System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input a current level of proficiency and a learning objective; means for a server to generate appropriate learning content and homework based on the input of the user; means for a terminal to display the learning content and the homework received from the server to the user; means for the user to transmit a homework result to the server; and means for the server to adjust next learning content and homework based on the homework result.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] Conventional learning systems often lack the means to flexibly provide learning content tailored to each user's individual proficiency level and progress. This makes it difficult for users to study effectively and efficiently, and it is also difficult to maintain motivation to study. Furthermore, there is a lack of means to objectively evaluate one's own proficiency level and compare it with other systems, making it difficult for users to grasp their own learning progress. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means: A means for a user to input their current level of proficiency and learning goals, and a server means for generating appropriate learning content and homework based on the user's input. The system further includes a means for a terminal to display the learning content and homework received from the server to the user, and for the user to send the results of their homework to the server. The server also includes a means for adjusting the next learning content and homework based on the results of the homework, and for generating data for comparing the user's proficiency with other companies' proficiency levels based on the user's current level. This allows users to effectively study according to their own level of proficiency and objectively evaluate their progress.

[0006] "User" refers to an individual or group that uses the system to study.

[0007] "Proficiency" is an index that represents the level of skill and knowledge that the user currently possesses.

[0008] A "learning goal" is a specific learning goal or purpose that a user wants to achieve.

[0009] "Input means" refers to a device or function that allows a user to input information into the system.

[0010] A "server" is a computer system that receives requests from users, processes the information, and returns an appropriate response.

[0011] "Learning content" refers to educational materials and information provided to users to help them advance their studies.

[0012] "Homework" refers to assignments or exercises that users complete to check their learning progress.

[0013] "Means for generating" refers to the function of creating learning content and homework based on user input data.

[0014] A "terminal" is a device, such as a computer or smartphone, that a user uses to access the system.

[0015] "Display means" refers to devices or functions that visually present information on the system.

[0016] "Transmission means" refers to a device or function that allows a user to transmit homework results, etc. to a server.

[0017] "Adjustment means" refers to the ability to change the next learning content or the difficulty or content of the homework based on the results of the homework.

[0018] "Comparative data" is information for comparing a user's proficiency with other companies or other standards.

[0019] "Means for verifying" refers to a device or function that allows a user to verify comparison data or other information. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention relates to a system that provides learning content and homework according to a user's level of proficiency and can objectively evaluate the learning progress. Hereinafter, an embodiment of the present invention will be described in detail.

[0042] For this system to function, the user, the terminal, and the server must cooperate to operate the program. First, the user accesses the system using the terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0043] The server receives the user's input data and generates appropriate learning content and homework. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0044] The user uses the learning content provided through the device to study and complete homework. The user inputs the results of the homework (e.g., the percentage of correct answers) into the device, which then transmits the results to the server.

[0045] The server adjusts the next learning content and homework based on the received homework results. For example, if the user answers the homework correctly, the next content may be more difficult. Conversely, if the user answers the homework correctly, the server may provide content of the same level or a lower level of difficulty.

[0046] Furthermore, the server generates data comparing the user's proficiency level with that of other companies based on the user's current level. This comparison data is presented in the form of, for example, "Intermediate level, compared to other companies, is within the range of passing X University." The terminal also displays this comparison data to the user, allowing them to objectively evaluate their own proficiency level.

[0047] As a specific example, if a user starts an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level and provides them to the user through the device. The user sends the results of the homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. In addition, if the user wishes to compare with other companies, the server can generate that data and display it on the device.

[0048] The above is an embodiment of the present invention. This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluations.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] A user accesses the system using a terminal and inputs their current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0052] Step 2:

[0053] The terminal sends the user's input data to the server, which receives the data.

[0054] Step 3:

[0055] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0056] Step 4:

[0057] The server sends the generated learning content and homework to the device, which receives it.

[0058] Step 5:

[0059] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0060] Step 6:

[0061] The user enters the results of their homework into the terminal, which then sends the results to the server.

[0062] Step 7:

[0063] The server analyzes the results of the homework received and adjusts the next learning content and homework. For example, if the percentage of correct answers in the homework is high, the difficulty level of the next content is increased, and if the percentage of correct answers is low, the difficulty level is maintained or decreased.

[0064] Step 8:

[0065] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0066] Step 9:

[0067] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0068] Step 10:

[0069] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0070] Step 11:

[0071] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0072] Example 1

[0073] 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."

[0074] Conventional learning systems have difficulty providing appropriate learning content and homework according to a user's level of proficiency, and lack a means to objectively evaluate learning progress. This makes it difficult for users to accurately grasp their own learning results, resulting in a decrease in learning efficiency.

[0075] 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.

[0076] In this invention, the server includes a means for a user to input their current level of proficiency and learning goals, a means for a terminal to transmit the input data to the server, a means for the server to generate appropriate learning content and homework based on the user's input, a means for the terminal to display the learning content and homework received from the server to the user, a means for the user to input homework results to the terminal and transmit them to the server, and a means for the server to adjust the next learning content and homework based on the homework results. This makes it possible to provide effective learning content according to the user's level of proficiency and objectively evaluate the user's learning progress.

[0077] "User" refers to a person who uses the system to receive learning content and homework.

[0078] "Terminal" refers to a device used by a user to access the system, such as a computer or smartphone.

[0079] "Server" refers to the central part of the system that receives data sent by users and generates and manages learning content and homework.

[0080] "Current proficiency" refers to the level of skill or knowledge that the user has currently acquired.

[0081] "Learning goal" refers to the specific learning objective or outcome that the user wants to achieve.

[0082] "Data" refers to information entered by the user and server-generated information, including learning content, homework, and comparison data.

[0083] "Learning content" refers to learning materials, practice questions, etc. that users use to study.

[0084] "Homework" refers to assignments or exercises that users complete to advance their learning.

[0085] "Homework results" refers to the grades and answers given by the user when they complete the homework.

[0086] "Comparative data" refers to data that shows the results of comparing a user's proficiency with other users or standards.

[0087] MODE FOR CARRYING OUT THE INVENTION

[0088] The present invention is a system that provides learning content and homework according to a user's level of proficiency and objectively evaluates the user's learning progress. For this system to function efficiently, the user, the terminal, and the server must cooperate to operate the program.

[0089] First, a user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0090] The server receives the user's input data and generates appropriate learning content and homework based on it. Specifically, the server uses a generative AI model to generate prompt sentences such as the following, and creates learning content and homework based on those prompts.

[0091] Example prompt sentence:

[0092] "Please generate intermediate level English conversation lesson content and homework to help me aim for a TOEIC score of 800. Also, please adjust the difficulty of the next lesson based on the learning results and provide comparative data with other companies."

[0093] The generated learning content and homework are sent from the server to the device, which then visually displays them to the user. The user studies using the learning content provided through the device, and when completing the homework, the results (e.g., percentage of correct answers) are entered into the device again. The device then sends this result data to the server.

[0094] The server analyzes the results of the homework assignments received and adjusts the next learning content and homework assignments. It also generates data comparing the user's current proficiency with other users, as needed. For example, it generates comparison data in the form of "Intermediate level, compared to other companies, and within the range of passing X University."

[0095] This allows users to be provided with learning content tailored to their level of proficiency, and also allows them to objectively evaluate their learning progress. Specifically, if a user begins intermediate-level English conversation lessons with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for that level and provides it to the user via their device. The user sends the results of their homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. Furthermore, if the user wishes to compare their services with those of other companies, the server can generate that data and display it on the device.

[0096] This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluation.

[0097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0098] Step 1:

[0099] The user inputs their current proficiency level, the subjects they are studying, and their learning goals. As input, the user enters data into a form on the terminal screen. The input data is the user's "current proficiency level," "study subjects," and "learning goals." For example, they enter "intermediate," "English conversation," and "TOEIC 800 points." These input data affect the user's learning level.

[0100] Step 2:

[0101] The device sends the data entered by the user to the server. Specifically, the device sends the data to the server using an HTTP POST request. This input data includes "current proficiency level," "study subjects," and "study goals." The server receives this data.

[0102] Step 3:

[0103] The server analyzes the received data and generates appropriate learning content and homework using a generative AI model. Inputs include the user's "current proficiency level," "study subjects," and "learning goals." The server generates and sends the following prompt to the generative AI model: "Please generate intermediate-level English conversation lesson content and homework to aim for a TOEIC score of 800." The generated learning content and homework are obtained as output.

[0104] Step 4:

[0105] The generated learning content and homework are sent from the server to the device. Specifically, the server sends the learning content and homework to the device as an HTTP response. This output data includes "Intermediate English Conversation Lessons" and "Intermediate Homework" appropriate for the user's level of proficiency.

[0106] Step 5:

[0107] The device displays the received learning content and homework to the user. As output, the device visually displays "Intermediate English Conversation Lessons" and "Intermediate Homework" on the user's screen. This operation allows the user to use the displayed data for study.

[0108] Step 6:

[0109] The user uses the provided learning content to progress through their studies and complete their homework. The input is the learning content and homework on the device. The user studies through quizzes and interactive practice sessions and answers the homework. The output is the results of their studies and the results of their homework (e.g., the percentage of correct answers).

[0110] Step 7:

[0111] The user enters the results of their homework into the device, and the device sends the results to the server. The user enters the results of their homework into a designated input field on the device. For example, "90% correct." The device sends this result data to the server using an HTTP POST request. The server receives the homework result data.

[0112] Step 8:

[0113] The server analyzes the homework results it receives and adjusts the next learning content and homework. The input is "homework result data." The server generates and sends the following prompt to the generative AI model: "Please adjust the difficulty of the next lesson based on the user's 90% correct answer rate." The adjusted learning content and homework are obtained as output.

[0114] Step 9:

[0115] In addition, the server generates data comparing the user's proficiency level with other companies' proficiency levels based on the user's current level. The inputs include the user's "current proficiency level" and "homework result data." The server uses a comparison algorithm to compare the data with other users' data. The output generates comparison data such as "Your TOEIC score is in the top 20% of other intermediate-level users."

[0116] Step 10:

[0117] The terminal receives the comparison data sent from the server and displays it to the user. As an output, the terminal displays on the user's screen, "Your TOEIC score is in the top 20% of other intermediate-level users." This allows the user to objectively evaluate their own proficiency level compared to other users.

[0118] The above is the specific processing flow of this system.

[0119] (Application example 1)

[0120] 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."

[0121] While conventional learning systems can provide appropriate learning content based on a user's level of proficiency, they are unable to compare the level of proficiency with that of other users, objectively evaluate the user's learning progress, or effectively suggest the next learning content. This makes it difficult for users to accurately understand their own learning progress and proceed with their studies appropriately.

[0122] 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.

[0123] In this invention, the server includes a means for generating comparison data using a generative AI model based on the learning progress of other users, a means for the user to check the comparison data, and a means for generating the comparison data and a prompt sentence for generating a suggestion for the next learning content, thereby enabling the user to objectively evaluate their own learning progress and receive effective learning suggestions.

[0124] "User" refers to an individual who uses the learning system to manage their own proficiency and learning progress.

[0125] "Proficiency" is a measure that indicates the level of knowledge and skill that a user currently possesses.

[0126] "Learning Objective" means a specific learning outcome or qualification that a user aims to achieve.

[0127] "Server" refers to a group of computers that receive data from users and generate and coordinate learning content and homework assignments.

[0128] "Learning content" refers to the teaching materials and resources that users use for learning.

[0129] "Homework" refers to assignments or exercises that a user must complete in relation to the learning content.

[0130] "Terminal" refers to the device (smartphone, tablet, PC, etc.) through which a user accesses the system and displays learning content and homework.

[0131] "Homework results" refers to data such as the grades and percentage of correct answers when a user completes homework.

[0132] "Comparison data" refers to evaluation information generated by comparing a user's proficiency level with the data of other users.

[0133] "Generative AI models" refer to artificial intelligence algorithms used to analyze data and generate and tailor learning content.

[0134] A "prompt" refers to input text that instructs a generative AI model to generate learning content and suggestions.

[0135] The present invention relates to a system for providing learning content and homework according to a user's level of proficiency and evaluating the user's learning progress. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS ... will be described in detail below with reference to exemplary embodiments.

[0136] System Configuration

[0137] To implement this invention, the user, the terminal, and the server must work together to run the program. The system includes the following main components:

[0138] 1. The user accesses the system using a terminal and enters their current proficiency level and learning goals.

[0139] 2. The terminal is a device such as a smartphone, tablet, or PC through which the user receives learning content and homework.

[0140] 3. The server is a set of computers that generate and coordinate learning content and homework based on user input. The following technologies are used to perform the necessary processing:

[0141] Generative AI models are used to generate learning content and tailor homework.

[0142] A database (e.g. MongoDB) is used to manage user data and learning progress.

[0143] Implementation Procedure

[0144] 1. User Registration and Data Entry:

[0145] The user inputs his / her current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800) using the terminal, and this data is sent from the terminal to the server.

[0146] 2. Learning content and homework generation:

[0147] The server generates appropriate learning content (e.g., intermediate-level English conversation lessons) and homework (e.g., intermediate-level practice questions) based on the user's proficiency and learning goals. A generative AI model is used in this process.

[0148] 3. Content Delivery and Learning:

[0149] The generated learning content and homework are sent from the server to the terminal and displayed to the user, who then uses the provided content to advance their studies and complete their homework.

[0150] 4. Progress Assessment and Adjustment:

[0151] The user inputs the results of the homework (e.g., percentage of correct answers) into the device and sends them to the server. The server then adjusts the difficulty and content of the next learning content and homework based on the results.

[0152] 5. Generating and Providing Comparative Data:

[0153] The server compares the user's learning progress with other users and generates comparison data using a generative AI model. For example, it includes evaluation information such as, "Your progress is about the same as other intermediate level users." This comparison data is sent to the device and can be confirmed by the user.

[0154] Specific examples

[0155] For example, if a user's goal is to achieve a TOEIC score of 800, they will enter their current proficiency level as intermediate when they first register. The server will generate intermediate-level English conversation lesson content and homework and provide it to the user. The user will then progress with their studies and enter their homework results. The server will then adjust the difficulty of the next content based on the results and generate evaluation information by comparing the user's data with that of other users.

[0156] Prompt Sentence Examples

[0157] "The user's current TOEIC score is 600, their goal is 800, and their current progress is 75%. Please generate the next learning content and quiz for them."

[0158] As a result, the present invention allows users to receive appropriate feedback and suggestions based on their own level of proficiency, enabling them to progress with their studies efficiently.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1: User registration and data entry

[0161] A user accesses the learning system using a terminal and enters their current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800). The entered data is sent from the terminal to the server. The server stores the received data in a database as a user profile. This profile includes the user's proficiency level, learning goal, and other necessary information.

[0162] Input: User proficiency and learning goals

[0163] Output: User profile (stored in database)

[0164] Step 2: Generate learning content and assignments

[0165] The server uses a generative AI model to generate learning content and homework based on the user's proficiency and learning goals. Specifically, it creates video lessons and quizzes appropriate for the user's current level. The generated content is sent from the server to the device and displayed to the user.

[0166] Input: User profile (proficiency and learning goals)

[0167] Output: Learning content and homework (sent to device)

[0168] Step 3: View and learn content

[0169] The device displays the learning content and homework received from the server to the user. The user watches the video lessons and works on the homework through the device. After the user completes the homework, they input the results (e.g., percentage of correct answers) into the device.

[0170] Input: Learning content and homework

[0171] Output: Homework results (entered by the user)

[0172] Step 4: Assess progress and adjust content

[0173] The server receives the homework results sent from the device and evaluates progress. It uses the generative AI model to adjust the difficulty and content of the next learning content and homework. For example, if the user has a high rate of correct answers on the homework, it will increase the difficulty of the next content. Conversely, if the rate of correct answers is low, it will decrease the difficulty.

[0174] Input: Homework results

[0175] Output: Next learning content and homework (adjusted for difficulty and sent to device)

[0176] Step 5: Generate and provide comparison data

[0177] The server compares the user's progress data with that of other users and generates comparison data using a generative AI model. The comparison data includes evaluation information such as "Your progress is about the same as other intermediate level users." This comparison data is also used to generate prompts. The generated comparison data and prompts are sent to the device and displayed to the user.

[0178] Input: User progress data and other users' data

[0179] Output: Comparison data and prompt (sent to terminal)

[0180] Step 6: Generate and view proposals

[0181] The device displays the comparison data and prompts received from the server to the user. Based on this information, the user can objectively evaluate their own learning progress and receive specific suggestions for their next study. For example, if a user's current TOEIC score is 600 and their goal is 800, the prompt displayed will read, "The user's current TOEIC score is 600, their goal is 800, their current progress is 75%, please generate the next learning content and quiz for them."

[0182] Input: Comparison data and prompt statement

[0183] Output: Suggestions for the user (displayed on the terminal)

[0184] Through these steps, the present invention provides learning content and homework according to the user's level of proficiency, evaluates progress, generates comparative data, and provides specific learning suggestions. This system allows users to objectively evaluate their own learning progress and study efficiently.

[0185] 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.

[0186] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an emotion engine that recognizes a user's emotions and adjusts the learning experience. The following describes in detail the embodiments of the present invention.

