learning goals, the amount of time they have available to learn, and their existing skill level in the initial setup form.

[1380] 2. The terminal sends the entered initial setting information to the server.

[1381] Generate a lesson plan

[1382] 1. The server analyzes the received initial setting information and generates an optimal learning plan tailored to the user's needs. For example, for a user who wants to learn English business communication, the server creates a plan that balances grammar, vocabulary, listening, and speaking skills.

[1383] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[1384] Track your learning and progress

[1385] 1. The user follows the presented study plan and performs daily study tasks.

[1386] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[1387] Checking progress and testing

[1388] 1. The server periodically analyzes the user's progress data and generates tests to check the user's progress. The tests may include listening and speaking exercises.

[1389] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[1390] Re-adjusting your study plan

[1391] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data. For example, if a user has difficulty with listening, it generates a new study plan that includes more listening practice.

[1392] 2. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[1393] Staying motivated

[1394] 1. The device displays encouraging messages and study advice to the user after each day's study. For example, it displays a message such as, "You did a great job today! Let's keep it up next time."

[1395] 2. The server generates customized encouraging messages based on the user's progress and sends them to the device.

[1396] Specific examples

[1397] For example, for a user who has set aside one hour of study time each day to pass a university English audio course, the system works as follows:

[1398] 1. In the initial setup form, the user enters the learning language as English, the goal as passing a university English audio course, and the daily study time as one hour.

[1399] 2. The device sends this information to the server.

[1400] 3. The server analyzes the input information and generates an optimal study plan. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations.

[1401] 4. The server sends this study plan to the user's terminal, and the user proceeds with their studies according to the plan.

[1402] 5. The device records the learning progress and sends the data to the server.

[1403] 6. The server generates periodic tests based on the progress data and sends them to the device.

[1404] 7. The user takes the test and sends the results from the terminal to the server.

[1405] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak at listening.

[1406] 9. The terminal provides the user with a new study plan, and the user continues studying based on it.

[1407] 10. After completing a lesson, the device will display an encouraging message such as "You did a great job today!" to help maintain motivation.

[1408] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[1409] The processing flow will be explained below.

[1410] Step 1:

[1411] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[1412] Step 2:

[1413] The terminal transmits the input initial setting information to the server.

[1414] Step 3:

[1415] The server analyzes the received initial setting information and generates an optimal learning plan based on the user's learning goals and available time.

[1416] Step 4:

[1417] The server transmits the generated study plan to the user terminal and presents it to the user.

[1418] Step 5:

[1419] The user follows the presented study plan and performs daily study tasks.

[1420] Step 6:

[1421] The device records the user's progress each time they complete a lesson.

[1422] Step 7:

[1423] The terminal periodically transmits the recorded learning progress data to the server.

[1424] Step 8:

[1425] The server receives the progress data and periodically generates tests to check the user's progress.

[1426] Step 9:

[1427] The server transmits the generated test to the user terminal and provides it to the user.

[1428] Step 10:

[1429] The user takes the test and has the results recorded on the terminal.

[1430] Step 11:

[1431] The terminal transmits the test results to the server.

[1432] Step 12:

[1433] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[1434] Step 13:

[1435] The server generates a new, re-adjusted lesson plan based on the analysis results.

[1436] Step 14:

[1437] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[1438] Step 15:

[1439] The user continues studying based on the new study plan.

[1440] Step 16:

[1441] The device displays encouraging messages and study advice to the user to help maintain motivation.

[1442] Step 17:

[1443] The server generates personalized encouraging messages based on the user's progress and test results and sends them to the device.

[1444] Step 18:

[1445] The terminal displays a customized encouraging message to the user.

[1446] Example 1

[1447] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1448] Conventional language learning systems lack the ability to flexibly readjust learning plans according to the user's individual needs and progress. They also lack the ability to generate customized encouraging messages to maintain user motivation. As a result, users have difficulty continuing their learning effectively and do not achieve the expected learning results.

[1449] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1450] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording progress each time the user completes a study task, means for periodically checking progress and generating tests including listening and speaking, means for analyzing test results and study progress data, analyzing the user's strengths and weaknesses, and readjusting the study plan as needed, means for generating customized encouraging messages based on the user's progress data and transmitting them to the user terminal, means for generating customized messages using a generative AI model, means for using a data analysis tool to analyze the user's progress, and means including a database for storing the study plan and progress data. This enables flexible plan readjustment according to the user's study progress and effective motivation maintenance.

[1451] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, learning time, and existing skill level that the user inputs into the language learning system.

[1452] A "study plan" is a plan that includes specific study tasks and schedules, and is generated based on the user's initial setting information.

[1453] "User terminal" refers to an electronic device used by a user to access the learning system, and includes a personal computer, smartphone, tablet, etc.

[1454] "Progress" is data that indicates the content and progress of what the user has learned according to the study plan.

[1455] "Tests" are exercises or assignments generated by the server to monitor a user's learning progress and assess their skill mastery.

[1456] "Test results" is data that indicates the results and evaluation points of a user when they take a test.

[1457] "Strengths and Weaknesses" are areas where the user's skills are strong and areas that need improvement, analyzed based on test results and learning progress data.

[1458] An "encouraging message" is a message of encouragement or advice that is generated by the server and sent to the user terminal with the aim of motivating the user to continue learning.

[1459] A "generative AI model" is an artificial intelligence model used for natural language generation, data analysis, and other tasks.

