system

Abstract

To provide a system that automatically provides optimal learning programs and content for individual learners, and monitors learning progress and provides feedback. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs the following processes: collecting the user's learning history and evaluation data; analyzing the learning history and evaluation data to score the user's level of understanding; generating a learning program suitable for the user based on the understanding score; providing the user with appropriate learning content based on the learning program; monitoring the user's learning progress, analyzing new progress data and updating the learning program; and providing the updated learning program to the user again.

Description

[Technical Field]

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

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

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

[0004] In conventional educational systems, it is common to provide learners with uniform teaching materials and learning programs, making it difficult to provide optimal instruction tailored to each learner's individual learning progress and level of understanding. Therefore, in order for learners to effectively progress in their studies and deepen their understanding, customized educational programs tailored to each individual learner are necessary, but no method for achieving this has been established. The present invention aims to provide a system that automatically provides optimal learning programs and content to individual learners, monitors their learning progress, and provides feedback. [Means for solving the problem]

[0005] In order to solve such problems, the present invention has the following means.

[0006] A system is provided that includes a means for collecting a user's learning history and evaluation data, a means for analyzing the learning history and evaluation data and scoring the user's level of understanding, a means for generating a learning program suitable for the user based on the comprehension score, a means for providing the user with appropriate learning content based on the learning program, a means for monitoring the user's learning progress, analyzing new progress data and updating the learning program, and a means for providing the updated learning program to the user again.

[0007] This system can automatically provide optimal learning programs and content for individual users, improving their learning efficiency. It also monitors learning progress in real time and dynamically adjusts learning programs as needed, enabling more effective education.

[0008] "Study history" is data that includes information such as the content and learning materials that the user has studied in the past, study time, progress, and so on.

[0009] "Evaluation data" refers to data that includes information such as test results, quiz scores, and assignment submission status for evaluating a user's learning outcomes.

[0010] A "comprehension score" is a numerical indicator of how much a user understands a particular learning content or subject.

[0011] A "learning program" is a series of educational plans that combines learning content, learning materials, learning plans, and learning procedures provided to a user.

[0012] "Learning content" refers to specific learning materials such as teaching materials, documents, videos, texts, and quizzes that users use to study.

[0013] "Learning progress" refers to specific situations or results that indicate how far a user has progressed in the process of working through a learning program.

[0014] "Monitoring" refers to the act of constantly observing a user's learning progress and behavior and recording any necessary information.

[0015] "Feedback" refers to information such as evaluations and advice provided to users regarding their learning results and progress, and instructions for the next learning step. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0037] The present invention provides a system that generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on the program, and monitors learning progress and provides feedback. Specific embodiments of the system are described below.

[0038] Overall system overview

[0039] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, dynamically providing learning programs and content to enable the user to study most effectively.

[0040] Data collection

[0041] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0042] Scoring comprehension

[0043] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0044] Creating and delivering learning programs

[0045] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0046] Monitoring learning progress

[0047] As a user follows a learning program, the device records their progress, actual learning content, test scores, etc., and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[0048] Feedback and Program Updates

[0049] The server generates feedback for the user based on the analysis results. For example, it may include specific instructions such as, "Now that you understand geometry better, try some applied problems next" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0050] Specific examples

[0051] 1. Collecting training data

[0052] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0053] 2. Scoring comprehension

[0054] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0055] 3. Creating and delivering appropriate learning programs

[0056] The server generates a learning program that progresses from the basics to applications of geometry, selecting video lectures and exercises.

[0057] The learning program is sent to the terminal, which displays it to User A.

[0058] 4. Monitoring your learning progress

[0059] User A watches a video lecture and the device records his / her progress solving the exercises.

[0060] The progress data is sent to the server.

[0061] 5. Feedback and Program Updates

[0062] The server analyzes the new progress data and determines that User A is struggling with a particular concept in geometry.

[0063] Provides instruction and supplementary materials for "focusing on specific concepts of geometry."

[0064] If necessary, the learning program is updated and resent to the terminal.

[0065] As described above, this system can provide educational programs and content that are optimized for each user's individual learning status, thereby improving learning efficiency and comprehension.

[0066] The processing flow will be explained below.

[0067] Step 1: Collect user learning data

[0068] A user logs in to a terminal.

[0069] The terminal sends the user's login information to the server.

[0070] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[0071] Step 2: Send user learning history and assessment data

[0072] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[0073] Step 3: Analyze the data and score comprehension

[0074] The server analyzes the received data and evaluates the user's current learning situation and level of understanding.

[0075] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[0076] Step 4: Generate an optimal learning program

[0077] The server generates an optimal learning program based on the generated comprehension score.

[0078] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from a database.

[0079] Step 5: Delivering the learning program

[0080] The server transmits the generated learning program to the terminal.

[0081] The terminal displays the received learning program to the user, and allows the user to start learning.

[0082] Step 6: Monitor and record your learning progress

[0083] Users progress through their studies via their terminals.

[0084] The device records the user's progress data in real time, including study time, study content, and test results.

[0085] Step 7: Submit your learning progress data

[0086] The terminal periodically transmits the recorded learning progress data to the server.

[0087] Step 8: Reanalyze progress data and generate feedback

[0088] The server analyzes the new progress data received and reassess the user's understanding and learning progress.

[0089] The server generates feedback based on the analysis, including specific advice such as "You should focus more on certain concepts of geometry."

[0090] Step 9: Feedback and delivery of updated learning programs

[0091] The server transmits the generated feedback and updated learning program to the terminal.

[0092] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[0093] Example 1

[0094] 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."

[0095] Conventional learning support systems have had difficulty monitoring users' learning progress in real time and dynamically providing appropriate feedback and learning materials. Furthermore, they lacked mechanisms for generating learning programs optimized for individual users and continuously updating them. As a result, users' learning efficiency declined, and their level of understanding did not improve sufficiently.

[0096] 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.

[0097] In this invention, the server includes means for collecting the user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, and means for generating a learning program suited to the user based on the comprehension score, thereby enabling the generation and updating of learning programs based on the individual user's learning status, as well as real-time progress monitoring and feedback.

[0098] "Study history" refers to a record of a user's past learning activities, specifically data such as the content studied, time spent, and grades.

[0099] "Evaluation data" refers to data used to evaluate the effectiveness of learning, such as a user's learning outcomes and test results.

[0100] The "understanding score" is a numerical value that quantitatively represents the user's understanding level based on the learning history and evaluation data.

[0101] A "learning program" is a set of individually optimized learning plans and tasks that are generated based on a user's comprehension score.

[0102] "Learning content" refers to specific learning materials and assignments provided to users based on a learning program, including video lectures, textbooks, and practice questions.

[0103] "Monitoring" refers to continuously observing a user's learning progress and collecting learning data.

[0104] "Progress data" is data recorded as the user progresses through the learning process according to the learning program.

[0105] "Feedback" refers to advice and instructions for next steps provided to the user based on the analysis results.

[0106] "Analysis algorithm" refers to a method or program for analyzing collected data and calculating comprehension scores.

[0107] "Terminal" refers to a device through which a user accesses a learning program and displays learning content.

[0108] The present invention is a system that generates an optimized learning program based on a user's learning history and evaluation data, provides learning content based on that program, and monitors learning progress and provides feedback.

[0109] To implement this system, the following hardware and software are used.

[0110] Hardware:

[0111] 1. Device: This refers to the computer or smartphone used by the user, and is used to input learning data and display learning content.

[0112] 2. Server: Data is stored and analyzed using cloud services (e.g., Amazon Web Services, Microsoft® Azure®).

[0113] software:

[0114] 1. Learning application: An application used by the user on a device to display learning content and input data.

[0115] 2. Analysis algorithm: Analyze the data using a Python machine learning library (e.g., scikit-learn) and generate a comprehension score.

[0116] A specific embodiment of the system will be described below.

[0117] Overall system overview

[0118] As users study using their devices, the server constantly monitors their progress and level of understanding, dynamically providing learning programs and content to help users study most effectively.

[0119] Data collection

[0120] When a user starts studying, the device collects evaluation data such as study history, past test results, and study time. The collected data is sent to the server, which analyzes it and evaluates the user's current learning status. For example, user A takes an online math test and the results are sent from the device to the server.

[0121] Scoring comprehension

[0122] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. For example, the server analyzes User A's level of algebra understanding and assigns him a score of 8 / 10.

[0123] Creating and delivering learning programs

[0124] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. For example, the server generates a learning program for User A, covering geometry from basics to applications, and selects video lectures and practice problems and sends them to the terminal.

[0125] Monitoring learning progress

[0126] As a user progresses through a learning program, the device records the user's progress, actual learning content, test scores, etc., and periodically sends them to the server. The server uses an analytical algorithm to evaluate the progress data in real time. As a concrete example, when User A is watching a video lecture, the device records the viewing time and progress, and sends them to the server.

[0127] Feedback and Program Updates

[0128] The server generates feedback for the user based on the analysis results. For example, instructions such as "focus on learning a specific concept of geometry" are provided along with corresponding supplementary learning materials. If necessary, the learning program is updated and resent to the device. As a specific example, the server analyzes User A's new progress data and identifies a lack of understanding of a specific concept of geometry, providing instructions and supplementary learning materials.

[0129] Prompt Sentence Examples

[0130] 1. "User A's algebra comprehension is 8 / 10 and his geometry comprehension is 6 / 10. What kind of learning content should we provide to him next?"

[0131] 2. "User A needs a learning program that progresses from basic to advanced geometry. Please suggest appropriate video lectures and exercises."

[0132] 3. "Analyze User A's learning progress data and suggest the next learning material or instruction needed."

[0133] 4. "I have some data showing that User A is struggling with a particular concept in geometry. Please provide appropriate feedback and suggest additional learning materials."

[0134] As described above, this system can provide optimal educational programs and content based on each user's individual learning status, thereby improving learning efficiency and comprehension.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] Data collection

[0138] When a user begins studying, the device collects data such as study history, past test results, and study time. The collected data is saved on the device as input from the user. The device then sends this data to the server. Specifically, when the user launches a study application and takes a test, the results are recorded in real time. For example, if user A takes an online algebra test, the results are saved on the device as "algebra test results."

[0139] Input: User's learning history, past test results, study time

[0140] Output: Training data stored on the device, data sent to the server

[0141] Step 2:

[0142] Scoring comprehension

[0143] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. The server analyzes the data using a Python machine learning library (e.g., scikit-learn) and generates a comprehension score. During this process, the server calculates scores such as 8 / 10 for algebra and 6 / 10 for geometry. For example, User A's algebra comprehension is analyzed and a score of 8 / 10 is generated.

[0144] Input: Training data sent to the server

[0145] Output: Comprehension score (e.g. Algebra 8 / 10, Geometry 6 / 10)

[0146] Step 3:

[0147] Creating and delivering learning programs

[0148] The server generates an optimal learning program for the user based on the comprehension score. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. The server selects online educational content and practice problems as learning content. For example, a learning program that covers geometry from basics to applications is generated and sent to the terminal.

[0149] Input: Comprehension score

[0150] Output: Generated training program, training program sent to the terminal

[0151] Step 4:

[0152] Monitoring learning progress

[0153] As a user progresses through a learning program, the device records the user's learning progress, learning content, test scores, etc. This data is periodically sent to a server, which uses an analytical algorithm to evaluate the progress data in real time. As a specific example, user A is watching a video lecture, and the viewing time and progress are recorded by the device and sent to the server.

[0154] Input: User's learning progress data

[0155] Output: Progress data sent to the server, real-time evaluation

[0156] Step 5:

[0157] Feedback and Program Updates

[0158] The server generates feedback for the user based on the analysis results. The learning program is updated as necessary and resent to the terminal. For example, an instruction to "concentrate on studying a specific concept of geometry" is generated and provided to the user via the terminal. If it is determined that User A lacks understanding of a specific concept of geometry, the server generates supplementary learning materials along with the feedback to "concentrate on studying" and sends them to the terminal.

[0159] Input: User progress data, real-time assessment results

[0160] Output: Generated feedback, updated learning program, feedback and program sent to the device

[0161] These are the specific processing steps of the system. Through this process, optimal educational programs and content are provided based on the individual learning status of each user, improving learning efficiency and comprehension.

[0162] (Application example 1)

[0163] 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."

[0164] While employee education and training are extremely important in brick-and-mortar stores, it is difficult to provide optimal training programs tailored to each employee's skill level and level of understanding. Furthermore, there is a lack of systems that can grasp each employee's progress in real time and dynamically update training content accordingly. For this reason, there is a need for a system that can provide efficient, individually optimized training.

[0165] 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.

[0166] In this invention, the server includes means for collecting employee skill assessment data and training history, means for analyzing the data to score the employee's level of understanding, means for generating a training program suited to the employee based on the score, means for providing the employee with appropriate training content based on the training program, means for monitoring the employee's training progress, analyzing new progress data to update the training program, and means for providing the updated training program to the employee again, thereby making it possible to provide efficient training optimized for each employee.

[0167] "User learning history" is a record of what learning activities the user has performed in the past.

[0168] "Evaluation data" is data for evaluating the user's level of understanding and progress regarding the learning content.

[0169] "Analysis" is the process of extracting meaningful information from collected data.

[0170] The "understanding score" is a numerical representation of the user's understanding of the learning content.

[0171] A "learning program" is a learning plan or learning system that is optimized based on the user's learning progress and level of understanding.

[0172] "Learning content" refers to learning materials such as video lectures, textbooks, and quizzes provided to users.

[0173] "Study progress" refers to the progress a user makes as they progress through a learning program.

[0174] "New progress data" refers to data relating to the learning situation that is newly collected as the user progresses through the learning program.

[0175] "Brick and mortar employees" are staff who work in actual physical stores.

[0176] "Skills assessment data" refers to data that indicates the results of an assessment of an employee's specific skills or abilities.

[0177] "Training history" is a record of the training and education an employee has received in the past.

[0178] "Training Program" means an educational plan or training system designed to improve the skills of employees.

[0179] "Training Content" means training materials, such as video lectures, practical tests, and study materials, that are provided to employees.

[0180] "Training Progress" is the progress an employee makes in progressing through a training program.

[0181] This invention is a system that provides individually optimized training programs to employees in brick-and-mortar stores. This system collects and analyzes employee skill assessment data and training history, and dynamically generates and provides optimal training programs and training content based on that data. This system makes it possible to achieve efficient training that is optimized for each employee.

[0182] Hardware and software used

[0183] The system uses the following hardware and software:

[0184] Hardware: Servers, smartphones, tablets

[0185] Software: Python, Pandas, Scikit-Learn, database system

[0186] Program processing

[0187] The server receives employee skill assessment data and training history, analyzes it, and scores the employee's skill level. Specifically, it uses an analysis algorithm using Python and Scikit-Learn to predict skill levels.

[0188] For example, when Employee A joins the company, his skill level is predicted based on his past training data. At this time, he is given a score such as high customer service skills (8 / 10) or insufficient product knowledge (5 / 10), and appropriate video learning materials and practical tests are automatically selected based on this.

[0189] Once the training program is generated, it is sent to the employee's device, such as a smartphone or tablet. Employees can view video materials and take practical tests via their device. Progress data is sent in real time to the server, which then updates the training program accordingly.

[0190] The server can provide feedback to employees based on the analysis results, such as issuing instructions like, "Your customer service skills have improved, so next you should learn about applied technology."

[0191] Specific examples

[0192] Employee A joins the company and his past training data is imported into the database. The server uses Python and Scikit-Learn to predict his skill level and generate a score, such as "high customer service skills but lacking product knowledge." Based on this score, the optimal video learning materials and practical tests are selected and sent to Employee A's smartphone. Employee A watches the videos and takes the tests, and their progress data is sent to the server. The server analyzes the new progress data and provides the next feedback and training content.

[0193] Prompt Sentence Examples

[0194] "Create a Python script that uses training data for new staff members to predict their skill levels and generate the optimal training program. I need a model that uses random forests for training and prediction."

[0195] As described above, this system makes it possible to efficiently provide training that is optimized for each employee, contributing to improving the service quality of stores.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1: Data collection

[0198] The user inputs or imports past training history and skill assessment data via the terminal. This data includes the employee's past training sessions, courses taken, and skill assessment test results. The terminal then sends this data to the server. The input data is information stored in a CSV file or database.

[0199] Step 2: Data analysis and scoring

[0200] The server uses the received data to score the employee's skill level. The server uses Python and Scikit-Learn to analyze the collected data and quantify the level of understanding of each skill (e.g., customer service, product knowledge, cash register operation, etc.). The input is past training history and skill evaluation data, and the output is the understanding score for each skill.

[0201] Step 3: Generate a training program

[0202] The server generates a training program suitable for the employee based on the comprehension score. This program includes video materials, practical tests, quizzes, etc. The server selects appropriate training content and generates this program. The input is the comprehension score, and the output is the training program.

[0203] Step 4: Provide training content

[0204] The server sends the generated training program to the employee's terminal. Through the terminal, the user can watch video materials and take practical tests and quizzes. The input is the training program, and the output is the training content displayed on the terminal.

[0205] Step 5: Progress monitoring

[0206] The device records the employee's progress as they progress through the training program and periodically sends the data to a server. The recorded data includes videos watched, quizzes completed, and practical test results. The input is the user's learning progress, and the output is the progress data.

[0207] Step 6: Analyze progress data

[0208] The server analyzes the progress data and evaluates changes in the employee's understanding. The server uses Python and Scikit-Learn to analyze new progress data and identify changes in the employee's skill level. The input is the progress data, and the output is the understanding score for the updated skill level.