[0187] The basic operation of this system is realized by the user, terminal, and server working together to run a program. First, the user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0188] The server receives the user's input data and generates appropriate learning content and homework based on the data. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0189] Furthermore, the device is equipped with an emotion engine that recognizes the user's real-time emotions. The emotion engine analyzes the user's facial expressions and tone of voice to determine how the user is feeling while studying. This emotion data is sent to the server.

[0190] The server analyzes the received emotional data and adjusts the user's learning experience. For example, this adjustment may provide more interesting content if it determines that the user is not concentrating, or suggest a short break if the user feels tired. The server also adjusts the next learning content and homework assignments based on the user's learning progress and emotional data. This allows the user to always study in the optimal learning environment.

[0191] After completing their homework, users enter their results into their device. The device then sends the results to a server, which then analyzes the homework results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high accuracy rate but they feel stressed, the difficulty of the next homework can be slightly reduced. Data comparing the user's proficiency with other companies is also generated along with the emotional data, allowing users to objectively evaluate their learning progress.

[0192] As a specific example, if a user begins an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level, and uses an emotion engine to collect and analyze the user's emotional data while studying. If the emotion engine determines that the user is not concentrating, the server provides learning content appropriate to that situation. When the user submits the results of their homework to the server, the difficulty and content of the next lesson are also adjusted. Furthermore, if the user wishes to compare their learning with other companies, the server can generate that data and display it on the device.

[0193] The above is an embodiment of the present invention. This system allows users to effectively study in a way that is tailored to their own level of proficiency and emotional state, and also allows them to understand their own progress through objective evaluations.

[0194] The processing flow will be explained below.

[0195] Step 1:

[0196] A user accesses the system using a terminal and inputs his / her current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0197] Step 2:

[0198] The terminal sends the user's input data to the server, which receives the data.

[0199] Step 3:

[0200] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0201] Step 4:

[0202] The server sends the generated learning content and homework to the device, which receives it.

[0203] Step 5:

[0204] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0205] Step 6:

[0206] The device's built-in emotion engine analyzes the user's facial expressions and tone of voice to collect real-time emotional data, including levels of concentration, stress, and fatigue.

[0207] Step 7:

[0208] The device sends the emotion data collected by the emotion engine to the server, which receives this data.

[0209] Step 8:

[0210] The server analyzes the received emotional data and combines it with the user's learning progress to adjust the next learning content and homework assignments. For example, if the user is not concentrating, it will provide more interesting content or suggest a short break.

[0211] Step 9:

[0212] The user completes the homework and enters the results into the terminal, which then sends the results to the server.

[0213] Step 10:

[0214] The server integrates and analyzes the homework results and emotional data it receives, and adjusts the difficulty and content of the next learning content and homework. For example, if the user has a high accuracy rate but is feeling stressed, the difficulty level will be slightly reduced.

[0215] Step 11:

[0216] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0217] Step 12:

[0218] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0219] Step 13:

[0220] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0221] Step 14:

[0222] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0223] Example 2

[0224] 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."

[0225] While conventional learning support systems provided content tailored to a user's proficiency level and learning goals to a certain extent, they were unable to appropriately adjust the learning experience based on the user's emotional state. This could lead to users losing concentration or becoming overly stressed, making it difficult to achieve optimal learning outcomes. Furthermore, because the next content was adjusted based solely on learning results, it was not possible to take into account the user's emotional changes, limiting the sustainability and effectiveness of learning.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input their current proficiency level and learning goals; a means for the terminal to transmit the user's input data to the server; a means for the server to generate appropriate learning content and homework based on the user's input; a means for the terminal to display the learning content and homework received from the server to the user; a means for the terminal to collect real-time emotional data of the user; a means for the terminal to transmit the emotional data to the server; a means for the server to adjust the learning experience based on the emotional data; a means for the user to input homework results to the terminal; a means for the terminal to transmit the homework results to the server; and a means for the server to adjust the next learning content and homework based on the homework results and emotional data. This makes it possible to provide optimal learning content while taking the user's emotional state into consideration and dynamically adjust the learning experience.

[0227] "User" refers to an individual who uses the system to receive learning content and homework.

[0228] "Proficiency" refers to the level of knowledge or skill that a user currently possesses in a particular subject of study.

[0229] "Learning goal" refers to the specific learning outcome or target value that the user is trying to achieve.

[0230] "Terminal" refers to a device used by a user, such as a PC or smartphone, that functions as an interface with the system.

[0231] "Server" refers to the central part of the system, a device that receives data from users and generates and provides appropriate learning content and homework.

[0232] "Learning content" refers to information such as learning materials and lessons for users to study.

[0233] "Homework" refers to tasks or problems that users complete for self-study.

[0234] "Emotional data" refers to data that indicates the user's real-time emotional state, analyzed from facial expressions, tone of voice, etc.

[0235] "Adjusting" refers to the system changing the difficulty and content of learning content and homework based on the user's emotional data and learning results.

[0236] "Comparison data" refers to information that allows a user to compare their proficiency with that of other users based on their current level.

[0237] The present invention provides a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, can objectively evaluate learning progress, and recognizes the user's emotions to adjust the learning experience. The following describes in detail the embodiments of the present invention.

[0238] This system is realized by a program that allows users, terminals, and servers to work together. Specifically, the system begins when a user inputs their current proficiency level, study subject (e.g., English conversation), and study goal (e.g., TOEIC 800 points) through their terminal, and this data is then sent to the server.

[0239] The terminal consists of a device such as a PC or smartphone, and accepts user input through a dedicated application. The input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0240] The server generates appropriate learning content and homework based on the data received from the user. The generative AI model used here is a machine learning model such as GPT-4 (registered trademark). Examples of prompts for the generative AI model include the following:

[0241] Example prompt sentence:

[0242] "Please create lesson content suitable for users aiming for intermediate level English conversation."

[0243] The learning content and homework generated by the server are sent to the terminal and displayed to the user through the application, allowing the user to proceed with their studies based on this information.

[0244] During the learning process, the device collects real-time emotional data from the user. This data is collected using an emotion engine (e.g., Microsoft® Azure® Cognitive Services) installed on the device, which analyzes the user's facial expressions, tone of voice, etc. This emotional data is then sent back to the server.

[0245] The server analyzes the user's emotional data and dynamically adjusts the learning experience. For example, if it determines that the user is not concentrating, it generates and provides more interesting content. If the user feels tired, it will suggest taking a break.

[0246] After a user completes their homework, they enter their results into the device, which then sends them to the server. The server analyzes the homework results and emotional data to adjust the content and difficulty of the next homework assignment. For example, if a high accuracy rate is observed but the user is still feeling stressed, the content of the next assignment can be reduced.

[0247] Furthermore, the server generates comparison data with other users based on the user's current proficiency level and transmits it to the terminal, allowing the user to objectively evaluate their own learning progress.

[0248] This invention allows users to study in an optimal learning environment that suits their emotional state and level of proficiency, thereby enhancing the effectiveness of their studies. Furthermore, by using comparative data to perform self-evaluation, it becomes possible to maintain motivation for studying.

[0249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0250] Step 1:

[0251] A user accesses the system's application using a terminal. The user enters their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goal (e.g., TOEIC 800 points) into a form within the app. Once the input is complete, the user clicks the "Submit" button. The input data is sent from the terminal to the server. The data entered here is the user's current proficiency level, the subject they are studying, and their learning goal. The output sent is data containing the user's learning information.

[0252] Step 2:

[0253] The terminal acquires the data entered by the user and sends it to the server via a secure communication protocol (e.g., HTTPS). The input includes the user's learning information. The output is the user's learning information sent to the server. Data processing involves temporarily storing the input data and converting it into an appropriate format before sending it.

[0254] Step 3:

[0255] The server generates appropriate learning content and homework based on the received user input data. The generative AI model used here is a machine learning model such as GPT-4. The following prompt is used for the generative AI model: "Please create lesson content suitable for users aiming for intermediate-level English conversation." The input includes the user's learning information. The output is learning content and homework. The server sends the generated content to the device.

[0256] Step 4:

[0257] The terminal displays the learning content and homework received from the server to the user. The input includes the learning content and homework from the server. The output is the learning content and homework displayed in a form that the user can check. Specific operations include displaying the lesson content and homework on the application screen.

[0258] Step 5:

[0259] Using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, the system analyzes the user's facial expressions and tone of voice during training to collect real-time emotional data. The input includes data on the user's facial expressions and voice. The output is data indicating the user's emotional state. Facial expression recognition algorithms and voice analysis algorithms are used for data processing.

[0260] Step 6:

[0261] The collected emotion data is sent from the device to the server. The input includes the user's emotion data. The output is the emotion data sent to the server. As a data processing step, the data is encrypted before transmission to ensure security.

[0262] Step 7:

[0263] The server analyzes the received emotional data and adjusts the user's learning experience. For example, it provides more interesting content if the user is not concentrating, or suggests a break if the user is tired. The input includes the user's emotional data. The output is new learning content or instructions tailored to the user's state. An algorithm for analyzing the emotional data is used for data processing.

[0264] Step 8:

[0265] After completing their studies, the user inputs their homework answers into the application. The device sends the input data to the server. The input includes the user's homework answer data. The output is the homework answer data sent to the server.

[0266] Step 9:

[0267] The terminal sends the homework results to the server. The input includes the user's homework results. The output is the homework results sent to the server. For data processing, a protocol for secure data transmission is used.

[0268] Step 10:

[0269] The server adjusts the next learning content and homework based on the homework results and emotional data. The input includes the user's homework results and emotional data. The output is the adjusted next learning content and homework. For example, if there is a high accuracy rate but stress is felt, the difficulty of the next homework is reduced. Data processing involves using machine learning algorithms to generate optimal content and homework.

[0270] Step 11:

[0271] The server generates comparison data with other users based on the user's current level and sends it to the terminal. The input includes the user's current proficiency data. The output is the comparison data with other users. Data processing includes the aggregation and analysis of user data.

[0272] Step 12:

[0273] The terminal displays the comparison data received from the server to the user within the application. The input includes the comparison data from the server. The output is the comparison data displayed in a format that can be viewed by the user. Specific operations include displaying the data in graphs or text.

[0274] (Application example 2)

[0275] 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."

[0276] Conventional online learning systems only provide learning content based on the user's proficiency level, but are unable to adjust the content to take into account the user's emotional state, resulting in poor learning efficiency. Furthermore, they do not provide sufficient data to compare the user's progress with other companies, making self-evaluation difficult. To address these issues, a system is needed that can recognize the user's emotional state in real time and adjust the learning content accordingly.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0278] In this invention, the server includes: means for a user to input their current proficiency level and learning goals; means for the server to generate appropriate learning content and homework based on the user's input; means for a terminal to display the learning content and homework received from the server to the user; means for analyzing the user's facial expressions and tone of voice to recognize emotions; means for transmitting the emotional data to the server; means for the server to adjust the learning experience based on the emotional data; means for the user to transmit homework results to the server; and means for the server to adjust the next learning content and homework based on the homework results and emotional data, thereby enabling efficient learning that takes the user's emotional state into consideration.

[0279] "User" refers to a learner who uses this system.

[0280] "Current proficiency" refers to the level of a particular skill or knowledge that a user currently possesses.

[0281] A "learning goal" is a specific skill or knowledge milestone that a user is trying to achieve.

[0282] "Server" refers to a remote computer system that receives data from users and generates and provides appropriate learning content and homework based on that data.

[0283] "Learning Content" refers to educational resources and information provided to users to further their learning.

[0284] "Homework" refers to tasks or problems that a user must complete to advance their learning.

[0285] "Terminal" refers to a smartphone, head-mounted display, or other electronic device used by a user.

[0286] "Facial expression" refers to the emotion or state shown by the user's facial movements and expressions.

[0287] "Tone of voice" refers to characteristics such as pitch, strength, and quality of a user's voice.

[0288] "Emotional data" refers to information about the user's emotional state obtained from facial expressions and tone of voice.

[0289] "Learning experience" refers to the process and overall experience a user has while using learning content.

[0290] "Progress" refers to the progress a user has made in the learning process.

[0291] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate learning content and homework based on user input.

[0292] A "prompt" is a starting point text that is given to a generative AI model to instruct it on how to generate learning content or homework.

[0293] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an "emotion engine" that recognizes the user's emotions and adjusts the learning experience. Hereinafter, embodiments of the present invention will be described in detail.

[0294] This system is realized by running a program in cooperation with a server, a user terminal, and an emotion engine.

[0295] Processing on the user's device

[0296] Users input their current level of proficiency, the subjects they are studying, and their learning goals using a smartphone or head-mounted display. This input data is sent from the device to the server. For example, if a user requests intermediate-level English conversation lessons with a goal of achieving a TOEIC score of 800, this information becomes the input data.

[0297] Processing on the server

[0298] The server receives the user's input data and generates appropriate learning content and homework based on that data using a generative AI model developed in Python. The model generates learning content using the following prompt sentence as input:

[0299] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[0300] The generated learning content and homework are sent to the user's terminal, which displays them to the user.

[0301] Emotion engine processing

[0302] The user's device is equipped with a camera and microphone for facial expression recognition and voice analysis. The emotion engine (using the TENSORFLOW® model) analyzes these input data to recognize the user's emotional state (concentration, fatigue, interest, etc.). This emotion data is sent to the server in real time. For example, if the user is determined to be not concentrating, the learning experience is adjusted using the following prompt sentence:

[0303] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[0304] Tailoring the learning experience

[0305] The server adjusts the learning content based on the emotional data. For example, if the user feels tired, it provides content suggesting a short break. It also changes the content and difficulty of the learning as needed to ensure the user has an optimal learning experience.

[0306] Assessment of learning progress and next adjustments

[0307] After completing their homework, users enter their results into their device, which then sends them to the server. The server then analyzes the results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high success rate but they feel stressed, the difficulty of the next homework will be reduced.

[0308] Providing progress comparison data

[0309] Furthermore, the server generates data for comparing proficiency levels based on the data of other users and provides this data to the user, allowing the user to objectively evaluate their own learning progress. For example, the user can send a prompt to the server requesting comparison with others, and the server generates comparison data.

[0310] In this way, the present invention makes it possible to provide an optimal learning experience according to the user's emotions and level of proficiency.

[0311] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0312] Step 1:

[0313] The user inputs their current proficiency level and learning goals.

[0314] Input details: Proficiency level (e.g., intermediate), study subject (e.g., English conversation), learning goal (e.g., TOEIC 800 points)

[0315] Specific operation: The user inputs the necessary information into a dedicated application using a smartphone or head-mounted display. The input data is saved on the device.

[0316] Step 2:

[0317] The terminal sends the input data to the server.

[0318] Input content: User proficiency, study subjects, learning goals

[0319] Output: User data received by the server

[0320] Specific operation: The terminal sends the user's input data to the server via the Internet, and the server stores this information in a database.

[0321] Step 3:

[0322] The server generates appropriate learning content and homework.

[0323] Input content: User data

[0324] Output: Generated learning content and homework

[0325] How it works: The server uses a generative AI model developed in Python to generate appropriate learning content and homework using the following prompts as input:

[0326] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[0327] The generated data is transmitted from the server to the terminal.

[0328] Step 4:

[0329] The terminal displays the received learning content and homework to the user.

[0330] Input content: Learning content and homework received from the server

[0331] Output: Displayed learning content and homework

[0332] Specific operation: The device displays the received content in a format that is easy for the user to understand. The user then begins learning.

[0333] Step 5:

[0334] The device analyzes the user's facial expressions and tone of voice to recognize emotions.

[0335] Input content: User's facial expression data, tone of voice data

[0336] Output: Recognized emotion data

[0337] Specific operation: Facial expression recognition and voice analysis are performed using the device's built-in camera and microphone. An emotion analysis engine using TensorFlow recognizes the user's emotions in real time and generates data.

[0338] Step 6:

[0339] The device transmits the emotion data to the server.

[0340] Input content: Emotion data

[0341] Output: Emotion data received by the server

[0342] Specific operation: The device sends the analyzed emotion data via the Internet to a server, which receives the data and stores it for analysis.

[0343] Step 7:

[0344] The server tailors the learning experience based on the emotional data.

[0345] Input content: Emotion data

[0346] Output: Tailored learning experience

[0347] What Happens: The server analyzes the emotion data and tailors the learning experience using the following prompt:

[0348] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[0349] Tailored learning content and break suggestions are sent to the device.

[0350] Step 8:

[0351] The user enters the results of their homework, and the terminal sends them to the server.

[0352] Input content: Homework results

[0353] Output: Homework result data received by the server

[0354] Specific operation: The user inputs the results of their homework using a smartphone or head-mounted display. The results are then sent from the device to the server.

[0355] Step 9:

[0356] The server analyzes the homework results and emotion data and adjusts the next learning content and homework.

[0357] Input content: Homework result data, emotion data

[0358] Output: Adjusted next learning content and homework

[0359] Specific operation: The server generates appropriate new learning content and homework based on the accuracy rate of homework and emotional data. The generated data is used for the next learning session.

[0360] Step 10:

[0361] The server generates data based on the user's current level to compare their proficiency with that of other companies.

[0362] Input: User progress data, other users' data

[0363] Output content: Comparison data

[0364] Specific operation: The server compares and analyzes the user's progress data with that of other users to generate proficiency comparison data, which is then sent to the user's device and displayed.

[0365] 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.

[0366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] 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.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0380] 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."