[1460] "Data analysis tools" are software and libraries used to analyze users' learning data and understand progress and trends.

[1461] A "database" is a digital storage system for efficiently storing and managing learning plans, progress data, etc.

[1462] This invention relates to a support system for helping users efficiently advance language learning. This system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, it provides messages of encouragement and advice to maintain the user's motivation. Specific embodiments of this system are described below.

[1463] Gathering initial configuration information

[1464] A user accesses the language learning system and enters information into the initial setup form, such as the language they want to learn, their learning goals, the time they have available for studying, their existing skill level, etc. For example, they may enter that their goal is to pass a university English audio course and that they will set aside one hour of study time every day.

[1465] Generate a lesson plan

[1466] The server uses an NLP library such as Apache OpenNLP to analyze the initial setting information received from the user. Based on the analysis results, it generates a learning plan that best suits the user's needs. For example, the first week could focus on basic listening and speaking, and the second week could focus on phrases needed for meetings and presentations. The generated learning plan is sent to the device via an HTTP request.

[1467] Track your learning and progress

[1468] The user performs daily learning tasks according to the presented learning plan. For example, 30 minutes of listening material and 30 minutes of speaking practice. After completing the learning, the device records the progress and sends the data in JSON format to the server. The progress data is stored in an SQL database (e.g., MySQL).

[1469] Checking progress and generating tests

[1470] The server periodically analyzes the user's progress using data analysis tools such as the Pandas library. Based on the analysis results, it generates tests to check the user's progress, including listening and speaking. The device provides the generated tests to the user, who then takes the tests online. For example, the test results for answering listening questions are stored in an SQL database.

[1471] Re-adjusting your study plan

[1472] The server analyzes the user's strengths and weaknesses using a machine learning model (for example, the Scikit-learn library) based on the test results and progress data. Based on the analysis results, the server readjusts the learning plan as necessary. For users who are weak at listening, the server generates a new learning plan to improve listening skills and sends it to the device.

[1473] Staying motivated

[1474] The device displays an encouraging message to the user after completing each day's study. For example, it displays a standard message such as "You did a great job today! Let's keep it up next time." In addition, the server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate customized encouraging messages based on progress data. The generated messages are input to a natural language generation model using prompt sentences.

[1475] Prompt Sentence Examples

[1476] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[1477] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and achieve high learning results while maintaining motivation.

[1478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1479] Program processing flow

[1480] Step 1:

[1481] A user accesses the language learning system and enters the language to be learned, learning objectives, available time for learning, and existing skill level in an initial setting form.

[1482] Input: Initial information such as user personal information, learning objectives, etc.

[1483] How it works: A user fills in information on a web form and clicks the "Submit" button.

[1484] Output: User preference information is sent to the system.

[1485] Step 2:

[1486] The terminal transmits the input initial setting information to the server.

[1487] Input: User initial configuration information (JSON format).

[1488] What it does: Sends data to the server via an HTTP POST request.

[1489] Output: The server receives the initialization information.

[1490] Step 3:

[1491] The server analyzes the received initial setting information and generates an optimal learning plan.

[1492] Input: Initial setting information (JSON format).

[1493] How it works: It uses the Apache OpenNLP library to parse the information and generate a learning plan in a Python script.

[1494] Output: A customized learning plan for each user.

[1495] Step 4:

[1496] The server transmits the generated study plan to the user terminal and presents it to the user.

[1497] Input: Learning plan (JSON format).

[1498] Behavior: Sends the lesson plan to the user's device via an HTTP POST request.

[1499] Output: The learning plan is displayed on the user's device.

[1500] Step 5:

[1501] The user follows the presented study plan and performs daily study tasks.

[1502] Input: lesson plan.

[1503] Action: The user follows a study plan and completes the materials and tasks, for example, studying listening material for 30 minutes, followed by 30 minutes of speaking practice.

[1504] Output: Progress of the learning task.

[1505] Step 6:

[1506] The device records the user's progress each time they complete a lesson and sends the data to the server.

[1507] Input: Learning progress data.

[1508] How it works: When you click the button to complete the learning task, the progress is sent to the server in JSON format.

[1509] Output: The server receives the progress data and stores it in a SQL database.

[1510] Step 7:

[1511] The server periodically analyzes the user's progress data and generates tests to check the progress.

[1512] Input: Progress data.

[1513] How it works: It uses the Pandas library to aggregate and analyze progress data and generate tests, including listening and speaking.

[1514] Output: A test question set.

[1515] Step 8:

[1516] The terminal provides the generated test to the user, who then performs the test.

[1517] Input: Test question.

[1518] Action: Take a test online, for example, answer a listening question.

[1519] Output: Test results.

[1520] Step 9:

[1521] The terminal records the results of the test and sends them to the server.

[1522] Input: Test results (JSON format).

[1523] What it does: Sends data to the server via an HTTP POST request.

[1524] Output: The server receives the test results and stores them in a SQL database.

[1525] Step 10:

[1526] Based on test results and progress data, the server analyzes the user's strengths and weaknesses and adjusts their study plan as needed.

[1527] Input: Test results and progress data.

[1528] What it does: Analyzes data using a machine learning model (e.g., Scikit-learn) and creates a new learning plan.

[1529] Output: A re-adjusted lesson plan.

[1530] Step 11:

[1531] The server transmits the readjusted study plan to the user terminal and provides it to the user.

[1532] Input: Realigned learning plan (JSON format).