[0209] Step 7: Feedback and Program Updates

[0210] The server provides feedback to employees based on the analysis results and updates the training program as necessary. For example, it provides specific instructions such as, "Your customer service skills have improved, so now it's time for applied skills training." The server then sends the updated training program back to the employee's device. The input is the comprehension score for the updated skill level, and the output is the updated training program.

[0211] 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.

[0212] The present invention is a system that uses a user's learning history and assessment data to generate an individually optimized learning program, provides learning content based on that program, and monitors learning progress and provides feedback. Furthermore, the system aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0213] Overall system overview

[0214] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0215] Data collection

[0216] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0217] Scoring comprehension

[0218] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0219] Emotional Recognition and Feedback

[0220] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[0221] Creating and delivering learning programs

[0222] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0223] Monitoring learning progress

[0224] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[0225] Feedback and Program Updates

[0226] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0227] Specific examples

[0228] 1. Collecting training data

[0229] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0230] 2. Scoring comprehension

[0231] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0232] 3. Emotional Recognition

[0233] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0234] The emotion engine analyzes this data and recognizes that User A is tired.

[0235] 4. Creating and delivering appropriate learning programs

[0236] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0237] The learning program is sent to the terminal, which displays it to User A.

[0238] 5. Monitoring your learning progress

[0239] User A watches a video lecture and the device records his / her progress solving the exercises.

[0240] The progress data is sent to the server.

[0241] 6. Feedback and Program Updates

[0242] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0243] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0244] If necessary, the learning program is updated and resent to the terminal.

[0245] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[0246] The processing flow will be explained below.

[0247] Step 1: Collect user learning data

[0248] A user logs in to a terminal.

[0249] The terminal sends the user's login information to the server.

[0250] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[0251] Step 2: Send user learning history and assessment data

[0252] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[0253] Step 3: Analyze the data and score comprehension

[0254] The server analyzes the received evaluation data and evaluates the user's current learning situation and level of understanding.

[0255] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[0256] Step 4: Collecting emotion data

[0257] As the user progresses with their learning, the device's camera, microphone, and temperature sensor collect the user's biometric data (facial expression, tone of voice, body temperature).

[0258] The collected emotion data is sent to a server in real time.

[0259] Step 5: Sentiment Analysis

[0260] An emotion engine installed on the server analyzes the received emotion data and recognizes the user's emotional state.

[0261] For example, it identifies whether the user is tired, has difficulty concentrating, is agitated, etc.

[0262] Step 6: Generate an optimal learning program

[0263] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine.

[0264] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from the database.

[0265] Step 7: Delivering the learning program

[0266] The server transmits the generated learning program to the terminal.

[0267] The terminal displays the received learning program to the user, and allows the user to start learning.

[0268] Step 8: Monitor and record your learning progress

[0269] The user progresses through the learning program.

[0270] The device records the user's progress data in real time, including study time, study content, and test results.

[0271] Step 9: Submit your learning progress data

[0272] The terminal periodically transmits the recorded learning progress data to the server.

[0273] Step 10: Reanalyze progress and sentiment data

[0274] The server analyzes the received learning progress data and emotional data, and reevaluates the user's level of understanding, learning progress, and emotional state.

[0275] Step 11: Feedback and Program Updates

[0276] The server generates feedback based on the analysis, including specific advice such as "focus more on a particular concept of geometry" or "take a short break."

[0277] Update learning programs and select new learning tasks and content based on feedback.

[0278] Step 12: Feedback and delivery of updated learning programs

[0279] The server transmits the generated feedback and updated learning program to the terminal.

[0280] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[0281] As a concrete example, let's consider the case where user A uses the system:

[0282] 1. User A logs in to the device, and the device sends evaluation data such as past test results to the server.

[0283] 2. The server analyzes the assessment data and generates comprehension scores of 8 / 10 for algebra and 6 / 10 for geometry.

[0284] 3. User A begins learning, and the device collects emotional data through the camera, microphone, and temperature sensor and sends it to the server.

[0285] 4. The emotion engine analyzes the data and determines that User A is tired.

[0286] 5. The server generates an optimal learning program based on the comprehension score and emotional data and sends it to the device.

[0287] 6. The terminal displays the program to User A and allows him to begin learning.

[0288] 7. User A's learning progress and new emotion data are periodically sent to the server.

[0289] 8. The server analyzes the data, creates feedback, and sends specific instructions to the device, such as "take a short break."

[0290] 9. User A receives the feedback and performs a new task reflected in the learning program.

[0291] This example enables the system to improve the user's learning efficiency and comprehension, and provide support that takes into account the user's emotional state.

[0292] Example 2

[0293] 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."

[0294] In conventional learning systems, it is difficult to provide an optimal learning program based on the individual user's learning progress and level of understanding, and furthermore, because the user's emotional state is not taken into consideration, there are problems with reduced learning efficiency and motivation. The present invention aims to solve these problems and provide an optimal learning environment that appropriately responds to the individual learning situation of each user and takes their emotional state into consideration.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0296] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for recognizing the user's emotional state, means for generating a learning program appropriate for the user based on the comprehension score and emotional state, means for providing the user with appropriate learning content, means for monitoring the user's learning progress, analyzing new progress data to update the learning program, and means for re-providing the updated learning program to the user. This improves the user's learning efficiency and level of understanding, and enables flexible learning support based on the user's emotional state.

[0297] "User" refers to an individual or organization that uses the System to study.

[0298] "Study history" refers to a record of a user's past learning activities, including subjects studied, study time, test results, and so on.

[0299] "Evaluation data" is data for evaluating a user's learning outcomes, and includes test scores, accuracy and speed of answering questions, etc.

[0300] The "comprehension score" is a numerical representation of how well a user understands a particular subject or content, based on an analysis of learning history and assessment data.

[0301] "Emotional state" is information that indicates the user's emotional and mental state, and is determined from biometric data such as facial expression, tone of voice, and body temperature.

[0302] A "learning program" is a plan containing the optimal learning activity schedule and content that is generated based on the user's comprehension score and emotional state.

[0303] "Learning content" refers to specific educational materials, quizzes, video lectures, and other educational resources provided to users in a learning program.

[0304] "Monitoring" means observing the user's learning process and recording their progress.

[0305] "Progress data" is data that indicates the progress of a user's learning activities, and includes the completion rate of video viewing and the correct answer rate for quizzes.

[0306] "Update" refers to modifying and improving existing learning programs and content based on new data and analytical results.

[0307] This invention is a system that uses a user's learning history and assessment data to generate an individually optimized learning program, provides learning content based on that program, monitors learning progress, and provides feedback. Furthermore, it aims to combine it with an emotion engine that recognizes the user's emotions to provide appropriate learning support according to the user's emotional state.

[0308] Overall system overview

[0309] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0310] Data collection

[0311] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0312] Scoring comprehension

[0313] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0314] Emotional Recognition and Feedback

[0315] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[0316] Creating and delivering learning programs

[0317] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0318] Monitoring learning progress

[0319] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[0320] Feedback and Program Updates

[0321] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0322] Specific examples

[0323] 1. Collecting training data

[0324] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0325] 2. Scoring comprehension

[0326] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0327] 3. Emotional Recognition

[0328] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0329] The emotion engine analyzes this data and recognizes that User A is tired.

[0330] 4. Creating and delivering appropriate learning programs

[0331] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0332] The learning program is sent to the terminal, which displays it to User A.

[0333] 5. Monitoring your learning progress

[0334] User A watches a video lecture and the device records his / her progress solving the exercises.

[0335] The progress data is sent to the server.

[0336] 6. Feedback and Program Updates

[0337] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0338] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0339] If necessary, the learning program is updated and resent to the terminal.

[0340] An example prompt is, "Please explain the steps necessary to assess the user's understanding of algebra and provide an appropriate learning program."

[0341] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: User Login

[0344] Specific details:

[0345] The user opens the system login screen using a terminal, enters the login ID and password, and presses the login button.

[0346] Input and Output:

[0347] The user enters a login ID and password, which the terminal sends to the server. The server checks the database to verify the validity of the login information. If the server authenticates the login, it returns the authentication information to the terminal.

[0348] Specific behavior:

[0349] The terminal receives the login information and displays the user's My Page.

[0350] Step 2: Collect and send data

[0351] Specific details:

[0352] After a user logs in to the system, the terminal reads past learning records and test results.

[0353] Input and Output:

[0354] The device collects the user's learning history and evaluation data, and sends it to a server, which stores the received data in a database for analysis.

[0355] Specific behavior:

[0356] The device sends the collected data to the server in a standard format such as JSON, which the server receives and stores in a database.

[0357] Step 3: Score comprehension

[0358] Specific details:

[0359] The server begins analysis based on the learning history and evaluation data, and scores the user's level of understanding.

[0360] Input and Output:

[0361] The server inputs learning history and assessment data, and outputs comprehension scores for each subject. The scores are calculated using analytical algorithms (e.g., decision trees or neural networks).

[0362] Specific behavior:

[0363] The server runs the analysis engine and generates a score, for example, "Algebra 8 / 10, Geometry 6 / 10." The generated scores are stored in the user's data profile.

[0364] Step 4: Recognize emotions

[0365] Specific details:

[0366] While the user is learning, the device collects real-time data using a camera, microphone, and temperature sensor, and the emotion engine analyzes this data to determine the user's emotional state.

[0367] Input and Output:

[0368] The device collects biometric data such as facial expressions, tone of voice, and body temperature, and the emotion engine outputs a judgment of the user's emotional state.

[0369] Specific behavior:

[0370] The device's camera captures the user's facial expressions, the microphone records the tone of voice, and the temperature sensor measures the user's body temperature. This data is pre-processed within the device, and the emotion engine analyzes it to determine the user's emotional state. The results are then sent to the server.

[0371] Step 5: Generate a learning program

[0372] Specific details:

[0373] The server designs an optimal learning program based on the comprehension score and emotional state.

[0374] Input and Output:

[0375] The server inputs the comprehension score and the emotional state assessment result, and outputs a learning program, which includes learning tasks and learning content.

[0376] Specific behavior:

[0377] The server uses the generative AI model to generate a learning program, for example, proposing a program that progresses from the basics of geometry to its applications. The generated program is then sent to the device.

[0378] Step 6: Deliver learning content

[0379] Specific details:

[0380] The terminal displays the learning program received from the server to the user, and the user starts learning according to the program.

[0381] Input and Output:

[0382] The input to the terminal is a learning program, and the output is specific learning content that is displayed to the user.

[0383] Specific behavior:

[0384] The device plays video lectures, displays text materials, and offers quizzes, allowing users to progress through the learning content.

[0385] Step 7: Monitoring your learning progress

[0386] Specific details:

[0387] As the user progresses through their studies, the device records progress data.

[0388] Input and Output:

[0389] The terminal inputs data related to the progress of learning and outputs progress data that is sent to the server.

[0390] Specific behavior:

[0391] The device tracks each learning step in real time and periodically sends the data to a server, which analyzes the data and tracks the user's learning progress.

[0392] Step 8: Generate and provide feedback

[0393] Specific details:

[0394] The server generates feedback based on the progress data and emotional state and provides it to the user.

[0395] Input and Output:

[0396] The server's inputs are progress data and emotional states, and its outputs are feedback and updated learning programs.

[0397] Specific behavior:

[0398] Based on the analysis results, the server may instruct the user to, for example, "focus on specific geometry concepts" and suggest "take a short break." The device receives the feedback and displays it to the user. If necessary, it resends an updated learning program.

[0399] Through this sequential processing, the system maximizes the user's learning efficiency and provides an optimal learning environment tailored to individual needs.

[0400] (Application example 2)

[0401] 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."

[0402] Conventional learning systems have the problem of being unable to provide individually optimized learning programs based on a user's learning history and assessment data, and they also lack real-time feedback and learning adjustments based on the user's emotional state. Furthermore, because they do not take the user's emotional state into account, learning efficiency and effectiveness may decline. This poses a challenge, making it difficult for users to continue learning with high motivation.

[0403] 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.

[0404] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for generating a learning program suited to the user based on the comprehension score, means for providing the user with appropriate learning content based on the learning program, means for monitoring the user's learning progress and analyzing new progress data to update the learning program, means for providing the updated learning program to the user again, means for adjusting the learning program based on the user's emotions, and means for providing real-time feedback to optimize the user's learning experience. This makes it possible to provide a learning program optimized for the user's individual learning and emotional state, thereby improving learning efficiency and maintaining motivation.

[0405] "User's learning history" refers to the content that the user has studied in the past and the activity records related to that learning.

[0406] "Evaluation data" refers to data that indicates the user's level of understanding and achievements regarding the learning content, and includes test results and evaluation scores.

[0407] The "understanding score" is a numerical representation of the user's understanding of the learning content, and is calculated by an analysis algorithm.

[0408] A "learning program" is a set of learning tasks and / or content designed to optimize a user's learning efficiency.

[0409] "Learning content" refers to learning materials that users use to study according to a learning program, including video lectures, textbooks, quizzes, and the like.

[0410] "Study progress" refers to the process by which a user advances in learning according to a learning program and the degree of progress.

[0411] The "emotion engine" is a system that uses a camera, microphone, body temperature sensor, etc. to recognize and analyze the user's emotional state.

[0412] "Feedback" is advice or instruction provided to a user based on their learning progress, level of understanding, and emotional state.

[0413] This invention is a system that generates an individually optimized learning program based on a user's learning history, evaluation data, and emotional state, and monitors the learning progress and provides feedback. Specific embodiments are described below.

[0414] Overall system overview

[0415] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0416] Data collection

[0417] When a user begins studying using a device, the server collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to the server, which analyzes it and evaluates the user's current study status.

[0418] Scoring comprehension

[0419] The server then uses the learning history and assessment data to score the user's level of understanding. This scoring is done using an analysis algorithm built into the server. For example, a detailed score such as 8 / 10 for algebra or 6 / 10 for geometry is calculated.

[0420] Emotional Recognition and Feedback

[0421] As the user progresses through their studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. For example, if the user is feeling impatient or tired, the server will use that information to adjust the study program.

[0422] Creating and delivering learning programs

[0423] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine. This program includes appropriate learning content (video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the device, which then displays it to the user.

[0424] Monitoring learning progress

[0425] As users study according to the learning program, the device records their progress, actual learning content, and test results, and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[0426] Feedback and Program Updates

[0427] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Now that you understand geometry better, try some applied problems" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0428] Specific examples

[0429] 1. Collecting training data

[0430] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0431] 2. Scoring comprehension

[0432] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0433] 3. Emotional Recognition

[0434] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0435] The emotion engine analyzes this data and recognizes that User A is tired.

[0436] 4. Creating and delivering appropriate learning programs

[0437] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0438] The learning program is sent to the terminal, which displays it to User A.

[0439] 5. Monitoring your learning progress

[0440] User A watches a video lecture and the device records his / her progress solving the exercises.

[0441] The progress data is sent to the server.

[0442] 6. Feedback and Program Updates

[0443] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0444] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0445] If necessary, the learning program is updated and resent to the terminal.

[0446] Prompt Sentence Examples

[0447] An example of a prompt sentence is "Study history: Algebra 8 / 10, Geometry 6 / 10; Emotional state: Fatigue." Based on this, the system generates an optimal learning program and appropriate feedback for the user.

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] The user logs in using a terminal.

[0451] Input: User login information (ID, password)

[0452] Output: User authentication status, learning history and assessment data

[0453] Operation: The device sends the user's login information to the server, which then authenticates them. If authentication is successful, the server retrieves the user's past learning history and evaluation data and sends them to the device.

[0454] Step 2:

[0455] The server analyzes the learning history and assessment data and calculates the comprehension score.

[0456] Input: User learning history and assessment data

[0457] Output: Comprehension score for each learning content (e.g., Algebra 8 / 10, Geometry 6 / 10)

[0458] How it works: The server uses an analysis algorithm to analyze the user's learning history and assessment data, calculates a comprehension score for each learning item, and stores it in a database.

[0459] Step 3:

[0460] The user begins learning.

[0461] Input: None (user operation)

[0462] Output: Signal to start learning, activate the device's camera and microphone

[0463] How it works: When a user starts learning, the device activates the camera, microphone, and temperature sensor and begins the learning session.

[0464] Step 4:

[0465] The emotion engine recognizes the user's emotional state in real time.

[0466] Input: Data from camera and microphone, body temperature sensor (facial expression, tone of voice, body temperature)

[0467] Output: User's emotional state (e.g., impatience, fatigue, concentration)

[0468] How it works: The device sends data from the camera, microphone, and temperature sensor to the emotion engine, which analyzes it and determines the user's emotional state. The results are then sent to the server.

[0469] Step 5:

[0470] The server generates a learning program that is optimal for the user.

[0471] Input: Comprehension score, emotional state

[0472] Output: Individually optimized learning program (learning tasks, appropriate learning content)

[0473] How it works: The server generates a learning program optimized for the user based on the user's comprehension score and emotional state. The generated program includes learning content such as video lectures, textbooks, and quizzes. The server then sends the program to the device.

[0474] Step 6:

[0475] The user proceeds with the study by following the instructions of the study program.

[0476] Input: Displayed learning program

[0477] Output: Learning progress data (viewing time, comprehension, answer results)

[0478] How it works: The user follows a learning program, watches video lectures, and answers quizzes. The device records this and sends the learning progress data to the server.

[0479] Step 7:

[0480] The server analyzes the learning progress data and generates feedback.

[0481] Input: Learning progress data

[0482] Output: Feedback instructions (e.g., "Revisit a specific concept in geometry," "Take a short break")

[0483] Operation: The server analyzes learning progress data in real time and generates necessary feedback for the user. The feedback is provided to the user via the terminal.

[0484] Step 8:

[0485] The server updates the learning program as needed and provides it again to the user.

