System

Abstract

A system is provided.SOLUTION: Means for a user to input and register a name, an age, a language proficiency, and a learning purpose, means for a server to receive the input information and generate a user profile, and means for the server to generate a test for initial evaluation based on the user profile; Means for collecting a test result of the user by the terminal and transmitting the test result to the server, means for analyzing the test result by the server and generating an individual learning path by the server, means for selecting learning content based on the learning path generated by the server and transmitting the learning content to the terminal, means for recording a learning progress of the user by the terminal and transmitting the learning progress to the server, means for analyzing progress data and optimizing the learning path and the content by the server, and means for providing feedback on the learning content by the user, the system including means for the server to analyze the feedback and make system-wide adjustments.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] The present invention solves the problem of a lack of learning materials suited to individual learning paces and styles in language learning. Conventional language learning systems use uniform learning materials and progress management, which is one factor that reduces users' learning effectiveness and motivation. The present invention aims to support effective and efficient language learning by providing individual learning plans suited to the user's proficiency level and learning style. [Means for solving the problem]

[0005] The present invention solves the above problems by including the following means: First, a means is provided for a user to input and register their name, age, language proficiency, and learning goals, and a server receives the input information and generates a user profile. Next, the server generates a test for initial assessment based on the user profile and sends the test to the terminal. Furthermore, the terminal collects the user's test results and sends them to the server. The server analyzes the test results and generates an individual learning path. Based on the generated learning path, the server selects appropriate learning content and sends it to the terminal. The terminal presents the received learning content to the user and records learning progress, which is sent to the server. The server analyzes the progress data and optimizes the learning path and content. Finally, the user provides feedback on the learning content, and the server analyzes the feedback and adjusts the entire system. These means allow users to effectively learn languages ​​at their own pace.

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

[0007] "Terminal" refers to the device (smartphone, tablet, computer, etc.) that a user uses to access the system and receive learning content.

[0008] "Server" refers to a central computing system for receiving and analyzing user information, and generating and transmitting learning content.

[0009] "Profile" refers to a user-specific data set that is generated based on the information the user provides during registration and the results of an initial assessment.

[0010] "Initial Assessment" refers to a test and its results provided by the server to assess a user's current language proficiency.

[0011] "Learning Path" refers to a learning plan optimized for an individual user that is generated based on the user's profile information and initial assessment results.

[0012] "Learning content" refers to language learning materials (videos, audio, quizzes, etc.) provided to users.

[0013] "Progress data" refers to data that records the user's learning progress (study time, correct answer rate, etc.).

[0014] "Feedback" refers to the opinions and impressions a user provides about their learning experience.

[0015] "Analysis" refers to the process by which the server analyzes user information and data to generate optimal learning paths and content. [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] The present invention relates to an online language learning system for providing and optimizing content related to language learning. The system functions through the interaction of a server, a terminal, and a user. The following describes in detail an embodiment of the system.

[0038] User registration and initial settings

[0039] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[0040] Initial evaluation

[0041] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[0042] Generate personalized learning paths

[0043] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0044] Providing learning content

[0045] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[0046] Track your learning progress

[0047] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0048] Progress data analysis and optimization

[0049] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with grammar questions, the server will adjust the next learning session to provide more grammar practice.

[0050] Feedback and System Tuning

[0051] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0052] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[0056] Step 2:

[0057] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[0058] Step 3:

[0059] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[0060] Step 4:

[0061] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[0062] Step 5:

[0063] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[0064] Step 6:

[0065] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[0066] Step 7:

[0067] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[0068] Step 8:

[0069] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[0070] Step 9:

[0071] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[0072] Step 10:

[0073] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[0074] Step 11:

[0075] The server analyzes the received progress data, evaluates the user's learning effectiveness, and optimizes the learning path and the next content provided, if necessary.

[0076] Step 12:

[0077] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[0078] Step 13:

[0079] The server analyzes user feedback to identify areas for improvement in the overall system and the content it provides, and makes any necessary adjustments to further optimize the user's learning experience.

[0080] Through these steps, users can effectively progress through language learning at their own pace.

[0081] Example 1

[0082] 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."

[0083] Conventional online language learning systems have difficulty effectively collecting individual users' learning progress and feedback and optimizing learning paths and content based on that information. Furthermore, they have been unable to provide optimal content tailored to users' learning goals and proficiency levels, which can lead to a decline in learning effectiveness.

[0084] 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.

[0085] In this invention, the server includes: means for a user to input and register personal information, age, language ability, and learning goals; means for the server to receive the input information and generate a user profile; means for the server to generate an evaluation test based on the user profile and send it to the terminal; means for the terminal to collect the user's test results and send them to the server; means for the server to analyze the test results and generate an individual learning path; means for the server to select learning content based on the generated learning path and send it to the terminal; means for the terminal to present the received learning content to the user; means for the terminal to record the user's learning progress and send it to the server; means for the server to analyze the progress data and optimize the learning path and content; means for the user to provide feedback on the learning content and the server to analyze the feedback and adjust the entire system; and means for optimizing the learning path and content using a generative AI model. This enables fast and effective optimization based on the user's individual learning progress and feedback.

[0086] "User" refers to an individual who uses the online language learning system to learn a language.

[0087] "Server" refers to a computer system that receives, processes, and analyzes data input by a user.

[0088] A "terminal" is a device operated by a user, and refers to an apparatus for receiving information from a server and providing it to the user.

[0089] "Profile" refers to information that compiles data such as a user's personal information, age, language ability, and learning goals.

[0090] "Initial Assessment Test" refers to a server-generated test to assess a user's current language proficiency.

[0091] "Generative AI Model" refers to the artificial intelligence model used by the server to optimize a user's learning path or content.

[0092] "Progress data" is data that indicates the user's learning progress, and includes the study time, the number of questions answered correctly, questions answered incorrectly, and so on.

[0093] "Learning path" refers to an individual learning plan generated by the server based on the user's proficiency and learning goals.

[0094] "Feedback" refers to the opinions and impressions a user provides regarding their learning experience.

[0095] "Optimization" refers to the process by which the server adjusts the learning path and content to maximize the user's learning effectiveness.

[0096] The present invention relates to an online language learning system for personalizing and optimizing content related to language learning, which functions through the interaction of a server, a terminal, and a user.

[0097] User registration and initial settings

[0098] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel goals," the server stores this information in the profile. Specifically, the server stores user information in a database using a SQL database or similar.

[0099] Initial evaluation

[0100] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server. The server evaluates the user's current ability based on the results.

[0101] Generate personalized learning paths

[0102] The server generates an individual learning path based on the user's initial evaluation results. This learning path is optimized for the user's proficiency level and learning goals, and its content includes daily learning content and goals. Specifically, a generative AI model is used to design an optimal curriculum for the user. For example, if a learning path for "learning basic grammar" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0103] Providing learning content

[0104] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, a pronunciation guide video followed by a quiz will be displayed on the device.

[0105] Track your learning progress

[0106] As a user studies using the designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0107] Progress data analysis and optimization

[0108] The server analyzes the received progress data and evaluates the user's learning effectiveness. The analysis involves reading data from an SQL database and analyzing it using Python or other tools. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is "struggling with grammar questions," the server will adjust the next learning session to provide more grammar practice.

[0109] Feedback and System Tuning

[0110] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements or new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0111] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[0112] Examples and prompts

[0113] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[0114] example:

[0115] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[0116] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] The user registers by entering personal information, age, language ability, and learning goals. The user enters this information using a terminal and presses the registration button. The entered data is sent from the terminal to the server.

[0120] Input: User's personal information, age, language ability, learning goals

[0121] Output: Send data to the server

[0122] Specific behavior:

[0123] When a user enters the required information into the web form on their device and presses the registration button, the data in the form is sent to the server in JSON format.

[0124] Step 2:

[0125] The server receives the entered information and generates a user profile. The server analyzes the received data, creates the profile, and stores it in a database.

[0126] Input: Personal data from the user

[0127] Output: User profile stored in the database

[0128] Specific behavior:

[0129] The server stores the received data in an SQL database and generates user profile information.

[0130] Step 3:

[0131] The server generates an initial assessment test based on the user profile and sends it to the device, where it uses a generative AI model to design the optimal test.

[0132] Input: User Profile

[0133] Output: Initial evaluation test sent to user device

[0134] Specific behavior:

[0135] The server's generative AI model creates an initial evaluation test based on the user's level of proficiency and sends it to the device.

[0136] Step 4:

[0137] The terminal presents the initial evaluation test to the user, who then takes the test. The user answers questions on the test screen and completes the test.

[0138] Input: Initial evaluation test sent from the server

[0139] Output: User test answer data

[0140] Specific behavior:

[0141] The terminal displays test questions to the user and receives the user's answers.

[0142] Step 5:

[0143] The device sends the user's test results to the server. The device collects the user's answers and sends them to the server.

[0144] Input: User test answer data

[0145] Output: Test results sent to the server

[0146] Specific behavior:

[0147] The device sends the user's response data in JSON format to the server.

[0148] Step 6:

[0149] The server analyzes the test results and generates an individual learning path, which is then analyzed using a generative AI model to design the optimal learning path.

[0150] Input: User test result data

[0151] Output: Individually optimized learning paths

[0152] Specific behavior:

[0153] The server analyzes the test results and uses a generative AI model to generate a learning path for each user.

[0154] Step 7:

[0155] The server selects learning content based on the generated learning path and sends it to the device. The server then selects appropriate learning resources (videos, quizzes, etc.) and sends them to the device.

[0156] Input: User's learning path

[0157] Output: Learning content sent to the user's device

[0158] Specific behavior:

[0159] The server selects the most appropriate content (video, audio, quiz) based on the user's learning path and sends it to the device.

[0160] Step 8:

[0161] The device presents the received study content to the user, who then uses this content to advance their studies.

[0162] Input: Learning content sent from the server

[0163] Output: The learning content that is displayed to the user

[0164] Specific behavior:

[0165] The terminal presents the received learning content to the user visually or audibly.

[0166] Step 9:

[0167] The device records the user's learning progress and sends it to the server. The device collects progress data such as study time, number of correct answers, and mistakes, and periodically sends it to the server.

[0168] Input: User's learning progress data

[0169] Output: Progress data sent to the server

[0170] Specific behavior:

[0171] The device transmits progress data collected during the learning activity to a server.

[0172] Step 10:

[0173] The server analyzes the progress data and optimizes the learning path and content. The server analyzes the progress data and determines the next learning content to be provided.

[0174] Input: User progress data

[0175] Output: Optimized next learning path and content

[0176] Specific behavior:

[0177] The server analyzes the progress data and uses a generative AI model to determine the next optimal learning content to provide.

[0178] Step 11:

[0179] Users provide feedback about their learning experience, and the server analyzes that feedback and adjusts the entire system. Users input feedback through their devices, and the server receives and analyzes it.

[0180] Input: User feedback

[0181] Output: System tuning and improvement

[0182] Specific behavior:

[0183] The server analyzes user feedback, identifies areas for improvement in the system, and makes appropriate adjustments.

[0184] Examples and prompts

[0185] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[0186] example:

[0187] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[0188] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[0189] (Application example 1)

[0190] 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."

[0191] In factories and other workplaces, where multinational workers often work, language differences can affect communication and safety. In such environments, workers need to be able to quickly and accurately understand work instructions and safety rules, but conventional methods have difficulty effectively resolving this issue. While there is a need for systems that can individually optimize and efficiently advance language learning, such systems are not widely available in reality. Therefore, there is a need for a language learning support system that can help multinational workers in factories overcome language barriers and communicate efficiently.

[0192] 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.

[0193] In this invention, the server includes a means for a user to input and register their name, age, language proficiency, and learning objectives, a means for the server to receive the input information and generate a user profile, and a means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, thereby enabling the server to generate an individual learning path and provide appropriate learning content.

[0194] In addition, in this invention, the server includes a means for the terminal to collect the user's test results and send them to the server, a means for the server to analyze the test results and generate an individual learning path, and a means for the server to select learning content based on the learning path generated and send it to the terminal, thereby making it possible to grasp the user's learning progress in real time and provide optimal content.

[0195] Furthermore, in this invention, the server includes means for presenting the learning content received by the terminal to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, and means for using smart devices to support learning of instructions and safety rules in multiple languages, thereby enabling multinational workers working in a factory to overcome language barriers, communicate efficiently, and work safely.

[0196] "User" refers to an individual person who uses the System.

[0197] "Name" refers to an individual notation for identifying a user.

[0198] "Age" refers to the user's age number.

[0199] "Language proficiency" refers to the user's current level of proficiency in a language.

[0200] "Learning objective" refers to a specific goal for a user to learn a language.

[0201] "Means for registering" refers to the process by which a user registers with the system by entering their name, age, language proficiency, and learning objectives.

[0202] "Server" refers to a computer system that receives and processes user input information.

[0203] "User profile" refers to a collection of data generated based on information entered about a user.

[0204] "Initial Assessment Test" refers to a test administered to assess a user's current language proficiency.

[0205] "Terminal" refers to the device on which a user takes an initial assessment test or accesses learning content.

[0206] "Test results" refers to the response data after a user has taken an initial evaluation test.

[0207] "Personalized Learning Path" refers to a customized learning plan generated to optimize a user's learning progress.

[0208] "Learning Content" refers to educational materials provided to users to further their learning.

[0209] "Study progress" refers to how far a user has progressed through the learning content.

[0210] "Progress data" refers to specific data regarding a user's learning progress.

[0211] "Feedback" refers to the opinions and reactions users provide about learning content.

[0212] "System-wide tuning" refers to the process by which the server improves the system based on user feedback.

[0213] "Smart devices" refer to wearable or portable devices that have computing functionality.

[0214] "Multilingual Instructions" refers to work instructions provided in multiple languages.

[0215] "Safety rules" refer to regulations to ensure safety at the workplace.

[0216] MODE FOR CARRYING OUT THE INVENTION

[0217] The present invention provides a language learning support system that enables multinational workers to overcome language barriers, communicate efficiently, and work safely. A specific embodiment of this system will be described below.

[0218] User registration and initial settings

[0219] First, a user registers in the system using a smart device (e.g., smart glasses) by inputting their name, age, language proficiency, and learning goals. This user information is then sent from the smart device to the server, which then creates a user profile based on the received information.

[0220] Initial evaluation

[0221] After the user profile is generated, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the smart device. The user takes the test through the smart device and sends the results to the server. The server analyzes the received test results and generates an individual learning path that is optimized for the user's proficiency and learning goals.

[0222] Providing learning content

[0223] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device. The smart device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a factory worker receives content on "understanding work instructions," they will be provided with videos and quizzes on specific work instructions and safety rules.

[0224] Track your learning progress

[0225] As a user progresses through the learning process using the designated learning content, the smart device records the progress. The recorded data includes the study time, number of correct answers, and questions answered incorrectly. The smart device periodically sends this progress data to the server.

[0226] Progress data analysis and optimization

[0227] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with a particular aspect of understanding factory safety rules, the server will adjust the next learning session to provide more practice on safety rules.

[0228] Feedback and System Tuning

[0229] Users can provide feedback about their learning experience via their smart devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the work instruction videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0230] Examples of specific examples and prompts

[0231] "Factory workers registered their name, age, language proficiency, and learning objectives through the smart glasses, and then took an initial assessment test. Based on the test results, they were provided with videos and quizzes on safety rules within the factory. The user's progress data was sent to the server for analysis. The next learning content was then optimized based on the analysis results."

[0232] Example prompts to input to a generative AI model:

[0233] Please complete the registration based on the information you provided during user registration.

[0234] Name: Taro Tanaka

[0235] Age: 35

[0236] Language Proficiency: Beginner

[0237] Learning Objective: Understand factory work instructions

[0238] This allows users to receive language learning support to carry out their work safely and efficiently within the factory.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] Users register by using a smart device to enter their name, age, language proficiency level, and learning purpose.

[0242] (Input) Name, age, language proficiency, learning purpose

[0243] (Processing) Send the entered data to the server

[0244] (Output) User information is stored on the server

[0245] Step 2:

[0246] The server generates a user profile based on the received user information.

[0247] (Input) User information (name, age, language proficiency, learning purpose)

[0248] (Processing) Store user information in a database and generate a profile

[0249] (Output) Generate user profile

[0250] Step 3:

[0251] The server generates an initial assessment test based on the user profile and sends it to the smart device.

[0252] (Input) User profile

[0253] (Processing) Generate appropriate test questions based on evaluation logic

[0254] (Output) The initial evaluation test is sent to the smart device.

[0255] Step 4:

[0256] Users take an initial evaluation test via their smart device.

[0257] (Input) Initial evaluation test

[0258] (Process) User answers the test

[0259] (Output) Test answer data is generated

[0260] Step 5:

[0261] The smart device sends the user's test results to the server.

[0262] (Input) Test response data

[0263] (Processing) Sending response data

[0264] (Output) The answer data is stored on the server.

[0265] Step 6:

[0266] The server analyzes the received test results and generates a personalized learning path optimized for the user.

[0267] (Input) Test results

[0268] (Processing) Generate learning paths based on analysis logic

[0269] (Output) Generate individual learning paths

[0270] Step 7:

[0271] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device.

