system

Abstract

Provide a system. 【Solution means】means for receiving a request from a user, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the learning progress of the user, means for providing feedback and additional materials based on the learning progress, means for receiving questions from the user, searching for appropriate answers from the database, and providing them to the user, means for integrating and analyzing the learning data of all users, means for generating and providing a report for the administrator based on the analysis results, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional training systems, a great deal of labor and time are required from the formulation of training content to learning support and progress management, and an increase in cost and a decrease in efficiency are problems. Also, it is difficult to provide feedback according to the progress of each learner and additional materials, and the quality of learning is not constant. Furthermore, the integrated analysis of learning progress and the generation of reports for administrators are often performed manually, imposing a large burden on administrators.

Means for Solving the Problems

[0005] This invention provides a system that includes means for receiving requests from users and searching a database based on the requested topic. Furthermore, it includes means for automatically generating training content using the acquired information and providing the generated training content to the user's terminal. It also includes means for monitoring the user's learning progress in real time and providing feedback and additional materials based on the progress. In addition, it includes means for receiving questions from users, searching for appropriate answers in the database, and providing them. Furthermore, it provides a system that includes means for integrating and analyzing the learning data of all users, and generating and providing reports for administrators based on the analysis results. This enables efficient training content formulation, learning support, and progress management, resulting in cost reduction and a reduction in front-end man-hours.

[0006] A "user" is an individual or group that uses the system to create training content or to learn.

[0007] A "request" is a request made by a user to the system, such as the creation of specific training content or the provision of information.

[0008] A "database" is a recording medium or system that stores the information and materials necessary for creating training content.

[0009] "Searching" is the act of retrieving information from a database based on specific criteria.

[0010] "Training content" refers to educational materials and resources provided for education and training, such as slides and practice exercises.

[0011] "Automated generation" refers to a process in which a system automatically creates training content with minimal human intervention.

[0012] A "terminal" is a device used by a user to access the system and utilize training content.

[0013] "Learning progress" refers to the progress and status of learning that the user has achieved through training content.

[0014] "Monitoring" refers to the act of tracking and recording the user's learning activities in real time.

[0015] "Feedback" refers to the advice and comments provided based on the user's learning progress.

[0016] "Supplementary materials" refer to supplementary teaching materials and information provided to assist the user's learning.

[0017] "Question" refers to an inquiry made by the user to the system to resolve doubts and uncertainties.

[0018] "Answer" refers to the information and explanations provided by the system in response to the user's question.

[0019] "Integration" refers to the process of consolidating learning data collected from multiple users into one.

[0020] "Analysis" refers to the examination work carried out to identify trends and problems from the integrated data.

[0021] "Administrator" refers to an individual or group responsible for the operation and management of the entire system.

[0022] "Report" refers to a report prepared based on the analysis results and provided to the administrator.

Brief Description of the Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0025] First, the language used in the following description will be explained.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. The system's configuration and operation methods are described in detail below, including specific examples.

[0045] Content generation module

[0046] Program processing

[0047] As part of this system, it receives requests from users and automatically generates training content. This significantly reduces the time and effort required to develop training materials.

[0048] The user sends a request via their device stating, "I want to create an introductory course on data analysis."

[0049] The server receives this request and searches for relevant information in the database.

[0050] The server uses artificial intelligence to automatically generate the content for the "Introduction to Data Analysis" course based on the search results.

[0051] The server sends the generated content to the user's terminal and displays it to the user.

[0052] For example, if the request is for a beginner's programming course, complete training content including beginner-level materials and practice problems will be automatically generated.

[0053] Learning support module

[0054] Program processing

[0055] The system monitors users' learning progress in real time and provides appropriate feedback and additional materials based on their progress. This maximizes the learning efficiency of each user.

[0056] The user begins learning from the provided content. For example, they might view slides and solve problems.

[0057] The device records the user's learning activities and sends that data to the server.

[0058] The server analyzes the user's progress data and, if the user encounters a particular problem, provides relevant explanatory videos or additional materials.

[0059] The server sends the feedback it provides to the user's device and displays it to the user.

[0060] For example, if a user is unable to solve a particular problem in an introductory data analysis course, additional learning materials and explanatory documents related to that problem will be automatically provided.

[0061] Management module

[0062] Program processing

[0063] By integrating learning data from all users and analyzing their progress, the system automatically generates and provides reports to administrators. This allows administrators to efficiently understand learning progress and take appropriate action.

[0064] The server periodically aggregates learning data from all users and analyzes their progress.

[0065] The server generates a report for administrators based on the analysis results.

[0066] The server sends the generated report to the administrator's terminal for them to view.

[0067] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[0068] Question and Answer

[0069] Program processing

[0070] We respond quickly to user questions and provide appropriate answers. This makes it easier for users to resolve their questions during their learning process.

[0071] A user submits the question, "What is a correlation coefficient?"

[0072] The server receives a question and searches the database for the appropriate answer.

[0073] The server sends the found answer to the user's device and displays it to the user.

[0074] In this way, the entire system works in coordination, enabling efficient generation of training content, learning support, and progress management. This invention offers significant benefits to companies and educational institutions, resulting in cost reductions and a reduction in front-end workload.

[0075] The following describes the processing flow.

[0076] Content generation module

[0077] Step 1:

[0078] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[0079] Step 2:

[0080] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[0081] Step 3:

[0082] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[0083] Step 4:

[0084] Based on the data acquired by the server, artificial intelligence is used to automatically generate course content (slides, practice problems, etc.).

[0085] Step 5:

[0086] The server sends the generated course content to the user's terminal.

[0087] Step 6:

[0088] The terminal displays the course content it received to the user.

[0089] Learning support module

[0090] Step 1:

[0091] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[0092] Step 2:

[0093] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[0094] Step 3:

[0095] The device sends the recorded data to the server.

[0096] Step 4:

[0097] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[0098] Step 5:

[0099] The server generates appropriate feedback and additional learning materials based on the analysis results.

[0100] Step 6:

[0101] The server sends generated feedback and additional materials to the user's device.

[0102] Step 7:

[0103] The device displays feedback and additional information received by the user.

[0104] Question and Answer

[0105] Step 1:

[0106] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[0107] Step 2:

[0108] The server receives a question from the user and analyzes the content of the question.

[0109] Step 3:

[0110] The server searches the database for the appropriate answer to the question.

[0111] Step 4:

[0112] The server sends the answer it finds to the user's device.

[0113] Step 5:

[0114] The device displays the received response to the user.

[0115] Management module

[0116] Step 1:

[0117] The server periodically collects and integrates the learning data of all users.

[0118] Step 2:

[0119] The server analyzes the integrated data to identify learning trends and problems.

[0120] Step 3:

[0121] The server generates a report for administrators based on the analysis results.

[0122] Step 4:

[0123] The server sends the generated report to the administrator's terminal.

[0124] Step 5:

[0125] The terminal displays the received reports to the administrator.

[0126] These processing steps ensure that the entire system operates efficiently, allowing for smooth development of training content, learning support, and progress management.

[0127] (Example 1)

[0128] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0129] Existing training systems require significant time and effort for developing training content, providing learning support, and managing progress. Furthermore, they struggle to provide feedback tailored to individual user progress, hindering efficient learning. Additionally, they lack mechanisms for integrating and analyzing overall learning data to provide useful insights for administrators. Therefore, a system is needed that centrally manages automated training content generation, learning support, and progress management, enabling efficient and effective training.

[0130] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0131] In this invention, the server includes means for receiving requests from users, means for searching an information storage device and obtaining relevant information, means for automatically generating training content using a generation AI model, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the information storage device and providing them to the user, means for integrating and analyzing learning data from all users, and means for generating and providing reports for administrators based on the analysis results. This enables efficient automatic generation of training content, learning support, and progress management, resulting in overall cost and man-hour reductions.

[0132] A "request" is a user's request for specific training content to be created and provided.

[0133] "Information storage device" is a general term for devices that store information, such as databases and storage media.

[0134] "Related information" refers to materials and data necessary for generating training content, which are retrieved from information storage devices based on user requests.

[0135] "Training content" refers to the entirety of materials, slides, videos, practice exercises, etc., provided for learning and training.

[0136] A "generative AI model" refers to an algorithm or software that uses artificial intelligence to automatically generate training content based on user requests.

[0137] A "device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0138] "Learning progress" refers to the progress a user has made in learning the training content.

[0139] "Feedback" refers to information such as explanations and suggestions for improvement that are provided according to the user's learning progress.

[0140] "Additional materials" refer to supplementary learning materials and information provided to help users gain a deeper understanding of the material they are learning.

[0141] A "question" is an inquiry that a user enters into the system regarding doubts or points of confusion that arise during the learning process.

[0142] An "answer" refers to the explanation or information that the system provides in response to a user's question.

[0143] "Learning data" is a general term for records and data related to a user's learning activities.

[0144] A "report" is a document or report that summarizes the analysis results provided by the server to administrators after analyzing training data.

[0145] An "administrator" is the person or individual responsible for the overall operation of the system and the management of user progress.

[0146] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. This system consists of a content generation module, a learning support module, and a management module, all of which automatically generate training content based on user requests. The details and operation methods of these modules are described below.

[0147] Content generation module

[0148] This module receives requests from users and automatically generates training content based on specified themes. Users send training content creation requests from their terminals. The server receives the request and searches for relevant materials in its information storage. Based on the retrieved information, it automatically generates training content using a generation AI model (e.g., GPT-4®). The generated content is sent from the server to the user's terminal and displayed.

[0149] For example, if a user requests to "create an introductory data analysis course," the server will search the database for relevant materials and use a generative AI model to automatically generate the course content for "Introduction to Data Analysis."

[0150] Example of a prompt:

[0151] "Please create training content for an introduction to data analysis."

[0152] "Create a beginner's programming course."

[0153] Learning support module

[0154] This module monitors the user's learning process in real time and provides appropriate feedback and additional materials based on their progress. As the user studies the provided content, the device records their learning activity and sends it to the server. The server analyzes this data and, if the user encounters difficulties with a particular task, provides relevant explanatory videos or additional materials. This allows the user to learn more efficiently.

[0155] For example, if a user is unable to solve a particular problem in the "Introduction to Data Analysis Course," related explanatory videos and additional learning materials will be automatically provided to the user's device.

[0156] Management module

[0157] This module integrates learning data from all users and analyzes their progress to automatically generate and provide reports to administrators. The server periodically aggregates and analyzes learning data submitted by all users. Based on the analysis results, it generates a report for administrators and sends it to their terminals for display.

[0158] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[0159] Question answering function

[0160] This feature provides quick answers to user questions. When a user submits a question, such as "What is a correlation coefficient?", the server receives the question and searches its information storage for the appropriate answer. The server then sends the answer to the user's device for display.

[0161] By working together as a whole, this system enables the efficient generation of training content tailored to user needs, learning support, and progress management. It also offers significant benefits to businesses and educational institutions, resulting in cost and labor savings.

[0162] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0163] Content generation module

[0164] Processing flow and specific actions

[0165] Step 1:

[0166] The user sends a request through their device.

[0167] Input: The user enters a request in the input form, saying "I want to create an introductory data analysis course," and presses the submit button.

[0168] Operation: User input is sent to the server as an HTTP request.

[0169] Output: The server receives the request.

[0170] Step 2:

[0171] The server receives the request.

[0172] Input: User request data (e.g., "I want to create an introductory course on data analysis")

[0173] Operation: The web server receives the request and passes the data to the backend application server.

[0174] Output: The application server parses the request content.

[0175] Step 3:

[0176] The server searches for related documents.

[0177] Input: Parsed request content (e.g., "Introduction to Data Analysis")

[0178] Operation: The server generates an SQL query and performs a search on the information storage device (database).

[0179] Output: Related materials are retrieved as search results.

[0180] Step 4:

[0181] The server generates the training content.

[0182] Input: Acquired related documents

[0183] Operation: The server sends a prompt message (e.g., "Create training content for an introduction to data analysis") to the generated AI model (e.g., GPT-4) and requests content generation.

[0184] Output: Generated training content (text, slides, practice questions, etc.)

[0185] Step 5:

[0186] The server sends the generated content to the user for display.

[0187] Input: Generated training content

[0188] Operation: The server packages the content in JSON format or similar and sends it to the user's terminal as an HTTP response.

[0189] Output: The user's device analyzes and displays the received content.

[0190] Learning support module

[0191] Processing flow and specific actions

[0192] Step 1:

[0193] The user begins the learning activity.

[0194] Input: Training content provided by the user (e.g., slides, practice questions)

[0195] Action: View content and solve problems.

[0196] Output: Learning activity data (which slides were viewed, which problems were solved)

[0197] Step 2:

[0198] The device records learning activity data.

[0199] Input: User's learning activity data

[0200] Operation: Records learning activities in real time to a local database or cache.

[0201] Output: Recorded training data

[0202] Step 3:

[0203] The device sends recorded data to the server.

[0204] Input: Recorded training data

[0205] Operation: At regular intervals, the recorded data is sent to the server in batch processing.

[0206] Output: Transmitted training data

[0207] Step 4:

[0208] The server analyzes the data.

[0209] Input: Submitted training data

[0210] Operation: Receives data and executes an algorithm to analyze learning progress.

[0211] Output: Analysis results (e.g., which issues users are struggling with)

[0212] Step 5:

[0213] The server provides feedback and additional information.

[0214] Input: Analysis results

[0215] Function: Select and provide explanatory videos and additional materials related to the parts where the user is having trouble.

[0216] Output: Feedback and additional materials

[0217] Step 6:

[0218] Display information provided by the server to the user.

[0219] Input: Feedback and additional materials

[0220] Operation: The server sends this information to the user's terminal.

[0221] Output: Displays information received by the user terminal.

[0222] Management module

[0223] Processing flow and specific actions

[0224] Step 1:

[0225] The server aggregates the learning data of all users.

[0226] Input: Training data submitted by each user

[0227] Operation: Aggregates data using databases and big data processing platforms.

[0228] Output: Aggregated training data

[0229] Step 2:

[0230] The server analyzes the data.

[0231] Input: Aggregated training data

[0232] Operation: Executes data analysis algorithms and analyzes the overall learning progress.

[0233] Output: Analysis results (popular courses, frequently occurring stumbling blocks, etc.)

[0234] Step 3:

[0235] The server generates a report for administrators.

[0236] Input: Analysis results

[0237] Function: Generates a report document for administrators based on the analysis results.

[0238] Output: Generated report

[0239] Step 4:

[0240] The server sends the report to the administrator for display.

[0241] Input: Generated report

[0242] Action: Sends the report as an HTTP response to the administrator's terminal.

[0243] Output: The administrator terminal receives the report and displays it on the administration screen.

[0244] Question answering function

[0245] Processing flow and specific actions

[0246] Step 1:

[0247] User submits a question

[0248] Input: Question (Example: "What is a correlation coefficient?")

[0249] Operation: Enter your question in the chat box or form and submit it.

[0250] Output: The server receives the question.

[0251] Step 2:

[0252] The server receives the question.

[0253] Input: Question data

[0254] Function: Analyzes the question and extracts appropriate keywords.

[0255] Output: Analyzed question data

[0256] Step 3:

[0257] The server searches for the appropriate answer.

[0258] Input: Analyzed question data

[0259] Operation: Executes an SQL query to search the information storage device and retrieve relevant answers.

[0260] Output: Related response data

[0261] Step 4:

[0262] The server sends the answer to the user and displays it.

[0263] Input: Response data

[0264] Action: Sends the response as an HTTP response to the user's device.

[0265] Output: The user terminal receives and displays the response.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0268] Traditional training systems were cumbersome in terms of developing training content and managing progress, making efficient learning support difficult. Furthermore, training for virtual store staff, in particular, requires individual feedback and progress management, but achieving this using traditional methods is costly and time-consuming.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data of all users, means for generating and providing reports for administrators based on the analysis results, means for generating training content for virtual store staff, means for recording staff learning activities and monitoring progress, and means for aggregating each staff member's learning data, analyzing progress, and providing feedback. This enables automatic formulation of training content, real-time monitoring of learning progress, provision of individual feedback, and automatic generation of reports for administrators.

[0271] A "user" is someone who will be using this system to receive training.

[0272] "Means for receiving requests" refers to the functionality of devices or software that receive requests from users, such as training topics or questions.

[0273] A "database" is a collection of data that is systematically managed, allowing for efficient searching and retrieval of necessary information.

[0274] "Means for obtaining relevant information" refers to the functions of devices or software that search for and retrieve information from a database based on a request.

[0275] "Means for automatically generating training content" refers to the functions of devices or software that automatically create training materials based on acquired information.

[0276] "Means of providing generated training content to the user's terminal" refers to the functions of devices or software that transmit and display automatically generated training content on the user's device.

[0277] "Means for monitoring user learning progress" refers to the functions of devices and software that track and record a user's learning status in real time.

[0278] "Means of providing feedback and additional materials" refers to the functions of devices and software that provide users with appropriate advice and supplementary materials based on their learning progress.

[0279] "Means for receiving questions" refers to the functions of devices or software that receive questions from users.

[0280] "Means of searching for appropriate answers" refers to the function of devices or software that search and retrieve the best answer to a question from a database.

[0281] "Means for integrating and analyzing learning data from all users" refers to the functions of devices and software that centrally manage and statistically analyze learning data collected from multiple users.

[0282] "Means for generating and providing reports for administrators" refers to the functionality of devices and software that create and provide detailed reports for administrators based on data analysis results.

[0283] "Means for generating training content for virtual store staff" refers to the functions of devices and software that automatically create training materials for staff working in virtual stores.

[0284] "Means for recording staff learning activities and monitoring progress" refers to the functions of devices and software that track staff learning behaviors and record their progress in real time.

[0285] The means of "aggregating learning data, analyzing progress, and providing feedback" refers to the functions of devices or software for collecting the learning data of individual staff and providing appropriate advice and supplementary teaching materials based on their progress.

[0286] This invention realizes a training system for staff in a virtual store and can be implemented using the following hardware and software. This system is composed of multiple modules that receive requests from users, search databases, generate training content using AI, monitor learning progress, and provide feedback.

[0287] 1. Hardware and Software

[0288] Hardware: Servers, user terminals (such as smartphones, head-mounted displays, etc.)

[0289] Software: Python, Flask (server-side framework), requests (HTTP request library)

[0290] 2. Program Processing

[0291] Receiving Requests

[0292] The server receives training topics and questions from the staff of the virtual store. For example, when a staff member wishes to receive training on "virtual customer service skills", the request is sent to the server.

[0293] Searching the Database and Obtaining Information

[0294] Based on the request, the server searches the database and obtains relevant information. This is a process of finding the most suitable materials for the training content desired by the staff.

[0295] Automatic generation of training content

[0296] Using the acquired information, the server utilizes an AI model to automatically generate training content. For example, if the training is on data analysis techniques, the generated materials will include basic methods and specific use cases.

[0297] Content provision

[0298] The generated training content is sent from the server to the user's terminal and made available for the user to view. This allows staff to immediately begin learning on their own devices.

[0299] Monitoring and feedback on learning progress

[0300] The device records the user's learning activities (viewing slides and answering questions) and sends this data to the server. The server analyzes this data in real time and provides appropriate feedback and additional learning materials based on the user's progress. For example, if a user gets stuck on a particular problem, they may be provided with explanatory videos or additional materials related to that problem.

[0301] Generating reports for administrators

[0302] The server aggregates learning data from all users and analyzes their progress. Based on the analysis results, it automatically generates and provides reports for administrators. For example, it reports to administrators which topics were most effective in last month's training and which areas many users are struggling with.

[0303] Question and Answer

[0304] A user's question is sent to the server, and relevant information is retrieved from the database. The server generates the best possible answer and provides it to the user. For example, if a staff member asks, "What is the product return policy?", the answer is provided immediately.

[0305] 3. Specific Examples and Examples of Prompt Statements

[0306] As a specific example, when a staff member requests "Please generate an introductory course on virtual customer service technology", the server searches for relevant materials and automatically generates training content using an AI model.

[0307] Example of a prompt sentence:

[0308] "Please generate an introductory course on virtual customer service technology for the staff of the virtual store."

[0309] In this way, this system realizes efficient training for the staff working in the virtual store, enabling significant reduction in costs and man-hours.

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user sends a request to the system. For example, the user, who is a staff member of a virtual store, requests "I hope to receive training on virtual customer service technology". This request is sent from the user terminal (such as a smartphone or a head-mounted display) to the server. The input is "virtual customer service technology", and the output is the request information for processing it.