[0381] The present invention relates to a system that provides learning content and homework according to a user's level of proficiency and can objectively evaluate the learning progress. Hereinafter, an embodiment of the present invention will be described in detail.

[0382] For this system to function, the user, the terminal, and the server must cooperate to operate the program. First, the user accesses the system using the terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0383] The server receives the user's input data and generates appropriate learning content and homework. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0384] The user uses the learning content provided through the device to study and complete homework. The user inputs the results of the homework (e.g., the percentage of correct answers) into the device, which then transmits the results to the server.

[0385] The server adjusts the next learning content and homework based on the received homework results. For example, if the user answers the homework correctly, the next content may be more difficult. Conversely, if the user answers the homework correctly, the server may provide content of the same level or a lower level of difficulty.

[0386] Furthermore, the server generates data comparing the user's proficiency level with that of other companies based on the user's current level. This comparison data is presented in the form of, for example, "Intermediate level, compared to other companies, is within the range of passing X University." The terminal also displays this comparison data to the user, allowing them to objectively evaluate their own proficiency level.

[0387] As a specific example, if a user starts an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level and provides them to the user through the device. The user sends the results of the homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. In addition, if the user wishes to compare with other companies, the server can generate that data and display it on the device.

[0388] The above is an embodiment of the present invention. This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluations.

[0389] The processing flow will be explained below.

[0390] Step 1:

[0391] A user accesses the system using a terminal and inputs their current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0392] Step 2:

[0393] The terminal sends the user's input data to the server, which receives the data.

[0394] Step 3:

[0395] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0396] Step 4:

[0397] The server sends the generated learning content and homework to the device, which receives it.

[0398] Step 5:

[0399] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0400] Step 6:

[0401] The user enters the results of their homework into the terminal, which then sends the results to the server.

[0402] Step 7:

[0403] The server analyzes the results of the homework received and adjusts the next learning content and homework. For example, if the percentage of correct answers in the homework is high, the difficulty level of the next content is increased, and if the percentage of correct answers is low, the difficulty level is maintained or decreased.

[0404] Step 8:

[0405] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0406] Step 9:

[0407] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0408] Step 10:

[0409] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0410] Step 11:

[0411] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0412] Example 1

[0413] 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."

[0414] Conventional learning systems have difficulty providing appropriate learning content and homework according to a user's level of proficiency, and lack a means to objectively evaluate learning progress. This makes it difficult for users to accurately grasp their own learning results, resulting in a decrease in learning efficiency.

[0415] 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.

[0416] In this invention, the server includes a means for a user to input their current level of proficiency and learning goals, a means for a terminal to transmit the input data to the server, a means for the server to generate appropriate learning content and homework based on the user's input, a means for the terminal to display the learning content and homework received from the server to the user, a means for the user to input homework results to the terminal and transmit them to the server, and a means for the server to adjust the next learning content and homework based on the homework results. This makes it possible to provide effective learning content according to the user's level of proficiency and objectively evaluate the user's learning progress.

[0417] "User" refers to a person who uses the system to receive learning content and homework.

[0418] "Terminal" refers to a device used by a user to access the system, such as a computer or smartphone.

[0419] "Server" refers to the central part of the system that receives data sent by users and generates and manages learning content and homework.

[0420] "Current proficiency" refers to the level of skill or knowledge that the user has currently acquired.

[0421] "Learning goal" refers to the specific learning objective or outcome that the user wants to achieve.

[0422] "Data" refers to information entered by the user and server-generated information, including learning content, homework, and comparison data.

[0423] "Learning content" refers to learning materials, practice questions, etc. that users use to study.

[0424] "Homework" refers to assignments or exercises that users complete to advance their learning.

[0425] "Homework results" refers to the grades and answers given by the user when they complete the homework.

[0426] "Comparative data" refers to data that shows the results of comparing a user's proficiency with other users or standards.

[0427] MODE FOR CARRYING OUT THE INVENTION

[0428] The present invention is a system that provides learning content and homework according to a user's level of proficiency and objectively evaluates the user's learning progress. For this system to function efficiently, the user, the terminal, and the server must cooperate to operate the program.

[0429] First, a user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0430] The server receives the user's input data and generates appropriate learning content and homework based on it. Specifically, the server uses a generative AI model to generate prompt sentences such as the following, and creates learning content and homework based on those prompts.

[0431] Example prompt sentence:

[0432] "Please generate intermediate level English conversation lesson content and homework to help me aim for a TOEIC score of 800. Also, please adjust the difficulty of the next lesson based on the learning results and provide comparative data with other companies."

[0433] The generated learning content and homework are sent from the server to the device, which then visually displays them to the user. The user studies using the learning content provided through the device, and when completing the homework, the results (e.g., percentage of correct answers) are entered into the device again. The device then sends this result data to the server.

[0434] The server analyzes the results of the homework assignments received and adjusts the next learning content and homework assignments. It also generates data comparing the user's current proficiency with other users, as needed. For example, it generates comparison data in the form of "Intermediate level, compared to other companies, and within the range of passing X University."

[0435] This allows users to be provided with learning content tailored to their level of proficiency, and also allows them to objectively evaluate their learning progress. Specifically, if a user begins intermediate-level English conversation lessons with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for that level and provides it to the user via their device. The user sends the results of their homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. Furthermore, if the user wishes to compare their services with those of other companies, the server can generate that data and display it on the device.

[0436] This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluation.

[0437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] The user inputs their current proficiency level, the subjects they are studying, and their learning goals. As input, the user enters data into a form on the terminal screen. The input data is the user's "current proficiency level," "study subjects," and "learning goals." For example, they enter "intermediate," "English conversation," and "TOEIC 800 points." These input data affect the user's learning level.

[0440] Step 2:

[0441] The device sends the data entered by the user to the server. Specifically, the device sends the data to the server using an HTTP POST request. This input data includes "current proficiency level," "study subjects," and "study goals." The server receives this data.

[0442] Step 3:

[0443] The server analyzes the received data and generates appropriate learning content and homework using a generative AI model. Inputs include the user's "current proficiency level," "study subjects," and "learning goals." The server generates and sends the following prompt to the generative AI model: "Please generate intermediate-level English conversation lesson content and homework to aim for a TOEIC score of 800." The generated learning content and homework are obtained as output.

[0444] Step 4:

[0445] The generated learning content and homework are sent from the server to the device. Specifically, the server sends the learning content and homework to the device as an HTTP response. This output data includes "Intermediate English Conversation Lessons" and "Intermediate Homework" appropriate for the user's level of proficiency.

[0446] Step 5:

[0447] The device displays the received learning content and homework to the user. As output, the device visually displays "Intermediate English Conversation Lessons" and "Intermediate Homework" on the user's screen. This operation allows the user to use the displayed data for study.

[0448] Step 6:

[0449] The user uses the provided learning content to progress through their studies and complete their homework. The input is the learning content and homework on the device. The user studies through quizzes and interactive practice sessions and answers the homework. The output is the results of their studies and the results of their homework (e.g., the percentage of correct answers).

[0450] Step 7:

[0451] The user enters the results of their homework into the device, and the device sends the results to the server. The user enters the results of their homework into a designated input field on the device. For example, "90% correct." The device sends this result data to the server using an HTTP POST request. The server receives the homework result data.

[0452] Step 8:

[0453] The server analyzes the homework results it receives and adjusts the next learning content and homework. The input is "homework result data." The server generates and sends the following prompt to the generative AI model: "Please adjust the difficulty of the next lesson based on the user's 90% correct answer rate." The adjusted learning content and homework are obtained as output.

[0454] Step 9:

[0455] In addition, the server generates data comparing the user's proficiency level with other companies' proficiency levels based on the user's current level. The inputs include the user's "current proficiency level" and "homework result data." The server uses a comparison algorithm to compare the data with other users' data. The output generates comparison data such as "Your TOEIC score is in the top 20% of other intermediate-level users."

[0456] Step 10:

[0457] The terminal receives the comparison data sent from the server and displays it to the user. As an output, the terminal displays on the user's screen, "Your TOEIC score is in the top 20% of other intermediate-level users." This allows the user to objectively evaluate their own proficiency level compared to other users.

[0458] The above is the specific processing flow of this system.

[0459] (Application example 1)

[0460] 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."

[0461] While conventional learning systems can provide appropriate learning content based on a user's level of proficiency, they are unable to compare the level of proficiency with that of other users, objectively evaluate the user's learning progress, or effectively suggest the next learning content. This makes it difficult for users to accurately understand their own learning progress and proceed with their studies appropriately.

[0462] 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.

[0463] In this invention, the server includes a means for generating comparison data using a generative AI model based on the learning progress of other users, a means for the user to check the comparison data, and a means for generating the comparison data and a prompt sentence for generating a suggestion for the next learning content, thereby enabling the user to objectively evaluate their own learning progress and receive effective learning suggestions.

[0464] "User" refers to an individual who uses the learning system to manage their own proficiency and learning progress.

[0465] "Proficiency" is a measure that indicates the level of knowledge and skill that a user currently possesses.

[0466] "Learning Objective" means a specific learning outcome or qualification that a user aims to achieve.

[0467] "Server" refers to a group of computers that receive data from users and generate and coordinate learning content and homework assignments.

[0468] "Learning content" refers to the teaching materials and resources that users use for learning.

[0469] "Homework" refers to assignments or exercises that a user must complete in relation to the learning content.

[0470] "Terminal" refers to the device (smartphone, tablet, PC, etc.) through which a user accesses the system and displays learning content and homework.

[0471] "Homework results" refers to data such as the grades and percentage of correct answers when a user completes homework.

[0472] "Comparison data" refers to evaluation information generated by comparing a user's proficiency level with the data of other users.

[0473] "Generative AI models" refer to artificial intelligence algorithms used to analyze data and generate and tailor learning content.

[0474] A "prompt" refers to input text that instructs a generative AI model to generate learning content and suggestions.

[0475] The present invention relates to a system for providing learning content and homework according to a user's level of proficiency and evaluating the user's learning progress. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS ... will be described in detail below with reference to exemplary embodiments.

[0476] System Configuration

[0477] To implement this invention, the user, the terminal, and the server must work together to run the program. The system includes the following main components:

[0478] 1. The user accesses the system using a terminal and enters their current proficiency level and learning goals.

[0479] 2. The terminal is a device such as a smartphone, tablet, or PC through which the user receives learning content and homework.

[0480] 3. The server is a set of computers that generate and coordinate learning content and homework based on user input. The following technologies are used to perform the necessary processing:

[0481] Generative AI models are used to generate learning content and tailor homework.

[0482] A database (e.g. MongoDB) is used to manage user data and learning progress.

[0483] Implementation Procedure

[0484] 1. User Registration and Data Entry:

[0485] The user inputs his / her current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800) using the terminal, and this data is sent from the terminal to the server.

[0486] 2. Learning content and homework generation:

[0487] The server generates appropriate learning content (e.g., intermediate-level English conversation lessons) and homework (e.g., intermediate-level practice questions) based on the user's proficiency and learning goals. A generative AI model is used in this process.

[0488] 3. Content Delivery and Learning:

[0489] The generated learning content and homework are sent from the server to the terminal and displayed to the user, who then uses the provided content to advance their studies and complete their homework.

[0490] 4. Progress Assessment and Adjustment:

[0491] The user inputs the results of the homework (e.g., percentage of correct answers) into the device and sends them to the server. The server then adjusts the difficulty and content of the next learning content and homework based on the results.

[0492] 5. Generating and Providing Comparative Data:

[0493] The server compares the user's learning progress with other users and generates comparison data using a generative AI model. For example, it includes evaluation information such as, "Your progress is about the same as other intermediate level users." This comparison data is sent to the device and can be confirmed by the user.

[0494] Specific examples

[0495] For example, if a user's goal is to achieve a TOEIC score of 800, they will enter their current proficiency level as intermediate when they first register. The server will generate intermediate-level English conversation lesson content and homework and provide it to the user. The user will then progress with their studies and enter their homework results. The server will then adjust the difficulty of the next content based on the results and generate evaluation information by comparing the user's data with that of other users.

[0496] Prompt Sentence Examples

[0497] "The user's current TOEIC score is 600, their goal is 800, and their current progress is 75%. Please generate the next learning content and quiz for them."

[0498] As a result, the present invention allows users to receive appropriate feedback and suggestions based on their own level of proficiency, enabling them to progress with their studies efficiently.

[0499] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0500] Step 1: User registration and data entry

[0501] A user accesses the learning system using a terminal and enters their current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800). The entered data is sent from the terminal to the server. The server stores the received data in a database as a user profile. This profile includes the user's proficiency level, learning goal, and other necessary information.

[0502] Input: User proficiency and learning goals

[0503] Output: User profile (stored in database)

[0504] Step 2: Generate learning content and assignments

[0505] The server uses a generative AI model to generate learning content and homework based on the user's proficiency and learning goals. Specifically, it creates video lessons and quizzes appropriate for the user's current level. The generated content is sent from the server to the device and displayed to the user.

[0506] Input: User profile (proficiency and learning goals)

[0507] Output: Learning content and homework (sent to device)

[0508] Step 3: View and learn content

[0509] The device displays the learning content and homework received from the server to the user. The user watches the video lessons and works on the homework through the device. After the user completes the homework, they input the results (e.g., percentage of correct answers) into the device.

[0510] Input: Learning content and homework

[0511] Output: Homework results (entered by the user)

[0512] Step 4: Assess progress and adjust content

[0513] The server receives the homework results sent from the device and evaluates progress. It uses the generative AI model to adjust the difficulty and content of the next learning content and homework. For example, if the user has a high rate of correct answers on the homework, it will increase the difficulty of the next content. Conversely, if the rate of correct answers is low, it will decrease the difficulty.

[0514] Input: Homework results

[0515] Output: Next learning content and homework (adjusted for difficulty and sent to device)

[0516] Step 5: Generate and provide comparison data

[0517] The server compares the user's progress data with that of other users and generates comparison data using a generative AI model. The comparison data includes evaluation information such as "Your progress is about the same as other intermediate level users." This comparison data is also used to generate prompts. The generated comparison data and prompts are sent to the device and displayed to the user.

[0518] Input: User progress data and other users' data

[0519] Output: Comparison data and prompt (sent to terminal)

[0520] Step 6: Generate and view proposals

[0521] The device displays the comparison data and prompts received from the server to the user. Based on this information, the user can objectively evaluate their own learning progress and receive specific suggestions for their next study. For example, if a user's current TOEIC score is 600 and their goal is 800, the prompt displayed will read, "The user's current TOEIC score is 600, their goal is 800, their current progress is 75%, please generate the next learning content and quiz for them."

[0522] Input: Comparison data and prompt statement

[0523] Output: Suggestions for the user (displayed on the terminal)

[0524] Through these steps, the present invention provides learning content and homework according to the user's level of proficiency, evaluates progress, generates comparative data, and provides specific learning suggestions. This system allows users to objectively evaluate their own learning progress and study efficiently.

[0525] 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.

[0526] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an emotion engine that recognizes a user's emotions and adjusts the learning experience. The following describes in detail the embodiments of the present invention.

[0527] The basic operation of this system is realized by the user, terminal, and server working together to run a program. First, the user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0528] The server receives the user's input data and generates appropriate learning content and homework based on the data. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0529] Furthermore, the device is equipped with an emotion engine that recognizes the user's real-time emotions. The emotion engine analyzes the user's facial expressions and tone of voice to determine how the user is feeling while studying. This emotion data is sent to the server.

[0530] The server analyzes the received emotional data and adjusts the user's learning experience. For example, this adjustment may provide more interesting content if it determines that the user is not concentrating, or suggest a short break if the user feels tired. The server also adjusts the next learning content and homework assignments based on the user's learning progress and emotional data. This allows the user to always study in the optimal learning environment.

[0531] After completing their homework, users enter their results into their device. The device then sends the results to a server, which then analyzes the homework results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high accuracy rate but they feel stressed, the difficulty of the next homework can be slightly reduced. Data comparing the user's proficiency with other companies is also generated along with the emotional data, allowing users to objectively evaluate their learning progress.

[0532] As a specific example, if a user begins an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level, and uses an emotion engine to collect and analyze the user's emotional data while studying. If the emotion engine determines that the user is not concentrating, the server provides learning content appropriate to that situation. When the user submits the results of their homework to the server, the difficulty and content of the next lesson are also adjusted. Furthermore, if the user wishes to compare their learning with other companies, the server can generate that data and display it on the device.

[0533] The above is an embodiment of the present invention. This system allows users to effectively study in a way that is tailored to their own level of proficiency and emotional state, and also allows them to understand their own progress through objective evaluations.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] A user accesses the system using a terminal and inputs his / her current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0537] Step 2:

[0538] The terminal sends the user's input data to the server, which receives the data.

[0539] Step 3:

[0540] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0541] Step 4:

[0542] The server sends the generated learning content and homework to the device, which receives it.

[0543] Step 5:

[0544] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0545] Step 6:

[0546] The device's built-in emotion engine analyzes the user's facial expressions and tone of voice to collect real-time emotional data, including levels of concentration, stress, and fatigue.

[0547] Step 7:

[0548] The device sends the emotion data collected by the emotion engine to the server, which receives this data.

[0549] Step 8:

[0550] The server analyzes the received emotional data and combines it with the user's learning progress to adjust the next learning content and homework assignments. For example, if the user is not concentrating, it will provide more interesting content or suggest a short break.