[1533] Operation: Sends an HTTP POST request to the user's device.

[1534] Output: The new lesson plan is displayed on the user's device.

[1535] Step 12:

[1536] The terminal displays encouraging messages and study advice to the user after completing each day's study.

[1537] Enter: an encouraging message.

[1538] What it does: After a lesson is finished, a message is displayed on the screen. For example, "You did a great job today! Let's keep it up next time."

[1539] Output: The user receives the message.

[1540] Step 13:

[1541] The server generates a customized encouraging message according to the user's progress and transmits it to the terminal.

[1542] Input: User progress data.

[1543] How it works: Using a generative AI model (e.g., OpenAI's ChatGPT), it generates encouraging messages using prompts.

[1544] Output: A customized encouraging message.

[1545] Prompt Sentence Examples

[1546] Prompt: "The user has completed another hour of listening practice today. Generate an encouraging message to encourage them to look forward to their next lesson."

[1547] (Application example 1)

[1548] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1549] To effectively advance language learning, it is important to flexibly adjust learning plans based on the user's progress and skill level and maintain motivation. However, existing systems do not adequately generate individually customized learning plans or readjust them based on progress, making it difficult for users to progress effectively. Furthermore, they do not provide appropriate messages to maintain motivation, increasing the risk of users giving up. The purpose of this invention is to solve these problems and support users in efficiently advancing their language learning.

[1550] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1551] In this invention, the server includes means for receiving initial setting information from a user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to a user terminal, means for recording study progress, means for periodically checking progress and generating tests, means for analyzing test results and study progress data and readjusting the study plan as necessary, means for providing motivational messages to the user, means for analyzing the user's strengths and weaknesses based on prompts using a generative AI model, means for providing daily study tasks based on the study plan and collecting and analyzing progress data, and means for providing customized encouraging messages according to the progress data. This enables users to efficiently progress through their individually customized study plans and achieve high learning outcomes while maintaining their motivation.

[1552] The "means for receiving initial setting information from the user" is a function for obtaining information such as the language of study, purpose of study, time available for study, and existing skill level provided by the user.

[1553] The "means for generating an optimal study plan based on the initial setting information" is a function for creating an effective study schedule according to the individual study needs of the user.

[1554] The "means for transmitting the generated study plan to the user terminal" is a communication function for providing the study plan generated by the server to the user terminal.

[1555] The "means for recording learning progress" is a function that tracks and records the progress of the user as they perform their daily learning tasks.

[1556] The "means for periodically checking progress and generating tests" is a function for periodically creating tests based on the user's learning progress data and checking the progress.

[1557] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to a function that analyzes the user's test results and learning progress data and flexibly reconfigures the learning plan according to the user's needs.

[1558] The "means for providing motivational messages to the user" is a function that displays messages of encouragement and advice to maintain the user's motivation after the user has finished studying.

[1559] "Means for using a generative AI model to analyze a user's strengths and weaknesses based on prompt sentences" refers to a function that utilizes a generative AI model to identify the strengths and weaknesses of a user's skills based on prompt sentences.

[1560] "Means for providing daily learning tasks based on a learning plan and collecting and analyzing progress data" refers to a function that presents daily learning tasks to users and records and analyzes progress data when they are completed.

[1561] The "means for providing an encouraging message customized according to progress data" is a function for generating and providing an encouraging message that is individually customized based on the user's progress data.

[1562] The present invention aims to build a system that provides users with individually customized learning plans and flexibly adjusts the plans according to their progress, with the aim of helping them efficiently advance their language learning. The system operates via communication between the user terminal and a server, and uses a generative AI model to perform a detailed analysis of the user's learning progress.

[1563] Basic configuration

[1564] The system consists of the following main components:

[1565] User device (smartphone, etc.)

[1566] Server (backend system)

[1567] Generative AI Models

[1568] Initial Setup

[1569] 1. The user terminal collects information from the user through an initial setup form, such as the language to be studied, learning objectives, learning time, and existing skill level. This information is sent to the server.

[1570] 2. The server analyzes the initial settings received from the user and generates an optimal learning plan based on that information. This plan balances skills such as grammar, vocabulary, listening, and speaking.

[1571] Implementing the learning plan

[1572] 1. The user device provides the user with daily learning tasks according to the learning plan sent from the server. Each time the user completes a task, their progress is recorded and sent to the server.

[1573] 2. The server periodically checks the user's progress and generates tests as needed. The test results are sent to the user's device and provided to the user.

[1574] Data analysis and reconditioning

[1575] 1. The server periodically analyzes the user's test results and learning progress data, and uses a generative AI model to analyze the user's strengths and weaknesses based on prompts. For example, "If the user's listening skills are lacking, generate a new learning plan to strengthen them."

[1576] 2. The user terminal provides the user with a new, re-adjusted study plan and allows the user to proceed with their studies based on that plan.

[1577] Staying motivated

[1578] 1. The server generates a customized encouraging message based on the user's progress data and sends it to the user's terminal.

[1579] 2. After completing the study, the user device displays a motivational message such as "You did a great job today!" to maintain the user's motivation to study.

[1580] Technology used

[1581] Hardware: User device (smartphone), server (backend system)

[1582] Software: Django (server-side framework), React Native (front-end framework), generative AI model

[1583] Specific examples

[1584] If a user sets aside 30 minutes per day to study English business communication, the system will operate as follows:

[1585] 1. On the user device, the user enters "English," "Business Communication," "30 minutes / day," and "Beginner" in the initial setup form.