[0313] Step 2:

[0314] The server searches the database based on the received request. It searches for and obtains information related to the requested theme (e.g., "virtual customer service technology") from the database. The input is the request information, and the output is a list of relevant training materials. In this process, the server generates a search query and queries the database using an SQL query or the like.

[0315] Step 3:

[0316] Based on the acquired information, the server automatically generates training content using a generative AI model. For example, it analyzes acquired materials and generates appropriate slides, videos, and explanatory text. The input is a list of relevant training materials, and the output is automatically generated training content. Specifically, the AI ​​model generates text data using natural language processing technology and attaches related media files.

[0317] Step 4:

[0318] The server provides the generated training content to the user's terminal. The terminal receives this content and displays it to the user. For example, generated slides or videos are displayed on the user's smartphone or head-mounted display. The input is the automatically generated training content, and the output is the content displayed on the user's terminal.

[0319] Step 5:

[0320] When a user studies training content, the device records their learning activity. Progress data, such as the user's slide viewing time and question answer history, is recorded and sent to the server. The input is the user's learning activity, and the output is progress data. Specifically, a learning log is created and periodically sent to the server.

[0321] Step 6:

[0322] The server monitors and analyzes received learning progress data in real time. Based on the learning progress, if the user is struggling at a specific point, it generates and provides additional learning materials or explanatory videos. The input is progress data, and the output is feedback and additional materials. Machine learning algorithms are used in the analysis process to identify the user's weaknesses.

[0323] Step 7:

[0324] When a user submits a question, the server receives it. The server searches the database, generates an appropriate answer, and provides it to the user. For example, if a user asks, "What is the product return policy?", the server searches the database for the relevant policy information and generates an answer. The input is the user's question, and the output is the appropriate answer. In this process, natural language processing techniques are used to analyze the question and find the most relevant answer.

[0325] Step 8:

[0326] The server integrates learning data from all users and periodically analyzes their progress. Based on the analysis results, it automatically generates and provides detailed reports to administrators. For example, when an administrator requests a monthly report, the server aggregates the learning status of all users and creates a report based on the analysis results. The input is the integrated learning data, and the output is a report for administrators. Statistical analysis and data visualization techniques are used to generate the report.

[0327] This series of processing steps allows virtual store staff to receive training efficiently, and enables managers to reliably track the progress of their training.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] This invention provides a more personalized learning experience by combining training content development, learning support, and progress management with user emotion recognition capabilities. The system's configuration and operation method are described in detail below, including specific examples.

[0330] Content generation module

[0331] Program processing

[0332] When a user requests training content, the content is automatically generated based on that topic. This saves time and effort.

[0333] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the terminal to the server.

[0334] The server receives the request and extracts keywords related to "Introduction to Data Analysis".

[0335] The server searches the database and retrieves relevant information and content, such as basic concepts and instructions on how to use tools.

[0336] The server automatically generates course content (slides, practice problems) based on information acquired using artificial intelligence.

[0337] The server sends the generated content to the user's device, and the device displays it to the user.

[0338] Learning support module

[0339] Program processing

[0340] It monitors the user's learning progress in real time and provides feedback and additional materials according to their progress.

[0341] The user begins learning the course content on their device. For example, they view slides and solve practice problems.

[0342] The device records the user's learning activity in real time and sends that data to the server.

[0343] The server receives and analyzes the user's learning progress data. It detects issues such as getting stuck on specific problems or taking an excessive amount of time to study.

[0344] The server generates appropriate feedback and additional learning materials and sends them to the user's device.

[0345] The device displays feedback and additional information to the user.

[0346] Emotion recognition module

[0347] Program processing

[0348] It recognizes user emotions and provides feedback and support to further improve the learning experience.

[0349] The device will be equipped with sensors and cameras to recognize the user's emotions.

[0350] The device sends the user's facial expressions, voice tone, and other information to the emotion engine to acquire emotional data.

[0351] The server receives emotion data in real time and analyzes it along with learning progress data.

[0352] The server generates feedback based on emotional data, such as when the user is stressed or has a low level of understanding.

[0353] The server generates feedback and sends it to the user's device, which then displays it to the user.

[0354] For example, if a user shows signs of frustration or stress while working on a practice problem, the emotion engine recognizes that emotion and the server makes a suggestion such as, "Would you like to review the explanation for this problem again?"

[0355] Management module

[0356] Program processing

[0357] Integrates learning and sentiment data from all users and generates reports for administrators.

[0358] The server periodically collects and integrates learning data and sentiment data from all users.

[0359] The server analyzes this data to identify learning trends and problems.

[0360] The server generates a report for administrators based on the analysis results.

[0361] The server generates a report which is sent to the administrator's terminal, and the terminal displays it to the administrator.

[0362] For example, a monthly report could be submitted to administrators detailing which courses are popular, where many users are struggling, and their emotional reactions to these issues. Based on this information, administrators can review and improve the course content.

[0363] In this way, this system, which incorporates an emotion engine, improves the user's learning experience, streamlines the formulation of training content, learning support, and progress management, and achieves overall cost and man-hour reductions.

[0364] The following describes the processing flow.

[0365] Content generation module

[0366] Step 1:

[0367] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[0368] Step 2:

[0369] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[0370] Step 3:

[0371] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[0372] Step 4:

[0373] Based on the data acquired by the server, artificial intelligence is used to automatically generate the course content (slides, practice problems, etc.) for the "Introduction to Data Analysis" course.

[0374] Step 5:

[0375] The server saves the generated course content and then sends it to the user's terminal.

[0376] Step 6:

[0377] The terminal displays the course content it received to the user.

[0378] Learning support module

[0379] Step 1:

[0380] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[0381] Step 2:

[0382] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[0383] Step 3:

[0384] The device periodically sends the data it has recorded to the server.

[0385] Step 4:

[0386] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[0387] Step 5:

[0388] The server generates appropriate feedback and additional learning materials based on the analysis results.

[0389] Step 6:

[0390] The server sends generated feedback and additional materials to the user's device.

[0391] Step 7:

[0392] The device displays feedback and additional information received by the user.

[0393] Emotion recognition module

[0394] Step 1:

[0395] The device will be equipped with sensors and cameras to recognize the user's emotions.

[0396] Step 2:

[0397] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the emotion engine.

[0398] Step 3:

[0399] The server receives emotion data in real time and analyzes it along with learning progress data.

[0400] Step 4:

[0401] Based on the analysis results, the server generates feedback that corresponds to the user's emotions. For example, if the user is feeling stressed, it might generate a suggestion such as, "Would you like to review this problem explanation again?"

[0402] Step 5:

[0403] The server sends the generated feedback to the user's device.

[0404] Step 6:

[0405] The device displays the feedback it has received to the user.

[0406] Question answering module

[0407] Step 1:

[0408] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[0409] Step 2:

[0410] The server receives a question from the user and analyzes the content of the question.

[0411] Step 3:

[0412] The server searches the database for the appropriate answer to the question.

[0413] Step 4:

[0414] The server sends the answer it finds to the user's device.

[0415] Step 5:

[0416] The device displays the received response to the user.

[0417] Management module

[0418] Step 1:

[0419] The server periodically collects and integrates learning and sentiment data from all users.

[0420] Step 2:

[0421] The server analyzes the integrated data to identify learning trends and problems.

[0422] Step 3:

[0423] The server generates a report for administrators based on the analysis results.

[0424] Step 4:

[0425] The server sends the generated report to the administrator's terminal.

[0426] Step 5:

[0427] The terminal displays the received reports to the administrator.

[0428] In this way, each module works together to realize a personalized learning experience that includes everything from training content development and learning support to progress management and even emotion recognition.

[0429] (Example 2)

[0430] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0431] Traditional learning systems struggled to provide personalized feedback and additional materials tailored to each user's learning progress, and they failed to consider users' emotional states. Therefore, learning efficiency and effectiveness could not be adequately guaranteed. Furthermore, it was difficult for administrators to grasp the overall learning situation and take appropriate measures.

[0432] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's display device, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for recognizing the user's emotions and collecting emotional data, means for analyzing the emotional data and reflecting it in the feedback, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing a report for administrators based on the analysis results. This makes it possible to provide personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. In addition, administrators can accurately grasp the overall learning situation and take appropriate measures.

[0433] "Means for receiving user requests" refers to a device or program that acquires request information when a user requests specific training content.

[0434] "Means for searching a database and retrieving relevant information" refers to a device or program that searches a database for relevant information based on a requested topic and retrieves the necessary data.

[0435] "Means for automatically generating training content using acquired information" refers to a device or program that automatically generates training content such as slides and practice problems using artificial intelligence, based on information acquired from a database.

[0436] "Means for providing generated training content to a user's display device" refers to a device or program that transmits automatically generated training content to a terminal used by the user and displays it.

[0437] "Means for monitoring user learning progress" refers to a device or program that records and monitors the user's progress in real time as they progress through the learning process.

[0438] "Means for providing feedback and additional materials based on learning progress" refers to a device or program that generates and provides appropriate feedback and additional learning materials based on the user's learning progress data.

[0439] "Means for recognizing user emotions and collecting emotional data" refers to a device or program that recognizes emotions from a user's facial expressions, tone of voice, etc., and collects that data.

[0440] "Means for analyzing emotional data and reflecting it in feedback" refers to a device or program that analyzes collected emotional data, adjusts the feedback content based on the results, and provides it to the user.

[0441] "Means for integrating and analyzing learning data and sentiment data from all users" refers to a device or program that integrates learning data and sentiment data collected from all users and analyzes that data.

[0442] "Means for generating and providing reports for administrators based on analysis results" refers to a device or program that generates and provides reports in a format usable by administrators, based on the results of integrated data analysis.

[0443] This invention provides a system that offers a more personalized learning experience by combining training content development, learning support, and progress management with a user emotion recognition function. The system consists of three main elements: a server, a terminal, and a user.

[0444] First, when a user requests training content, they do so via their device. When the user clicks the "Request creation of an introductory data analysis course" button, the device sends this request to the server. After receiving the request, the server searches the database based on the requested topic and retrieves relevant information. The hardware used includes a server and a database server, while the software used includes a database management system (e.g., MySQL®) and a text analysis tool (e.g., NLTK).

[0445] Based on the acquired information, the server automatically generates training content (slides, practice questions, etc.) using a generative AI model (e.g., GPT-3®). The server then sends the generated content to the user's device, which displays it to the user.

[0446] For example, if a user clicks the "Request creation of an introductory data analysis course" button, materials related to "Introduction to Data Analysis" are retrieved from the database based on the request, and slides and practice problems are automatically generated using a generative AI model. The generated content is then displayed on the user's device.

[0447] Example prompt: "Automatically generate a data analysis course for beginners."

[0448] Next, as the user progresses through the learning process, the device records the user's actions in real time and sends this data to the server. The server analyzes the user's learning progress data to detect issues such as getting stuck on specific problems or taking too long to learn. Data analysis tools (e.g., Pandas) are used for this analysis. The server generates appropriate feedback and additional learning materials and sends them to the user's device. The device then displays the generated feedback and additional materials to the user.

[0449] For example, if a user encounters a problem while studying the "Introduction to Data Analysis Course," the server analyzes the information, generates additional explanations and reference materials, and sends them to the user's device. The user then relearns the material based on the provided resources.

[0450] Example prompt: "Automatically provide additional explanatory materials to users who are falling behind in their learning progress."

[0451] Furthermore, it also has a function to recognize the user's emotions. The device uses sensors and cameras to acquire the user's emotional data (facial expressions, tone of voice, etc.) and sends it to the server. The server analyzes the emotional data and provides feedback based on whether the user is feeling stressed or has a low level of understanding. Emotion analysis tools (e.g., OpenCV, TENSORFLOW®) are used for this analysis.

[0452] For example, if a user shows signs of dissatisfaction or stress while working on a practice problem, the server generates feedback such as "Would you like to review this problem explanation again?" and sends it to the user's device.

[0453] Example prompt: "If the user is experiencing stress, please provide an appropriate feedback message."

[0454] Finally, the server integrates all users' learning and sentiment data and periodically generates reports for administrators. The server analyzes this data to identify learning trends and problems. Data visualization tools (e.g., Tableau) are used for this analysis. The generated reports are sent to the administrator's terminal for their viewing.

[0455] As a concrete example, administrators can review monthly reports to see which courses are popular, where many users are struggling, and what their emotional reactions were to these issues, and then revise and improve the course content accordingly.

[0456] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[0457] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0458] Step 1: Sending and receiving requests

[0459] The user clicks the "Request creation of an introductory data analysis course" button. The input is the user's click action and the request content, and the output is the HTTP request generated by the terminal.

[0460] The terminal sends this request to the server. The input is the request information from the user, and the output is the request data sent to the server.

[0461] Specific actions:

[0462] 1. The user performs the action of clicking a button.

[0463] 2. The device captures the click event, generates an HTTP request, and sends it to the server.

[0464] Step 2: Keyword extraction and database search

[0465] The server receives the request and extracts keywords related to "Introduction to Data Analysis". The input is the user's request data, and the output is the extracted keywords.

[0466] The server searches the database and retrieves relevant documents and content. The input is extracted keywords, and the output is related informational data.

[0467] Specific actions:

[0468] 1. The server parses the request data and performs natural language processing to extract keywords.

[0469] 2. The server generates database queries and searches for and retrieves relevant information.

[0470] Step 3: Content Generation

[0471] The server automatically generates training content based on the information it acquires. The input is the acquired information data, and the output is the generated training content.

[0472] The server uses a generated AI model (e.g., GPT-3) to create slides and practice problems.

[0473] Specific actions:

[0474] 1. The server inputs the acquired information into the model and generates training content.

[0475] 2. The server converts the generated slides and practice questions into a data format.

[0476] Step 4: Content Delivery

[0477] The server provides the generated training content to the user's terminal. The input is the generated training content, and the output is the content data sent to the terminal.

[0478] The device displays the content it has received to the user.

[0479] Specific actions:

[0480] 1. The server sends the content data to the terminal.

[0481] 2. The content received by the device is displayed on the user interface.

[0482] Step 5: Monitoring Learning Progress

[0483] The user begins learning the course content on their device. The input is the user's learning activity, and the output is progress data.

[0484] The device records the user's learning activity in real time and sends that data to the server.

[0485] Specific actions:

[0486] 1. The user views the slides and performs the actions of solving the practice problems.

[0487] 2. The terminal records user operation events and sends them to the server as progress data.

[0488] Step 6: Analyze progress and provide feedback

[0489] The server receives user learning progress data and analyzes it to identify issues such as difficulty with specific problems or excessive learning time. The input is progress data, and the output is the analysis results.

[0490] The server generates appropriate feedback and additional learning materials and sends them to the user's terminal. The input is the analysis result, and the output is the feedback data.

[0491] Specific actions:

[0492] 1. The server analyzes the progress data using an analysis tool.

[0493] 2. The server uses the generated AI model to create feedback and additional materials, and sends them to the terminal.

[0494] Step 7: View feedback and additional materials

[0495] The device displays feedback and additional materials to the user. The input is feedback data, and the output is the feedback and materials reflected in the user interface.

[0496] Specific actions:

[0497] 1. Receive feedback and additional information for the device to display.

[0498] 2. The device displays feedback and information on the user interface.

[0499] Step 8: Acquisition and transmission of emotional data

[0500] The device will be equipped with sensors and cameras to recognize the user's emotions. The input will be the user's facial expressions and tone of voice, and the output will be emotion data.

[0501] The device sends emotional data to the server in real time.

[0502] Specific actions:

[0503] 1. The device uses its camera and microphone to capture user emotion data.

[0504] 2. The device sends the acquired emotion data to the server.

[0505] Step 9: Analysis of emotional data and provision of feedback

[0506] The server receives and analyzes emotion data in real time. The input is emotion data, and the output is the analysis result.

[0507] The server generates feedback based on emotional data and sends it to the user's terminal. The input is the analysis result, and the output is emotion-based feedback data.

[0508] Specific actions:

[0509] 1. The server analyzes the emotional data using an emotional analysis tool.

[0510] 2. The server generates feedback based on the analysis results and sends it to the user's terminal.

[0511] Step 10: Data integration and report generation for all users

[0512] The server periodically collects and integrates learning and sentiment data from all users. The input is data from all users, and the output is the integrated data.

[0513] The server analyzes the integrated data and generates reports for administrators. The input is the integrated data, and the output is the report data.

[0514] Specific actions:

[0515] 1. The server periodically collects data from all users and integrates it into the database.

[0516] 2. The server generates a report using a data visualization tool.

[0517] Step 11: Provide reports for administrators

[0518] The server generates a report which is then provided to the administrator's terminal, and the terminal displays it to the administrator. The input is the report data, and the output is the report reflected on the administrator's display device.

[0519] Specific actions:

[0520] 1. The server sends the report data to the administrator's terminal.

[0521] 2. The terminal displays the report on the administrator's user interface.

[0522] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[0523] (Application Example 2)

[0524] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0525] Traditional learning support systems were limited to monitoring users' learning progress and providing feedback and additional materials, lacking personalized support tailored to individual users' emotions and levels of understanding. As a result, they failed to address the stress and frustration users experienced during learning, potentially leading to decreased learning effectiveness. Furthermore, administrators lacked the information necessary to effectively integrate and analyze all users' learning data and implement appropriate countermeasures. This made it difficult to make appropriate improvements to enhance the quality of training.

[0526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0527] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for using sensors and cameras to recognize the user's emotions, means for analyzing the acquired emotional data and reflecting it in the feedback, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing reports for administrators based on the analysis results. This makes it possible to provide personalized support that responds to the user's emotions, and an improvement in learning effectiveness can be expected. In addition, detailed analysis based on aggregated data allows administrators to take appropriate improvement measures, thereby improving the overall quality of the training.

[0528] "Means for receiving user requests" refers to the functionality of a device or software that has an interface that allows users to request training content on a specific theme or topic.

[0529] "Means for searching a database and retrieving relevant information" refers to the function of a device or software that searches a database for relevant information based on a requested topic and retrieves that information.

[0530] "Means for automatically generating training content" refers to the function of a device or software that automatically generates the content necessary for training based on acquired information.

[0531] "Means of providing training content to a user's terminal" refers to the function of a device or software that transmits generated training content to a terminal used by the user, enabling the user to utilize that content.

[0532] "Means for monitoring user learning progress" refers to the function of a device or software that tracks a user's learning activities in real time and records their progress.

[0533] "Means of providing feedback and additional materials based on learning progress" refers to the function of a device or software that automatically generates and provides appropriate feedback and additional learning materials according to the user's learning progress.

[0534] "Means of using sensors or cameras to recognize user emotions" refers to the function of a device or software that includes sensors or cameras to detect the user's facial expressions and tone of voice and analyze their emotions.

[0535] "Means for analyzing acquired emotional data and reflecting it in feedback" refers to the function of a device or software that analyzes detected emotional data and generates feedback based on that analysis.

[0536] "Means of receiving questions from users, searching for appropriate answers in a database, and providing them to users" refers to the function of a device or software that receives a question entered by a user, searches for an answer to that question in a database, and returns it to the user.

[0537] "Means for integrating and analyzing learning data and emotional data of all users" refers to the function of a device or software that collects, integrates, and analyzes learning progress data and emotional data of all users.

[0538] "Means of generating and providing reports for administrators based on analysis results" refers to the function of a device or software that automatically generates reports for administrators based on the results of integrated data analysis and provides them for administrators to use.

[0539] This invention is a learning support system that helps users request learning content on specific themes and progress through their learning while receiving progress management and individual feedback. The system can recognize the user's emotions and provide personalized support based on them. Specifically, the program configuration and processing of this system are described in detail below.

[0540] System Configuration

[0541] hardware

[0542] Server: Receives requests, searches databases, generates content, and analyzes progress and sentiment data.

[0543] Devices (smartphones, tablets, etc.): These are the devices used by users to display content, record progress, and collect sentiment data.

[0544] software

[0545] Flask (Web server framework): Handles requests and provides APIs.

[0546] EmotionRecognizer (emotion recognition module): Analysis of emotion data (e.g., OpenCV, DeepFace)

[0547] ContentGenerator (Content Generation Module): Automatic generation of training content (e.g., AI generation model, GPT-3)

[0548] FeedbackGenerator (Feedback Generation Module): Generates feedback based on progress data.

[0549] Database: Data storage and retrieval (e.g., Firebase, SQLite)

[0550] Program Processing Description

[0551] 1. Receiving requests and generating content

[0552] The server receives learning topic requests from users, searches the database to retrieve relevant information, and then automatically generates training content based on that information and provides it to the user's device. For example, if a user requests "Basic Data Analysis," the server will collect relevant materials and use them to generate tutorials and practice problems.