[0551] Step 9:

[0552] The user completes the homework and enters the results into the terminal, which then sends the results to the server.

[0553] Step 10:

[0554] The server integrates and analyzes the homework results and emotional data it receives, and adjusts the difficulty and content of the next learning content and homework. For example, if the user has a high accuracy rate but is feeling stressed, the difficulty level will be slightly reduced.

[0555] Step 11:

[0556] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0557] Step 12:

[0558] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0559] Step 13:

[0560] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0561] Step 14:

[0562] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0563] Example 2

[0564] 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."

[0565] While conventional learning support systems provided content tailored to a user's proficiency level and learning goals to a certain extent, they were unable to appropriately adjust the learning experience based on the user's emotional state. This could lead to users losing concentration or becoming overly stressed, making it difficult to achieve optimal learning outcomes. Furthermore, because the next content was adjusted based solely on learning results, it was not possible to take into account the user's emotional changes, limiting the sustainability and effectiveness of learning.

[0566] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input their current proficiency level and learning goals; a means for the terminal to transmit the user's input data to the server; a means for the server to generate appropriate learning content and homework based on the user's input; a means for the terminal to display the learning content and homework received from the server to the user; a means for the terminal to collect real-time emotional data of the user; a means for the terminal to transmit the emotional data to the server; a means for the server to adjust the learning experience based on the emotional data; a means for the user to input homework results to the terminal; a means for the terminal to transmit the homework results to the server; and a means for the server to adjust the next learning content and homework based on the homework results and emotional data. This makes it possible to provide optimal learning content while taking the user's emotional state into consideration and dynamically adjust the learning experience.

[0567] "User" refers to an individual who uses the system to receive learning content and homework.

[0568] "Proficiency" refers to the level of knowledge or skill that a user currently possesses in a particular subject of study.

[0569] "Learning goal" refers to the specific learning outcome or target value that the user is trying to achieve.

[0570] "Terminal" refers to a device used by a user, such as a PC or smartphone, that functions as an interface with the system.

[0571] "Server" refers to the central part of the system, a device that receives data from users and generates and provides appropriate learning content and homework.

[0572] "Learning content" refers to information such as learning materials and lessons for users to study.

[0573] "Homework" refers to tasks or problems that users complete for self-study.

[0574] "Emotional data" refers to data that indicates the user's real-time emotional state, analyzed from facial expressions, tone of voice, etc.

[0575] "Adjusting" refers to the system changing the difficulty and content of learning content and homework based on the user's emotional data and learning results.

[0576] "Comparison data" refers to information that allows a user to compare their proficiency with that of other users based on their current level.

[0577] The present invention provides a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, can objectively evaluate learning progress, and recognizes the user's emotions to adjust the learning experience. The following describes in detail the embodiments of the present invention.

[0578] This system is realized by a program that allows users, terminals, and servers to work together. Specifically, the system begins when a user inputs their current proficiency level, study subject (e.g., English conversation), and study goal (e.g., TOEIC 800 points) through their terminal, and this data is then sent to the server.

[0579] The terminal consists of a device such as a PC or smartphone, and accepts user input through a dedicated application. The input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0580] The server generates appropriate learning content and homework based on the data received from the user. The generative AI model used here is a machine learning model such as GPT-4. Examples of prompts for the generative AI model include the following:

[0581] Example prompt sentence:

[0582] "Please create lesson content suitable for users aiming for intermediate level English conversation."

[0583] The learning content and homework generated by the server are sent to the terminal and displayed to the user through the application, allowing the user to proceed with their studies based on this information.

[0584] During the learning process, the device collects real-time emotional data from the user. This data is collected using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, which analyzes the user's facial expressions, tone of voice, etc. This emotional data is then sent back to the server.

[0585] The server analyzes the user's emotional data and dynamically adjusts the learning experience. For example, if it determines that the user is not concentrating, it generates and provides more interesting content. If the user feels tired, it will suggest taking a break.

[0586] After a user completes their homework, they enter their results into the device, which then sends them to the server. The server analyzes the homework results and emotional data to adjust the content and difficulty of the next homework assignment. For example, if a high accuracy rate is observed but the user is still feeling stressed, the content of the next assignment can be reduced.

[0587] Furthermore, the server generates comparison data with other users based on the user's current proficiency level and transmits it to the terminal, allowing the user to objectively evaluate their own learning progress.

[0588] This invention allows users to study in an optimal learning environment that suits their emotional state and level of proficiency, thereby enhancing the effectiveness of their studies. Furthermore, by using comparative data to perform self-evaluation, it becomes possible to maintain motivation for studying.

[0589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0590] Step 1:

[0591] A user accesses the system's application using a terminal. The user enters their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goal (e.g., TOEIC 800 points) into a form within the app. Once the input is complete, the user clicks the "Submit" button. The input data is sent from the terminal to the server. The data entered here is the user's current proficiency level, the subject they are studying, and their learning goal. The output sent is data containing the user's learning information.

[0592] Step 2:

[0593] The terminal acquires the data entered by the user and sends it to the server via a secure communication protocol (e.g., HTTPS). The input includes the user's learning information. The output is the user's learning information sent to the server. Data processing involves temporarily storing the input data and converting it into an appropriate format before sending it.

[0594] Step 3:

[0595] The server generates appropriate learning content and homework based on the received user input data. The generative AI model used here is a machine learning model such as GPT-4. The following prompt is used for the generative AI model: "Please create lesson content suitable for users aiming for intermediate-level English conversation." The input includes the user's learning information. The output is learning content and homework. The server sends the generated content to the device.

[0596] Step 4:

[0597] The terminal displays the learning content and homework received from the server to the user. The input includes the learning content and homework from the server. The output is the learning content and homework displayed in a form that the user can check. Specific operations include displaying the lesson content and homework on the application screen.

[0598] Step 5:

[0599] Using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, the system analyzes the user's facial expressions and tone of voice during training to collect real-time emotional data. The input includes data on the user's facial expressions and voice. The output is data indicating the user's emotional state. Facial expression recognition algorithms and voice analysis algorithms are used for data processing.

[0600] Step 6:

[0601] The collected emotion data is sent from the device to the server. The input includes the user's emotion data. The output is the emotion data sent to the server. As a data processing step, the data is encrypted before transmission to ensure security.

[0602] Step 7:

[0603] The server analyzes the received emotional data and adjusts the user's learning experience. For example, it provides more interesting content if the user is not concentrating, or suggests a break if the user is tired. The input includes the user's emotional data. The output is new learning content or instructions tailored to the user's state. An algorithm for analyzing the emotional data is used for data processing.

[0604] Step 8:

[0605] After completing their studies, the user inputs their homework answers into the application. The device sends the input data to the server. The input includes the user's homework answer data. The output is the homework answer data sent to the server.

[0606] Step 9:

[0607] The terminal sends the homework results to the server. The input includes the user's homework results. The output is the homework results sent to the server. For data processing, a protocol for secure data transmission is used.

[0608] Step 10:

[0609] The server adjusts the next learning content and homework based on the homework results and emotional data. The input includes the user's homework results and emotional data. The output is the adjusted next learning content and homework. For example, if there is a high accuracy rate but stress is felt, the difficulty of the next homework is reduced. Data processing involves using machine learning algorithms to generate optimal content and homework.

[0610] Step 11:

[0611] The server generates comparison data with other users based on the user's current level and sends it to the terminal. The input includes the user's current proficiency data. The output is the comparison data with other users. Data processing includes the aggregation and analysis of user data.

[0612] Step 12:

[0613] The terminal displays the comparison data received from the server to the user within the application. The input includes the comparison data from the server. The output is the comparison data displayed in a format that can be viewed by the user. Specific operations include displaying the data in graphs or text.

[0614] (Application example 2)

[0615] 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."

[0616] Conventional online learning systems only provide learning content based on the user's proficiency level, but are unable to adjust the content to take into account the user's emotional state, resulting in poor learning efficiency. Furthermore, they do not provide sufficient data to compare the user's progress with other companies, making self-evaluation difficult. To address these issues, a system is needed that can recognize the user's emotional state in real time and adjust the learning content accordingly.

[0617] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0618] In this invention, the server includes: means for a user to input their current proficiency level and learning goals; means for the server to generate appropriate learning content and homework based on the user's input; means for a terminal to display the learning content and homework received from the server to the user; means for analyzing the user's facial expressions and tone of voice to recognize emotions; means for transmitting the emotional data to the server; means for the server to adjust the learning experience based on the emotional data; means for the user to transmit homework results to the server; and means for the server to adjust the next learning content and homework based on the homework results and emotional data, thereby enabling efficient learning that takes the user's emotional state into consideration.

[0619] "User" refers to a learner who uses this system.

[0620] "Current proficiency" refers to the level of a particular skill or knowledge that a user currently possesses.

[0621] A "learning goal" is a specific skill or knowledge milestone that a user is trying to achieve.

[0622] "Server" refers to a remote computer system that receives data from users and generates and provides appropriate learning content and homework based on that data.

[0623] "Learning Content" refers to educational resources and information provided to users to further their learning.

[0624] "Homework" refers to tasks or problems that a user must complete to advance their learning.

[0625] "Terminal" refers to a smartphone, head-mounted display, or other electronic device used by a user.

[0626] "Facial expression" refers to the emotion or state shown by the user's facial movements and expressions.

[0627] "Tone of voice" refers to characteristics such as pitch, strength, and quality of a user's voice.

[0628] "Emotional data" refers to information about the user's emotional state obtained from facial expressions and tone of voice.

[0629] "Learning experience" refers to the process and overall experience a user has while using learning content.

[0630] "Progress" refers to the progress a user has made in the learning process.

[0631] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate learning content and homework based on user input.

[0632] A "prompt" is a starting point text that is given to a generative AI model to instruct it on how to generate learning content or homework.

[0633] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an "emotion engine" that recognizes the user's emotions and adjusts the learning experience. Hereinafter, embodiments of the present invention will be described in detail.

[0634] This system is realized by running a program in cooperation with a server, a user terminal, and an emotion engine.

[0635] Processing on the user's device

[0636] Users input their current level of proficiency, the subjects they are studying, and their learning goals using a smartphone or head-mounted display. This input data is sent from the device to the server. For example, if a user requests intermediate-level English conversation lessons with a goal of achieving a TOEIC score of 800, this information becomes the input data.

[0637] Processing on the server

[0638] The server receives the user's input data and generates appropriate learning content and homework based on that data using a generative AI model developed in Python. The model generates learning content using the following prompt sentence as input:

[0639] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[0640] The generated learning content and homework are sent to the user's terminal, which displays them to the user.

[0641] Emotion engine processing

[0642] The user's device is equipped with a camera and microphone for facial expression recognition and voice analysis. The emotion engine (using a TensorFlow model) analyzes these input data to recognize the user's emotional state (concentration, fatigue, interest, etc.). This emotion data is sent to the server in real time. For example, if the user is determined to be not concentrating, the learning experience is adjusted using the following prompt sentence:

[0643] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[0644] Tailoring the learning experience

[0645] The server adjusts the learning content based on the emotional data. For example, if the user feels tired, it provides content suggesting a short break. It also changes the content and difficulty of the learning as needed to ensure the user has an optimal learning experience.

[0646] Assessment of learning progress and next adjustments

[0647] After completing their homework, users enter their results into their device, which then sends them to the server. The server then analyzes the results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high success rate but they feel stressed, the difficulty of the next homework will be reduced.

[0648] Providing progress comparison data

[0649] Furthermore, the server generates data for comparing proficiency levels based on the data of other users and provides this data to the user, allowing the user to objectively evaluate their own learning progress. For example, the user can send a prompt to the server requesting comparison with others, and the server generates comparison data.

[0650] In this way, the present invention makes it possible to provide an optimal learning experience according to the user's emotions and level of proficiency.

[0651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0652] Step 1:

[0653] The user inputs their current proficiency level and learning goals.

[0654] Input details: Proficiency level (e.g., intermediate), study subject (e.g., English conversation), learning goal (e.g., TOEIC 800 points)

[0655] Specific operation: The user inputs the necessary information into a dedicated application using a smartphone or head-mounted display. The input data is saved on the device.

[0656] Step 2:

[0657] The terminal sends the input data to the server.

[0658] Input content: User proficiency, study subjects, learning goals

[0659] Output: User data received by the server

[0660] Specific operation: The terminal sends the user's input data to the server via the Internet, and the server stores this information in a database.

[0661] Step 3:

[0662] The server generates appropriate learning content and homework.

[0663] Input content: User data

[0664] Output: Generated learning content and homework

[0665] How it works: The server uses a generative AI model developed in Python to generate appropriate learning content and homework using the following prompts as input:

[0666] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[0667] The generated data is transmitted from the server to the terminal.

[0668] Step 4:

[0669] The terminal displays the received learning content and homework to the user.

[0670] Input content: Learning content and homework received from the server

[0671] Output: Displayed learning content and homework

[0672] Specific operation: The device displays the received content in a format that is easy for the user to understand. The user then begins learning.

[0673] Step 5:

[0674] The device analyzes the user's facial expressions and tone of voice to recognize emotions.

[0675] Input content: User's facial expression data, tone of voice data

[0676] Output: Recognized emotion data

[0677] Specific operation: Facial expression recognition and voice analysis are performed using the device's built-in camera and microphone. An emotion analysis engine using TensorFlow recognizes the user's emotions in real time and generates data.

[0678] Step 6:

[0679] The device transmits the emotion data to the server.

[0680] Input content: Emotion data

[0681] Output: Emotion data received by the server

[0682] Specific operation: The device sends the analyzed emotion data via the Internet to a server, which receives the data and stores it for analysis.

[0683] Step 7:

[0684] The server tailors the learning experience based on the emotional data.

[0685] Input content: Emotion data

[0686] Output: Tailored learning experience

[0687] What Happens: The server analyzes the emotion data and tailors the learning experience using the following prompt:

[0688] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[0689] Tailored learning content and break suggestions are sent to the device.

[0690] Step 8:

[0691] The user enters the results of their homework, and the terminal sends them to the server.

[0692] Input content: Homework results

[0693] Output: Homework result data received by the server

[0694] Specific operation: The user inputs the results of their homework using a smartphone or head-mounted display. The results are then sent from the device to the server.

[0695] Step 9:

[0696] The server analyzes the homework results and emotion data and adjusts the next learning content and homework.

[0697] Input content: Homework result data, emotion data

[0698] Output: Adjusted next learning content and homework

[0699] Specific operation: The server generates appropriate new learning content and homework based on the accuracy rate of homework and emotional data. The generated data is used for the next learning session.

[0700] Step 10:

[0701] The server generates data based on the user's current level to compare their proficiency with that of other companies.

[0702] Input: User progress data, other users' data

[0703] Output content: Comparison data

[0704] Specific operation: The server compares and analyzes the user's progress data with that of other users to generate proficiency comparison data, which is then sent to the user's device and displayed.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] [Third embodiment]

[0709] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0710] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0711] 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).

[0712] 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.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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."

[0721] The present invention relates to a system that provides learning content and homework according to a user's level of proficiency and can objectively evaluate the learning progress. Hereinafter, an embodiment of the present invention will be described in detail.

[0722] For this system to function, the user, the terminal, and the server must cooperate to operate the program. First, the user accesses the system using the terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0723] The server receives the user's input data and generates appropriate learning content and homework. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0724] The user uses the learning content provided through the device to study and complete homework. The user inputs the results of the homework (e.g., the percentage of correct answers) into the device, which then transmits the results to the server.

[0725] The server adjusts the next learning content and homework based on the received homework results. For example, if the user answers the homework correctly, the next content may be more difficult. Conversely, if the user answers the homework correctly, the server may provide content of the same level or a lower level of difficulty.

[0726] Furthermore, the server generates data comparing the user's proficiency level with that of other companies based on the user's current level. This comparison data is presented in the form of, for example, "Intermediate level, compared to other companies, is within the range of passing X University." The terminal also displays this comparison data to the user, allowing them to objectively evaluate their own proficiency level.

[0727] As a specific example, if a user starts an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level and provides them to the user through the device. The user sends the results of the homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. In addition, if the user wishes to compare with other companies, the server can generate that data and display it on the device.

[0728] The above is an embodiment of the present invention. This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluations.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] A user accesses the system using a terminal and inputs their current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0732] Step 2:

[0733] The terminal sends the user's input data to the server, which receives the data.

[0734] Step 3:

[0735] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0736] Step 4:

[0737] The server sends the generated learning content and homework to the device, which receives it.

[0738] Step 5:

[0739] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0740] Step 6:

[0741] The user enters the results of their homework into the terminal, which then sends the results to the server.

[0742] Step 7:

[0743] The server analyzes the results of the homework received and adjusts the next learning content and homework. For example, if the percentage of correct answers in the homework is high, the difficulty level of the next content is increased, and if the percentage of correct answers is low, the difficulty level is maintained or decreased.

[0744] Step 8:

[0745] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0746] Step 9:

[0747] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0748] Step 10:

[0749] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0750] Step 11:

[0751] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0752] Example 1

[0753] 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."

[0754] Conventional learning systems have difficulty providing appropriate learning content and homework according to a user's level of proficiency, and lack a means to objectively evaluate learning progress. This makes it difficult for users to accurately grasp their own learning results, resulting in a decrease in learning efficiency.

[0755] 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.