[0486] Input: Feedback instructions, changes in emotional state

[0487] Output: Updated learning program

[0488] Operation: The server updates the learning program based on feedback and changes in emotional state. The updated learning program is resent to the device and displayed to the user.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] [Second embodiment]

[0493] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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).

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0504] 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."

[0505] The present invention provides a system that generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on the program, and monitors learning progress and provides feedback. Specific embodiments of the system are described below.

[0506] Overall system overview

[0507] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, dynamically providing learning programs and content to enable the user to study most effectively.

[0508] Data collection

[0509] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0510] Scoring comprehension

[0511] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0512] Creating and delivering learning programs

[0513] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0514] Monitoring learning progress

[0515] As a user follows a learning program, the device records their progress, actual learning content, test scores, etc., and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[0516] Feedback and Program Updates

[0517] The server generates feedback for the user based on the analysis results. For example, it may include specific instructions such as, "Now that you understand geometry better, try some applied problems next" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0518] Specific examples

[0519] 1. Collecting training data

[0520] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0521] 2. Scoring comprehension

[0522] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0523] 3. Creating and delivering appropriate learning programs

[0524] The server generates a learning program that progresses from the basics to applications of geometry, selecting video lectures and exercises.

[0525] The learning program is sent to the terminal, which displays it to User A.

[0526] 4. Monitoring your learning progress

[0527] User A watches a video lecture and the device records his / her progress solving the exercises.

[0528] The progress data is sent to the server.

[0529] 5. Feedback and Program Updates

[0530] The server analyzes the new progress data and determines that User A is struggling with a particular concept in geometry.

[0531] Provides instruction and supplementary materials for "focusing on specific concepts of geometry."

[0532] If necessary, the learning program is updated and resent to the terminal.

[0533] As described above, this system can provide educational programs and content that are optimized for each user's individual learning status, thereby improving learning efficiency and comprehension.

[0534] The processing flow will be explained below.

[0535] Step 1: Collect user learning data

[0536] A user logs in to a terminal.

[0537] The terminal sends the user's login information to the server.

[0538] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[0539] Step 2: Send user learning history and assessment data

[0540] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[0541] Step 3: Analyze the data and score comprehension

[0542] The server analyzes the received data and evaluates the user's current learning situation and level of understanding.

[0543] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[0544] Step 4: Generate an optimal learning program

[0545] The server generates an optimal learning program based on the generated comprehension score.

[0546] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from a database.

[0547] Step 5: Delivering the learning program

[0548] The server transmits the generated learning program to the terminal.

[0549] The terminal displays the received learning program to the user, and allows the user to start learning.

[0550] Step 6: Monitor and record your learning progress

[0551] Users progress through their studies via their terminals.

[0552] The device records the user's progress data in real time, including study time, study content, and test results.

[0553] Step 7: Submit your learning progress data

[0554] The terminal periodically transmits the recorded learning progress data to the server.

[0555] Step 8: Reanalyze progress data and generate feedback

[0556] The server analyzes the new progress data received and reassess the user's understanding and learning progress.

[0557] The server generates feedback based on the analysis, including specific advice such as "You should focus more on certain concepts of geometry."

[0558] Step 9: Feedback and delivery of updated learning programs

[0559] The server transmits the generated feedback and updated learning program to the terminal.

[0560] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[0561] Example 1

[0562] 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."

[0563] Conventional learning support systems have had difficulty monitoring users' learning progress in real time and dynamically providing appropriate feedback and learning materials. Furthermore, they lacked mechanisms for generating learning programs optimized for individual users and continuously updating them. As a result, users' learning efficiency declined, and their level of understanding did not improve sufficiently.

[0564] 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.

[0565] In this invention, the server includes means for collecting the user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, and means for generating a learning program suited to the user based on the comprehension score, thereby enabling the generation and updating of learning programs based on the individual user's learning status, as well as real-time progress monitoring and feedback.

[0566] "Study history" refers to a record of a user's past learning activities, specifically data such as the content studied, time spent, and grades.

[0567] "Evaluation data" refers to data used to evaluate the effectiveness of learning, such as a user's learning outcomes and test results.

[0568] The "understanding score" is a numerical value that quantitatively represents the user's understanding level based on the learning history and evaluation data.

[0569] A "learning program" is a set of individually optimized learning plans and tasks that are generated based on a user's comprehension score.

[0570] "Learning content" refers to specific learning materials and assignments provided to users based on a learning program, including video lectures, textbooks, and practice questions.

[0571] "Monitoring" refers to continuously observing a user's learning progress and collecting learning data.

[0572] "Progress data" is data recorded as the user progresses through the learning process according to the learning program.

[0573] "Feedback" refers to advice and instructions for next steps provided to the user based on the analysis results.

[0574] "Analysis algorithm" refers to a method or program for analyzing collected data and calculating comprehension scores.

[0575] "Terminal" refers to a device through which a user accesses a learning program and displays learning content.

[0576] The present invention is a system that generates an optimized learning program based on a user's learning history and evaluation data, provides learning content based on that program, and monitors learning progress and provides feedback.

[0577] To implement this system, the following hardware and software are used.

[0578] Hardware:

[0579] 1. Device: This refers to the computer or smartphone used by the user, and is used to input learning data and display learning content.

[0580] 2. Server: Data is stored and analyzed using cloud services (e.g., Amazon Web Services, Microsoft Azure).

[0581] software:

[0582] 1. Learning application: An application used by the user on a device to display learning content and input data.

[0583] 2. Analysis algorithm: Analyze the data using a Python machine learning library (e.g., scikit-learn) and generate a comprehension score.

[0584] A specific embodiment of the system will be described below.

[0585] Overall system overview

[0586] As users study using their devices, the server constantly monitors their progress and level of understanding, dynamically providing learning programs and content to help users study most effectively.

[0587] Data collection

[0588] When a user starts studying, the device collects evaluation data such as study history, past test results, and study time. The collected data is sent to the server, which analyzes it and evaluates the user's current learning status. For example, user A takes an online math test and the results are sent from the device to the server.

[0589] Scoring comprehension

[0590] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. For example, the server analyzes User A's level of algebra understanding and assigns him a score of 8 / 10.

[0591] Creating and delivering learning programs

[0592] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. For example, the server generates a learning program for User A, covering geometry from basics to applications, and selects video lectures and practice problems and sends them to the terminal.

[0593] Monitoring learning progress

[0594] As a user progresses through a learning program, the device records the user's progress, actual learning content, test scores, etc., and periodically sends them to the server. The server uses an analytical algorithm to evaluate the progress data in real time. As a concrete example, when User A is watching a video lecture, the device records the viewing time and progress, and sends them to the server.

[0595] Feedback and Program Updates

[0596] The server generates feedback for the user based on the analysis results. For example, instructions such as "focus on learning a specific concept of geometry" are provided along with corresponding supplementary learning materials. If necessary, the learning program is updated and resent to the device. As a specific example, the server analyzes User A's new progress data and identifies a lack of understanding of a specific concept of geometry, providing instructions and supplementary learning materials.

[0597] Prompt Sentence Examples

[0598] 1. "User A's algebra comprehension is 8 / 10 and his geometry comprehension is 6 / 10. What kind of learning content should we provide to him next?"

[0599] 2. "User A needs a learning program that progresses from basic to advanced geometry. Please suggest appropriate video lectures and exercises."

[0600] 3. "Analyze User A's learning progress data and suggest the next learning material or instruction needed."

[0601] 4. "I have some data showing that User A is struggling with a particular concept in geometry. Please provide appropriate feedback and suggest additional learning materials."

[0602] As described above, this system can provide optimal educational programs and content based on each user's individual learning status, thereby improving learning efficiency and comprehension.

[0603] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] Data collection

[0606] When a user begins studying, the device collects data such as study history, past test results, and study time. The collected data is saved on the device as input from the user. The device then sends this data to the server. Specifically, when the user launches a study application and takes a test, the results are recorded in real time. For example, if user A takes an online algebra test, the results are saved on the device as "algebra test results."

[0607] Input: User's learning history, past test results, study time

[0608] Output: Training data stored on the device, data sent to the server

[0609] Step 2:

[0610] Scoring comprehension

[0611] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. The server analyzes the data using a Python machine learning library (e.g., scikit-learn) and generates a comprehension score. During this process, the server calculates scores such as 8 / 10 for algebra and 6 / 10 for geometry. For example, User A's algebra comprehension is analyzed and a score of 8 / 10 is generated.

[0612] Input: Training data sent to the server

[0613] Output: Comprehension score (e.g. Algebra 8 / 10, Geometry 6 / 10)

[0614] Step 3:

[0615] Creating and delivering learning programs

[0616] The server generates an optimal learning program for the user based on the comprehension score. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. The server selects online educational content and practice problems as learning content. For example, a learning program that covers geometry from basics to applications is generated and sent to the terminal.

[0617] Input: Comprehension score

[0618] Output: Generated training program, training program sent to the terminal

[0619] Step 4:

[0620] Monitoring learning progress

[0621] As a user progresses through a learning program, the device records the user's learning progress, learning content, test scores, etc. This data is periodically sent to a server, which uses an analytical algorithm to evaluate the progress data in real time. As a specific example, user A is watching a video lecture, and the viewing time and progress are recorded by the device and sent to the server.

[0622] Input: User's learning progress data

[0623] Output: Progress data sent to the server, real-time evaluation

[0624] Step 5:

[0625] Feedback and Program Updates

[0626] The server generates feedback for the user based on the analysis results. The learning program is updated as necessary and resent to the terminal. For example, an instruction to "concentrate on studying a specific concept of geometry" is generated and provided to the user via the terminal. If it is determined that User A lacks understanding of a specific concept of geometry, the server generates supplementary learning materials along with the feedback to "concentrate on studying" and sends them to the terminal.

[0627] Input: User progress data, real-time assessment results

[0628] Output: Generated feedback, updated learning program, feedback and program sent to the device

[0629] These are the specific processing steps of the system. Through this process, optimal educational programs and content are provided based on the individual learning status of each user, improving learning efficiency and comprehension.

[0630] (Application example 1)

[0631] 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."

[0632] While employee education and training are extremely important in brick-and-mortar stores, it is difficult to provide optimal training programs tailored to each employee's skill level and level of understanding. Furthermore, there is a lack of systems that can grasp each employee's progress in real time and dynamically update training content accordingly. For this reason, there is a need for a system that can provide efficient, individually optimized training.

[0633] 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.

[0634] In this invention, the server includes means for collecting employee skill assessment data and training history, means for analyzing the data to score the employee's level of understanding, means for generating a training program suited to the employee based on the score, means for providing the employee with appropriate training content based on the training program, means for monitoring the employee's training progress, analyzing new progress data to update the training program, and means for providing the updated training program to the employee again, thereby making it possible to provide efficient training optimized for each employee.

[0635] "User learning history" is a record of what learning activities the user has performed in the past.

[0636] "Evaluation data" is data for evaluating the user's level of understanding and progress regarding the learning content.

[0637] "Analysis" is the process of extracting meaningful information from collected data.

[0638] The "understanding score" is a numerical representation of the user's understanding of the learning content.

[0639] A "learning program" is a learning plan or learning system that is optimized based on the user's learning progress and level of understanding.

[0640] "Learning content" refers to learning materials such as video lectures, textbooks, and quizzes provided to users.

[0641] "Study progress" refers to the progress a user makes as they progress through a learning program.

[0642] "New progress data" refers to data relating to the learning situation that is newly collected as the user progresses through the learning program.

[0643] "Brick and mortar employees" are staff who work in actual physical stores.

[0644] "Skills assessment data" refers to data that indicates the results of an assessment of an employee's specific skills or abilities.

[0645] "Training history" is a record of the training and education an employee has received in the past.

[0646] "Training Program" means an educational plan or training system designed to improve the skills of employees.

[0647] "Training Content" means training materials, such as video lectures, practical tests, and study materials, that are provided to employees.

[0648] "Training Progress" is the progress an employee makes in progressing through a training program.

[0649] This invention is a system that provides individually optimized training programs to employees in brick-and-mortar stores. This system collects and analyzes employee skill assessment data and training history, and dynamically generates and provides optimal training programs and training content based on that data. This system makes it possible to achieve efficient training that is optimized for each employee.

[0650] Hardware and software used

[0651] The system uses the following hardware and software:

[0652] Hardware: Servers, smartphones, tablets

[0653] Software: Python, Pandas, Scikit-Learn, database system

[0654] Program processing

[0655] The server receives employee skill assessment data and training history, analyzes it, and scores the employee's skill level. Specifically, it uses an analysis algorithm using Python and Scikit-Learn to predict skill levels.

[0656] For example, when Employee A joins the company, his skill level is predicted based on his past training data. At this time, he is given a score such as high customer service skills (8 / 10) or insufficient product knowledge (5 / 10), and appropriate video learning materials and practical tests are automatically selected based on this.

[0657] Once the training program is generated, it is sent to the employee's device, such as a smartphone or tablet. Employees can view video materials and take practical tests via their device. Progress data is sent in real time to the server, which then updates the training program accordingly.

[0658] The server can provide feedback to employees based on the analysis results, such as issuing instructions like, "Your customer service skills have improved, so next you should learn about applied technology."

[0659] Specific examples

[0660] Employee A joins the company and his past training data is imported into the database. The server uses Python and Scikit-Learn to predict his skill level and generate a score, such as "high customer service skills but lacking product knowledge." Based on this score, the optimal video learning materials and practical tests are selected and sent to Employee A's smartphone. Employee A watches the videos and takes the tests, and their progress data is sent to the server. The server analyzes the new progress data and provides the next feedback and training content.

[0661] Prompt Sentence Examples

[0662] "Create a Python script that uses training data for new staff members to predict their skill levels and generate the optimal training program. I need a model that uses random forests for training and prediction."

[0663] As described above, this system makes it possible to efficiently provide training that is optimized for each employee, contributing to improving the service quality of stores.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1: Data collection

[0666] The user inputs or imports past training history and skill assessment data via the terminal. This data includes the employee's past training sessions, courses taken, and skill assessment test results. The terminal then sends this data to the server. The input data is information stored in a CSV file or database.

[0667] Step 2: Data analysis and scoring

[0668] The server uses the received data to score the employee's skill level. The server uses Python and Scikit-Learn to analyze the collected data and quantify the level of understanding of each skill (e.g., customer service, product knowledge, cash register operation, etc.). The input is past training history and skill evaluation data, and the output is the understanding score for each skill.

[0669] Step 3: Generate a training program

[0670] The server generates a training program suitable for the employee based on the comprehension score. This program includes video materials, practical tests, quizzes, etc. The server selects appropriate training content and generates this program. The input is the comprehension score, and the output is the training program.

[0671] Step 4: Provide training content

[0672] The server sends the generated training program to the employee's terminal. Through the terminal, the user can watch video materials and take practical tests and quizzes. The input is the training program, and the output is the training content displayed on the terminal.

[0673] Step 5: Progress monitoring

[0674] The device records the employee's progress as they progress through the training program and periodically sends the data to a server. The recorded data includes videos watched, quizzes completed, and practical test results. The input is the user's learning progress, and the output is the progress data.

[0675] Step 6: Analyze progress data

[0676] The server analyzes the progress data and evaluates changes in the employee's understanding. The server uses Python and Scikit-Learn to analyze new progress data and identify changes in the employee's skill level. The input is the progress data, and the output is the understanding score for the updated skill level.

[0677] Step 7: Feedback and Program Updates

[0678] The server provides feedback to employees based on the analysis results and updates the training program as necessary. For example, it provides specific instructions such as, "Your customer service skills have improved, so now it's time for applied skills training." The server then sends the updated training program back to the employee's device. The input is the comprehension score for the updated skill level, and the output is the updated training program.

[0679] 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.

[0680] The present invention is a system that uses a user's learning history and assessment data to generate an individually optimized learning program, provides learning content based on that program, and monitors learning progress and provides feedback. Furthermore, the system aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0681] Overall system overview

[0682] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0683] Data collection

[0684] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0685] Scoring comprehension

[0686] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0687] Emotional Recognition and Feedback

[0688] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[0689] Creating and delivering learning programs

[0690] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0691] Monitoring learning progress

[0692] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[0693] Feedback and Program Updates

[0694] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0695] Specific examples

[0696] 1. Collecting training data

[0697] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0698] 2. Scoring comprehension

[0699] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0700] 3. Emotional Recognition

[0701] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0702] The emotion engine analyzes this data and recognizes that User A is tired.

[0703] 4. Creating and delivering appropriate learning programs

[0704] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0705] The learning program is sent to the terminal, which displays it to User A.

[0706] 5. Monitoring your learning progress

[0707] User A watches a video lecture and the device records his / her progress solving the exercises.

[0708] The progress data is sent to the server.

[0709] 6. Feedback and Program Updates

[0710] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0711] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0712] If necessary, the learning program is updated and resent to the terminal.

[0713] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[0714] The processing flow will be explained below.

[0715] Step 1: Collect user learning data

[0716] A user logs in to a terminal.

[0717] The terminal sends the user's login information to the server.

[0718] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[0719] Step 2: Send user learning history and assessment data

[0720] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[0721] Step 3: Analyze the data and score comprehension

[0722] The server analyzes the received evaluation data and evaluates the user's current learning situation and level of understanding.

[0723] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[0724] Step 4: Collecting emotion data

[0725] As the user progresses with their learning, the device's camera, microphone, and temperature sensor collect the user's biometric data (facial expression, tone of voice, body temperature).

[0726] The collected emotion data is sent to a server in real time.

[0727] Step 5: Sentiment Analysis

[0728] An emotion engine installed on the server analyzes the received emotion data and recognizes the user's emotional state.