[0553] 2. Monitoring learning progress and providing feedback

[0554] As a user progresses through the learning process on their device, the device records progress data in real time and sends it to the server. The server analyzes the progress data and generates and provides feedback and additional materials tailored to the user's progress. For example, if a user gets stuck on a practice problem, additional explanatory materials are provided.

[0555] 3. Emotion Recognition and Personalized Support

[0556] The device uses sensors and cameras to collect user emotional data (facial expressions and tone of voice) and sends it to a server. The server analyzes the emotional data and generates feedback to improve the user's learning experience. For example, if the user is feeling stressed, it might suggest, "Would you like to review this problem explanation again?"

[0557] 4. Generate management reports

[0558] The server integrates and analyzes all users' learning and emotional data, and generates and provides reports for administrators based on the analysis results. This allows administrators to evaluate the overall quality of training and take steps to improve it. For example, the report visualizes popular courses and points where many users stumble.

[0559] Example of a prompt

[0560] "Please generate detailed content on introductory data analysis."

[0561] This allows users to have a personalized learning experience, receive effective learning support, and enable administrators to implement appropriate data-driven improvements.

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] User submits a request

[0565] The user requests a specific learning topic using their device. For example, they might select "Basic Data Analysis" and submit the request. In this step, the input is the user's request, and the output is the request data being sent to the server. The server then receives this request data.

[0566] Step 2:

[0567] The server searches the database and retrieves the information.

[0568] Based on the request data received by the server, it searches the database and retrieves relevant information (e.g., learning materials, tutorials, practice problems). The input to this step is the request data, and the output is the retrieved information. The server extracts the appropriate data from the database and integrates it.

[0569] Step 3:

[0570] Automatic generation of training content

[0571] The server uses the acquired information to automatically generate training content using a generative AI model (e.g., GPT-3). In this step, the input is the acquired information, and the output is the generated training content. The server inputs a prompt (e.g., "Please generate detailed content on basic data analysis.") to the generative AI model and generates the content.

[0572] Step 4:

[0573] Content provision

[0574] The server generates training content and provides it to the user's terminal. The input to this step is the generated training content, and the output is that the content is sent to the user's terminal. The terminal displays the received content to the user.

[0575] Step 5:

[0576] Monitoring learning progress

[0577] As a user progresses through the learning process using the device, the device monitors the user's learning progress in real time and sends that data to the server. In this step, the input is the learning progress data, and the output is the transmission of that data to the server. The device records the learning log and transfers it to the server.

[0578] Step 6:

[0579] Providing progress-based feedback

[0580] The server receives and analyzes progress data. It generates feedback and additional materials based on the progress and sends them to the terminal. The input for this step is progress data, and the output is the generated feedback. The server uses the FeedbackGenerator module to generate appropriate feedback and send it to the terminal.

[0581] Step 7:

[0582] Recognition of emotional data

[0583] The device collects user emotional data (facial expressions, tone of voice) using sensors and cameras and sends this data to a server. In this step, the input is emotional data, and the output is the transmission of that data to the server. The device captures emotional data in real time and transfers it to the server.

[0584] Step 8:

[0585] Analysis and feedback of emotional data

[0586] The server receives and analyzes emotion data. It generates personalized feedback based on the emotion and sends it to the terminal. The input for this step is emotion data, and the output is the generated feedback. The server uses the EmotionRecognizer module to analyze the emotion data and the FeedbackGenerator module to generate the feedback.

[0587] Step 9:

[0588] Generating management reports

[0589] The server integrates all users' learning and sentiment data and generates and provides monthly and weekly reports for administrators. In this step, the input is the integrated data, and the output is the generated management report. The server aggregates all the data, creates a report based on the analysis results, and sends it to the administrator.

[0590] Through the processing steps described above, the present invention personalizes the user's learning experience, provides effective learning support, and enables administrators to implement appropriate improvement measures based on data.

[0591] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0592] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0593] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0594] [Second Embodiment]

[0595] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0596] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0597] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0598] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0599] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0600] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0601] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0602] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0603] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0604] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0605] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0606] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0607] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. The system's configuration and operation methods are described in detail below, including specific examples.

[0608] Content generation module

[0609] Program processing

[0610] As part of this system, it receives requests from users and automatically generates training content. This significantly reduces the time and effort required to develop training materials.

[0611] The user sends a request via their device stating, "I want to create an introductory course on data analysis."

[0612] The server receives this request and searches for relevant information in the database.

[0613] The server uses artificial intelligence to automatically generate the content for the "Introduction to Data Analysis" course based on the search results.

[0614] The server sends the generated content to the user's terminal and displays it to the user.

[0615] For example, if the request is for a beginner's programming course, complete training content including beginner-level materials and practice problems will be automatically generated.

[0616] Learning support module

[0617] Program processing

[0618] The system monitors users' learning progress in real time and provides appropriate feedback and additional materials based on their progress. This maximizes the learning efficiency of each user.

[0619] The user begins learning from the provided content. For example, they might view slides and solve problems.

[0620] The device records the user's learning activities and sends that data to the server.

[0621] The server analyzes the user's progress data and, if the user encounters a particular problem, provides relevant explanatory videos or additional materials.

[0622] The server sends the feedback it provides to the user's device and displays it to the user.

[0623] For example, if a user is unable to solve a particular problem in an introductory data analysis course, additional learning materials and explanatory documents related to that problem will be automatically provided.

[0624] Management module

[0625] Program processing

[0626] By integrating learning data from all users and analyzing their progress, the system automatically generates and provides reports to administrators. This allows administrators to efficiently understand learning progress and take appropriate action.

[0627] The server periodically aggregates learning data from all users and analyzes their progress.

[0628] The server generates a report for administrators based on the analysis results.

[0629] The server sends the generated report to the administrator's terminal for them to view.

[0630] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[0631] Question and Answer

[0632] Program processing

[0633] We respond quickly to user questions and provide appropriate answers. This makes it easier for users to resolve their questions during their learning process.

[0634] A user submits the question, "What is a correlation coefficient?"

[0635] The server receives a question and searches the database for the appropriate answer.

[0636] The server sends the found answer to the user's device and displays it to the user.

[0637] In this way, the entire system works in coordination, enabling efficient generation of training content, learning support, and progress management. This invention offers significant benefits to companies and educational institutions, resulting in cost reductions and a reduction in front-end workload.

[0638] The following describes the processing flow.

[0639] Content generation module

[0640] Step 1:

[0641] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[0642] Step 2:

[0643] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[0644] Step 3:

[0645] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[0646] Step 4:

[0647] Based on the data acquired by the server, artificial intelligence is used to automatically generate course content (slides, practice problems, etc.).

[0648] Step 5:

[0649] The server sends the generated course content to the user's terminal.

[0650] Step 6:

[0651] The terminal displays the course content it received to the user.

[0652] Learning support module

[0653] Step 1:

[0654] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[0655] Step 2:

[0656] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[0657] Step 3:

[0658] The device sends the recorded data to the server.

[0659] Step 4:

[0660] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[0661] Step 5:

[0662] The server generates appropriate feedback and additional learning materials based on the analysis results.

[0663] Step 6:

[0664] The server sends generated feedback and additional materials to the user's device.

[0665] Step 7:

[0666] The device displays feedback and additional information received by the user.

[0667] Question and Answer

[0668] Step 1:

[0669] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[0670] Step 2:

[0671] The server receives a question from the user and analyzes the content of the question.

[0672] Step 3:

[0673] The server searches the database for the appropriate answer to the question.

[0674] Step 4:

[0675] The server sends the answer it finds to the user's device.

[0676] Step 5:

[0677] The device displays the received response to the user.

[0678] Management module

[0679] Step 1:

[0680] The server periodically collects and integrates learning data from all users.

[0681] Step 2:

[0682] The server analyzes the integrated data to identify learning trends and problems.

[0683] Step 3:

[0684] The server generates a report for administrators based on the analysis results.

[0685] Step 4:

[0686] The server sends the generated report to the administrator's terminal.

[0687] Step 5:

[0688] The terminal displays the received reports to the administrator.

[0689] These processing steps ensure that the entire system operates efficiently, allowing for smooth development of training content, learning support, and progress management.

[0690] (Example 1)

[0691] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0692] Existing training systems require significant time and effort for developing training content, providing learning support, and managing progress. Furthermore, they struggle to provide feedback tailored to individual user progress, hindering efficient learning. Additionally, they lack mechanisms for integrating and analyzing overall learning data to provide useful insights for administrators. Therefore, a system is needed that centrally manages automated training content generation, learning support, and progress management, enabling efficient and effective training.

[0693] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0694] In this invention, the server includes means for receiving requests from users, means for searching an information storage device and obtaining relevant information, means for automatically generating training content using a generation AI model, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the information storage device and providing them to the user, means for integrating and analyzing learning data from all users, and means for generating and providing reports for administrators based on the analysis results. This enables efficient automatic generation of training content, learning support, and progress management, resulting in overall cost and man-hour reductions.

[0695] A "request" is a user's request for specific training content to be created and provided.

[0696] "Information storage device" is a general term for devices that store information, such as databases and storage media.

[0697] "Related information" refers to materials and data necessary for generating training content, which are retrieved from information storage devices based on user requests.

[0698] "Training content" refers to the entirety of materials, slides, videos, practice exercises, etc., provided for learning and training.

[0699] A "generative AI model" refers to an algorithm or software that uses artificial intelligence to automatically generate training content based on user requests.

[0700] A "device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0701] "Learning progress" refers to the progress a user has made in learning the training content.

[0702] "Feedback" refers to information such as explanations and suggestions for improvement that are provided according to the user's learning progress.

[0703] "Additional materials" refer to supplementary learning materials and information provided to help users gain a deeper understanding of the material they are learning.

[0704] A "question" is an inquiry that a user enters into the system regarding doubts or points of confusion that arise during the learning process.

[0705] An "answer" refers to the explanation or information that the system provides in response to a user's question.

[0706] "Learning data" is a general term for records and data related to a user's learning activities.

[0707] A "report" is a document or report that summarizes the analysis results provided by the server to administrators after analyzing training data.

[0708] An "administrator" is the person or individual responsible for the overall operation of the system and the management of user progress.

[0709] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. This system consists of a content generation module, a learning support module, and a management module, all of which automatically generate training content based on user requests. The details and operation methods of these modules are described below.

[0710] Content generation module

[0711] This module receives requests from users and automatically generates training content based on specified themes. Users send training content creation requests from their terminals. The server receives the request and searches for relevant materials in its information storage. Based on the retrieved information, it automatically generates training content using a generation AI model (e.g., GPT-4). The generated content is sent from the server to the user's terminal and displayed.

[0712] For example, if a user requests to "create an introductory data analysis course," the server will search the database for relevant materials and use a generative AI model to automatically generate the course content for "Introduction to Data Analysis."

[0713] Example of a prompt:

[0714] "Please create training content for an introduction to data analysis."

[0715] "Create a beginner's programming course."

[0716] Learning support module

[0717] This module monitors the user's learning process in real time and provides appropriate feedback and additional materials based on their progress. As the user studies the provided content, the device records their learning activity and sends it to the server. The server analyzes this data and, if the user encounters difficulties with a particular task, provides relevant explanatory videos or additional materials. This allows the user to learn more efficiently.

[0718] For example, if a user is unable to solve a particular problem in the "Introduction to Data Analysis Course," related explanatory videos and additional learning materials will be automatically provided to the user's device.

[0719] Management module

[0720] This module integrates learning data from all users and analyzes their progress to automatically generate and provide reports to administrators. The server periodically aggregates and analyzes learning data submitted by all users. Based on the analysis results, it generates a report for administrators and sends it to their terminals for display.

[0721] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[0722] Question answering function

[0723] This feature provides quick answers to user questions. When a user submits a question, such as "What is a correlation coefficient?", the server receives the question and searches its information storage for the appropriate answer. The server then sends the answer to the user's device for display.

[0724] By working together as a whole, this system enables the efficient generation of training content tailored to user needs, learning support, and progress management. It also offers significant benefits to businesses and educational institutions, resulting in cost and labor savings.

[0725] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0726] Content generation module

[0727] Processing flow and specific actions

[0728] Step 1:

[0729] The user sends a request through their device.

[0730] Input: The user enters a request in the input form, saying "I want to create an introductory data analysis course," and presses the submit button.

[0731] Operation: User input is sent to the server as an HTTP request.

[0732] Output: The server receives the request.

[0733] Step 2:

[0734] The server receives the request.

[0735] Input: User request data (e.g., "I want to create an introductory course on data analysis")

[0736] Operation: The web server receives the request and passes the data to the backend application server.

[0737] Output: The application server parses the request content.

[0738] Step 3:

[0739] The server searches for related documents.

[0740] Input: Parsed request content (e.g., "Introduction to Data Analysis")

[0741] Operation: The server generates an SQL query and performs a search on the information storage device (database).

[0742] Output: Related materials are retrieved as search results.

[0743] Step 4:

[0744] The server generates the training content.

[0745] Input: Acquired related documents

[0746] Operation: The server sends a prompt message (e.g., "Create training content for an introduction to data analysis") to the generated AI model (e.g., GPT-4) and requests content generation.

[0747] Output: Generated training content (text, slides, practice questions, etc.)

[0748] Step 5:

[0749] The server sends the generated content to the user for display.

[0750] Input: Generated training content

[0751] Operation: The server packages the content in JSON format or similar and sends it to the user's terminal as an HTTP response.

[0752] Output: The user's device analyzes and displays the received content.

[0753] Learning support module

[0754] Processing flow and specific actions

[0755] Step 1:

[0756] The user begins the learning activity.

[0757] Input: Training content provided by the user (e.g., slides, practice questions)

[0758] Action: View content and solve problems.

[0759] Output: Learning activity data (which slides were viewed, which problems were solved)

[0760] Step 2:

[0761] The device records learning activity data.

[0762] Input: User's learning activity data

[0763] Operation: Records learning activities in real time to a local database or cache.

[0764] Output: Recorded training data

[0765] Step 3:

[0766] The device sends recorded data to the server.

[0767] Input: Recorded training data

[0768] Operation: At regular intervals, the recorded data is sent to the server in batch processing.

[0769] Output: Transmitted training data

[0770] Step 4:

[0771] The server analyzes the data.

[0772] Input: Submitted training data

[0773] Operation: Receives data and executes an algorithm to analyze learning progress.

[0774] Output: Analysis results (e.g., which issues users are struggling with)

[0775] Step 5:

[0776] The server provides feedback and additional information.

[0777] Input: Analysis results

[0778] Function: Select and provide explanatory videos and additional materials related to the parts where the user is having trouble.

[0779] Output: Feedback and additional materials

[0780] Step 6:

[0781] Display information provided by the server to the user.

[0782] Input: Feedback and additional materials

[0783] Operation: The server sends this information to the user's terminal.

[0784] Output: Displays information received by the user terminal.

[0785] Management module

[0786] Processing flow and specific actions

[0787] Step 1:

[0788] The server aggregates the learning data of all users.

[0789] Input: Training data submitted by each user

[0790] Operation: Aggregates data using databases and big data processing platforms.

[0791] Output: Aggregated training data

[0792] Step 2:

[0793] The server analyzes the data.

[0794] Input: Aggregated training data

[0795] Operation: Executes data analysis algorithms and analyzes the overall learning progress.

[0796] Output: Analysis results (popular courses, frequently occurring stumbling blocks, etc.)

[0797] Step 3:

[0798] The server generates a report for administrators.

[0799] Input: Analysis results

[0800] Function: Generates a report document for administrators based on the analysis results.

[0801] Output: Generated report

[0802] Step 4:

[0803] The server sends the report to the administrator for display.

[0804] Input: Generated report

[0805] Action: Sends the report as an HTTP response to the administrator's terminal.

[0806] Output: The administrator terminal receives the report and displays it on the administration screen.

[0807] Question answering function

[0808] Processing flow and specific actions

[0809] Step 1:

[0810] User submits a question

[0811] Input: Question (Example: "What is a correlation coefficient?")

[0812] Operation: Enter your question in the chat box or form and submit it.

[0813] Output: The server receives the question.

[0814] Step 2:

[0815] The server receives the question.

[0816] Input: Question data

[0817] Function: Analyzes the question and extracts appropriate keywords.

[0818] Output: Analyzed question data

[0819] Step 3:

[0820] The server searches for the appropriate answer.

[0821] Input: Analyzed question data

[0822] Operation: Executes an SQL query to search the information storage device and retrieve relevant answers.

[0823] Output: Related response data

[0824] Step 4:

[0825] The server sends the answer to the user and displays it.

[0826] Input: Response data

[0827] Action: Sends the response as an HTTP response to the user's device.

[0828] Output: The user terminal receives and displays the response.

[0829] (Application Example 1)

[0830] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0831] Traditional training systems were cumbersome in terms of developing training content and managing progress, making efficient learning support difficult. Furthermore, training for virtual store staff, in particular, requires individual feedback and progress management, but achieving this using traditional methods is costly and time-consuming.

[0832] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0833] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data of all users, means for generating and providing reports for administrators based on the analysis results, means for generating training content for virtual store staff, means for recording staff learning activities and monitoring progress, and means for aggregating each staff member's learning data, analyzing progress, and providing feedback. This enables automatic formulation of training content, real-time monitoring of learning progress, provision of individual feedback, and automatic generation of reports for administrators.

[0834] A "user" is someone who will be using this system to receive training.

[0835] "Means for receiving requests" refers to the functionality of devices or software that receive requests from users, such as training topics or questions.

[0836] A "database" is a collection of data that is systematically managed, allowing for efficient searching and retrieval of necessary information.

[0837] "Means for obtaining relevant information" refers to the functions of devices or software that search for and retrieve information from a database based on a request.

[0838] "Means for automatically generating training content" refers to the functions of devices or software that automatically create training materials based on acquired information.

[0839] "Means of providing generated training content to the user's terminal" refers to the functions of devices or software that transmit and display automatically generated training content on the user's device.

[0840] "Means for monitoring user learning progress" refers to the functions of devices and software that track and record a user's learning status in real time.

[0841] "Means of providing feedback and additional materials" refers to the functions of devices and software that provide users with appropriate advice and supplementary materials based on their learning progress.

[0842] "Means for receiving questions" refers to the functions of devices or software that receive questions from users.

[0843] "Means of searching for appropriate answers" refers to the function of devices or software that search and retrieve the best answer to a question from a database.

[0844] "Means for integrating and analyzing learning data from all users" refers to the functions of devices and software that centrally manage and statistically analyze learning data collected from multiple users.

[0845] "Means for generating and providing reports for administrators" refers to the functionality of devices and software that create and provide detailed reports for administrators based on data analysis results.

[0846] "Means for generating training content for virtual store staff" refers to the functions of devices and software that automatically create training materials for staff working in virtual stores.

[0847] "Means for recording staff learning activities and monitoring progress" refers to the functions of devices and software that track staff learning behaviors and record their progress in real time.

[0848] "Means for aggregating learning data, analyzing progress, and providing feedback" refers to the functions of devices and software that collect individual staff members' learning data and provide appropriate advice and supplementary materials based on that progress.

[0849] This invention provides a training system for staff in virtual stores, which can be implemented using the following hardware and software. The system consists of multiple modules that receive requests from users, search a database, generate training content using AI, and monitor learning progress and provide feedback.

[0850] 1. Hardware and software

[0851] Hardware: Servers, user terminals (smartphones, head-mounted displays, etc.)

[0852] Software: Python, Flask (server-side framework), requests (HTTP request library)

[0853] 2. Program Processing

[0854] Receiving a request

[0855] The server receives training topics and questions from virtual store staff. For example, if a staff member wants training on "virtual customer service techniques," that request is sent to the server.

[0856] Database search and information retrieval

[0857] Based on the request, the server searches the database and retrieves relevant information. This is the process of finding the most suitable materials for the training content requested by the staff.

[0858] Automatic generation of training content

[0859] Using the acquired information, the server utilizes an AI model to automatically generate training content. For example, if the training is on data analysis techniques, the generated materials will include basic methods and specific use cases.

[0860] Content provision

[0861] The generated training content is sent from the server to the user's terminal and made available for the user to view. This allows staff to immediately begin learning on their own devices.

[0862] Monitoring and feedback on learning progress

[0863] The device records the user's learning activities (viewing slides and answering questions) and sends this data to the server. The server analyzes this data in real time and provides appropriate feedback and additional learning materials based on the user's progress. For example, if a user gets stuck on a particular problem, they may be provided with explanatory videos or additional materials related to that problem.