[0756] In this invention, the server includes a means for a user to input their current level of proficiency and learning goals, a means for a terminal to transmit the input data to the server, a means for the server to generate appropriate learning content and homework based on the user's input, a means for the terminal to display the learning content and homework received from the server to the user, a means for the user to input homework results to the terminal and transmit them to the server, and a means for the server to adjust the next learning content and homework based on the homework results. This makes it possible to provide effective learning content according to the user's level of proficiency and objectively evaluate the user's learning progress.

[0757] "User" refers to a person who uses the system to receive learning content and homework.

[0758] "Terminal" refers to a device used by a user to access the system, such as a computer or smartphone.

[0759] "Server" refers to the central part of the system that receives data sent by users and generates and manages learning content and homework.

[0760] "Current proficiency" refers to the level of skill or knowledge that the user has currently acquired.

[0761] "Learning goal" refers to the specific learning objective or outcome that the user wants to achieve.

[0762] "Data" refers to information entered by the user and server-generated information, including learning content, homework, and comparison data.

[0763] "Learning content" refers to learning materials, practice questions, etc. that users use to study.

[0764] "Homework" refers to assignments or exercises that users complete to advance their learning.

[0765] "Homework results" refers to the grades and answers given by the user when they complete the homework.

[0766] "Comparative data" refers to data that shows the results of comparing a user's proficiency with other users or standards.

[0767] MODE FOR CARRYING OUT THE INVENTION

[0768] The present invention is a system that provides learning content and homework according to a user's level of proficiency and objectively evaluates the user's learning progress. For this system to function efficiently, the user, the terminal, and the server must cooperate to operate the program.

[0769] First, a user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0770] The server receives the user's input data and generates appropriate learning content and homework based on it. Specifically, the server uses a generative AI model to generate prompt sentences such as the following, and creates learning content and homework based on those prompts.

[0771] Example prompt sentence:

[0772] "Please generate intermediate level English conversation lesson content and homework to help me aim for a TOEIC score of 800. Also, please adjust the difficulty of the next lesson based on the learning results and provide comparative data with other companies."

[0773] The generated learning content and homework are sent from the server to the device, which then visually displays them to the user. The user studies using the learning content provided through the device, and when completing the homework, the results (e.g., percentage of correct answers) are entered into the device again. The device then sends this result data to the server.

[0774] The server analyzes the results of the homework assignments received and adjusts the next learning content and homework assignments. It also generates data comparing the user's current proficiency with other users, as needed. For example, it generates comparison data in the form of "Intermediate level, compared to other companies, and within the range of passing X University."

[0775] This allows users to be provided with learning content tailored to their level of proficiency, and also allows them to objectively evaluate their learning progress. Specifically, if a user begins intermediate-level English conversation lessons with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for that level and provides it to the user via their device. The user sends the results of their homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. Furthermore, if the user wishes to compare their services with those of other companies, the server can generate that data and display it on the device.

[0776] This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluation.

[0777] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0778] Step 1:

[0779] The user inputs their current proficiency level, the subjects they are studying, and their learning goals. As input, the user enters data into a form on the terminal screen. The input data is the user's "current proficiency level," "study subjects," and "learning goals." For example, they enter "intermediate," "English conversation," and "TOEIC 800 points." These input data affect the user's learning level.

[0780] Step 2:

[0781] The device sends the data entered by the user to the server. Specifically, the device sends the data to the server using an HTTP POST request. This input data includes "current proficiency level," "study subjects," and "study goals." The server receives this data.

[0782] Step 3:

[0783] The server analyzes the received data and generates appropriate learning content and homework using a generative AI model. Inputs include the user's "current proficiency level," "study subjects," and "learning goals." The server generates and sends the following prompt to the generative AI model: "Please generate intermediate-level English conversation lesson content and homework to aim for a TOEIC score of 800." The generated learning content and homework are obtained as output.

[0784] Step 4:

[0785] The generated learning content and homework are sent from the server to the device. Specifically, the server sends the learning content and homework to the device as an HTTP response. This output data includes "Intermediate English Conversation Lessons" and "Intermediate Homework" appropriate for the user's level of proficiency.

[0786] Step 5:

[0787] The device displays the received learning content and homework to the user. As output, the device visually displays "Intermediate English Conversation Lessons" and "Intermediate Homework" on the user's screen. This operation allows the user to use the displayed data for study.

[0788] Step 6:

[0789] The user uses the provided learning content to progress through their studies and complete their homework. The input is the learning content and homework on the device. The user studies through quizzes and interactive practice sessions and answers the homework. The output is the results of their studies and the results of their homework (e.g., the percentage of correct answers).

[0790] Step 7:

[0791] The user enters the results of their homework into the device, and the device sends the results to the server. The user enters the results of their homework into a designated input field on the device. For example, "90% correct." The device sends this result data to the server using an HTTP POST request. The server receives the homework result data.

[0792] Step 8:

[0793] The server analyzes the homework results it receives and adjusts the next learning content and homework. The input is "homework result data." The server generates and sends the following prompt to the generative AI model: "Please adjust the difficulty of the next lesson based on the user's 90% correct answer rate." The adjusted learning content and homework are obtained as output.

[0794] Step 9:

[0795] In addition, the server generates data comparing the user's proficiency level with other companies' proficiency levels based on the user's current level. The inputs include the user's "current proficiency level" and "homework result data." The server uses a comparison algorithm to compare the data with other users' data. The output generates comparison data such as "Your TOEIC score is in the top 20% of other intermediate-level users."

[0796] Step 10:

[0797] The terminal receives the comparison data sent from the server and displays it to the user. As an output, the terminal displays on the user's screen, "Your TOEIC score is in the top 20% of other intermediate-level users." This allows the user to objectively evaluate their own proficiency level compared to other users.

[0798] The above is the specific processing flow of this system.

[0799] (Application example 1)

[0800] 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."

[0801] While conventional learning systems can provide appropriate learning content based on a user's level of proficiency, they are unable to compare the level of proficiency with that of other users, objectively evaluate the user's learning progress, or effectively suggest the next learning content. This makes it difficult for users to accurately understand their own learning progress and proceed with their studies appropriately.

[0802] 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.

[0803] In this invention, the server includes a means for generating comparison data using a generative AI model based on the learning progress of other users, a means for the user to check the comparison data, and a means for generating the comparison data and a prompt sentence for generating a suggestion for the next learning content, thereby enabling the user to objectively evaluate their own learning progress and receive effective learning suggestions.

[0804] "User" refers to an individual who uses the learning system to manage their own proficiency and learning progress.

[0805] "Proficiency" is a measure that indicates the level of knowledge and skill that a user currently possesses.

[0806] "Learning Objective" means a specific learning outcome or qualification that a user aims to achieve.

[0807] "Server" refers to a group of computers that receive data from users and generate and coordinate learning content and homework assignments.

[0808] "Learning content" refers to the teaching materials and resources that users use for learning.

[0809] "Homework" refers to assignments or exercises that a user must complete in relation to the learning content.

[0810] "Terminal" refers to the device (smartphone, tablet, PC, etc.) through which a user accesses the system and displays learning content and homework.

[0811] "Homework results" refers to data such as the grades and percentage of correct answers when a user completes homework.

[0812] "Comparison data" refers to evaluation information generated by comparing a user's proficiency level with the data of other users.

[0813] "Generative AI models" refer to artificial intelligence algorithms used to analyze data and generate and tailor learning content.

[0814] A "prompt" refers to input text that instructs a generative AI model to generate learning content and suggestions.

[0815] The present invention relates to a system for providing learning content and homework according to a user's level of proficiency and evaluating the user's learning progress. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS ... will be described in detail below with reference to exemplary embodiments.

[0816] System Configuration

[0817] To implement this invention, the user, the terminal, and the server must work together to run the program. The system includes the following main components:

[0818] 1. The user accesses the system using a terminal and enters their current proficiency level and learning goals.

[0819] 2. The terminal is a device such as a smartphone, tablet, or PC through which the user receives learning content and homework.

[0820] 3. The server is a set of computers that generate and coordinate learning content and homework based on user input. The following technologies are used to perform the necessary processing:

[0821] Generative AI models are used to generate learning content and tailor homework.

[0822] A database (e.g. MongoDB) is used to manage user data and learning progress.

[0823] Implementation Procedure

[0824] 1. User Registration and Data Entry:

[0825] The user inputs his / her current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800) using the terminal, and this data is sent from the terminal to the server.

[0826] 2. Learning content and homework generation:

[0827] The server generates appropriate learning content (e.g., intermediate-level English conversation lessons) and homework (e.g., intermediate-level practice questions) based on the user's proficiency and learning goals. A generative AI model is used in this process.

[0828] 3. Content Delivery and Learning:

[0829] The generated learning content and homework are sent from the server to the terminal and displayed to the user, who then uses the provided content to advance their studies and complete their homework.

[0830] 4. Progress Assessment and Adjustment:

[0831] The user inputs the results of the homework (e.g., percentage of correct answers) into the device and sends them to the server. The server then adjusts the difficulty and content of the next learning content and homework based on the results.

[0832] 5. Generating and Providing Comparative Data:

[0833] The server compares the user's learning progress with other users and generates comparison data using a generative AI model. For example, it includes evaluation information such as, "Your progress is about the same as other intermediate level users." This comparison data is sent to the device and can be confirmed by the user.

[0834] Specific examples

[0835] For example, if a user's goal is to achieve a TOEIC score of 800, they will enter their current proficiency level as intermediate when they first register. The server will generate intermediate-level English conversation lesson content and homework and provide it to the user. The user will then progress with their studies and enter their homework results. The server will then adjust the difficulty of the next content based on the results and generate evaluation information by comparing the user's data with that of other users.

[0836] Prompt Sentence Examples

[0837] "The user's current TOEIC score is 600, their goal is 800, and their current progress is 75%. Please generate the next learning content and quiz for them."

[0838] As a result, the present invention allows users to receive appropriate feedback and suggestions based on their own level of proficiency, enabling them to progress with their studies efficiently.

[0839] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0840] Step 1: User registration and data entry

[0841] A user accesses the learning system using a terminal and enters their current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800). The entered data is sent from the terminal to the server. The server stores the received data in a database as a user profile. This profile includes the user's proficiency level, learning goal, and other necessary information.

[0842] Input: User proficiency and learning goals

[0843] Output: User profile (stored in database)

[0844] Step 2: Generate learning content and assignments

[0845] The server uses a generative AI model to generate learning content and homework based on the user's proficiency and learning goals. Specifically, it creates video lessons and quizzes appropriate for the user's current level. The generated content is sent from the server to the device and displayed to the user.

[0846] Input: User profile (proficiency and learning goals)

[0847] Output: Learning content and homework (sent to device)

[0848] Step 3: View and learn content

[0849] The device displays the learning content and homework received from the server to the user. The user watches the video lessons and works on the homework through the device. After the user completes the homework, they input the results (e.g., percentage of correct answers) into the device.

[0850] Input: Learning content and homework

[0851] Output: Homework results (entered by the user)

[0852] Step 4: Assess progress and adjust content

[0853] The server receives the homework results sent from the device and evaluates progress. It uses the generative AI model to adjust the difficulty and content of the next learning content and homework. For example, if the user has a high rate of correct answers on the homework, it will increase the difficulty of the next content. Conversely, if the rate of correct answers is low, it will decrease the difficulty.

[0854] Input: Homework results

[0855] Output: Next learning content and homework (adjusted for difficulty and sent to device)

[0856] Step 5: Generate and provide comparison data

[0857] The server compares the user's progress data with that of other users and generates comparison data using a generative AI model. The comparison data includes evaluation information such as "Your progress is about the same as other intermediate level users." This comparison data is also used to generate prompts. The generated comparison data and prompts are sent to the device and displayed to the user.

[0858] Input: User progress data and other users' data

[0859] Output: Comparison data and prompt (sent to terminal)

[0860] Step 6: Generate and view proposals

[0861] The device displays the comparison data and prompts received from the server to the user. Based on this information, the user can objectively evaluate their own learning progress and receive specific suggestions for their next study. For example, if a user's current TOEIC score is 600 and their goal is 800, the prompt displayed will read, "The user's current TOEIC score is 600, their goal is 800, their current progress is 75%, please generate the next learning content and quiz for them."

[0862] Input: Comparison data and prompt statement

[0863] Output: Suggestions for the user (displayed on the terminal)

[0864] Through these steps, the present invention provides learning content and homework according to the user's level of proficiency, evaluates progress, generates comparative data, and provides specific learning suggestions. This system allows users to objectively evaluate their own learning progress and study efficiently.

[0865] 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.

[0866] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an emotion engine that recognizes a user's emotions and adjusts the learning experience. The following describes in detail the embodiments of the present invention.

[0867] The basic operation of this system is realized by the user, terminal, and server working together to run a program. First, the user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[0868] The server receives the user's input data and generates appropriate learning content and homework based on the data. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[0869] Furthermore, the device is equipped with an emotion engine that recognizes the user's real-time emotions. The emotion engine analyzes the user's facial expressions and tone of voice to determine how the user is feeling while studying. This emotion data is sent to the server.

[0870] The server analyzes the received emotional data and adjusts the user's learning experience. For example, this adjustment may provide more interesting content if it determines that the user is not concentrating, or suggest a short break if the user feels tired. The server also adjusts the next learning content and homework assignments based on the user's learning progress and emotional data. This allows the user to always study in the optimal learning environment.

[0871] After completing their homework, users enter their results into their device. The device then sends the results to a server, which then analyzes the homework results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high accuracy rate but they feel stressed, the difficulty of the next homework can be slightly reduced. Data comparing the user's proficiency with other companies is also generated along with the emotional data, allowing users to objectively evaluate their learning progress.

[0872] As a specific example, if a user begins an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level, and uses an emotion engine to collect and analyze the user's emotional data while studying. If the emotion engine determines that the user is not concentrating, the server provides learning content appropriate to that situation. When the user submits the results of their homework to the server, the difficulty and content of the next lesson are also adjusted. Furthermore, if the user wishes to compare their learning with other companies, the server can generate that data and display it on the device.

[0873] The above is an embodiment of the present invention. This system allows users to effectively study in a way that is tailored to their own level of proficiency and emotional state, and also allows them to understand their own progress through objective evaluations.

[0874] The processing flow will be explained below.

[0875] Step 1:

[0876] A user accesses the system using a terminal and inputs his / her current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[0877] Step 2:

[0878] The terminal sends the user's input data to the server, which receives the data.

[0879] Step 3:

[0880] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[0881] Step 4:

[0882] The server sends the generated learning content and homework to the device, which receives it.

[0883] Step 5:

[0884] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[0885] Step 6:

[0886] The device's built-in emotion engine analyzes the user's facial expressions and tone of voice to collect real-time emotional data, including levels of concentration, stress, and fatigue.

[0887] Step 7:

[0888] The device sends the emotion data collected by the emotion engine to the server, which receives this data.

[0889] Step 8:

[0890] The server analyzes the received emotional data and combines it with the user's learning progress to adjust the next learning content and homework assignments. For example, if the user is not concentrating, it will provide more interesting content or suggest a short break.

[0891] Step 9:

[0892] The user completes the homework and enters the results into the terminal, which then sends the results to the server.

[0893] Step 10:

[0894] The server integrates and analyzes the homework results and emotional data it receives, and adjusts the difficulty and content of the next learning content and homework. For example, if the user has a high accuracy rate but is feeling stressed, the difficulty level will be slightly reduced.

[0895] Step 11:

[0896] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[0897] Step 12:

[0898] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[0899] Step 13:

[0900] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[0901] Step 14:

[0902] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[0903] Example 2

[0904] 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."

[0905] While conventional learning support systems provided content tailored to a user's proficiency level and learning goals to a certain extent, they were unable to appropriately adjust the learning experience based on the user's emotional state. This could lead to users losing concentration or becoming overly stressed, making it difficult to achieve optimal learning outcomes. Furthermore, because the next content was adjusted based solely on learning results, it was not possible to take into account the user's emotional changes, limiting the sustainability and effectiveness of learning.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input their current proficiency level and learning goals; a means for the terminal to transmit the user's input data to the server; a means for the server to generate appropriate learning content and homework based on the user's input; a means for the terminal to display the learning content and homework received from the server to the user; a means for the terminal to collect real-time emotional data of the user; a means for the terminal to transmit the emotional data to the server; a means for the server to adjust the learning experience based on the emotional data; a means for the user to input homework results to the terminal; a means for the terminal to transmit the homework results to the server; and a means for the server to adjust the next learning content and homework based on the homework results and emotional data. This makes it possible to provide optimal learning content while taking the user's emotional state into consideration and dynamically adjust the learning experience.

[0907] "User" refers to an individual who uses the system to receive learning content and homework.

[0908] "Proficiency" refers to the level of knowledge or skill that a user currently possesses in a particular subject of study.

[0909] "Learning goal" refers to the specific learning outcome or target value that the user is trying to achieve.

[0910] "Terminal" refers to a device used by a user, such as a PC or smartphone, that functions as an interface with the system.

[0911] "Server" refers to the central part of the system, a device that receives data from users and generates and provides appropriate learning content and homework.

[0912] "Learning content" refers to information such as learning materials and lessons for users to study.

[0913] "Homework" refers to tasks or problems that users complete for self-study.

[0914] "Emotional data" refers to data that indicates the user's real-time emotional state, analyzed from facial expressions, tone of voice, etc.