[1586] 2. The server generates an optimal learning plan based on the received information, incorporating basic listening and speaking practice in the first week and phrases for meetings and presentations in the second week.

[1587] 3. The user device provides the user with daily learning tasks, records the progress, and sends it to the server.

[1588] 4. The server uses the generative AI model based on progress to generate new learning plans when necessary and provide them to the user.

[1589] 5. After each day's study, the user device displays an encouraging message such as "You did a great job today!"

[1590] Example prompts for generative AI models

[1591] "Generate an English study plan for learning business communication. The user is a beginner and has set aside 30 minutes a day to study."

[1592] With this configuration and operation, users can efficiently progress with language learning and achieve high learning results while maintaining their motivation.

[1593] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1594] Step 1:

[1595] The user enters information such as the language to learn, learning objectives, learning time, and existing skill level through the initial setup form. This is the input data. The user terminal sends this initial setup information to the server.

[1596] Step 2:

[1597] The server receives and analyzes the initial setup information. Based on the analyzed data, it generates a study plan suited to the user's needs. For example, the server may consider the user's learning goals and available study time and place basic listening and speaking practice in the first week, and phrases for meetings and presentations in the second week. The generated study plan is sent to the user's device as output data.

[1598] Step 3:

[1599] The user terminal presents the study plan received from the server to the user. The user performs daily study tasks according to the study plan. As each study task is completed, progress data is entered into the terminal.

[1600] Step 4:

[1601] Each time a learning task is completed, the user device records progress data, including the time it took to complete the task and the number of correct answers, and transmits the data to the server.

[1602] Step 5:

[1603] The server periodically checks the progress data and generates progress check tests based on the data. These tests are set for each skill, such as listening, speaking, grammar, and vocabulary. The generated tests are sent to the user's device.

[1604] Step 6:

[1605] The user terminal provides the generated test to the user, who takes the test and inputs the result data into the terminal.

[1606] Step 7:

[1607] The user terminal transmits test result data to the server, which analyzes the test result data and learning progress data to identify the user's strengths and weaknesses.

[1608] Step 8:

[1609] The server uses a generative AI model to analyze the user's strengths and weaknesses based on the prompt, and performs data calculations such as "if the user's listening skills are lacking, generate a new learning plan to strengthen them."

[1610] Step 9:

[1611] The server readjusts the lesson plan as needed based on the analysis results, and the new lesson plan is sent back to the user's device.

[1612] Step 10:

[1613] The user terminal provides the user with the readjusted study plan and proceeds with the study based on it.

[1614] Step 11:

[1615] After completing a study session, the user device displays an encouraging message such as "You did a great job today!" to the user, which is expected to help maintain the user's motivation. The encouraging message is customized based on the user's progress data.

[1616] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1617] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized study plan based on initial setting information provided by the user, and flexibly readjusts the plan according to the user's progress. Furthermore, the system provides encouraging and advice messages to maintain the user's motivation, and recognizes the user's emotions to provide more accurate support.

[1618] Specific examples of the system

[1619] User Preferences

[1620] 1. The user accesses the language learning system and enters the necessary information in the initial setup form (language to learn, learning objectives, available time for learning, and existing skill level).

[1621] 2. The terminal sends the entered initial setting information to the server.

[1622] Generate a lesson plan

[1623] 1. The server analyzes the received initial setting information and generates an optimal learning plan based on the user's needs.

[1624] 2. The server sends the generated study plan to the user's terminal and presents it to the user.

[1625] Track your learning and progress

[1626] 1. The user follows the presented study plan and performs daily study tasks.

[1627] 2. The device records the user's progress each time they complete a lesson and sends that data to the server.

[1628] Checking progress and testing

[1629] 1. The server periodically analyzes the user's progress data and generates a test to check the user's progress. For example, the test may include listening and speaking practice.

[1630] 2. The terminal provides the generated test to the user, records the results, and sends them to the server.

[1631] Re-adjusting your study plan

[1632] 1. The server analyzes the user's strengths and weaknesses based on the test results and progress data.

[1633] 2. The server generates a re-adjusted learning plan based on the analysis results.

[1634] 3. The server transmits the re-adjusted study plan to the user terminal and provides it to the user.

[1635] Use of emotion engine

[1636] 1. The device collects emotional data from the user's facial expressions, voice, etc. and analyzes it using an emotion engine.

[1637] 2. The server recognizes the user's emotional state based on the emotional data obtained from the emotion engine.

[1638] 3. The server adjusts the study plan and motivational messages taking into account the user's emotional state.

[1639] Staying motivated

[1640] 1. The device displays encouraging messages and study advice to the user after completing each day's study.

[1641] 2. The server generates a customized encouraging message based on the user's emotional data and progress and sends it to the device.

[1642] 3. The device displays a customized encouraging message to the user.

[1643] Specific examples

[1644] When a user is learning business English, the following scenarios can be envisaged using the emotion engine:

[1645] 1. The user enters the initial information, setting aside one hour each day to learn business English.

[1646] 2. The device sends this information to the server.

[1647] 3. The server analyzes the configuration information and generates a study plan that allows the user to learn vocabulary and phrases necessary for business situations.

[1648] 4. The server sends the study plan to the user's terminal, and the user proceeds with the study according to the plan.

[1649] 5. The device records the user's learning progress and periodically sends the data to the server.

[1650] 6. The server analyzes the progress data, generates a progress confirmation test, and sends it to the user terminal.