[0864] Generating reports for administrators

[0865] The server aggregates learning data from all users and analyzes their progress. Based on the analysis results, it automatically generates and provides reports for administrators. For example, it reports to administrators which topics were most effective in last month's training and which areas many users are struggling with.

[0866] Question and Answer

[0867] A user's question is sent to the server, and relevant information is retrieved from the database. The server generates the best possible answer and provides it to the user. For example, if a staff member asks, "What is the product return policy?", the answer is provided immediately.

[0868] 3. Specific Examples and Examples of Prompt Statements

[0869] For example, if a staff member requests, "Please generate an introductory course on virtual customer service techniques," the server will search for relevant materials and automatically generate training content using an AI model.

[0870] Example of a prompt:

[0871] "Please create an introductory course on virtual customer service techniques for virtual store staff."

[0872] In this way, this system enables efficient training of staff working in virtual stores, resulting in significant reductions in costs and man-hours.

[0873] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0874] Step 1:

[0875] A user sends a request to the system. For example, a user who is a staff member at a virtual store requests "training on virtual customer service techniques." This request is sent from the user's terminal (such as a smartphone or head-mounted display) to the server. The input is "virtual customer service techniques," and the output is the request information for processing it.

[0876] Step 2:

[0877] The server searches the database based on the received request. It searches the database for and retrieves information related to the requested topic (e.g., "Virtual Customer Service Techniques"). The input is the request information, and the output is a list of related training materials. In this process, the server generates a search query and queries the database using SQL queries or similar methods.

[0878] Step 3:

[0879] Based on the acquired information, the server automatically generates training content using a generative AI model. For example, it analyzes acquired materials and generates appropriate slides, videos, and explanatory text. The input is a list of relevant training materials, and the output is automatically generated training content. Specifically, the AI ​​model generates text data using natural language processing technology and attaches related media files.

[0880] Step 4:

[0881] The server provides the generated training content to the user's terminal. The terminal receives this content and displays it to the user. For example, generated slides or videos are displayed on the user's smartphone or head-mounted display. The input is the automatically generated training content, and the output is the content displayed on the user's terminal.

[0882] Step 5:

[0883] When a user studies training content, the device records their learning activity. Progress data, such as the user's slide viewing time and question answer history, is recorded and sent to the server. The input is the user's learning activity, and the output is progress data. Specifically, a learning log is created and periodically sent to the server.

[0884] Step 6:

[0885] The server monitors and analyzes received learning progress data in real time. Based on the learning progress, if the user is struggling at a specific point, it generates and provides additional learning materials or explanatory videos. The input is progress data, and the output is feedback and additional materials. Machine learning algorithms are used in the analysis process to identify the user's weaknesses.

[0886] Step 7:

[0887] When a user submits a question, the server receives it. The server searches the database, generates an appropriate answer, and provides it to the user. For example, if a user asks, "What is the product return policy?", the server searches the database for the relevant policy information and generates an answer. The input is the user's question, and the output is the appropriate answer. In this process, natural language processing techniques are used to analyze the question and find the most relevant answer.

[0888] Step 8:

[0889] The server integrates learning data from all users and periodically analyzes their progress. Based on the analysis results, it automatically generates and provides detailed reports to administrators. For example, when an administrator requests a monthly report, the server aggregates the learning status of all users and creates a report based on the analysis results. The input is the integrated learning data, and the output is a report for administrators. Statistical analysis and data visualization techniques are used to generate the report.

[0890] This series of processing steps allows virtual store staff to receive training efficiently, and enables managers to reliably track the progress of their training.

[0891] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0892] This invention provides a more personalized learning experience by combining training content development, learning support, and progress management with user emotion recognition capabilities. The system's configuration and operation method are described in detail below, including specific examples.

[0893] Content generation module

[0894] Program processing

[0895] When a user requests training content, the content is automatically generated based on that topic. This saves time and effort.

[0896] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the terminal to the server.

[0897] The server receives the request and extracts keywords related to "Introduction to Data Analysis".

[0898] The server searches the database and retrieves relevant information and content, such as basic concepts and instructions on how to use tools.

[0899] The server automatically generates course content (slides, practice problems) based on information acquired using artificial intelligence.

[0900] The server sends the generated content to the user's device, and the device displays it to the user.

[0901] Learning support module

[0902] Program processing

[0903] It monitors the user's learning progress in real time and provides feedback and additional materials according to their progress.

[0904] The user begins learning the course content on their device. For example, they view slides and solve practice problems.

[0905] The device records the user's learning activity in real time and sends that data to the server.

[0906] The server receives and analyzes the user's learning progress data. It detects issues such as getting stuck on specific problems or taking an excessive amount of time to learn.

[0907] The server generates appropriate feedback and additional learning materials and sends them to the user's device.

[0908] The device displays feedback and additional information to the user.

[0909] Emotion recognition module

[0910] Program processing

[0911] It recognizes user emotions and provides feedback and support to further improve the learning experience.

[0912] The device will be equipped with sensors and cameras to recognize the user's emotions.

[0913] The device sends the user's facial expressions, voice tone, and other information to the emotion engine to acquire emotional data.

[0914] The server receives emotion data in real time and analyzes it along with learning progress data.

[0915] The server generates feedback based on emotional data, such as when the user is stressed or has a low level of understanding.

[0916] The server generates feedback and sends it to the user's device, which then displays it to the user.

[0917] For example, if a user shows signs of frustration or stress while working on a practice problem, the emotion engine recognizes that emotion and the server makes a suggestion such as, "Would you like to review the explanation for this problem again?"

[0918] Management module

[0919] Program processing

[0920] Integrates learning and sentiment data from all users and generates reports for administrators.

[0921] The server periodically collects and integrates learning data and sentiment data from all users.

[0922] The server analyzes this data to identify learning trends and problems.

[0923] The server generates a report for administrators based on the analysis results.

[0924] The server generates a report which is sent to the administrator's terminal, and the terminal displays it to the administrator.

[0925] For example, a monthly report could be submitted to administrators detailing which courses are popular, where many users are struggling, and their emotional reactions to these issues. Based on this information, administrators can review and improve the course content.

[0926] In this way, this system, which incorporates an emotion engine, improves the user's learning experience, streamlines the formulation of training content, learning support, and progress management, and achieves overall cost and man-hour reductions.

[0927] The following describes the processing flow.

[0928] Content generation module

[0929] Step 1:

[0930] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[0931] Step 2:

[0932] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[0933] Step 3:

[0934] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[0935] Step 4:

[0936] Based on the data acquired by the server, artificial intelligence is used to automatically generate the course content (slides, practice problems, etc.) for the "Introduction to Data Analysis" course.

[0937] Step 5:

[0938] The server saves the generated course content and then sends it to the user's terminal.

[0939] Step 6:

[0940] The terminal displays the course content it received to the user.

[0941] Learning support module

[0942] Step 1:

[0943] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[0944] Step 2:

[0945] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[0946] Step 3:

[0947] The device periodically sends the data it has recorded to the server.

[0948] Step 4:

[0949] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[0950] Step 5:

[0951] The server generates appropriate feedback and additional learning materials based on the analysis results.

[0952] Step 6:

[0953] The server sends generated feedback and additional materials to the user's device.

[0954] Step 7:

[0955] The device displays feedback and additional information received by the user.

[0956] Emotion recognition module

[0957] Step 1:

[0958] The device will be equipped with sensors and cameras to recognize the user's emotions.

[0959] Step 2:

[0960] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the emotion engine.

[0961] Step 3:

[0962] The server receives emotion data in real time and analyzes it along with learning progress data.

[0963] Step 4:

[0964] Based on the analysis results, the server generates feedback that corresponds to the user's emotions. For example, if the user is feeling stressed, it might generate a suggestion such as, "Would you like to review this problem explanation again?"

[0965] Step 5:

[0966] The server sends the generated feedback to the user's device.

[0967] Step 6:

[0968] The device displays the feedback it has received to the user.

[0969] Question answering module

[0970] Step 1:

[0971] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[0972] Step 2:

[0973] The server receives a question from the user and analyzes the content of the question.

[0974] Step 3:

[0975] The server searches the database for the appropriate answer to the question.

[0976] Step 4:

[0977] The server sends the answer it finds to the user's device.

[0978] Step 5:

[0979] The device displays the received response to the user.

[0980] Management module

[0981] Step 1:

[0982] The server periodically collects and integrates learning and sentiment data from all users.

[0983] Step 2:

[0984] The server analyzes the integrated data to identify learning trends and problems.

[0985] Step 3:

[0986] The server generates a report for administrators based on the analysis results.

[0987] Step 4:

[0988] The server sends the generated report to the administrator's terminal.

[0989] Step 5:

[0990] The terminal displays the received reports to the administrator.

[0991] In this way, each module works together to realize a personalized learning experience that includes everything from training content development and learning support to progress management and even emotion recognition.

[0992] (Example 2)

[0993] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0994] Traditional learning systems struggled to provide personalized feedback and additional materials tailored to each user's learning progress, and they failed to consider users' emotional states. Therefore, learning efficiency and effectiveness could not be adequately guaranteed. Furthermore, it was difficult for administrators to grasp the overall learning situation and take appropriate measures.

[0995] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's display device, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for recognizing the user's emotions and collecting emotional data, means for analyzing the emotional data and reflecting it in the feedback, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing a report for administrators based on the analysis results. This makes it possible to provide personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. In addition, administrators can accurately grasp the overall learning situation and take appropriate measures.

[0996] "Means for receiving user requests" refers to a device or program that acquires request information when a user requests specific training content.

[0997] "Means for searching a database and retrieving relevant information" refers to a device or program that searches a database for relevant information based on a requested topic and retrieves the necessary data.

[0998] "Means for automatically generating training content using acquired information" refers to a device or program that automatically generates training content such as slides and practice problems using artificial intelligence, based on information acquired from a database.

[0999] "Means for providing generated training content to a user's display device" refers to a device or program that transmits automatically generated training content to a terminal used by the user and displays it.

[1000] "Means for monitoring user learning progress" refers to a device or program that records and monitors the user's progress in real time as they progress through the learning process.

[1001] "Means for providing feedback and additional materials based on learning progress" refers to a device or program that generates and provides appropriate feedback and additional learning materials based on the user's learning progress data.

[1002] "Means for recognizing user emotions and collecting emotional data" refers to a device or program that recognizes emotions from a user's facial expressions, tone of voice, etc., and collects that data.

[1003] "Means for analyzing emotional data and reflecting it in feedback" refers to a device or program that analyzes collected emotional data, adjusts the feedback content based on the results, and provides it to the user.

[1004] "Means for integrating and analyzing learning data and sentiment data from all users" refers to a device or program that integrates learning data and sentiment data collected from all users and analyzes that data.

[1005] "Means for generating and providing reports for administrators based on analysis results" refers to a device or program that generates and provides reports in a format usable by administrators, based on the results of integrated data analysis.

[1006] This invention provides a system that offers a more personalized learning experience by combining training content development, learning support, and progress management with a user emotion recognition function. The system consists of three main elements: a server, a terminal, and a user.

[1007] First, when a user requests training content, they do so via their device. When the user clicks the "Request creation of an introductory data analysis course" button, the device sends this request to the server. After receiving the request, the server searches the database based on the requested topic and retrieves relevant information. The hardware used includes a server and a database server, while the software used includes a database management system (e.g., MySQL) and a text analysis tool (e.g., NLTK).

[1008] Based on the acquired information, the server automatically generates training content (slides, practice questions, etc.) using a generative AI model (e.g., GPT-3). The server then sends the generated content to the user's device, which displays it to the user.

[1009] For example, if a user clicks the "Request creation of an introductory data analysis course" button, materials related to "Introduction to Data Analysis" are retrieved from the database based on the request, and slides and practice problems are automatically generated using a generative AI model. The generated content is then displayed on the user's device.

[1010] Example prompt: "Automatically generate a data analysis course for beginners."

[1011] Next, as the user progresses through the learning process, the device records the user's actions in real time and sends this data to the server. The server analyzes the user's learning progress data to detect issues such as getting stuck on specific problems or taking too long to learn. Data analysis tools (e.g., Pandas) are used for this analysis. The server generates appropriate feedback and additional learning materials and sends them to the user's device. The device then displays the generated feedback and additional materials to the user.

[1012] For example, if a user encounters a problem while studying the "Introduction to Data Analysis Course," the server analyzes the information, generates additional explanations and reference materials, and sends them to the user's device. The user then relearns the material based on the provided resources.

[1013] Example prompt: "Automatically provide additional explanatory materials to users who are falling behind in their learning progress."

[1014] Furthermore, it also has a function to recognize the user's emotions. The device uses sensors and a camera to acquire the user's emotional data (facial expressions, tone of voice, etc.) and sends it to the server. The server analyzes the emotional data and provides feedback based on whether the user is feeling stressed or has a low level of understanding. Emotion analysis tools (e.g., OpenCV, TensorFlow) are used for this analysis.

[1015] For example, if a user shows signs of dissatisfaction or stress while working on a practice problem, the server generates feedback such as "Would you like to review this problem explanation again?" and sends it to the user's device.

[1016] Example prompt: "If the user is experiencing stress, please provide an appropriate feedback message."

[1017] Finally, the server integrates all users' learning and sentiment data and periodically generates reports for administrators. The server analyzes this data to identify learning trends and problems. Data visualization tools (e.g., Tableau) are used for this analysis. The generated reports are sent to the administrator's terminal for their viewing.

[1018] As a concrete example, administrators can review monthly reports to see which courses are popular, where many users are struggling, and what their emotional reactions were to these issues, and then revise and improve the course content accordingly.

[1019] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[1020] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1021] Step 1: Sending and receiving requests

[1022] The user clicks the "Request creation of an introductory data analysis course" button. The input is the user's click action and the request content, and the output is the HTTP request generated by the terminal.

[1023] The terminal sends this request to the server. The input is the request information from the user, and the output is the request data sent to the server.

[1024] Specific actions:

[1025] 1. The user performs the action of clicking a button.

[1026] 2. The device captures the click event, generates an HTTP request, and sends it to the server.

[1027] Step 2: Keyword extraction and database search

[1028] The server receives the request and extracts keywords related to "Introduction to Data Analysis". The input is the user's request data, and the output is the extracted keywords.

[1029] The server searches the database and retrieves relevant documents and content. The input is extracted keywords, and the output is related informational data.

[1030] Specific actions:

[1031] 1. The server parses the request data and performs natural language processing to extract keywords.

[1032] 2. The server generates database queries and searches for and retrieves relevant information.

[1033] Step 3: Content Generation

[1034] The server automatically generates training content based on the information it acquires. The input is the acquired information data, and the output is the generated training content.

[1035] The server uses a generated AI model (e.g., GPT-3) to create slides and practice problems.

[1036] Specific actions:

[1037] 1. The server inputs the acquired information into the model and generates training content.

[1038] 2. The server converts the generated slides and practice questions into a data format.

[1039] Step 4: Content Delivery

[1040] The server provides the generated training content to the user's terminal. The input is the generated training content, and the output is the content data sent to the terminal.

[1041] The device displays the content it has received to the user.

[1042] Specific actions:

[1043] 1. The server sends the content data to the terminal.

[1044] 2. The content received by the device is displayed on the user interface.

[1045] Step 5: Monitoring Learning Progress

[1046] The user begins learning the course content on their device. The input is the user's learning activity, and the output is progress data.

[1047] The device records the user's learning activity in real time and sends that data to the server.

[1048] Specific actions:

[1049] 1. The user views the slides and performs the actions of solving the practice problems.

[1050] 2. The terminal records user operation events and sends them to the server as progress data.

[1051] Step 6: Analyze progress and provide feedback

[1052] The server receives user learning progress data and analyzes it to identify issues such as difficulty with specific problems or excessive learning time. The input is progress data, and the output is the analysis results.

[1053] The server generates appropriate feedback and additional learning materials and sends them to the user's terminal. The input is the analysis result, and the output is the feedback data.

[1054] Specific actions:

[1055] 1. The server analyzes the progress data using an analysis tool.

[1056] 2. The server uses the generated AI model to create feedback and additional materials, and sends them to the terminal.

[1057] Step 7: View feedback and additional materials

[1058] The device displays feedback and additional materials to the user. The input is feedback data, and the output is the feedback and materials reflected in the user interface.

[1059] Specific actions:

[1060] 1. Receive feedback and additional information for the device to display.

[1061] 2. The device displays feedback and information on the user interface.

[1062] Step 8: Acquisition and transmission of emotional data

[1063] The device will be equipped with sensors and cameras to recognize the user's emotions. The input will be the user's facial expressions and tone of voice, and the output will be emotion data.

[1064] The device sends emotional data to the server in real time.

[1065] Specific actions:

[1066] 1. The device uses its camera and microphone to capture user emotion data.

[1067] 2. The device sends the acquired emotion data to the server.

[1068] Step 9: Analysis of emotional data and provision of feedback

[1069] The server receives and analyzes emotion data in real time. The input is emotion data, and the output is the analysis result.

[1070] The server generates feedback based on emotional data and sends it to the user's terminal. The input is the analysis result, and the output is emotion-based feedback data.

[1071] Specific actions:

[1072] 1. The server analyzes the emotional data using an emotional analysis tool.

[1073] 2. The server generates feedback based on the analysis results and sends it to the user's terminal.

[1074] Step 10: Data integration and report generation for all users

[1075] The server periodically collects and integrates learning and sentiment data from all users. The input is data from all users, and the output is the integrated data.

[1076] The server analyzes the integrated data and generates reports for administrators. The input is the integrated data, and the output is the report data.

[1077] Specific actions:

[1078] 1. The server periodically collects data from all users and integrates it into the database.

[1079] 2. The server generates a report using a data visualization tool.

[1080] Step 11: Provide reports for administrators

[1081] The server generates a report which is then provided to the administrator's terminal, and the terminal displays it to the administrator. The input is the report data, and the output is the report reflected on the administrator's display device.

[1082] Specific actions:

[1083] 1. The server sends the report data to the administrator's terminal.

[1084] 2. The terminal displays the report on the administrator's user interface.

[1085] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[1086] (Application Example 2)

[1087] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1088] Traditional learning support systems were limited to monitoring users' learning progress and providing feedback and additional materials, lacking personalized support tailored to individual users' emotions and levels of understanding. As a result, they failed to address the stress and frustration users experienced during learning, potentially leading to decreased learning effectiveness. Furthermore, administrators lacked the information necessary to effectively integrate and analyze all users' learning data and implement appropriate countermeasures. This made it difficult to make appropriate improvements to enhance the quality of training.

[1089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1090] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for using sensors and cameras to recognize the user's emotions, means for analyzing the acquired emotional data and reflecting it in the feedback, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing reports for administrators based on the analysis results. This makes it possible to provide personalized support that responds to the user's emotions, and an improvement in learning effectiveness can be expected. In addition, detailed analysis based on aggregated data allows administrators to take appropriate improvement measures, thereby improving the overall quality of the training.

[1091] "Means for receiving user requests" refers to the functionality of a device or software that has an interface that allows users to request training content on a specific theme or topic.

[1092] "Means for searching a database and retrieving relevant information" refers to the function of a device or software that searches a database for relevant information based on a requested topic and retrieves that information.

[1093] "Means for automatically generating training content" refers to the function of a device or software that automatically generates the content necessary for training based on acquired information.

[1094] "Means of providing training content to a user's terminal" refers to the function of a device or software that transmits generated training content to a terminal used by the user, enabling the user to utilize that content.

[1095] "Means for monitoring user learning progress" refers to the function of a device or software that tracks a user's learning activities in real time and records their progress.

[1096] "Means of providing feedback and additional materials based on learning progress" refers to the function of a device or software that automatically generates and provides appropriate feedback and additional learning materials according to the user's learning progress.

[1097] "Means of using sensors or cameras to recognize user emotions" refers to the function of a device or software that includes sensors or cameras to detect the user's facial expressions and tone of voice and analyze their emotions.

[1098] "Means for analyzing acquired emotional data and reflecting it in feedback" refers to the function of a device or software that analyzes detected emotional data and generates feedback based on that analysis.

[1099] "Means of receiving questions from users, searching for appropriate answers in a database, and providing them to users" refers to the function of a device or software that receives a question entered by a user, searches for an answer to that question in a database, and returns it to the user.

[1100] "Means for integrating and analyzing learning data and emotional data of all users" refers to the function of a device or software that collects, integrates, and analyzes learning progress data and emotional data of all users.

[1101] "Means of generating and providing reports for administrators based on analysis results" refers to the function of a device or software that automatically generates reports for administrators based on the results of integrated data analysis and provides them for administrators to use.