[0915] "Adjusting" refers to the system changing the difficulty and content of learning content and homework based on the user's emotional data and learning results.

[0916] "Comparison data" refers to information that allows a user to compare their proficiency with that of other users based on their current level.

[0917] The present invention provides a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, can objectively evaluate learning progress, and recognizes the user's emotions to adjust the learning experience. The following describes in detail the embodiments of the present invention.

[0918] This system is realized by a program that allows users, terminals, and servers to work together. Specifically, the system begins when a user inputs their current proficiency level, study subject (e.g., English conversation), and study goal (e.g., TOEIC 800 points) through their terminal, and this data is then sent to the server.

[0919] The terminal consists of a device such as a PC or smartphone, and accepts user input through a dedicated application. The input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0920] The server generates appropriate learning content and homework based on the data received from the user. The generative AI model used here is a machine learning model such as GPT-4. Examples of prompts for the generative AI model include the following:

[0921] Example prompt sentence:

[0922] "Please create lesson content suitable for users aiming for intermediate level English conversation."

[0923] The learning content and homework generated by the server are sent to the terminal and displayed to the user through the application, allowing the user to proceed with their studies based on this information.

[0924] During the learning process, the device collects real-time emotional data from the user. This data is collected using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, which analyzes the user's facial expressions, tone of voice, etc. This emotional data is then sent back to the server.

[0925] The server analyzes the user's emotional data and dynamically adjusts the learning experience. For example, if it determines that the user is not concentrating, it generates and provides more interesting content. If the user feels tired, it will suggest taking a break.

[0926] After a user completes their homework, they enter their results into the device, which then sends them to the server. The server analyzes the homework results and emotional data to adjust the content and difficulty of the next homework assignment. For example, if a high accuracy rate is observed but the user is still feeling stressed, the content of the next assignment can be reduced.

[0927] Furthermore, the server generates comparison data with other users based on the user's current proficiency level and transmits it to the terminal, allowing the user to objectively evaluate their own learning progress.

[0928] This invention allows users to study in an optimal learning environment that suits their emotional state and level of proficiency, thereby enhancing the effectiveness of their studies. Furthermore, by using comparative data to perform self-evaluation, it becomes possible to maintain motivation for studying.

[0929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0930] Step 1:

[0931] A user accesses the system's application using a terminal. The user enters their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goal (e.g., TOEIC 800 points) into a form within the app. Once the input is complete, the user clicks the "Submit" button. The input data is sent from the terminal to the server. The data entered here is the user's current proficiency level, the subject they are studying, and their learning goal. The output sent is data containing the user's learning information.

[0932] Step 2:

[0933] The terminal acquires the data entered by the user and sends it to the server via a secure communication protocol (e.g., HTTPS). The input includes the user's learning information. The output is the user's learning information sent to the server. Data processing involves temporarily storing the input data and converting it into an appropriate format before sending it.

[0934] Step 3:

[0935] The server generates appropriate learning content and homework based on the received user input data. The generative AI model used here is a machine learning model such as GPT-4. The following prompt is used for the generative AI model: "Please create lesson content suitable for users aiming for intermediate-level English conversation." The input includes the user's learning information. The output is learning content and homework. The server sends the generated content to the device.

[0936] Step 4:

[0937] The terminal displays the learning content and homework received from the server to the user. The input includes the learning content and homework from the server. The output is the learning content and homework displayed in a form that the user can check. Specific operations include displaying the lesson content and homework on the application screen.

[0938] Step 5:

[0939] Using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, the system analyzes the user's facial expressions and tone of voice during training to collect real-time emotional data. The input includes data on the user's facial expressions and voice. The output is data indicating the user's emotional state. Facial expression recognition algorithms and voice analysis algorithms are used for data processing.

[0940] Step 6:

[0941] The collected emotion data is sent from the device to the server. The input includes the user's emotion data. The output is the emotion data sent to the server. As a data processing step, the data is encrypted before transmission to ensure security.

[0942] Step 7:

[0943] The server analyzes the received emotional data and adjusts the user's learning experience. For example, it provides more interesting content if the user is not concentrating, or suggests a break if the user is tired. The input includes the user's emotional data. The output is new learning content or instructions tailored to the user's state. An algorithm for analyzing the emotional data is used for data processing.

[0944] Step 8:

[0945] After completing their studies, the user inputs their homework answers into the application. The device sends the input data to the server. The input includes the user's homework answer data. The output is the homework answer data sent to the server.

[0946] Step 9:

[0947] The terminal sends the homework results to the server. The input includes the user's homework results. The output is the homework results sent to the server. For data processing, a protocol for secure data transmission is used.

[0948] Step 10:

[0949] The server adjusts the next learning content and homework based on the homework results and emotional data. The input includes the user's homework results and emotional data. The output is the adjusted next learning content and homework. For example, if there is a high accuracy rate but stress is felt, the difficulty of the next homework is reduced. Data processing involves using machine learning algorithms to generate optimal content and homework.

[0950] Step 11:

[0951] The server generates comparison data with other users based on the user's current level and sends it to the terminal. The input includes the user's current proficiency data. The output is the comparison data with other users. Data processing includes the aggregation and analysis of user data.

[0952] Step 12:

[0953] The terminal displays the comparison data received from the server to the user within the application. The input includes the comparison data from the server. The output is the comparison data displayed in a format that can be viewed by the user. Specific operations include displaying the data in graphs or text.

[0954] (Application example 2)

[0955] 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."

[0956] Conventional online learning systems only provide learning content based on the user's proficiency level, but are unable to adjust the content to take into account the user's emotional state, resulting in poor learning efficiency. Furthermore, they do not provide sufficient data to compare the user's progress with other companies, making self-evaluation difficult. To address these issues, a system is needed that can recognize the user's emotional state in real time and adjust the learning content accordingly.

[0957] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0958] In this invention, the server includes: means for a user to input their current proficiency level and learning goals; means for the server to generate appropriate learning content and homework based on the user's input; means for a terminal to display the learning content and homework received from the server to the user; means for analyzing the user's facial expressions and tone of voice to recognize emotions; means for transmitting the emotional data to the server; means for the server to adjust the learning experience based on the emotional data; means for the user to transmit homework results to the server; and means for the server to adjust the next learning content and homework based on the homework results and emotional data, thereby enabling efficient learning that takes the user's emotional state into consideration.

[0959] "User" refers to a learner who uses this system.

[0960] "Current proficiency" refers to the level of a particular skill or knowledge that a user currently possesses.

[0961] A "learning goal" is a specific skill or knowledge milestone that a user is trying to achieve.

[0962] "Server" refers to a remote computer system that receives data from users and generates and provides appropriate learning content and homework based on that data.

[0963] "Learning Content" refers to educational resources and information provided to users to further their learning.

[0964] "Homework" refers to tasks or problems that a user must complete to advance their learning.

[0965] "Terminal" refers to a smartphone, head-mounted display, or other electronic device used by a user.

[0966] "Facial expression" refers to the emotion or state shown by the user's facial movements and expressions.

[0967] "Tone of voice" refers to characteristics such as pitch, strength, and quality of a user's voice.

[0968] "Emotional data" refers to information about the user's emotional state obtained from facial expressions and tone of voice.

[0969] "Learning experience" refers to the process and overall experience a user has while using learning content.

[0970] "Progress" refers to the progress a user has made in the learning process.

[0971] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate learning content and homework based on user input.

[0972] A "prompt" is a starting point text that is given to a generative AI model to instruct it on how to generate learning content or homework.

[0973] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an "emotion engine" that recognizes the user's emotions and adjusts the learning experience. Hereinafter, embodiments of the present invention will be described in detail.

[0974] This system is realized by running a program in cooperation with a server, a user terminal, and an emotion engine.

[0975] Processing on the user's device

[0976] Users input their current level of proficiency, the subjects they are studying, and their learning goals using a smartphone or head-mounted display. This input data is sent from the device to the server. For example, if a user requests intermediate-level English conversation lessons with a goal of achieving a TOEIC score of 800, this information becomes the input data.

[0977] Processing on the server

[0978] The server receives the user's input data and generates appropriate learning content and homework based on that data using a generative AI model developed in Python. The model generates learning content using the following prompt sentence as input:

[0979] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[0980] The generated learning content and homework are sent to the user's terminal, which displays them to the user.

[0981] Emotion engine processing

[0982] The user's device is equipped with a camera and microphone for facial expression recognition and voice analysis. The emotion engine (using a TensorFlow model) analyzes these input data to recognize the user's emotional state (concentration, fatigue, interest, etc.). This emotion data is sent to the server in real time. For example, if the user is determined to be not concentrating, the learning experience is adjusted using the following prompt sentence:

[0983] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[0984] Tailoring the learning experience

[0985] The server adjusts the learning content based on the emotional data. For example, if the user feels tired, it provides content suggesting a short break. It also changes the content and difficulty of the learning as needed to ensure the user has an optimal learning experience.

[0986] Assessment of learning progress and next adjustments

[0987] After completing their homework, users enter their results into their device, which then sends them to the server. The server then analyzes the results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high success rate but they feel stressed, the difficulty of the next homework will be reduced.

[0988] Providing progress comparison data

[0989] Furthermore, the server generates data for comparing proficiency levels based on the data of other users and provides this data to the user, allowing the user to objectively evaluate their own learning progress. For example, the user can send a prompt to the server requesting comparison with others, and the server generates comparison data.

[0990] In this way, the present invention makes it possible to provide an optimal learning experience according to the user's emotions and level of proficiency.

[0991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0992] Step 1:

[0993] The user inputs their current proficiency level and learning goals.

[0994] Input details: Proficiency level (e.g., intermediate), study subject (e.g., English conversation), learning goal (e.g., TOEIC 800 points)

[0995] Specific operation: The user inputs the necessary information into a dedicated application using a smartphone or head-mounted display. The input data is saved on the device.

[0996] Step 2:

[0997] The terminal sends the input data to the server.

[0998] Input content: User proficiency, study subjects, learning goals

[0999] Output: User data received by the server

[1000] Specific operation: The terminal sends the user's input data to the server via the Internet, and the server stores this information in a database.

[1001] Step 3:

[1002] The server generates appropriate learning content and homework.

[1003] Input content: User data

[1004] Output: Generated learning content and homework

[1005] How it works: The server uses a generative AI model developed in Python to generate appropriate learning content and homework using the following prompts as input:

[1006] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[1007] The generated data is transmitted from the server to the terminal.

[1008] Step 4:

[1009] The terminal displays the received learning content and homework to the user.

[1010] Input content: Learning content and homework received from the server

[1011] Output: Displayed learning content and homework

[1012] Specific operation: The device displays the received content in a format that is easy for the user to understand. The user then begins learning.

[1013] Step 5:

[1014] The device analyzes the user's facial expressions and tone of voice to recognize emotions.

[1015] Input content: User's facial expression data, tone of voice data

[1016] Output: Recognized emotion data

[1017] Specific operation: Facial expression recognition and voice analysis are performed using the device's built-in camera and microphone. An emotion analysis engine using TensorFlow recognizes the user's emotions in real time and generates data.

[1018] Step 6:

[1019] The device transmits the emotion data to the server.

[1020] Input content: Emotion data

[1021] Output: Emotion data received by the server

[1022] Specific operation: The device sends the analyzed emotion data via the Internet to a server, which receives the data and stores it for analysis.

[1023] Step 7:

[1024] The server tailors the learning experience based on the emotional data.

[1025] Input content: Emotion data

[1026] Output: Tailored learning experience

[1027] What Happens: The server analyzes the emotion data and tailors the learning experience using the following prompt:

[1028] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[1029] Tailored learning content and break suggestions are sent to the device.

[1030] Step 8:

[1031] The user enters the results of their homework, and the terminal sends them to the server.

[1032] Input content: Homework results

[1033] Output: Homework result data received by the server

[1034] Specific operation: The user inputs the results of their homework using a smartphone or head-mounted display. The results are then sent from the device to the server.

[1035] Step 9:

[1036] The server analyzes the homework results and emotion data and adjusts the next learning content and homework.

[1037] Input content: Homework result data, emotion data

[1038] Output: Adjusted next learning content and homework

[1039] Specific operation: The server generates appropriate new learning content and homework based on the accuracy rate of homework and emotional data. The generated data is used for the next learning session.

[1040] Step 10:

[1041] The server generates data based on the user's current level to compare their proficiency with that of other companies.

[1042] Input: User progress data, other users' data

[1043] Output content: Comparison data

[1044] Specific operation: The server compares and analyzes the user's progress data with that of other users to generate proficiency comparison data, which is then sent to the user's device and displayed.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] [Fourth embodiment]

[1049] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1050] 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.

[1051] 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).

[1052] 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.

[1053] 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.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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."

[1062] The present invention relates to a system that provides learning content and homework according to a user's level of proficiency and can objectively evaluate the learning progress. Hereinafter, an embodiment of the present invention will be described in detail.

[1063] For this system to function, the user, the terminal, and the server must cooperate to operate the program. First, the user accesses the system using the terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[1064] The server receives the user's input data and generates appropriate learning content and homework. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[1065] The user uses the learning content provided through the device to study and complete homework. The user inputs the results of the homework (e.g., the percentage of correct answers) into the device, which then transmits the results to the server.

[1066] The server adjusts the next learning content and homework based on the received homework results. For example, if the user answers the homework correctly, the next content may be more difficult. Conversely, if the user answers the homework correctly, the server may provide content of the same level or a lower level of difficulty.

[1067] Furthermore, the server generates data comparing the user's proficiency level with that of other companies based on the user's current level. This comparison data is presented in the form of, for example, "Intermediate level, compared to other companies, is within the range of passing X University." The terminal also displays this comparison data to the user, allowing them to objectively evaluate their own proficiency level.

[1068] As a specific example, if a user starts an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level and provides them to the user through the device. The user sends the results of the homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. In addition, if the user wishes to compare with other companies, the server can generate that data and display it on the device.

[1069] The above is an embodiment of the present invention. This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluations.

[1070] The processing flow will be explained below.

[1071] Step 1:

[1072] A user accesses the system using a terminal and inputs their current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[1073] Step 2:

[1074] The terminal sends the user's input data to the server, which receives the data.

[1075] Step 3:

[1076] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[1077] Step 4:

[1078] The server sends the generated learning content and homework to the device, which receives it.

[1079] Step 5:

[1080] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[1081] Step 6:

[1082] The user enters the results of their homework into the terminal, which then sends the results to the server.

[1083] Step 7:

[1084] The server analyzes the results of the homework received and adjusts the next learning content and homework. For example, if the percentage of correct answers in the homework is high, the difficulty level of the next content is increased, and if the percentage of correct answers is low, the difficulty level is maintained or decreased.

[1085] Step 8:

[1086] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[1087] Step 9:

[1088] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[1089] Step 10:

[1090] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[1091] Step 11:

[1092] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[1093] Example 1

[1094] 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."

[1095] Conventional learning systems have difficulty providing appropriate learning content and homework according to a user's level of proficiency, and lack a means to objectively evaluate learning progress. This makes it difficult for users to accurately grasp their own learning results, resulting in a decrease in learning efficiency.

[1096] 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.

[1097] In this invention, the server includes a means for a user to input their current level of proficiency and learning goals, a means for a terminal to transmit the input data to the server, a means for the server to generate appropriate learning content and homework based on the user's input, a means for the terminal to display the learning content and homework received from the server to the user, a means for the user to input homework results to the terminal and transmit them to the server, and a means for the server to adjust the next learning content and homework based on the homework results. This makes it possible to provide effective learning content according to the user's level of proficiency and objectively evaluate the user's learning progress.

[1098] "User" refers to a person who uses the system to receive learning content and homework.

[1099] "Terminal" refers to a device used by a user to access the system, such as a computer or smartphone.

[1100] "Server" refers to the central part of the system that receives data sent by users and generates and manages learning content and homework.

[1101] "Current proficiency" refers to the level of skill or knowledge that the user has currently acquired.

[1102] "Learning goal" refers to the specific learning objective or outcome that the user wants to achieve.

[1103] "Data" refers to information entered by the user and server-generated information, including learning content, homework, and comparison data.

[1104] "Learning content" refers to learning materials, practice questions, etc. that users use to study.

[1105] "Homework" refers to assignments or exercises that users complete to advance their learning.

[1106] "Homework results" refers to the grades and answers given by the user when they complete the homework.

[1107] "Comparative data" refers to data that shows the results of comparing a user's proficiency with other users or standards.

[1108] MODE FOR CARRYING OUT THE INVENTION

[1109] The present invention is a system that provides learning content and homework according to a user's level of proficiency and objectively evaluates the user's learning progress. For this system to function efficiently, the user, the terminal, and the server must cooperate to operate the program.

[1110] First, a user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[1111] The server receives the user's input data and generates appropriate learning content and homework based on it. Specifically, the server uses a generative AI model to generate prompt sentences such as the following, and creates learning content and homework based on those prompts.

[1112] Example prompt sentence:

[1113] "Please generate intermediate level English conversation lesson content and homework to help me aim for a TOEIC score of 800. Also, please adjust the difficulty of the next lesson based on the learning results and provide comparative data with other companies."

[1114] The generated learning content and homework are sent from the server to the device, which then visually displays them to the user. The user studies using the learning content provided through the device, and when completing the homework, the results (e.g., percentage of correct answers) are entered into the device again. The device then sends this result data to the server.