[0729] For example, it identifies whether the user is tired, has difficulty concentrating, is agitated, etc.

[0730] Step 6: Generate an optimal learning program

[0731] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine.

[0732] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from the database.

[0733] Step 7: Delivering the learning program

[0734] The server transmits the generated learning program to the terminal.

[0735] The terminal displays the received learning program to the user, and allows the user to start learning.

[0736] Step 8: Monitor and record your learning progress

[0737] The user progresses through the learning program.

[0738] The device records the user's progress data in real time, including study time, study content, and test results.

[0739] Step 9: Submit your learning progress data

[0740] The terminal periodically transmits the recorded learning progress data to the server.

[0741] Step 10: Reanalyze progress and sentiment data

[0742] The server analyzes the received learning progress data and emotional data, and reevaluates the user's level of understanding, learning progress, and emotional state.

[0743] Step 11: Feedback and Program Updates

[0744] The server generates feedback based on the analysis, including specific advice such as "focus more on a particular concept of geometry" or "take a short break."

[0745] Update learning programs and select new learning tasks and content based on feedback.

[0746] Step 12: Feedback and delivery of updated learning programs

[0747] The server transmits the generated feedback and updated learning program to the terminal.

[0748] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[0749] As a concrete example, let's consider the case where user A uses the system:

[0750] 1. User A logs in to the device, and the device sends evaluation data such as past test results to the server.

[0751] 2. The server analyzes the assessment data and generates comprehension scores of 8 / 10 for algebra and 6 / 10 for geometry.

[0752] 3. User A begins learning, and the device collects emotional data through the camera, microphone, and temperature sensor and sends it to the server.

[0753] 4. The emotion engine analyzes the data and determines that User A is tired.

[0754] 5. The server generates an optimal learning program based on the comprehension score and emotional data and sends it to the device.

[0755] 6. The terminal displays the program to User A and allows him to begin learning.

[0756] 7. User A's learning progress and new emotion data are periodically sent to the server.

[0757] 8. The server analyzes the data, creates feedback, and sends specific instructions to the device, such as "take a short break."

[0758] 9. User A receives the feedback and performs a new task reflected in the learning program.

[0759] This example enables the system to improve the user's learning efficiency and comprehension, and provide support that takes into account the user's emotional state.

[0760] Example 2

[0761] 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."

[0762] In conventional learning systems, it is difficult to provide an optimal learning program based on the individual user's learning progress and level of understanding, and furthermore, because the user's emotional state is not taken into consideration, there are problems with reduced learning efficiency and motivation. The present invention aims to solve these problems and provide an optimal learning environment that appropriately responds to the individual learning situation of each user and takes their emotional state into consideration.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0764] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for recognizing the user's emotional state, means for generating a learning program appropriate for the user based on the comprehension score and emotional state, means for providing the user with appropriate learning content, means for monitoring the user's learning progress, analyzing new progress data to update the learning program, and means for re-providing the updated learning program to the user. This improves the user's learning efficiency and level of understanding, and enables flexible learning support based on the user's emotional state.

[0765] "User" refers to an individual or organization that uses the System to study.

[0766] "Study history" refers to a record of a user's past learning activities, including subjects studied, study time, test results, and so on.

[0767] "Evaluation data" is data for evaluating a user's learning outcomes, and includes test scores, accuracy and speed of answering questions, etc.

[0768] The "comprehension score" is a numerical representation of how well a user understands a particular subject or content, based on an analysis of learning history and assessment data.

[0769] "Emotional state" is information that indicates the user's emotional and mental state, and is determined from biometric data such as facial expression, tone of voice, and body temperature.

[0770] A "learning program" is a plan containing the optimal learning activity schedule and content that is generated based on the user's comprehension score and emotional state.

[0771] "Learning content" refers to specific educational materials, quizzes, video lectures, and other educational resources provided to users in a learning program.

[0772] "Monitoring" means observing the user's learning process and recording their progress.

[0773] "Progress data" is data that indicates the progress of a user's learning activities, and includes the completion rate of video viewing and the correct answer rate for quizzes.

[0774] "Update" refers to modifying and improving existing learning programs and content based on new data and analytical results.

[0775] This system generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on that program, monitors learning progress, and provides feedback. Furthermore, it aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0776] Overall system overview

[0777] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0778] Data collection

[0779] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0780] Scoring comprehension

[0781] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0782] Emotional Recognition and Feedback

[0783] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[0784] Creating and delivering learning programs

[0785] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0786] Monitoring learning progress

[0787] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[0788] Feedback and Program Updates

[0789] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0790] Specific examples

[0791] 1. Collecting training data

[0792] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0793] 2. Scoring comprehension

[0794] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0795] 3. Emotional Recognition

[0796] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0797] The emotion engine analyzes this data and recognizes that User A is tired.

[0798] 4. Creating and delivering appropriate learning programs

[0799] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0800] The learning program is sent to the terminal, which displays it to User A.

[0801] 5. Monitoring your learning progress

[0802] User A watches a video lecture and the device records his / her progress solving the exercises.

[0803] The progress data is sent to the server.

[0804] 6. Feedback and Program Updates

[0805] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0806] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0807] If necessary, the learning program is updated and resent to the terminal.

[0808] An example prompt is, "Please explain the steps necessary to assess the user's understanding of algebra and provide an appropriate learning program."

[0809] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[0810] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0811] Step 1: User Login

[0812] Specific details:

[0813] The user opens the system login screen using a terminal, enters the login ID and password, and presses the login button.

[0814] Input and Output:

[0815] The user enters a login ID and password, which the terminal sends to the server. The server checks the database to verify the validity of the login information. If the server authenticates the login, it returns the authentication information to the terminal.

[0816] Specific behavior:

[0817] The terminal receives the login information and displays the user's My Page.

[0818] Step 2: Collect and send data

[0819] Specific details:

[0820] After a user logs in to the system, the terminal reads past learning records and test results.

[0821] Input and Output:

[0822] The device collects the user's learning history and evaluation data, and sends it to a server, which stores the received data in a database for analysis.

[0823] Specific behavior:

[0824] The device sends the collected data to the server in a standard format such as JSON, which the server receives and stores in a database.

[0825] Step 3: Score comprehension

[0826] Specific details:

[0827] The server begins analysis based on the learning history and evaluation data, and scores the user's level of understanding.

[0828] Input and Output:

[0829] The server inputs learning history and assessment data, and outputs comprehension scores for each subject. The scores are calculated using analytical algorithms (e.g., decision trees or neural networks).

[0830] Specific behavior:

[0831] The server runs the analysis engine and generates a score, for example, "Algebra 8 / 10, Geometry 6 / 10." The generated scores are stored in the user's data profile.

[0832] Step 4: Recognize emotions

[0833] Specific details:

[0834] While the user is learning, the device collects real-time data using a camera, microphone, and temperature sensor, and the emotion engine analyzes this data to determine the user's emotional state.

[0835] Input and Output:

[0836] The device collects biometric data such as facial expressions, tone of voice, and body temperature, and the emotion engine outputs a judgment of the user's emotional state.

[0837] Specific behavior:

[0838] The device's camera captures the user's facial expressions, the microphone records the tone of voice, and the temperature sensor measures the user's body temperature. This data is pre-processed within the device, and the emotion engine analyzes it to determine the user's emotional state. The results are then sent to the server.

[0839] Step 5: Generate a learning program

[0840] Specific details:

[0841] The server designs an optimal learning program based on the comprehension score and emotional state.

[0842] Input and Output:

[0843] The server inputs the comprehension score and the emotional state assessment result, and outputs a learning program, which includes learning tasks and learning content.

[0844] Specific behavior:

[0845] The server uses the generative AI model to generate a learning program, for example, proposing a program that progresses from the basics of geometry to its applications. The generated program is then sent to the device.

[0846] Step 6: Deliver learning content

[0847] Specific details:

[0848] The terminal displays the learning program received from the server to the user, and the user starts learning according to the program.

[0849] Input and Output:

[0850] The input to the terminal is a learning program, and the output is specific learning content that is displayed to the user.

[0851] Specific behavior:

[0852] The device plays video lectures, displays text materials, and offers quizzes, allowing users to progress through the learning content.

[0853] Step 7: Monitoring your learning progress

[0854] Specific details:

[0855] As the user progresses through their studies, the device records progress data.

[0856] Input and Output:

[0857] The terminal inputs data related to the progress of learning and outputs progress data that is sent to the server.

[0858] Specific behavior:

[0859] The device tracks each learning step in real time and periodically sends the data to a server, which analyzes the data and tracks the user's learning progress.

[0860] Step 8: Generate and provide feedback

[0861] Specific details:

[0862] The server generates feedback based on the progress data and emotional state and provides it to the user.

[0863] Input and Output:

[0864] The server's inputs are progress data and emotional states, and its outputs are feedback and updated learning programs.

[0865] Specific behavior:

[0866] Based on the analysis results, the server may instruct the user to, for example, "focus on specific geometry concepts" and suggest "take a short break." The device receives the feedback and displays it to the user. If necessary, it resends an updated learning program.

[0867] Through this sequential processing, the system maximizes the user's learning efficiency and provides an optimal learning environment tailored to individual needs.

[0868] (Application example 2)

[0869] 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."

[0870] Conventional learning systems have the problem of being unable to provide individually optimized learning programs based on a user's learning history and assessment data, and they also lack real-time feedback and learning adjustments based on the user's emotional state. Furthermore, because they do not take the user's emotional state into account, learning efficiency and effectiveness may decline. This poses a challenge, making it difficult for users to continue learning with high motivation.

[0871] 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.

[0872] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for generating a learning program suited to the user based on the comprehension score, means for providing the user with appropriate learning content based on the learning program, means for monitoring the user's learning progress and analyzing new progress data to update the learning program, means for providing the updated learning program to the user again, means for adjusting the learning program based on the user's emotions, and means for providing real-time feedback to optimize the user's learning experience. This makes it possible to provide a learning program optimized for the user's individual learning and emotional state, thereby improving learning efficiency and maintaining motivation.

[0873] "User's learning history" refers to the content that the user has studied in the past and the activity records related to that learning.

[0874] "Evaluation data" refers to data that indicates the user's level of understanding and achievements regarding the learning content, and includes test results and evaluation scores.

[0875] The "understanding score" is a numerical representation of the user's understanding of the learning content, and is calculated by an analysis algorithm.

[0876] A "learning program" is a set of learning tasks and / or content designed to optimize a user's learning efficiency.

[0877] "Learning content" refers to learning materials that users use to study according to a learning program, including video lectures, textbooks, quizzes, and the like.

[0878] "Study progress" refers to the process by which a user advances in learning according to a learning program and the degree of progress.

[0879] The "emotion engine" is a system that uses a camera, microphone, body temperature sensor, etc. to recognize and analyze the user's emotional state.

[0880] "Feedback" is advice or instruction provided to a user based on their learning progress, level of understanding, and emotional state.

[0881] This invention is a system that generates an individually optimized learning program based on a user's learning history, evaluation data, and emotional state, and monitors the learning progress and provides feedback. Specific embodiments are described below.

[0882] Overall system overview

[0883] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[0884] Data collection

[0885] When a user begins studying using a device, the server collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to the server, which analyzes it and evaluates the user's current study status.

[0886] Scoring comprehension

[0887] The server then uses the learning history and assessment data to score the user's level of understanding. This scoring is done using an analysis algorithm built into the server. For example, a detailed score such as 8 / 10 for algebra or 6 / 10 for geometry is calculated.

[0888] Emotional Recognition and Feedback

[0889] As the user progresses through their studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. For example, if the user is feeling impatient or tired, the server will use that information to adjust the study program.

[0890] Creating and delivering learning programs

[0891] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine. This program includes appropriate learning content (video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the device, which then displays it to the user.

[0892] Monitoring learning progress

[0893] As users study according to the learning program, the device records their progress, actual learning content, and test results, and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[0894] Feedback and Program Updates

[0895] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Now that you understand geometry better, try some applied problems" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0896] Specific examples

[0897] 1. Collecting training data

[0898] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0899] 2. Scoring comprehension

[0900] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0901] 3. Emotional Recognition

[0902] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[0903] The emotion engine analyzes this data and recognizes that User A is tired.

[0904] 4. Creating and delivering appropriate learning programs

[0905] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[0906] The learning program is sent to the terminal, which displays it to User A.

[0907] 5. Monitoring your learning progress

[0908] User A watches a video lecture and the device records his / her progress solving the exercises.

[0909] The progress data is sent to the server.

[0910] 6. Feedback and Program Updates

[0911] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[0912] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[0913] If necessary, the learning program is updated and resent to the terminal.

[0914] Prompt Sentence Examples

[0915] An example of a prompt sentence is "Study history: Algebra 8 / 10, Geometry 6 / 10; Emotional state: Fatigue." Based on this, the system generates an optimal learning program and appropriate feedback for the user.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] The user logs in using a terminal.

[0919] Input: User login information (ID, password)

[0920] Output: User authentication status, learning history and assessment data

[0921] Operation: The device sends the user's login information to the server, which then authenticates them. If authentication is successful, the server retrieves the user's past learning history and evaluation data and sends them to the device.

[0922] Step 2:

[0923] The server analyzes the learning history and assessment data and calculates the comprehension score.

[0924] Input: User learning history and assessment data

[0925] Output: Comprehension score for each learning content (e.g., Algebra 8 / 10, Geometry 6 / 10)

[0926] How it works: The server uses an analysis algorithm to analyze the user's learning history and assessment data, calculates a comprehension score for each learning item, and stores it in a database.

[0927] Step 3:

[0928] The user begins learning.

[0929] Input: None (user operation)

[0930] Output: Signal to start learning, activate the device's camera and microphone

[0931] How it works: When a user starts learning, the device activates the camera, microphone, and temperature sensor and begins the learning session.

[0932] Step 4:

[0933] The emotion engine recognizes the user's emotional state in real time.

[0934] Input: Data from camera and microphone, body temperature sensor (facial expression, tone of voice, body temperature)

[0935] Output: User's emotional state (e.g., impatience, fatigue, concentration)

[0936] How it works: The device sends data from the camera, microphone, and temperature sensor to the emotion engine, which analyzes it and determines the user's emotional state. The results are then sent to the server.

[0937] Step 5:

[0938] The server generates a learning program that is optimal for the user.

[0939] Input: Comprehension score, emotional state

[0940] Output: Individually optimized learning program (learning tasks, appropriate learning content)

[0941] How it works: The server generates a learning program optimized for the user based on the user's comprehension score and emotional state. The generated program includes learning content such as video lectures, textbooks, and quizzes. The server then sends the program to the device.

[0942] Step 6:

[0943] The user proceeds with the study by following the instructions of the study program.

[0944] Input: Displayed learning program

[0945] Output: Learning progress data (viewing time, comprehension, answer results)

[0946] How it works: The user follows a learning program, watches video lectures, and answers quizzes. The device records this and sends the learning progress data to the server.

[0947] Step 7:

[0948] The server analyzes the learning progress data and generates feedback.

[0949] Input: Learning progress data

[0950] Output: Feedback instructions (e.g., "Revisit a specific concept in geometry," "Take a short break")

[0951] Operation: The server analyzes learning progress data in real time and generates necessary feedback for the user. The feedback is provided to the user via the terminal.

[0952] Step 8:

[0953] The server updates the learning program as needed and provides it again to the user.

[0954] Input: Feedback instructions, changes in emotional state

[0955] Output: Updated learning program

[0956] Operation: The server updates the learning program based on feedback and changes in emotional state. The updated learning program is resent to the device and displayed to the user.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] [Third embodiment]

[0961] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0962] 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.

[0963] 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).

[0964] 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.

[0965] 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.

[0966] 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).

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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."

[0973] The present invention provides a system that generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on the program, and monitors learning progress and provides feedback. Specific embodiments of the system are described below.

[0974] Overall system overview

[0975] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, dynamically providing learning programs and content to enable the user to study most effectively.

[0976] Data collection

[0977] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[0978] Scoring comprehension

[0979] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[0980] Creating and delivering learning programs

[0981] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[0982] Monitoring learning progress

[0983] As a user follows a learning program, the device records their progress, actual learning content, test scores, etc., and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[0984] Feedback and Program Updates

[0985] The server generates feedback for the user based on the analysis results. For example, it may include specific instructions such as, "Now that you understand geometry better, try some applied problems next" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[0986] Specific examples

[0987] 1. Collecting training data

[0988] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[0989] 2. Scoring comprehension

[0990] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[0991] 3. Creating and delivering appropriate learning programs

[0992] The server generates a learning program that progresses from the basics to applications of geometry, selecting video lectures and exercises.

[0993] The learning program is sent to the terminal, which displays it to User A.

[0994] 4. Monitoring your learning progress

[0995] User A watches a video lecture and the device records his / her progress solving the exercises.

[0996] The progress data is sent to the server.

[0997] 5. Feedback and Program Updates

[0998] The server analyzes the new progress data and determines that User A is struggling with a particular concept in geometry.

[0999] Provides instruction and supplementary materials for "focusing on specific concepts of geometry."

[1000] If necessary, the learning program is updated and resent to the terminal.

[1001] As described above, this system can provide educational programs and content that are optimized for each user's individual learning status, thereby improving learning efficiency and comprehension.

[1002] The processing flow will be explained below.

[1003] Step 1: Collect user learning data

[1004] A user logs in to a terminal.

[1005] The terminal sends the user's login information to the server.

[1006] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[1007] Step 2: Send user learning history and assessment data

[1008] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[1009] Step 3: Analyze the data and score comprehension

[1010] The server analyzes the received data and evaluates the user's current learning situation and level of understanding.