[1102] This invention is a learning support system that helps users request learning content on specific themes and progress through their learning while receiving progress management and individual feedback. The system can recognize the user's emotions and provide personalized support based on them. Specifically, the program configuration and processing of this system are described in detail below.

[1103] System Configuration

[1104] hardware

[1105] Server: Receives requests, searches databases, generates content, and analyzes progress and sentiment data.

[1106] Devices (smartphones, tablets, etc.): These are the devices used by users to display content, record progress, and collect sentiment data.

[1107] software

[1108] Flask (Web server framework): Handles requests and provides APIs.

[1109] EmotionRecognizer (emotion recognition module): Analysis of emotion data (e.g., OpenCV, DeepFace)

[1110] ContentGenerator (Content Generation Module): Automatic generation of training content (e.g., AI generation model, GPT-3)

[1111] FeedbackGenerator (Feedback Generation Module): Generates feedback based on progress data.

[1112] Database: Data storage and retrieval (e.g., Firebase, SQLite)

[1113] Program Processing Description

[1114] 1. Receiving requests and generating content

[1115] The server receives learning topic requests from users, searches the database to retrieve relevant information, and then automatically generates training content based on that information and provides it to the user's device. For example, if a user requests "Basic Data Analysis," the server will collect relevant materials and use them to generate tutorials and practice problems.

[1116] 2. Monitoring learning progress and providing feedback

[1117] As a user progresses through the learning process on their device, the device records progress data in real time and sends it to the server. The server analyzes the progress data and generates and provides feedback and additional materials tailored to the user's progress. For example, if a user gets stuck on a practice problem, additional explanatory materials are provided.

[1118] 3. Emotion Recognition and Personalized Support

[1119] The device uses sensors and cameras to collect user emotional data (facial expressions and tone of voice) and sends it to a server. The server analyzes the emotional data and generates feedback to improve the user's learning experience. For example, if the user is feeling stressed, it might suggest, "Would you like to review this problem explanation again?"

[1120] 4. Generate management reports

[1121] The server integrates and analyzes all users' learning and emotional data, and generates and provides reports for administrators based on the analysis results. This allows administrators to evaluate the overall quality of training and take steps to improve it. For example, the report visualizes popular courses and points where many users stumble.

[1122] Example of a prompt

[1123] "Please generate detailed content on introductory data analysis."

[1124] This allows users to have a personalized learning experience, receive effective learning support, and enable administrators to implement appropriate data-driven improvements.

[1125] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1126] Step 1:

[1127] User submits a request

[1128] The user requests a specific learning topic using their device. For example, they might select "Basic Data Analysis" and submit the request. In this step, the input is the user's request, and the output is the request data being sent to the server. The server then receives this request data.

[1129] Step 2:

[1130] The server searches the database and retrieves the information.

[1131] Based on the request data received by the server, it searches the database and retrieves relevant information (e.g., learning materials, tutorials, practice problems). The input to this step is the request data, and the output is the retrieved information. The server extracts the appropriate data from the database and integrates it.

[1132] Step 3:

[1133] Automatic generation of training content

[1134] The server uses the acquired information to automatically generate training content using a generative AI model (e.g., GPT-3). In this step, the input is the acquired information, and the output is the generated training content. The server inputs a prompt (e.g., "Please generate detailed content on basic data analysis.") to the generative AI model and generates the content.

[1135] Step 4:

[1136] Content provision

[1137] The server generates training content and provides it to the user's terminal. The input to this step is the generated training content, and the output is that the content is sent to the user's terminal. The terminal displays the received content to the user.

[1138] Step 5:

[1139] Monitoring learning progress

[1140] As a user progresses through the learning process using the device, the device monitors the user's learning progress in real time and sends that data to the server. In this step, the input is the learning progress data, and the output is the transmission of that data to the server. The device records the learning log and transfers it to the server.

[1141] Step 6:

[1142] Providing progress-based feedback

[1143] The server receives and analyzes progress data. It generates feedback and additional materials based on the progress and sends them to the terminal. The input for this step is progress data, and the output is the generated feedback. The server uses the FeedbackGenerator module to generate appropriate feedback and send it to the terminal.

[1144] Step 7:

[1145] Recognition of emotional data

[1146] The device collects user emotional data (facial expressions, tone of voice) using sensors and cameras and sends this data to a server. In this step, the input is emotional data, and the output is the transmission of that data to the server. The device captures emotional data in real time and transfers it to the server.

[1147] Step 8:

[1148] Analysis and feedback of emotional data

[1149] The server receives and analyzes emotion data. It generates personalized feedback based on the emotion and sends it to the terminal. The input for this step is emotion data, and the output is the generated feedback. The server uses the EmotionRecognizer module to analyze the emotion data and the FeedbackGenerator module to generate the feedback.

[1150] Step 9:

[1151] Generating management reports

[1152] The server integrates all users' learning and sentiment data and generates and provides monthly and weekly reports for administrators. In this step, the input is the integrated data, and the output is the generated management report. The server aggregates all the data, creates a report based on the analysis results, and sends it to the administrator.

[1153] Through the processing steps described above, the present invention personalizes the user's learning experience, provides effective learning support, and enables administrators to implement appropriate improvement measures based on data.

[1154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1155] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1156] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1157] [Third Embodiment]

[1158] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1162] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1166] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1167] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1168] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1169] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1170] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. The system's configuration and operation methods are described in detail below, including specific examples.

[1171] Content generation module

[1172] Program processing

[1173] As part of this system, it receives requests from users and automatically generates training content. This significantly reduces the time and effort required to develop training materials.

[1174] The user sends a request via their device stating, "I want to create an introductory course on data analysis."

[1175] The server receives this request and searches for relevant information in the database.

[1176] The server uses artificial intelligence to automatically generate the content for the "Introduction to Data Analysis" course based on the search results.

[1177] The server sends the generated content to the user's terminal and displays it to the user.

[1178] For example, if the request is for a beginner's programming course, complete training content including beginner-level materials and practice problems will be automatically generated.

[1179] Learning support module

[1180] Program processing

[1181] The system monitors users' learning progress in real time and provides appropriate feedback and additional materials based on their progress. This maximizes the learning efficiency of each user.

[1182] The user begins learning from the provided content. For example, they might view slides and solve problems.

[1183] The device records the user's learning activities and sends that data to the server.

[1184] The server analyzes the user's progress data and, if the user encounters a particular problem, provides relevant explanatory videos or additional materials.

[1185] The server sends the feedback it provides to the user's device and displays it to the user.

[1186] For example, if a user is unable to solve a particular problem in an introductory data analysis course, additional learning materials and explanatory documents related to that problem will be automatically provided.

[1187] Management module

[1188] Program processing

[1189] By integrating learning data from all users and analyzing their progress, the system automatically generates and provides reports to administrators. This allows administrators to efficiently understand learning progress and take appropriate action.

[1190] The server periodically aggregates learning data from all users and analyzes their progress.

[1191] The server generates a report for administrators based on the analysis results.

[1192] The server sends the generated report to the administrator's terminal for them to view.

[1193] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[1194] Question and Answer

[1195] Program processing

[1196] We respond quickly to user questions and provide appropriate answers. This makes it easier for users to resolve their questions during their learning process.

[1197] A user submits the question, "What is a correlation coefficient?"

[1198] The server receives a question and searches the database for the appropriate answer.

[1199] The server sends the found answer to the user's device and displays it to the user.

[1200] In this way, the entire system works in coordination, enabling efficient generation of training content, learning support, and progress management. This invention offers significant benefits to companies and educational institutions, resulting in cost reductions and a reduction in front-end workload.

[1201] The following describes the processing flow.

[1202] Content generation module

[1203] Step 1:

[1204] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[1205] Step 2:

[1206] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[1207] Step 3:

[1208] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[1209] Step 4:

[1210] Based on the data acquired by the server, artificial intelligence is used to automatically generate course content (slides, practice problems, etc.).

[1211] Step 5:

[1212] The server sends the generated course content to the user's terminal.

[1213] Step 6:

[1214] The terminal displays the course content it received to the user.

[1215] Learning support module

[1216] Step 1:

[1217] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[1218] Step 2:

[1219] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[1220] Step 3:

[1221] The device sends the recorded data to the server.

[1222] Step 4:

[1223] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[1224] Step 5:

[1225] The server generates appropriate feedback and additional learning materials based on the analysis results.

[1226] Step 6:

[1227] The server sends generated feedback and additional materials to the user's device.

[1228] Step 7:

[1229] The device displays feedback and additional information received by the user.

[1230] Question and Answer

[1231] Step 1:

[1232] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[1233] Step 2:

[1234] The server receives a question from the user and analyzes the content of the question.

[1235] Step 3:

[1236] The server searches the database for the appropriate answer to the question.

[1237] Step 4:

[1238] The server sends the answer it finds to the user's device.

[1239] Step 5:

[1240] The device displays the received response to the user.

[1241] Management module

[1242] Step 1:

[1243] The server periodically collects and integrates the learning data of all users.

[1244] Step 2:

[1245] The server analyzes the integrated data to identify learning trends and problems.

[1246] Step 3:

[1247] The server generates a report for administrators based on the analysis results.

[1248] Step 4:

[1249] The server sends the generated report to the administrator's terminal.

[1250] Step 5:

[1251] The terminal displays the received reports to the administrator.

[1252] These processing steps ensure that the entire system operates efficiently, allowing for smooth development of training content, learning support, and progress management.

[1253] (Example 1)

[1254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1255] Existing training systems require significant time and effort for developing training content, providing learning support, and managing progress. Furthermore, they struggle to provide feedback tailored to individual user progress, hindering efficient learning. Additionally, they lack mechanisms for integrating and analyzing overall learning data to provide useful insights for administrators. Therefore, a system is needed that centrally manages automated training content generation, learning support, and progress management, enabling efficient and effective training.

[1256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1257] In this invention, the server includes means for receiving requests from users, means for searching an information storage device and obtaining relevant information, means for automatically generating training content using a generation AI model, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the information storage device and providing them to the user, means for integrating and analyzing learning data from all users, and means for generating and providing reports for administrators based on the analysis results. This enables efficient automatic generation of training content, learning support, and progress management, resulting in overall cost and man-hour reductions.

[1258] A "request" is a user's request for specific training content to be created and provided.

[1259] "Information storage device" is a general term for devices that store information, such as databases and storage media.

[1260] "Related information" refers to materials and data necessary for generating training content, which are retrieved from information storage devices based on user requests.

[1261] "Training content" refers to the entirety of materials, slides, videos, practice exercises, etc., provided for learning and training.

[1262] A "generative AI model" refers to an algorithm or software that uses artificial intelligence to automatically generate training content based on user requests.

[1263] A "device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1264] "Learning progress" refers to the progress a user has made in learning the training content.

[1265] "Feedback" refers to information such as explanations and suggestions for improvement that are provided according to the user's learning progress.

[1266] "Additional materials" refer to supplementary learning materials and information provided to help users gain a deeper understanding of the material they are learning.

[1267] A "question" is an inquiry that a user enters into the system regarding doubts or points of confusion that arise during the learning process.

[1268] An "answer" refers to the explanation or information that the system provides in response to a user's question.

[1269] "Learning data" is a general term for records and data related to a user's learning activities.

[1270] A "report" is a document or report that summarizes the analysis results provided by the server to administrators after analyzing training data.

[1271] An "administrator" is the person or individual responsible for the overall operation of the system and the management of user progress.

[1272] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. This system consists of a content generation module, a learning support module, and a management module, all of which automatically generate training content based on user requests. The details and operation methods of these modules are described below.

[1273] Content generation module

[1274] This module receives requests from users and automatically generates training content based on specified themes. Users send training content creation requests from their terminals. The server receives the request and searches for relevant materials in its information storage. Based on the retrieved information, it automatically generates training content using a generation AI model (e.g., GPT-4). The generated content is sent from the server to the user's terminal and displayed.

[1275] For example, if a user requests to "create an introductory data analysis course," the server will search the database for relevant materials and use a generative AI model to automatically generate the course content for "Introduction to Data Analysis."

[1276] Example of a prompt:

[1277] "Please create training content for an introduction to data analysis."

[1278] "Create a beginner's programming course."

[1279] Learning support module

[1280] This module monitors the user's learning process in real time and provides appropriate feedback and additional materials based on their progress. As the user studies the provided content, the device records their learning activity and sends it to the server. The server analyzes this data and, if the user encounters difficulties with a particular task, provides relevant explanatory videos or additional materials. This allows the user to learn more efficiently.

[1281] For example, if a user is unable to solve a particular problem in the "Introduction to Data Analysis Course," related explanatory videos and additional learning materials will be automatically provided to the user's device.

[1282] Management module

[1283] This module integrates learning data from all users and analyzes their progress to automatically generate and provide reports to administrators. The server periodically aggregates and analyzes learning data submitted by all users. Based on the analysis results, it generates a report for administrators and sends it to their terminals for display.

[1284] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[1285] Question answering function

[1286] This feature provides quick answers to user questions. When a user submits a question, such as "What is a correlation coefficient?", the server receives the question and searches its information storage for the appropriate answer. The server then sends the answer to the user's device for display.

[1287] By working together as a whole, this system enables the efficient generation of training content tailored to user needs, learning support, and progress management. It also offers significant benefits to businesses and educational institutions, resulting in cost and labor savings.

[1288] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1289] Content generation module

[1290] Processing flow and specific actions

[1291] Step 1:

[1292] The user sends a request through their device.

[1293] Input: The user enters a request in the input form, saying "I want to create an introductory data analysis course," and presses the submit button.

[1294] Operation: User input is sent to the server as an HTTP request.

[1295] Output: The server receives the request.

[1296] Step 2:

[1297] The server receives the request.

[1298] Input: User request data (e.g., "I want to create an introductory course on data analysis")

[1299] Operation: The web server receives the request and passes the data to the backend application server.

[1300] Output: The application server parses the request content.

[1301] Step 3:

[1302] The server searches for related documents.

[1303] Input: Parsed request content (e.g., "Introduction to Data Analysis")

[1304] Operation: The server generates an SQL query and performs a search on the information storage device (database).

[1305] Output: Related materials are retrieved as search results.

[1306] Step 4:

[1307] The server generates the training content.

[1308] Input: Acquired related documents

[1309] Operation: The server sends a prompt message (e.g., "Create training content for an introduction to data analysis") to the generated AI model (e.g., GPT-4) and requests content generation.

[1310] Output: Generated training content (text, slides, practice questions, etc.)

[1311] Step 5:

[1312] The server sends the generated content to the user for display.

[1313] Input: Generated training content

[1314] Operation: The server packages the content in JSON format or similar and sends it to the user's terminal as an HTTP response.

[1315] Output: The user's device analyzes and displays the received content.

[1316] Learning support module

[1317] Processing flow and specific actions

[1318] Step 1:

[1319] The user begins the learning activity.

[1320] Input: Training content provided by the user (e.g., slides, practice questions)

[1321] Action: View content and solve problems.

[1322] Output: Learning activity data (which slides were viewed, which problems were solved)

[1323] Step 2:

[1324] The device records learning activity data.

[1325] Input: User's learning activity data

[1326] Operation: Records learning activities in real time to a local database or cache.

[1327] Output: Recorded training data

[1328] Step 3:

[1329] The device sends recorded data to the server.

[1330] Input: Recorded training data

[1331] Operation: At regular intervals, the recorded data is sent to the server in batch processing.

[1332] Output: Transmitted training data

[1333] Step 4:

[1334] The server analyzes the data.

[1335] Input: Submitted training data

[1336] Operation: Receives data and executes an algorithm to analyze learning progress.

[1337] Output: Analysis results (e.g., which issues users are struggling with)

[1338] Step 5:

[1339] The server provides feedback and additional information.

[1340] Input: Analysis results

[1341] Function: Select and provide explanatory videos and additional materials related to the parts where the user is having trouble.

[1342] Output: Feedback and additional materials

[1343] Step 6:

[1344] Display information provided by the server to the user.

[1345] Input: Feedback and additional materials

[1346] Operation: The server sends this information to the user's terminal.

[1347] Output: Displays information received by the user terminal.

[1348] Management module

[1349] Processing flow and specific actions

[1350] Step 1:

[1351] The server aggregates the learning data of all users.

[1352] Input: Training data submitted by each user

[1353] Operation: Aggregates data using databases and big data processing platforms.

[1354] Output: Aggregated training data

[1355] Step 2:

[1356] The server analyzes the data.

[1357] Input: Aggregated training data

[1358] Operation: Executes data analysis algorithms and analyzes the overall learning progress.

[1359] Output: Analysis results (popular courses, frequently occurring stumbling blocks, etc.)

[1360] Step 3:

[1361] The server generates a report for administrators.

[1362] Input: Analysis results

[1363] Function: Generates a report document for administrators based on the analysis results.

[1364] Output: Generated report

[1365] Step 4:

[1366] The server sends the report to the administrator for display.

[1367] Input: Generated report

[1368] Action: Sends the report as an HTTP response to the administrator's terminal.

[1369] Output: The administrator terminal receives the report and displays it on the administration screen.

[1370] Question answering function

[1371] Processing flow and specific actions

[1372] Step 1:

[1373] User submits a question

[1374] Input: Question (Example: "What is a correlation coefficient?")

[1375] Operation: Enter your question in the chat box or form and submit it.

[1376] Output: The server receives the question.

[1377] Step 2:

[1378] The server receives the question.

[1379] Input: Question data

[1380] Function: Analyzes the question and extracts appropriate keywords.

[1381] Output: Analyzed question data

[1382] Step 3:

[1383] The server searches for the appropriate answer.

[1384] Input: Analyzed question data

[1385] Operation: Executes an SQL query to search the information storage device and retrieve relevant answers.

[1386] Output: Related response data

[1387] Step 4:

[1388] The server sends the answer to the user and displays it.

[1389] Input: Response data

[1390] Action: Sends the response as an HTTP response to the user's device.

[1391] Output: The user terminal receives and displays the response.

[1392] (Application Example 1)

[1393] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1394] Traditional training systems were cumbersome in terms of developing training content and managing progress, making efficient learning support difficult. Furthermore, training for virtual store staff, in particular, requires individual feedback and progress management, but achieving this using traditional methods is costly and time-consuming.

[1395] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1396] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data of all users, means for generating and providing reports for administrators based on the analysis results, means for generating training content for virtual store staff, means for recording staff learning activities and monitoring progress, and means for aggregating each staff member's learning data, analyzing progress, and providing feedback. This enables automatic formulation of training content, real-time monitoring of learning progress, provision of individual feedback, and automatic generation of reports for administrators.

[1397] A "user" is someone who will be using this system to receive training.

[1398] "Means for receiving requests" refers to the functionality of devices or software that receive requests from users, such as training topics or questions.

[1399] A "database" is a collection of data that is systematically managed, allowing for efficient searching and retrieval of necessary information.

[1400] "Means for obtaining relevant information" refers to the functions of devices or software that search for and retrieve information from a database based on a request.

[1401] "Means for automatically generating training content" refers to the functions of devices or software that automatically create training materials based on acquired information.

[1402] "Means of providing generated training content to the user's terminal" refers to the functions of devices or software that transmit and display automatically generated training content on the user's device.

[1403] "Means for monitoring user learning progress" refers to the functions of devices and software that track and record a user's learning status in real time.

[1404] "Means of providing feedback and additional materials" refers to the functions of devices and software that provide users with appropriate advice and supplementary materials based on their learning progress.

[1405] "Means for receiving questions" refers to the functions of devices or software that receive questions from users.

[1406] "Means of searching for appropriate answers" refers to the function of devices or software that search and retrieve the best answer to a question from a database.

[1407] "Means for integrating and analyzing learning data from all users" refers to the functions of devices and software that centrally manage and statistically analyze learning data collected from multiple users.

[1408] "Means for generating and providing reports for administrators" refers to the functionality of devices and software that create and provide detailed reports for administrators based on data analysis results.

[1409] "Means for generating training content for virtual store staff" refers to the functions of devices and software that automatically create training materials for staff working in virtual stores.

[1410] "Means for recording staff learning activities and monitoring progress" refers to the functions of devices and software that track staff learning behaviors and record their progress in real time.

[1411] "Means for aggregating learning data, analyzing progress, and providing feedback" refers to the functions of devices and software that collect individual staff members' learning data and provide appropriate advice and supplementary materials based on that progress.

[1412] This invention provides a training system for staff in virtual stores, which can be implemented using the following hardware and software. The system consists of multiple modules that receive requests from users, search a database, generate training content using AI, and monitor learning progress and provide feedback.