[1651] 7. The user takes the test and sends the results from the device to the server.

[1652] 8. The server analyzes the test results and generates a new study plan to improve listening skills for users who are weak in listening.

[1653] 9. The device collects the user's emotional state using facial recognition and voice data and analyzes it using an emotion engine.

[1654] 10. Based on the emotional data, if the user feels frustrated, the server generates an encouraging message such as, "You're doing great! You'll definitely get results."

[1655] 11. The terminal displays the generated message to the user to keep them motivated.

[1656] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing the emotion engine, it can further increase users' motivation and help them achieve their ultimate learning goals.

[1657] The processing flow will be explained below.

[1658] Step 1:

[1659] The user accesses the language learning system and enters the necessary information (language to be learned, learning purpose, available time for learning, existing skill level) into the initial setting form.

[1660] Step 2:

[1661] The terminal transmits the input initial setting information to the server.

[1662] Step 3:

[1663] The server analyzes the received initial setting information and generates an optimal learning plan that meets the user's needs.

[1664] Step 4:

[1665] The server transmits the generated study plan to the user terminal and presents it to the user.

[1666] Step 5:

[1667] The user follows the presented study plan and performs daily study tasks.

[1668] Step 6:

[1669] The device records the user's progress each time they complete a lesson.

[1670] Step 7:

[1671] The terminal periodically transmits the recorded learning progress data to the server.

[1672] Step 8:

[1673] The server receives the progress data and periodically generates tests to check the user's progress.

[1674] Step 9:

[1675] The server transmits the generated test to the user terminal and provides it to the user.

[1676] Step 10:

[1677] The user takes the test and has the results recorded on the terminal.

[1678] Step 11:

[1679] The terminal transmits the test results to the server.

[1680] Step 12:

[1681] The server analyzes the test results and progress data to identify the user's strengths and weaknesses.

[1682] Step 13:

[1683] The server generates a new, re-adjusted lesson plan based on the analysis results.

[1684] Step 14:

[1685] The server transmits the readjusted study plan to the user terminal and presents it to the user.

[1686] Step 15:

[1687] The user continues studying based on the new study plan.

[1688] Step 16:

[1689] The device displays encouraging messages and study advice to the user to help maintain motivation.

[1690] Step 17:

[1691] The device collects emotion data using facial recognition and voice data from the user.

[1692] Step 18:

[1693] The terminal transmits the collected emotion data to an emotion engine to analyze the user's emotional state.

[1694] Step 19:

[1695] The server recognizes the emotions the user is feeling while studying based on the emotion data obtained from the emotion engine.

[1696] Step 20:

[1697] The server adjusts the study plan and motivational messages to take into account the user's emotional state and generates new customized messages.

[1698] Step 21:

[1699] The server sends a customized encouraging message to the user terminal.

[1700] Step 22:

[1701] The terminal displays the generated encouraging message to the user to maintain motivation for learning.

[1702] Example 2

[1703] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1704] Conventional language learning systems have the problem that they are unable to accurately grasp the user's learning progress, and that it is difficult to maintain motivation or flexibly respond to individual learning needs. Furthermore, providing learning plans and messages that ignore the user's emotional state can cause users to feel frustrated with learning, making it difficult for them to continue studying.

[1705] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user terminal, means for recording study progress, means for periodically checking the progress status and generating a test, means for analyzing the test results and study progress data and readjusting the study plan as necessary, means for recognizing the user's emotions and providing an adjusted motivational message, means for analyzing the user's facial expressions and voice data to collect emotion data, and means for generating customized messages using a generative AI model. This makes it possible to provide an individually customized study plan and motivational message based on the user's study progress and emotional state.

[1706] "Initial setting information" refers to basic information such as the language to be learned, learning objectives, available learning time, and existing skill level that is input when a user accesses the language learning system.

[1707] A "study plan" is a set of individually customized study schedules and tasks that are generated based on the user's initial setting information.

[1708] A "user terminal" is a device such as a computer or smartphone used by a user, and is a medium for displaying study plans, tests, messages, etc.

[1709] "Study progress" is data that indicates the progress and achievement level of the learning tasks that the user has performed according to the learning plan.

[1710] "Tests" are server-generated tests to assess a user's listening, speaking, or other language skills in order to assess their learning progress.

[1711] "Test results" is data that indicates the scores and evaluations of users when they take a test.

[1712] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expressions and voice.

[1713] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate content, specifically for generating customized messages.

[1714] "Motivational messages" are messages of encouragement and advice provided to increase the user's motivation to learn and encourage them to continue.

[1715] This invention relates to a support system for helping users efficiently advance language learning. The system generates an individually customized learning plan based on initial setting information input by the user, supports the progress of learning, and provides motivational messages that incorporate the user's emotional state.

[1716] 1. Receive initial setup information and generate a lesson plan

[1717] A user accesses the language learning system and inputs initial information such as the language to be learned, learning objectives, available learning time, existing skill level, etc. The terminal collects this information and sends it to the server.

[1718] The server uses Python data analysis libraries (e.g., Pandas, SciPy) to analyze the received initial configuration information and generate an optimal learning plan based on the user's needs. The generated learning plan includes specific tasks and schedules to achieve the user's learning goals.

[1719] 2. Recording your learning progress

[1720] The user performs daily learning tasks according to the presented learning plan. The device records the user's learning progress and manages the progress data using a database such as MySQL or SQLite. This data is periodically sent to a server to grasp the user's learning status.