[1011] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[1012] Step 4: Generate an optimal learning program

[1013] The server generates an optimal learning program based on the generated comprehension score.

[1014] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from a database.

[1015] Step 5: Delivering the learning program

[1016] The server transmits the generated learning program to the terminal.

[1017] The terminal displays the received learning program to the user, and allows the user to start learning.

[1018] Step 6: Monitor and record your learning progress

[1019] Users progress through their studies via their terminals.

[1020] The device records the user's progress data in real time, including study time, study content, and test results.

[1021] Step 7: Submit your learning progress data

[1022] The terminal periodically transmits the recorded learning progress data to the server.

[1023] Step 8: Reanalyze progress data and generate feedback

[1024] The server analyzes the new progress data received and reassess the user's understanding and learning progress.

[1025] The server generates feedback based on the analysis, including specific advice such as "You should focus more on certain concepts of geometry."

[1026] Step 9: Feedback and delivery of updated learning programs

[1027] The server transmits the generated feedback and updated learning program to the terminal.

[1028] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[1029] Example 1

[1030] 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."

[1031] Conventional learning support systems have had difficulty monitoring users' learning progress in real time and dynamically providing appropriate feedback and learning materials. Furthermore, they lacked mechanisms for generating learning programs optimized for individual users and continuously updating them. As a result, users' learning efficiency declined, and their level of understanding did not improve sufficiently.

[1032] 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.

[1033] In this invention, the server includes means for collecting the user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, and means for generating a learning program suited to the user based on the comprehension score, thereby enabling the generation and updating of learning programs based on the individual user's learning status, as well as real-time progress monitoring and feedback.

[1034] "Study history" refers to a record of a user's past learning activities, specifically data such as the content studied, time spent, and grades.

[1035] "Evaluation data" refers to data used to evaluate the effectiveness of learning, such as a user's learning outcomes and test results.

[1036] The "understanding score" is a numerical value that quantitatively represents the user's understanding level based on the learning history and evaluation data.

[1037] A "learning program" is a set of individually optimized learning plans and tasks that are generated based on a user's comprehension score.

[1038] "Learning content" refers to specific learning materials and assignments provided to users based on a learning program, including video lectures, textbooks, and practice questions.

[1039] "Monitoring" refers to continuously observing a user's learning progress and collecting learning data.

[1040] "Progress data" is data recorded as the user progresses through the learning process according to the learning program.

[1041] "Feedback" refers to advice and instructions for next steps provided to the user based on the analysis results.

[1042] "Analysis algorithm" refers to a method or program for analyzing collected data and calculating comprehension scores.

[1043] "Terminal" refers to a device through which a user accesses a learning program and displays learning content.

[1044] The present invention is a system that generates an optimized learning program based on a user's learning history and evaluation data, provides learning content based on that program, and monitors learning progress and provides feedback.

[1045] To implement this system, the following hardware and software are used.

[1046] Hardware:

[1047] 1. Device: This refers to the computer or smartphone used by the user, and is used to input learning data and display learning content.

[1048] 2. Server: Data is stored and analyzed using cloud services (e.g., Amazon Web Services, Microsoft Azure).

[1049] software:

[1050] 1. Learning application: An application used by the user on a device to display learning content and input data.

[1051] 2. Analysis algorithm: Analyze the data using a Python machine learning library (e.g., scikit-learn) and generate a comprehension score.

[1052] A specific embodiment of the system will be described below.

[1053] Overall system overview

[1054] As users study using their devices, the server constantly monitors their progress and level of understanding, dynamically providing learning programs and content to help users study most effectively.

[1055] Data collection

[1056] When a user starts studying, the device collects evaluation data such as study history, past test results, and study time. The collected data is sent to the server, which analyzes it and evaluates the user's current learning status. For example, user A takes an online math test and the results are sent from the device to the server.

[1057] Scoring comprehension

[1058] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. For example, the server analyzes User A's level of algebra understanding and assigns him a score of 8 / 10.

[1059] Creating and delivering learning programs

[1060] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. For example, the server generates a learning program for User A, covering geometry from basics to applications, and selects video lectures and practice problems and sends them to the terminal.

[1061] Monitoring learning progress

[1062] As a user progresses through a learning program, the device records the user's progress, actual learning content, test scores, etc., and periodically sends them to the server. The server uses an analytical algorithm to evaluate the progress data in real time. As a concrete example, when User A is watching a video lecture, the device records the viewing time and progress, and sends them to the server.

[1063] Feedback and Program Updates

[1064] The server generates feedback for the user based on the analysis results. For example, instructions such as "focus on learning a specific concept of geometry" are provided along with corresponding supplementary learning materials. If necessary, the learning program is updated and resent to the device. As a specific example, the server analyzes User A's new progress data and identifies a lack of understanding of a specific concept of geometry, providing instructions and supplementary learning materials.

[1065] Prompt Sentence Examples

[1066] 1. "User A's algebra comprehension is 8 / 10 and his geometry comprehension is 6 / 10. What kind of learning content should we provide to him next?"

[1067] 2. "User A needs a learning program that progresses from basic to advanced geometry. Please suggest appropriate video lectures and exercises."

[1068] 3. "Analyze User A's learning progress data and suggest the next learning material or instruction needed."

[1069] 4. "I have some data showing that User A is struggling with a particular concept in geometry. Please provide appropriate feedback and suggest additional learning materials."

[1070] As described above, this system can provide optimal educational programs and content based on each user's individual learning status, thereby improving learning efficiency and comprehension.

[1071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1072] Step 1:

[1073] Data collection

[1074] When a user begins studying, the device collects data such as study history, past test results, and study time. The collected data is saved on the device as input from the user. The device then sends this data to the server. Specifically, when the user launches a study application and takes a test, the results are recorded in real time. For example, if user A takes an online algebra test, the results are saved on the device as "algebra test results."

[1075] Input: User's learning history, past test results, study time

[1076] Output: Training data stored on the device, data sent to the server

[1077] Step 2:

[1078] Scoring comprehension

[1079] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. The server analyzes the data using a Python machine learning library (e.g., scikit-learn) and generates a comprehension score. During this process, the server calculates scores such as 8 / 10 for algebra and 6 / 10 for geometry. For example, User A's algebra comprehension is analyzed and a score of 8 / 10 is generated.

[1080] Input: Training data sent to the server

[1081] Output: Comprehension score (e.g. Algebra 8 / 10, Geometry 6 / 10)

[1082] Step 3:

[1083] Creating and delivering learning programs

[1084] The server generates an optimal learning program for the user based on the comprehension score. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. The server selects online educational content and practice problems as learning content. For example, a learning program that covers geometry from basics to applications is generated and sent to the terminal.

[1085] Input: Comprehension score

[1086] Output: Generated training program, training program sent to the terminal

[1087] Step 4:

[1088] Monitoring learning progress

[1089] As a user progresses through a learning program, the device records the user's learning progress, learning content, test scores, etc. This data is periodically sent to a server, which uses an analytical algorithm to evaluate the progress data in real time. As a specific example, user A is watching a video lecture, and the viewing time and progress are recorded by the device and sent to the server.

[1090] Input: User's learning progress data

[1091] Output: Progress data sent to the server, real-time evaluation

[1092] Step 5:

[1093] Feedback and Program Updates

[1094] The server generates feedback for the user based on the analysis results. The learning program is updated as necessary and resent to the terminal. For example, an instruction to "concentrate on studying a specific concept of geometry" is generated and provided to the user via the terminal. If it is determined that User A lacks understanding of a specific concept of geometry, the server generates supplementary learning materials along with the feedback to "concentrate on studying" and sends them to the terminal.

[1095] Input: User progress data, real-time assessment results

[1096] Output: Generated feedback, updated learning program, feedback and program sent to the device

[1097] These are the specific processing steps of the system. Through this process, optimal educational programs and content are provided based on the individual learning status of each user, improving learning efficiency and comprehension.

[1098] (Application example 1)

[1099] 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."

[1100] While employee education and training are extremely important in brick-and-mortar stores, it is difficult to provide optimal training programs tailored to each employee's skill level and level of understanding. Furthermore, there is a lack of systems that can grasp each employee's progress in real time and dynamically update training content accordingly. For this reason, there is a need for a system that can provide efficient, individually optimized training.

[1101] 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.

[1102] In this invention, the server includes means for collecting employee skill assessment data and training history, means for analyzing the data to score the employee's level of understanding, means for generating a training program suited to the employee based on the score, means for providing the employee with appropriate training content based on the training program, means for monitoring the employee's training progress, analyzing new progress data to update the training program, and means for providing the updated training program to the employee again, thereby making it possible to provide efficient training optimized for each employee.

[1103] "User learning history" is a record of what learning activities the user has performed in the past.

[1104] "Evaluation data" is data for evaluating the user's level of understanding and progress regarding the learning content.

[1105] "Analysis" is the process of extracting meaningful information from collected data.

[1106] The "understanding score" is a numerical representation of the user's understanding of the learning content.

[1107] A "learning program" is a learning plan or learning system that is optimized based on the user's learning progress and level of understanding.

[1108] "Learning content" refers to learning materials such as video lectures, textbooks, and quizzes provided to users.

[1109] "Study progress" refers to the progress a user makes as they progress through a learning program.

[1110] "New progress data" refers to data relating to the learning situation that is newly collected as the user progresses through the learning program.

[1111] "Brick and mortar employees" are staff who work in actual physical stores.

[1112] "Skills assessment data" refers to data that indicates the results of an assessment of an employee's specific skills or abilities.

[1113] "Training history" is a record of the training and education an employee has received in the past.

[1114] "Training Program" means an educational plan or training system designed to improve the skills of employees.

[1115] "Training Content" means training materials, such as video lectures, practical tests, and study materials, that are provided to employees.

[1116] "Training Progress" is the progress an employee makes in progressing through a training program.

[1117] This invention is a system that provides individually optimized training programs to employees in brick-and-mortar stores. This system collects and analyzes employee skill assessment data and training history, and dynamically generates and provides optimal training programs and training content based on that data. This system makes it possible to achieve efficient training that is optimized for each employee.

[1118] Hardware and software used

[1119] The system uses the following hardware and software:

[1120] Hardware: Servers, smartphones, tablets

[1121] Software: Python, Pandas, Scikit-Learn, database system

[1122] Program processing

[1123] The server receives employee skill assessment data and training history, analyzes it, and scores the employee's skill level. Specifically, it uses an analysis algorithm using Python and Scikit-Learn to predict skill levels.

[1124] For example, when Employee A joins the company, his skill level is predicted based on his past training data. At this time, he is given a score such as high customer service skills (8 / 10) or insufficient product knowledge (5 / 10), and appropriate video learning materials and practical tests are automatically selected based on this.

[1125] Once the training program is generated, it is sent to the employee's device, such as a smartphone or tablet. Employees can view video materials and take practical tests via their device. Progress data is sent in real time to the server, which then updates the training program accordingly.

[1126] The server can provide feedback to employees based on the analysis results, such as issuing instructions like, "Your customer service skills have improved, so next you should learn about applied technology."

[1127] Specific examples

[1128] Employee A joins the company and his past training data is imported into the database. The server uses Python and Scikit-Learn to predict his skill level and generate a score, such as "high customer service skills but lacking product knowledge." Based on this score, the optimal video learning materials and practical tests are selected and sent to Employee A's smartphone. Employee A watches the videos and takes the tests, and their progress data is sent to the server. The server analyzes the new progress data and provides the next feedback and training content.

[1129] Prompt Sentence Examples

[1130] "Create a Python script that uses training data for new staff members to predict their skill levels and generate the optimal training program. I need a model that uses random forests for training and prediction."

[1131] As described above, this system makes it possible to efficiently provide training that is optimized for each employee, contributing to improving the service quality of stores.

[1132] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1133] Step 1: Data collection

[1134] The user inputs or imports past training history and skill assessment data via the terminal. This data includes the employee's past training sessions, courses taken, and skill assessment test results. The terminal then sends this data to the server. The input data is information stored in a CSV file or database.

[1135] Step 2: Data analysis and scoring

[1136] The server uses the received data to score the employee's skill level. The server uses Python and Scikit-Learn to analyze the collected data and quantify the level of understanding of each skill (e.g., customer service, product knowledge, cash register operation, etc.). The input is past training history and skill evaluation data, and the output is the understanding score for each skill.

[1137] Step 3: Generate a training program

[1138] The server generates a training program suitable for the employee based on the comprehension score. This program includes video materials, practical tests, quizzes, etc. The server selects appropriate training content and generates this program. The input is the comprehension score, and the output is the training program.

[1139] Step 4: Provide training content

[1140] The server sends the generated training program to the employee's terminal. Through the terminal, the user can watch video materials and take practical tests and quizzes. The input is the training program, and the output is the training content displayed on the terminal.

[1141] Step 5: Progress monitoring

[1142] The device records the employee's progress as they progress through the training program and periodically sends the data to a server. The recorded data includes videos watched, quizzes completed, and practical test results. The input is the user's learning progress, and the output is the progress data.

[1143] Step 6: Analyze progress data

[1144] The server analyzes the progress data and evaluates changes in the employee's understanding. The server uses Python and Scikit-Learn to analyze new progress data and identify changes in the employee's skill level. The input is the progress data, and the output is the understanding score for the updated skill level.

[1145] Step 7: Feedback and Program Updates

[1146] The server provides feedback to employees based on the analysis results and updates the training program as necessary. For example, it provides specific instructions such as, "Your customer service skills have improved, so now it's time for applied skills training." The server then sends the updated training program back to the employee's device. The input is the comprehension score for the updated skill level, and the output is the updated training program.

[1147] 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.

[1148] The present invention is a system that uses a user's learning history and assessment data to generate an individually optimized learning program, provides learning content based on that program, and monitors learning progress and provides feedback. Furthermore, the system aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1149] Overall system overview

[1150] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1151] Data collection

[1152] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[1153] Scoring comprehension

[1154] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[1155] Emotional Recognition and Feedback

[1156] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[1157] Creating and delivering learning programs

[1158] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[1159] Monitoring learning progress

[1160] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[1161] Feedback and Program Updates

[1162] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1163] Specific examples

[1164] 1. Collecting training data

[1165] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1166] 2. Scoring comprehension

[1167] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1168] 3. Emotional Recognition

[1169] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1170] The emotion engine analyzes this data and recognizes that User A is tired.

[1171] 4. Creating and delivering appropriate learning programs

[1172] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1173] The learning program is sent to the terminal, which displays it to User A.

[1174] 5. Monitoring your learning progress

[1175] User A watches a video lecture and the device records his / her progress solving the exercises.

[1176] The progress data is sent to the server.

[1177] 6. Feedback and Program Updates

[1178] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1179] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1180] If necessary, the learning program is updated and resent to the terminal.

[1181] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[1182] The processing flow will be explained below.

[1183] Step 1: Collect user learning data

[1184] A user logs in to a terminal.

[1185] The terminal sends the user's login information to the server.

[1186] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[1187] Step 2: Send user learning history and assessment data

[1188] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[1189] Step 3: Analyze the data and score comprehension

[1190] The server analyzes the received evaluation data and evaluates the user's current learning situation and level of understanding.

[1191] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[1192] Step 4: Collecting emotion data

[1193] As the user progresses with their learning, the device's camera, microphone, and temperature sensor collect the user's biometric data (facial expression, tone of voice, body temperature).

[1194] The collected emotion data is sent to a server in real time.

[1195] Step 5: Sentiment Analysis

[1196] An emotion engine installed on the server analyzes the received emotion data and recognizes the user's emotional state.

[1197] For example, it identifies whether the user is tired, has difficulty concentrating, is agitated, etc.

[1198] Step 6: Generate an optimal learning program

[1199] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine.

[1200] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from the database.

[1201] Step 7: Delivering the learning program

[1202] The server transmits the generated learning program to the terminal.

[1203] The terminal displays the received learning program to the user, and allows the user to start learning.

[1204] Step 8: Monitor and record your learning progress

[1205] The user progresses through the learning program.

[1206] The device records the user's progress data in real time, including study time, study content, and test results.

[1207] Step 9: Submit your learning progress data

[1208] The terminal periodically transmits the recorded learning progress data to the server.

[1209] Step 10: Reanalyze progress and sentiment data

[1210] The server analyzes the received learning progress data and emotional data, and reevaluates the user's level of understanding, learning progress, and emotional state.

[1211] Step 11: Feedback and Program Updates

[1212] The server generates feedback based on the analysis, including specific advice such as "focus more on a particular concept of geometry" or "take a short break."

[1213] Update learning programs and select new learning tasks and content based on feedback.

[1214] Step 12: Feedback and delivery of updated learning programs

[1215] The server transmits the generated feedback and updated learning program to the terminal.

[1216] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[1217] As a concrete example, let's consider the case where user A uses the system:

[1218] 1. User A logs in to the device, and the device sends evaluation data such as past test results to the server.

[1219] 2. The server analyzes the assessment data and generates comprehension scores of 8 / 10 for algebra and 6 / 10 for geometry.

[1220] 3. User A begins learning, and the device collects emotional data through the camera, microphone, and temperature sensor and sends it to the server.

[1221] 4. The emotion engine analyzes the data and determines that User A is tired.

[1222] 5. The server generates an optimal learning program based on the comprehension score and emotional data and sends it to the device.

[1223] 6. The terminal displays the program to User A and allows him to begin learning.

[1224] 7. User A's learning progress and new emotion data are periodically sent to the server.

[1225] 8. The server analyzes the data, creates feedback, and sends specific instructions to the device, such as "take a short break."

[1226] 9. User A receives the feedback and performs a new task reflected in the learning program.

[1227] This example enables the system to improve the user's learning efficiency and comprehension, and provide support that takes into account the user's emotional state.