[1413] 1. Hardware and software

[1414] Hardware: Servers, user terminals (smartphones, head-mounted displays, etc.)

[1415] Software: Python, Flask (server-side framework), requests (HTTP request library)

[1416] 2. Program Processing

[1417] Receiving a request

[1418] The server receives training topics and questions from virtual store staff. For example, if a staff member wants training on "virtual customer service techniques," that request is sent to the server.

[1419] Database search and information retrieval

[1420] Based on the request, the server searches the database and retrieves relevant information. This is the process of finding the most suitable materials for the training content requested by the staff.

[1421] Automatic generation of training content

[1422] Using the acquired information, the server utilizes an AI model to automatically generate training content. For example, if the training is on data analysis techniques, the generated materials will include basic methods and specific use cases.

[1423] Content provision

[1424] The generated training content is sent from the server to the user's terminal and made available for the user to view. This allows staff to immediately begin learning on their own devices.

[1425] Monitoring and feedback on learning progress

[1426] The device records the user's learning activities (viewing slides and answering questions) and sends this data to the server. The server analyzes this data in real time and provides appropriate feedback and additional learning materials based on the user's progress. For example, if a user gets stuck on a particular problem, they may be provided with explanatory videos or additional materials related to that problem.

[1427] Generating reports for administrators

[1428] The server aggregates learning data from all users and analyzes their progress. Based on the analysis results, it automatically generates and provides reports for administrators. For example, it reports to administrators which topics were most effective in last month's training and which areas many users are struggling with.

[1429] Question and Answer

[1430] A user's question is sent to the server, and relevant information is retrieved from the database. The server generates the best possible answer and provides it to the user. For example, if a staff member asks, "What is the product return policy?", the answer is provided immediately.

[1431] 3. Specific Examples and Examples of Prompt Statements

[1432] For example, if a staff member requests, "Please generate an introductory course on virtual customer service techniques," the server will search for relevant materials and automatically generate training content using an AI model.

[1433] Example of a prompt:

[1434] "Please create an introductory course on virtual customer service techniques for virtual store staff."

[1435] In this way, this system enables efficient training of staff working in virtual stores, resulting in significant reductions in costs and man-hours.

[1436] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1437] Step 1:

[1438] A user sends a request to the system. For example, a user who is a staff member at a virtual store requests "training on virtual customer service techniques." This request is sent from the user's terminal (such as a smartphone or head-mounted display) to the server. The input is "virtual customer service techniques," and the output is the request information for processing it.

[1439] Step 2:

[1440] The server searches the database based on the received request. It searches the database for and retrieves information related to the requested topic (e.g., "Virtual Customer Service Techniques"). The input is the request information, and the output is a list of related training materials. In this process, the server generates a search query and queries the database using SQL queries or similar methods.

[1441] Step 3:

[1442] Based on the acquired information, the server automatically generates training content using a generative AI model. For example, it analyzes acquired materials and generates appropriate slides, videos, and explanatory text. The input is a list of relevant training materials, and the output is automatically generated training content. Specifically, the AI ​​model generates text data using natural language processing technology and attaches related media files.

[1443] Step 4:

[1444] The server provides the generated training content to the user's terminal. The terminal receives this content and displays it to the user. For example, generated slides or videos are displayed on the user's smartphone or head-mounted display. The input is the automatically generated training content, and the output is the content displayed on the user's terminal.

[1445] Step 5:

[1446] When a user studies training content, the device records their learning activity. Progress data, such as the user's slide viewing time and question answer history, is recorded and sent to the server. The input is the user's learning activity, and the output is progress data. Specifically, a learning log is created and periodically sent to the server.

[1447] Step 6:

[1448] The server monitors and analyzes received learning progress data in real time. Based on the learning progress, if the user is struggling at a specific point, it generates and provides additional learning materials or explanatory videos. The input is progress data, and the output is feedback and additional materials. Machine learning algorithms are used in the analysis process to identify the user's weaknesses.

[1449] Step 7:

[1450] When a user submits a question, the server receives it. The server searches the database, generates an appropriate answer, and provides it to the user. For example, if a user asks, "What is the product return policy?", the server searches the database for the relevant policy information and generates an answer. The input is the user's question, and the output is the appropriate answer. In this process, natural language processing techniques are used to analyze the question and find the most relevant answer.

[1451] Step 8:

[1452] The server integrates learning data from all users and periodically analyzes their progress. Based on the analysis results, it automatically generates and provides detailed reports to administrators. For example, when an administrator requests a monthly report, the server aggregates the learning status of all users and creates a report based on the analysis results. The input is the integrated learning data, and the output is a report for administrators. Statistical analysis and data visualization techniques are used to generate the report.

[1453] This series of processing steps allows virtual store staff to receive training efficiently, and enables managers to reliably track the progress of their training.

[1454] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1455] This invention provides a more personalized learning experience by combining training content development, learning support, and progress management with user emotion recognition capabilities. The system's configuration and operation method are described in detail below, including specific examples.

[1456] Content generation module

[1457] Program processing

[1458] When a user requests training content, the content is automatically generated based on that topic. This saves time and effort.

[1459] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the terminal to the server.

[1460] The server receives the request and extracts keywords related to "Introduction to Data Analysis".

[1461] The server searches the database and retrieves relevant information and content, such as basic concepts and instructions on how to use tools.

[1462] The server automatically generates course content (slides, practice problems) based on information acquired using artificial intelligence.

[1463] The server sends the generated content to the user's device, and the device displays it to the user.

[1464] Learning support module

[1465] Program processing

[1466] It monitors the user's learning progress in real time and provides feedback and additional materials according to their progress.

[1467] The user begins learning the course content on their device. For example, they view slides and solve practice problems.

[1468] The device records the user's learning activity in real time and sends that data to the server.

[1469] The server receives and analyzes the user's learning progress data. It detects issues such as getting stuck on specific problems or taking an excessive amount of time to learn.

[1470] The server generates appropriate feedback and additional learning materials and sends them to the user's device.

[1471] The device displays feedback and additional information to the user.

[1472] Emotion recognition module

[1473] Program processing

[1474] It recognizes user emotions and provides feedback and support to further improve the learning experience.

[1475] The device will be equipped with sensors and cameras to recognize the user's emotions.

[1476] The device sends the user's facial expressions, voice tone, and other information to the emotion engine to acquire emotional data.

[1477] The server receives emotion data in real time and analyzes it along with learning progress data.

[1478] The server generates feedback based on emotional data, such as when the user is stressed or has a low level of understanding.

[1479] The server generates feedback and sends it to the user's device, which then displays it to the user.

[1480] For example, if a user shows signs of frustration or stress while working on a practice problem, the emotion engine recognizes that emotion and the server makes a suggestion such as, "Would you like to review the explanation for this problem again?"

[1481] Management module

[1482] Program processing

[1483] Integrates learning and sentiment data from all users and generates reports for administrators.

[1484] The server periodically collects and integrates learning data and sentiment data from all users.

[1485] The server analyzes this data to identify learning trends and problems.

[1486] The server generates a report for administrators based on the analysis results.

[1487] The server generates a report which is sent to the administrator's terminal, and the terminal displays it to the administrator.

[1488] For example, a monthly report could be submitted to administrators detailing which courses are popular, where many users are struggling, and their emotional reactions to these issues. Based on this information, administrators can review and improve the course content.

[1489] In this way, this system, which incorporates an emotion engine, improves the user's learning experience, streamlines the formulation of training content, learning support, and progress management, and achieves overall cost and man-hour reductions.

[1490] The following describes the processing flow.

[1491] Content generation module

[1492] Step 1:

[1493] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[1494] Step 2:

[1495] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[1496] Step 3:

[1497] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[1498] Step 4:

[1499] Based on the data acquired by the server, artificial intelligence is used to automatically generate the course content (slides, practice problems, etc.) for the "Introduction to Data Analysis" course.

[1500] Step 5:

[1501] The server saves the generated course content and then sends it to the user's terminal.

[1502] Step 6:

[1503] The terminal displays the course content it received to the user.

[1504] Learning support module

[1505] Step 1:

[1506] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[1507] Step 2:

[1508] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[1509] Step 3:

[1510] The device periodically sends the data it has recorded to the server.

[1511] Step 4:

[1512] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[1513] Step 5:

[1514] The server generates appropriate feedback and additional learning materials based on the analysis results.

[1515] Step 6:

[1516] The server sends generated feedback and additional materials to the user's device.

[1517] Step 7:

[1518] The device displays feedback and additional information received by the user.

[1519] Emotion recognition module

[1520] Step 1:

[1521] The device will be equipped with sensors and cameras to recognize the user's emotions.

[1522] Step 2:

[1523] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the emotion engine.

[1524] Step 3:

[1525] The server receives emotion data in real time and analyzes it along with learning progress data.

[1526] Step 4:

[1527] Based on the analysis results, the server generates feedback that corresponds to the user's emotions. For example, if the user is feeling stressed, it might generate a suggestion such as, "Would you like to review this problem explanation again?"

[1528] Step 5:

[1529] The server sends the generated feedback to the user's device.

[1530] Step 6:

[1531] The device displays the feedback it has received to the user.

[1532] Question answering module

[1533] Step 1:

[1534] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[1535] Step 2:

[1536] The server receives a question from the user and analyzes the content of the question.

[1537] Step 3:

[1538] The server searches the database for the appropriate answer to the question.

[1539] Step 4:

[1540] The server sends the answer it finds to the user's device.

[1541] Step 5:

[1542] The device displays the received response to the user.

[1543] Management module

[1544] Step 1:

[1545] The server periodically collects and integrates learning and sentiment data from all users.

[1546] Step 2:

[1547] The server analyzes the integrated data to identify learning trends and problems.

[1548] Step 3:

[1549] The server generates a report for administrators based on the analysis results.

[1550] Step 4:

[1551] The server sends the generated report to the administrator's terminal.

[1552] Step 5:

[1553] The terminal displays the received reports to the administrator.

[1554] In this way, each module works together to realize a personalized learning experience that includes everything from training content development and learning support to progress management and even emotion recognition.

[1555] (Example 2)

[1556] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1557] Traditional learning systems struggled to provide personalized feedback and additional materials tailored to each user's learning progress, and they failed to consider users' emotional states. Therefore, learning efficiency and effectiveness could not be adequately guaranteed. Furthermore, it was difficult for administrators to grasp the overall learning situation and take appropriate measures.

[1558] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's display device, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for recognizing the user's emotions and collecting emotional data, means for analyzing the emotional data and reflecting it in the feedback, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing a report for administrators based on the analysis results. This makes it possible to provide personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. In addition, administrators can accurately grasp the overall learning situation and take appropriate measures.

[1559] "Means for receiving user requests" refers to a device or program that acquires request information when a user requests specific training content.

[1560] "Means for searching a database and retrieving relevant information" refers to a device or program that searches a database for relevant information based on a requested topic and retrieves the necessary data.

[1561] "Means for automatically generating training content using acquired information" refers to a device or program that automatically generates training content such as slides and practice problems using artificial intelligence, based on information acquired from a database.

[1562] "Means for providing generated training content to a user's display device" refers to a device or program that transmits automatically generated training content to a terminal used by the user and displays it.

[1563] "Means for monitoring user learning progress" refers to a device or program that records and monitors the user's progress in real time as they progress through the learning process.

[1564] "Means for providing feedback and additional materials based on learning progress" refers to a device or program that generates and provides appropriate feedback and additional learning materials based on the user's learning progress data.

[1565] "Means for recognizing user emotions and collecting emotional data" refers to a device or program that recognizes emotions from a user's facial expressions, tone of voice, etc., and collects that data.

[1566] "Means for analyzing emotional data and reflecting it in feedback" refers to a device or program that analyzes collected emotional data, adjusts the feedback content based on the results, and provides it to the user.

[1567] "Means for integrating and analyzing learning data and sentiment data from all users" refers to a device or program that integrates learning data and sentiment data collected from all users and analyzes that data.

[1568] "Means for generating and providing reports for administrators based on analysis results" refers to a device or program that generates and provides reports in a format usable by administrators, based on the results of integrated data analysis.

[1569] This invention provides a system that offers a more personalized learning experience by combining training content development, learning support, and progress management with a user emotion recognition function. The system consists of three main elements: a server, a terminal, and a user.

[1570] First, when a user requests training content, they do so via their device. When the user clicks the "Request creation of an introductory data analysis course" button, the device sends this request to the server. After receiving the request, the server searches the database based on the requested topic and retrieves relevant information. The hardware used includes a server and a database server, while the software used includes a database management system (e.g., MySQL) and a text analysis tool (e.g., NLTK).

[1571] Based on the acquired information, the server automatically generates training content (slides, practice questions, etc.) using a generative AI model (e.g., GPT-3). The server then sends the generated content to the user's device, which displays it to the user.

[1572] For example, if a user clicks the "Request creation of an introductory data analysis course" button, materials related to "Introduction to Data Analysis" are retrieved from the database based on the request, and slides and practice problems are automatically generated using a generative AI model. The generated content is then displayed on the user's device.

[1573] Example prompt: "Automatically generate a data analysis course for beginners."

[1574] Next, as the user progresses through the learning process, the device records the user's actions in real time and sends this data to the server. The server analyzes the user's learning progress data to detect issues such as getting stuck on specific problems or taking too long to learn. Data analysis tools (e.g., Pandas) are used for this analysis. The server generates appropriate feedback and additional learning materials and sends them to the user's device. The device then displays the generated feedback and additional materials to the user.

[1575] For example, if a user encounters a problem while studying the "Introduction to Data Analysis Course," the server analyzes the information, generates additional explanations and reference materials, and sends them to the user's device. The user then relearns the material based on the provided resources.

[1576] Example prompt: "Automatically provide additional explanatory materials to users who are falling behind in their learning progress."

[1577] Furthermore, it also has a function to recognize the user's emotions. The device uses sensors and a camera to acquire the user's emotional data (facial expressions, tone of voice, etc.) and sends it to the server. The server analyzes the emotional data and provides feedback based on whether the user is feeling stressed or has a low level of understanding. Emotion analysis tools (e.g., OpenCV, TensorFlow) are used for this analysis.

[1578] For example, if a user shows signs of dissatisfaction or stress while working on a practice problem, the server generates feedback such as "Would you like to review this problem explanation again?" and sends it to the user's device.

[1579] Example prompt: "If the user is experiencing stress, please provide an appropriate feedback message."

[1580] Finally, the server integrates all users' learning and sentiment data and periodically generates reports for administrators. The server analyzes this data to identify learning trends and problems. Data visualization tools (e.g., Tableau) are used for this analysis. The generated reports are sent to the administrator's terminal for their viewing.

[1581] As a concrete example, administrators can review monthly reports to see which courses are popular, where many users are struggling, and what their emotional reactions were to these issues, and then revise and improve the course content accordingly.

[1582] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[1583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1584] Step 1: Sending and receiving requests

[1585] The user clicks the "Request creation of an introductory data analysis course" button. The input is the user's click action and the request content, and the output is the HTTP request generated by the terminal.

[1586] The terminal sends this request to the server. The input is the request information from the user, and the output is the request data sent to the server.

[1587] Specific actions:

[1588] 1. The user performs the action of clicking a button.

[1589] 2. The device captures the click event, generates an HTTP request, and sends it to the server.

[1590] Step 2: Keyword extraction and database search

[1591] The server receives the request and extracts keywords related to "Introduction to Data Analysis". The input is the user's request data, and the output is the extracted keywords.

[1592] The server searches the database and retrieves relevant documents and content. The input is extracted keywords, and the output is related informational data.

[1593] Specific actions:

[1594] 1. The server parses the request data and performs natural language processing to extract keywords.

[1595] 2. The server generates database queries and searches for and retrieves relevant information.

[1596] Step 3: Content Generation

[1597] The server automatically generates training content based on the information it acquires. The input is the acquired information data, and the output is the generated training content.

[1598] The server uses a generated AI model (e.g., GPT-3) to create slides and practice problems.

[1599] Specific actions:

[1600] 1. The server inputs the acquired information into the model and generates training content.

[1601] 2. The server converts the generated slides and practice questions into a data format.

[1602] Step 4: Content Delivery

[1603] The server provides the generated training content to the user's terminal. The input is the generated training content, and the output is the content data sent to the terminal.

[1604] The device displays the content it has received to the user.

[1605] Specific actions:

[1606] 1. The server sends the content data to the terminal.

[1607] 2. The content received by the device is displayed on the user interface.

[1608] Step 5: Monitoring Learning Progress

[1609] The user begins learning the course content on their device. The input is the user's learning activity, and the output is progress data.

[1610] The device records the user's learning activity in real time and sends that data to the server.

[1611] Specific actions:

[1612] 1. The user views the slides and performs the actions of solving the practice problems.

[1613] 2. The terminal records user operation events and sends them to the server as progress data.

[1614] Step 6: Analyze progress and provide feedback

[1615] The server receives user learning progress data and analyzes it to identify issues such as difficulty with specific problems or excessive learning time. The input is progress data, and the output is the analysis results.

[1616] The server generates appropriate feedback and additional learning materials and sends them to the user's terminal. The input is the analysis result, and the output is the feedback data.

[1617] Specific actions:

[1618] 1. The server analyzes the progress data using an analysis tool.

[1619] 2. The server uses the generated AI model to create feedback and additional materials, and sends them to the terminal.

[1620] Step 7: View feedback and additional materials

[1621] The device displays feedback and additional materials to the user. The input is feedback data, and the output is the feedback and materials reflected in the user interface.

[1622] Specific actions:

[1623] 1. Receive feedback and additional information for the device to display.

[1624] 2. The device displays feedback and information on the user interface.

[1625] Step 8: Acquisition and transmission of emotional data

[1626] The device will be equipped with sensors and cameras to recognize the user's emotions. The input will be the user's facial expressions and tone of voice, and the output will be emotion data.

[1627] The device sends emotional data to the server in real time.

[1628] Specific actions:

[1629] 1. The device uses its camera and microphone to capture user emotion data.

[1630] 2. The device sends the acquired emotion data to the server.

[1631] Step 9: Analysis of emotional data and provision of feedback

[1632] The server receives and analyzes emotion data in real time. The input is emotion data, and the output is the analysis result.

[1633] The server generates feedback based on emotional data and sends it to the user's terminal. The input is the analysis result, and the output is emotion-based feedback data.

[1634] Specific actions:

[1635] 1. The server analyzes the emotional data using an emotional analysis tool.

[1636] 2. The server generates feedback based on the analysis results and sends it to the user's terminal.

[1637] Step 10: Data integration and report generation for all users

[1638] The server periodically collects and integrates learning and sentiment data from all users. The input is data from all users, and the output is the integrated data.

[1639] The server analyzes the integrated data and generates reports for administrators. The input is the integrated data, and the output is the report data.

[1640] Specific actions:

[1641] 1. The server periodically collects data from all users and integrates it into the database.

[1642] 2. The server generates a report using a data visualization tool.

[1643] Step 11: Provide reports for administrators

[1644] The server generates a report which is then provided to the administrator's terminal, and the terminal displays it to the administrator. The input is the report data, and the output is the report reflected on the administrator's display device.

[1645] Specific actions:

[1646] 1. The server sends the report data to the administrator's terminal.

[1647] 2. The terminal displays the report on the administrator's user interface.

[1648] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[1649] (Application Example 2)

[1650] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1651] Traditional learning support systems were limited to monitoring users' learning progress and providing feedback and additional materials, lacking personalized support tailored to individual users' emotions and levels of understanding. As a result, they failed to address the stress and frustration users experienced during learning, potentially leading to decreased learning effectiveness. Furthermore, administrators lacked the information necessary to effectively integrate and analyze all users' learning data and implement appropriate countermeasures. This made it difficult to make appropriate improvements to enhance the quality of training.

[1652] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1653] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for using sensors and cameras to recognize the user's emotions, means for analyzing the acquired emotional data and reflecting it in the feedback, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing reports for administrators based on the analysis results. This makes it possible to provide personalized support that responds to the user's emotions, and an improvement in learning effectiveness can be expected. In addition, detailed analysis based on aggregated data allows administrators to take appropriate improvement measures, thereby improving the overall quality of the training.

[1654] "Means for receiving user requests" refers to the functionality of a device or software that has an interface that allows users to request training content on a specific theme or topic.

[1655] "Means for searching a database and retrieving relevant information" refers to the function of a device or software that searches a database for relevant information based on a requested topic and retrieves that information.

[1656] "Means for automatically generating training content" refers to the function of a device or software that automatically generates the content necessary for training based on acquired information.