[0272] (Input) Individual Learning Path

[0273] (Processing) Select the most suitable learning content

[0274] (Output) Learning content is sent to the smart device

[0275] Step 8:

[0276] The smart device presents the received learning content to the user.

[0277] (Input) Learning content

[0278] (Processing) Viewing learning content

[0279] (Output) User views learning content

[0280] Step 9:

[0281] Users use the learning content to advance their studies.

[0282] (Input) Learning content

[0283] (Processing) Content Learning

[0284] (Output) Generate learning progress data

[0285] Step 10:

[0286] The smart device records the user's learning progress and sends it to the server.

[0287] (Input) Learning progress data

[0288] (Processing) Recording and sending progress data

[0289] (Output) Progress data is stored on the server.

[0290] Step 11:

[0291] The server analyzes the received progress data and optimizes the user's learning path and content.

[0292] (Input) Progress data

[0293] (Processing) Optimization of learning paths and content based on analytical logic

[0294] (Output) Optimized learning paths and content

[0295] Step 12:

[0296] Users provide feedback on their learning experience through their smart devices.

[0297] (Input) Feedback data

[0298] (Process) Entering and sending feedback

[0299] (Output) Feedback data is stored on the server

[0300] Step 13:

[0301] The server analyzes the feedback received and identifies needed improvements and new features.

[0302] (Input) Feedback data

[0303] (Processing) Feedback analysis and system-wide adjustment

[0304] (Output) Adjusted system settings

[0305] 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.

[0306] The present invention relates to an online language learning system that recognizes a user's emotional state using an emotion engine to further optimize learning paths and content. This system functions in cooperation with a server, a terminal, a user, and an emotion engine. A specific embodiment of this system will be described below.

[0307] User registration and initial settings

[0308] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[0309] Initial evaluation

[0310] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[0311] Generate personalized learning paths

[0312] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0313] Emotion recognition with emotion engine

[0314] During training, the emotion engine recognizes the user's emotional state. The emotion engine identifies the user's emotional state by analyzing the user's facial expressions, tone of voice, input data, etc. For example, it uses a camera and microphone to detect changes in facial expressions and voice while the user is watching a video.

[0315] Providing learning content

[0316] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine, and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[0317] Track your learning progress

[0318] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0319] Progress data analysis and optimization

[0320] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is "struggling with grammar questions" and also "feels stressed," the server will make adjustments such as inserting a break to allow the user to relax in the next lesson and starting with easier questions.

[0321] Feedback and System Tuning

[0322] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server will make adjustments, such as simplifying the content.

[0323] As described above, by combining an emotion engine, the present invention provides a personalized learning experience that takes into account the user's emotional state. Through analysis of emotion data and learning progress data, it is possible to optimize learning content and maintain user motivation.

[0324] The processing flow will be explained below.

[0325] Step 1:

[0326] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[0327] Step 2:

[0328] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[0329] Step 3:

[0330] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[0331] Step 4:

[0332] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[0333] Step 5:

[0334] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[0335] Step 6:

[0336] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[0337] Step 7:

[0338] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[0339] Step 8:

[0340] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[0341] Step 9:

[0342] During training, the emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, tone of voice, and input data to identify the emotional state.

[0343] Step 10:

[0344] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[0345] Step 11:

[0346] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[0347] Step 12:

[0348] The server analyzes the received progress data, evaluates the user's learning effectiveness, and, if necessary, optimizes the learning path and the next content provided in comparison with the analysis results of the emotion engine.

[0349] Step 13:

[0350] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[0351] Step 14:

[0352] The server analyzes user feedback and also uses data from the emotion engine when identifying areas for improvement in the overall system and the content it provides.

[0353] Step 15:

[0354] The server adjusts the entire system and content based on the feedback. For example, if a user says, "The pronunciation practice video was helpful, but it was too difficult," the server will adjust the content by simplifying it.

[0355] These steps allow users to effectively progress through language learning at their own pace. Furthermore, by utilizing emotion recognition through the emotion engine, the learning experience is constantly optimized, helping to maintain user motivation.

[0356] Example 2

[0357] 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."

[0358] Conventional online language learning systems lack the ability to recognize a user's emotional state and optimize learning paths and content, making it difficult to provide personalized support to maximize learning outcomes. This is particularly true when users are stressed or lacking concentration, leading to a decline in motivation and efficiency. Furthermore, there is no established method for adjusting the system using user emotional data, in addition to learning progress.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0360] In this invention, the server includes means for a user to input and register their name, age, language proficiency, and learning objectives, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and transmit the test to the terminal, means for the terminal to collect the user's test results and transmit them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the learning path generated and transmit the selected learning content to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and transmit the recorded learning data to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the terminal to recognize the user's emotional state using an emotion engine and transmit the data to the server, means for the server to adjust the learning content based on the data from the emotion engine to provide an optimal learning experience, and means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system. This enables the system to recognize the user's emotional state in real time, optimize the learning path and content based on the user's emotional state, and maximize learning effectiveness through individualized support.

[0361] "User" refers to an individual who uses the system to learn a language.

[0362] "Server" refers to the central computing unit that receives, processes, analyzes, and generates and delivers appropriate learning content to users.

[0363] A "terminal" is a device that a user uses to interface with the system, including a computer, tablet, smartphone, etc.

[0364] An "emotion engine" refers to a software or hardware algorithm that analyzes a user's facial expressions, tone of voice, input data, etc. to recognize the user's emotional state.

[0365] "User profile" refers to a data set generated based on information provided by a user at the time of registration (such as name, age, language proficiency, and learning objectives).

[0366] "Initial assessment test" refers to a test generated by the server and taken by the user via a terminal in order to assess the user's current language proficiency.

[0367] "Individualized learning path" refers to a user-specific learning plan generated by the server based on the user's initial assessment results and learning objectives.

[0368] "Learning content" refers to information resources including learning materials, quizzes, videos, audio, etc. provided by the server to help users advance their studies.

[0369] "Study progress" refers to data that indicates the progress and results (study time, number of correct answers, questions answered incorrectly, etc.) when a user studies using learning content.

[0370] "Emotion data" refers to the emotional state of the user recognized by the emotion engine and recorded as numerical or text data.

[0371] "Feedback" refers to the opinions and thoughts that a user provides to the system regarding their learning experience.

[0372] "Optimization" refers to the act of individually adjusting a user's learning path and learning content based on progress data and emotional data collected by the server, in order to maximize learning effectiveness.

[0373] This invention relates to an online language learning system that recognizes a user's emotional state and optimizes learning paths and content based on that information. This system functions in cooperation with a server, a terminal, and an emotion engine. Detailed embodiments of this system are described below.

[0374] User registration and initial settings

[0375] A user first registers with the system. They enter information such as their name, age, language proficiency, and learning purpose, and send it from their terminal to the server. The server generates a user profile based on the received information and stores it in a database. For example, if a user enters "Name: Yamada Hanako, Age: 25, Language Proficiency: English Intermediate, Learning Purpose: Business," the server registers this in the profile.

[0376] Initial evaluation

[0377] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server. The server analyzes the results and evaluates the user's proficiency. For example, a "vocabulary test" or "grammar test" may be given.

[0378] Generate personalized learning paths

[0379] The server generates an individual learning path based on the initial evaluation results. This learning path includes daily learning content and goals. The server then sends the generated learning path to the device and notifies the user. For example, if a learning path for "Basic Grammar Acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0380] Emotion recognition with emotion engine

[0381] During learning, the emotion engine recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to identify their emotional state. For example, it uses the device's camera and microphone to detect changes in the user's facial expressions and voice and identify emotions such as "stress," "joy," and "concentration."

[0382] Providing learning content

[0383] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine. The selected content is sent to the device, which then presents it to the user. For example, if "pronunciation practice" content is provided, it will include a pronunciation guide video followed by a quiz.

[0384] Track your learning progress

[0385] As a user studies using the designated learning content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0386] Progress data analysis and optimization

[0387] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is struggling with grammar questions and is also feeling stressed, the server will insert a break for the user to relax in the next lesson and start with easier questions.

[0388] Feedback and System Tuning

[0389] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server can make adjustments, such as simplifying the content.

[0390] Prompt Sentence Examples

[0391] "Please suggest ways to optimize the following learning path to improve the user's learning experience: The user is a beginner in English and is focused on learning grammar for travel purposes."

[0392] "Please suggest ways to adjust the content to reduce the stress users feel while learning. Data from the emotion engine indicates that users are experiencing stress."

[0393] As described above, this invention combines an emotion engine to provide a personalized learning experience that takes into account the user's emotional state. Through analysis of learning progress data and emotion data, it is possible to optimize learning content and maintain user motivation.

[0394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0395] Specific flow of program processing

[0396] Step 1:

[0397] User registration and initial settings

[0398] Input: User enters name, age, language proficiency, and learning objectives into the device

[0399] Data processing: The device formats the user's input information and sends it to the server

[0400] Output: The server generates and stores the user profile in the database.

[0401] Specific operation: The user enters "Name: Yamada Taro, Age: 30, Language Proficiency: Beginner English, Learning Objective: Travel", and the device sends this to the server and stores it.

[0402] Step 2:

[0403] Initial evaluation

[0404] Input: The server generates an initial assessment test based on the user profile.

[0405] Data processing: The server sends the initial evaluation test to the device.

[0406] Output: Device displays test to user, user enters result, device sends result to server

[0407] Specific operation: The server generates "vocabulary tests" and "grammar tests," and the device displays them to the user. The user enters answers, and the device sends the results to the server.

[0408] Step 3:

[0409] Generate personalized learning paths

[0410] Input: Initial evaluation test results from the server

[0411] Data processing: The server analyzes and generates an individual learning path

[0412] Output: The server sends the generated learning pass to the device.

[0413] Specific operation: The server generates a learning path for beginner grammar acquisition, sends the content to the terminal, and notifies the user.

[0414] Step 4:

[0415] Emotion recognition with emotion engine

[0416] Input: The device captures the user's facial expressions and tone of voice using the camera and microphone.

[0417] Data processing: The device analyzes data with an emotion engine to identify the emotional state

[0418] Output: The device sends the analysis results to the server.

[0419] Specific operation: While the device is learning, it analyzes the user's facial expressions and tone of voice in real time, identifies their "stress" and "concentration" states, and sends the results to the server.

[0420] Step 5:

[0421] Providing learning content

[0422] Input: The server receives the user's learning path and emotion data.

[0423] Data processing: The server selects appropriate learning content

[0424] Output: Send selected learning content to the device

[0425] Specific operation: The server selects "pronunciation practice videos" and "listening quizzes," sends them to the device, and presents them to the user.

[0426] Step 6:

[0427] Track your learning progress

[0428] Input: Users consume learning content and record their progress

[0429] Data processing: The device sends progress data (study time, number of correct answers, etc.) to the server

[0430] Output: The server receives and stores the progress data.

[0431] Specific operation: The user studies words for 30 minutes, and the device records the study time and accuracy rate and sends the results to the server.

[0432] Step 7:

[0433] Progress data analysis and optimization

[0434] Input: Server receives progress and emotion data

[0435] Data processing: The server analyzes and optimizes the learning path and next content as needed.

[0436] Output: Send new learning paths and content to your device

[0437] Specific behavior: If the user is struggling with a grammar problem and feeling stressed, the server sends a new learning path to the device, inserting relaxation time.

[0438] Step 8:

[0439] Feedback and System Tuning

[0440] Input: Users provide feedback about their learning experience

[0441] Data processing: The device sends feedback to the server, which analyzes it.

[0442] Output: The server adjusts the entire system and content based on the analysis results.

[0443] Specific operation: The user gives feedback such as "The pronunciation practice video was difficult," and the server uses that feedback to simplify the video content.

[0444] (Application example 2)

[0445] 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."

[0446] Conventional online learning systems and e-readers often provide uniform content without considering the user's emotional state, resulting in a failure to optimize the user's comprehension and interest. Furthermore, ignoring emotions such as stress and excitement felt by users while learning or reading can lead to a decline in learning effectiveness and reading experience. Such systems make it difficult to maintain users' motivation to continue learning, resulting in poor learning outcomes and poor reading satisfaction.

[0447] 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 a user to input and register their name, age, language proficiency, and learning purpose, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, means for the terminal to collect the user's test results and send them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the generated learning path and send it to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, means for the server to recognize the user's emotional state using an emotion engine and optimize the reading experience, means for the emotion engine to analyze the user's facial expressions, voice, and input data, and means for dynamically selecting and displaying appropriate content based on the recognized emotional state. This allows for an optimized learning or reading experience based on the user's emotional state.

[0448] A "user" is an individual who uses a system or application.

[0449] "Name, age, language proficiency, learning objectives" refers to personal and learning details provided by users when they register.

[0450] A "server" is a computer on a network that stores and processes data.

[0451] A "user profile" is a data structure that is generated based on personal information provided by a user.

[0452] An "initial assessment test" is a test administered to assess a user's current level of knowledge.

[0453] "Terminal" means the device through which a user accesses the system.

[0454] "Test Results" means the answers provided by a user to an initial assessment test.

[0455] A "personalized learning path" is a customized learning plan based on the user's assessment results.

[0456] "Learning content" refers to the learning materials and information provided to users to help them advance their studies.

[0457] "Study progress" refers to data that indicates how far a user has progressed in their studies.

[0458] An "emotion engine" is software that recognizes a user's emotional state.

[0459] "Facial expressions, voice, and input data" refers to the data that the emotion engine uses to analyze the user's emotions.

[0460] A "recognized emotional state" is a user's emotion as identified by the emotion engine.

[0461] "Appropriate content" refers to content selected based on the user's emotional state.

[0462] A "displaying means" is a method or device for showing content to a user.

[0463] The present invention relates to a system that recognizes a user's emotional state and optimizes the learning or reading experience. This system functions through the interaction of a server, a terminal, a user, and an emotion engine. Specific embodiments for implementing this system are described below.

[0464] User registration and initial settings

[0465] Users first register by entering personal information such as their name, age, language proficiency, and learning goals into the system. The terminal then sends this information to the server, which then creates a user profile based on that information.

[0466] Initial evaluation

[0467] The server generates an initial assessment test based on the user profile to evaluate the user's current knowledge level and sends it to the device. The user takes the test through the device and sends the results to the server. The server analyzes the results and generates a personalized learning path for the user.

[0468] Learning paths and content offerings

[0469] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device presents the content to the user, who then uses it to advance their learning. Learning progress is recorded on the device and periodically sent to the server.

[0470] Emotion recognition by emotion engine

[0471] While studying or reading, the emotion engine analyzes the user's facial expressions, voice, and input data to identify their emotional state. If the user is feeling stressed, the emotion engine notifies the server, which then takes appropriate action.

[0472] Content Optimization

[0473] The server analyzes the emotion data and progress data obtained from the emotion engine to optimize the user's learning path and content. For example, if a user is feeling stressed, it will suggest chapters with relaxing content.

[0474] Specific examples of reading experiences

[0475] If a user is reading a thriller novel and the emotion engine identifies that they are feeling stressed, the server can offer another relaxing chapter, allowing the user to continue enjoying the book.

[0476] Hardware and software used

[0477] Hardware: Smartphone, tablet, or PC with webcam

[0478] software:

[0479] EmotionRecognition: A library for analyzing emotions from user facial expressions and voice (e.g., OpenCV)

[0480] EbookProvider: Backend service that provides e-books and coordinates content

[0481] Prompt Sentence Examples

[0482] "How do you adjust your content if users are stressed?"

[0483] "If a user is excited, what kind of interesting content do you recommend?"

[0484] In this way, a learning and reading experience can be provided that is individually optimized based on the user's emotional state.

[0485] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0486] Step 1:

[0487] The user enters personal information such as name, age, language proficiency, and learning goals, and sends it from the device to the server. Based on the entered information, the server generates a user profile. Specifically, the data entered by the user is sent to the server and saved in JSON format on the server side.

[0488] Step 2:

[0489] The server creates an initial evaluation test based on the generated user profile and sends it to the device. Specifically, the server's AI model analyzes the user profile, selects appropriate questions, and sends test-format data to the device. The output is test-format data.

[0490] Step 3:

[0491] The user takes the initial evaluation test using a terminal and inputs the results. The terminal collects the test results and sends them to the server. The input data is the user's answers, and the output is the test results sent to the server.

[0492] Step 4:

[0493] The server analyzes the test results and generates an individual learning path based on that information. The input data is the test results, and the output is an individual learning path. The server's AI model analyzes the data and creates a learning curriculum tailored to the user's needs.

[0494] Step 5:

[0495] The server selects the most appropriate learning content based on the generated learning path and sends it to the device. The input data is the learning path, and the output is the learning content. Appropriate learning materials are selected from the content database.

[0496] Step 6:

[0497] The device presents the received learning content to the user. Specifically, text, audio, video, etc. are displayed through a user interface. The input data is the learning content, and the output is the display on the user interface.

[0498] Step 7:

[0499] As the user studies or reads, the device records learning progress data, including study time, number of correct answers, questions missed, etc. The input data is the user's learning actions, and the output is progress data.

[0500] Step 8:

[0501] The server periodically receives and analyzes progress data sent from the device. The input data is the progress data, and the output is the analysis results. The server optimizes the learning path and content provided based on the analysis results.