[1115] The server analyzes the results of the homework assignments received and adjusts the next learning content and homework assignments. It also generates data comparing the user's current proficiency with other users, as needed. For example, it generates comparison data in the form of "Intermediate level, compared to other companies, and within the range of passing X University."

[1116] This allows users to be provided with learning content tailored to their level of proficiency, and also allows them to objectively evaluate their learning progress. Specifically, if a user begins intermediate-level English conversation lessons with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for that level and provides it to the user via their device. The user sends the results of their homework to the server, which then adjusts the difficulty and content of the next lesson based on the results. Furthermore, if the user wishes to compare their services with those of other companies, the server can generate that data and display it on the device.

[1117] This system allows users to effectively study according to their own level of proficiency, and also allows them to grasp their own progress through objective evaluation.

[1118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1119] Step 1:

[1120] The user inputs their current proficiency level, the subjects they are studying, and their learning goals. As input, the user enters data into a form on the terminal screen. The input data is the user's "current proficiency level," "study subjects," and "learning goals." For example, they enter "intermediate," "English conversation," and "TOEIC 800 points." These input data affect the user's learning level.

[1121] Step 2:

[1122] The device sends the data entered by the user to the server. Specifically, the device sends the data to the server using an HTTP POST request. This input data includes "current proficiency level," "study subjects," and "study goals." The server receives this data.

[1123] Step 3:

[1124] The server analyzes the received data and generates appropriate learning content and homework using a generative AI model. Inputs include the user's "current proficiency level," "study subjects," and "learning goals." The server generates and sends the following prompt to the generative AI model: "Please generate intermediate-level English conversation lesson content and homework to aim for a TOEIC score of 800." The generated learning content and homework are obtained as output.

[1125] Step 4:

[1126] The generated learning content and homework are sent from the server to the device. Specifically, the server sends the learning content and homework to the device as an HTTP response. This output data includes "Intermediate English Conversation Lessons" and "Intermediate Homework" appropriate for the user's level of proficiency.

[1127] Step 5:

[1128] The device displays the received learning content and homework to the user. As output, the device visually displays "Intermediate English Conversation Lessons" and "Intermediate Homework" on the user's screen. This operation allows the user to use the displayed data for study.

[1129] Step 6:

[1130] The user uses the provided learning content to progress through their studies and complete their homework. The input is the learning content and homework on the device. The user studies through quizzes and interactive practice sessions and answers the homework. The output is the results of their studies and the results of their homework (e.g., the percentage of correct answers).

[1131] Step 7:

[1132] The user enters the results of their homework into the device, and the device sends the results to the server. The user enters the results of their homework into a designated input field on the device. For example, "90% correct." The device sends this result data to the server using an HTTP POST request. The server receives the homework result data.

[1133] Step 8:

[1134] The server analyzes the homework results it receives and adjusts the next learning content and homework. The input is "homework result data." The server generates and sends the following prompt to the generative AI model: "Please adjust the difficulty of the next lesson based on the user's 90% correct answer rate." The adjusted learning content and homework are obtained as output.

[1135] Step 9:

[1136] In addition, the server generates data comparing the user's proficiency level with other companies' proficiency levels based on the user's current level. The inputs include the user's "current proficiency level" and "homework result data." The server uses a comparison algorithm to compare the data with other users' data. The output generates comparison data such as "Your TOEIC score is in the top 20% of other intermediate-level users."

[1137] Step 10:

[1138] The terminal receives the comparison data sent from the server and displays it to the user. As an output, the terminal displays on the user's screen, "Your TOEIC score is in the top 20% of other intermediate-level users." This allows the user to objectively evaluate their own proficiency level compared to other users.

[1139] The above is the specific processing flow of this system.

[1140] (Application example 1)

[1141] 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."

[1142] While conventional learning systems can provide appropriate learning content based on a user's level of proficiency, they are unable to compare the level of proficiency with that of other users, objectively evaluate the user's learning progress, or effectively suggest the next learning content. This makes it difficult for users to accurately understand their own learning progress and proceed with their studies appropriately.

[1143] 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.

[1144] In this invention, the server includes a means for generating comparison data using a generative AI model based on the learning progress of other users, a means for the user to check the comparison data, and a means for generating the comparison data and a prompt sentence for generating a suggestion for the next learning content, thereby enabling the user to objectively evaluate their own learning progress and receive effective learning suggestions.

[1145] "User" refers to an individual who uses the learning system to manage their own proficiency and learning progress.

[1146] "Proficiency" is a measure that indicates the level of knowledge and skill that a user currently possesses.

[1147] "Learning Objective" means a specific learning outcome or qualification that a user aims to achieve.

[1148] "Server" refers to a group of computers that receive data from users and generate and coordinate learning content and homework assignments.

[1149] "Learning content" refers to the teaching materials and resources that users use for learning.

[1150] "Homework" refers to assignments or exercises that a user must complete in relation to the learning content.

[1151] "Terminal" refers to the device (smartphone, tablet, PC, etc.) through which a user accesses the system and displays learning content and homework.

[1152] "Homework results" refers to data such as the grades and percentage of correct answers when a user completes homework.

[1153] "Comparison data" refers to evaluation information generated by comparing a user's proficiency level with the data of other users.

[1154] "Generative AI models" refer to artificial intelligence algorithms used to analyze data and generate and tailor learning content.

[1155] A "prompt" refers to input text that instructs a generative AI model to generate learning content and suggestions.

[1156] The present invention relates to a system for providing learning content and homework according to a user's level of proficiency and evaluating the user's learning progress. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS ... will be described in detail below with reference to exemplary embodiments.

[1157] System Configuration

[1158] To implement this invention, the user, the terminal, and the server must work together to run the program. The system includes the following main components:

[1159] 1. The user accesses the system using a terminal and enters their current proficiency level and learning goals.

[1160] 2. The terminal is a device such as a smartphone, tablet, or PC through which the user receives learning content and homework.

[1161] 3. The server is a set of computers that generate and coordinate learning content and homework based on user input. The following technologies are used to perform the necessary processing:

[1162] Generative AI models are used to generate learning content and tailor homework.

[1163] A database (e.g. MongoDB) is used to manage user data and learning progress.

[1164] Implementation Procedure

[1165] 1. User Registration and Data Entry:

[1166] The user inputs his / her current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800) using the terminal, and this data is sent from the terminal to the server.

[1167] 2. Learning content and homework generation:

[1168] The server generates appropriate learning content (e.g., intermediate-level English conversation lessons) and homework (e.g., intermediate-level practice questions) based on the user's proficiency and learning goals. A generative AI model is used in this process.

[1169] 3. Content Delivery and Learning:

[1170] The generated learning content and homework are sent from the server to the terminal and displayed to the user, who then uses the provided content to advance their studies and complete their homework.

[1171] 4. Progress Assessment and Adjustment:

[1172] The user inputs the results of the homework (e.g., percentage of correct answers) into the device and sends them to the server. The server then adjusts the difficulty and content of the next learning content and homework based on the results.

[1173] 5. Generating and Providing Comparative Data:

[1174] The server compares the user's learning progress with other users and generates comparison data using a generative AI model. For example, it includes evaluation information such as, "Your progress is about the same as other intermediate level users." This comparison data is sent to the device and can be confirmed by the user.

[1175] Specific examples

[1176] For example, if a user's goal is to achieve a TOEIC score of 800, they will enter their current proficiency level as intermediate when they first register. The server will generate intermediate-level English conversation lesson content and homework and provide it to the user. The user will then progress with their studies and enter their homework results. The server will then adjust the difficulty of the next content based on the results and generate evaluation information by comparing the user's data with that of other users.

[1177] Prompt Sentence Examples

[1178] "The user's current TOEIC score is 600, their goal is 800, and their current progress is 75%. Please generate the next learning content and quiz for them."

[1179] As a result, the present invention allows users to receive appropriate feedback and suggestions based on their own level of proficiency, enabling them to progress with their studies efficiently.

[1180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1181] Step 1: User registration and data entry

[1182] A user accesses the learning system using a terminal and enters their current proficiency level (e.g., intermediate level) and learning goal (e.g., TOEIC score 800). The entered data is sent from the terminal to the server. The server stores the received data in a database as a user profile. This profile includes the user's proficiency level, learning goal, and other necessary information.

[1183] Input: User proficiency and learning goals

[1184] Output: User profile (stored in database)

[1185] Step 2: Generate learning content and assignments

[1186] The server uses a generative AI model to generate learning content and homework based on the user's proficiency and learning goals. Specifically, it creates video lessons and quizzes appropriate for the user's current level. The generated content is sent from the server to the device and displayed to the user.

[1187] Input: User profile (proficiency and learning goals)

[1188] Output: Learning content and homework (sent to device)

[1189] Step 3: View and learn content

[1190] The device displays the learning content and homework received from the server to the user. The user watches the video lessons and works on the homework through the device. After the user completes the homework, they input the results (e.g., percentage of correct answers) into the device.

[1191] Input: Learning content and homework

[1192] Output: Homework results (entered by the user)

[1193] Step 4: Assess progress and adjust content

[1194] The server receives the homework results sent from the device and evaluates progress. It uses the generative AI model to adjust the difficulty and content of the next learning content and homework. For example, if the user has a high rate of correct answers on the homework, it will increase the difficulty of the next content. Conversely, if the rate of correct answers is low, it will decrease the difficulty.

[1195] Input: Homework results

[1196] Output: Next learning content and homework (adjusted for difficulty and sent to device)

[1197] Step 5: Generate and provide comparison data

[1198] The server compares the user's progress data with that of other users and generates comparison data using a generative AI model. The comparison data includes evaluation information such as "Your progress is about the same as other intermediate level users." This comparison data is also used to generate prompts. The generated comparison data and prompts are sent to the device and displayed to the user.

[1199] Input: User progress data and other users' data

[1200] Output: Comparison data and prompt (sent to terminal)

[1201] Step 6: Generate and view proposals

[1202] The device displays the comparison data and prompts received from the server to the user. Based on this information, the user can objectively evaluate their own learning progress and receive specific suggestions for their next study. For example, if a user's current TOEIC score is 600 and their goal is 800, the prompt displayed will read, "The user's current TOEIC score is 600, their goal is 800, their current progress is 75%, please generate the next learning content and quiz for them."

[1203] Input: Comparison data and prompt statement

[1204] Output: Suggestions for the user (displayed on the terminal)

[1205] Through these steps, the present invention provides learning content and homework according to the user's level of proficiency, evaluates progress, generates comparative data, and provides specific learning suggestions. This system allows users to objectively evaluate their own learning progress and study efficiently.

[1206] 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.

[1207] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an emotion engine that recognizes a user's emotions and adjusts the learning experience. The following describes in detail the embodiments of the present invention.

[1208] The basic operation of this system is realized by the user, terminal, and server working together to run a program. First, the user accesses the system using a terminal and inputs their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goals. The terminal then sends this input data to the server.

[1209] The server receives the user's input data and generates appropriate learning content and homework based on the data. For example, it generates "intermediate level English conversation lesson content" and "intermediate level homework." The generated learning content and homework are sent to the user's terminal, and the terminal displays them to the user.

[1210] Furthermore, the device is equipped with an emotion engine that recognizes the user's real-time emotions. The emotion engine analyzes the user's facial expressions and tone of voice to determine how the user is feeling while studying. This emotion data is sent to the server.

[1211] The server analyzes the received emotional data and adjusts the user's learning experience. For example, this adjustment may provide more interesting content if it determines that the user is not concentrating, or suggest a short break if the user feels tired. The server also adjusts the next learning content and homework assignments based on the user's learning progress and emotional data. This allows the user to always study in the optimal learning environment.

[1212] After completing their homework, users enter their results into their device. The device then sends the results to a server, which then analyzes the homework results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high accuracy rate but they feel stressed, the difficulty of the next homework can be slightly reduced. Data comparing the user's proficiency with other companies is also generated along with the emotional data, allowing users to objectively evaluate their learning progress.

[1213] As a specific example, if a user begins an intermediate-level English conversation lesson with the goal of achieving a TOEIC score of 800, the server generates lesson content and homework appropriate for the intermediate level, and uses an emotion engine to collect and analyze the user's emotional data while studying. If the emotion engine determines that the user is not concentrating, the server provides learning content appropriate to that situation. When the user submits the results of their homework to the server, the difficulty and content of the next lesson are also adjusted. Furthermore, if the user wishes to compare their learning with other companies, the server can generate that data and display it on the device.

[1214] The above is an embodiment of the present invention. This system allows users to effectively study in a way that is tailored to their own level of proficiency and emotional state, and also allows them to understand their own progress through objective evaluations.

[1215] The processing flow will be explained below.

[1216] Step 1:

[1217] A user accesses the system using a terminal and inputs his / her current proficiency level (e.g., intermediate), learning goal (e.g., TOEIC 800 points), and subject to be studied (e.g., English conversation).

[1218] Step 2:

[1219] The terminal sends the user's input data to the server, which receives the data.

[1220] Step 3:

[1221] The server generates appropriate learning content and homework based on the user's input data, for example, "Intermediate level English conversation lesson content" and "Intermediate level homework."

[1222] Step 4:

[1223] The server sends the generated learning content and homework to the device, which receives it.

[1224] Step 5:

[1225] The device displays learning content and homework to the user, who then studies using the provided content and completes the homework.

[1226] Step 6:

[1227] The device's built-in emotion engine analyzes the user's facial expressions and tone of voice to collect real-time emotional data, including levels of concentration, stress, and fatigue.

[1228] Step 7:

[1229] The device sends the emotion data collected by the emotion engine to the server, which receives this data.

[1230] Step 8:

[1231] The server analyzes the received emotional data and combines it with the user's learning progress to adjust the next learning content and homework assignments. For example, if the user is not concentrating, it will provide more interesting content or suggest a short break.

[1232] Step 9:

[1233] The user completes the homework and enters the results into the terminal, which then sends the results to the server.

[1234] Step 10:

[1235] The server integrates and analyzes the homework results and emotional data it receives, and adjusts the difficulty and content of the next learning content and homework. For example, if the user has a high accuracy rate but is feeling stressed, the difficulty level will be slightly reduced.

[1236] Step 11:

[1237] The server generates new learning content and homework and sends them to the terminal, which receives them and displays them again to the user.

[1238] Step 12:

[1239] The server generates data to compare the user's proficiency with that of other companies based on the user's current level. Specifically, it compares the user's learning progress with the standards of other companies and creates data showing the user's relative position.

[1240] Step 13:

[1241] The server sends the generated comparison data to the terminal, which receives the data and displays it to the user.

[1242] Step 14:

[1243] Users can review the comparison data and objectively assess their own proficiency, and based on the results, set further learning goals and adjust their learning methods.

[1244] Example 2

[1245] 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."

[1246] While conventional learning support systems provided content tailored to a user's proficiency level and learning goals to a certain extent, they were unable to appropriately adjust the learning experience based on the user's emotional state. This could lead to users losing concentration or becoming overly stressed, making it difficult to achieve optimal learning outcomes. Furthermore, because the next content was adjusted based solely on learning results, it was not possible to take into account the user's emotional changes, limiting the sustainability and effectiveness of learning.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input their current proficiency level and learning goals; a means for the terminal to transmit the user's input data to the server; a means for the server to generate appropriate learning content and homework based on the user's input; a means for the terminal to display the learning content and homework received from the server to the user; a means for the terminal to collect real-time emotional data of the user; a means for the terminal to transmit the emotional data to the server; a means for the server to adjust the learning experience based on the emotional data; a means for the user to input homework results to the terminal; a means for the terminal to transmit the homework results to the server; and a means for the server to adjust the next learning content and homework based on the homework results and emotional data. This makes it possible to provide optimal learning content while taking the user's emotional state into consideration and dynamically adjust the learning experience.

[1248] "User" refers to an individual who uses the system to receive learning content and homework.

[1249] "Proficiency" refers to the level of knowledge or skill that a user currently possesses in a particular subject of study.

[1250] "Learning goal" refers to the specific learning outcome or target value that the user is trying to achieve.

[1251] "Terminal" refers to a device used by a user, such as a PC or smartphone, that functions as an interface with the system.

[1252] "Server" refers to the central part of the system, a device that receives data from users and generates and provides appropriate learning content and homework.

[1253] "Learning content" refers to information such as learning materials and lessons for users to study.

[1254] "Homework" refers to tasks or problems that users complete for self-study.

[1255] "Emotional data" refers to data that indicates the user's real-time emotional state, analyzed from facial expressions, tone of voice, etc.

[1256] "Adjusting" refers to the system changing the difficulty and content of learning content and homework based on the user's emotional data and learning results.

[1257] "Comparison data" refers to information that allows a user to compare their proficiency with that of other users based on their current level.

[1258] The present invention provides a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, can objectively evaluate learning progress, and recognizes the user's emotions to adjust the learning experience. The following describes in detail the embodiments of the present invention.

[1259] This system is realized by a program that allows users, terminals, and servers to work together. Specifically, the system begins when a user inputs their current proficiency level, study subject (e.g., English conversation), and study goal (e.g., TOEIC 800 points) through their terminal, and this data is then sent to the server.