[1657] "Means of providing training content to a user's terminal" refers to the function of a device or software that transmits generated training content to a terminal used by the user, enabling the user to utilize that content.

[1658] "Means for monitoring user learning progress" refers to the function of a device or software that tracks a user's learning activities in real time and records their progress.

[1659] "Means of providing feedback and additional materials based on learning progress" refers to the function of a device or software that automatically generates and provides appropriate feedback and additional learning materials according to the user's learning progress.

[1660] "Means of using sensors or cameras to recognize user emotions" refers to the function of a device or software that includes sensors or cameras to detect the user's facial expressions and tone of voice and analyze their emotions.

[1661] "Means for analyzing acquired emotional data and reflecting it in feedback" refers to the function of a device or software that analyzes detected emotional data and generates feedback based on that analysis.

[1662] "Means of receiving questions from users, searching for appropriate answers in a database, and providing them to users" refers to the function of a device or software that receives a question entered by a user, searches for an answer to that question in a database, and returns it to the user.

[1663] "Means for integrating and analyzing learning data and emotional data of all users" refers to the function of a device or software that collects, integrates, and analyzes learning progress data and emotional data of all users.

[1664] "Means of generating and providing reports for administrators based on analysis results" refers to the function of a device or software that automatically generates reports for administrators based on the results of integrated data analysis and provides them for administrators to use.

[1665] This invention is a learning support system that helps users request learning content on specific themes and progress through their learning while receiving progress management and individual feedback. The system can recognize the user's emotions and provide personalized support based on them. Specifically, the program configuration and processing of this system are described in detail below.

[1666] System Configuration

[1667] hardware

[1668] Server: Receives requests, searches databases, generates content, and analyzes progress and sentiment data.

[1669] Devices (smartphones, tablets, etc.): These are the devices used by users to display content, record progress, and collect sentiment data.

[1670] software

[1671] Flask (Web server framework): Handles requests and provides APIs.

[1672] EmotionRecognizer (emotion recognition module): Analysis of emotion data (e.g., OpenCV, DeepFace)

[1673] ContentGenerator (Content Generation Module): Automatic generation of training content (e.g., AI generation model, GPT-3)

[1674] FeedbackGenerator (Feedback Generation Module): Generates feedback based on progress data.

[1675] Database: Data storage and retrieval (e.g., Firebase, SQLite)

[1676] Program Processing Description

[1677] 1. Receiving requests and generating content

[1678] The server receives learning topic requests from users, searches the database to retrieve relevant information, and then automatically generates training content based on that information and provides it to the user's device. For example, if a user requests "Basic Data Analysis," the server will collect relevant materials and use them to generate tutorials and practice problems.

[1679] 2. Monitoring learning progress and providing feedback

[1680] As a user progresses through the learning process on their device, the device records progress data in real time and sends it to the server. The server analyzes the progress data and generates and provides feedback and additional materials tailored to the user's progress. For example, if a user gets stuck on a practice problem, additional explanatory materials are provided.

[1681] 3. Emotion Recognition and Personalized Support

[1682] The device uses sensors and cameras to collect user emotional data (facial expressions and tone of voice) and sends it to a server. The server analyzes the emotional data and generates feedback to improve the user's learning experience. For example, if the user is feeling stressed, it might suggest, "Would you like to review this problem explanation again?"

[1683] 4. Generate management reports

[1684] The server integrates and analyzes all users' learning and emotional data, and generates and provides reports for administrators based on the analysis results. This allows administrators to evaluate the overall quality of training and take steps to improve it. For example, the report visualizes popular courses and points where many users stumble.

[1685] Example of a prompt

[1686] "Please generate detailed content on introductory data analysis."

[1687] This allows users to have a personalized learning experience, receive effective learning support, and enable administrators to implement appropriate data-driven improvements.

[1688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1689] Step 1:

[1690] User submits a request

[1691] The user requests a specific learning topic using their device. For example, they might select "Basic Data Analysis" and submit the request. In this step, the input is the user's request, and the output is the request data being sent to the server. The server then receives this request data.

[1692] Step 2:

[1693] The server searches the database and retrieves the information.

[1694] Based on the request data received by the server, it searches the database and retrieves relevant information (e.g., learning materials, tutorials, practice problems). The input to this step is the request data, and the output is the retrieved information. The server extracts the appropriate data from the database and integrates it.

[1695] Step 3:

[1696] Automatic generation of training content

[1697] The server uses the acquired information to automatically generate training content using a generative AI model (e.g., GPT-3). In this step, the input is the acquired information, and the output is the generated training content. The server inputs a prompt (e.g., "Please generate detailed content on basic data analysis.") to the generative AI model and generates the content.

[1698] Step 4:

[1699] Content provision

[1700] The server generates training content and provides it to the user's terminal. The input to this step is the generated training content, and the output is that the content is sent to the user's terminal. The terminal displays the received content to the user.

[1701] Step 5:

[1702] Monitoring learning progress

[1703] As a user progresses through the learning process using the device, the device monitors the user's learning progress in real time and sends that data to the server. In this step, the input is the learning progress data, and the output is the transmission of that data to the server. The device records the learning log and transfers it to the server.

[1704] Step 6:

[1705] Providing progress-based feedback

[1706] The server receives and analyzes progress data. It generates feedback and additional materials based on the progress and sends them to the terminal. The input for this step is progress data, and the output is the generated feedback. The server uses the FeedbackGenerator module to generate appropriate feedback and send it to the terminal.

[1707] Step 7:

[1708] Recognition of emotional data

[1709] The device collects user emotional data (facial expressions, tone of voice) using sensors and cameras and sends this data to a server. In this step, the input is emotional data, and the output is the transmission of that data to the server. The device captures emotional data in real time and transfers it to the server.

[1710] Step 8:

[1711] Analysis and feedback of emotional data

[1712] The server receives and analyzes emotion data. It generates personalized feedback based on the emotion and sends it to the terminal. The input for this step is emotion data, and the output is the generated feedback. The server uses the EmotionRecognizer module to analyze the emotion data and the FeedbackGenerator module to generate the feedback.

[1713] Step 9:

[1714] Generating management reports

[1715] The server integrates all users' learning and sentiment data and generates and provides monthly and weekly reports for administrators. In this step, the input is the integrated data, and the output is the generated management report. The server aggregates all the data, creates a report based on the analysis results, and sends it to the administrator.

[1716] Through the processing steps described above, the present invention personalizes the user's learning experience, provides effective learning support, and enables administrators to implement appropriate improvement measures based on data.

[1717] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1718] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1719] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1720] [Fourth Embodiment]

[1721] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1722] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1723] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1724] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1725] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1726] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1727] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1728] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1729] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1730] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1731] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1732] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1733] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1734] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. The system's configuration and operation methods are described in detail below, including specific examples.

[1735] Content generation module

[1736] Program processing

[1737] As part of this system, it receives requests from users and automatically generates training content. This significantly reduces the time and effort required to develop training materials.

[1738] The user sends a request via their device stating, "I want to create an introductory course on data analysis."

[1739] The server receives this request and searches for relevant information in the database.

[1740] The server uses artificial intelligence to automatically generate the content for the "Introduction to Data Analysis" course based on the search results.

[1741] The server sends the generated content to the user's terminal and displays it to the user.

[1742] For example, if the request is for a beginner's programming course, complete training content including beginner-level materials and practice problems will be automatically generated.

[1743] Learning support module

[1744] Program processing

[1745] The system monitors users' learning progress in real time and provides appropriate feedback and additional materials based on their progress. This maximizes the learning efficiency of each user.

[1746] The user begins learning from the provided content. For example, they might view slides and solve problems.

[1747] The device records the user's learning activities and sends that data to the server.

[1748] The server analyzes the user's progress data and, if the user encounters a particular problem, provides relevant explanatory videos or additional materials.

[1749] The server sends the feedback it provides to the user's device and displays it to the user.

[1750] For example, if a user is unable to solve a particular problem in an introductory data analysis course, additional learning materials and explanatory documents related to that problem will be automatically provided.

[1751] Management module

[1752] Program processing

[1753] By integrating learning data from all users and analyzing their progress, the system automatically generates and provides reports to administrators. This allows administrators to efficiently understand learning progress and take appropriate action.

[1754] The server periodically aggregates learning data from all users and analyzes their progress.

[1755] The server generates a report for administrators based on the analysis results.

[1756] The server sends the generated report to the administrator's terminal for them to view.

[1757] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[1758] Question and Answer

[1759] Program processing

[1760] We respond quickly to user questions and provide appropriate answers. This makes it easier for users to resolve their questions during their learning process.

[1761] A user submits the question, "What is a correlation coefficient?"

[1762] The server receives a question and searches the database for the appropriate answer.

[1763] The server sends the found answer to the user's device and displays it to the user.

[1764] In this way, the entire system works in coordination, enabling efficient generation of training content, learning support, and progress management. This invention offers significant benefits to companies and educational institutions, resulting in cost reductions and a reduction in front-end workload.

[1765] The following describes the processing flow.

[1766] Content generation module

[1767] Step 1:

[1768] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[1769] Step 2:

[1770] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[1771] Step 3:

[1772] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[1773] Step 4:

[1774] Based on the data acquired by the server, artificial intelligence is used to automatically generate course content (slides, practice problems, etc.).

[1775] Step 5:

[1776] The server sends the generated course content to the user's terminal.

[1777] Step 6:

[1778] The terminal displays the course content it received to the user.

[1779] Learning support module

[1780] Step 1:

[1781] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[1782] Step 2:

[1783] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[1784] Step 3:

[1785] The device sends the recorded data to the server.

[1786] Step 4:

[1787] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[1788] Step 5:

[1789] The server generates appropriate feedback and additional learning materials based on the analysis results.

[1790] Step 6:

[1791] The server sends generated feedback and additional materials to the user's device.

[1792] Step 7:

[1793] The device displays feedback and additional information received by the user.

[1794] Question and Answer

[1795] Step 1:

[1796] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[1797] Step 2:

[1798] The server receives a question from the user and analyzes the content of the question.

[1799] Step 3:

[1800] The server searches the database for the appropriate answer to the question.

[1801] Step 4:

[1802] The server sends the answer it finds to the user's device.

[1803] Step 5:

[1804] The device displays the received response to the user.

[1805] Management module

[1806] Step 1:

[1807] The server periodically collects and integrates the learning data of all users.

[1808] Step 2:

[1809] The server analyzes the integrated data to identify learning trends and problems.

[1810] Step 3:

[1811] The server generates a report for administrators based on the analysis results.

[1812] Step 4:

[1813] The server sends the generated report to the administrator's terminal.

[1814] Step 5:

[1815] The terminal displays the received reports to the administrator.

[1816] These processing steps ensure that the entire system operates efficiently, allowing for smooth development of training content, learning support, and progress management.

[1817] (Example 1)

[1818] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1819] Existing training systems require significant time and effort for developing training content, providing learning support, and managing progress. Furthermore, they struggle to provide feedback tailored to individual user progress, hindering efficient learning. Additionally, they lack mechanisms for integrating and analyzing overall learning data to provide useful insights for administrators. Therefore, a system is needed that centrally manages automated training content generation, learning support, and progress management, enabling efficient and effective training.

[1820] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1821] In this invention, the server includes means for receiving requests from users, means for searching an information storage device and obtaining relevant information, means for automatically generating training content using a generation AI model, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the information storage device and providing them to the user, means for integrating and analyzing learning data from all users, and means for generating and providing reports for administrators based on the analysis results. This enables efficient automatic generation of training content, learning support, and progress management, resulting in overall cost and man-hour reductions.

[1822] A "request" is a user's request for specific training content to be created and provided.

[1823] "Information storage device" is a general term for devices that store information, such as databases and storage media.

[1824] "Related information" refers to materials and data necessary for generating training content, which are retrieved from information storage devices based on user requests.

[1825] "Training content" refers to the entirety of materials, slides, videos, practice exercises, etc., provided for learning and training.

[1826] A "generative AI model" refers to an algorithm or software that uses artificial intelligence to automatically generate training content based on user requests.

[1827] A "device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1828] "Learning progress" refers to the progress a user has made in learning the training content.

[1829] "Feedback" refers to information such as explanations and suggestions for improvement that are provided according to the user's learning progress.

[1830] "Additional materials" refer to supplementary learning materials and information provided to help users gain a deeper understanding of the material they are learning.

[1831] A "question" is an inquiry that a user enters into the system regarding doubts or points of confusion that arise during the learning process.

[1832] An "answer" refers to the explanation or information that the system provides in response to a user's question.

[1833] "Learning data" is a general term for records and data related to a user's learning activities.

[1834] A "report" is a document or report that summarizes the analysis results provided by the server to administrators after analyzing training data.

[1835] An "administrator" is the person or individual responsible for the overall operation of the system and the management of user progress.

[1836] This invention provides a system that efficiently handles training content development, learning support, and progress management, thereby reducing costs and man-hours. This system consists of a content generation module, a learning support module, and a management module, all of which automatically generate training content based on user requests. The details and operation methods of these modules are described below.

[1837] Content generation module

[1838] This module receives requests from users and automatically generates training content based on specified themes. Users send training content creation requests from their terminals. The server receives the request and searches for relevant materials in its information storage. Based on the retrieved information, it automatically generates training content using a generation AI model (e.g., GPT-4). The generated content is sent from the server to the user's terminal and displayed.

[1839] For example, if a user requests to "create an introductory data analysis course," the server will search the database for relevant materials and use a generative AI model to automatically generate the course content for "Introduction to Data Analysis."

[1840] Example of a prompt:

[1841] "Please create training content for an introduction to data analysis."

[1842] "Create a beginner's programming course."

[1843] Learning support module

[1844] This module monitors the user's learning process in real time and provides appropriate feedback and additional materials based on their progress. As the user studies the provided content, the device records their learning activity and sends it to the server. The server analyzes this data and, if the user encounters difficulties with a particular task, provides relevant explanatory videos or additional materials. This allows the user to learn more efficiently.

[1845] For example, if a user is unable to solve a particular problem in the "Introduction to Data Analysis Course," related explanatory videos and additional learning materials will be automatically provided to the user's device.

[1846] Management module

[1847] This module integrates learning data from all users and analyzes their progress to automatically generate and provide reports to administrators. The server periodically aggregates and analyzes learning data submitted by all users. Based on the analysis results, it generates a report for administrators and sends it to their terminals for display.

[1848] For example, a monthly report could be submitted to the administrator detailing which courses are popular and where users are struggling. Based on this information, the administrator can review and improve the course content.

[1849] Question answering function

[1850] This feature provides quick answers to user questions. When a user submits a question, such as "What is a correlation coefficient?", the server receives the question and searches its information storage for the appropriate answer. The server then sends the answer to the user's device for display.

[1851] By working together as a whole, this system enables the efficient generation of training content tailored to user needs, learning support, and progress management. It also offers significant benefits to businesses and educational institutions, resulting in cost and labor savings.

[1852] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1853] Content generation module

[1854] Processing flow and specific actions

[1855] Step 1:

[1856] The user sends a request through their device.

[1857] Input: The user enters a request in the input form, saying "I want to create an introductory data analysis course," and presses the submit button.

[1858] Operation: User input is sent to the server as an HTTP request.

[1859] Output: The server receives the request.

[1860] Step 2:

[1861] The server receives the request.

[1862] Input: User request data (e.g., "I want to create an introductory course on data analysis")

[1863] Operation: The web server receives the request and passes the data to the backend application server.

[1864] Output: The application server parses the request content.

[1865] Step 3:

[1866] The server searches for related documents.

[1867] Input: Parsed request content (e.g., "Introduction to Data Analysis")

[1868] Operation: The server generates an SQL query and performs a search on the information storage device (database).

[1869] Output: Related materials are retrieved as search results.

[1870] Step 4:

[1871] The server generates the training content.

[1872] Input: Acquired related documents

[1873] Operation: The server sends a prompt message (e.g., "Create training content for an introduction to data analysis") to the generated AI model (e.g., GPT-4) and requests content generation.

[1874] Output: Generated training content (text, slides, practice questions, etc.)

[1875] Step 5:

[1876] The server sends the generated content to the user for display.

[1877] Input: Generated training content

[1878] Operation: The server packages the content in JSON format or similar and sends it to the user's terminal as an HTTP response.

[1879] Output: The user's device analyzes and displays the received content.

[1880] Learning support module

[1881] Processing flow and specific actions

[1882] Step 1:

[1883] The user begins the learning activity.

[1884] Input: Training content provided by the user (e.g., slides, practice questions)

[1885] Action: View content and solve problems.

[1886] Output: Learning activity data (which slides were viewed, which problems were solved)

[1887] Step 2:

[1888] The device records learning activity data.

[1889] Input: User's learning activity data

[1890] Operation: Records learning activities in real time to a local database or cache.

[1891] Output: Recorded training data

[1892] Step 3:

[1893] The device sends recorded data to the server.

[1894] Input: Recorded training data

[1895] Operation: At regular intervals, the recorded data is sent to the server in batch processing.

[1896] Output: Transmitted training data

[1897] Step 4:

[1898] The server analyzes the data.

[1899] Input: Submitted training data

[1900] Operation: Receives data and executes an algorithm to analyze learning progress.

[1901] Output: Analysis results (e.g., which issues users are struggling with)

[1902] Step 5:

[1903] The server provides feedback and additional information.

[1904] Input: Analysis results

[1905] Function: Select and provide explanatory videos and additional materials related to the parts where the user is having trouble.

[1906] Output: Feedback and additional materials

[1907] Step 6:

[1908] Display information provided by the server to the user.

[1909] Input: Feedback and additional materials

[1910] Operation: The server sends this information to the user's terminal.

[1911] Output: Displays information received by the user terminal.

[1912] Management module

[1913] Processing flow and specific actions

[1914] Step 1:

[1915] The server aggregates the learning data of all users.

[1916] Input: Training data submitted by each user

[1917] Operation: Aggregates data using databases and big data processing platforms.

[1918] Output: Aggregated training data

[1919] Step 2:

[1920] The server analyzes the data.

[1921] Input: Aggregated training data

[1922] Operation: Executes data analysis algorithms and analyzes the overall learning progress.

[1923] Output: Analysis results (popular courses, frequently occurring stumbling blocks, etc.)

[1924] Step 3:

[1925] The server generates a report for administrators.

[1926] Input: Analysis results

[1927] Function: Generates a report document for administrators based on the analysis results.

[1928] Output: Generated report

[1929] Step 4:

[1930] The server sends the report to the administrator for display.

[1931] Input: Generated report

[1932] Action: Sends the report as an HTTP response to the administrator's terminal.

[1933] Output: The administrator terminal receives the report and displays it on the administration screen.

[1934] Question answering function

[1935] Processing flow and specific actions

[1936] Step 1:

[1937] User submits a question

[1938] Input: Question (Example: "What is a correlation coefficient?")

[1939] Operation: Enter your question in the chat box or form and submit it.

[1940] Output: The server receives the question.

[1941] Step 2:

[1942] The server receives the question.

[1943] Input: Question data

[1944] Function: Analyzes the question and extracts appropriate keywords.

[1945] Output: Analyzed question data

[1946] Step 3:

[1947] The server searches for the appropriate answer.

[1948] Input: Analyzed question data

[1949] Operation: Executes an SQL query to search the information storage device and retrieve relevant answers.

[1950] Output: Related response data

[1951] Step 4:

[1952] The server sends the answer to the user and displays it.

[1953] Input: Response data

[1954] Action: Sends the response as an HTTP response to the user's device.

[1955] Output: The user terminal receives and displays the response.

[1956] (Application Example 1)

[1957] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1958] Traditional training systems were cumbersome in terms of developing training content and managing progress, making efficient learning support difficult. Furthermore, training for virtual store staff, in particular, requires individual feedback and progress management, but achieving this using traditional methods is costly and time-consuming.

[1959] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1960] In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's terminal, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for receiving questions from users, searching for appropriate answers from the database and providing them to the user, means for integrating and analyzing the learning data of all users, means for generating and providing reports for administrators based on the analysis results, means for generating training content for virtual store staff, means for recording staff learning activities and monitoring progress, and means for aggregating each staff member's learning data, analyzing progress, and providing feedback. This enables automatic formulation of training content, real-time monitoring of learning progress, provision of individual feedback, and automatic generation of reports for administrators.

[1961] A "user" is someone who will be using this system to receive training.

[1962] "Means for receiving requests" refers to the functionality of devices or software that receive requests from users, such as training topics or questions.

[1963] A "database" is a collection of data that is systematically managed, allowing for efficient searching and retrieval of necessary information.