[0502] Step 9:

[0503] While studying or reading, the emotion engine analyzes the user's facial expressions and voice to identify emotions. The input data is facial expressions and voice captured in real time, and the output is the recognized emotional state.

[0504] Step 10:

[0505] The server receives the emotional state from the emotion engine, dynamically selects the most appropriate content, and sends it to the device. The input data is the recognized emotional state, and the output is the adjusted content. If it recognizes that the user is feeling stressed, it selects relaxing content.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] [Second embodiment]

[0510] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0511] 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.

[0512] 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).

[0513] 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.

[0514] 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.

[0515] 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).

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] In the smart glasses 214, 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.

[0521] 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."

[0522] The present invention relates to an online language learning system for providing and optimizing content related to language learning. The system functions through the interaction of a server, a terminal, and a user. The following describes in detail an embodiment of the system.

[0523] User registration and initial settings

[0524] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[0525] Initial evaluation

[0526] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[0527] Generate personalized learning paths

[0528] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0529] Providing learning content

[0530] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[0531] Track your learning progress

[0532] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0533] Progress data analysis and optimization

[0534] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with grammar questions, the server will adjust the next learning session to provide more grammar practice.

[0535] Feedback and System Tuning

[0536] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0537] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[0538] The processing flow will be explained below.

[0539] Step 1:

[0540] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[0541] Step 2:

[0542] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[0543] Step 3:

[0544] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[0545] Step 4:

[0546] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[0547] Step 5:

[0548] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[0549] Step 6:

[0550] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[0551] Step 7:

[0552] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[0553] Step 8:

[0554] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[0555] Step 9:

[0556] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[0557] Step 10:

[0558] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[0559] Step 11:

[0560] The server analyzes the received progress data, evaluates the user's learning effectiveness, and optimizes the learning path and the next content provided, if necessary.

[0561] Step 12:

[0562] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[0563] Step 13:

[0564] The server analyzes user feedback to identify areas for improvement in the overall system and the content it provides, and makes any necessary adjustments to further optimize the user's learning experience.

[0565] Through these steps, users can effectively progress through language learning at their own pace.

[0566] Example 1

[0567] 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."

[0568] Conventional online language learning systems have difficulty effectively collecting individual users' learning progress and feedback and optimizing learning paths and content based on that information. Furthermore, they have been unable to provide optimal content tailored to users' learning goals and proficiency levels, which can lead to a decline in learning effectiveness.

[0569] 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.

[0570] In this invention, the server includes: means for a user to input and register personal information, age, language ability, and learning goals; means for the server to receive the input information and generate a user profile; means for the server to generate an evaluation test based on the user profile and send it to the terminal; means for the terminal to collect the user's test results and send them to the server; means for the server to analyze the test results and generate an individual learning path; means for the server to select learning content based on the generated learning path and send it to the terminal; means for the terminal to present the received learning content to the user; means for the terminal to record the user's learning progress and send it to the server; means for the server to analyze the progress data and optimize the learning path and content; means for the user to provide feedback on the learning content and the server to analyze the feedback and adjust the entire system; and means for optimizing the learning path and content using a generative AI model. This enables fast and effective optimization based on the user's individual learning progress and feedback.

[0571] "User" refers to an individual who uses the online language learning system to learn a language.

[0572] "Server" refers to a computer system that receives, processes, and analyzes data input by a user.

[0573] A "terminal" is a device operated by a user, and refers to an apparatus for receiving information from a server and providing it to the user.

[0574] "Profile" refers to information that compiles data such as a user's personal information, age, language ability, and learning goals.

[0575] "Initial Assessment Test" refers to a server-generated test to assess a user's current language proficiency.

[0576] "Generative AI Model" refers to the artificial intelligence model used by the server to optimize a user's learning path or content.

[0577] "Progress data" is data that indicates the user's learning progress, and includes the study time, the number of questions answered correctly, questions answered incorrectly, and so on.

[0578] "Learning path" refers to an individual learning plan generated by the server based on the user's proficiency and learning goals.

[0579] "Feedback" refers to the opinions and impressions a user provides regarding their learning experience.

[0580] "Optimization" refers to the process by which the server adjusts the learning path and content to maximize the user's learning effectiveness.

[0581] The present invention relates to an online language learning system for personalizing and optimizing content related to language learning, which functions through the interaction of a server, a terminal, and a user.

[0582] User registration and initial settings

[0583] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel goals," the server stores this information in the profile. Specifically, the server stores user information in a database using a SQL database or similar.

[0584] Initial evaluation

[0585] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server. The server evaluates the user's current ability based on the results.

[0586] Generate personalized learning paths

[0587] The server generates an individual learning path based on the user's initial evaluation results. This learning path is optimized for the user's proficiency level and learning goals, and its content includes daily learning content and goals. Specifically, a generative AI model is used to design an optimal curriculum for the user. For example, if a learning path for "learning basic grammar" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0588] Providing learning content

[0589] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, a pronunciation guide video followed by a quiz will be displayed on the device.

[0590] Track your learning progress

[0591] As a user studies using the designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0592] Progress data analysis and optimization

[0593] The server analyzes the received progress data and evaluates the user's learning effectiveness. The analysis involves reading data from an SQL database and analyzing it using Python or other tools. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is "struggling with grammar questions," the server will adjust the next learning session to provide more grammar practice.

[0594] Feedback and System Tuning

[0595] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements or new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0596] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[0597] Examples and prompts

[0598] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[0599] example:

[0600] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[0601] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[0602] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0603] Step 1:

[0604] The user registers by entering personal information, age, language ability, and learning goals. The user enters this information using a terminal and presses the registration button. The entered data is sent from the terminal to the server.

[0605] Input: User's personal information, age, language ability, learning goals

[0606] Output: Send data to the server

[0607] Specific behavior:

[0608] When a user enters the required information into the web form on their device and presses the registration button, the data in the form is sent to the server in JSON format.

[0609] Step 2:

[0610] The server receives the entered information and generates a user profile. The server analyzes the received data, creates the profile, and stores it in a database.

[0611] Input: Personal data from the user

[0612] Output: User profile stored in the database

[0613] Specific behavior:

[0614] The server stores the received data in an SQL database and generates user profile information.

[0615] Step 3:

[0616] The server generates an initial assessment test based on the user profile and sends it to the device, where it uses a generative AI model to design the optimal test.

[0617] Input: User Profile

[0618] Output: Initial evaluation test sent to user device

[0619] Specific behavior:

[0620] The server's generative AI model creates an initial evaluation test based on the user's level of proficiency and sends it to the device.

[0621] Step 4:

[0622] The terminal presents the initial evaluation test to the user, who then takes the test. The user answers questions on the test screen and completes the test.

[0623] Input: Initial evaluation test sent from the server

[0624] Output: User test answer data

[0625] Specific behavior:

[0626] The terminal displays test questions to the user and receives the user's answers.

[0627] Step 5:

[0628] The device sends the user's test results to the server. The device collects the user's answers and sends them to the server.

[0629] Input: User test answer data

[0630] Output: Test results sent to the server

[0631] Specific behavior:

[0632] The device sends the user's response data in JSON format to the server.

[0633] Step 6:

[0634] The server analyzes the test results and generates an individual learning path, which is then analyzed using a generative AI model to design the optimal learning path.

[0635] Input: User test result data

[0636] Output: Individually optimized learning paths

[0637] Specific behavior:

[0638] The server analyzes the test results and uses a generative AI model to generate a learning path for each user.

[0639] Step 7:

[0640] The server selects learning content based on the generated learning path and sends it to the device. The server then selects appropriate learning resources (videos, quizzes, etc.) and sends them to the device.

[0641] Input: User's learning path

[0642] Output: Learning content sent to the user's device

[0643] Specific behavior:

[0644] The server selects the most appropriate content (video, audio, quiz) based on the user's learning path and sends it to the device.

[0645] Step 8:

[0646] The device presents the received study content to the user, who then uses this content to advance their studies.

[0647] Input: Learning content sent from the server

[0648] Output: The learning content that is displayed to the user

[0649] Specific behavior:

[0650] The terminal presents the received learning content to the user visually or audibly.

[0651] Step 9:

[0652] The device records the user's learning progress and sends it to the server. The device collects progress data such as study time, number of correct answers, and mistakes, and periodically sends it to the server.

[0653] Input: User's learning progress data

[0654] Output: Progress data sent to the server

[0655] Specific behavior:

[0656] The device transmits progress data collected during the learning activity to a server.

[0657] Step 10:

[0658] The server analyzes the progress data and optimizes the learning path and content. The server analyzes the progress data and determines the next learning content to be provided.

[0659] Input: User progress data

[0660] Output: Optimized next learning path and content

[0661] Specific behavior:

[0662] The server analyzes the progress data and uses a generative AI model to determine the next optimal learning content to provide.

[0663] Step 11:

[0664] Users provide feedback about their learning experience, and the server analyzes that feedback and adjusts the entire system. Users input feedback through their devices, and the server receives and analyzes it.

[0665] Input: User feedback

[0666] Output: System tuning and improvement

[0667] Specific behavior:

[0668] The server analyzes user feedback, identifies areas for improvement in the system, and makes appropriate adjustments.

[0669] Examples and prompts

[0670] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[0671] example:

[0672] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[0673] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[0674] (Application example 1)

[0675] 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."

[0676] In factories and other workplaces, where multinational workers often work, language differences can affect communication and safety. In such environments, workers need to be able to quickly and accurately understand work instructions and safety rules, but conventional methods have difficulty effectively resolving this issue. While there is a need for systems that can individually optimize and efficiently advance language learning, such systems are not widely available in reality. Therefore, there is a need for a language learning support system that can help multinational workers in factories overcome language barriers and communicate efficiently.

[0677] 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.

[0678] In this invention, the server includes a means for a user to input and register their name, age, language proficiency, and learning objectives, a means for the server to receive the input information and generate a user profile, and a means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, thereby enabling the server to generate an individual learning path and provide appropriate learning content.

[0679] In addition, in this invention, the server includes a means for the terminal to collect the user's test results and send them to the server, a means for the server to analyze the test results and generate an individual learning path, and a means for the server to select learning content based on the learning path generated and send it to the terminal, thereby making it possible to grasp the user's learning progress in real time and provide optimal content.

[0680] Furthermore, in this invention, the server includes means for presenting the learning content received by the terminal to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, and means for using smart devices to support learning of instructions and safety rules in multiple languages, thereby enabling multinational workers working in a factory to overcome language barriers, communicate efficiently, and work safely.

[0681] "User" refers to an individual person who uses the System.

[0682] "Name" refers to an individual notation for identifying a user.

[0683] "Age" refers to the user's age number.

[0684] "Language proficiency" refers to the user's current level of proficiency in a language.

[0685] "Learning objective" refers to a specific goal for a user to learn a language.

[0686] "Means for registering" refers to the process by which a user registers with the system by entering their name, age, language proficiency, and learning objectives.

[0687] "Server" refers to a computer system that receives and processes user input information.

[0688] "User profile" refers to a collection of data generated based on information entered about a user.

[0689] "Initial Assessment Test" refers to a test administered to assess a user's current language proficiency.

[0690] "Terminal" refers to the device on which a user takes an initial assessment test or accesses learning content.

[0691] "Test results" refers to the response data after a user has taken an initial evaluation test.

[0692] "Personalized Learning Path" refers to a customized learning plan generated to optimize a user's learning progress.

[0693] "Learning Content" refers to educational materials provided to users to further their learning.

[0694] "Study progress" refers to how far a user has progressed through the learning content.

[0695] "Progress data" refers to specific data regarding a user's learning progress.

[0696] "Feedback" refers to the opinions and reactions users provide about learning content.

[0697] "System-wide tuning" refers to the process by which the server improves the system based on user feedback.

[0698] "Smart devices" refer to wearable or portable devices that have computing functionality.

[0699] "Multilingual Instructions" refers to work instructions provided in multiple languages.

[0700] "Safety rules" refer to regulations to ensure safety at the workplace.

[0701] MODE FOR CARRYING OUT THE INVENTION

[0702] The present invention provides a language learning support system that enables multinational workers to overcome language barriers, communicate efficiently, and work safely. A specific embodiment of this system will be described below.

[0703] User registration and initial settings

[0704] First, a user registers in the system using a smart device (e.g., smart glasses) by inputting their name, age, language proficiency, and learning goals. This user information is then sent from the smart device to the server, which then creates a user profile based on the received information.

[0705] Initial evaluation

[0706] After the user profile is generated, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the smart device. The user takes the test through the smart device and sends the results to the server. The server analyzes the received test results and generates an individual learning path that is optimized for the user's proficiency and learning goals.

[0707] Providing learning content

[0708] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device. The smart device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a factory worker receives content on "understanding work instructions," they will be provided with videos and quizzes on specific work instructions and safety rules.

[0709] Track your learning progress

[0710] As a user progresses through the learning process using the designated learning content, the smart device records the progress. The recorded data includes the study time, number of correct answers, and questions answered incorrectly. The smart device periodically sends this progress data to the server.

[0711] Progress data analysis and optimization

[0712] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with a particular aspect of understanding factory safety rules, the server will adjust the next learning session to provide more practice on safety rules.

[0713] Feedback and System Tuning

[0714] Users can provide feedback about their learning experience via their smart devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the work instruction videos were helpful," the server will make adjustments such as increasing the number of similar content.

[0715] Examples of specific examples and prompts

[0716] "Factory workers registered their name, age, language proficiency, and learning objectives through the smart glasses, and then took an initial assessment test. Based on the test results, they were provided with videos and quizzes on safety rules within the factory. The user's progress data was sent to the server for analysis. The next learning content was then optimized based on the analysis results."

[0717] Example prompts to input to a generative AI model:

[0718] Please complete the registration based on the information you provided during user registration.

[0719] Name: Taro Tanaka

[0720] Age: 35

[0721] Language Proficiency: Beginner

[0722] Learning Objective: Understand factory work instructions

[0723] This allows users to receive language learning support to carry out their work safely and efficiently within the factory.

[0724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0725] Step 1:

[0726] Users register by using a smart device to enter their name, age, language proficiency level, and learning purpose.

[0727] (Input) Name, age, language proficiency, learning purpose

[0728] (Processing) Send the entered data to the server

[0729] (Output) User information is stored on the server

[0730] Step 2:

[0731] The server generates a user profile based on the received user information.

[0732] (Input) User information (name, age, language proficiency, learning purpose)

[0733] (Processing) Store user information in a database and generate a profile

[0734] (Output) Generate user profile

[0735] Step 3:

[0736] The server generates an initial assessment test based on the user profile and sends it to the smart device.

[0737] (Input) User profile

[0738] (Processing) Generate appropriate test questions based on evaluation logic

[0739] (Output) The initial evaluation test is sent to the smart device.

[0740] Step 4:

[0741] Users take an initial evaluation test via their smart device.

[0742] (Input) Initial evaluation test

[0743] (Process) User answers the test

[0744] (Output) Test answer data is generated

[0745] Step 5:

[0746] The smart device sends the user's test results to the server.

[0747] (Input) Test response data

[0748] (Processing) Sending response data

[0749] (Output) The answer data is stored on the server.

[0750] Step 6:

[0751] The server analyzes the received test results and generates a personalized learning path optimized for the user.

[0752] (Input) Test results

[0753] (Processing) Generate learning paths based on analysis logic

[0754] (Output) Generate individual learning paths

[0755] Step 7:

[0756] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device.

[0757] (Input) Individual Learning Path

[0758] (Processing) Select the most suitable learning content

[0759] (Output) Learning content is sent to the smart device

[0760] Step 8:

[0761] The smart device presents the received learning content to the user.

[0762] (Input) Learning content

[0763] (Processing) Viewing learning content

[0764] (Output) User views learning content

[0765] Step 9:

[0766] Users use the learning content to advance their studies.

[0767] (Input) Learning content

[0768] (Processing) Content Learning

[0769] (Output) Generate learning progress data

[0770] Step 10:

[0771] The smart device records the user's learning progress and sends it to the server.

[0772] (Input) Learning progress data

[0773] (Processing) Recording and sending progress data

[0774] (Output) Progress data is stored on the server.

[0775] Step 11:

[0776] The server analyzes the received progress data and optimizes the user's learning path and content.

[0777] (Input) Progress data

[0778] (Processing) Optimization of learning paths and content based on analytical logic

[0779] (Output) Optimized learning paths and content

[0780] Step 12:

[0781] Users provide feedback on their learning experience through their smart devices.

[0782] (Input) Feedback data

[0783] (Process) Entering and sending feedback

[0784] (Output) Feedback data is stored on the server

[0785] Step 13:

[0786] The server analyzes the feedback received and identifies needed improvements and new features.

[0787] (Input) Feedback data

[0788] (Processing) Feedback analysis and system-wide adjustment

[0789] (Output) Adjusted system settings

[0790] 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.

[0791] The present invention relates to an online language learning system that recognizes a user's emotional state using an emotion engine to further optimize learning paths and content. This system functions in cooperation with a server, a terminal, a user, and an emotion engine. A specific embodiment of this system will be described below.