[1228] Example 2

[1229] 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."

[1230] In conventional learning systems, it is difficult to provide an optimal learning program based on the individual user's learning progress and level of understanding, and furthermore, because the user's emotional state is not taken into consideration, there are problems with reduced learning efficiency and motivation. The present invention aims to solve these problems and provide an optimal learning environment that appropriately responds to the individual learning situation of each user and takes their emotional state into consideration.

[1231] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1232] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for recognizing the user's emotional state, means for generating a learning program appropriate for the user based on the comprehension score and emotional state, means for providing the user with appropriate learning content, means for monitoring the user's learning progress, analyzing new progress data to update the learning program, and means for re-providing the updated learning program to the user. This improves the user's learning efficiency and level of understanding, and enables flexible learning support based on the user's emotional state.

[1233] "User" refers to an individual or organization that uses the System to study.

[1234] "Study history" refers to a record of a user's past learning activities, including subjects studied, study time, test results, and so on.

[1235] "Evaluation data" is data for evaluating a user's learning outcomes, and includes test scores, accuracy and speed of answering questions, etc.

[1236] The "comprehension score" is a numerical representation of how well a user understands a particular subject or content, based on an analysis of learning history and assessment data.

[1237] "Emotional state" is information that indicates the user's emotional and mental state, and is determined from biometric data such as facial expression, tone of voice, and body temperature.

[1238] A "learning program" is a plan containing the optimal learning activity schedule and content that is generated based on the user's comprehension score and emotional state.

[1239] "Learning content" refers to specific educational materials, quizzes, video lectures, and other educational resources provided to users in a learning program.

[1240] "Monitoring" means observing the user's learning process and recording their progress.

[1241] "Progress data" is data that indicates the progress of a user's learning activities, and includes the completion rate of video viewing and the correct answer rate for quizzes.

[1242] "Update" refers to modifying and improving existing learning programs and content based on new data and analytical results.

[1243] This system generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on that program, monitors learning progress, and provides feedback. Furthermore, it aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1244] Overall system overview

[1245] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1246] Data collection

[1247] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[1248] Scoring comprehension

[1249] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[1250] Emotional Recognition and Feedback

[1251] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[1252] Creating and delivering learning programs

[1253] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[1254] Monitoring learning progress

[1255] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[1256] Feedback and Program Updates

[1257] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1258] Specific examples

[1259] 1. Collecting training data

[1260] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1261] 2. Scoring comprehension

[1262] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1263] 3. Emotional Recognition

[1264] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1265] The emotion engine analyzes this data and recognizes that User A is tired.

[1266] 4. Creating and delivering appropriate learning programs

[1267] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1268] The learning program is sent to the terminal, which displays it to User A.

[1269] 5. Monitoring your learning progress

[1270] User A watches a video lecture and the device records his / her progress solving the exercises.

[1271] The progress data is sent to the server.

[1272] 6. Feedback and Program Updates

[1273] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1274] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1275] If necessary, the learning program is updated and resent to the terminal.

[1276] An example prompt is, "Please explain the steps necessary to assess the user's understanding of algebra and provide an appropriate learning program."

[1277] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[1278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1279] Step 1: User Login

[1280] Specific details:

[1281] The user opens the system login screen using a terminal, enters the login ID and password, and presses the login button.

[1282] Input and Output:

[1283] The user enters a login ID and password, which the terminal sends to the server. The server checks the database to verify the validity of the login information. If the server authenticates the login, it returns the authentication information to the terminal.

[1284] Specific behavior:

[1285] The terminal receives the login information and displays the user's My Page.

[1286] Step 2: Collect and send data

[1287] Specific details:

[1288] After a user logs in to the system, the terminal reads past learning records and test results.

[1289] Input and Output:

[1290] The device collects the user's learning history and evaluation data, and sends it to a server, which stores the received data in a database for analysis.

[1291] Specific behavior:

[1292] The device sends the collected data to the server in a standard format such as JSON, which the server receives and stores in a database.

[1293] Step 3: Score comprehension

[1294] Specific details:

[1295] The server begins analysis based on the learning history and evaluation data, and scores the user's level of understanding.

[1296] Input and Output:

[1297] The server inputs learning history and assessment data, and outputs comprehension scores for each subject. The scores are calculated using analytical algorithms (e.g., decision trees or neural networks).

[1298] Specific behavior:

[1299] The server runs the analysis engine and generates a score, for example, "Algebra 8 / 10, Geometry 6 / 10." The generated scores are stored in the user's data profile.

[1300] Step 4: Recognize emotions

[1301] Specific details:

[1302] While the user is learning, the device collects real-time data using a camera, microphone, and temperature sensor, and the emotion engine analyzes this data to determine the user's emotional state.

[1303] Input and Output:

[1304] The device collects biometric data such as facial expressions, tone of voice, and body temperature, and the emotion engine outputs a judgment of the user's emotional state.

[1305] Specific behavior:

[1306] The device's camera captures the user's facial expressions, the microphone records the tone of voice, and the temperature sensor measures the user's body temperature. This data is pre-processed within the device, and the emotion engine analyzes it to determine the user's emotional state. The results are then sent to the server.

[1307] Step 5: Generate a learning program

[1308] Specific details:

[1309] The server designs an optimal learning program based on the comprehension score and emotional state.

[1310] Input and Output:

[1311] The server inputs the comprehension score and the emotional state assessment result, and outputs a learning program, which includes learning tasks and learning content.

[1312] Specific behavior:

[1313] The server uses the generative AI model to generate a learning program, for example, proposing a program that progresses from the basics of geometry to its applications. The generated program is then sent to the device.

[1314] Step 6: Deliver learning content

[1315] Specific details:

[1316] The terminal displays the learning program received from the server to the user, and the user starts learning according to the program.

[1317] Input and Output:

[1318] The input to the terminal is a learning program, and the output is specific learning content that is displayed to the user.

[1319] Specific behavior:

[1320] The device plays video lectures, displays text materials, and offers quizzes, allowing users to progress through the learning content.

[1321] Step 7: Monitoring your learning progress

[1322] Specific details:

[1323] As the user progresses through their studies, the device records progress data.

[1324] Input and Output:

[1325] The terminal inputs data related to the progress of learning and outputs progress data that is sent to the server.

[1326] Specific behavior:

[1327] The device tracks each learning step in real time and periodically sends the data to a server, which analyzes the data and tracks the user's learning progress.

[1328] Step 8: Generate and provide feedback

[1329] Specific details:

[1330] The server generates feedback based on the progress data and emotional state and provides it to the user.

[1331] Input and Output:

[1332] The server's inputs are progress data and emotional states, and its outputs are feedback and updated learning programs.

[1333] Specific behavior:

[1334] Based on the analysis results, the server may instruct the user to, for example, "focus on specific geometry concepts" and suggest "take a short break." The device receives the feedback and displays it to the user. If necessary, it resends an updated learning program.

[1335] Through this sequential processing, the system maximizes the user's learning efficiency and provides an optimal learning environment tailored to individual needs.

[1336] (Application example 2)

[1337] 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."

[1338] Conventional learning systems have the problem of being unable to provide individually optimized learning programs based on a user's learning history and assessment data, and they also lack real-time feedback and learning adjustments based on the user's emotional state. Furthermore, because they do not take the user's emotional state into account, learning efficiency and effectiveness may decline. This poses a challenge, making it difficult for users to continue learning with high motivation.

[1339] 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.

[1340] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for generating a learning program suited to the user based on the comprehension score, means for providing the user with appropriate learning content based on the learning program, means for monitoring the user's learning progress and analyzing new progress data to update the learning program, means for providing the updated learning program to the user again, means for adjusting the learning program based on the user's emotions, and means for providing real-time feedback to optimize the user's learning experience. This makes it possible to provide a learning program optimized for the user's individual learning and emotional state, thereby improving learning efficiency and maintaining motivation.

[1341] "User's learning history" refers to the content that the user has studied in the past and the activity records related to that learning.

[1342] "Evaluation data" refers to data that indicates the user's level of understanding and achievements regarding the learning content, and includes test results and evaluation scores.

[1343] The "understanding score" is a numerical representation of the user's understanding of the learning content, and is calculated by an analysis algorithm.

[1344] A "learning program" is a set of learning tasks and / or content designed to optimize a user's learning efficiency.

[1345] "Learning content" refers to learning materials that users use to study according to a learning program, including video lectures, textbooks, quizzes, and the like.

[1346] "Study progress" refers to the process by which a user advances in learning according to a learning program and the degree of progress.

[1347] The "emotion engine" is a system that uses a camera, microphone, body temperature sensor, etc. to recognize and analyze the user's emotional state.

[1348] "Feedback" is advice or instruction provided to a user based on their learning progress, level of understanding, and emotional state.

[1349] This invention is a system that generates an individually optimized learning program based on a user's learning history, evaluation data, and emotional state, and monitors the learning progress and provides feedback. Specific embodiments are described below.

[1350] Overall system overview

[1351] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1352] Data collection

[1353] When a user begins studying using a device, the server collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to the server, which analyzes it and evaluates the user's current study status.

[1354] Scoring comprehension

[1355] The server then uses the learning history and assessment data to score the user's level of understanding. This scoring is done using an analysis algorithm built into the server. For example, a detailed score such as 8 / 10 for algebra or 6 / 10 for geometry is calculated.

[1356] Emotional Recognition and Feedback

[1357] As the user progresses through their studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. For example, if the user is feeling impatient or tired, the server will use that information to adjust the study program.

[1358] Creating and delivering learning programs

[1359] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine. This program includes appropriate learning content (video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the device, which then displays it to the user.

[1360] Monitoring learning progress

[1361] As users study according to the learning program, the device records their progress, actual learning content, and test results, and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[1362] Feedback and Program Updates

[1363] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Now that you understand geometry better, try some applied problems" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1364] Specific examples

[1365] 1. Collecting training data

[1366] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1367] 2. Scoring comprehension

[1368] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1369] 3. Emotional Recognition

[1370] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1371] The emotion engine analyzes this data and recognizes that User A is tired.

[1372] 4. Creating and delivering appropriate learning programs

[1373] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1374] The learning program is sent to the terminal, which displays it to User A.

[1375] 5. Monitoring your learning progress

[1376] User A watches a video lecture and the device records his / her progress solving the exercises.

[1377] The progress data is sent to the server.

[1378] 6. Feedback and Program Updates

[1379] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1380] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1381] If necessary, the learning program is updated and resent to the terminal.

[1382] Prompt Sentence Examples

[1383] An example of a prompt sentence is "Study history: Algebra 8 / 10, Geometry 6 / 10; Emotional state: Fatigue." Based on this, the system generates an optimal learning program and appropriate feedback for the user.

[1384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1385] Step 1:

[1386] The user logs in using a terminal.

[1387] Input: User login information (ID, password)

[1388] Output: User authentication status, learning history and assessment data

[1389] Operation: The device sends the user's login information to the server, which then authenticates them. If authentication is successful, the server retrieves the user's past learning history and evaluation data and sends them to the device.

[1390] Step 2:

[1391] The server analyzes the learning history and assessment data and calculates the comprehension score.

[1392] Input: User learning history and assessment data

[1393] Output: Comprehension score for each learning content (e.g., Algebra 8 / 10, Geometry 6 / 10)

[1394] How it works: The server uses an analysis algorithm to analyze the user's learning history and assessment data, calculates a comprehension score for each learning item, and stores it in a database.

[1395] Step 3:

[1396] The user begins learning.

[1397] Input: None (user operation)

[1398] Output: Signal to start learning, activate the device's camera and microphone

[1399] How it works: When a user starts learning, the device activates the camera, microphone, and temperature sensor and begins the learning session.

[1400] Step 4:

[1401] The emotion engine recognizes the user's emotional state in real time.

[1402] Input: Data from camera and microphone, body temperature sensor (facial expression, tone of voice, body temperature)

[1403] Output: User's emotional state (e.g., impatience, fatigue, concentration)

[1404] How it works: The device sends data from the camera, microphone, and temperature sensor to the emotion engine, which analyzes it and determines the user's emotional state. The results are then sent to the server.

[1405] Step 5:

[1406] The server generates a learning program that is optimal for the user.

[1407] Input: Comprehension score, emotional state

[1408] Output: Individually optimized learning program (learning tasks, appropriate learning content)

[1409] How it works: The server generates a learning program optimized for the user based on the user's comprehension score and emotional state. The generated program includes learning content such as video lectures, textbooks, and quizzes. The server then sends the program to the device.

[1410] Step 6:

[1411] The user proceeds with the study by following the instructions of the study program.

[1412] Input: Displayed learning program

[1413] Output: Learning progress data (viewing time, comprehension, answer results)

[1414] How it works: The user follows a learning program, watches video lectures, and answers quizzes. The device records this and sends the learning progress data to the server.

[1415] Step 7:

[1416] The server analyzes the learning progress data and generates feedback.

[1417] Input: Learning progress data

[1418] Output: Feedback instructions (e.g., "Revisit a specific concept in geometry," "Take a short break")

[1419] Operation: The server analyzes learning progress data in real time and generates necessary feedback for the user. The feedback is provided to the user via the terminal.

[1420] Step 8:

[1421] The server updates the learning program as needed and provides it again to the user.

[1422] Input: Feedback instructions, changes in emotional state

[1423] Output: Updated learning program

[1424] Operation: The server updates the learning program based on feedback and changes in emotional state. The updated learning program is resent to the device and displayed to the user.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] [Fourth embodiment]

[1429] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1430] 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.

[1431] 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).

[1432] 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.

[1433] 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.

[1434] 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).

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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."

[1442] The present invention provides a system that generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on the program, and monitors learning progress and provides feedback. Specific embodiments of the system are described below.

[1443] Overall system overview

[1444] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, dynamically providing learning programs and content to enable the user to study most effectively.

[1445] Data collection

[1446] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[1447] Scoring comprehension

[1448] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[1449] Creating and delivering learning programs

[1450] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[1451] Monitoring learning progress

[1452] As a user follows a learning program, the device records their progress, actual learning content, test scores, etc., and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[1453] Feedback and Program Updates

[1454] The server generates feedback for the user based on the analysis results. For example, it may include specific instructions such as, "Now that you understand geometry better, try some applied problems next" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1455] Specific examples

[1456] 1. Collecting training data

[1457] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1458] 2. Scoring comprehension

[1459] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1460] 3. Creating and delivering appropriate learning programs

[1461] The server generates a learning program that progresses from the basics to applications of geometry, selecting video lectures and exercises.

[1462] The learning program is sent to the terminal, which displays it to User A.

[1463] 4. Monitoring your learning progress

[1464] User A watches a video lecture and the device records his / her progress solving the exercises.

[1465] The progress data is sent to the server.

[1466] 5. Feedback and Program Updates

[1467] The server analyzes the new progress data and determines that User A is struggling with a particular concept in geometry.

[1468] Provides instruction and supplementary materials for "focusing on specific concepts of geometry."

[1469] If necessary, the learning program is updated and resent to the terminal.

[1470] As described above, this system can provide educational programs and content that are optimized for each user's individual learning status, thereby improving learning efficiency and comprehension.

[1471] The processing flow will be explained below.

[1472] Step 1: Collect user learning data

[1473] A user logs in to a terminal.

[1474] The terminal sends the user's login information to the server.

[1475] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[1476] Step 2: Send user learning history and assessment data

[1477] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[1478] Step 3: Analyze the data and score comprehension

[1479] The server analyzes the received data and evaluates the user's current learning situation and level of understanding.

[1480] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[1481] Step 4: Generate an optimal learning program

[1482] The server generates an optimal learning program based on the generated comprehension score.

[1483] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from a database.

[1484] Step 5: Delivering the learning program

[1485] The server transmits the generated learning program to the terminal.

[1486] The terminal displays the received learning program to the user, and allows the user to start learning.

[1487] Step 6: Monitor and record your learning progress

[1488] Users progress through their studies via their terminals.

[1489] The device records the user's progress data in real time, including study time, study content, and test results.

[1490] Step 7: Submit your learning progress data

[1491] The terminal periodically transmits the recorded learning progress data to the server.

[1492] Step 8: Reanalyze progress data and generate feedback

[1493] The server analyzes the new progress data received and reassess the user's understanding and learning progress.

[1494] The server generates feedback based on the analysis, including specific advice such as "You should focus more on certain concepts of geometry."

[1495] Step 9: Feedback and delivery of updated learning programs

[1496] The server transmits the generated feedback and updated learning program to the terminal.

[1497] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[1498] Example 1

[1499] 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."

[1500] Conventional learning support systems have had difficulty monitoring users' learning progress in real time and dynamically providing appropriate feedback and learning materials. Furthermore, they lacked mechanisms for generating learning programs optimized for individual users and continuously updating them. As a result, users' learning efficiency declined, and their level of understanding did not improve sufficiently.

[1501] 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.

[1502] In this invention, the server includes means for collecting the user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, and means for generating a learning program suited to the user based on the comprehension score, thereby enabling the generation and updating of learning programs based on the individual user's learning status, as well as real-time progress monitoring and feedback.

[1503] "Study history" refers to a record of a user's past learning activities, specifically data such as the content studied, time spent, and grades.

[1504] "Evaluation data" refers to data used to evaluate the effectiveness of learning, such as a user's learning outcomes and test results.

[1505] The "understanding score" is a numerical value that quantitatively represents the user's understanding level based on the learning history and evaluation data.

[1506] A "learning program" is a set of individually optimized learning plans and tasks that are generated based on a user's comprehension score.

[1507] "Learning content" refers to specific learning materials and assignments provided to users based on a learning program, including video lectures, textbooks, and practice questions.

[1508] "Monitoring" refers to continuously observing a user's learning progress and collecting learning data.