[1964] "Means for obtaining relevant information" refers to the functions of devices or software that search for and retrieve information from a database based on a request.

[1965] "Means for automatically generating training content" refers to the functions of devices or software that automatically create training materials based on acquired information.

[1966] "Means of providing generated training content to the user's terminal" refers to the functions of devices or software that transmit and display automatically generated training content on the user's device.

[1967] "Means for monitoring user learning progress" refers to the functions of devices and software that track and record a user's learning status in real time.

[1968] "Means of providing feedback and additional materials" refers to the functions of devices and software that provide users with appropriate advice and supplementary materials based on their learning progress.

[1969] "Means for receiving questions" refers to the functions of devices or software that receive questions from users.

[1970] "Means of searching for appropriate answers" refers to the function of devices or software that search and retrieve the best answer to a question from a database.

[1971] "Means for integrating and analyzing learning data from all users" refers to the functions of devices and software that centrally manage and statistically analyze learning data collected from multiple users.

[1972] "Means for generating and providing reports for administrators" refers to the functionality of devices and software that create and provide detailed reports for administrators based on data analysis results.

[1973] "Means for generating training content for virtual store staff" refers to the functions of devices and software that automatically create training materials for staff working in virtual stores.

[1974] "Means for recording staff learning activities and monitoring progress" refers to the functions of devices and software that track staff learning behaviors and record their progress in real time.

[1975] "Means for aggregating learning data, analyzing progress, and providing feedback" refers to the functions of devices and software that collect individual staff members' learning data and provide appropriate advice and supplementary materials based on that progress.

[1976] This invention provides a training system for staff in virtual stores, which can be implemented using the following hardware and software. The system consists of multiple modules that receive requests from users, search a database, generate training content using AI, and monitor learning progress and provide feedback.

[1977] 1. Hardware and software

[1978] Hardware: Servers, user terminals (smartphones, head-mounted displays, etc.)

[1979] Software: Python, Flask (server-side framework), requests (HTTP request library)

[1980] 2. Program Processing

[1981] Receiving a request

[1982] The server receives training topics and questions from virtual store staff. For example, if a staff member wants training on "virtual customer service techniques," that request is sent to the server.

[1983] Database search and information retrieval

[1984] Based on the request, the server searches the database and retrieves relevant information. This is the process of finding the most suitable materials for the training content requested by the staff.

[1985] Automatic generation of training content

[1986] Using the acquired information, the server utilizes an AI model to automatically generate training content. For example, if the training is on data analysis techniques, the generated materials will include basic methods and specific use cases.

[1987] Content provision

[1988] The generated training content is sent from the server to the user's terminal and made available for the user to view. This allows staff to immediately begin learning on their own devices.

[1989] Monitoring and feedback on learning progress

[1990] The device records the user's learning activities (viewing slides and answering questions) and sends this data to the server. The server analyzes this data in real time and provides appropriate feedback and additional learning materials based on the user's progress. For example, if a user gets stuck on a particular problem, they may be provided with explanatory videos or additional materials related to that problem.

[1991] Generating reports for administrators

[1992] The server aggregates learning data from all users and analyzes their progress. Based on the analysis results, it automatically generates and provides reports for administrators. For example, it reports to administrators which topics were most effective in last month's training and which areas many users are struggling with.

[1993] Question and Answer

[1994] A user's question is sent to the server, and relevant information is retrieved from the database. The server generates the best possible answer and provides it to the user. For example, if a staff member asks, "What is the product return policy?", the answer is provided immediately.

[1995] 3. Specific Examples and Examples of Prompt Statements

[1996] For example, if a staff member requests, "Please generate an introductory course on virtual customer service techniques," the server will search for relevant materials and automatically generate training content using an AI model.

[1997] Example of a prompt:

[1998] "Please create an introductory course on virtual customer service techniques for virtual store staff."

[1999] In this way, this system enables efficient training of staff working in virtual stores, resulting in significant reductions in costs and man-hours.

[2000] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2001] Step 1:

[2002] A user sends a request to the system. For example, a user who is a staff member at a virtual store requests "training on virtual customer service techniques." This request is sent from the user's terminal (such as a smartphone or head-mounted display) to the server. The input is "virtual customer service techniques," and the output is the request information for processing it.

[2003] Step 2:

[2004] The server searches the database based on the received request. It searches the database for and retrieves information related to the requested topic (e.g., "Virtual Customer Service Techniques"). The input is the request information, and the output is a list of related training materials. In this process, the server generates a search query and queries the database using SQL queries or similar methods.

[2005] Step 3:

[2006] Based on the acquired information, the server automatically generates training content using a generative AI model. For example, it analyzes acquired materials and generates appropriate slides, videos, and explanatory text. The input is a list of relevant training materials, and the output is automatically generated training content. Specifically, the AI ​​model generates text data using natural language processing technology and attaches related media files.

[2007] Step 4:

[2008] The server provides the generated training content to the user's terminal. The terminal receives this content and displays it to the user. For example, generated slides or videos are displayed on the user's smartphone or head-mounted display. The input is the automatically generated training content, and the output is the content displayed on the user's terminal.

[2009] Step 5:

[2010] When a user studies training content, the device records their learning activity. Progress data, such as the user's slide viewing time and question answer history, is recorded and sent to the server. The input is the user's learning activity, and the output is progress data. Specifically, a learning log is created and periodically sent to the server.

[2011] Step 6:

[2012] The server monitors and analyzes received learning progress data in real time. Based on the learning progress, if the user is struggling at a specific point, it generates and provides additional learning materials or explanatory videos. The input is progress data, and the output is feedback and additional materials. Machine learning algorithms are used in the analysis process to identify the user's weaknesses.

[2013] Step 7:

[2014] When a user submits a question, the server receives it. The server searches the database, generates an appropriate answer, and provides it to the user. For example, if a user asks, "What is the product return policy?", the server searches the database for the relevant policy information and generates an answer. The input is the user's question, and the output is the appropriate answer. In this process, natural language processing techniques are used to analyze the question and find the most relevant answer.

[2015] Step 8:

[2016] The server integrates learning data from all users and periodically analyzes their progress. Based on the analysis results, it automatically generates and provides detailed reports to administrators. For example, when an administrator requests a monthly report, the server aggregates the learning status of all users and creates a report based on the analysis results. The input is the integrated learning data, and the output is a report for administrators. Statistical analysis and data visualization techniques are used to generate the report.

[2017] This series of processing steps allows virtual store staff to receive training efficiently, and enables managers to reliably track the progress of their training.

[2018] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2019] This invention provides a more personalized learning experience by combining training content development, learning support, and progress management with user emotion recognition capabilities. The system's configuration and operation method are described in detail below, including specific examples.

[2020] Content generation module

[2021] Program processing

[2022] When a user requests training content, the content is automatically generated based on that topic. This saves time and effort.

[2023] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the terminal to the server.

[2024] The server receives the request and extracts keywords related to "Introduction to Data Analysis".

[2025] The server searches the database and retrieves relevant information and content, such as basic concepts and instructions on how to use tools.

[2026] The server automatically generates course content (slides, practice problems) based on information acquired using artificial intelligence.

[2027] The server sends the generated content to the user's device, and the device displays it to the user.

[2028] Learning support module

[2029] Program processing

[2030] It monitors the user's learning progress in real time and provides feedback and additional materials according to their progress.

[2031] The user begins learning the course content on their device. For example, they view slides and solve practice problems.

[2032] The device records the user's learning activity in real time and sends that data to the server.

[2033] The server receives and analyzes the user's learning progress data. It detects issues such as getting stuck on specific problems or taking an excessive amount of time to learn.

[2034] The server generates appropriate feedback and additional learning materials and sends them to the user's device.

[2035] The device displays feedback and additional information to the user.

[2036] Emotion recognition module

[2037] Program processing

[2038] It recognizes user emotions and provides feedback and support to further improve the learning experience.

[2039] The device will be equipped with sensors and cameras to recognize the user's emotions.

[2040] The device sends the user's facial expressions, voice tone, and other information to the emotion engine to acquire emotional data.

[2041] The server receives emotion data in real time and analyzes it along with learning progress data.

[2042] The server generates feedback based on emotional data, such as when the user is stressed or has a low level of understanding.

[2043] The server generates feedback and sends it to the user's device, which then displays it to the user.

[2044] For example, if a user shows signs of frustration or stress while working on a practice problem, the emotion engine recognizes that emotion and the server makes a suggestion such as, "Would you like to review the explanation for this problem again?"

[2045] Management module

[2046] Program processing

[2047] Integrates learning and sentiment data from all users and generates reports for administrators.

[2048] The server periodically collects and integrates learning data and sentiment data from all users.

[2049] The server analyzes this data to identify learning trends and problems.

[2050] The server generates a report for administrators based on the analysis results.

[2051] The server generates a report which is sent to the administrator's terminal, and the terminal displays it to the administrator.

[2052] For example, a monthly report could be submitted to administrators detailing which courses are popular, where many users are struggling, and their emotional reactions to these issues. Based on this information, administrators can review and improve the course content.

[2053] In this way, this system, which incorporates an emotion engine, improves the user's learning experience, streamlines the formulation of training content, learning support, and progress management, and achieves overall cost and man-hour reductions.

[2054] The following describes the processing flow.

[2055] Content generation module

[2056] Step 1:

[2057] The user clicks the "Request creation of an introductory data analysis course" button. This request is sent from the user's device.

[2058] Step 2:

[2059] The server receives a request from the user and extracts keywords related to "Introduction to Data Analysis".

[2060] Step 3:

[2061] The server searches the database and retrieves materials and content related to "Introduction to Data Analysis." For example, it collects information on basic concepts, methods, and tools.

[2062] Step 4:

[2063] Based on the data acquired by the server, artificial intelligence is used to automatically generate the course content (slides, practice problems, etc.) for the "Introduction to Data Analysis" course.

[2064] Step 5:

[2065] The server saves the generated course content and then sends it to the user's terminal.

[2066] Step 6:

[2067] The terminal displays the course content it received to the user.

[2068] Learning support module

[2069] Step 1:

[2070] The user begins learning the provided course content on their device. For example, they might view slides and solve practice problems.

[2071] Step 2:

[2072] The device records the user's learning behavior in real time. It collects data such as which slides were viewed and which practice problems were solved.

[2073] Step 3:

[2074] The device periodically sends the data it has recorded to the server.

[2075] Step 4:

[2076] The server receives and analyzes the user's learning progress data. For example, it can detect if a user repeatedly makes mistakes on a particular practice problem or if they are taking an excessive amount of time to study.

[2077] Step 5:

[2078] The server generates appropriate feedback and additional learning materials based on the analysis results.

[2079] Step 6:

[2080] The server sends generated feedback and additional materials to the user's device.

[2081] Step 7:

[2082] The device displays feedback and additional information received by the user.

[2083] Emotion recognition module

[2084] Step 1:

[2085] The device will be equipped with sensors and cameras to recognize the user's emotions.

[2086] Step 2:

[2087] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the emotion engine.

[2088] Step 3:

[2089] The server receives emotion data in real time and analyzes it along with learning progress data.

[2090] Step 4:

[2091] Based on the analysis results, the server generates feedback that corresponds to the user's emotions. For example, if the user is feeling stressed, it might generate a suggestion such as, "Would you like to review this problem explanation again?"

[2092] Step 5:

[2093] The server sends the generated feedback to the user's device.

[2094] Step 6:

[2095] The device displays the feedback it has received to the user.

[2096] Question answering module

[2097] Step 1:

[2098] The user uses their device to type and submit a question. For example, they might submit the question, "What is a correlation coefficient?"

[2099] Step 2:

[2100] The server receives a question from the user and analyzes the content of the question.

[2101] Step 3:

[2102] The server searches the database for the appropriate answer to the question.

[2103] Step 4:

[2104] The server sends the answer it finds to the user's device.

[2105] Step 5:

[2106] The device displays the received response to the user.

[2107] Management module

[2108] Step 1:

[2109] The server periodically collects and integrates learning and sentiment data from all users.

[2110] Step 2:

[2111] The server analyzes the integrated data to identify learning trends and problems.

[2112] Step 3:

[2113] The server generates a report for administrators based on the analysis results.

[2114] Step 4:

[2115] The server sends the generated report to the administrator's terminal.

[2116] Step 5:

[2117] The terminal displays the received reports to the administrator.

[2118] In this way, each module works together to realize a personalized learning experience that includes everything from training content development and learning support to progress management and even emotion recognition.

[2119] (Example 2)

[2120] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2121] Traditional learning systems struggled to provide personalized feedback and additional materials tailored to each user's learning progress, and they failed to consider users' emotional states. Therefore, learning efficiency and effectiveness could not be adequately guaranteed. Furthermore, it was difficult for administrators to grasp the overall learning situation and take appropriate measures.

[2122] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving requests from users, means for searching a database based on the requested theme and obtaining relevant information, means for automatically generating training content using the obtained information, means for providing the generated training content to the user's display device, means for monitoring the user's learning progress, means for providing feedback and additional materials based on the learning progress, means for recognizing the user's emotions and collecting emotional data, means for analyzing the emotional data and reflecting it in the feedback, means for integrating and analyzing the learning data and emotional data of all users, and means for generating and providing a report for administrators based on the analysis results. This makes it possible to provide personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. In addition, administrators can accurately grasp the overall learning situation and take appropriate measures.

[2123] "Means for receiving user requests" refers to a device or program that acquires request information when a user requests specific training content.

[2124] "Means for searching a database and retrieving relevant information" refers to a device or program that searches a database for relevant information based on a requested topic and retrieves the necessary data.

[2125] "Means for automatically generating training content using acquired information" refers to a device or program that automatically generates training content such as slides and practice problems using artificial intelligence, based on information acquired from a database.

[2126] "Means for providing generated training content to a user's display device" refers to a device or program that transmits automatically generated training content to a terminal used by the user and displays it.

[2127] "Means for monitoring user learning progress" refers to a device or program that records and monitors the user's progress in real time as they progress through the learning process.

[2128] "Means for providing feedback and additional materials based on learning progress" refers to a device or program that generates and provides appropriate feedback and additional learning materials based on the user's learning progress data.

[2129] "Means for recognizing user emotions and collecting emotional data" refers to a device or program that recognizes emotions from a user's facial expressions, tone of voice, etc., and collects that data.

[2130] "Means for analyzing emotional data and reflecting it in feedback" refers to a device or program that analyzes collected emotional data, adjusts the feedback content based on the results, and provides it to the user.

[2131] "Means for integrating and analyzing learning data and sentiment data from all users" refers to a device or program that integrates learning data and sentiment data collected from all users and analyzes that data.

[2132] "Means for generating and providing reports for administrators based on analysis results" refers to a device or program that generates and provides reports in a format usable by administrators, based on the results of integrated data analysis.

[2133] This invention provides a system that offers a more personalized learning experience by combining training content development, learning support, and progress management with a user emotion recognition function. The system consists of three main elements: a server, a terminal, and a user.

[2134] First, when a user requests training content, they do so via their device. When the user clicks the "Request creation of an introductory data analysis course" button, the device sends this request to the server. After receiving the request, the server searches the database based on the requested topic and retrieves relevant information. The hardware used includes a server and a database server, while the software used includes a database management system (e.g., MySQL) and a text analysis tool (e.g., NLTK).

[2135] Based on the acquired information, the server automatically generates training content (slides, practice questions, etc.) using a generative AI model (e.g., GPT-3). The server then sends the generated content to the user's device, which displays it to the user.

[2136] For example, if a user clicks the "Request creation of an introductory data analysis course" button, materials related to "Introduction to Data Analysis" are retrieved from the database based on the request, and slides and practice problems are automatically generated using a generative AI model. The generated content is then displayed on the user's device.

[2137] Example prompt: "Automatically generate a data analysis course for beginners."

[2138] Next, as the user progresses through the learning process, the device records the user's actions in real time and sends this data to the server. The server analyzes the user's learning progress data to detect issues such as getting stuck on specific problems or taking too long to learn. Data analysis tools (e.g., Pandas) are used for this analysis. The server generates appropriate feedback and additional learning materials and sends them to the user's device. The device then displays the generated feedback and additional materials to the user.

[2139] For example, if a user encounters a problem while studying the "Introduction to Data Analysis Course," the server analyzes the information, generates additional explanations and reference materials, and sends them to the user's device. The user then relearns the material based on the provided resources.

[2140] Example prompt: "Automatically provide additional explanatory materials to users who are falling behind in their learning progress."

[2141] Furthermore, it also has a function to recognize the user's emotions. The device uses sensors and a camera to acquire the user's emotional data (facial expressions, tone of voice, etc.) and sends it to the server. The server analyzes the emotional data and provides feedback based on whether the user is feeling stressed or has a low level of understanding. Emotion analysis tools (e.g., OpenCV, TensorFlow) are used for this analysis.

[2142] For example, if a user shows signs of dissatisfaction or stress while working on a practice problem, the server generates feedback such as "Would you like to review this problem explanation again?" and sends it to the user's device.

[2143] Example prompt: "If the user is experiencing stress, please provide an appropriate feedback message."

[2144] Finally, the server integrates all users' learning and sentiment data and periodically generates reports for administrators. The server analyzes this data to identify learning trends and problems. Data visualization tools (e.g., Tableau) are used for this analysis. The generated reports are sent to the administrator's terminal for their viewing.

[2145] As a concrete example, administrators can review monthly reports to see which courses are popular, where many users are struggling, and what their emotional reactions were to these issues, and then revise and improve the course content accordingly.

[2146] As described above, the present invention provides personalized feedback and additional materials that take into account the user's learning progress and emotional state, thereby improving learning efficiency and effectiveness. Furthermore, administrators can accurately grasp the overall learning situation and take appropriate measures.

[2147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2148] Step 1: Sending and receiving requests

[2149] The user clicks the "Request creation of an introductory data analysis course" button. The input is the user's click action and the request content, and the output is the HTTP request generated by the terminal.

[2150] The terminal sends this request to the server. The input is the request information from the user, and the output is the request data sent to the server.

[2151] Specific actions:

[2152] 1. The user performs the action of clicking a button.

[2153] 2. The device captures the click event, generates an HTTP request, and sends it to the server.

[2154] Step 2: Keyword extraction and database search

[2155] The server receives the request and extracts keywords related to "Introduction to Data Analysis". The input is the user's request data, and the output is the extracted keywords.

[2156] The server searches the database and retrieves relevant documents and content. The input is extracted keywords, and the output is related informational data.

[2157] Specific actions:

[2158] 1. The server parses the request data and performs natural language processing to extract keywords.

[2159] 2. The server generates database queries and searches for and retrieves relevant information.

[2160] Step 3: Content Generation

[2161] The server automatically generates training content based on the information it acquires. The input is the acquired information data, and the output is the generated training content.

[2162] The server uses a generated AI model (e.g., GPT-3) to create slides and practice problems.

[2163] Specific actions:

[2164] 1. The server inputs the acquired information into the model and generates training content.

[2165] 2. The server converts the generated slides and practice questions into a data format.

[2166] Step 4: Content Delivery

[2167] The server provides the generated training content to the user's terminal. The input is the generated training content, and the output is the content data sent to the terminal.

[2168] The device displays the content it has received to the user.

[2169] Specific actions:

[2170] 1. The server sends the content data to the terminal.

[2171] 2. The content received by the device is displayed on the user interface.

[2172] Step 5: Monitoring Learning Progress

[2173] The user begins learning the course content on their device. The input is the user's learning activity, and the output is progress data.

[2174] The device records the user's learning activity in real time and sends that data to the server.

[2175] Specific actions:

[2176] 1. The user views the slides and performs the actions of solving the practice problems.

[2177] 2. The terminal records user operation events and sends them to the server as progress data.

[2178] Step 6: Analyze progress and provide feedback

[2179] The server receives user learning progress data and analyzes it to identify issues such as difficulty with specific problems or excessive learning time. The input is progress data, and the output is the analysis results.

[2180] The server generates appropriate feedback and additional...

Claims

1. A means of receiving requests from users, A means of searching the database based on the requested theme and retrieving relevant information, A means of automatically generating training content using acquired information, A means of providing the generated training content to the user's device, A means of monitoring the user's learning progress, A means of providing feedback and additional materials based on learning progress, A means of receiving questions from users, searching for appropriate answers in a database, and providing them to the user. A means of integrating and analyzing the learning data of all users, A means of generating and providing reports for administrators based on the analysis results, A system that includes this.

2. The system according to claim 1, characterized by monitoring the user's learning progress data in real time.

3. The system according to claim 1, characterized in that it generates training content using artificial intelligence based on acquired information.

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