[0792] User registration and initial settings

[0793] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[0794] Initial evaluation

[0795] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[0796] Generate personalized learning paths

[0797] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0798] Emotion recognition with emotion engine

[0799] During training, the emotion engine recognizes the user's emotional state. The emotion engine identifies the user's emotional state by analyzing the user's facial expressions, tone of voice, input data, etc. For example, it uses a camera and microphone to detect changes in facial expressions and voice while the user is watching a video.

[0800] Providing learning content

[0801] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine, and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[0802] Track your learning progress

[0803] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0804] Progress data analysis and optimization

[0805] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is "struggling with grammar questions" and also "feels stressed," the server will make adjustments such as inserting a break to allow the user to relax in the next lesson and starting with easier questions.

[0806] Feedback and System Tuning

[0807] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server will make adjustments, such as simplifying the content.

[0808] As described above, by combining an emotion engine, the present invention provides a personalized learning experience that takes into account the user's emotional state. Through analysis of emotion data and learning progress data, it is possible to optimize learning content and maintain user motivation.

[0809] The processing flow will be explained below.

[0810] Step 1:

[0811] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[0812] Step 2:

[0813] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[0814] Step 3:

[0815] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[0816] Step 4:

[0817] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[0818] Step 5:

[0819] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[0820] Step 6:

[0821] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[0822] Step 7:

[0823] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[0824] Step 8:

[0825] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[0826] Step 9:

[0827] During training, the emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, tone of voice, and input data to identify the emotional state.

[0828] Step 10:

[0829] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[0830] Step 11:

[0831] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[0832] Step 12:

[0833] The server analyzes the received progress data, evaluates the user's learning effectiveness, and, if necessary, optimizes the learning path and the next content provided in comparison with the analysis results of the emotion engine.

[0834] Step 13:

[0835] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[0836] Step 14:

[0837] The server analyzes user feedback and also uses data from the emotion engine when identifying areas for improvement in the overall system and the content it provides.

[0838] Step 15:

[0839] The server adjusts the entire system and content based on the feedback. For example, if a user says, "The pronunciation practice video was helpful, but it was too difficult," the server will adjust the content by simplifying it.

[0840] These steps allow users to effectively progress through language learning at their own pace. Furthermore, by utilizing emotion recognition through the emotion engine, the learning experience is constantly optimized, helping to maintain user motivation.

[0841] Example 2

[0842] 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."

[0843] Conventional online language learning systems lack the ability to recognize a user's emotional state and optimize learning paths and content, making it difficult to provide personalized support to maximize learning outcomes. This is particularly true when users are stressed or lacking concentration, leading to a decline in motivation and efficiency. Furthermore, there is no established method for adjusting the system using user emotional data, in addition to learning progress.

[0844] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0845] In this invention, the server includes means for a user to input and register their name, age, language proficiency, and learning objectives, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and transmit the test to the terminal, means for the terminal to collect the user's test results and transmit them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the learning path generated and transmit the selected learning content to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and transmit the recorded learning data to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the terminal to recognize the user's emotional state using an emotion engine and transmit the data to the server, means for the server to adjust the learning content based on the data from the emotion engine to provide an optimal learning experience, and means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system. This enables the system to recognize the user's emotional state in real time, optimize the learning path and content based on the user's emotional state, and maximize learning effectiveness through individualized support.

[0846] "User" refers to an individual who uses the system to learn a language.

[0847] "Server" refers to the central computing unit that receives, processes, analyzes, and generates and delivers appropriate learning content to users.

[0848] A "terminal" is a device that a user uses to interface with the system, including a computer, tablet, smartphone, etc.

[0849] An "emotion engine" refers to a software or hardware algorithm that analyzes a user's facial expressions, tone of voice, input data, etc. to recognize the user's emotional state.

[0850] "User profile" refers to a data set generated based on information provided by a user at the time of registration (such as name, age, language proficiency, and learning objectives).

[0851] "Initial assessment test" refers to a test generated by the server and taken by the user via a terminal in order to assess the user's current language proficiency.

[0852] "Individualized learning path" refers to a user-specific learning plan generated by the server based on the user's initial assessment results and learning objectives.

[0853] "Learning content" refers to information resources including learning materials, quizzes, videos, audio, etc. provided by the server to help users advance their studies.

[0854] "Study progress" refers to data that indicates the progress and results (study time, number of correct answers, questions answered incorrectly, etc.) when a user studies using learning content.

[0855] "Emotion data" refers to the emotional state of the user recognized by the emotion engine and recorded as numerical or text data.

[0856] "Feedback" refers to the opinions and thoughts that a user provides to the system regarding their learning experience.

[0857] "Optimization" refers to the act of individually adjusting a user's learning path and learning content based on progress data and emotional data collected by the server, in order to maximize learning effectiveness.

[0858] This invention relates to an online language learning system that recognizes a user's emotional state and optimizes learning paths and content based on that information. This system functions in cooperation with a server, a terminal, and an emotion engine. Detailed embodiments of this system are described below.

[0859] User registration and initial settings

[0860] A user first registers with the system. They enter information such as their name, age, language proficiency, and learning purpose, and send it from their terminal to the server. The server generates a user profile based on the received information and stores it in a database. For example, if a user enters "Name: Yamada Hanako, Age: 25, Language Proficiency: English Intermediate, Learning Purpose: Business," the server registers this in the profile.

[0861] Initial evaluation

[0862] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server. The server analyzes the results and evaluates the user's proficiency. For example, a "vocabulary test" or "grammar test" may be given.

[0863] Generate personalized learning paths

[0864] The server generates an individual learning path based on the initial evaluation results. This learning path includes daily learning content and goals. The server then sends the generated learning path to the device and notifies the user. For example, if a learning path for "Basic Grammar Acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[0865] Emotion recognition with emotion engine

[0866] During learning, the emotion engine recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to identify their emotional state. For example, it uses the device's camera and microphone to detect changes in the user's facial expressions and voice and identify emotions such as "stress," "joy," and "concentration."

[0867] Providing learning content

[0868] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine. The selected content is sent to the device, which then presents it to the user. For example, if "pronunciation practice" content is provided, it will include a pronunciation guide video followed by a quiz.

[0869] Track your learning progress

[0870] As a user studies using the designated learning content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[0871] Progress data analysis and optimization

[0872] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is struggling with grammar questions and is also feeling stressed, the server will insert a break for the user to relax in the next lesson and start with easier questions.

[0873] Feedback and System Tuning

[0874] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server can make adjustments, such as simplifying the content.

[0875] Prompt Sentence Examples

[0876] "Please suggest ways to optimize the following learning path to improve the user's learning experience: The user is a beginner in English and is focused on learning grammar for travel purposes."

[0877] "Please suggest ways to adjust the content to reduce the stress users feel while learning. Data from the emotion engine indicates that users are experiencing stress."

[0878] As described above, this invention combines an emotion engine to provide a personalized learning experience that takes into account the user's emotional state. Through analysis of learning progress data and emotion data, it is possible to optimize learning content and maintain user motivation.

[0879] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0880] Specific flow of program processing

[0881] Step 1:

[0882] User registration and initial settings

[0883] Input: User enters name, age, language proficiency, and learning objectives into the device

[0884] Data processing: The device formats the user's input information and sends it to the server

[0885] Output: The server generates and stores the user profile in the database.

[0886] Specific operation: The user enters "Name: Yamada Taro, Age: 30, Language Proficiency: Beginner English, Learning Objective: Travel", and the device sends this to the server and stores it.

[0887] Step 2:

[0888] Initial evaluation

[0889] Input: The server generates an initial assessment test based on the user profile.

[0890] Data processing: The server sends the initial evaluation test to the device.

[0891] Output: Device displays test to user, user enters result, device sends result to server

[0892] Specific operation: The server generates "vocabulary tests" and "grammar tests," and the device displays them to the user. The user enters answers, and the device sends the results to the server.

[0893] Step 3:

[0894] Generate personalized learning paths

[0895] Input: Initial evaluation test results from the server

[0896] Data processing: The server analyzes and generates an individual learning path

[0897] Output: The server sends the generated learning pass to the device.

[0898] Specific operation: The server generates a learning path for beginner grammar acquisition, sends the content to the terminal, and notifies the user.

[0899] Step 4:

[0900] Emotion recognition with emotion engine

[0901] Input: The device captures the user's facial expressions and tone of voice using the camera and microphone.

[0902] Data processing: The device analyzes data with an emotion engine to identify the emotional state

[0903] Output: The device sends the analysis results to the server.

[0904] Specific operation: While the device is learning, it analyzes the user's facial expressions and tone of voice in real time, identifies their "stress" and "concentration" states, and sends the results to the server.

[0905] Step 5:

[0906] Providing learning content

[0907] Input: The server receives the user's learning path and emotion data.

[0908] Data processing: The server selects appropriate learning content

[0909] Output: Send selected learning content to the device

[0910] Specific operation: The server selects "pronunciation practice videos" and "listening quizzes," sends them to the device, and presents them to the user.

[0911] Step 6:

[0912] Track your learning progress

[0913] Input: Users consume learning content and record their progress

[0914] Data processing: The device sends progress data (study time, number of correct answers, etc.) to the server

[0915] Output: The server receives and stores the progress data.

[0916] Specific operation: The user studies words for 30 minutes, and the device records the study time and accuracy rate and sends the results to the server.

[0917] Step 7:

[0918] Progress data analysis and optimization

[0919] Input: Server receives progress and emotion data

[0920] Data processing: The server analyzes and optimizes the learning path and next content as needed.

[0921] Output: Send new learning paths and content to your device

[0922] Specific behavior: If the user is struggling with a grammar problem and feeling stressed, the server sends a new learning path to the device, inserting relaxation time.

[0923] Step 8:

[0924] Feedback and System Tuning

[0925] Input: Users provide feedback about their learning experience

[0926] Data processing: The device sends feedback to the server, which analyzes it.

[0927] Output: The server adjusts the entire system and content based on the analysis results.

[0928] Specific operation: The user gives feedback such as "The pronunciation practice video was difficult," and the server uses that feedback to simplify the video content.

[0929] (Application example 2)

[0930] 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."

[0931] Conventional online learning systems and e-readers often provide uniform content without considering the user's emotional state, resulting in a failure to optimize the user's comprehension and interest. Furthermore, ignoring emotions such as stress and excitement felt by users while learning or reading can lead to a decline in learning effectiveness and reading experience. Such systems make it difficult to maintain users' motivation to continue learning, resulting in poor learning outcomes and poor reading satisfaction.

[0932] 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 a user to input and register their name, age, language proficiency, and learning purpose, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, means for the terminal to collect the user's test results and send them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the generated learning path and send it to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, means for the server to recognize the user's emotional state using an emotion engine and optimize the reading experience, means for the emotion engine to analyze the user's facial expressions, voice, and input data, and means for dynamically selecting and displaying appropriate content based on the recognized emotional state. This allows for an optimized learning or reading experience based on the user's emotional state.

[0933] A "user" is an individual who uses a system or application.

[0934] "Name, age, language proficiency, learning objectives" refers to personal and learning details provided by users when they register.

[0935] A "server" is a computer on a network that stores and processes data.

[0936] A "user profile" is a data structure that is generated based on personal information provided by a user.

[0937] An "initial assessment test" is a test administered to assess a user's current level of knowledge.

[0938] "Terminal" means the device through which a user accesses the system.

[0939] "Test Results" means the answers provided by a user to an initial assessment test.

[0940] A "personalized learning path" is a customized learning plan based on the user's assessment results.

[0941] "Learning content" refers to the learning materials and information provided to users to help them advance their studies.

[0942] "Study progress" refers to data that indicates how far a user has progressed in their studies.

[0943] An "emotion engine" is software that recognizes a user's emotional state.

[0944] "Facial expressions, voice, and input data" refers to the data that the emotion engine uses to analyze the user's emotions.

[0945] A "recognized emotional state" is a user's emotion as identified by the emotion engine.

[0946] "Appropriate content" refers to content selected based on the user's emotional state.

[0947] A "displaying means" is a method or device for showing content to a user.

[0948] The present invention relates to a system that recognizes a user's emotional state and optimizes the learning or reading experience. This system functions through the interaction of a server, a terminal, a user, and an emotion engine. Specific embodiments for implementing this system are described below.

[0949] User registration and initial settings

[0950] Users first register by entering personal information such as their name, age, language proficiency, and learning goals into the system. The terminal then sends this information to the server, which then creates a user profile based on that information.

[0951] Initial evaluation

[0952] The server generates an initial assessment test based on the user profile to evaluate the user's current knowledge level and sends it to the device. The user takes the test through the device and sends the results to the server. The server analyzes the results and generates a personalized learning path for the user.

[0953] Learning paths and content offerings

[0954] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device presents the content to the user, who then uses it to advance their learning. Learning progress is recorded on the device and periodically sent to the server.

[0955] Emotion recognition by emotion engine

[0956] While studying or reading, the emotion engine analyzes the user's facial expressions, voice, and input data to identify their emotional state. If the user is feeling stressed, the emotion engine notifies the server, which then takes appropriate action.

[0957] Content Optimization

[0958] The server analyzes the emotion data and progress data obtained from the emotion engine to optimize the user's learning path and content. For example, if a user is feeling stressed, it will suggest chapters with relaxing content.

[0959] Specific examples of reading experiences

[0960] If a user is reading a thriller novel and the emotion engine identifies that they are feeling stressed, the server can offer another relaxing chapter, allowing the user to continue enjoying the book.

[0961] Hardware and software used

[0962] Hardware: Smartphone, tablet, or PC with webcam

[0963] software:

[0964] EmotionRecognition: A library for analyzing emotions from user facial expressions and voice (e.g., OpenCV)

[0965] EbookProvider: Backend service that provides e-books and coordinates content

[0966] Prompt Sentence Examples

[0967] "How do you adjust your content if users are stressed?"

[0968] "If a user is excited, what kind of interesting content do you recommend?"

[0969] In this way, a learning and reading experience can be provided that is individually optimized based on the user's emotional state.

[0970] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0971] Step 1:

[0972] The user enters personal information such as name, age, language proficiency, and learning goals, and sends it from the device to the server. Based on the entered information, the server generates a user profile. Specifically, the data entered by the user is sent to the server and saved in JSON format on the server side.

[0973] Step 2:

[0974] The server creates an initial evaluation test based on the generated user profile and sends it to the device. Specifically, the server's AI model analyzes the user profile, selects appropriate questions, and sends test-format data to the device. The output is test-format data.

[0975] Step 3:

[0976] The user takes the initial evaluation test using a terminal and inputs the results. The terminal collects the test results and sends them to the server. The input data is the user's answers, and the output is the test results sent to the server.

[0977] Step 4:

[0978] The server analyzes the test results and generates an individual learning path based on that information. The input data is the test results, and the output is an individual learning path. The server's AI model analyzes the data and creates a learning curriculum tailored to the user's needs.

[0979] Step 5:

[0980] The server selects the most appropriate learning content based on the generated learning path and sends it to the device. The input data is the learning path, and the output is the learning content. Appropriate learning materials are selected from the content database.

[0981] Step 6:

[0982] The device presents the received learning content to the user. Specifically, text, audio, video, etc. are displayed through a user interface. The input data is the learning content, and the output is the display on the user interface.

[0983] Step 7:

[0984] As the user studies or reads, the device records learning progress data, including study time, number of correct answers, questions missed, etc. The input data is the user's learning actions, and the output is progress data.

[0985] Step 8:

[0986] The server periodically receives and analyzes progress data sent from the device. The input data is the progress data, and the output is the analysis results. The server optimizes the learning path and content provided based on the analysis results.

[0987] Step 9:

[0988] While studying or reading, the emotion engine analyzes the user's facial expressions and voice to identify emotions. The input data is facial expressions and voice captured in real time, and the output is the recognized emotional state.

[0989] Step 10:

[0990] The server receives the emotional state from the emotion engine, dynamically selects the most appropriate content, and sends it to the device. The input data is the recognized emotional state, and the output is the adjusted content. If it recognizes that the user is feeling stressed, it selects relaxing content.

[0991] 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.

[0992] 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.

[0993] 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.

[0994] [Third embodiment]

[0995] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0996] 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.

[0997] 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).

[0998] 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.

[0999] 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.

[1000] 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).

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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."

[1007] The present invention relates to an online language learning system for providing and optimizing content related to language learning. The system functions through the interaction of a server, a terminal, and a user. The following describes in detail an embodiment of the system.

[1008] User registration and initial settings

[1009] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[1010] Initial evaluation

[1011] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[1012] Generate personalized learning paths

[1013] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1014] Providing learning content

[1015] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[1016] Track your learning progress

[1017] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1018] Progress data analysis and optimization

[1019] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with grammar questions, the server will adjust the next learning session to provide more grammar practice.

[1020] Feedback and System Tuning

[1021] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1022] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[1023] The processing flow will be explained below.

[1024] Step 1:

[1025] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[1026] Step 2:

[1027] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[1028] Step 3:

[1029] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[1030] Step 4:

[1031] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[1032] Step 5:

[1033] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[1034] Step 6:

[1035] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[1036] Step 7:

[1037] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[1038] Step 8:

[1039] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[1040] Step 9:

[1041] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[1042] Step 10:

[1043] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[1044] Step 11:

[1045] The server analyzes the received progress data, evaluates the user's learning effectiveness, and optimizes the learning path and the next content provided, if necessary.