[1260] The terminal consists of a device such as a PC or smartphone, and accepts user input through a dedicated application. The input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[1261] The server generates appropriate learning content and homework based on the data received from the user. The generative AI model used here is a machine learning model such as GPT-4. Examples of prompts for the generative AI model include the following:

[1262] Example prompt sentence:

[1263] "Please create lesson content suitable for users aiming for intermediate level English conversation."

[1264] The learning content and homework generated by the server are sent to the terminal and displayed to the user through the application, allowing the user to proceed with their studies based on this information.

[1265] During the learning process, the device collects real-time emotional data from the user. This data is collected using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, which analyzes the user's facial expressions, tone of voice, etc. This emotional data is then sent back to the server.

[1266] The server analyzes the user's emotional data and dynamically adjusts the learning experience. For example, if it determines that the user is not concentrating, it generates and provides more interesting content. If the user feels tired, it will suggest taking a break.

[1267] After a user completes their homework, they enter their results into the device, which then sends them to the server. The server analyzes the homework results and emotional data to adjust the content and difficulty of the next homework assignment. For example, if a high accuracy rate is observed but the user is still feeling stressed, the content of the next assignment can be reduced.

[1268] Furthermore, the server generates comparison data with other users based on the user's current proficiency level and transmits it to the terminal, allowing the user to objectively evaluate their own learning progress.

[1269] This invention allows users to study in an optimal learning environment that suits their emotional state and level of proficiency, thereby enhancing the effectiveness of their studies. Furthermore, by using comparative data to perform self-evaluation, it becomes possible to maintain motivation for studying.

[1270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1271] Step 1:

[1272] A user accesses the system's application using a terminal. The user enters their current proficiency level, the subject they are studying (e.g., English conversation), and their learning goal (e.g., TOEIC 800 points) into a form within the app. Once the input is complete, the user clicks the "Submit" button. The input data is sent from the terminal to the server. The data entered here is the user's current proficiency level, the subject they are studying, and their learning goal. The output sent is data containing the user's learning information.

[1273] Step 2:

[1274] The terminal acquires the data entered by the user and sends it to the server via a secure communication protocol (e.g., HTTPS). The input includes the user's learning information. The output is the user's learning information sent to the server. Data processing involves temporarily storing the input data and converting it into an appropriate format before sending it.

[1275] Step 3:

[1276] The server generates appropriate learning content and homework based on the received user input data. The generative AI model used here is a machine learning model such as GPT-4. The following prompt is used for the generative AI model: "Please create lesson content suitable for users aiming for intermediate-level English conversation." The input includes the user's learning information. The output is learning content and homework. The server sends the generated content to the device.

[1277] Step 4:

[1278] The terminal displays the learning content and homework received from the server to the user. The input includes the learning content and homework from the server. The output is the learning content and homework displayed in a form that the user can check. Specific operations include displaying the lesson content and homework on the application screen.

[1279] Step 5:

[1280] Using an emotion engine (e.g., Microsoft Azure Cognitive Services) installed on the device, the system analyzes the user's facial expressions and tone of voice during training to collect real-time emotional data. The input includes data on the user's facial expressions and voice. The output is data indicating the user's emotional state. Facial expression recognition algorithms and voice analysis algorithms are used for data processing.

[1281] Step 6:

[1282] The collected emotion data is sent from the device to the server. The input includes the user's emotion data. The output is the emotion data sent to the server. As a data processing step, the data is encrypted before transmission to ensure security.

[1283] Step 7:

[1284] The server analyzes the received emotional data and adjusts the user's learning experience. For example, it provides more interesting content if the user is not concentrating, or suggests a break if the user is tired. The input includes the user's emotional data. The output is new learning content or instructions tailored to the user's state. An algorithm for analyzing the emotional data is used for data processing.

[1285] Step 8:

[1286] After completing their studies, the user inputs their homework answers into the application. The device sends the input data to the server. The input includes the user's homework answer data. The output is the homework answer data sent to the server.

[1287] Step 9:

[1288] The terminal sends the homework results to the server. The input includes the user's homework results. The output is the homework results sent to the server. For data processing, a protocol for secure data transmission is used.

[1289] Step 10:

[1290] The server adjusts the next learning content and homework based on the homework results and emotional data. The input includes the user's homework results and emotional data. The output is the adjusted next learning content and homework. For example, if there is a high accuracy rate but stress is felt, the difficulty of the next homework is reduced. Data processing involves using machine learning algorithms to generate optimal content and homework.

[1291] Step 11:

[1292] The server generates comparison data with other users based on the user's current level and sends it to the terminal. The input includes the user's current proficiency data. The output is the comparison data with other users. Data processing includes the aggregation and analysis of user data.

[1293] Step 12:

[1294] The terminal displays the comparison data received from the server to the user within the application. The input includes the comparison data from the server. The output is the comparison data displayed in a format that can be viewed by the user. Specific operations include displaying the data in graphs or text.

[1295] (Application example 2)

[1296] 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."

[1297] Conventional online learning systems only provide learning content based on the user's proficiency level, but are unable to adjust the content to take into account the user's emotional state, resulting in poor learning efficiency. Furthermore, they do not provide sufficient data to compare the user's progress with other companies, making self-evaluation difficult. To address these issues, a system is needed that can recognize the user's emotional state in real time and adjust the learning content accordingly.

[1298] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1299] In this invention, the server includes: means for a user to input their current proficiency level and learning goals; means for the server to generate appropriate learning content and homework based on the user's input; means for a terminal to display the learning content and homework received from the server to the user; means for analyzing the user's facial expressions and tone of voice to recognize emotions; means for transmitting the emotional data to the server; means for the server to adjust the learning experience based on the emotional data; means for the user to transmit homework results to the server; and means for the server to adjust the next learning content and homework based on the homework results and emotional data, thereby enabling efficient learning that takes the user's emotional state into consideration.

[1300] "User" refers to a learner who uses this system.

[1301] "Current proficiency" refers to the level of a particular skill or knowledge that a user currently possesses.

[1302] A "learning goal" is a specific skill or knowledge milestone that a user is trying to achieve.

[1303] "Server" refers to a remote computer system that receives data from users and generates and provides appropriate learning content and homework based on that data.

[1304] "Learning Content" refers to educational resources and information provided to users to further their learning.

[1305] "Homework" refers to tasks or problems that a user must complete to advance their learning.

[1306] "Terminal" refers to a smartphone, head-mounted display, or other electronic device used by a user.

[1307] "Facial expression" refers to the emotion or state shown by the user's facial movements and expressions.

[1308] "Tone of voice" refers to characteristics such as pitch, strength, and quality of a user's voice.

[1309] "Emotional data" refers to information about the user's emotional state obtained from facial expressions and tone of voice.

[1310] "Learning experience" refers to the process and overall experience a user has while using learning content.

[1311] "Progress" refers to the progress a user has made in the learning process.

[1312] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate learning content and homework based on user input.

[1313] A "prompt" is a starting point text that is given to a generative AI model to instruct it on how to generate learning content or homework.

[1314] The present invention combines a system that provides appropriate learning content and homework based on a user's proficiency and learning goals, and can objectively evaluate learning progress, with an "emotion engine" that recognizes the user's emotions and adjusts the learning experience. Hereinafter, embodiments of the present invention will be described in detail.

[1315] This system is realized by running a program in cooperation with a server, a user terminal, and an emotion engine.

[1316] Processing on the user's device

[1317] Users input their current level of proficiency, the subjects they are studying, and their learning goals using a smartphone or head-mounted display. This input data is sent from the device to the server. For example, if a user requests intermediate-level English conversation lessons with a goal of achieving a TOEIC score of 800, this information becomes the input data.

[1318] Processing on the server

[1319] The server receives the user's input data and generates appropriate learning content and homework based on that data using a generative AI model developed in Python. The model generates learning content using the following prompt sentence as input:

[1320] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[1321] The generated learning content and homework are sent to the user's terminal, which displays them to the user.

[1322] Emotion engine processing

[1323] The user's device is equipped with a camera and microphone for facial expression recognition and voice analysis. The emotion engine (using a TensorFlow model) analyzes these input data to recognize the user's emotional state (concentration, fatigue, interest, etc.). This emotion data is sent to the server in real time. For example, if the user is determined to be not concentrating, the learning experience is adjusted using the following prompt sentence:

[1324] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[1325] Tailoring the learning experience

[1326] The server adjusts the learning content based on the emotional data. For example, if the user feels tired, it provides content suggesting a short break. It also changes the content and difficulty of the learning as needed to ensure the user has an optimal learning experience.

[1327] Assessment of learning progress and next adjustments

[1328] After completing their homework, users enter their results into their device, which then sends them to the server. The server then analyzes the results and emotional data to further adjust the learning content and homework for the next time. For example, if a user's homework results show a high success rate but they feel stressed, the difficulty of the next homework will be reduced.

[1329] Providing progress comparison data

[1330] Furthermore, the server generates data for comparing proficiency levels based on the data of other users and provides this data to the user, allowing the user to objectively evaluate their own learning progress. For example, the user can send a prompt to the server requesting comparison with others, and the server generates comparison data.

[1331] In this way, the present invention makes it possible to provide an optimal learning experience according to the user's emotions and level of proficiency.

[1332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] The user inputs their current proficiency level and learning goals.

[1335] Input details: Proficiency level (e.g., intermediate), study subject (e.g., English conversation), learning goal (e.g., TOEIC 800 points)

[1336] Specific operation: The user inputs the necessary information into a dedicated application using a smartphone or head-mounted display. The input data is saved on the device.

[1337] Step 2:

[1338] The terminal sends the input data to the server.

[1339] Input content: User proficiency, study subjects, learning goals

[1340] Output: User data received by the server

[1341] Specific operation: The terminal sends the user's input data to the server via the Internet, and the server stores this information in a database.

[1342] Step 3:

[1343] The server generates appropriate learning content and homework.

[1344] Input content: User data

[1345] Output: Generated learning content and homework

[1346] How it works: The server uses a generative AI model developed in Python to generate appropriate learning content and homework using the following prompts as input:

[1347] The user is currently studying intermediate level English conversation and is aiming for a TOEIC score of 800. Please generate new learning content and homework appropriate for the intermediate level.

[1348] The generated data is transmitted from the server to the terminal.

[1349] Step 4:

[1350] The terminal displays the received learning content and homework to the user.

[1351] Input content: Learning content and homework received from the server

[1352] Output: Displayed learning content and homework

[1353] Specific operation: The device displays the received content in a format that is easy for the user to understand. The user then begins learning.

[1354] Step 5:

[1355] The device analyzes the user's facial expressions and tone of voice to recognize emotions.

[1356] Input content: User's facial expression data, tone of voice data

[1357] Output: Recognized emotion data

[1358] Specific operation: Facial expression recognition and voice analysis are performed using the device's built-in camera and microphone. An emotion analysis engine using TensorFlow recognizes the user's emotions in real time and generates data.

[1359] Step 6:

[1360] The device transmits the emotion data to the server.

[1361] Input content: Emotion data

[1362] Output: Emotion data received by the server

[1363] Specific operation: The device sends the analyzed emotion data via the Internet to a server, which receives the data and stores it for analysis.

[1364] Step 7:

[1365] The server tailors the learning experience based on the emotional data.

[1366] Input content: Emotion data

[1367] Output: Tailored learning experience

[1368] What Happens: The server analyzes the emotion data and tailors the learning experience using the following prompt:

[1369] Generate adjustment suggestions to provide an optimal learning experience based on the emotions users feel while learning (concentration, fatigue, interest, etc.) For example, if the user is fatigued, suggest a short break.

[1370] Tailored learning content and break suggestions are sent to the device.

[1371] Step 8:

[1372] The user enters the results of their homework, and the terminal sends them to the server.

[1373] Input content: Homework results

[1374] Output: Homework result data received by the server

[1375] Specific operation: The user inputs the results of their homework using a smartphone or head-mounted display. The results are then sent from the device to the server.

[1376] Step 9:

[1377] The server analyzes the homework results and emotion data and adjusts the next learning content and homework.

[1378] Input content: Homework result data, emotion data

[1379] Output: Adjusted next learning content and homework

[1380] Specific operation: The server generates appropriate new learning content and homework based on the accuracy rate of homework and emotional data. The generated data is used for the next learning session.

[1381] Step 10:

[1382] The server generates data based on the user's current level to compare their proficiency with that of other companies.

[1383] Input: User progress data, other users' data

[1384] Output content: Comparison data

[1385] Specific operation: The server compares and analyzes the user's progress data with that of other users to generate proficiency comparison data, which is then sent to the user's device and displayed.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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).

[1393] 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.

[1394] 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."

[1395] 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.

[1396] 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).

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1406] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1407] The following is further disclosed regarding the above embodiment.

[1408] (Claim 1)

[1409] a means for the user to input current proficiency and learning goals;

[1410] means for the server to generate appropriate learning content and homework based on the user's input;

[1411] a means for displaying the learning content and homework received by the terminal from the server to the user;

[1412] means for the user to transmit the results of the homework to a server;

[1413] a means for the server to adjust next learning content and homework based on the results of the homework;

[1414] A system including:

[1415] (Claim 2)

[1416] 2. The system of claim 1, wherein the server further comprises means for generating data for comparing proficiency levels with other companies based on the user's current level.

[1417] (Claim 3)

[1418] 10. The system of claim 1, further comprising means for the user to verify the comparison data received from the server.

[1419] "Example 1"

[1420] (Claim 1)

[1421] a means for the user to input current proficiency and learning goals;

[1422] means for the terminal to transmit the input data to a server;

[1423] means for the server to generate appropriate learning content and homework based on the user's input;

[1424] a means for displaying the learning content and homework received by the terminal from the server to the user;

[1425] A means for the user to input the results of the homework into a terminal and transmit the results to a server;

[1426] a means for the server to adjust next learning content and homework based on the results of the homework;

[1427] A system including:

[1428] (Claim 2)

[1429] 2. The system of claim 1, wherein the server further comprises means for generating data for comparing proficiency levels with other companies based on the user's current level.

[1430] (Claim 3)

[1431] 10. The system of claim 1, further comprising means for the user to verify the comparison data received from the server.

[1432] "Application Example 1"

[1433] (Claim 1)

[1434] a means for the user to input current proficiency and learning goals;

[1435] means for the server to generate appropriate learning content and homework based on the user's input;

[1436] a means for displaying the learning content and homework received by the terminal from the server to the user;

[1437] means for the user to transmit the results of the homework to a server;

[1438] a means for the server to adjust next learning content and homework based on the results of the homework;

[1439] A means for the server to generate comparison data using a generation AI model based on the learning progress of other users;

[1440] a means for the user to view the comparison data;

[1441] means for generating a prompt sentence for generating the comparison data and a suggestion for the next learning content;

[1442] A system including:

[1443] (Claim 2)

[1444] 10. The system of claim 1, further comprising means for the user to confirm the received comparison data and suggestions on the terminal.

[1445] (Claim 3)

[1446] The system of claim 1 , wherein the server further comprises means for generating and adjusting learning content using a generative AI model.

[1447] "Example 2: Combining Emotion Engines"

[1448] (Claim 1)

[1449] a means for the user to input current proficiency and learning goals;

[1450] means for transmitting the user input data to a server by a terminal;

[1451] means for the server to generate appropriate learning content and homework based on the user's input;

[1452] a means for displaying the learning content and homework received by the terminal from the server to the user;

[1453] means for the terminal to collect real-time emotional data of the user;

[1454] A means for the terminal to transmit emotion data to a server;

[1455] means for the server to tailor the learning experience based on said emotional data;

[1456] a means for the user to input the results of the homework into the terminal;

[1457] A means for the terminal to transmit the results of the homework to a server;

[1458] A server adjusts the next learning content and homework based on the homework results and emotion data;

[1459] A system including:

[1460] (Claim 2)

[1461] 2. The system of claim 1, wherein the server further comprises means for generating data for comparing the user's proficiency level with that of others based on the user's current level.

[1462] (Claim 3)

[1463] 10. The system of claim 1, further comprising means for a user to verify the comparison data received from the server.

[1464] "Application example 2 when combining emotion engines"

[1465] (Claim 1)

[1466] a means for the user to input current proficiency and learning goals;

[1467] means for the server to generate appropriate learning content and homework based on the user's input;

[1468] a means for displaying the learning content and homework received by the terminal from the server to the user;

[1469] means for recognizing emotions by analyzing the facial expressions and tone of voice of the user;

[1470] means for transmitting the emotion data to a server;

[1471] means for the server to tailor the learning experience based on said emotional data;

[1472] means for the user to transmit the results of the homework to a server;

[1473] a means for the server to adjust the next learning content and homework based on the homework results and emotion data;

[1474] A system including:

[1475] (Claim 2)

[1476] 2. The system of claim 1, wherein the server further comprises means for generating data for comparing proficiency levels with other companies based on the user's current level.

[1477] (Claim 3)

[1478] 10. The system of claim 1, further comprising means for the user to verify the comparison data received from the server. [Explanation of symbols]

[1479] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for the user to input current proficiency and learning goals; means for the server to generate appropriate learning content and homework based on the user's input; a means for displaying the learning content and homework received by the terminal from the server to the user; means for the user to transmit the results of the homework to a server; a means for the server to adjust next learning content and homework based on the results of the homework; A system including:

2. 2. The system according to claim 1, wherein the server further comprises means for generating data for comparing proficiency levels with other companies based on the user's current level.

3. 2. The system of claim 1, further comprising means for the user to verify the comparison data received from the server.

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