[1509] "Progress data" is data recorded as the user progresses through the learning process according to the learning program.

[1510] "Feedback" refers to advice and instructions for next steps provided to the user based on the analysis results.

[1511] "Analysis algorithm" refers to a method or program for analyzing collected data and calculating comprehension scores.

[1512] "Terminal" refers to a device through which a user accesses a learning program and displays learning content.

[1513] The present invention is a system that generates an optimized learning program based on a user's learning history and evaluation data, provides learning content based on that program, and monitors learning progress and provides feedback.

[1514] To implement this system, the following hardware and software are used.

[1515] Hardware:

[1516] 1. Device: This refers to the computer or smartphone used by the user, and is used to input learning data and display learning content.

[1517] 2. Server: Data is stored and analyzed using cloud services (e.g., Amazon Web Services, Microsoft Azure).

[1518] software:

[1519] 1. Learning application: An application used by the user on a device to display learning content and input data.

[1520] 2. Analysis algorithm: Analyze the data using a Python machine learning library (e.g., scikit-learn) and generate a comprehension score.

[1521] A specific embodiment of the system will be described below.

[1522] Overall system overview

[1523] As users study using their devices, the server constantly monitors their progress and level of understanding, dynamically providing learning programs and content to help users study most effectively.

[1524] Data collection

[1525] When a user starts studying, the device collects evaluation data such as study history, past test results, and study time. The collected data is sent to the server, which analyzes it and evaluates the user's current learning status. For example, user A takes an online math test and the results are sent from the device to the server.

[1526] Scoring comprehension

[1527] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. For example, the server analyzes User A's level of algebra understanding and assigns him a score of 8 / 10.

[1528] Creating and delivering learning programs

[1529] The server generates an optimal learning program for the user based on the scoring data. This program includes appropriate learning content (e.g., video lectures, textbooks, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. For example, the server generates a learning program for User A, covering geometry from basics to applications, and selects video lectures and practice problems and sends them to the terminal.

[1530] Monitoring learning progress

[1531] As a user progresses through a learning program, the device records the user's progress, actual learning content, test scores, etc., and periodically sends them to the server. The server uses an analytical algorithm to evaluate the progress data in real time. As a concrete example, when User A is watching a video lecture, the device records the viewing time and progress, and sends them to the server.

[1532] Feedback and Program Updates

[1533] The server generates feedback for the user based on the analysis results. For example, instructions such as "focus on learning a specific concept of geometry" are provided along with corresponding supplementary learning materials. If necessary, the learning program is updated and resent to the device. As a specific example, the server analyzes User A's new progress data and identifies a lack of understanding of a specific concept of geometry, providing instructions and supplementary learning materials.

[1534] Prompt Sentence Examples

[1535] 1. "User A's algebra comprehension is 8 / 10 and his geometry comprehension is 6 / 10. What kind of learning content should we provide to him next?"

[1536] 2. "User A needs a learning program that progresses from basic to advanced geometry. Please suggest appropriate video lectures and exercises."

[1537] 3. "Analyze User A's learning progress data and suggest the next learning material or instruction needed."

[1538] 4. "I have some data showing that User A is struggling with a particular concept in geometry. Please provide appropriate feedback and suggest additional learning materials."

[1539] As described above, this system can provide optimal educational programs and content based on each user's individual learning status, thereby improving learning efficiency and comprehension.

[1540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1541] Step 1:

[1542] Data collection

[1543] When a user begins studying, the device collects data such as study history, past test results, and study time. The collected data is saved on the device as input from the user. The device then sends this data to the server. Specifically, when the user launches a study application and takes a test, the results are recorded in real time. For example, if user A takes an online algebra test, the results are saved on the device as "algebra test results."

[1544] Input: User's learning history, past test results, study time

[1545] Output: Training data stored on the device, data sent to the server

[1546] Step 2:

[1547] Scoring comprehension

[1548] The server uses an analysis algorithm to score the user's level of understanding based on the received learning history and evaluation data. The server analyzes the data using a Python machine learning library (e.g., scikit-learn) and generates a comprehension score. During this process, the server calculates scores such as 8 / 10 for algebra and 6 / 10 for geometry. For example, User A's algebra comprehension is analyzed and a score of 8 / 10 is generated.

[1549] Input: Training data sent to the server

[1550] Output: Comprehension score (e.g. Algebra 8 / 10, Geometry 6 / 10)

[1551] Step 3:

[1552] Creating and delivering learning programs

[1553] The server generates an optimal learning program for the user based on the comprehension score. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.). The generated learning program is sent to the terminal, which displays it to the user. The server selects online educational content and practice problems as learning content. For example, a learning program that covers geometry from basics to applications is generated and sent to the terminal.

[1554] Input: Comprehension score

[1555] Output: Generated training program, training program sent to the terminal

[1556] Step 4:

[1557] Monitoring learning progress

[1558] As a user progresses through a learning program, the device records the user's learning progress, learning content, test scores, etc. This data is periodically sent to a server, which uses an analytical algorithm to evaluate the progress data in real time. As a specific example, user A is watching a video lecture, and the viewing time and progress are recorded by the device and sent to the server.

[1559] Input: User's learning progress data

[1560] Output: Progress data sent to the server, real-time evaluation

[1561] Step 5:

[1562] Feedback and Program Updates

[1563] The server generates feedback for the user based on the analysis results. The learning program is updated as necessary and resent to the terminal. For example, an instruction to "concentrate on studying a specific concept of geometry" is generated and provided to the user via the terminal. If it is determined that User A lacks understanding of a specific concept of geometry, the server generates supplementary learning materials along with the feedback to "concentrate on studying" and sends them to the terminal.

[1564] Input: User progress data, real-time assessment results

[1565] Output: Generated feedback, updated learning program, feedback and program sent to the device

[1566] These are the specific processing steps of the system. Through this process, optimal educational programs and content are provided based on the individual learning status of each user, improving learning efficiency and comprehension.

[1567] (Application example 1)

[1568] 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."

[1569] While employee education and training are extremely important in brick-and-mortar stores, it is difficult to provide optimal training programs tailored to each employee's skill level and level of understanding. Furthermore, there is a lack of systems that can grasp each employee's progress in real time and dynamically update training content accordingly. For this reason, there is a need for a system that can provide efficient, individually optimized training.

[1570] 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.

[1571] In this invention, the server includes means for collecting employee skill assessment data and training history, means for analyzing the data to score the employee's level of understanding, means for generating a training program suited to the employee based on the score, means for providing the employee with appropriate training content based on the training program, means for monitoring the employee's training progress, analyzing new progress data to update the training program, and means for providing the updated training program to the employee again, thereby making it possible to provide efficient training optimized for each employee.

[1572] "User learning history" is a record of what learning activities the user has performed in the past.

[1573] "Evaluation data" is data for evaluating the user's level of understanding and progress regarding the learning content.

[1574] "Analysis" is the process of extracting meaningful information from collected data.

[1575] The "understanding score" is a numerical representation of the user's understanding of the learning content.

[1576] A "learning program" is a learning plan or learning system that is optimized based on the user's learning progress and level of understanding.

[1577] "Learning content" refers to learning materials such as video lectures, textbooks, and quizzes provided to users.

[1578] "Study progress" refers to the progress a user makes as they progress through a learning program.

[1579] "New progress data" refers to data relating to the learning situation that is newly collected as the user progresses through the learning program.

[1580] "Brick and mortar employees" are staff who work in actual physical stores.

[1581] "Skills assessment data" refers to data that indicates the results of an assessment of an employee's specific skills or abilities.

[1582] "Training history" is a record of the training and education an employee has received in the past.

[1583] "Training Program" means an educational plan or training system designed to improve the skills of employees.

[1584] "Training Content" means training materials, such as video lectures, practical tests, and study materials, that are provided to employees.

[1585] "Training Progress" is the progress an employee makes in progressing through a training program.

[1586] This invention is a system that provides individually optimized training programs to employees in brick-and-mortar stores. This system collects and analyzes employee skill assessment data and training history, and dynamically generates and provides optimal training programs and training content based on that data. This system makes it possible to achieve efficient training that is optimized for each employee.

[1587] Hardware and software used

[1588] The system uses the following hardware and software:

[1589] Hardware: Servers, smartphones, tablets

[1590] Software: Python, Pandas, Scikit-Learn, database system

[1591] Program processing

[1592] The server receives employee skill assessment data and training history, analyzes it, and scores the employee's skill level. Specifically, it uses an analysis algorithm using Python and Scikit-Learn to predict skill levels.

[1593] For example, when Employee A joins the company, his skill level is predicted based on his past training data. At this time, he is given a score such as high customer service skills (8 / 10) or insufficient product knowledge (5 / 10), and appropriate video learning materials and practical tests are automatically selected based on this.

[1594] Once the training program is generated, it is sent to the employee's device, such as a smartphone or tablet. Employees can view video materials and take practical tests via their device. Progress data is sent in real time to the server, which then updates the training program accordingly.

[1595] The server can provide feedback to employees based on the analysis results, such as issuing instructions like, "Your customer service skills have improved, so next you should learn about applied technology."

[1596] Specific examples

[1597] Employee A joins the company and his past training data is imported into the database. The server uses Python and Scikit-Learn to predict his skill level and generate a score, such as "high customer service skills but lacking product knowledge." Based on this score, the optimal video learning materials and practical tests are selected and sent to Employee A's smartphone. Employee A watches the videos and takes the tests, and their progress data is sent to the server. The server analyzes the new progress data and provides the next feedback and training content.

[1598] Prompt Sentence Examples

[1599] "Create a Python script that uses training data for new staff members to predict their skill levels and generate the optimal training program. I need a model that uses random forests for training and prediction."

[1600] As described above, this system makes it possible to efficiently provide training that is optimized for each employee, contributing to improving the service quality of stores.

[1601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1602] Step 1: Data collection

[1603] The user inputs or imports past training history and skill assessment data via the terminal. This data includes the employee's past training sessions, courses taken, and skill assessment test results. The terminal then sends this data to the server. The input data is information stored in a CSV file or database.

[1604] Step 2: Data analysis and scoring

[1605] The server uses the received data to score the employee's skill level. The server uses Python and Scikit-Learn to analyze the collected data and quantify the level of understanding of each skill (e.g., customer service, product knowledge, cash register operation, etc.). The input is past training history and skill evaluation data, and the output is the understanding score for each skill.

[1606] Step 3: Generate a training program

[1607] The server generates a training program suitable for the employee based on the comprehension score. This program includes video materials, practical tests, quizzes, etc. The server selects appropriate training content and generates this program. The input is the comprehension score, and the output is the training program.

[1608] Step 4: Provide training content

[1609] The server sends the generated training program to the employee's terminal. Through the terminal, the user can watch video materials and take practical tests and quizzes. The input is the training program, and the output is the training content displayed on the terminal.

[1610] Step 5: Progress monitoring

[1611] The device records the employee's progress as they progress through the training program and periodically sends the data to a server. The recorded data includes videos watched, quizzes completed, and practical test results. The input is the user's learning progress, and the output is the progress data.

[1612] Step 6: Analyze progress data

[1613] The server analyzes the progress data and evaluates changes in the employee's understanding. The server uses Python and Scikit-Learn to analyze new progress data and identify changes in the employee's skill level. The input is the progress data, and the output is the understanding score for the updated skill level.

[1614] Step 7: Feedback and Program Updates

[1615] The server provides feedback to employees based on the analysis results and updates the training program as necessary. For example, it provides specific instructions such as, "Your customer service skills have improved, so now it's time for applied skills training." The server then sends the updated training program back to the employee's device. The input is the comprehension score for the updated skill level, and the output is the updated training program.

[1616] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1617] The present invention is a system that uses a user's learning history and assessment data to generate an individually optimized learning program, provides learning content based on that program, and monitors learning progress and provides feedback. Furthermore, the system aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1618] Overall system overview

[1619] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1620] Data collection

[1621] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[1622] Scoring comprehension

[1623] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[1624] Emotional Recognition and Feedback

[1625] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[1626] Creating and delivering learning programs

[1627] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[1628] Monitoring learning progress

[1629] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[1630] Feedback and Program Updates

[1631] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1632] Specific examples

[1633] 1. Collecting training data

[1634] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1635] 2. Scoring comprehension

[1636] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1637] 3. Emotional Recognition

[1638] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1639] The emotion engine analyzes this data and recognizes that User A is tired.

[1640] 4. Creating and delivering appropriate learning programs

[1641] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1642] The learning program is sent to the terminal, which displays it to User A.

[1643] 5. Monitoring your learning progress

[1644] User A watches a video lecture and the device records his / her progress solving the exercises.

[1645] The progress data is sent to the server.

[1646] 6. Feedback and Program Updates

[1647] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1648] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1649] If necessary, the learning program is updated and resent to the terminal.

[1650] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[1651] The processing flow will be explained below.

[1652] Step 1: Collect user learning data

[1653] A user logs in to a terminal.

[1654] The terminal sends the user's login information to the server.

[1655] The server performs user authentication and, if authentication is successful, sends a confirmation message to the terminal.

[1656] Step 2: Send user learning history and assessment data

[1657] The terminal transmits evaluation data such as the user's learning history, past test results, and study time to the server.

[1658] Step 3: Analyze the data and score comprehension

[1659] The server analyzes the received evaluation data and evaluates the user's current learning situation and level of understanding.

[1660] The server generates a comprehension score based on the assessment data, for example, 8 / 10 for algebra, 6 / 10 for geometry, etc.

[1661] Step 4: Collecting emotion data

[1662] As the user progresses with their learning, the device's camera, microphone, and temperature sensor collect the user's biometric data (facial expression, tone of voice, body temperature).

[1663] The collected emotion data is sent to a server in real time.

[1664] Step 5: Sentiment Analysis

[1665] An emotion engine installed on the server analyzes the received emotion data and recognizes the user's emotional state.

[1666] For example, it identifies whether the user is tired, has difficulty concentrating, is agitated, etc.

[1667] Step 6: Generate an optimal learning program

[1668] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine.

[1669] The server selects relevant learning content (video lectures, text materials, quizzes, etc.) from the database.

[1670] Step 7: Delivering the learning program

[1671] The server transmits the generated learning program to the terminal.

[1672] The terminal displays the received learning program to the user, and allows the user to start learning.

[1673] Step 8: Monitor and record your learning progress

[1674] The user progresses through the learning program.

[1675] The device records the user's progress data in real time, including study time, study content, and test results.

[1676] Step 9: Submit your learning progress data

[1677] The terminal periodically transmits the recorded learning progress data to the server.

[1678] Step 10: Reanalyze progress and sentiment data

[1679] The server analyzes the received learning progress data and emotional data, and reevaluates the user's level of understanding, learning progress, and emotional state.

[1680] Step 11: Feedback and Program Updates

[1681] The server generates feedback based on the analysis, including specific advice such as "focus more on a particular concept of geometry" or "take a short break."

[1682] Update learning programs and select new learning tasks and content based on feedback.

[1683] Step 12: Feedback and delivery of updated learning programs

[1684] The server transmits the generated feedback and updated learning program to the terminal.

[1685] The terminal displays new feedback to the user and allows them to continue learning based on the updated program.

[1686] As a concrete example, let's consider the case where user A uses the system:

[1687] 1. User A logs in to the device, and the device sends evaluation data such as past test results to the server.

[1688] 2. The server analyzes the assessment data and generates comprehension scores of 8 / 10 for algebra and 6 / 10 for geometry.

[1689] 3. User A begins learning, and the device collects emotional data through the camera, microphone, and temperature sensor and sends it to the server.

[1690] 4. The emotion engine analyzes the data and determines that User A is tired.

[1691] 5. The server generates an optimal learning program based on the comprehension score and emotional data and sends it to the device.

[1692] 6. The terminal displays the program to User A and allows him to begin learning.

[1693] 7. User A's learning progress and new emotion data are periodically sent to the server.

[1694] 8. The server analyzes the data, creates feedback, and sends specific instructions to the device, such as "take a short break."

[1695] 9. User A receives the feedback and performs a new task reflected in the learning program.

[1696] This example enables the system to improve the user's learning efficiency and comprehension, and provide support that takes into account the user's emotional state.

[1697] Example 2

[1698] 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."

[1699] In conventional learning systems, it is difficult to provide an optimal learning program based on the individual user's learning progress and level of understanding, and furthermore, because the user's emotional state is not taken into consideration, there are problems with reduced learning efficiency and motivation. The present invention aims to solve these problems and provide an optimal learning environment that appropriately responds to the individual learning situation of each user and takes their emotional state into consideration.

[1700] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1701] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for recognizing the user's emotional state, means for generating a learning program appropriate for the user based on the comprehension score and emotional state, means for providing the user with appropriate learning content, means for monitoring the user's learning progress, analyzing new progress data to update the learning program, and means for re-providing the updated learning program to the user. This improves the user's learning efficiency and level of understanding, and enables flexible learning support based on the user's emotional state.

[1702] "User" refers to an individual or organization that uses the System to study.

[1703] "Study history" refers to a record of a user's past learning activities, including subjects studied, study time, test results, and so on.

[1704] "Evaluation data" is data for evaluating a user's learning outcomes, and includes test scores, accuracy and speed of answering questions, etc.

[1705] The "comprehension score" is a numerical representation of how well a user understands a particular subject or content, based on an analysis of learning history and assessment data.

[1706] "Emotional state" is information that indicates the user's emotional and mental state, and is determined from biometric data such as facial expression, tone of voice, and body temperature.

[1707] A "learning program" is a plan containing the optimal learning activity schedule and content that is generated based on the user's comprehension score and emotional state.

[1708] "Learning content" refers to specific educational materials, quizzes, video lectures, and other educational resources provided to users in a learning program.