[1046] Step 12:

[1047] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[1048] Step 13:

[1049] The server analyzes user feedback to identify areas for improvement in the overall system and the content it provides, and makes any necessary adjustments to further optimize the user's learning experience.

[1050] Through these steps, users can effectively progress through language learning at their own pace.

[1051] Example 1

[1052] 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."

[1053] Conventional online language learning systems have difficulty effectively collecting individual users' learning progress and feedback and optimizing learning paths and content based on that information. Furthermore, they have been unable to provide optimal content tailored to users' learning goals and proficiency levels, which can lead to a decline in learning effectiveness.

[1054] 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.

[1055] In this invention, the server includes: means for a user to input and register personal information, age, language ability, and learning goals; means for the server to receive the input information and generate a user profile; means for the server to generate an evaluation test based on the user profile and send it to the terminal; means for the terminal to collect the user's test results and send them to the server; means for the server to analyze the test results and generate an individual learning path; means for the server to select learning content based on the generated learning path and send it to the terminal; means for the terminal to present the received learning content to the user; means for the terminal to record the user's learning progress and send it to the server; means for the server to analyze the progress data and optimize the learning path and content; means for the user to provide feedback on the learning content and the server to analyze the feedback and adjust the entire system; and means for optimizing the learning path and content using a generative AI model. This enables fast and effective optimization based on the user's individual learning progress and feedback.

[1056] "User" refers to an individual who uses the online language learning system to learn a language.

[1057] "Server" refers to a computer system that receives, processes, and analyzes data input by a user.

[1058] A "terminal" is a device operated by a user, and refers to an apparatus for receiving information from a server and providing it to the user.

[1059] "Profile" refers to information that compiles data such as a user's personal information, age, language ability, and learning goals.

[1060] "Initial Assessment Test" refers to a server-generated test to assess a user's current language proficiency.

[1061] "Generative AI Model" refers to the artificial intelligence model used by the server to optimize a user's learning path or content.

[1062] "Progress data" is data that indicates the user's learning progress, and includes the study time, the number of questions answered correctly, questions answered incorrectly, and so on.

[1063] "Learning path" refers to an individual learning plan generated by the server based on the user's proficiency and learning goals.

[1064] "Feedback" refers to the opinions and impressions a user provides regarding their learning experience.

[1065] "Optimization" refers to the process by which the server adjusts the learning path and content to maximize the user's learning effectiveness.

[1066] The present invention relates to an online language learning system for personalizing and optimizing content related to language learning, which functions through the interaction of a server, a terminal, and a user.

[1067] User registration and initial settings

[1068] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel goals," the server stores this information in the profile. Specifically, the server stores user information in a database using a SQL database or similar.

[1069] Initial evaluation

[1070] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server. The server evaluates the user's current ability based on the results.

[1071] Generate personalized learning paths

[1072] The server generates an individual learning path based on the user's initial evaluation results. This learning path is optimized for the user's proficiency level and learning goals, and its content includes daily learning content and goals. Specifically, a generative AI model is used to design an optimal curriculum for the user. For example, if a learning path for "learning basic grammar" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1073] Providing learning content

[1074] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, a pronunciation guide video followed by a quiz will be displayed on the device.

[1075] Track your learning progress

[1076] As a user studies using the designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1077] Progress data analysis and optimization

[1078] The server analyzes the received progress data and evaluates the user's learning effectiveness. The analysis involves reading data from an SQL database and analyzing it using Python or other tools. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is "struggling with grammar questions," the server will adjust the next learning session to provide more grammar practice.

[1079] Feedback and System Tuning

[1080] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements or new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1081] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[1082] Examples and prompts

[1083] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[1084] example:

[1085] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[1086] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[1087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1088] Step 1:

[1089] The user registers by entering personal information, age, language ability, and learning goals. The user enters this information using a terminal and presses the registration button. The entered data is sent from the terminal to the server.

[1090] Input: User's personal information, age, language ability, learning goals

[1091] Output: Send data to the server

[1092] Specific behavior:

[1093] When a user enters the required information into the web form on their device and presses the registration button, the data in the form is sent to the server in JSON format.

[1094] Step 2:

[1095] The server receives the entered information and generates a user profile. The server analyzes the received data, creates the profile, and stores it in a database.

[1096] Input: Personal data from the user

[1097] Output: User profile stored in the database

[1098] Specific behavior:

[1099] The server stores the received data in an SQL database and generates user profile information.

[1100] Step 3:

[1101] The server generates an initial assessment test based on the user profile and sends it to the device, where it uses a generative AI model to design the optimal test.

[1102] Input: User Profile

[1103] Output: Initial evaluation test sent to user device

[1104] Specific behavior:

[1105] The server's generative AI model creates an initial evaluation test based on the user's level of proficiency and sends it to the device.

[1106] Step 4:

[1107] The terminal presents the initial evaluation test to the user, who then takes the test. The user answers questions on the test screen and completes the test.

[1108] Input: Initial evaluation test sent from the server

[1109] Output: User test answer data

[1110] Specific behavior:

[1111] The terminal displays test questions to the user and receives the user's answers.

[1112] Step 5:

[1113] The device sends the user's test results to the server. The device collects the user's answers and sends them to the server.

[1114] Input: User test answer data

[1115] Output: Test results sent to the server

[1116] Specific behavior:

[1117] The device sends the user's response data in JSON format to the server.

[1118] Step 6:

[1119] The server analyzes the test results and generates an individual learning path, which is then analyzed using a generative AI model to design the optimal learning path.

[1120] Input: User test result data

[1121] Output: Individually optimized learning paths

[1122] Specific behavior:

[1123] The server analyzes the test results and uses a generative AI model to generate a learning path for each user.

[1124] Step 7:

[1125] The server selects learning content based on the generated learning path and sends it to the device. The server then selects appropriate learning resources (videos, quizzes, etc.) and sends them to the device.

[1126] Input: User's learning path

[1127] Output: Learning content sent to the user's device

[1128] Specific behavior:

[1129] The server selects the most appropriate content (video, audio, quiz) based on the user's learning path and sends it to the device.

[1130] Step 8:

[1131] The device presents the received study content to the user, who then uses this content to advance their studies.

[1132] Input: Learning content sent from the server

[1133] Output: The learning content that is displayed to the user

[1134] Specific behavior:

[1135] The terminal presents the received learning content to the user visually or audibly.

[1136] Step 9:

[1137] The device records the user's learning progress and sends it to the server. The device collects progress data such as study time, number of correct answers, and mistakes, and periodically sends it to the server.

[1138] Input: User's learning progress data

[1139] Output: Progress data sent to the server

[1140] Specific behavior:

[1141] The device transmits progress data collected during the learning activity to a server.

[1142] Step 10:

[1143] The server analyzes the progress data and optimizes the learning path and content. The server analyzes the progress data and determines the next learning content to be provided.

[1144] Input: User progress data

[1145] Output: Optimized next learning path and content

[1146] Specific behavior:

[1147] The server analyzes the progress data and uses a generative AI model to determine the next optimal learning content to provide.

[1148] Step 11:

[1149] Users provide feedback about their learning experience, and the server analyzes that feedback and adjusts the entire system. Users input feedback through their devices, and the server receives and analyzes it.

[1150] Input: User feedback

[1151] Output: System tuning and improvement

[1152] Specific behavior:

[1153] The server analyzes user feedback, identifies areas for improvement in the system, and makes appropriate adjustments.

[1154] Examples and prompts

[1155] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[1156] example:

[1157] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[1158] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[1159] (Application example 1)

[1160] 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."

[1161] In factories and other workplaces, where multinational workers often work, language differences can affect communication and safety. In such environments, workers need to be able to quickly and accurately understand work instructions and safety rules, but conventional methods have difficulty effectively resolving this issue. While there is a need for systems that can individually optimize and efficiently advance language learning, such systems are not widely available in reality. Therefore, there is a need for a language learning support system that can help multinational workers in factories overcome language barriers and communicate efficiently.

[1162] 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.

[1163] In this invention, the server includes a means for a user to input and register their name, age, language proficiency, and learning objectives, a means for the server to receive the input information and generate a user profile, and a means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, thereby enabling the server to generate an individual learning path and provide appropriate learning content.

[1164] In addition, in this invention, the server includes a means for the terminal to collect the user's test results and send them to the server, a means for the server to analyze the test results and generate an individual learning path, and a means for the server to select learning content based on the learning path generated and send it to the terminal, thereby making it possible to grasp the user's learning progress in real time and provide optimal content.

[1165] Furthermore, in this invention, the server includes means for presenting the learning content received by the terminal to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, and means for using smart devices to support learning of instructions and safety rules in multiple languages, thereby enabling multinational workers working in a factory to overcome language barriers, communicate efficiently, and work safely.

[1166] "User" refers to an individual person who uses the System.

[1167] "Name" refers to an individual notation for identifying a user.

[1168] "Age" refers to the user's age number.

[1169] "Language proficiency" refers to the user's current level of proficiency in a language.

[1170] "Learning objective" refers to a specific goal for a user to learn a language.

[1171] "Means for registering" refers to the process by which a user registers with the system by entering their name, age, language proficiency, and learning objectives.

[1172] "Server" refers to a computer system that receives and processes user input information.

[1173] "User profile" refers to a collection of data generated based on information entered about a user.

[1174] "Initial Assessment Test" refers to a test administered to assess a user's current language proficiency.

[1175] "Terminal" refers to the device on which a user takes an initial assessment test or accesses learning content.

[1176] "Test results" refers to the response data after a user has taken an initial evaluation test.

[1177] "Personalized Learning Path" refers to a customized learning plan generated to optimize a user's learning progress.

[1178] "Learning Content" refers to educational materials provided to users to further their learning.

[1179] "Study progress" refers to how far a user has progressed through the learning content.

[1180] "Progress data" refers to specific data regarding a user's learning progress.

[1181] "Feedback" refers to the opinions and reactions users provide about learning content.

[1182] "System-wide tuning" refers to the process by which the server improves the system based on user feedback.

[1183] "Smart devices" refer to wearable or portable devices that have computing functionality.

[1184] "Multilingual Instructions" refers to work instructions provided in multiple languages.

[1185] "Safety rules" refer to regulations to ensure safety at the workplace.

[1186] MODE FOR CARRYING OUT THE INVENTION

[1187] The present invention provides a language learning support system that enables multinational workers to overcome language barriers, communicate efficiently, and work safely. A specific embodiment of this system will be described below.

[1188] User registration and initial settings

[1189] First, a user registers in the system using a smart device (e.g., smart glasses) by inputting their name, age, language proficiency, and learning goals. This user information is then sent from the smart device to the server, which then creates a user profile based on the received information.

[1190] Initial evaluation

[1191] After the user profile is generated, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the smart device. The user takes the test through the smart device and sends the results to the server. The server analyzes the received test results and generates an individual learning path that is optimized for the user's proficiency and learning goals.

[1192] Providing learning content

[1193] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device. The smart device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a factory worker receives content on "understanding work instructions," they will be provided with videos and quizzes on specific work instructions and safety rules.

[1194] Track your learning progress

[1195] As a user progresses through the learning process using the designated learning content, the smart device records the progress. The recorded data includes the study time, number of correct answers, and questions answered incorrectly. The smart device periodically sends this progress data to the server.

[1196] Progress data analysis and optimization

[1197] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with a particular aspect of understanding factory safety rules, the server will adjust the next learning session to provide more practice on safety rules.

[1198] Feedback and System Tuning

[1199] Users can provide feedback about their learning experience via their smart devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the work instruction videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1200] Examples of specific examples and prompts

[1201] "Factory workers registered their name, age, language proficiency, and learning objectives through the smart glasses, and then took an initial assessment test. Based on the test results, they were provided with videos and quizzes on safety rules within the factory. The user's progress data was sent to the server for analysis. The next learning content was then optimized based on the analysis results."

[1202] Example prompts to input to a generative AI model:

[1203] Please complete the registration based on the information you provided during user registration.

[1204] Name: Taro Tanaka

[1205] Age: 35

[1206] Language Proficiency: Beginner

[1207] Learning Objective: Understand factory work instructions

[1208] This allows users to receive language learning support to carry out their work safely and efficiently within the factory.

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] Users register by using a smart device to enter their name, age, language proficiency level, and learning purpose.

[1212] (Input) Name, age, language proficiency, learning purpose

[1213] (Processing) Send the entered data to the server

[1214] (Output) User information is stored on the server

[1215] Step 2:

[1216] The server generates a user profile based on the received user information.

[1217] (Input) User information (name, age, language proficiency, learning purpose)

[1218] (Processing) Store user information in a database and generate a profile

[1219] (Output) Generate user profile

[1220] Step 3:

[1221] The server generates an initial assessment test based on the user profile and sends it to the smart device.

[1222] (Input) User profile

[1223] (Processing) Generate appropriate test questions based on evaluation logic

[1224] (Output) The initial evaluation test is sent to the smart device.

[1225] Step 4:

[1226] Users take an initial evaluation test via their smart device.

[1227] (Input) Initial evaluation test

[1228] (Process) User answers the test

[1229] (Output) Test answer data is generated

[1230] Step 5:

[1231] The smart device sends the user's test results to the server.

[1232] (Input) Test response data

[1233] (Processing) Sending response data

[1234] (Output) The answer data is stored on the server.

[1235] Step 6:

[1236] The server analyzes the received test results and generates a personalized learning path optimized for the user.

[1237] (Input) Test results

[1238] (Processing) Generate learning paths based on analysis logic

[1239] (Output) Generate individual learning paths

[1240] Step 7:

[1241] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device.

[1242] (Input) Individual Learning Path

[1243] (Processing) Select the most suitable learning content

[1244] (Output) Learning content is sent to the smart device

[1245] Step 8:

[1246] The smart device presents the received learning content to the user.

[1247] (Input) Learning content

[1248] (Processing) Viewing learning content

[1249] (Output) User views learning content

[1250] Step 9:

[1251] Users use the learning content to advance their studies.

[1252] (Input) Learning content

[1253] (Processing) Content Learning

[1254] (Output) Generate learning progress data

[1255] Step 10:

[1256] The smart device records the user's learning progress and sends it to the server.

[1257] (Input) Learning progress data

[1258] (Processing) Recording and sending progress data

[1259] (Output) Progress data is stored on the server.

[1260] Step 11:

[1261] The server analyzes the received progress data and optimizes the user's learning path and content.

[1262] (Input) Progress data

[1263] (Processing) Optimization of learning paths and content based on analytical logic

[1264] (Output) Optimized learning paths and content

[1265] Step 12:

[1266] Users provide feedback on their learning experience through their smart devices.

[1267] (Input) Feedback data

[1268] (Process) Entering and sending feedback

[1269] (Output) Feedback data is stored on the server

[1270] Step 13:

[1271] The server analyzes the feedback received and identifies needed improvements and new features.

[1272] (Input) Feedback data

[1273] (Processing) Feedback analysis and system-wide adjustment

[1274] (Output) Adjusted system settings

[1275] 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.

[1276] The present invention relates to an online language learning system that recognizes a user's emotional state using an emotion engine to further optimize learning paths and content. This system functions in cooperation with a server, a terminal, a user, and an emotion engine. A specific embodiment of this system will be described below.

[1277] User registration and initial settings

[1278] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[1279] Initial evaluation

[1280] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[1281] Generate personalized learning paths

[1282] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1283] Emotion recognition with emotion engine

[1284] During training, the emotion engine recognizes the user's emotional state. The emotion engine identifies the user's emotional state by analyzing the user's facial expressions, tone of voice, input data, etc. For example, it uses a camera and microphone to detect changes in facial expressions and voice while the user is watching a video.

[1285] Providing learning content

[1286] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine, and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[1287] Track your learning progress

[1288] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1289] Progress data analysis and optimization

[1290] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is "struggling with grammar questions" and also "feels stressed," the server will make adjustments such as inserting a break to allow the user to relax in the next lesson and starting with easier questions.

[1291] Feedback and System Tuning

[1292] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server will make adjustments, such as simplifying the content.

[1293] As described above, by combining an emotion engine, the present invention provides a personalized learning experience that takes into account the user's emotional state. Through analysis of emotion data and learning progress data, it is possible to optimize learning content and maintain user motivation.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[1297] Step 2:

[1298] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[1299] Step 3:

[1300] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[1301] Step 4:

[1302] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[1303] Step 5:

[1304] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[1305] Step 6:

[1306] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[1307] Step 7:

[1308] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[1309] Step 8:

[1310] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[1311] Step 9:

[1312] During training, the emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, tone of voice, and input data to identify the emotional state.

[1313] Step 10:

[1314] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[1315] Step 11:

[1316] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[1317] Step 12:

[1318] The server analyzes the received progress data, evaluates the user's learning effectiveness, and, if necessary, optimizes the learning path and the next content provided in comparison with the analysis results of the emotion engine.

[1319] Step 13:

[1320] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[1321] Step 14:

[1322] The server analyzes user feedback and also uses data from the emotion engine when identifying areas for improvement in the overall system and the content it provides.

[1323] Step 15:

[1324] The server adjusts the entire system and content based on the feedback. For example, if a user says, "The pronunciation practice video was helpful, but it was too difficult," the server will adjust the content by simplifying it.