[1721] 3. Check your progress and test

[1722] The server analyzes the user's progress data and generates tests to check progress as needed. The tests evaluate skills such as listening and speaking, and are generated using Python data analysis libraries (e.g., Pandas, NumPy).

[1723] 4. Readjust your study plan

[1724] The server analyzes test results and progress data to identify the user's strengths and weaknesses. This is done using machine learning algorithms (e.g., Scikit-learn). Based on the analysis results, the server generates an adjusted study plan and sends it to the user's device. This allows the user to always study based on the optimal study plan.

[1725] 5. Use of Emotion Engine

[1726] The device collects the user's facial expression and voice data and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow).The server recognizes the user's emotional state based on the emotional data obtained from the emotion recognition engine and reflects this in the content of the study plan and motivational messages.

[1727] 6. Staying motivated

[1728] The device displays encouraging messages and study advice to the user after completing each day's study. The server uses a generative AI model (e.g., a natural language generation algorithm) to generate encouraging messages based on the user's emotional data and progress. The generated messages are sent to the user's device and displayed to the user. This helps to motivate the user and encourage them to continue studying.

[1729] Examples of concrete examples and prompts

[1730] As a concrete example, if a user is learning business English, they would enter the initial settings information as "Business English," "1 hour daily," and "Intermediate level." Based on this information, the server would generate a study plan for learning vocabulary and phrases needed in business situations and send it to the user's device. The user would proceed with their study, and the device would record and send the progress. Based on the progress data, the server would generate a test and provide it to the user. The study plan would be readjusted depending on the test results. The server would also analyze the user's emotional state from their facial expressions and voice, and generate appropriate encouraging messages that would be displayed on the device.

[1731] An example of a prompt to input to a generative AI model is as follows:

[1732] Generate encouraging messages based on the user's learning goals and progress data. For example, if the user is frustrated with listening, the message might be "You're doing great! You'll see results!"

[1733] In this way, this system allows users to progress through language learning efficiently and flexibly, supporting them in continuing their studies and achieving their ultimate goals.

[1734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1735] Step 1: User enters initial setup information

[1736] A user accesses the language learning system and enters information into an initial setup form, such as the language to learn, learning objectives, available study time, and existing skill level. The entered information becomes the basis for generating a learning plan tailored to the user's learning needs. The terminal collects this initial setup information and sends it to the server. Input is via text input fields and selection boxes, and output is the data sent to the server as initial setup information.

[1737] Step 2: Generate a lesson plan

[1738] The server generates an optimal learning plan based on the received initial setup information. It uses Python data analysis libraries (e.g., Pandas, SciPy) to create a schedule based on learning objectives and available time. Specifically, it analyzes the initial setup information (vocabulary, phrases, skill level, etc.) and assigns learning tasks by time. The output is a customized learning plan, which is sent to the user's device.

[1739] Step 3: Practice and record your progress

[1740] The user performs daily learning tasks according to a learning plan provided by the server. The device records the user's progress each time they complete a study and stores it in a database such as MySQL or SQLite. Specifically, when the user presses a button after completing a task, the time it took to complete the task and the degree of achievement are recorded. The input is the user's operation, and the output is information stored in the database as progress data.

[1741] Step 4: Check progress and generate tests

[1742] The server periodically analyzes the user's progress data and generates tests to check progress. The progress data is aggregated using Python analysis libraries (e.g., Pandas, NumPy) and tests (listening, speaking, etc.) are created according to the user's learning status. The output tests are sent to the user's device and provided to the user. Specifically, a screen for the user to take the test is displayed on the device.

[1743] Step 5: Test results and readjust your study plan

[1744] The user takes the provided test and sends the results from their device to the server. The server uses a machine learning algorithm (e.g., Scikit-learn) to analyze the user's strengths and weaknesses based on the received test results and progress data. Based on the analysis results, the server readjusts the learning plan and sets new learning tasks, if necessary. The output is a readjusted learning plan, which is sent to the user's device. For example, for a user who is weak at listening, a new listening improvement task is added.

[1745] Step 6: Collect and analyze emotion data

[1746] The device collects facial and voice data from the user during and after learning, and analyzes it using an emotion recognition engine (e.g., OpenCV, TensorFlow). This determines the user's emotional state and sends the data to a server. Specifically, the device uses a camera and microphone to capture facial and voice data and recognizes emotions in real time. The input is the user's facial and voice data, and the output is emotional data resulting from the analysis.

[1747] Step 7: Generate and deliver motivational messages

[1748] The server generates a motivational message customized to the user's emotional state based on the emotional data and progress data. For this purpose, it uses a generative AI model (e.g., a natural language generation algorithm). For example, if the user is feeling frustrated, it generates a message saying, "You're doing great! You'll succeed." The generated message is sent to the device and displayed to the user. Specifically, the message appears as a pop-up on the screen. The input is emotional data and progress data, and the output is an encouraging message.

[1749] This detailed step-by-step process allows users to progress through language learning efficiently and flexibly. Furthermore, the use of emotional data further enhances the user's learning experience, helping to maintain motivation.

[1750] (Application example 2)

[1751] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1752] While existing language learning support systems provide functions for recording users' progress and readjusting plans, they lack specific emotional recognition and learning support through virtual interaction. Furthermore, they struggle to maintain users' motivation or provide advice based on their emotional state, and lack flexible, individualized support to maximize learning outcomes. This creates problems that make it difficult for users to continue studying.