[1709] "Monitoring" means observing the user's learning process and recording their progress.

[1710] "Progress data" is data that indicates the progress of a user's learning activities, and includes the completion rate of video viewing and the correct answer rate for quizzes.

[1711] "Update" refers to modifying and improving existing learning programs and content based on new data and analytical results.

[1712] This system generates an individually optimized learning program using a user's learning history and assessment data, provides learning content based on that program, monitors learning progress, and provides feedback. Furthermore, it aims to provide appropriate learning support according to the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1713] Overall system overview

[1714] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1715] Data collection

[1716] When a user starts studying using a device, the device collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to a server, which analyzes it and evaluates the user's current study status.

[1717] Scoring comprehension

[1718] The server then scores the user's level of understanding based on the received learning history and assessment data. This scoring is performed using an analysis algorithm installed on the server, which calculates a detailed score for each subject and item. For example, a score of 8 / 10 for algebra and 6 / 10 for geometry in mathematics might be assigned.

[1719] Emotional Recognition and Feedback

[1720] As the user studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. If the user is feeling impatient or tired, the server will use that information to adjust the learning program.

[1721] Creating and delivering learning programs

[1722] The server generates an optimal learning program for the user based on the scoring data and feedback from the emotion engine. This program includes appropriate learning content (e.g., video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the terminal, which then displays it to the user.

[1723] Monitoring learning progress

[1724] As the user studies according to the learning program, the device records the user's progress, actual learning content, test results, etc., and periodically sends them to the server. The server analyzes this data in real time to understand the user's learning progress and changes in understanding.

[1725] Feedback and Program Updates

[1726] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Your understanding of geometry has improved, so try an applied problem next," "Review the basics of algebra again," or "Your concentration is declining, so take a short break." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1727] Specific examples

[1728] 1. Collecting training data

[1729] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1730] 2. Scoring comprehension

[1731] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1732] 3. Emotional Recognition

[1733] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1734] The emotion engine analyzes this data and recognizes that User A is tired.

[1735] 4. Creating and delivering appropriate learning programs

[1736] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1737] The learning program is sent to the terminal, which displays it to User A.

[1738] 5. Monitoring your learning progress

[1739] User A watches a video lecture and the device records his / her progress solving the exercises.

[1740] The progress data is sent to the server.

[1741] 6. Feedback and Program Updates

[1742] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1743] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1744] If necessary, the learning program is updated and resent to the terminal.

[1745] An example prompt is, "Please explain the steps necessary to assess the user's understanding of algebra and provide an appropriate learning program."

[1746] As described above, this system can provide educational programs and content optimized for each user's individual learning and emotional state, improving learning efficiency and comprehension. Furthermore, the emotion engine provides support according to the user's emotional state, allowing the user to continue learning in a healthy mental state.

[1747] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1748] Step 1: User Login

[1749] Specific details:

[1750] The user opens the system login screen using a terminal, enters the login ID and password, and presses the login button.

[1751] Input and Output:

[1752] The user enters a login ID and password, which the terminal sends to the server. The server checks the database to verify the validity of the login information. If the server authenticates the login, it returns the authentication information to the terminal.

[1753] Specific behavior:

[1754] The terminal receives the login information and displays the user's My Page.

[1755] Step 2: Collect and send data

[1756] Specific details:

[1757] After a user logs in to the system, the terminal reads past learning records and test results.

[1758] Input and Output:

[1759] The device collects the user's learning history and evaluation data, and sends it to a server, which stores the received data in a database for analysis.

[1760] Specific behavior:

[1761] The device sends the collected data to the server in a standard format such as JSON, which the server receives and stores in a database.

[1762] Step 3: Score comprehension

[1763] Specific details:

[1764] The server begins analysis based on the learning history and evaluation data, and scores the user's level of understanding.

[1765] Input and Output:

[1766] The server inputs learning history and assessment data, and outputs comprehension scores for each subject. The scores are calculated using analytical algorithms (e.g., decision trees or neural networks).

[1767] Specific behavior:

[1768] The server runs the analysis engine and generates a score, for example, "Algebra 8 / 10, Geometry 6 / 10." The generated scores are stored in the user's data profile.

[1769] Step 4: Recognize emotions

[1770] Specific details:

[1771] While the user is learning, the device collects real-time data using a camera, microphone, and temperature sensor, and the emotion engine analyzes this data to determine the user's emotional state.

[1772] Input and Output:

[1773] The device collects biometric data such as facial expressions, tone of voice, and body temperature, and the emotion engine outputs a judgment of the user's emotional state.

[1774] Specific behavior:

[1775] The device's camera captures the user's facial expressions, the microphone records the tone of voice, and the temperature sensor measures the user's body temperature. This data is pre-processed within the device, and the emotion engine analyzes it to determine the user's emotional state. The results are then sent to the server.

[1776] Step 5: Generate a learning program

[1777] Specific details:

[1778] The server designs an optimal learning program based on the comprehension score and emotional state.

[1779] Input and Output:

[1780] The server inputs the comprehension score and the emotional state assessment result, and outputs a learning program, which includes learning tasks and learning content.

[1781] Specific behavior:

[1782] The server uses the generative AI model to generate a learning program, for example, proposing a program that progresses from the basics of geometry to its applications. The generated program is then sent to the device.

[1783] Step 6: Deliver learning content

[1784] Specific details:

[1785] The terminal displays the learning program received from the server to the user, and the user starts learning according to the program.

[1786] Input and Output:

[1787] The input to the terminal is a learning program, and the output is specific learning content that is displayed to the user.

[1788] Specific behavior:

[1789] The device plays video lectures, displays text materials, and offers quizzes, allowing users to progress through the learning content.

[1790] Step 7: Monitoring your learning progress

[1791] Specific details:

[1792] As the user progresses through their studies, the device records progress data.

[1793] Input and Output:

[1794] The terminal inputs data related to the progress of learning and outputs progress data that is sent to the server.

[1795] Specific behavior:

[1796] The device tracks each learning step in real time and periodically sends the data to a server, which analyzes the data and tracks the user's learning progress.

[1797] Step 8: Generate and provide feedback

[1798] Specific details:

[1799] The server generates feedback based on the progress data and emotional state and provides it to the user.

[1800] Input and Output:

[1801] The server's inputs are progress data and emotional states, and its outputs are feedback and updated learning programs.

[1802] Specific behavior:

[1803] Based on the analysis results, the server may instruct the user to, for example, "focus on specific geometry concepts" and suggest "take a short break." The device receives the feedback and displays it to the user. If necessary, it resends an updated learning program.

[1804] Through this sequential processing, the system maximizes the user's learning efficiency and provides an optimal learning environment tailored to individual needs.

[1805] (Application example 2)

[1806] 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."

[1807] Conventional learning systems have the problem of being unable to provide individually optimized learning programs based on a user's learning history and assessment data, and they also lack real-time feedback and learning adjustments based on the user's emotional state. Furthermore, because they do not take the user's emotional state into account, learning efficiency and effectiveness may decline. This poses a challenge, making it difficult for users to continue learning with high motivation.

[1808] 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.

[1809] In this invention, the server includes means for collecting a user's learning history and assessment data, means for analyzing the learning history and assessment data to score the user's level of understanding, means for generating a learning program suited to the user based on the comprehension score, means for providing the user with appropriate learning content based on the learning program, means for monitoring the user's learning progress and analyzing new progress data to update the learning program, means for providing the updated learning program to the user again, means for adjusting the learning program based on the user's emotions, and means for providing real-time feedback to optimize the user's learning experience. This makes it possible to provide a learning program optimized for the user's individual learning and emotional state, thereby improving learning efficiency and maintaining motivation.

[1810] "User's learning history" refers to the content that the user has studied in the past and the activity records related to that learning.

[1811] "Evaluation data" refers to data that indicates the user's level of understanding and achievements regarding the learning content, and includes test results and evaluation scores.

[1812] The "understanding score" is a numerical representation of the user's understanding of the learning content, and is calculated by an analysis algorithm.

[1813] A "learning program" is a set of learning tasks and / or content designed to optimize a user's learning efficiency.

[1814] "Learning content" refers to learning materials that users use to study according to a learning program, including video lectures, textbooks, quizzes, and the like.

[1815] "Study progress" refers to the process by which a user advances in learning according to a learning program and the degree of progress.

[1816] The "emotion engine" is a system that uses a camera, microphone, body temperature sensor, etc. to recognize and analyze the user's emotional state.

[1817] "Feedback" is advice or instruction provided to a user based on their learning progress, level of understanding, and emotional state.

[1818] This invention is a system that generates an individually optimized learning program based on a user's learning history, evaluation data, and emotional state, and monitors the learning progress and provides feedback. Specific embodiments are described below.

[1819] Overall system overview

[1820] In this system, when a user studies using a device, the server constantly monitors the user's progress and level of understanding, and the emotion engine analyzes the user's emotional state, dynamically providing learning programs and content to help the user study most effectively.

[1821] Data collection

[1822] When a user begins studying using a device, the server collects evaluation data such as the user's study history, past test results, study time, etc. The collected data is sent to the server, which analyzes it and evaluates the user's current study status.

[1823] Scoring comprehension

[1824] The server then uses the learning history and assessment data to score the user's level of understanding. This scoring is done using an analysis algorithm built into the server. For example, a detailed score such as 8 / 10 for algebra or 6 / 10 for geometry is calculated.

[1825] Emotional Recognition and Feedback

[1826] As the user progresses through their studies, the device is equipped with a camera, microphone, and temperature sensor, and the emotion engine analyzes this biometric data to determine the user's emotional state. For example, if the user is feeling impatient or tired, the server will use that information to adjust the study program.

[1827] Creating and delivering learning programs

[1828] The server generates an optimal learning program for the user based on the comprehension score and feedback from the emotion engine. This program includes appropriate learning content (video lectures, text materials, quizzes, etc.) and learning tasks. The generated learning program is sent from the server to the device, which then displays it to the user.

[1829] Monitoring learning progress

[1830] As users study according to the learning program, the device records their progress, actual learning content, and test results, and periodically sends them to the server. The server analyzes this data in real time to understand changes in the user's learning progress and level of understanding.

[1831] Feedback and Program Updates

[1832] The server generates feedback for the user based on the analysis results and feedback from the emotion engine. For example, it may include specific instructions such as "Now that you understand geometry better, try some applied problems" or "Review the basics of algebra again." This feedback is provided to the user via their device. At the same time, the learning program is updated as necessary and resent to the device.

[1833] Specific examples

[1834] 1. Collecting training data

[1835] User A logs in to the system and his terminal sends his past test results for algebra and geometry to the server.

[1836] 2. Scoring comprehension

[1837] The server uses an analytical algorithm to calculate a score of 8 / 10 for algebra and 6 / 10 for geometry.

[1838] 3. Emotional Recognition

[1839] While User A is studying, the device's camera, microphone, and temperature sensor monitor the user's facial expression, tone of voice, and body temperature.

[1840] The emotion engine analyzes this data and recognizes that User A is tired.

[1841] 4. Creating and delivering appropriate learning programs

[1842] Based on the comprehension score and data from the emotion engine, the server generates a learning program that progresses from basic to advanced geometry and selects video lectures and exercises.

[1843] The learning program is sent to the terminal, which displays it to User A.

[1844] 5. Monitoring your learning progress

[1845] User A watches a video lecture and the device records his / her progress solving the exercises.

[1846] The progress data is sent to the server.

[1847] 6. Feedback and Program Updates

[1848] The server analyzes the new progress data and feedback from the emotion engine and determines that User A is struggling with a particular concept in geometry and is losing focus.

[1849] It provides instructions for "focusing on specific geometry concepts," suggestions for "taking short breaks," and supplementary materials.

[1850] If necessary, the learning program is updated and resent to the terminal.

[1851] Prompt Sentence Examples

[1852] An example of a prompt sentence is "Study history: Algebra 8 / 10, Geometry 6 / 10; Emotional state: Fatigue." Based on this, the system generates an optimal learning program and appropriate feedback for the user.

[1853] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1854] Step 1:

[1855] The user logs in using a terminal.

[1856] Input: User login information (ID, password)

[1857] Output: User authentication status, learning history and assessment data

[1858] Operation: The device sends the user's login information to the server, which then authenticates them. If authentication is successful, the server retrieves the user's past learning history and evaluation data and sends them to the device.

[1859] Step 2:

[1860] The server analyzes the learning history and assessment data and calculates the comprehension score.

[1861] Input: User learning history and assessment data

[1862] Output: Comprehension score for each learning content (e.g., Algebra 8 / 10, Geometry 6 / 10)

[1863] How it works: The server uses an analysis algorithm to analyze the user's learning history and assessment data, calculates a comprehension score for each learning item, and stores it in a database.

[1864] Step 3:

[1865] The user begins learning.

[1866] Input: None (user operation)

[1867] Output: Signal to start learning, activate the device's camera and microphone

[1868] How it works: When a user starts learning, the device activates the camera, microphone, and temperature sensor and begins the learning session.

[1869] Step 4:

[1870] The emotion engine recognizes the user's emotional state in real time.

[1871] Input: Data from camera and microphone, body temperature sensor (facial expression, tone of voice, body temperature)

[1872] Output: User's emotional state (e.g., impatience, fatigue, concentration)

[1873] How it works: The device sends data from the camera, microphone, and temperature sensor to the emotion engine, which analyzes it and determines the user's emotional state. The results are then sent to the server.

[1874] Step 5:

[1875] The server generates a learning program that is optimal for the user.

[1876] Input: Comprehension score, emotional state

[1877] Output: Individually optimized learning program (learning tasks, appropriate learning content)

[1878] How it works: The server generates a learning program optimized for the user based on the user's comprehension score and emotional state. The generated program includes learning content such as video lectures, textbooks, and quizzes. The server then sends the program to the device.

[1879] Step 6:

[1880] The user proceeds with the study by following the instructions of the study program.

[1881] Input: Displayed learning program

[1882] Output: Learning progress data (viewing time, comprehension, answer results)

[1883] How it works: The user follows a learning program, watches video lectures, and answers quizzes. The device records this and sends the learning progress data to the server.

[1884] Step 7:

[1885] The server analyzes the learning progress data and generates feedback.

[1886] Input: Learning progress data

[1887] Output: Feedback instructions (e.g., "Revisit a specific concept in geometry," "Take a short break")

[1888] Operation: The server analyzes learning progress data in real time and generates necessary feedback for the user. The feedback is provided to the user via the terminal.

[1889] Step 8:

[1890] The server updates the learning program as needed and provides it again to the user.

[1891] Input: Feedback instructions, changes in emotional state

[1892] Output: Updated learning program

[1893] Operation: The server updates the learning program based on feedback and changes in emotional state. The updated learning program is resent to the device and displayed to the user.

[1894] 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.

[1895] 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.

[1896] 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 robot 414.

[1897] 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.

[1898] FIG. 9 illustrates 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 behaviors 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.

[1899] 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.

[1900] 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).

[1901] 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.

[1902] 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."

[1903] 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.

[1904] 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).

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] The following is further disclosed regarding the above embodiment.

[1916] (Claim 1)

[1917] means for collecting user learning history and assessment data;

[1918] means for analyzing the learning history and evaluation data to score the user's level of understanding;

[1919] means for generating a learning program suited to the user based on the comprehension score;

[1920] means for providing appropriate learning content to the user based on the learning program;

[1921] means for monitoring the user's learning progress and analyzing new progress data to update the learning program;

[1922] means for providing the updated learning program to the user again;

[1923] A system including:

[1924] (Claim 2)

[1925] 2. The system according to claim 1, further comprising means for a user to access the learning content via a terminal and proceed with the learning.

[1926] (Claim 3)

[1927] 10. The system of claim 1, further comprising a server having an analytical algorithm for calculating the comprehension score.

[1928] "Example 1"

[1929] (Claim 1)

[1930] means for collecting user learning history and assessment data;

[1931] means for analyzing the learning history and evaluation data to score the user's level of understanding;

[1932] means for generating a learning program suited to the user based on the comprehension score;

[1933] means for providing appropriate learning content to the user based on the learning program;

[1934] means for monitoring the user's learning progress and analyzing new progress data to update the learning program;

[1935] means for providing the updated learning program to the user again;

[1936] means for analyzing the learning progress data in real time;

[1937] a means for selecting learning materials including video lectures, textbooks, and exercises as the appropriate learning content;

[1938] A system including:

[1939] (Claim 2)

[1940] 2. The system according to claim 1, further comprising means for a user to access the learning content via a terminal and proceed with the learning.

[1941] (Claim 3)

[1942] 10. The system of claim 1, further comprising a server having an analytical algorithm for calculating the comprehension score.

[1943] "Application Example 1"

[1944] (Claim 1)

[1945] means for collecting user learning history and assessment data;

[1946] means for analyzing the learning history and evaluation data to score the user's level of understanding;

[1947] means for generating a learning program suited to the user based on the comprehension score;

[1948] means for providing appropriate learning content to the user based on the learning program;

[1949] means for monitoring the user's learning progress and analyzing new progress data to update the learning program;

[1950] means for providing the updated learning program...

Claims

1. means for collecting user learning history and assessment data; means for analyzing the learning history and evaluation data to score the user's level of understanding; means for generating a learning program suited to the user based on the comprehension score; means for providing appropriate learning content to the user based on the learning program; means for monitoring the user's learning progress and analyzing new progress data to update the learning program; means for providing the updated learning program to the user again; A system including:

2. The system according to claim 1 , further comprising means for a user to access the learning content via a terminal and proceed with the learning.

3. The system of claim 1 , further comprising a server having an analytical algorithm for calculating the comprehension score.

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