[1325] These steps allow users to effectively progress through language learning at their own pace. Furthermore, by utilizing emotion recognition through the emotion engine, the learning experience is constantly optimized, helping to maintain user motivation.

[1326] Example 2

[1327] 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."

[1328] Conventional online language learning systems lack the ability to recognize a user's emotional state and optimize learning paths and content, making it difficult to provide personalized support to maximize learning outcomes. This is particularly true when users are stressed or lacking concentration, leading to a decline in motivation and efficiency. Furthermore, there is no established method for adjusting the system using user emotional data, in addition to learning progress.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1330] In this invention, the server includes means for a user to input and register their name, age, language proficiency, and learning objectives, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and transmit the test to the terminal, means for the terminal to collect the user's test results and transmit them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the learning path generated and transmit the selected learning content to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and transmit the recorded learning data to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the terminal to recognize the user's emotional state using an emotion engine and transmit the data to the server, means for the server to adjust the learning content based on the data from the emotion engine to provide an optimal learning experience, and means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system. This enables the system to recognize the user's emotional state in real time, optimize the learning path and content based on the user's emotional state, and maximize learning effectiveness through individualized support.

[1331] "User" refers to an individual who uses the system to learn a language.

[1332] "Server" refers to the central computing unit that receives, processes, analyzes, and generates and delivers appropriate learning content to users.

[1333] A "terminal" is a device that a user uses to interface with the system, including a computer, tablet, smartphone, etc.

[1334] An "emotion engine" refers to a software or hardware algorithm that analyzes a user's facial expressions, tone of voice, input data, etc. to recognize the user's emotional state.

[1335] "User profile" refers to a data set generated based on information provided by a user at the time of registration (such as name, age, language proficiency, and learning objectives).

[1336] "Initial assessment test" refers to a test generated by the server and taken by the user via a terminal in order to assess the user's current language proficiency.

[1337] "Individualized learning path" refers to a user-specific learning plan generated by the server based on the user's initial assessment results and learning objectives.

[1338] "Learning content" refers to information resources including learning materials, quizzes, videos, audio, etc. provided by the server to help users advance their studies.

[1339] "Study progress" refers to data that indicates the progress and results (study time, number of correct answers, questions answered incorrectly, etc.) when a user studies using learning content.

[1340] "Emotion data" refers to the emotional state of the user recognized by the emotion engine and recorded as numerical or text data.

[1341] "Feedback" refers to the opinions and thoughts that a user provides to the system regarding their learning experience.

[1342] "Optimization" refers to the act of individually adjusting a user's learning path and learning content based on progress data and emotional data collected by the server, in order to maximize learning effectiveness.

[1343] This invention relates to an online language learning system that recognizes a user's emotional state and optimizes learning paths and content based on that information. This system functions in cooperation with a server, a terminal, and an emotion engine. Detailed embodiments of this system are described below.

[1344] User registration and initial settings

[1345] A user first registers with the system. They enter information such as their name, age, language proficiency, and learning purpose, and send it from their terminal to the server. The server generates a user profile based on the received information and stores it in a database. For example, if a user enters "Name: Yamada Hanako, Age: 25, Language Proficiency: English Intermediate, Learning Purpose: Business," the server registers this in the profile.

[1346] Initial evaluation

[1347] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server. The server analyzes the results and evaluates the user's proficiency. For example, a "vocabulary test" or "grammar test" may be given.

[1348] Generate personalized learning paths

[1349] The server generates an individual learning path based on the initial evaluation results. This learning path includes daily learning content and goals. The server then sends the generated learning path to the device and notifies the user. For example, if a learning path for "Basic Grammar Acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1350] Emotion recognition with emotion engine

[1351] During learning, the emotion engine recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to identify their emotional state. For example, it uses the device's camera and microphone to detect changes in the user's facial expressions and voice and identify emotions such as "stress," "joy," and "concentration."

[1352] Providing learning content

[1353] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine. The selected content is sent to the device, which then presents it to the user. For example, if "pronunciation practice" content is provided, it will include a pronunciation guide video followed by a quiz.

[1354] Track your learning progress

[1355] As a user studies using the designated learning content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1356] Progress data analysis and optimization

[1357] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is struggling with grammar questions and is also feeling stressed, the server will insert a break for the user to relax in the next lesson and start with easier questions.

[1358] Feedback and System Tuning

[1359] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server can make adjustments, such as simplifying the content.

[1360] Prompt Sentence Examples

[1361] "Please suggest ways to optimize the following learning path to improve the user's learning experience: The user is a beginner in English and is focused on learning grammar for travel purposes."

[1362] "Please suggest ways to adjust the content to reduce the stress users feel while learning. Data from the emotion engine indicates that users are experiencing stress."

[1363] As described above, this invention combines an emotion engine to provide a personalized learning experience that takes into account the user's emotional state. Through analysis of learning progress data and emotion data, it is possible to optimize learning content and maintain user motivation.

[1364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1365] Specific flow of program processing

[1366] Step 1:

[1367] User registration and initial settings

[1368] Input: User enters name, age, language proficiency, and learning objectives into the device

[1369] Data processing: The device formats the user's input information and sends it to the server

[1370] Output: The server generates and stores the user profile in the database.

[1371] Specific operation: The user enters "Name: Yamada Taro, Age: 30, Language Proficiency: Beginner English, Learning Objective: Travel", and the device sends this to the server and stores it.

[1372] Step 2:

[1373] Initial evaluation

[1374] Input: The server generates an initial assessment test based on the user profile.

[1375] Data processing: The server sends the initial evaluation test to the device.

[1376] Output: Device displays test to user, user enters result, device sends result to server

[1377] Specific operation: The server generates "vocabulary tests" and "grammar tests," and the device displays them to the user. The user enters answers, and the device sends the results to the server.

[1378] Step 3:

[1379] Generate personalized learning paths

[1380] Input: Initial evaluation test results from the server

[1381] Data processing: The server analyzes and generates an individual learning path

[1382] Output: The server sends the generated learning pass to the device.

[1383] Specific operation: The server generates a learning path for beginner grammar acquisition, sends the content to the terminal, and notifies the user.

[1384] Step 4:

[1385] Emotion recognition with emotion engine

[1386] Input: The device captures the user's facial expressions and tone of voice using the camera and microphone.

[1387] Data processing: The device analyzes data with an emotion engine to identify the emotional state

[1388] Output: The device sends the analysis results to the server.

[1389] Specific operation: While the device is learning, it analyzes the user's facial expressions and tone of voice in real time, identifies their "stress" and "concentration" states, and sends the results to the server.

[1390] Step 5:

[1391] Providing learning content

[1392] Input: The server receives the user's learning path and emotion data.

[1393] Data processing: The server selects appropriate learning content

[1394] Output: Send selected learning content to the device

[1395] Specific operation: The server selects "pronunciation practice videos" and "listening quizzes," sends them to the device, and presents them to the user.

[1396] Step 6:

[1397] Track your learning progress

[1398] Input: Users consume learning content and record their progress

[1399] Data processing: The device sends progress data (study time, number of correct answers, etc.) to the server

[1400] Output: The server receives and stores the progress data.

[1401] Specific operation: The user studies words for 30 minutes, and the device records the study time and accuracy rate and sends the results to the server.

[1402] Step 7:

[1403] Progress data analysis and optimization

[1404] Input: Server receives progress and emotion data

[1405] Data processing: The server analyzes and optimizes the learning path and next content as needed.

[1406] Output: Send new learning paths and content to your device

[1407] Specific behavior: If the user is struggling with a grammar problem and feeling stressed, the server sends a new learning path to the device, inserting relaxation time.

[1408] Step 8:

[1409] Feedback and System Tuning

[1410] Input: Users provide feedback about their learning experience

[1411] Data processing: The device sends feedback to the server, which analyzes it.

[1412] Output: The server adjusts the entire system and content based on the analysis results.

[1413] Specific operation: The user gives feedback such as "The pronunciation practice video was difficult," and the server uses that feedback to simplify the video content.

[1414] (Application example 2)

[1415] 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."

[1416] Conventional online learning systems and e-readers often provide uniform content without considering the user's emotional state, resulting in a failure to optimize the user's comprehension and interest. Furthermore, ignoring emotions such as stress and excitement felt by users while learning or reading can lead to a decline in learning effectiveness and reading experience. Such systems make it difficult to maintain users' motivation to continue learning, resulting in poor learning outcomes and poor reading satisfaction.

[1417] 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 a user to input and register their name, age, language proficiency, and learning purpose, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, means for the terminal to collect the user's test results and send them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the generated learning path and send it to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, means for the server to recognize the user's emotional state using an emotion engine and optimize the reading experience, means for the emotion engine to analyze the user's facial expressions, voice, and input data, and means for dynamically selecting and displaying appropriate content based on the recognized emotional state. This allows for an optimized learning or reading experience based on the user's emotional state.

[1418] A "user" is an individual who uses a system or application.

[1419] "Name, age, language proficiency, learning objectives" refers to personal and learning details provided by users when they register.

[1420] A "server" is a computer on a network that stores and processes data.

[1421] A "user profile" is a data structure that is generated based on personal information provided by a user.

[1422] An "initial assessment test" is a test administered to assess a user's current level of knowledge.

[1423] "Terminal" means the device through which a user accesses the system.

[1424] "Test Results" means the answers provided by a user to an initial assessment test.

[1425] A "personalized learning path" is a customized learning plan based on the user's assessment results.

[1426] "Learning content" refers to the learning materials and information provided to users to help them advance their studies.

[1427] "Study progress" refers to data that indicates how far a user has progressed in their studies.

[1428] An "emotion engine" is software that recognizes a user's emotional state.

[1429] "Facial expressions, voice, and input data" refers to the data that the emotion engine uses to analyze the user's emotions.

[1430] A "recognized emotional state" is a user's emotion as identified by the emotion engine.

[1431] "Appropriate content" refers to content selected based on the user's emotional state.

[1432] A "displaying means" is a method or device for showing content to a user.

[1433] The present invention relates to a system that recognizes a user's emotional state and optimizes the learning or reading experience. This system functions through the interaction of a server, a terminal, a user, and an emotion engine. Specific embodiments for implementing this system are described below.

[1434] User registration and initial settings

[1435] Users first register by entering personal information such as their name, age, language proficiency, and learning goals into the system. The terminal then sends this information to the server, which then creates a user profile based on that information.

[1436] Initial evaluation

[1437] The server generates an initial assessment test based on the user profile to evaluate the user's current knowledge level and sends it to the device. The user takes the test through the device and sends the results to the server. The server analyzes the results and generates a personalized learning path for the user.

[1438] Learning paths and content offerings

[1439] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device presents the content to the user, who then uses it to advance their learning. Learning progress is recorded on the device and periodically sent to the server.

[1440] Emotion recognition by emotion engine

[1441] While studying or reading, the emotion engine analyzes the user's facial expressions, voice, and input data to identify their emotional state. If the user is feeling stressed, the emotion engine notifies the server, which then takes appropriate action.

[1442] Content Optimization

[1443] The server analyzes the emotion data and progress data obtained from the emotion engine to optimize the user's learning path and content. For example, if a user is feeling stressed, it will suggest chapters with relaxing content.

[1444] Specific examples of reading experiences

[1445] If a user is reading a thriller novel and the emotion engine identifies that they are feeling stressed, the server can offer another relaxing chapter, allowing the user to continue enjoying the book.

[1446] Hardware and software used

[1447] Hardware: Smartphone, tablet, or PC with webcam

[1448] software:

[1449] EmotionRecognition: A library for analyzing emotions from user facial expressions and voice (e.g., OpenCV)

[1450] EbookProvider: Backend service that provides e-books and coordinates content

[1451] Prompt Sentence Examples

[1452] "How do you adjust your content if users are stressed?"

[1453] "If a user is excited, what kind of interesting content do you recommend?"

[1454] In this way, a learning and reading experience can be provided that is individually optimized based on the user's emotional state.

[1455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1456] Step 1:

[1457] The user enters personal information such as name, age, language proficiency, and learning goals, and sends it from the device to the server. Based on the entered information, the server generates a user profile. Specifically, the data entered by the user is sent to the server and saved in JSON format on the server side.

[1458] Step 2:

[1459] The server creates an initial evaluation test based on the generated user profile and sends it to the device. Specifically, the server's AI model analyzes the user profile, selects appropriate questions, and sends test-format data to the device. The output is test-format data.

[1460] Step 3:

[1461] The user takes the initial evaluation test using a terminal and inputs the results. The terminal collects the test results and sends them to the server. The input data is the user's answers, and the output is the test results sent to the server.

[1462] Step 4:

[1463] The server analyzes the test results and generates an individual learning path based on that information. The input data is the test results, and the output is an individual learning path. The server's AI model analyzes the data and creates a learning curriculum tailored to the user's needs.

[1464] Step 5:

[1465] The server selects the most appropriate learning content based on the generated learning path and sends it to the device. The input data is the learning path, and the output is the learning content. Appropriate learning materials are selected from the content database.

[1466] Step 6:

[1467] The device presents the received learning content to the user. Specifically, text, audio, video, etc. are displayed through a user interface. The input data is the learning content, and the output is the display on the user interface.

[1468] Step 7:

[1469] As the user studies or reads, the device records learning progress data, including study time, number of correct answers, questions missed, etc. The input data is the user's learning actions, and the output is progress data.

[1470] Step 8:

[1471] The server periodically receives and analyzes progress data sent from the device. The input data is the progress data, and the output is the analysis results. The server optimizes the learning path and content provided based on the analysis results.

[1472] Step 9:

[1473] While studying or reading, the emotion engine analyzes the user's facial expressions and voice to identify emotions. The input data is facial expressions and voice captured in real time, and the output is the recognized emotional state.

[1474] Step 10:

[1475] The server receives the emotional state from the emotion engine, dynamically selects the most appropriate content, and sends it to the device. The input data is the recognized emotional state, and the output is the adjusted content. If it recognizes that the user is feeling stressed, it selects relaxing content.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] [Fourth embodiment]

[1480] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1481] 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.

[1482] 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).

[1483] 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.

[1484] 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.

[1485] 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).

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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."

[1493] The present invention relates to an online language learning system for providing and optimizing content related to language learning. The system functions through the interaction of a server, a terminal, and a user. The following describes in detail an embodiment of the system.

[1494] User registration and initial settings

[1495] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[1496] Initial evaluation

[1497] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[1498] Generate personalized learning paths

[1499] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1500] Providing learning content

[1501] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[1502] Track your learning progress

[1503] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1504] Progress data analysis and optimization

[1505] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with grammar questions, the server will adjust the next learning session to provide more grammar practice.

[1506] Feedback and System Tuning

[1507] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1508] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[1512] Step 2:

[1513] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[1514] Step 3:

[1515] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[1516] Step 4:

[1517] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[1518] Step 5:

[1519] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[1520] Step 6:

[1521] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[1522] Step 7:

[1523] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[1524] Step 8:

[1525] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[1526] Step 9:

[1527] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[1528] Step 10:

[1529] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[1530] Step 11:

[1531] The server analyzes the received progress data, evaluates the user's learning effectiveness, and optimizes the learning path and the next content provided, if necessary.

[1532] Step 12:

[1533] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[1534] Step 13:

[1535] The server analyzes user feedback to identify areas for improvement in the overall system and the content it provides, and makes any necessary adjustments to further optimize the user's learning experience.

[1536] Through these steps, users can effectively progress through language learning at their own pace.

[1537] Example 1

[1538] 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."

[1539] Conventional online language learning systems have difficulty effectively collecting individual users' learning progress and feedback and optimizing learning paths and content based on that information. Furthermore, they have been unable to provide optimal content tailored to users' learning goals and proficiency levels, which can lead to a decline in learning effectiveness.

[1540] 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.

[1541] In this invention, the server includes: means for a user to input and register personal information, age, language ability, and learning goals; means for the server to receive the input information and generate a user profile; means for the server to generate an evaluation test based on the user profile and send it to the terminal; means for the terminal to collect the user's test results and send them to the server; means for the server to analyze the test results and generate an individual learning path; means for the server to select learning content based on the generated learning path and send it to the terminal; means for the terminal to present the received learning content to the user; means for the terminal to record the user's learning progress and send it to the server; means for the server to analyze the progress data and optimize the learning path and content; means for the user to provide feedback on the learning content and the server to analyze the feedback and adjust the entire system; and means for optimizing the learning path and content using a generative AI model. This enables fast and effective optimization based on the user's individual learning progress and feedback.

[1542] "User" refers to an individual who uses the online language learning system to learn a language.

[1543] "Server" refers to a computer system that receives, processes, and analyzes data input by a user.

[1544] A "terminal" is a device operated by a user, and refers to an apparatus for receiving information from a server and providing it to the user.

[1545] "Profile" refers to information that compiles data such as a user's personal information, age, language ability, and learning goals.

[1546] "Initial Assessment Test" refers to a server-generated test to assess a user's current language proficiency.

[1547] "Generative AI Model" refers to the artificial intelligence model used by the server to optimize a user's learning path or content.

[1548] "Progress data" is data that indicates the user's learning progress, and includes the study time, the number of questions answered correctly, questions answered incorrectly, and so on.