[1753] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving initial setting information from the user, means for generating an optimal study plan based on the initial setting information, means for transmitting the generated study plan to the user device, means for recording study progress, means for collecting and analyzing the user's emotional data, means for generating motivational messages based on the emotional data and providing them to the user, means for recording and analyzing the user's comments and actions in the virtual space, and means for analyzing test results and study progress data and readjusting the study plan as necessary. This enables individualized study support that takes the user's emotional state into consideration, and enables effective language learning through interaction in a virtual environment.

[1754] The "means for receiving initial setting information from a user" refers to the means by which a user accesses the language learning system and inputs information such as the language to be learned, learning objectives, available time for learning, and existing skill level.

[1755] The "means for generating an optimal study plan based on initial setting information" is a means for analyzing the received initial setting information and automatically generating an individual study plan that is most suitable for the user.

[1756] The "means for transmitting the generated study plan to the user device" refers to means for transmitting the generated study plan to the user terminal and presenting it to the user.

[1757] "Means for recording learning progress" refers to means for collecting and recording the progress of users as they proceed with their learning.

[1758] The "means for periodically checking the progress and generating tests" refers to a means for periodically analyzing the user's progress data and generating tests for checking the progress based on that data.

[1759] "Means for analyzing test results and learning progress data and readjusting the learning plan as necessary" refers to means for detecting the user's strengths and weaknesses based on test results and learning progress data, and for readjusting the learning plan according to the analysis results.

[1760] The "means for collecting and analyzing emotional data" refers to a means for collecting emotional data from the user's facial expressions, voice, etc., and analyzing it.

[1761] The "means for generating a motivational message based on emotional data and providing it to the user" is a means for grasping the emotional state of the user based on the emotional data, and generating a message of encouragement or advice in response to that, and providing it to the user.

[1762] "Means for supporting language learning through interaction with users in a virtual space" refers to means for supporting language learning through dialogue between users and virtual characters in a virtual space such as a virtual store.

[1763] "Means for recording and analyzing user's statements and actions in a virtual space" refers to means for recording and analyzing the statements and actions that a user makes in a virtual space.

[1764] The present invention is a system for efficiently providing specific language learning support in a virtual store environment. This system generates an optimal learning plan based on the user's initial setting information, supports language learning in a virtual space, and has the function of readjusting the learning plan taking into account the user's emotions and progress.

[1765] Hardware and software used

[1766] Hardware: Smartphones, head-mounted displays, smart glasses

[1767] Software: Generative AI model (GPT-4), emotion recognition engine (e.g., Microsoft Azure's Emotion API), database

[1768] System Overview

[1769] 1. Receiving user preferences

[1770] Users input initial setup information via a smartphone, head-mounted display, or smart glasses, including the language to learn, learning goals, available time, and existing skill level.

[1771] 2. Generate a learning plan

[1772] The input initial setting information is sent to the server, which uses a generative AI model (GPT-4) to generate an optimal learning plan and sends it to the user's device.

[1773] 3. Language learning in virtual stores

[1774] Users can learn a language by interacting with virtual characters in a virtual space (such as a virtual cafe or library). The user's comments and actions are recorded and stored as progress data.

[1775] 4. Emotional Data Collection and Analysis

[1776] The user's facial expressions and voice data are analyzed using an emotion recognition engine, which outputs emotion data and sends it to the server.

[1777] 5. Checking progress and generating tests

[1778] The server periodically checks the user's progress data and generates a test for checking the progress. The user takes the test through the terminal and sends the results to the server.

[1779] 6. Readjust your study plan

[1780] The server analyzes the test results and progress data and adjusts the study plan as needed, which is then sent back to the user's device.

[1781] 7. Providing motivational messages

[1782] Based on the user's emotional data, a generative AI model is used to generate personalized motivational messages, which are then displayed on the user's device after training is complete.

[1783] Specific examples

[1784] For example, use prompts such as the following to provide guidance and encouragement to the user:

[1785] "Choose a language to learn." "Choose a time each day to avoid."

[1786] "Hello! Today we're going to learn some new business English vocabulary. Are you ready?"

[1787] "We will give you a short test to check your progress. Please answer the listening questions."

[1788] "You did a great job today! Let's work even harder."

[1789] "You're making great progress!"

[1790] In this way, this system allows users to proceed with language learning in a planned and flexible manner, and by utilizing emotional data, it is possible to increase motivation and enable sustained learning.

[1791] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1792] Step 1:

[1793] The user accesses the language learning system and inputs initial setup information, including the language to be learned, learning goals, available time for learning, and existing skill level. The terminal then transmits this initial setup information to the server. Based on the input initial setup information, the server analyzes the data and generates an optimal learning plan for the user.

[1794] Step 2:

[1795] The server uses a generative AI model (GPT-4) to analyze the user's initial setting information and generate an optimal learning plan. This generated learning plan is customized to the user's learning goals and time. The server then sends the generated learning plan to the user's device. The device then presents this plan to the user, allowing them to begin learning.

[1796] Step 3:

[1797] Users learn a language by interacting with virtual characters in a virtual store. The device records the user's comments and actions and sends them to a server as progress data. Learning in the virtual space is done in an interactive format based on real situations, improving the user's communication skills.

[1798] Step 4:

[1799] The server periodically checks the user's progress and generates a test based on the user's progress. The test may include listening and speaking exercises to assess the user's learning status. The test results are provided to the user via their device and are then sent back to the server.