[1549] "Learning path" refers to an individual learning plan generated by the server based on the user's proficiency and learning goals.

[1550] "Feedback" refers to the opinions and impressions a user provides regarding their learning experience.

[1551] "Optimization" refers to the process by which the server adjusts the learning path and content to maximize the user's learning effectiveness.

[1552] The present invention relates to an online language learning system for personalizing and optimizing content related to language learning, which functions through the interaction of a server, a terminal, and a user.

[1553] User registration and initial settings

[1554] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel goals," the server stores this information in the profile. Specifically, the server stores user information in a database using a SQL database or similar.

[1555] Initial evaluation

[1556] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server. The server evaluates the user's current ability based on the results.

[1557] Generate personalized learning paths

[1558] The server generates an individual learning path based on the user's initial evaluation results. This learning path is optimized for the user's proficiency level and learning goals, and its content includes daily learning content and goals. Specifically, a generative AI model is used to design an optimal curriculum for the user. For example, if a learning path for "learning basic grammar" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1559] Providing learning content

[1560] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, a pronunciation guide video followed by a quiz will be displayed on the device.

[1561] Track your learning progress

[1562] As a user studies using the designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1563] Progress data analysis and optimization

[1564] The server analyzes the received progress data and evaluates the user's learning effectiveness. The analysis involves reading data from an SQL database and analyzing it using Python or other tools. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is "struggling with grammar questions," the server will adjust the next learning session to provide more grammar practice.

[1565] Feedback and System Tuning

[1566] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and identifies necessary improvements or new features. For example, if a user provides feedback that "the pronunciation practice videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1567] Through this process, users can effectively learn languages ​​at their own pace. The system is constantly optimized based on users' progress data and feedback, increasing the effectiveness and motivation of their learning.

[1568] Examples and prompts

[1569] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[1570] example:

[1571] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[1572] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[1573] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1574] Step 1:

[1575] The user registers by entering personal information, age, language ability, and learning goals. The user enters this information using a terminal and presses the registration button. The entered data is sent from the terminal to the server.

[1576] Input: User's personal information, age, language ability, learning goals

[1577] Output: Send data to the server

[1578] Specific behavior:

[1579] When a user enters the required information into the web form on their device and presses the registration button, the data in the form is sent to the server in JSON format.

[1580] Step 2:

[1581] The server receives the entered information and generates a user profile. The server analyzes the received data, creates the profile, and stores it in a database.

[1582] Input: Personal data from the user

[1583] Output: User profile stored in the database

[1584] Specific behavior:

[1585] The server stores the received data in an SQL database and generates user profile information.

[1586] Step 3:

[1587] The server generates an initial assessment test based on the user profile and sends it to the device, where it uses a generative AI model to design the optimal test.

[1588] Input: User Profile

[1589] Output: Initial evaluation test sent to user device

[1590] Specific behavior:

[1591] The server's generative AI model creates an initial evaluation test based on the user's level of proficiency and sends it to the device.

[1592] Step 4:

[1593] The terminal presents the initial evaluation test to the user, who then takes the test. The user answers questions on the test screen and completes the test.

[1594] Input: Initial evaluation test sent from the server

[1595] Output: User test answer data

[1596] Specific behavior:

[1597] The terminal displays test questions to the user and receives the user's answers.

[1598] Step 5:

[1599] The device sends the user's test results to the server. The device collects the user's answers and sends them to the server.

[1600] Input: User test answer data

[1601] Output: Test results sent to the server

[1602] Specific behavior:

[1603] The device sends the user's response data in JSON format to the server.

[1604] Step 6:

[1605] The server analyzes the test results and generates an individual learning path, which is then analyzed using a generative AI model to design the optimal learning path.

[1606] Input: User test result data

[1607] Output: Individually optimized learning paths

[1608] Specific behavior:

[1609] The server analyzes the test results and uses a generative AI model to generate a learning path for each user.

[1610] Step 7:

[1611] The server selects learning content based on the generated learning path and sends it to the device. The server then selects appropriate learning resources (videos, quizzes, etc.) and sends them to the device.

[1612] Input: User's learning path

[1613] Output: Learning content sent to the user's device

[1614] Specific behavior:

[1615] The server selects the most appropriate content (video, audio, quiz) based on the user's learning path and sends it to the device.

[1616] Step 8:

[1617] The device presents the received study content to the user, who then uses this content to advance their studies.

[1618] Input: Learning content sent from the server

[1619] Output: The learning content that is displayed to the user

[1620] Specific behavior:

[1621] The terminal presents the received learning content to the user visually or audibly.

[1622] Step 9:

[1623] The device records the user's learning progress and sends it to the server. The device collects progress data such as study time, number of correct answers, and mistakes, and periodically sends it to the server.

[1624] Input: User's learning progress data

[1625] Output: Progress data sent to the server

[1626] Specific behavior:

[1627] The device transmits progress data collected during the learning activity to a server.

[1628] Step 10:

[1629] The server analyzes the progress data and optimizes the learning path and content. The server analyzes the progress data and determines the next learning content to be provided.

[1630] Input: User progress data

[1631] Output: Optimized next learning path and content

[1632] Specific behavior:

[1633] The server analyzes the progress data and uses a generative AI model to determine the next optimal learning content to provide.

[1634] Step 11:

[1635] Users provide feedback about their learning experience, and the server analyzes that feedback and adjusts the entire system. Users input feedback through their devices, and the server receives and analyzes it.

[1636] Input: User feedback

[1637] Output: System tuning and improvement

[1638] Specific behavior:

[1639] The server analyzes user feedback, identifies areas for improvement in the system, and makes appropriate adjustments.

[1640] Examples and prompts

[1641] By inputting prompts such as the following into the generative AI model, it is possible to select and optimize learning content for a specific topic.

[1642] example:

[1643] "For a user who wants to learn basic English grammar, what kind of learning content (videos, quizzes, etc.) should be provided per day?"

[1644] Based on this prompt, the generative AI model automatically selects appropriate content such as grammar videos and quizzes, providing an efficient learning experience tailored to the user.

[1645] (Application example 1)

[1646] 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."

[1647] In factories and other workplaces, where multinational workers often work, language differences can affect communication and safety. In such environments, workers need to be able to quickly and accurately understand work instructions and safety rules, but conventional methods have difficulty effectively resolving this issue. While there is a need for systems that can individually optimize and efficiently advance language learning, such systems are not widely available in reality. Therefore, there is a need for a language learning support system that can help multinational workers in factories overcome language barriers and communicate efficiently.

[1648] 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.

[1649] In this invention, the server includes a means for a user to input and register their name, age, language proficiency, and learning objectives, a means for the server to receive the input information and generate a user profile, and a means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, thereby enabling the server to generate an individual learning path and provide appropriate learning content.

[1650] In addition, in this invention, the server includes a means for the terminal to collect the user's test results and send them to the server, a means for the server to analyze the test results and generate an individual learning path, and a means for the server to select learning content based on the learning path generated and send it to the terminal, thereby making it possible to grasp the user's learning progress in real time and provide optimal content.

[1651] Furthermore, in this invention, the server includes means for presenting the learning content received by the terminal to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, and means for using smart devices to support learning of instructions and safety rules in multiple languages, thereby enabling multinational workers working in a factory to overcome language barriers, communicate efficiently, and work safely.

[1652] "User" refers to an individual person who uses the System.

[1653] "Name" refers to an individual notation for identifying a user.

[1654] "Age" refers to the user's age number.

[1655] "Language proficiency" refers to the user's current level of proficiency in a language.

[1656] "Learning objective" refers to a specific goal for a user to learn a language.

[1657] "Means for registering" refers to the process by which a user registers with the system by entering their name, age, language proficiency, and learning objectives.

[1658] "Server" refers to a computer system that receives and processes user input information.

[1659] "User profile" refers to a collection of data generated based on information entered about a user.

[1660] "Initial Assessment Test" refers to a test administered to assess a user's current language proficiency.

[1661] "Terminal" refers to the device on which a user takes an initial assessment test or accesses learning content.

[1662] "Test results" refers to the response data after a user has taken an initial evaluation test.

[1663] "Personalized Learning Path" refers to a customized learning plan generated to optimize a user's learning progress.

[1664] "Learning Content" refers to educational materials provided to users to further their learning.

[1665] "Study progress" refers to how far a user has progressed through the learning content.

[1666] "Progress data" refers to specific data regarding a user's learning progress.

[1667] "Feedback" refers to the opinions and reactions users provide about learning content.

[1668] "System-wide tuning" refers to the process by which the server improves the system based on user feedback.

[1669] "Smart devices" refer to wearable or portable devices that have computing functionality.

[1670] "Multilingual Instructions" refers to work instructions provided in multiple languages.

[1671] "Safety rules" refer to regulations to ensure safety at the workplace.

[1672] MODE FOR CARRYING OUT THE INVENTION

[1673] The present invention provides a language learning support system that enables multinational workers to overcome language barriers, communicate efficiently, and work safely. A specific embodiment of this system will be described below.

[1674] User registration and initial settings

[1675] First, a user registers in the system using a smart device (e.g., smart glasses) by inputting their name, age, language proficiency, and learning goals. This user information is then sent from the smart device to the server, which then creates a user profile based on the received information.

[1676] Initial evaluation

[1677] After the user profile is generated, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the smart device. The user takes the test through the smart device and sends the results to the server. The server analyzes the received test results and generates an individual learning path that is optimized for the user's proficiency and learning goals.

[1678] Providing learning content

[1679] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device. The smart device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a factory worker receives content on "understanding work instructions," they will be provided with videos and quizzes on specific work instructions and safety rules.

[1680] Track your learning progress

[1681] As a user progresses through the learning process using the designated learning content, the smart device records the progress. The recorded data includes the study time, number of correct answers, and questions answered incorrectly. The smart device periodically sends this progress data to the server.

[1682] Progress data analysis and optimization

[1683] The server analyzes the received progress data and evaluates the user's learning effectiveness. Based on this evaluation, the server optimizes the learning content and tasks it provides next. For example, if it determines that the user is struggling with a particular aspect of understanding factory safety rules, the server will adjust the next learning session to provide more practice on safety rules.

[1684] Feedback and System Tuning

[1685] Users can provide feedback about their learning experience via their smart devices. The server analyzes this feedback and identifies necessary improvements and new features. For example, if a user provides feedback that "the work instruction videos were helpful," the server will make adjustments such as increasing the number of similar content.

[1686] Examples of specific examples and prompts

[1687] "Factory workers registered their name, age, language proficiency, and learning objectives through the smart glasses, and then took an initial assessment test. Based on the test results, they were provided with videos and quizzes on safety rules within the factory. The user's progress data was sent to the server for analysis. The next learning content was then optimized based on the analysis results."

[1688] Example prompts to input to a generative AI model:

[1689] Please complete the registration based on the information you provided during user registration.

[1690] Name: Taro Tanaka

[1691] Age: 35

[1692] Language Proficiency: Beginner

[1693] Learning Objective: Understand factory work instructions

[1694] This allows users to receive language learning support to carry out their work safely and efficiently within the factory.

[1695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1696] Step 1:

[1697] Users register by using a smart device to enter their name, age, language proficiency level, and learning purpose.

[1698] (Input) Name, age, language proficiency, learning purpose

[1699] (Processing) Send the entered data to the server

[1700] (Output) User information is stored on the server

[1701] Step 2:

[1702] The server generates a user profile based on the received user information.

[1703] (Input) User information (name, age, language proficiency, learning purpose)

[1704] (Processing) Store user information in a database and generate a profile

[1705] (Output) Generate user profile

[1706] Step 3:

[1707] The server generates an initial assessment test based on the user profile and sends it to the smart device.

[1708] (Input) User profile

[1709] (Processing) Generate appropriate test questions based on evaluation logic

[1710] (Output) The initial evaluation test is sent to the smart device.

[1711] Step 4:

[1712] Users take an initial evaluation test via their smart device.

[1713] (Input) Initial evaluation test

[1714] (Process) User answers the test

[1715] (Output) Test answer data is generated

[1716] Step 5:

[1717] The smart device sends the user's test results to the server.

[1718] (Input) Test response data

[1719] (Processing) Sending response data

[1720] (Output) The answer data is stored on the server.

[1721] Step 6:

[1722] The server analyzes the received test results and generates a personalized learning path optimized for the user.

[1723] (Input) Test results

[1724] (Processing) Generate learning paths based on analysis logic

[1725] (Output) Generate individual learning paths

[1726] Step 7:

[1727] Based on the generated learning path, the server selects appropriate learning content and sends it to the smart device.

[1728] (Input) Individual Learning Path

[1729] (Processing) Select the most suitable learning content

[1730] (Output) Learning content is sent to the smart device

[1731] Step 8:

[1732] The smart device presents the received learning content to the user.

[1733] (Input) Learning content

[1734] (Processing) Viewing learning content

[1735] (Output) User views learning content

[1736] Step 9:

[1737] Users use the learning content to advance their studies.

[1738] (Input) Learning content

[1739] (Processing) Content Learning

[1740] (Output) Generate learning progress data

[1741] Step 10:

[1742] The smart device records the user's learning progress and sends it to the server.

[1743] (Input) Learning progress data

[1744] (Processing) Recording and sending progress data

[1745] (Output) Progress data is stored on the server.

[1746] Step 11:

[1747] The server analyzes the received progress data and optimizes the user's learning path and content.

[1748] (Input) Progress data

[1749] (Processing) Optimization of learning paths and content based on analytical logic

[1750] (Output) Optimized learning paths and content

[1751] Step 12:

[1752] Users provide feedback on their learning experience through their smart devices.

[1753] (Input) Feedback data

[1754] (Process) Entering and sending feedback

[1755] (Output) Feedback data is stored on the server

[1756] Step 13:

[1757] The server analyzes the feedback received and identifies needed improvements and new features.

[1758] (Input) Feedback data

[1759] (Processing) Feedback analysis and system-wide adjustment

[1760] (Output) Adjusted system settings

[1761] 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.

[1762] The present invention relates to an online language learning system that recognizes a user's emotional state using an emotion engine to further optimize learning paths and content. This system functions in cooperation with a server, a terminal, a user, and an emotion engine. A specific embodiment of this system will be described below.

[1763] User registration and initial settings

[1764] Users must first register with the system. They enter information such as their name, age, current language proficiency level, and learning goals, and send it from their device to the server. Based on this information, the server generates a user profile. For example, if a user registers as "30 years old, beginner level in English, travel purpose," the server stores that information in the profile.

[1765] Initial evaluation

[1766] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server after completion. For example, if a vocabulary question or simple grammar question is asked, the user answers it and the results are sent to the server.

[1767] Generate personalized learning paths

[1768] The server generates an individual learning path based on the user's initial assessment results. This learning path is optimized for the user's proficiency level and learning goals, and includes daily learning content and goals. For example, if a learning path for "basic grammar acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1769] Emotion recognition with emotion engine

[1770] During training, the emotion engine recognizes the user's emotional state. The emotion engine identifies the user's emotional state by analyzing the user's facial expressions, tone of voice, input data, etc. For example, it uses a camera and microphone to detect changes in facial expressions and voice while the user is watching a video.

[1771] Providing learning content

[1772] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine, and sends it to the device. The device then presents the received content to the user. This content can include videos, audio, quizzes, etc. For example, if a user receives "pronunciation practice" content, they will be provided with a pronunciation guide video followed by a quiz.

[1773] Track your learning progress

[1774] As a user progresses through their studies using designated study content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1775] Progress data analysis and optimization

[1776] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is "struggling with grammar questions" and also "feels stressed," the server will make adjustments such as inserting a break to allow the user to relax in the next lesson and starting with easier questions.

[1777] Feedback and System Tuning

[1778] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server will make adjustments, such as simplifying the content.

[1779] As described above, by combining an emotion engine, the present invention provides a personalized learning experience that takes into account the user's emotional state. Through analysis of emotion data and learning progress data, it is possible to optimize learning content and maintain user motivation.

[1780] The processing flow will be explained below.

[1781] Step 1:

[1782] Users visit the website or app and enter their name, age, current language proficiency level, and learning goals, which completes their registration with the system.

[1783] Step 2:

[1784] The user's input information is sent from the terminal to the server, which then creates a user profile based on the received information.

[1785] Step 3:

[1786] The server generates an initial assessment test based on the user profile, which includes vocabulary and grammar questions.

[1787] Step 4:

[1788] The server sends the generated initial evaluation test to the terminal, which presents the test to the user.

[1789] Step 5:

[1790] The user answers the initial evaluation test through the terminal, and once the answers are complete, the test results are sent from the terminal to the server.

[1791] Step 6:

[1792] The server analyzes the results of the initial assessment test to assess the user's language proficiency and creates a personalized learning path based on the assessment results.

[1793] Step 7:

[1794] The server selects appropriate learning content based on the generated learning path, and the selected learning content is sent to the device.

[1795] Step 8:

[1796] The device then presents the received learning content to the user, which may include videos, audio, quizzes, etc.

[1797] Step 9:

[1798] During training, the emotion engine recognizes the user's emotional state by analyzing the user's facial expressions, tone of voice, and input data to identify the emotional state.

[1799] Step 10:

[1800] The user uses the presented learning content to progress through their studies, while the device records the user's learning progress.

[1801] Step 11:

[1802] The device periodically transmits recorded learning progress data to the server, including the learning time, number of correct answers, number of incorrect answers, etc.

[1803] Step 12:

[1804] The server analyzes the received progress data, evaluates the user's learning effectiveness, and, if necessary, optimizes the learning path and the next content provided in comparison with the analysis results of the emotion engine.

[1805] Step 13:

[1806] The user provides feedback about the learning experience, which is transmitted to the server via the device.

[1807] Step 14:

[1808] The server analyzes user feedback and also uses data from the emotion engine when identifying areas for improvement in the overall system and the content it provides.

[1809] Step 15:

[1810] The server adjusts the entire system and content based on the feedback. For example, if a user says, "The pronunciation practice video was helpful, but it was too difficult," the server will adjust the content by simplifying it.

[1811] These steps allow users to effectively progress through language learning at their own pace. Furthermore, by utilizing emotion recognition through the emotion engine, the learning experience is constantly optimized, helping to maintain user motivation.

[1812] Example 2

[1813] 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."

[1814] Conventional online language learning systems lack the ability to recognize a user's emotional state and optimize learning paths and content, making it difficult to provide personalized support to maximize learning outcomes. This is particularly true when users are stressed or lacking concentration, leading to a decline in motivation and efficiency. Furthermore, there is no established method for adjusting the system using user emotional data, in addition to learning progress.

[1815] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1816] In this invention, the server includes means for a user to input and register their name, age, language proficiency, and learning objectives, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and transmit the test to the terminal, means for the terminal to collect the user's test results and transmit them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the learning path generated and transmit the selected learning content to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and transmit the recorded learning data to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the terminal to recognize the user's emotional state using an emotion engine and transmit the data to the server, means for the server to adjust the learning content based on the data from the emotion engine to provide an optimal learning experience, and means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system. This enables the system to recognize the user's emotional state in real time, optimize the learning path and content based on the user's emotional state, and maximize learning effectiveness through individualized support.

[1817] "User" refers to an individual who uses the system to learn a language.

[1818] "Server" refers to the central computing unit that receives, processes, analyzes, and generates and delivers appropriate learning content to users.

[1819] A "terminal" is a device that a user uses to interface with the system, including a computer, tablet, smartphone, etc.

[1820] An "emotion engine" refers to a software or hardware algorithm that analyzes a user's facial expressions, tone of voice, input data, etc. to recognize the user's emotional state.

[1821] "User profile" refers to a data set generated based on information provided by a user at the time of registration (such as name, age, language proficiency, and learning objectives).

[1822] "Initial assessment test" refers to a test generated by the server and taken by the user via a terminal in order to assess the user's current language proficiency.

[1823] "Individualized learning path" refers to a user-specific learning plan generated by the server based on the user's initial assessment results and learning objectives.

[1824] "Learning content" refers to information resources including learning materials, quizzes, videos, audio, etc. provided by the server to help users advance their studies.

[1825] "Study progress" refers to data that indicates the progress and results (study time, number of correct answers, questions answered incorrectly, etc.) when a user studies using learning content.

[1826] "Emotion data" refers to the emotional state of the user recognized by the emotion engine and recorded as numerical or text data.

[1827] "Feedback" refers to the opinions and thoughts that a user provides to the system regarding their learning experience.

[1828] "Optimization" refers to the act of individually adjusting a user's learning path and learning content based on progress data and emotional data collected by the server, in order to maximize learning effectiveness.

[1829] This invention relates to an online language learning system that recognizes a user's emotional state and optimizes learning paths and content based on that information. This system functions in cooperation with a server, a terminal, and an emotion engine. Detailed embodiments of this system are described below.

[1830] User registration and initial settings

[1831] A user first registers with the system. They enter information such as their name, age, language proficiency, and learning purpose, and send it from their terminal to the server. The server generates a user profile based on the received information and stores it in a database. For example, if a user enters "Name: Yamada Hanako, Age: 25, Language Proficiency: English Intermediate, Learning Purpose: Business," the server registers this in the profile.

[1832] Initial evaluation

[1833] After the user profile is created, the server generates an initial assessment test to evaluate the user's current language proficiency and sends it to the terminal. The user takes the test through the terminal and sends the results to the server. The server analyzes the results and evaluates the user's proficiency. For example, a "vocabulary test" or "grammar test" may be given.

[1834] Generate personalized learning paths

[1835] The server generates an individual learning path based on the initial evaluation results. This learning path includes daily learning content and goals. The server then sends the generated learning path to the device and notifies the user. For example, if a learning path for "Basic Grammar Acquisition" is generated, the daily learning content will include basic grammar rules and related vocabulary.

[1836] Emotion recognition with emotion engine

[1837] During learning, the emotion engine recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to identify their emotional state. For example, it uses the device's camera and microphone to detect changes in the user's facial expressions and voice and identify emotions such as "stress," "joy," and "concentration."

[1838] Providing learning content

[1839] The server selects appropriate learning content based on the generated learning path and the analysis results of the emotion engine. The selected content is sent to the device, which then presents it to the user. For example, if "pronunciation practice" content is provided, it will include a pronunciation guide video followed by a quiz.

[1840] Track your learning progress

[1841] As a user studies using the designated learning content, the device records their progress. The recorded data includes study time, number of correct answers, and questions answered incorrectly. The device periodically sends this progress data to the server. For example, if a user studies vocabulary for 30 minutes, the study time and correct answer rate will be recorded.

[1842] Progress data analysis and optimization

[1843] The server analyzes the received progress data and evaluates the user's learning effectiveness. If necessary, it compares this with the emotion engine's analysis results and optimizes the learning path and the next content provided. For example, if the emotion engine determines that the user is struggling with grammar questions and is also feeling stressed, the server will insert a break for the user to relax in the next lesson and start with easier questions.

[1844] Feedback and System Tuning

[1845] Users can provide feedback about their learning experience through their devices. The server analyzes this feedback and uses the emotion engine data to identify areas for improvement in the system as a whole and in the content it provides. For example, if a user says, "The pronunciation practice video was helpful, but too difficult," the server can make adjustments, such as simplifying the content.

[1846] Prompt Sentence Examples

[1847] "Please suggest ways to optimize the following learning path to improve the user's learning experience: The user is a beginner in English and is focused on learning grammar for travel purposes."

[1848] "Please suggest ways to adjust the content to reduce the stress users feel while learning. Data from the emotion engine indicates that users are experiencing stress."

[1849] As described above, this invention combines an emotion engine to provide a personalized learning experience that takes into account the user's emotional state. Through analysis of learning progress data and emotion data, it is possible to optimize learning content and maintain user motivation.

[1850] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1851] Specific flow of program processing

[1852] Step 1:

[1853] User registration and initial settings

[1854] Input: User enters name, age, language proficiency, and learning objectives into the device

[1855] Data processing: The device formats the user's input information and sends it to the server

[1856] Output: The server generates and stores the user profile in the database.

[1857] Specific operation: The user enters "Name: Yamada Taro, Age: 30, Language Proficiency: Beginner English, Learning Objective: Travel", and the device sends this to the server and stores it.

[1858] Step 2:

[1859] Initial evaluation

[1860] Input: The server generates an initial assessment test based on the user profile.

[1861] Data processing: The server sends the initial evaluation test to the device.

[1862] Output: Device displays test to user, user enters result, device sends result to server

[1863] Specific operation: The server generates "vocabulary tests" and "grammar tests," and the device displays them to the user. The user enters answers, and the device sends the results to the server.

[1864] Step 3:

[1865] Generate personalized learning paths

[1866] Input: Initial evaluation test results from the server

[1867] Data processing: The server analyzes and generates an individual learning path

[1868] Output: The server sends the generated learning pass to the device.

[1869] Specific operation: The server generates a learning path for beginner grammar acquisition, sends the content to the terminal, and notifies the user.

[1870] Step 4:

[1871] Emotion recognition with emotion engine

[1872] Input: The device captures the user's facial expressions and tone of voice using the camera and microphone.

[1873] Data processing: The device analyzes data with an emotion engine to identify the emotional state

[1874] Output: The device sends the analysis results to the server.

[1875] Specific operation: While the device is learning, it analyzes the user's facial expressions and tone of voice in real time, identifies their "stress" and "concentration" states, and sends the results to the server.

[1876] Step 5:

[1877] Providing learning content

[1878] Input: The server receives the user's learning path and emotion data.

[1879] Data processing: The server selects appropriate learning content

[1880] Output: Send selected learning content to the device

[1881] Specific operation: The server selects "pronunciation practice videos" and "listening quizzes," sends them to the device, and presents them to the user.

[1882] Step 6:

[1883] Track your learning progress

[1884] Input: Users consume learning content and record their progress

[1885] Data processing: The device sends progress data (study time, number of correct answers, etc.) to the server

[1886] Output: The server receives and stores the progress data.

[1887] Specific operation: The user studies words for 30 minutes, and the device records the study time and accuracy rate and sends the results to the server.

[1888] Step 7:

[1889] Progress data analysis and optimization

[1890] Input: Server receives progress and emotion data

[1891] Data processing: The server analyzes and optimizes the learning path and next content as needed.

[1892] Output: Send new learning paths and content to your device

[1893] Specific behavior: If the user is struggling with a grammar problem and feeling stressed, the server sends a new learning path to the device, inserting relaxation time.

[1894] Step 8:

[1895] Feedback and System Tuning

[1896] Input: Users provide feedback about their learning experience

[1897] Data processing: The device sends feedback to the server, which analyzes it.

[1898] Output: The server adjusts the entire system and content based on the analysis results.

[1899] Specific operation: The user gives feedback such as "The pronunciation practice video was difficult," and the server uses that feedback to simplify the video content.

[1900] (Application example 2)

[1901] 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."

[1902] Conventional online learning systems and e-readers often provide uniform content without considering the user's emotional state, resulting in a failure to optimize the user's comprehension and interest. Furthermore, ignoring emotions such as stress and excitement felt by users while learning or reading can lead to a decline in learning effectiveness and reading experience. Such systems make it difficult to maintain users' motivation to continue learning, resulting in poor learning outcomes and poor reading satisfaction.

[1903] 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 a user to input and register their name, age, language proficiency, and learning purpose, means for the server to receive the input information and generate a user profile, means for the server to generate a test for initial evaluation based on the user profile and send it to the terminal, means for the terminal to collect the user's test results and send them to the server, means for the server to analyze the test results and generate an individual learning path, means for the server to select learning content based on the generated learning path and send it to the terminal, means for the terminal to present the received learning content to the user, means for the terminal to record the user's learning progress and send it to the server, means for the server to analyze the progress data and optimize the learning path and content, means for the user to provide feedback on the learning content and for the server to analyze the feedback and adjust the entire system, means for the server to recognize the user's emotional state using an emotion engine and optimize the reading experience, means for the emotion engine to analyze the user's facial expressions, voice, and input data, and means for dynamically selecting and displaying appropriate content based on the recognized emotional state. This allows for an optimized learning or reading experience based on the user's emotional state.

[1904] A "user" is an individual who uses a system or application.

[1905] "Name, age, language proficiency, learning objectives" refers to personal and learning details provided by users when they register.

[1906] A "server" is a computer on a network that stores and processes data.

[1907] A "user profile" is a data structure that is generated based on personal information provided by a user.

[1908] An "initial assessment test" is a test administered to assess a user's current level of knowledge.

[1909] "Terminal" means the device through which a user accesses the system.

[1910] "Test Results" means the answers provided by a user to an initial assessment test.

[1911] A "personalized learning path" is a customized learning plan based on the user's assessment results.

[1912] "Learning content" refers to the learning materials and information provided to users to help them advance their studies.

[1913] "Study progress" refers to data that indicates how far a user has progressed in their studies.

[1914] An "emotion engine" is software that recognizes a user's emotional state.

[1915] "Facial expressions, voice, and input data" refers to the data that the emotion engine uses to analyze the user's emotions.

[1916] A "recognized emotional state" is a user's emotion as identified by the emotion engine.

[1917] "Appropriate content" refers to content selected based on the user's emotional state.

[1918] A "displaying means" is a method or device for showing content to a user.

[1919] The present invention relates to a system that recognizes a user's emotional state and optimizes the learning or reading experience. This system functions through the interaction of a server, a terminal, a user, and an emotion engine. Specific embodiments for implementing this system are described below.

[1920] User registration and initial settings

[1921] Users first register by entering personal information such as their name, age, language proficiency, and learning goals into the system. The terminal then sends this information to the server, which then creates a user profile based on that information.

[1922] Initial evaluation

[1923] The server generates an initial assessment test based on the user profile to evaluate the user's current knowledge level and sends it to the device. The user takes the test through the device and sends the results to the server. The server analyzes the results and generates a personalized learning path for the user.

[1924] Learning paths and content offerings

[1925] The server selects appropriate learning content based on the generated learning path and sends it to the device. The device presents the content to the user, who then uses it to advance their learning. Learning progress is recorded on the device and periodically sent to the server.

[1926] Emotion recognition by emotion engine

[1927] While studying or reading, the emotion engine analyzes the user's facial expressions, voice, and input data to identify their emotional state. If the user is feeling stressed, the emotion engine notifies the server, which then takes appropriate action.

[1928] Content Optimization

[1929] The server analyzes the emotion data and progress data obtained from the emotion engine to optimize the user's learning path and content. For example, if a user is feeling stressed, it will suggest chapters with relaxing content.

[1930] Specific examples of reading experiences

[1931] If a user is reading a thriller novel and the emotion engine identifies that they are feeling stressed, the server can offer another relaxing chapter, allowing the user to continue enjoying the book.

[1932] Hardware and software used

[1933] Hardware: Smartphone, tablet, or PC with webcam

[1934] software:

[1935] EmotionRecognition: A library for analyzing emotions from user facial expressions and voice (e.g., OpenCV)

[1936] EbookProvider: Backend service that provides e-books and coordinates content

[1937] Prompt Sentence Examples

[1938] "How do you adjust your content if users are stressed?"

[1939] "If a user is excited, what kind of interesting content do you recommend?"

[1940] In this way, a learning and reading experience can be provided that is individually optimized based on the user's emotional state.

[1941] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1942] Step 1:

[1943] The user enters personal information such as name, age, language proficiency, and learning goals, and sends it from the device to the server. Based on the entered information, the server generates a user profile. Specifically, the data entered by the user is sent to the server and saved in JSON format on the server side.

[1944] Step 2:

[1945] The server creates an initial evaluation test based on the generated user profile and sends it to the device. Specifically, the server's AI model analyzes the user profile, selects appropriate questions, and sends test-format data to the device. The output is test-format data.

[1946] Step 3:

[1947] The user takes the initial evaluation test using a terminal and inputs the results. The terminal collects the test results and sends them to the server. The input data is the user's answers, and the output is the test results sent to the server.

[1948] Step 4:

[1949] The server analyzes the test results and generates an individual learning path based on that information. The input data is the test results, and the output is an individual learning path. The server's AI model analyzes the data and creates a learning curriculum tailored to the user's needs.

[1950] Step 5:

[1951] The server selects the most appropriate learning content based on the generated learning path and sends it to the device. The input data is the learning path, and the output is the learning content. Appropriate learning materials are selected from the content database.

[1952] Step 6:

[1953] The device presents the received learning content to the user. Specifically, text, audio, video, etc. are displayed through a user interface. The input data is the learning content, and the output is the display on the user interface.

[1954] Step 7:

[1955] As the user studies or reads, the device records learning progress data, including study time, number of correct answers, questions missed, etc. The input data is the user's learning actions, and the output is progress data.

[1956] Step 8:

[1957] The server periodically receives and analyzes progress data sent from the device. The input data is the progress data, and the output is the analysis results. The server optimizes the learning path and content provided based on the analysis results.

[1958] Step 9:

[1959] While studying or reading, the emotion engine analyzes the user's facial expressions and voice to identify emotions. The input data is facial expressions and voice captured in real time, and the output is the recognized emotional state.

[1960] Step 10:

[1961] The server receives the emotional state from the emotion engine, dynamically selects the most appropriate content, and sends it to the device. The input data is the recognized emotional state, and the output is the adjusted content. If it recognizes that the user is feeling stressed, it selects relaxing content.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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).

[1969] 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.

[1970] 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."

[1971] 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.

[1972] 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).

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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...

Claims

1. a means for users to register by entering their name, age, language proficiency, and learning goals; means for the server to receive the input information and generate a user profile; A means for the server to generate a test for initial evaluation based on the user profile and transmit the test to the terminal; A means for the terminal to collect the user's test results and transmit them to the server; a means for the server to analyze the test results and generate a personalized learning path; A means for selecting learning content based on the learning path generated by the server and transmitting the content to the terminal; means for presenting the received learning content to the user; A means for the terminal to record the user's learning progress and transmit the record to a server; a means by which the server analyzes the progress data and optimizes learning paths and content; A system that includes a means for users to provide feedback on learning content and for a server to analyze the feedback and make adjustments to the system as a whole.

2. 10. The system of claim 1, further comprising means for generating and analyzing initial assessment tests.

3. 10. The system of claim 1, further comprising means for recording learning progress data and transmitting it to a server.

Images