[1800] Step 5:

[1801] The server uses an emotion recognition engine to collect and analyze emotions from the user's facial expressions and voice data. The emotion data is used to maintain motivation and optimize the user's learning experience. The server readjusts the user's study plan based on the emotion data and generates appropriate encouraging messages.

[1802] Step 6:

[1803] The server readjusts the study plan based on the test results, progress data, and the analysis of the emotional data. The readjusted study plan focuses on the user's weaknesses and areas that need improvement. The server then sends the readjusted study plan to the user's device.

[1804] Step 7:

[1805] The device displays encouraging and advising messages to users after each day's study. These messages are individually customized using a generative AI model and are intended to motivate users. For example, prompts such as "You did a great job today! Keep trying!" and "You're making great progress."

[1806] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1807] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1808] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1809] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1810] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1811] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1812] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1813] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1814] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1816] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1817] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1818] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1819] 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.

[1820] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1821] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1822] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1823] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1824] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1825] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1826] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1827] The following is further disclosed regarding the above embodiment.

[1828] (Claim 1)

[1829] means for receiving initial setting information from a user;

[1830] means for generating an optimal learning plan based on the initial setting information;

[1831] means for transmitting the generated study plan to a user terminal;

[1832] a means of recording learning progress;

[1833] A means to periodically check progress and generate tests;

[1834] A means of analyzing test results and learning progress data and readjusting learning plans as needed; and

[1835] A system including a means for providing a motivational message to a user.

[1836] (Claim 2)

[1837] 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

[1838] (Claim 3)

[1839] 10. The system of claim 1, further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results.

[1840] "Example 1"

[1841] (Claim 1)

[1842] means for receiving initial setting information from a user;

[1843] means for generating an optimal learning plan based on the initial setting information;

[1844] means for transmitting the generated study plan to a user terminal;

[1845] a means for recording progress as the user completes a learning task;

[1846] A means to regularly check progress and generate tests including listening and speaking;

[1847] A means of analyzing test results and learning progress data to analyze the user's strengths and weaknesses and adjust the learning plan as needed;

[1848] means for generating a customized encouraging message based on the user's progress data and sending the message to the user terminal;

[1849] a means for generating customized messages using a generative AI model;

[1850] a means for using data analysis tools to analyze a user's progress;

[1851] A system including means including a database for storing lesson plans and progress data.

[1852] (Claim 2)

[1853] 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

[1854] (Claim 3)

[1855] 10. The system of claim 1, further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results.

[1856] "Application Example 1"

[1857] (Claim 1)

[1858] means for receiving initial setting information from a user;

[1859] means for generating an optimal learning plan based on the initial setting information;

[1860] means for transmitting the generated study plan to a user terminal;

[1861] a means of recording learning progress;

[1862] A means to periodically check progress and generate tests;

[1863] A means of analyzing test results and learning progress data and readjusting learning plans as needed; and

[1864] means for providing motivational messages to the user;

[1865] A means for using a generative AI model to analyze a user's strengths and weaknesses based on a prompt;

[1866] Provide daily learning tasks based on the learning plan, and collect and analyze progress data.

[1867] A system including means for providing customized encouraging messages in response to progress data.

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

[1870] (Claim 3)

[1871] 10. The system of claim 1, further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results.

[1872] "Example 2: Combining Emotion Engines"

[1873] (Claim 1)

[1874] means for receiving initial setting information from a user;

[1875] means for generating an optimal learning plan based on the initial setting information;

[1876] means for transmitting the generated study plan to a user terminal;

[1877] a means of recording learning progress;

[1878] A means to periodically check progress and generate tests;

[1879] A means of analyzing test results and learning progress data and readjusting learning plans as needed; and

[1880] means for recognizing a user's emotions and providing tailored motivational messages;

[1881] A means for collecting emotion data by analyzing facial expressions and voice data of a user;

[1882] A system including means for generating a customized message using a generative AI model.

[1883] (Claim 2)

[1884] 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

[1885] (Claim 3)

[1886] 10. The system of claim 1, further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results.

[1887] "Application example 2 when combining emotion engines"

[1888] (Claim 1)

[1889] means for receiving initial setting information from a user;

[1890] means for generating an optimal learning plan based on the initial setting information;

[1891] means for transmitting the generated study plan to a user device;

[1892] a means of recording learning progress;

[1893] A means to periodically check progress and generate tests;

[1894] A means of analyzing test results and learning progress data and readjusting learning plans as needed; and

[1895] a means for collecting and analyzing emotion data;

[1896] means for generating a motivational message based on the emotion data and providing the message to the user;

[1897] A means for supporting language learning while interacting with a user in a virtual space;

[1898] A means for recording and analyzing user's statements and actions within the virtual space

[1899] A system including:

[1900] (Claim 2)

[1901] 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

[1902] (Claim 3)

[1903] 10. The system of claim 1, further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results. [Explanation of symbols]

[1904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving initial setting information from a user; means for generating an optimal learning plan based on the initial setting information; means for transmitting the generated study plan to a user terminal; a means of recording learning progress; A means to periodically check progress and generate tests; A means of analyzing test results and learning progress data and readjusting learning plans as needed; and and means for providing a motivational message to the user.

2. 10. The system of claim 1, further comprising means for periodically sending the user progress check reminders.

3. The system of claim 1 further comprising means for analyzing the strengths and weaknesses of the user's skills based on the test results.

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